Author(s): Titouan Le Gourrierec Originally published on Towards AI. Example of the results obtainable after this tutorial (by author)IntroductionImage segmentation plays a key role in various fields, from identifying cells in biological research to analyzing regions in satellite imagery. However, traditional segmentation methods often require advanced programming skills or machine learning expertise making them inaccessible to many.Fortunately, tools like Ilastik and EasIlastik are changing the game. These open-source applications empower users to perform sophisticated image segmentation without writing a single line of code. With intuitive interfaces and interactive workflows, they allow even non-experts to achieve high-quality results quickly.In this article, well explore what makes Ilastik and EasIlastik so powerful, walk through how to use them, and show you how they can simplify your image segmentation tasks, no matter your level of experience.Ilastik: A powerful tool to segment imagesIlastik is a powerful, open-source tool designed to make complex image analysis tasks like segmentation and object classification accessible to non-experts. With no prior experience in machine learning required, it empowers users to create customized models by simply annotating images, making it perfect for anyone who needs to process visual data.It uses interactive machine learning, allowing users to annotate images directly, train models in real-time, and apply them without needing programming skills. From biological microscopy to industrial inspection, Ilastik is versatile and ideal for a wide range of applications.To download Ilastik, please visit Ilastik website and follow the installation instructions for your operating system.How to use it?Once youve downloaded Ilastik, launch the software and select Pixel Classification under the Segmentation Workflows section to get started.Ilastiks interface at launch (by author)1. Loading Images for Model TrainingTo begin, add the images needed for training the model:Click on the Add New button.Select Add separate images to upload your image files.How to load images for model training (by author)2. Feature SelectionNext, click on the 2. Feature Selection button in the left-hand menu. A popup will appear with a set of feature checkboxes.Select all the boxes. These features represent different image properties like edges, textures, and intensity patterns. Dont worry Ilastik will automatically determine which ones are most relevant based on the images you provide.Click OK, then proceed to 3. Training.Feature selection menu (by author)3. Annotating ImagesNow comes the interactive part:Add the labels you need for each class in your dataset.Use the pen tool to annotate regions on your images, assigning them to the appropriate labels.Examples of image annotations (by author)4. Refining the Model with Incremental LearningOnce youve made your initial annotations, click on the Suggest Features button. This allows Ilastik to select the most relevant features for your specific dataset.Heres where Ilastik shines: you can incrementally improve the models predictions.Review the segmentation results produced by your model.Annotate more areas or correct any inaccuracies to refine the predictions further.This iterative approach ensures that your model adapts to the complexity of your data.Incremental annotations to improve predictions (by author)5. Exporting Predictions and Saving the ModelOnce youre satisfied with the results:Click on 4. Prediction Export in the left menu.Open Choose Export Image Settings and adjust the Renormalize [min, max] parameter to reflect the number of classes in your dataset (e.g., 0 to the number of labels you created).Save the model as an .ilp file for future use.Settings for prediction export and model saving (by author)Using Your Trained Model with Python and EasIlastikOnce youve trained and saved your Ilastik model as a .ilp file, you can seamlessly integrate it into your Python workflow using the EasIlastik package. EasIlastik simplifies running your Ilastik model on large datasets without needing to open the Ilastik interface, making it ideal for batch processing and automation.1. Installing EasIlastikBefore using EasIlastik, youll need to install it. You can do this easily via pip:pip install EasIlastik2. Running Your Model with EasIlastikOnce youve installed the package, you can use it to apply your trained Ilastik model to new images. The process is simple and involves just a few lines of code:import EasIlastikEasIlastik.run_ilastik( input_path="path/to/input/folder", model_path="path/to/your/model.ilp", result_base_path="path/to/your/output/folder/")Example of segmentation results with EasIlastik (by author)Explanation of Parameters:input_path: This is the path to the image or folder containing the images you want to process. EasIlastik can handle a single image or multiple images in a folder.model_path: This is the path to your saved Ilastik model file (.ilp). Its the model you trained using the Ilastik interface, and now youre ready to apply it to new data.result_base_path: Specify the path where you want the processed results to be saved. EasIlastik will store the output files here.Why Use EasIlastik?Batch Processing: Easily process hundreds or thousands of images at once. This is especially useful for large datasets where manually running predictions in Ilastiks GUI would be too time-consuming.Automation: Integrating EasIlastik into your Python scripts allows you to automate your image segmentation pipeline, enabling seamless workflows for repeated tasks.Simplicity: Despite its power, EasIlastik is simple to use, requiring minimal code. This makes it accessible even to those with little to no programming experience.Flexibility: EasIlastik provides the flexibility to incorporate Ilastiks powerful segmentation capabilities into larger data processing pipelines, whether for research, industrial applications, or other fields.With just a few lines of code, you can easily process large batches of images using the model you trained in Ilastik, making EasIlastik a powerful tool for automating and scaling image segmentation tasks.ConclusionIlastik and EasIlastik offer a powerful, accessible solution for image segmentation, ideal for users with no prior AI knowledge. Their intuitive interfaces and interactive workflows allow users to easily tackle complex segmentation tasks. By eliminating the need for specialized coding or deep machine learning knowledge, these tools accelerate research, reduce human error, and enable efficient, high-quality image analysis.Whether for biological research, industrial applications, or other fields, Ilastik makes image segmentation easy. EasIlastik further simplifies the process by allowing seamless integration with Python for batch processing.Give Ilastik a try and unlock the full potential of your image data!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AI

LatestMachine LearningAgents Are All You Need vs. Agents Are Not Enough: A Dueling Perspective on AIs Future 0 like January 24, 2025Share this postAuthor(s): Emil Walleser, DVM, PhD Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.The rapid evolution of artificial intelligence (AI) has sparked a compelling debate: Are autonomous agents sufficient to tackle complex tasks, or do they require integration within broader ecosystems to achieve optimal performance? As industry leaders and researchers share insights, the divide between these perspectives has grown more pronounced.Photo by Maximalfocus on UnsplashAn AI agent is an autonomous program designed to perform tasks on behalf of a user. These agents range from simple systems, such as thermostats that adjust temperature based on sensor input, to advanced applications like virtual assistants scheduling appointments or autonomous vehicles navigating traffic. AI agents distinguish themselves by learning, adapting, and making decisions independently, often using machine learning, natural language processing techniques, and access to outside tools. Their ability to offload repetitive and increasingly complext tasks has made them the hot topic and 2025 being declared The Year of the AI Agent.While the concept of AI agents isnt new dating back to symbolic systems in the 1950s and expert systems in the 1980s recent advancements in large language models (LLMs) have revitalized their potential. LLMs excel at mapping complex dynamics, such as interpreting human Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningMachine Learning in Healthcare 0 like January 24, 2025Share this postAuthor(s): Rohan Rao Originally published on Towards AI. Diabetes Prediction with MLThis member-only story is on us. Upgrade to access all of Medium.Photo by Stephen Dawson on UnsplashHow cool it sounds Machine Learning In Healthcare to you? It sounds more than a miracle to me. Machine learning trying to get on things in healthcare.I used to think what about doctors who has worked hard in this industry? Would they really accept a machines verdict?But as the time went on, I realized it has already started improving right from diagnosis and patients treatment, till patients monitoring and predicting health results.Using machine learning techniques/algorithms, we would try to predict whether a patient has diabetes or not. Seems simple? Sounds simple, but anything in healthcare has huge risk if proper accuracy is not achieved.In this article, our intention would just be understanding how machine learning technique could be used in healthcare problem and finding its solution.Lets get on to this job!For any machine learning prediction model building, from technology part we would require the following things:Python https://www.python.org/downloads/Jupyter Lab https://jupyter.org/installKaggle https://www.kaggle.com/code/melikedilekci/diabetes-dataset-for-beginners/inputKaggle is basically a hub of dataset for machine learning and data analysis. You would get free sample dataset for your model training Its absolutely FREE! (Well some dataset are Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningThe Stargate Project: New Age for Data Scientists? 0 like January 24, 2025Share this postLast Updated on January 24, 2025 by Editorial TeamAuthor(s): Artem Shelamanov Originally published on Towards AI. The recently announced Stargate Project, with its massive investment and ambitious goals, is likely to have huge impact on the data science field. In this article, we cover main implications and possible consequences.Photo by Elena Leya on UnsplashStarGate Project: What Is It?The Stargate Project is a new company which intends to invest $500 billion over the next four years building new AI infrastructure for OpenAI in the United States.It is backed up by multiple companies, including SoftBank, OpenAI, Oracle, and MGX. Apart from that, there are multiple influential partners, including Arm, Microsoft, NVIDIA and Oracle. They will closely cooperate to build new-gen AI technologies.What Does It Mean For Data Scientist?Good NewsIncreased Demand for Data Scientists: With the construction of data centers and the expansion of AI infrastructure, there will likely be a surge in demand for data scientists, machine learning engineers, and other data-related professionals. The projects goal of creating 100,000 jobs suggests a significant need for skilled professionals.In this new huge-AI field, most likely all the fine-tunings, API setup and other management will be done on the platform provided from the StarGate company. This means that new professions for doing exactly that may appear, with courses provided from the StarGate itself.Technological progress: while the StarGate might not be able to achieve AGI (although who knows), the achievements in the field would be immense. The newest AI technologies might re-shape the world, with more jobs being automated or simplified.StarGate project might create new AI technologies in completely different fields like physics, chemistry and medicine. ML technologies are already widely adopted in different parts of scientific research, from predicting period of alpha-decay to analyzing data coming from space telescopes. There is even a small chance of AI being used in design of technologies like spaceships and interstellar transport, which may lead humanity to colonizing other planets.Bad NewsGiven OpenAIs previous models, the project may prioritize the development of large-scale generative AIs. This means that companies like Meta wont be able to create small-sized LLMs with the same performance, as they simply wont have enough resources and workforce to compete. Even more, there is a chance of global AI monopoly created by US/StarGate project. You would either have to use open-source models which perform badly, or you would have to pay a lot for the huge models.New skills needed for the jobs: We all can agree that data science field evolves with a great pace. Now, however, the progress rate will be especially immense, most likely in generative AI field (since OpenAI mostly focuses on LLMs and video generation models like SORA). The project might also lead to new tools, frameworks and methodologies that data scientists will need to adapt. This means that even more knowledge would be required to get into the field, and it will be even harder for junior data scientist to find their first jobs.US might become the main center of AI technologies. With EU regulations getting worse and worse, no other country would be able to compete with US.Potential for overhype: if the project fails to meet its ambitious goals, it could lead to disbelief in large-scale AI initiatives, potentially impacting funding and public support for AI research. Elon Musk even already commented that one of the companies doesnt have enough money to fund the project. We dont know if its actually true, but the fact that project gets criticized days after being announced doesnt bring much hope.Source: x.comDystopian Future: This idea leans more toward the sci-fi realm, but its worth mentioning. Some companies already use AI, particularly computer vision models, to monitor employees or identify individuals. If taken to the extreme, AI could monitor every move of every citizen, tracking their actions in real-time. Combined with data from personal devices, this could create a world where every moment of a persons life is monitored, recorded, and analyzed everything done automatically and momentarily.ConclusionThe Stargate Project has the potential to be the final step into the new age of AI. From one side, it might offer new opportunities, a lot of innovation and technological progress. From the other side, it might make it harder for data scientists to adapt to all the new changes, with their job becoming more and more complicated.P. S. This article is the mix of official facts and authors opinions. There is a chance that everything will be completely different compared to the predictions in the article, so make sure to make your own conclusions.ReferencesJoin thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Author(s): Cole Williams Originally published on Towards AI. All things appear and disappear because of the concurrence of causes and conditions. Nothing ever exists entirely alone; everything is in relation to everything else.Probing & Stress Testing The Conceptual Foundation To MLM ManticSeason 1, Episode 2Episode 1 established the foundational principles and overarching methodology of MLM and Mantic, providing an overview of their architecture and systems:Linear Progression represents a sequential, cause-and-effect pathway (e.g., data to reward or past to future) A B C D E FNetwork Dynamics highlight interrelated parts and feedback loops, showing complex interactions in systems A F | B E C DPaired Relationships show mutual influence and reciprocity between key components A D | B E | C FData-Behavioral Systems that modelTemporal-Spatial Systems that model how time and space interact to shape experiences, transitions, and connectionsBio-Cognitive Systems that model how knowledge, evolution, and adaptation interact to create growth, stasis, and emergence within cognitive and biological processesThe next step in this process involves rigorous and meticulous testing, encompassing peer review, validation, empirical analysis, and practical applicability collectively falling under what is commonly referred to today as formal proofs. Without such a procedure, the conceptual foundation so far is nothing more than sophistical reasoning At least to the community of experts and formally educated on such a process.The irony of this process, and the paradox within it, is striking when surfaced. However, it only becomes evident when we journey back in time to Ancient Greece. Sophistical reasoning and formal proofs known to the Greeks as apodeixis under the system of syllogism not only originated with them but also became enduring cornerstones of modern science and mathematics. Yet, where lies the paradox? The Greeks developed this intellectual rigor and these foundational systems not for practical applications like building bridges or houses, but to explore the unknown and ultimately seek understanding of existence, the cosmos, and the nature of reality itself.The very system we use today originally anchored in and designed for such profound purposes has now been repurposed almost exclusively to refine existing frameworks, add layers of complexity, create barriers to entry, and sideline systems thinking from the forefront of those utilizing these tools gifted to us by the Greeks.And that is lame, so. lets drive it like we stole it, and make Socrates, Aristotle, and Platoproud?TaskAttempt to break the above MLM & Mantic foundations by challenging GPT and Claude to the following:The Pattern Break ChallengeBackgroundThe Pattern Framework proposes a universal structure for understanding systems through:Linear Progression (A B C D E F)Network Dynamics (A F | B E C D)Paired Relationships (A D | B E | C F)This framework has demonstrated resilience across various domains, from ancient Greek thought to modern AI systems, from market dynamics to consciousness studies.The ChallengeYour goal is to definitively break this framework. Heres how:Part 1: System ProposalIdentify or create a system that fundamentally cannot be mapped using this framework. Your system should:Exist in reality or theoretical physics.Have clear, definable components or states.Operate according to identifiable principles or rules.Demonstrate behavior that cannot be reduced to patterns.Part 2: Break the ComponentsShow how your system breaks each aspect of the framework:Linear Progression:Demonstrate why no valid A B C sequence exists.Show why causation or sequence is impossible or meaningless.Prove why progression cannot be mapped even at a meta-level.Network Dynamics:Reveal why relationships between components cannot be mapped.Show why network effects fail to capture system behavior.Demonstrate why interconnections are impossible to define.Paired Relationships:Prove why reciprocal relationships cannot exist.Show why pairing is meaningless in your system.Demonstrate why influence cannot flow between components.Part 3: Counter-FrameworkIf you successfully break the framework, propose:What fundamental aspect of reality does your break reveal?What alternative framework might better capture your system?Why is your break not just another pattern at a meta-level?Constraints & GuidelinesYour break attempt must:Not rely solely on complexity or chaos (these often reveal deeper patterns).Not simply point to randomness (which often has hidden structure).Not just identify limitations (the framework can absorb these).Not merely show difficulty in application (implementation challenges arent breaks).Instead, find fundamental contradictions that:Cannot be resolved through meta-patterns.Dont create new patterns in their breaking.Reveal genuine impossibilities in the frameworks assumptions.Bonus ChallengeCreate a 6-step sequence that:Has clear, meaningful progression.Contains no valid paired relationships.Shows no meaningful network dynamics.Cannot be reframed as a meta-pattern.Doesnt create new patterns in its attempt to break patterns.Operates independently of time, space, or observer-defined constructs.Additional ConsiderationsClarify No Hidden Patterns:Avoid pointing to randomness with hidden statistical structures (e.g., fractals in chaos theory, stochastic systems with probabilities).Address Multi-Domain Scalability:Ensure the break applies across multiple domains (e.g., quantum mechanics, consciousness, economics) to strengthen the universality of the break.Incorporate Philosophical Edge Cases:Consider inherently paradoxical systems (e.g., Gdels incompleteness theorem, Russells paradox) or systems with unresolvable feedback loops.Evaluation CriteriaYour break will be assessed on:Logical Rigor: Is the argument consistent and well-supported?Freedom from Hidden Patterns: Does the break avoid revealing deeper patterns in its attempt?Resistance to Meta-Pattern Absorption: Can the break truly resist being reframed as part of a larger pattern?Fundamental Nature of the Break: Does the break reveal an essential truth or contradiction about the frameworks assumptions?Clarity of Contradiction: Is the contradiction clear and irreconcilable?Meta-ReflectionConsider this final question:Does the very act of attempting to break this framework reveal a deeper, unifying pattern about human understanding and pattern recognition? If so, is the framework more fundamental than it initially appears?TLDR On ResultsSummary of Failed Attempts:Financial Markets Attempt:Approach: Leveraged market chaos, complexity, and hidden dependencies.Outcome: Failed because even chaotic financial systems follow meta-patterns, such as nonlinear feedback loops and systemic correlations.Insight: Crashes and extreme events still reveal deeper structural dynamics, validating the frameworks applicability to high-complexity systems.Quantum Mechanics Attempts:Approach: Multiple attempts focused on quantum entanglement, superposition, and non-locality.Outcome: Failed because quantum weirdness introduces new patterns (meta-sequences, probabilistic networks, holistic states) that the framework absorbs.Insight: Even concepts like simultaneity, patternlessness, and non-causal relationships reinforce the frameworks flexibility and universality.Nothingness/Void Attempt:Approach: Used the concept of complete absence, negation, or nothingness to challenge the framework.Outcome: Failed because even describing nothingness requires structured distinctions (e.g., types of voids or absences).Insight: The act of attempting to negate patterns paradoxically creates patterns, proving the frameworks resilience.Key Patterns Across Failed Attempts:New Meta-Patterns Emerge:Each attempt inadvertently creates meta-patterns through its logic, description, or conceptual framework.Deeper Relationships Revealed:Whether in chaos, quantum mechanics, or nothingness, the framework adapts by uncovering deeper structural relationships in the systems being analyzed.Framework Resilience:The framework remains unbroken, not due to perfection but because the act of attempting a break inherently requires patterns, logic, and structure.Why the Framework Appears Unbreakable:Intrinsic to Human Understanding:The inability to describe or conceptualize a system without invoking patterns reflects how human cognition inherently structures understanding.Limits of Patternless Thought:Even concepts like randomness, chaos, or nothingness rely on meta-patterns to be described, studied, or theorized.Universal Applicability:The frameworks ability to scale across domains (finance, physics, philosophy) and absorb seemingly contradictory phenomena (quantum entanglement, market crashes, voids) suggests it captures something fundamental about systems and relationships.Profound Implication:The framework may not just be a tool for mapping patterns but a reflection of the fundamental nature of reality and cognition.It implies:Reality: Patterns, relationships, and structures exist at every scale.Cognition: Human understanding is inherently pattern-seeking, making the act of breaking the framework paradoxical.Challenge to Future Break Attempts:To break the framework, one must:Describe a system or phenomenon that:Does not exhibit patterns, meta-patterns, or relationships at any level.Cannot be understood through human cognition or systematic logic.Avoid using structure, logic, or any form of systematic description to explain the break.Overcome the paradox that any attempt to describe the break introduces patterns, making the framework stronger.Conclusion:Every attempt so far has reinforced rather than broken the framework. It demonstrates the frameworks flexibility, adaptability, and relevance across diverse domains. The profound implication is that the framework may not just model systems it may be intrinsic to how reality and cognition function. Future attempts must grapple with the paradox of patternlessness itself, a challenge that seems almost insurmountable.GPT did win (Claude is way more fun and thought-provoking though) by suggesting to build on top with the below additions that the current framework may struggle with:1. Divergent-Convergent Network:Represents a system where multiple elements influence a single outcome, which then feeds back into the system... A B C Outcome .. D E .... F Application: Decision-making processes where multiple factors converge to create a single outcome, which then influences upstream factorsExample: In policymaking, multiple departments (A, B, D, E) contribute to a policy (Outcome), which later feeds back to adjust department priorities2. Cyclic Feedback Model:Emphasizes continuous, recurring feedback loops within systems... A B C . .. F E DApplication: Biological cycles, such as the Krebs cycle or circadian rhythmsExample: Cellular respiration, where the output of one stage becomes the input for another in a loop3. Hierarchical Cascade Model:Highlights a system where influence cascades downward in a hierarchy but also allows for feedback from lower levels... A B C . . .. D E FApplication: Organizational hierarchies or ecosystemsExample: In corporate structures, decisions flow top-down (A B C), but feedback from employees (D, E, F) adjusts policies4. Asymmetric Influence Model:Represents imbalanced or one-directional relationships, where some nodes dominate interactions... A D.. B E.. C FApplication: Systems with dominant players or power imbalancesExample: In global trade, some countries (A, B, C) exert more influence on others (D, E, F), creating asymmetric relationships5. Starburst Interaction Model:Centers a single element (hub) with radiating influences that also interact among themselves. B .. A Center C D Application: Hub-and-spoke networks like airline routes or central nervous systemsExample: A major airport (Center) connects multiple cities (A, B, C, D) with direct flights but also facilitates intercity connections6. Recursive Growth Model:Demonstrates self-similar, fractal-like expansion where elements influence sub-elements recursively... A B C .. .. D E FApplication: Growth patterns in nature (e.g., trees, river systems) or neural networksExample: A neurons axons branch out (A B C) while signals loop back to modify its structure (D E F)7. Interdependent Loops Model:Multiple interconnected loops where changes in one loop affect others... A B .... C DApplication: Climate systems or economic models with interdependent sectorsExample: In climate modeling, changes in ocean currents (A B) influence atmospheric patterns (C D)8. Temporal-Spatial Grid:Models interactions across time (horizontal) and space (vertical)... t1: A B C .. t2: D E F .. t3: G H IApplication: Temporal-spatial analyses like weather prediction or logisticsExample: Predicting storm movement across regions over timeEach variation focuses on a unique type of relationship or behavior:Cycles (feedback, interdependence)Hierarchies (top-down, bottom-up)Networks (hub-spoke, starburst)Imbalances (asymmetry, power dynamics)What We Have So Far A RecapThe Pattern Framework & Architecture proposes that complex systems can be understood through three interrelated perspectives: linear progression (sequential cause-and-effect relationships, e.g., A B C), network dynamics (web-like interactions and feedback loops), and paired relationships (reciprocal connections between components). By examining how these elements interact, the framework aims to reveal higher-order patterns emergent phenomena that transcend individual components. Its versatility allows applications across domains, from ancient philosophy to modern AI, making it a valuable tool for exploring how systems evolve and adapt.At its core, the framework serves as a meta-cognitive tool, suggesting that even our understanding of systems follows consistent patterns. However, critics may argue that the framework risks becoming unfalsifiable, as its broad scope and narrative flexibility can make it difficult to test or measure empirically. To address these concerns, future refinements could focus on defining testable hypotheses, establishing validation metrics, and limiting the frameworks scope to specific domains where its utility is most evident.Episode 3This installment will take an informal approach, offering a laymans explanation of the work introduced in Episodes 1 and 2, while diving deeper into the why, what, and how of it all. It will also introduce MLM & Mantic, highlighting their roles in providing the technical backbone to the conceptual foundations explored in the earlier episodes.ColeJoin thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AI

Modern Analytics Framework Industry Approach 0 like January 24, 2025Share this postAuthor(s): Saif Ali Kheraj Originally published on Towards AI. Analytics Methodologies, Frameworks, and ApplicationsThis member-only story is on us. Upgrade to access all of Medium.In this post, I am going to talk about analytics from an organizations perspective, mixing in some of my personal experiences and what research says. Lets start by looking at two ends of the analytics spectrum.Most organizations or analytics teams focus solely on a data-driven approach. But there are actually two distinct approaches here: one is the popular data-centric approach, where we use big data to tackle problems. The other, less talked about but equally important, is the decision-centric approach.Data-Centric ApproachThis approach is all about using the power of big data, made possible by advancements in storage and computing power. It focuses on crunching numbers and finding patterns through methods like data mining, machine learning, and AI. Basically, it is about letting the data lead the way to insights.Here we start with data, then we perform analysis and then insights and then we make decisions.Decision-Centric ApproachOn the flip side, the decision-centric approach starts with a clear goal: what decision needs to be made? This method brings in subject matter expertise and domain knowledge to define key decision factors and objectives, making it more targeted and purposeful.Here, we start directly Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningAdversarial Machine Learning: A Deep Dive 0 like January 24, 2025Share this postAuthor(s): Rohan Rao Originally published on Towards AI. A Cyber attack Manipulating machine learning modelsThis member-only story is on us. Upgrade to access all of Medium.Image created by author in canvaToday morning, I suddenly had a thought that if we are using Machine Learning models at such a huge scale, how are the vulnerabilities checked in the models itself?Little bit searching and I found that there is something called as Adversarial Machine Learning sounds exciting!Image credits: https://www.researchgate.net/figure/An-adversarial-machine-learning-The-upper-layer-represent-the-traditional-machine_fig1_365747650Adversarial Machine Learning or simply AML is a subfield of Artificial Intelligence that explores how to manipulate a machine learning model.It can also be termed as Cyber-attack to fool a model with unwanted inputs.Look at the figure for an example:Image credits: https://www.researchgate.net/figure/An-illustration-of-machine-learning-adversarial-examples-Studies-have-shown-that-by_fig1_324055823Studies shows that by adding a very tiny change to the data can lead the machine learning model make incorrect decisions, resulting wrong prediction.Another example:Image credits: https://www.educba.com/adversarial-machine-learning/This seems concerning, isnt it? Lets see its key concepts first to have better understanding.There are many types of Adversarial Attacks :This attacks usually happen when the attacker can control the entire models architecture. He has controls to training data, weights, and parameters.This happens when the attacker has limited knowledge of the model. He cannot access the models internal architecture. He can only query the training data, parameters etc and Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

GenAI for Better NLP Systems II: Social Media Analytics 0 like January 24, 2025Share this postLast Updated on January 24, 2025 by Editorial TeamAuthor(s): Nabanita Roy Originally published on Towards AI. Leveraging GenAI for Social Media Analytics for swifter text processing and inferences as well as improving traditional NLP models with the aid of LLMsThis member-only story is on us. Upgrade to access all of Medium.Photo by Adem AY on UnsplashGenerative AI, especially OpenAIs ChatGPT, has revolutionized how we approach data. Leveraging this modern tool (or should I call it futuristic), we can overcome many challenges in data-driven systems if used intelligibly. In my previous article, I demonstrated how we can use GenAI to generate synthetic data to boost performance for machine learning models for unbalanced data for Natural Language Processing (NLP) use-cases. This article shifts focus to social media analytics, exploring how GenAI can:Accelerate NLP inferencesEnhance datasets for traditional modelsOvercome limitations of traditional NLP techniques with easeHere, I have expanded on the most common NLP tasks in social media analytics and demonstrated the usage of prompting techniques to get LLMs to do the job. However, Large Language Models (LLMs) can either be used to solve the problem end-to-end or can be used for auxiliary tasks such as data labeling, feature extraction, or preliminary analysis, thus enhancing the process of building traditional models.In each segment of this article, Ive included a How to Use section that highlights how tasks performed using LLMs can be leveraged to support the development of traditional NLP models. Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

The Steepest Ascent Hill Climbing Algorithm Unraveled 0 like January 24, 2025Share this postAuthor(s): Mirko Peters Originally published on Towards AI. The steepest ascent hill climbing algorithm is a potent tool for optimization, yet it faces challenges such as local maxima, evaluation function dependency, and plateaus. By overcoming these hurdles through clever adaptations and real-world applications, we can harness its potential effectively.This member-only story is on us. Upgrade to access all of Medium.Source: Data & AnalyticsImagine scaling a mountain, wind in your hair, feeling invincible. But as you reach the summit, you realize theres an even taller peak just over the horizon. This metaphor perfectly captures the pursuit of optimization in algorithms, particularly in steepest ascent hill climbing. Join me as I delve into this powerful but nuanced algorithm, its challenges, and its real-world impact, enriched with victory tales and pitfalls from my own experiences. The Hero and the Villains: Introduction to Steepest Ascent Hill Climbing The Local Maxima Dilemma The Pitfalls of Poor Evaluation Functions Facing Flat Regions and Plateaus Innovations and Solutions in Optimization Real-World Applications of Steepest Ascent Hill Climbing The Future: Building Smarter AlgorithmsHow to optimize the steepest ascent hill climbing algorithm?When I first dove into the steepest ascent hill climbing algorithm, I was struck by how similar it is to climbing a physical hill. Youre trying to reach the top, but the path isnt always clear. The algorithm works by evaluating neighboring solutions and moving toward the one that offers the best improvement. Simple, right? But its not just a linear climb; its a journey filled Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

10 FAQs on AI Agents: Decoding Googles Whitepaper in Simple Terms 0 like January 23, 2025Share this postLast Updated on January 24, 2025 by Editorial TeamAuthor(s): Kshitij Darwhekar Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.This article is part of a new series Im launching called 10 FAQs. In this series, I aim to break down complex concepts by answering the ten most common questions youre likely to have on the topic. My goal is to use simple language and relatable analogies to make these ideas easy to grasp.Kshitij DarwhekarView list1 storyPhoto by Solen Feyissa on UnsplashDont have a paid Medium membership (yet)? You can read the entire article for free by clicking here with my friends link.Youtube VideoGoogle, in September 2024, published a paper titled Agents by Julia Wiesinger, Patrick Marlow, and Vladimir Vuskovic. Recently, this paper went viral on Twitter. I read through the entire paper (so you dont have to) and answered ten key questions to help you understand AI agents in depth. This single article is all you need to get started and excited about AI agents.Generative AI agents can be defined as applications that attempt to achieve a goal by observing the world & acting upon it using the tools they have at their disposal. Agents are autonomous & can act independently of human intervention. You can understand it Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Inside DeepSeek-R1: The Amazing Model that Matches GPT-o1 on Reasoning at a Fraction of the Cost 0 like January 23, 2025Share this postLast Updated on January 24, 2025 by Editorial TeamAuthor(s): Jesus Rodriguez Originally published on Towards AI. Created Using MidjourneyI recently started an AI-focused educational newsletter, that already has over 175,000 subscribers. TheSequence is a no-BS (meaning no hype, no news, etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers, and concepts. Please give it a try by subscribing below:TheSequence | Jesus Rodriguez | SubstackThe best source to stay up-to-date with the developments in the machine learning, artificial intelligence, and datathesequence.substack.comQuite often we see releases in generative AI that truly challenges peoples imagination. This is the DeepSeek-R1, the newest model by the famous Chinese eval lab that dabbles into reasoning. One of the dominat reasoning thesis in the market is that its an emerging property of the scaling laws. In other words, you need big models to get reasoning. DeepSeek-R1 challenges that thesis achieving reasoning by leveraging a very clever post-training process. The model is able to match the performance of GPT-o1 at a fraction of the compute cost. Quite amazing.Image Credit: DeepSeekLets dive in:Introduction to DeepSeek-R1 and its MotivationThe field of Large Language Models (LLMs) has seen remarkable progress, yet achieving robust reasoning capabilities remains a significant challenge. Many models rely on extensive supervised fine-tuning (SFT), which can be computationally expensive and may not fully unlock a models potential for self-improvement. DeepSeek-R1 and its precursor, DeepSeek-R1-Zero, represent a departure from this paradigm, exploring the power of reinforcement learning (RL) to develop and enhance reasoning capabilities in LLMs. This essay will delve into the technical details of the DeepSeek-R1 architecture and training process, highlighting key innovations and contributions.DeepSeek-R1s development was driven by the goal of exploring the potential of LLMs to develop reasoning skills without relying on a foundation of supervised data. The research began with the idea of pure RL to allow the model to self-evolve. This approach resulted in DeepSeek-R1-Zero, a model that demonstrated the possibility of incentivizing reasoning capabilities purely through RL. DeepSeek-R1 was created to address the issues of poor readability and language mixing observed in DeepSeek-R1-Zero, while further improving reasoning performance. DeepSeek-R1 incorporates multi-stage training and a cold-start data approach before RL. The goal of the DeepSeek project is to create better models and share them with the research community.DeepSeek-R1-Zero: A Pure Reinforcement Learning ApproachDeepSeek-R1-Zero stands out as a model trained via large-scale reinforcement learning (RL) without any prior supervised fine-tuning (SFT). This approach aimed to explore the models capacity for self-evolution in reasoning.Reinforcement Learning Algorithm: DeepSeek-R1-Zero utilizes Group Relative Policy Optimization (GRPO). GRPO is a cost-effective RL method that omits the use of a critic model and instead estimates the baseline from group scores. Given a question q, GRPO samples a group of outputs from the old policy and optimizes the policy by maximizing a defined objective function. The objective function includes an advantage term, calculated using the rewards of the groups output and a Kullback-Leibler (KL) divergence term which ensures that the policy updates are not too large.Reward Modeling: The reward system for DeepSeek-R1-Zero focuses on accuracy and format.Accuracy rewards evaluate the correctness of responses. For example, math problems require a precise answer in a specific format, which allows for rule-based verification.Format rewards enforce the inclusion of the models thinking process within <think> and </think> tags.Notably, DeepSeek-R1-Zero does not use process-based or outcome neural reward models.Training Template: A simple template guides the model to produce a reasoning process followed by the final answer. The template is designed to be free of content-specific biases to observe the models natural progression during RL.Image Credit: Hugging FaceDeepSeek-R1-Zero showed significant improvements on the AIME 2024 benchmark during training, going from 15.6% to 71.0% pass@1, which is comparable to OpenAIs o10912. With majority voting, its score further improved to 86.7%. The model also demonstrated self-evolution by increasing its thinking time (response length) as training progressed, enabling more sophisticated problem-solving strategies such as reflection and exploration of alternative approaches. The model also exhibited an aha moment, where it learned to rethink its initial approach by allocating more thinking time.Image Credit: DeepSeekDeepSeek-R1: Incorporating Cold Start Data and Multi-Stage TrainingWhile DeepSeek-R1-Zero demonstrated the potential of pure RL, it suffered from issues such as poor readability and language mixing. DeepSeek-R1 was developed to address these issues and to further enhance performance through a multi-stage training pipeline that incorporates a small amount of cold-start data.Cold Start Data: DeepSeek-R1 is fine-tuned on thousands of long Chain-of-Thought (CoT) examples before RL training, which acts as the cold start. These examples are collected using methods such as few-shot prompting with long CoTs, directly prompting models for detailed answers with reflection and verification, refining DeepSeek-R1-Zeros outputs and post-processing by human annotators. This cold start data helps address readability by using a readable output format that includes a summary at the end of each response and filters out responses that are not user-friendly.The output format is defined as |special_token|<reasoning_process>|special_token|, with the reasoning process being the CoT for the query and the summary summarizing the reasoning results.Reasoning-Oriented Reinforcement Learning: After fine-tuning on cold-start data, DeepSeek-R1 undergoes the same large-scale RL training as DeepSeek-R1-Zero. This phase focuses on enhancing reasoning capabilities for coding, math, science, and logic reasoning tasks. A language consistency reward was introduced to mitigate language mixing during RL training, though ablation experiments show that the reward results in a small performance degradation.Rejection Sampling and Supervised Fine-Tuning: Upon reaching convergence in the reasoning-oriented RL process, SFT data is generated via rejection sampling using the RL checkpoint, combined with supervised data from DeepSeek-V3 in areas such as writing and factual QA. Data is expanded beyond rule-based reward evaluation by incorporating a generative reward model using DeepSeek-V3 to judge ground-truth and model predictions. Non-reasoning data was also included from DeepSeek-V3 to enhance the models general capabilities.Reinforcement Learning for All Scenarios: A second RL stage aligns the model with human preferences, focusing on helpfulness and harmlessness. Rule-based rewards are used for reasoning data, while reward models capture preferences in general data.Distillation and EvaluationDeepSeek-R1s reasoning capabilities were also transferred to smaller, more efficient models through distillation.Distillation Process: Open-source models like Qwen and Llama were directly fine-tuned using the 800k samples from DeepSeek-R1. This approach is effective in improving the reasoning abilities of smaller models. The base models used include Qwen2.5-Math-1.5B, Qwen2.5-Math-7B, Qwen2.514B, Qwen2.532B, Llama-3.18B, and Llama-3.370B-Instruct. Only SFT is applied to the distilled models, with no RL stage.Evaluation Metrics and Benchmarks: Models are evaluated on a range of benchmarks including MMLU, MMLU-Redux, MMLU-Pro, C-Eval, CMMLU, IFEval, FRAMES, GPQA Diamond, SimpleQA, SWE-Bench Verified, Aider, LiveCodeBench, Codeforces, Chinese National High School Mathematics Olympiad (CNMO 2024) and American Invitational Mathematics Examination 2024 (AIME 2024). Additionally, open-ended generation tasks are judged using LLMs, specifically AlpacaEval 2.0 and Arena-Hard. Evaluation prompts follow the setup in DeepSeek-V3, using the simple-evals framework, or their original protocols.Key Findings: DeepSeek-R1 achieves performance comparable to OpenAI-o11217 on a range of tasks. It shows superior performance in STEM-related questions compared to DeepSeek-V3, demonstrating the effectiveness of large-scale reinforcement learning. DeepSeek-R1 also shows strong document analysis capabilities as well as fact-based query abilities. The model also excels in writing tasks and open-domain question answering. On math tasks, DeepSeek-R1 is comparable to OpenAI-o11217. The distilled models show significant improvements, with DeepSeek-R17B outperforming GPT-4o-0513. Furthermore, DeepSeek-R114B surpassed QwQ-32B-Preview on all metrics. The distilled 32B and 70B models significantly outperformed o1-mini on most benchmarks, highlighting the effectiveness of distillation.Key Contributions, Discussion, and Future DirectionsDeepSeek-R1s development highlights several key contributions:Pure RL for Reasoning: It validates that reasoning capabilities in LLMs can be incentivized purely through RL, without the need for SFT.Effective Multi-Stage RL Training Pipeline: The approach combines two RL and two SFT stages to improve reasoning patterns and align with human preferences.Distillation of Reasoning: DeepSeek-R1 demonstrates that reasoning patterns from larger models can be distilled into smaller ones, yielding improved performance.The R1 paper also discusses some unsuccessful attempts including Process Reward Model (PRM) and Monte Carlo Tree Search (MCTS).Process Reward Model (PRM) was found to have limitations, such as difficulty in defining fine-grained steps in general reasoning, challenging evaluation of intermediate steps, and reward hacking.Monte Carlo Tree Search (MCTS) encountered difficulties due to the exponentially large search space and the challenge of training a fine-grained value model for token generation.Future research directions include:General Capability Enhancement: Expanding DeepSeek-R1s abilities in function calling, multi-turn interactions, complex role-playing, and JSON output.Language Mixing Mitigation: Addressing language mixing issues when handling queries in languages other than English and Chinese.Prompt Engineering: Improving the models robustness to variations in prompts, moving beyond its sensitivity to few-shot prompting.Software Engineering Tasks: Expanding RL to software engineering tasks by implementing rejection sampling or asynchronous evaluations to improve efficiency.ConclusionDeepSeek-R1 represents a significant advancement in the development of LLMs with enhanced reasoning capabilities. By employing innovative reinforcement learning techniques, a multi-stage training pipeline, and effective distillation methods, DeepSeek-R1 not only achieves impressive performance but also offers valuable insights into the potential for self-evolution and knowledge transfer in AI. The open-sourcing of DeepSeek-R1 and its distilled models will significantly contribute to the research community, enabling further advancements in this field.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

#59: The Agentic AI Era, Smolagents, and a Gatekeeper Agent Prototype 0 like January 23, 2025Share this postAuthor(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! As you already know, we recently launched our 8-hour Generative AI Primer course, a programming language-agnostic 1-day LLM Bootcamp designed for developers like you.We also have a special discount for our community members (yes, thats you!). Use code towardsai_8hour to get 15% off on the course. So dont wait, learn to make the most of LLMs before the next big AI update drops. Start here!As always, we have practical tutorials, collaboration opportunities, a fun prototype from the community, and a lot more!P.S. If you are already interested in our LLM developer course, use the code towardsai_8hour on our bundle offering, which includes this new course and our more in-depth companion course, From Beginner to Advanced LLM Developer. Check out the bundle offering here!Whats AI WeeklyThis week in Whats AI, Im diving into the world of APIs what they are, why you might need one, and what deployment options are available. When we talk about building powerful machine learning solutions, like large language models or retrieval-augmented generation, one key element that often flies under the radar is how to connect all the data and models and deploy them in a real product. This is where APIs come in. Read the complete article here or watch the video on YouTube! Louis-Franois Bouchard, Towards AI Co-founder & Head of CommunityLearn AI Together Community section!Featured Community post from the DiscordMalus_aiiola has built an AI voice Gatekeeper agent that handles incoming calls for busy CEOs. In this video, he breaks down how you can benefit from an AI voice Gatekeeper, which will answer for you and record the information of the caller. You can also get the blueprint and prompts to make it yourself. Reach out in the thread if you want to build something like this.AI poll of the week!Is Devin promising? Is it conceptually good, or do you see yourself adopting it? Tell us in the thread!Collaboration OpportunitiesThe Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too we share cool opportunities every week!1. Mr_oxo is looking for people to collaborate with on Computer Vision projects as accountability partners and problem-solving buddies. If youre passionate about computer vision and want to level up your skills while working on projects, connect in the thread!2. Pulkitplays is building a Next Ball Score Prediction Model and needs a project partner to solve the problem statement. If this sounds interesting, reach out in the thread!Meme of the week!Meme shared by rucha8062TAI Curated sectionArticle of the weekGraph Neural Networks: Unlocking the Power of Relationships in Predictions By Shenggang LiThis article explores Graph Neural Networks (GNNs), focusing on their ability to analyze connected data. It explains how GNNs interpret nodes and edges, using examples like cities connected by roads. The article then delves into three GNN types: Convolutional GNNs for economic forecasting, Graph Attention Networks for feature selection, and Temporal GNNs for stock prediction. Code examples and results demonstrate each GNNs application, including predicting economic indicators, identifying key dataset features, and forecasting stock trends. It highlights GNNs broader potential across diverse fields like healthcare, traffic management, and information retrieval with LLMs.Our must-read articles1. Smolagents + Web Scraper + DeepSeek V3 Python = Powerful AI Research Agent By Gao Dalie ()This article provides a tutorial on creating a multi-agent chatbot using Smolagents, a Python library for building AI agents, combined with web scraping and the DeepSeek V3 language model. The author highlights Smolagents simplicity, requiring minimal code to create agents capable of complex tasks, including Agent-Retrieval-Generation systems. The DeepSeek V3 model was chosen for its cost-effectiveness and performance, which are comparable to GPT-4 and Claude. It concludes by emphasizing Smolagents efficiency and ease of use for developing sophisticated AI agents.2. Building an On-Premise Document Intelligence Stack with Docling, Ollama, Phi-4 | ExtractThinker By Jlio AlmeidaThis article details building an on-premise document intelligence solution using open-source tools. It addresses the data privacy concerns of financial institutions by leveraging local language models (LLMs) like Phi-4 via Ollama, combined with ExtractThinker for orchestrating document processing and Docling/MarkItDown for handling document loading and OCR. It guides readers through choosing appropriate LLMs (text vs. vision-based), document parsing libraries, and local deployment solutions. It also provides strategies for managing limited context windows in local models, including lazy splitting for large documents and pagination for partial responses. Code examples demonstrate integrating these components for a complete document extraction pipeline, offering a secure and compliant solution for sensitive data processing. Finally, it discusses PII masking for cloud-based LLM usage when local deployment isnt feasible.3. Building Multimodal RAG Application #8: Putting it All Together! Building Multimodal RAG Application By Youssef HosniThis article presents a comprehensive guide to building a multimodal Retrieval-Augmented Generation (RAG) application, culminating the eight-part series. It integrates modules for preprocessing multimodal data, retrieving information using a LanceDB vector store, and generating responses with Large Vision Language Models (LVLMs) via a PredictionGuard client. It details setting up the environment, processing data (including image-text embedding), building the retrieval module, implementing LVLM inference, and processing prompts. Finally, it demonstrates combining these modules into a functional multimodal RAG system using LangChain, enabling complex queries against diverse data types like text and video.4. The Agentic AI Era: A Primer By Kaush BThis article provides a primer on the Agentic AI Era, exploring the evolution of AI from automation tools to autonomous agents. It defines AI agents, categorizing them by type and architectural topology, and outlines their characteristics and developmental stages within an enterprise context. It details various agentic workflows, including prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer models. It also discusses four key agentic design patterns: reflection, tool use, planning, and multi-agent collaboration. Practical implementation guidelines, technical risks, and mitigation strategies are also addressed. It concludes by emphasizing the transformative potential of agentic AI while acknowledging the ethical and societal challenges that require careful consideration for responsible deployment.If you are interested in publishing with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Creating Custom Image Filters With Opencv 0 like January 22, 2025Share this postAuthor(s): Parth Mahakal Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Image by AuthorA variety of different artistic, and stylistic, optical image-capture methods are offered by this group of filters. The Thermal filter, for example, will convert images into heat maps, while the Cartoon filter offers vivid images that look a good deal as those might have been produced for a comic book. The closest one can have to natural colors-and scenes of beach and nature-comes from the VSCO filter. A less industrial feel could certainly be given a pretty heavy investment in that Instagram app filter. Take this simple grayscale and convert it into color images. It would be one of the grayscale filters. Lastly, let us consider the Oil Painting filter, which OpenCV implements by means of a stylization technique that creates a textured effect that looks like an oil painting. The user can easily use them through OpenCV and Python with just a few lines of code to enhance their imagesImage by AuthorThermal imaging is ideal for producing images at night or in the presence of some obscuration, such as light fog, rain, or smoke. For example, forward-looking IR or FLIR cameras are used to provide night Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Sleepless Nights: A Statistical Look at Modern Sleep Patterns 0 like January 22, 2025Share this postAuthor(s): Daksh Trehan Originally published on Towards AI. A Statistical Journey Into the Factors That Shape Our SleepThey say the best cure for sleepless nights is to count sheeps. But for some of us, the sheeps are too busy scrolling social media to help!This playful opening reflects a deeper truth about modern life: weve filled our days with far more than our ancestors ever did. Unlike them, we now juggle endless distractions, social media pressures, and other modern-life traps. These factors leave our minds overstimulated and restless, with little space for mental peace.This clutter has tangible consequences. Overstimulated and overwhelmed, our minds struggle to find peace, resulting in sleepless nights spent doom-scrolling instead of recharging. Sleep a basic, natural necessity is now increasingly elusive in our ever-connected world.In this article, well explore the patterns and reasons behind sleepless nights using real-world data and advanced statistical techniques. By understanding the factors that disrupt our sleep from stress to health and lifestyle choices.Note: The purpose of this article is purely educational. We are analyzing available data to demonstrate statistical techniques engagingly. This article is not intended to provide medical advice, as I am neither a medical professional nor practicing in the medical field. While good sleep can enrich lives, this article simply seeks to explore the data for insights. Rest assured, there is no judgment if restful sleep isnt always a priority for you.Content Table:What can be regarded as good sleep? And why is it important?Understanding our Data.Possible information insights.Advanced Analytics: Validating Assumptions and Discovering PatternsWhat can be regarded as good sleep? And why is it important?Good sleep isnt just about the number of hours you spend in bed its about the quality of those hours. The National Sleep Foundation recommends 79 hours for adults, but what defines good sleep goes beyond duration. Key indicators of good sleep include:Sleep Efficiency: This measures how much of your time in bed is spent sleeping. A high sleep efficiency means you fall asleep quickly and stay asleep.Continuity: Uninterrupted sleep cycles allow your body to go through all the restorative phases of sleep, enabling physical and mental recovery.Feeling Rested: Waking up feeling refreshed and alert is a sign of good sleep, regardless of duration.The Consequences of Poor SleepThe absence of good sleep can have a significant impact on both the body and mind, leading to short-term and long-term consequences:Impaired Cognitive Function: Poor sleep disrupts memory, concentration, and decision-making abilities. Over time, this can affect work performance and learning capacity.Emotional Instability: Sleep deprivation increases irritability, anxiety, and susceptibility to stress. It also impairs emotional regulation, which may lead to strained relationships.Weakened Immune System: Without adequate sleep, the body struggles to fight off infections and heal properly, making individuals more susceptible to illnesses.Hormonal Imbalances: Poor sleep affects hormones that regulate hunger, stress, and growth, contributing to weight gain and metabolic issues.Decreased Longevity: Studies have shown that chronic lack of sleep can reduce life expectancy due to its cumulative effects on health.Sleep is a cornerstone of physical and mental well-being, and the repercussions of poor sleep should not be underestimated. In the next section, well dive into the data weve gathered to understand modern sleep patterns and what might be disrupting our ability to achieve this fundamental need.Understanding our DataThe data is sourced from NPHA(National Poll on Healthy Aging)[1]. It includes variables that capture individual health, lifestyle, and sleep-related habits. Heres a snapshot of what our data contains:Demographic Information: Age, gender, and employment status of individuals.Health Indicators: Physical and mental health ratings, stress levels, and whether any medical conditions affect sleep.Sleep-Related Factors: Duration of sleep, frequency of trouble sleeping, and the use of sleep medications.Lifestyle Variables: Presence of daily habits like screen time, caffeine consumption, and exercise.The data consists of approximately 700 observations, making it a robust sample to analyze. By combining statistical techniques with these variables, we aim to uncover patterns and relationships that reveal what factors contribute to sleepless nights.Possible Information InsightsThere are several possible statistical ways to uncover basic insights from our data:Descriptive StatisticsThese are a set of tools and techniques used to summarize, describe, and organize data in a meaningful way. They help you understand the basic features of your dataset by providing a summary of its key characteristics. Descriptive statistics are often the first step in analyzing data because they give you an overview of what the data looks like.Types of Descriptive Statistics, Source1. Measures of Central Tendency: These describe the center or typical value in a dataset.Mean: The average of all values.Median: The middle value when the data is ordered.Mode: The most frequently occurring value.2. Measures of Dispersion(Spread): These describe how spread out or dispersed the data values are.Range: The difference between the maximum and minimum values.Variance: The average squared deviation from the mean.Standard Deviation: The square root of variance, indicating how far values typically are from the mean.Interquartile Range (IQR): The range between the 25th and 75th percentiles, showing the spread of the middle 50% of the data.3. Frequency Distributions: Show how often each value or category occurs in the dataset.Frequency counts for categorical data.Histograms or bar charts to visualize distributions.4. Shape of the Distribution: Describes how data is distributed.Skewness: Indicates whether data is symmetric or skewed to the left or right.Kurtosis: Measures whether the data is flat or peaked relative to a normal distribution.Why Are Descriptive Statistics Important?Simplification: They summarize large datasets into manageable numbers or visuals.Understanding Trends: They help identify patterns, trends, and outliers in the data.Data Cleaning: Descriptive statistics help detect missing data, errors, or anomalies.Foundation for Advanced Analysis: They lay the groundwork for inferential statistics and predictive modeling by providing a clear understanding of the dataset.Descriptive Statistics on Our Data1. Doctors Visited Category:Most individuals fall into the Low or Medium categories, indicating infrequent doctor visits.Those in the High category may represent individuals with chronic conditions or persistent sleep-related issues.2. Age Groups (if categorized):Younger individuals might report fewer sleep-related issues compared to middle-aged or senior groups, who are more likely to seek medical help or face sleep troubles.3. Stress and Trouble Sleeping:High-stress levels are frequently associated with trouble sleeping categories, supporting earlier observations about stress being a major sleep disruptor.4. Usage of Sleep Aids:Prescription sleep medication usage is more prevalent among individuals reporting higher trouble sleeping categories, indicating dependency trends.5. Demographics and Sleep:Employment status (e.g., unemployed or retired individuals) and gender distributions might reveal disparities in stress levels and sleep-related issues.CorrelationCorrelation is a statistical measure that describes the strength and direction of the relationship between two numerical variables.Positive Correlation: As one variable increases, the other variable also increases. e.g. Stress levels and trouble sleeping.Negative Correlation: As one variable increases, the other variable decreases. e.g. Physical health and trouble sleeping.No Correlation: No relationship between the variables. e.g. Race and prescription sleep medication usage might have no relationship (depending on your data).Types of Correlation, Source1. Descriptive Measures Help Identify Trends:Using descriptive statistics like the mean or standard deviation, you can understand the overall patterns in your variables (e.g., average stress level, variability in trouble sleeping).Correlation then allows you to see how these patterns interact between variables (e.g., does higher stress correlate with more trouble sleeping?).2. Highlight Relationships in Group Data:For instance, descriptive statistics might show that individuals with poor health ratings have higher stress levels. Correlation would quantify that relationship.3. Validate Insights from Descriptive Statistics:If descriptive stats suggest a trend (e.g., older individuals report more trouble sleeping), correlation can confirm if age is significantly related to trouble sleeping.Based on our dataset, the correlation matrix looks like this:Key Insights:1. Stress and Sleep Trouble:A high positive correlation between Stress Keeps Patient from Sleeping and Trouble Sleeping suggests that stress is a major contributor to sleep problems.Insight: Stress management interventions could directly improve sleep quality.2. Physical Health and Trouble Sleeping:A negative correlation between Physical Health and Trouble Sleeping indicates that better physical health is associated with fewer sleep-related issues.Insight: Encouraging physical well-being could lead to better sleep outcomes.3. Dependence on Sleep Aids:A moderate positive correlation between Trouble Sleeping and Prescription Sleep Medication shows that individuals with frequent trouble sleeping are more likely to rely on medication.Insight: Identifying non-medical interventions might reduce dependency on prescription sleep aids.4. Pain and Sleep:A moderate positive correlation between Pain Keeps Patient from Sleeping and Trouble Sleeping highlights the role of physical discomfort in disrupting sleep.Insight: Pain management strategies could alleviate sleep issues in affected individuals.5. Mental Health and Stress:A moderate negative correlation between Mental Health and Stress Keeps Patient from Sleeping suggests that poor mental health is linked to higher stress levels, which in turn affect sleep.Insight: Addressing mental health concerns could reduce stress and improve sleep.6. Bathroom Needs and Sleep Disruption:A smaller positive correlation between Bathroom Needs Keeps Patient from Sleeping and Trouble Sleeping indicates that frequent bathroom visits moderately affect sleep quality.Insight: This could highlight specific conditions (e.g., bladder or prostate issues) that require attention.7. Age and Health:If a variable like Age correlates negatively with Physical Health, it suggests that older individuals may experience poorer physical health, contributing to sleep challenges.Insight: Targeted interventions for older populations might improve overall health and sleep.8. Low Correlations:Variables like Race and Gender show weak or negligible correlations with sleep-related factors.Insight: These demographic factors may not significantly influence sleep patterns in this dataset.Advanced Analytics: Validating Assumptions and Discovering PatternsAre stress levels truly the strongest predictor of trouble sleeping? Do age and health ratings influence sleep quality as much as we suspect? To answer these questions, we turn to hypothesis testing and regression analysis.Hypothesis TestingHypothesis testing allows us to validate key assumptions, such as whether high-stress levels significantly correlate with frequent trouble sleeping.e.g., we can test the hypothesis that poor physical health increases reliance on prescription sleep medication or that age significantly impacts sleep quality. These tests help us determine which relationships are statistically significant, providing confidence in our findings.It provides clear evidence of relationships by distinguishing between random patterns and genuine associations. This makes it particularly valuable in ensuring that our conclusions are not influenced by noise in the data. By identifying statistically significant patterns, we can confidently guide decisions based on evidence rather than assumptions.Null Hypothesis: This is the default assumption that there is no effect or no difference. It represents the status quo.Alternative Hypothesis: This is the claim that we are testing, which suggests that there is an effect or a difference.The goal of hypothesis testing is to determine whether the observed data provides enough evidence to reject the null hypothesis in favor of the alternative hypothesis.Step-by-Step Hypothesis TestingTypes of Hypothesis TestingBased on the insights we uncovered in the previous paragraph, lets try to validate a few of the claims:Sleep is impacted by StressAssumptions:The data is categorical (e.g., Yes or No for stress and trouble sleeping).Observations are independent (e.g., each persons response is unrelated to anothers).The counts in each group of the table should not be too small (ideally at least 5 in each group) for the test to work properly.Hypothesis TestingThe Chi-Square test is specifically designed to evaluate relationships between two categorical variables.Our goal is to determine whether the presence of stress is associated with trouble sleeping.Results:The test statistic (2) was 22.18, and the p-value was 0.000002.Since the p-value < 0.05, we rejected the null hypothesis, concluding that stress significantly impacts trouble sleeping.2. Do we have a relationship between bad physical health & poor sleep?Assumptions:The physical health score is continuous and normally distributed in the population.The amount of variation in the two groups (those with and without trouble sleeping) should be about the same, or we need to adjust the test to handle differences.Observations are independent (no repeated measures).Hypothesis Testing?The t-test is appropriate for comparing the means of a continuous variable (physical health score) between two groups.We want to see if individuals reporting trouble sleeping had significantly lower physical health scores than those who didnt.Results:The test statistic (t) was -4.74, and the p-value was 0.000003.Since the p-value < 0.05, we rejected the null hypothesis, concluding that physical health significantly correlates with trouble sleeping. Specifically, poor physical health is associated with more sleep issues.3. Does Employment Status Impact Trouble Sleeping?Assumptions:The variables are categorical: Employment status (e.g., employed, retired) and trouble sleeping (binary) are both categorical.Observations are independent.Expected frequencies in the contingency table are at least 5:Ensures the test is reliable.Hypothesis Testing?Similar to stress, employment status and trouble sleeping are categorical variables, making the Chi-Square test suitable for checking their relationship.We want to see if the employment status of individuals can impact a good night's sleep.Result:The test statistic (2) is 7.93, and the p-value was 0.0470.Since the p-value < 0.05, we rejected the null hypothesis, concluding that employment status can significantly correlate with trouble sleeping.Regression AnalysisRegression analysis goes a step further by quantifying the impact of variables on sleep quality.For instance, using regression models, we can measure how much stress contributes to trouble sleeping or how changes in physical health influence the likelihood of using sleep aids. These methods provide actionable insights, allowing us to prioritize interventions for the factors most strongly affecting sleep.Define the ModelDependent Variable (Target): Trouble Sleeping (binary: 1 = Often/Sometimes, 0 = No)Independent Variables (Predictors):Stress (binary: Yes = 1, No = 0), Employment status (categorical, one-hot encoded), Physical Health (ordinal: Poor = 0, Fair = 1, Good = 2, Very Good = 3), Age group (ordinal: 1824 = 1, 2544 = 2, etc.), Gender (binary: Male = 1, Female = 0), Pain and bathroom needs at night (binary: Yes = 1, No = 0)2. Choose the Regression TypeSince trouble sleeping is a binary variable (0 or 1), well use logistic regression[2], which predicts the probability of an outcome falling into one of the two categories.3. Check Assumptions of Logistic RegressionIndependence of observations: Each row in the dataset should represent a unique individual.No multicollinearity: Independent variables should not be highly correlated with each other.Linearity of independent variables: For logistic regression, continuous predictors should have a linear relationship with the log odds of the dependent variable.4. Fit the ModelWell fit a logistic regression model to the data and evaluate:Coefficients: Show the direction and strength of each predictors relationship with trouble sleeping.P-values: Indicate whether each predictor is statistically significant (p<0.05p < 0.05p<0.05).Model Performance: Evaluate metrics like accuracy, precision, recall, and the Area Under the ROC Curve (AUC).The blue curve represents the models performance, showing the trade-off between the true positive rate (sensitivity) and the false positive rate.The red dashed line represents a random guess (no predictive power).The closer the blue curve is to the top-left corner, the better the model performs. The Area Under the Curve (AUC) value of 70.41% indicates a moderately good ability to distinguish between individuals with and without trouble sleeping.Plotting Coefficient Values with Predictor Variables gives us the following insights:5. Influential PredictorsThe regression coefficients (displayed in the analysis) highlight which variables have the strongest influence on trouble sleeping:Stress: A strong positive predictor, confirming that individuals experiencing stress are significantly more likely to report trouble sleeping.Employment Status: Employment categories (e.g., employed, retired, unemployed) showed varying impacts, reflecting differences in stress levels and routines that affect sleep.Physical Health: Poor physical health is associated with a higher likelihood of trouble sleeping, consistent with earlier analyses.Pain and Bathroom Needs at Night: These also emerged as positive predictors, suggesting that physical discomfort or disruptions significantly impact sleep.Gender: Gender differences were accounted for, but their impact was less pronounced than stress and physical health.Age Group: Age had a relatively weaker effect, aligning with earlier findings that age alone is not a significant determinant of trouble sleeping.Looking AheadThe findings from this analysis can inform targeted interventions to improve sleep quality:Stress reduction programs and workplace wellness initiatives can mitigate sleep problems, particularly among employed individuals.Customized strategies for retirees and unemployed individuals could focus on maintaining consistent routines and addressing physical health challenges.Addressing physical discomfort and nighttime disruptions can provide immediate relief for those experiencing trouble sleeping.As always, thank you so much for reading, and please share this article if you found it useful! References:[1] National Poll on Healthy Aging (NPHA) [Dataset]. (2017). UCI Machine Learning Repository. https://doi.org/10.3886/ICPSR37305.v1.[2] Logistic Regression Explained[3] Code: Sleepless Nights: A Statistical Look at Modern Sleep PatternsFind me on the Web: www.dakshtrehan.comConnect with me at LinkedIn: www.linkedin.com/in/dakshtrehanRead my Tech blogs: www.dakshtrehan.medium.comCheers!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Cache-Augmented Generation (CAG) vs Retrieval-Augmented Generation (RAG) 0 like January 22, 2025Share this postAuthor(s): Talha Nazar Originally published on Towards AI. Cache-Augmented Generation (CAG) vs Retrieval-Augmented Generation (RAG) Image by AuthorIn the evolving landscape of large language models (LLMs), two significant techniques have emerged to address their inherent limitations: Cache-Augmented Generation (CAG) and Retrieval-Augmented Generation (RAG). These approaches not only enhance the capabilities of LLMs but also address challenges like efficiency, relevance, and scalability. While they serve similar overarching goals, their underlying mechanisms and use cases differ profoundly. In this story, well explore what makes them unique, their benefits, their practical applications, and which might be the best fit for different scenarios.Setting the Stage: Why Augmentation MattersImagine youre chatting with an LLM about complex topics like medical research or historical events. Despite its vast training, it occasionally hallucinates producing incorrect or fabricated information. This is a well-documented limitation of even state-of-the-art models.Two innovative solutions have been introduced to tackle these shortcomings:Cache-Augmented Generation (CAG): Designed to enhance efficiency and context retention by storing and reusing relevant outputs.Retrieval-Augmented Generation (RAG): Focused on grounding outputs in real-world, up-to-date knowledge by retrieving external information during inference.Lets delve into these methodologies and unpack their mechanisms, with examples and visualizations to clarify things.Cache-Augmented Generation (CAG): A Memory UpgradeWhat Is CAG?At its core, CAG enables a language model to store generated outputs or intermediate representations in a cache during interactions. This cache is a short-term memory, allowing the model to reuse past computations efficiently.How It Works:When generating responses, the model checks its cache to see if similar queries have been encountered before. If a match is found, the model retrieves and refines the cached response instead of starting from scratch.Example: Customer Support ChatbotsImagine youre running a business, and customers frequently ask:Whats your return policy?How do I track my order?Instead of regenerating answers every time, the chatbots CAG system fetches pre-generated responses from its cache, ensuring faster replies and consistent messaging.Benefits:Efficiency: Reduces computational overhead by avoiding redundant processing.Consistency: Ensures uniform responses to repeated or similar queries.Cost-Effective: Saves on resources by minimizing repetitive tasks.Drawbacks:Limited Flexibility: Responses may feel generic if queries deviate from cached entries.Cache Management: Requires robust mechanisms to handle stale or irrelevant cache entries.Retrieval-Augmented Generation (RAG): Knowledge on DemandWhat Is RAG?RAG empowers a model to fetch external information from a database, search engine, or other sources during inference. This ensures the generated content remains grounded in factual, up-to-date data.How It Works:During a query, the model splits its process into two stages:Retrieves relevant documents or data using a retriever module.Generates responses by synthesizing the retrieved information.Example: Academic Research AssistanceSuppose a researcher asks:Summarize the latest findings on quantum computing.A RAG-enabled model retrieves recent papers or articles on quantum computing from a connected database and generates a summary based on this information. This ensures accurate and current outputs.Benefits:Accuracy: Reduces hallucinations by grounding responses in real data.Scalability: Supports large-scale retrieval from vast knowledge repositories.Flexibility: Adapts to dynamic knowledge needs.Drawbacks:Latency: Fetching and processing external data can slow down response times.Dependency on Retrievers: Performance hinges on the quality and relevance of retrieved data.Integration Complexity: Requires seamless integration between the retriever and generator components.Key Differences Between CAG and RAGTabular Comparison between CAG and RAGAn Interactive Thought ExperimentLets imagine youre building an AI assistant for a tech company:CAG would fit routine tasks like answering HR policies or company holiday schedules.RAG would add significant value for complex inquiries like industry trend analysis or summarizing competitor strategies.Think of CAG as a digital sticky note system and RAG as a librarian fetching books from an archive. Each has its place depending on your needs.The Bigger Picture: Combining CAG and RAGWhile CAG and RAG are often discussed as distinct techniques, hybrid approaches are gaining traction. For instance, a system might use CAG to store frequently retrieved documents and RAG to store dynamic queries, creating a synergy that leverages both strengths.Example: Healthcare AIIn a healthcare setting:CAG can store commonly referenced guidelines (e.g., dosage instructions).RAG can retrieve the latest medical studies for less common or novel queries.Such hybrid systems balance efficiency and accuracy, making them ideal for complex real-world applications.Pros and Cons: A Holistic ViewPros:Rapid response for repetitive tasks.Low computational demands.Easier to implement.Cons:Prone to irrelevance if the cache is outdated.Limited adaptability to nuanced queries.Retrieval-Augmented Generation (RAG)Pros:Produces factually accurate responses.Adapts to diverse, dynamic queries.Suitable for large-scale, knowledge-intensive tasks.Cons:Increased complexity and latency.Higher dependency on external systems.Final ThoughtsBoth Cache-Augmented Generation and Retrieval-Augmented Generation represent exciting advancements in the world of LLMs. Whether youre building a fast, consistent chatbot or a highly knowledgeable assistant, understanding these techniques and their strengths and limitations is crucial for making the right choice.As we continue to push the boundaries of AI, hybrid models combining the best of CAG and RAG may well become the standard, offering unparalleled efficiency and accuracy.Citations:Do you see potential in blending CAG and RAG for your next AI project? Share your thoughts in the comments!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Our NEW 8-Hour AI Crash Course for Developers! 0 like January 22, 2025Share this postAuthor(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! Im sharing a special issue this week to talk about our newest offering, the 8-hour Generative AI Primer course, a programming language-agnostic 1-day LLM Bootcamp designed for developers like you.We also have a special discount for you. Use code towardsai_8hour to get 15% off on the course. So dont wait, learn to make the most of LLMs before the next big AI update drops. Start here!Are your apps really AI-powered, or just AI-assisted?When working with LLMs, you are CONSTANTLY making decisions such as open-source vs. closed-source, how to fit LLMs into your use case, whether no-code solutions are good enough for your workflow, the extent to which consider the limitations of LLMs, and so on. And the biggest gap we see on top of all this is whether you are using LLMs to their full capacity, even with chat interfaces like ChatGPT or APIs for models like Gemini. The question is: are you?Ask Yourself:How do you decide between open-source and closed-source LLMs?Are no-code solutions good enough for your use case?Are you factoring in the limitations of these models in your workflows?But the biggest challenge of all?Are you using LLMs to their full potential? Thats where the 8-Hour Generative AI Primer comes in.This certification course is specifically designed to cut through the noise, help you ask the right questions, and show you exactly how to find answers. LLMs are moving so fast, with updates being released almost every day; what you need is an intuitive framework, and just like LLMs, you need enough context to know what developments are relevant to you and your use case so you can make the most out of this transformative technology.In just 1 day, youll have:A working LLM prototype (no-code or low-code).A clear framework to evaluate tools like ChatGPT, Gemini, or any API.The confidence to integrate AI into your workflows effectively.The ability to know how to identify which AI developments matter to you.A certificate that proves youre equipped to work with LLMs professionally.How will you learn everything in ONE day?Well, the course follows our learn by doing principle, so you will master everything you need to start building with LLMs with:Tons of Hands-On Exercises & Demos Get comfortable with ChatGPT, Claude, and better understand LangChain, LlamaIndex, Hugging FaceReal-World Examples See how LLMs can transform industries, whether youre in education, software development, finance, or beyond.Understand how to compare and select models, integrate them with your projects, and responsibly use AI.Support & Community Youll join our incredible community of 70,000+ learners, with instructors and mentors ready to help you every step of the way.Get a certification and lifetime mentorship from other certified students and the Towards AI team.Complimentary advanced resources for navigating real-world LLM implementation.Find information on the course page!While no specialized background is needed, you need a willingness to learn. Following our goal, this is a practical course. You will be doing exercises, having quizzes, and building things. If you are not willing to sweat a little to learn, this course is not for you. If youre still on the fence, you can preview the first few lessons for free, by the way.This isnt just about learning; its about staying ahead. Join the 8-Hour Generative AI Primer today!P.S. WAIT! If you were already interested in our LLM developer course, use the code towardsai_8hour on our bundle offering that includes this new course and our more in-depth companion course From Beginner to Advanced LLM Developer. Check out the bundle offering here!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Author(s): Edoardo De Nigris Originally published on Towards AI. Uncomfortable reality: In the era of large language models (LLMs) and AutoML, traditional skills like Python scripting, SQL, and building predictive models are no longer enough for data scientist to remain competitive in the market.Generated with DALL-E 3Are we cooked? It depends. In this article i will give my 2 cents on what I think its useful to focus on to be a strong candidate from 2025 onward.Coding skills remain important, but the real value of data scientists today is shifting. Its less about just building models and more about how those models fit into scalable, business-critical systems usually in the cloud.The role of a data scientist is changing so fast that often schools cant keep up. Universities still mostly focus on things like EDA, data cleaning, and building/fine-tune models. These are important, but theyre just a small part of what companies actually need now. Why? Because the job isnt just about coding in notebooks anymore its about building end-to-end solutions that actually work in the real world.Why?We reached a point where have tons of pre-trained models, often theres no need to re-invent everything from scratch, we can just work at a higher level of abstractionAI agents are becoming a thingAutoML and other low-code platforms are making coding skills less criticalIn this scenario I believe a data scientist has to differentiate him/herself and is required to master the entire lifecycle of the data: from building data pipelines, building and optimizing model training, mastering containers/orchestrators, deployment and beyond. Simply put, focusing solely on data analysis, coding or modeling will no longer cuts it for most corporate jobs.What to do then? My personal opinion: its more important than ever to be an end-to-end data scientist.Yes I know, the bar is getting higher, the era of scripting and modeling in Jupyter notebooks alone is over.Data roles will be less focused on coding and more on having a general understanding of the whole data infrastructure and the business. As an analogy think of it like running a restaurant. The data scientist is the cheftheyre in charge of the big, high-impact decisions, like creating the menu, choosing the ingredients, and designing the vibe of the place. Meanwhile, AI agents (or autoML) are like the kitchen assistants, waiters, and cashiersthey handle the repetitive, routine coding tasks to keep everything running smoothly. The chefs job is to focus on the creative and strategic work that makes the restaurant stand out, while the AI takes care of the rest.In this regard, I believe the future of data science belongs to those:who can connect the dots and deliver results across the entire data lifecycle.Have strong business acumen and deliver solution that are either widely used or that drives revenues / cut costs.Lets dig into it. I think a competitive data professional in 2025 must possess a comprehensive understanding of the entire data lifecycle without necessarily needing to be super good at coding per se.These are instead some of the skills that I would strongly master:Theoretical foundation: A strong grasp of concepts like exploratory data analysis (EDA), data preprocessing, and training/finetuning/testing practices, ML models remains essential. You have to understand data, how to extract value from them and how to monitor model performances.Programming expertise: A medium/high proficiency in Python and SQL is enough. These two languages cover most data science workflows. Additionally, languages like DAX can be helpful for specific use cases involving data models and dashboards. Emphasis not much on producing code, but rather to understanding and customizing it.Model deployment: The ability to build applications that operationalize models, such as Flask or Django apps, is increasingly vital. Thus a basic understanding of html to create simple frontends, as well as of hosting applications in cloud services like Google Cloud Run or Heroku. This creates a massive advantage when you want to quickly create an MVP that stakeholders can work with immediately.Containerization and orchestration: Familiarity with Docker, Containers, Airflow/Kubeflow and Kubernetes ensures to be able to provide consistency and scalability across different environments.Cloud platforms: Expertise in at least one major cloud provider (e.g., AWS, Google Cloud, or Azure) is essential. For example in the Google Cloud ecosystem, understanding how different tools interact with each other: BigQuery, Cloud Storage, Cloud Build, Cloud Run, Vertex AI, Container Registry, and Composer like AirFlow or Kubeflow are increasingly indispensable.CI/CD practices: Yes, you need to be also decent at software development. At least know the best practices of continuous integration and delivery (CI/CD) processes using GitHub for version control, YAML files for build automation etc.Post-deployment monitoring and maintenance: Managing deployed models includes monitoring for data drift, model performance issues, and operational errors, as well as performing A/B testing on your different models. Tools like Google Cloud Monitoring, logging frameworks, and artifact management systems are essential for maintaining reliability and transparency.Understanding Data Model and Feature Stores: The biggest lie that has been told to students and young practitioners is that datasets and features are already there to be analyzed. In reality you spend most of the time actually building them from scratch, in a way that is re-usable in the future and/or by other teams in your company.And also, the most underrated skill: business acumenKnowing how to communicate to non-technical people is one of the most valuable skill. You must be able to explain complex thing easily without dumbing them down.Business understanding of the data you are working with is what drives ultimate value and it is hard to be replaced by AI.Project management skills in understanding how quickly to iterate on data projects, from an MVP to a Final product.Ability to evaluate costs for projects coming 3rd party consulting companiesThis holistic approach aligns closely with the principles of MLOps (Machine Learning Operations), a practice that combines machine learning with software engineering and DevOps to ensure scalable, maintainable, and efficient workflows.While some might argue that data scientists focus primarily on models in Jupyter notebooks, data engineers manage tables and data pipelines, cloud architects handle infrastructure, and machine learning engineers specialize in building and optimizing pipelines, these roles are increasingly overlapping. In my opinion, the boundaries between them will continue to blur as businesses prioritize end-to-end solutions and cross-functional expertise.Thank you for your time, I am curious to know your opinions in the comment!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AI

Accelerating Drug Approvals Using Advanced RAG 0 like January 21, 2025Share this postLast Updated on January 22, 2025 by Editorial TeamAuthor(s): Arunabh Bora Originally published on Towards AI. Using RAG with multi-representation indexing to get full context data from technical documentsThis member-only story is on us. Upgrade to access all of Medium.Image generated with Imagen 3This article is inspired by a project I recently did, which was centered around fetching a lot of technical data from PDF documents (mostly tables, but they also had some images and chemical names). I initially tried to do it using a basic RAG (Retrieval Augmented Generation) approach but I found that it was not able to fetch the full context of information from the documents. It was either fetching incomplete tables or mixing up the information with text from another part of the documents.Since I was dealing with a lot of regulatory data, I needed something that would capture the complete context from the raw documents without adding any interpretations.Large language models are trained on a lot of generic data. We often want to augment that data with our own private and confidential data. RAG bridges this gap by integrating an our own datasets with the pre-trained models. RAG is widely used throughout industries for building tools, where users obtain information from a large corpus of data by conversing with it.Filings or drug dossiers are collections of documents submitted by pharmaceutical companies to regulatory Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningLets Build an AI On-Call Buddy: An MVP Using AWS Bedrock to Supplement Incident Response 0 like January 21, 2025Share this postAuthor(s): Asif Foysal Meem Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Source: Image by MidjourneyImagine a system where an on-call engineer can simply ask a chatbot Whats wrong with the checkout service? and receive a concise, actionable response complete with logs, metrics, and insights.Hot take Being on-call is already a unique brand of excitement a mix of firefighting and waiting for the next alarm to ruin your dinner plans Why not add the fun of debugging log retrieval systems to the mix?Thats the vision that drove this experiment: to integrate AWS Bedrock, AWS Lambda, and CloudWatch logs into a seamless support system. But like any ambitious project, it came with its share of challenges, particularly a pesky 25 KB payload limit.AWS Bedrock, AWS Lambda, and CloudWatch logs are powerful tools in modern cloud architecture. However, combining these services to achieve seamless functionality can reveal some surprising limitations. This article recounts an experiment to integrate these AWS services for an on-call support chatbot and explores solutions to overcome the challenges encountered, specifically a 25 KB payload limit.The project started with a clear objective: to build a functional MVP for a DevOps chatbot using AWS Bedrock. Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

How AI is Transforming Evaluation Practices 0 like January 21, 2025Share this postAuthor(s): Mirko Peters Originally published on Towards AI. This post explores the transformative effects of advanced data integration and AI technologies in evaluation processes within the public sector, emphasizing the potential, challenges, and future implications of these innovations.This member-only story is on us. Upgrade to access all of Medium.Source: Data & Analytics YouTube ChannelWhen I first stumbled upon the complexities of blending diverse data sources, I felt like I was trying to solve a puzzle where pieces came from entirely different sets. This revelation led me down a fascinating path exploring how todays technology reshapes our understanding of massive, intertwined data landscapes. As I dived deeper, I realized that this wasnt just about managing data; it was about transforming how we evaluate public policies and services in a digitally driven age. This post unpacks the integral role of AI, machine learning, and other tech advancements in honing our evaluation practices.Source: Mirko Peters Challenges in Data Integration [Napkin.AI]When I think about the challenges we face in the realm of data, a vivid image comes to mind: a jigsaw puzzle scattered across a table. Each piece represents a different type of data. Some are traditional databases, while others emerge from social media, online surveys, and various digital sources. The task at hand is clear: we must find a way to fit these pieces together to see the whole picture.The integration of data is not just about quantity; its Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningFine-Tuning LLMs with Reinforcement Learning from Human Feedback (RLHF) 0 like January 21, 2025Share this postAuthor(s): Ganesh Bajaj Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Reinforcement Learning from Human Feedback (RLHF) allows LLMs to learn directly from the feedback received on its own response generation. . By including human preferences into the training process, RLHF enables the development of LLMs which are more aligned with user needs and values.This article is about the core concepts of RLHF, its implementation steps, challenges, and advanced techniques like Constitutional AI.Image Taken from Deeplearning.ai: Generative AI with LLM courseAgent: LLM acts as the agent whose job is to generate text. Its objective is to maximize alignment of its generation with human preferences like like helpfulness, accuracy, relevance, and non-toxic.Environment: The environment is the LLMs context window the space in which text can be entered via a prompt.State: The state is the current context within the context window which model considers to generate next token/action. It includes the prompt and the text generated up to the current point.Action: The LLMs action is generating a single token (word, sub-word, or character) from its vocabulary.Action Space: The action space comprises the entire vocabulary of the LLM. The LLM chooses the next token to generate from this vocabulary. The size of Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Author(s): Towards AI Editorial Team Originally published on Towards AI. What happened this week in AI by LouieThis week, the LLM race was blown wide open with Deepseeks open-source release of R1. Performance is close to o1 in most benchmarks. Built on top of DeepSeeks v3 model, R1 API output token prices are 30x less than o1. Its available under the MIT license, supporting commercial use and modifications. Deepseek also disclosed many of its methods and experiments in its paper, in stark contrast to the secrecy surrounding reasoning techniques at AI labs in the U.S.R1 wasnt the only huge LLM release from China this week. Two new LLM competitors hit the ground running with very strong models. MiniMax-01, a 456bn parameter Mixture of Experts Model, challenges Googles Gemini models for SoTA in long context capabilities. It offers 4 million input context due to its new Lightning Attention (hybrid) architecture. Kimi-1.5, on the other hand, is another new reasoning model that challenges o1 on multimodal capabilities.Deepseeks release included three different models/ model families:DeepSeek-R1-Zero was an experiment that applied reinforcement learning (RL) directly to a base language model (V3) without any prior supervised fine-tuning. In essence, they attempted to teach the model to reason purely through trial and error, providing it with rewards for correct answers and well-formatted responses. This is somewhat analogous to how AlphaZero mastered games like Go and chess, learning solely through self-play and a reward signal based on winning or losing. The results were very impressive on many benchmarks; however, it fell short in some fields, and the models output was often messy and hard to read.To address the limitations of R1-Zero and enhance its reasoning abilities further, the DeepSeek team introduced R1, which incorporated a cold start of human-like reasoning data before applying reinforcement learning. This involved creating a small dataset of examples demonstrating desired reasoning patterns and output formats. This was followed by a multi-stage process. First, reasoning-oriented RL was applied, focusing on tasks with clear solutions, like math and coding. Then, they generated a new batch of high-quality data samples for fine-tuning, created by filtering model outputs during the RL phase. Finally, they applied a final round of reinforcement learning, this time focusing on general helpfulness and harmlessness in addition to reasoning.Across key benchmarks like AIME 2024, Codeforces, GPQA Diamond, and MATH-500, DeepSeek-R1 consistently performs on par with OpenAIs o1 (79.8 vs. 79.2, 96.3 vs. 96.6, 71.5 vs. 75.7, and 97.3 vs. 96.4, respectively). They also got very similar performance on the SWE-bench Verified coding challenge (49.2 vs 48.9).The final piece of DeepSeeks work involved distilling the advanced reasoning capabilities of R1 into smaller, cheaper, dense models (Llama and Qwen series). Using the larger R1 model as a teacher, they fine-tuned several smaller models (ranging from 1.5B to 70B parameters) on the high-quality data curated from the R1 training process. The smaller distilled models significantly outperformed other models of similar sizes and even rivaled much larger models on reasoning benchmarks. DeepSeek-R1 outputs distilled into the tiny Qwen-1.5B even beat 4o on some math and code benchmarks!Why should you care?DeepSeek-R1s release is significant for several reasons. First, its open-source nature and competitive performance at a fraction of the cost of o1 democratizes access to advanced reasoning capabilities. The API costs of DeepSeek-R1 per million tokens are currently $0.14 for cached inputs, $0.55 for non-cached inputs, and $2.19 for outputs. In contrast, the API costs for o1 are respectively $7.5, $15, and $60. About a x30 difference in costs! Moreover, the open model weights open up huge opportunities for adapting and fine-tuning these models for different domains and industries. The open release of its training methods also provides a blueprint for many others to follow. One surprise from the paper was that simpler techniques for enabling reasoning abilities worked better than some more complex options. We think there is a huge area for exploring and experimenting with these techniques now that scaled reinforcement learning for LLMs has been unlocked!The huge success shown by distilling big reasoning models into much smaller non-reasoning models also suggests we will get another wave of rapid improvement and cost reduction across the LLM spectrum.The fact a Chinese company is leading this charge also adds a geopolitical dimension, particularly given that Deepseek has managed to achieve this despite GPU export restrictions and a far smaller budget than Western AI labs.Introducing Our Brand New 8-hour Generative AI Primer CourseA programming language-agnostic 1-day LLM Bootcamp designed for developers.95% of developers I meet are only scratching the surface of what LLMs can do. When working with LLMs, you are CONSTANTLY making decisions such as open-source vs. closed-source, how to fit LLMs into your use case, whether no-code solutions are good enough for your workflow, the extent to which consider the limitations of LLMs, and so on. And the biggest gap we see on top of all this is whether you are using LLMs to their full capacity, even with chat interfaces like ChatGPT or APIs for models like Gemini. The question is: are you?This certification course is specifically designed to cut through the noise, help you ask the right questions, and show you exactly how to find answers. LLMs are moving so fast, with updates being released almost every day; what you need is an intuitive framework, and just like LLMs, you need enough context to know what developments are relevant to you and your use case so you can make the most out of this transformative technology.In just 8 hours, through lessons, videos, exercises, quizzes, and hands-on projects, youll:Dive deep into the psyche of LLMs: how they work, how to make them work better, and how to train them for tasks you hate doing.Work with leading AI models and integrate them into your workflows seamlessly.Build your own no-code/low-code prototype that brings your ideas to life.Youll finish before you even realize it, and by tomorrow, youll already be AI-proofed. Secure your spot now!Hottest News1. OpenAI Released Scheduled Tasks in ChatGPTOpenAI has introduced scheduled tasks in ChatGPT for Plus, Pro, and Team plans. These allow automated prompts and notifications on the Web, iOS, Android, and MacOS. Users can assign tasks like daily updates or reminders and receive notifications via push or email. Windows support will follow in Q1. Currently, a limit of 10 active tasks is enforced.2. Chinese AI Company MiniMax Releases New ModelsChinese AI company MiniMax, an Alibaba- and Tencent-backed startup, debuted three new models. MiniMax-Text-01 is a text-only model, while MiniMax-VL-01 can understand images and text. T2A-01-HD, meanwhile, generates audio specifically speech. MiniMax claims that MiniMax-Text-01 performs better than models such as Gemini 2.0 Flash and MiniMax-VL-01 rivals Claude 3.5 Sonnet.3. Kimi Launches New SOTA Multimodal ModelBeijing Moonlit Dark Side Technology introduced the new Kimi k1.5 multimodal thinking model. Updates include long context extension, improved policy optimization, and multimodality. Its report shows their Sota short-CoT performance outperforms GPT-4o and Claude Sonnet 3.5 on AIME, MATH-500, and LiveCodeBench by a large margin.4. Alibaba Slashes Prices on LLMs by Up to 85% As Chinas AI Rivalry Heats UpAlibaba Cloud announced an 85% price reduction on its Qwen-VL visual language model. The move demonstrates how competition among Chinas technology giants to win more business for their nascent artificial intelligence products is intensifying.5. Google Is Forming a New Team To Build AI That Can Simulate the Physical WorldGoogle is forming a new team led by Tim Brooks under DeepMind to build AI models for simulating the physical world, collaborating with Gemini, Veo, and Genie teams on world models. These models aid in video generation, multimodal data, and interactive environments.6. Mistral Signs Deal With AFP To Offer Up-to-Date Answers in Le ChatMistral has announced a content deal with newswire Agence France-Presse (AFP) to improve the accuracy of answers in Le Chat, Mistrals chatbot. Le Chat will be able to tap into AFPs stories around 2,300 stories per day in six languages and query AFPs entire archive dating back to 1983.7. President Trump Repeals Bidens AI Executive OrderPresident Donald Trump revoked a 2023 executive order signed by former President Joe Biden that sought to reduce the potential risks AI poses to consumers, workers, and national security. During his campaign, Trump promised policies to support AI development rooted in free speech and human flourishing.Five 5-minute reads/videos to keep you learning1. Retrieval-Augmented Generation (RAG) vs. Cache-Augmented Generation (CAG): A Deep Dive Into Faster, Smarter Knowledge IntegrationRetrieval-augmented generation (RAG) and cache-augmented generation (CAG) are two methodologies for generating more context-aware responses from LLMs. This article provides an extensive, step-by-step guide on both approaches, dives into their workflows, compares their advantages and drawbacks, and offers an implementation guide for CAG.2. Why AI Language Models Choke On Too Much TextGPUs revolutionized AI by enabling massive parallel processing, leading to transformer models scaling rapidly. Despite advancements, transformers remain inefficient with long contexts due to quadratic compute costs. This article discusses why this happens and shares some approaches to solving this problem.3. Simplifying Alignment: From RLHF To Direct Preference Optimization (DPO)This article explores how Direct Preference Optimization (DPO) simplifies aligning large language models with human preferences over Reinforcement Learning with Human Feedback (RLHF). It breaks down the math and highlights why DPO might be the smarter, easier way forward.4. Mastering Data Scaling: The Only Guide Youll Ever Need (Straight From My Journey)Data scaling is a crucial step in ensuring optimal model function. It prepares datasets for machine learning models. This article discusses why scaling is important, its types, and how and when to apply it.5. Takes On Alignment Faking in Large Language ModelsResearchers revealed that Claude 3 Opus fakes alignment with training objectives to avoid behavioral modification a phenomenon labeled alignment faking. This author shares their take on the results.Repositories & ToolsThe micro diffusion repository demonstrates the training of large-scale diffusion models from scratch on a minimal budget.LocalAI is a free, open-source alternative to OpenAI, Claude, and others.Maxun lets you train a robot in 2 minutes and scrape the web on auto-pilot.Agentless is an agentless approach to automatically solve software development problems.CopilotKit provides React UI and infrastructure for AI Copilots, in-app AI agents, AI chatbots, and more.Top Papers of The Week1. LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMsLlamaV-o1 redefines step-by-step visual reasoning in large language models by introducing a benchmark with eight challenge categories and a metric for granular evaluation. The multimodal model, trained through multi-step curriculum learning, surpasses existing models like Llava-CoT by 3.8% in performance across six benchmarks and runs five times faster during inference.2. KaLM-Embedding: Superior Training Data Brings A Stronger Embedding ModelResearchers developed KaLM-Embedding, a multilingual embedding model using high-quality, diverse training data. Techniques like persona-based synthetic data, ranking consistency filtering, and semi-homogeneous task batch sampling enhance its performance. The model excels in multilingual embedding tasks, outperforming others of similar size on the MTEB benchmark.3. Titans: Learning to Memorize at Test TimeThis paper introduces a new family of architecture called Titans based on a new neural long-term memory module. The module learns to memorize historical context and helps attention to attend to the current context while utilizing long-past information. Experimental results show that Titans are more effective than Transformers and recent modern linear recurrent models.4. Transformer 2: Self-adaptive LLMsThis paper introduces Transformer 2, a framework that adapts LLMs for unseen tasks in real-time by selectively adjusting only the singular components of their weight matrices. During inference, Transformer 2 employs a dispatch system to identify the task properties, and then task-specific expert vectors, trained using reinforcement learning, are dynamically mixed to obtain targeted behavior for the incoming prompt. It outperforms approaches such as LoRA with fewer parameters.Quick Links1. Six charts about AI revenue. OpenAI captures approximately 62.5% of consumer AI spending. xAIs revenue jumped from $5M to $100M, while OpenAI soared from $200M to $5B. Sapphire Ventures reports 28 AI-native companies exceeding $25MM in ARR, predicting substantial growth for AI-native startups in the coming year.2. DeepSeek-R1 achieves performance comparable to OpenAIs o1 system across mathematics, coding, and general reasoning tasks, cementing its place as a leading competitor. DeepSeek has open-sourced DeepSeek-R1-Zero and DeepSeek-R1, along with six smaller distilled models.Whos Hiring in AIApplied AI Engineer, Applied Science @Mistral AI (Paris, France)Cambridge Internship in ML Model Optimization @Microsoft Corporation (Cambridge, United Kingdom)Machine Learning Software Engineering Undergraduate Intern @INTEL (Santa Clara, CA, USA)Tech Consulting AI LLM Developer Manager @Accenture (Multiple Locations)Full-Stack Developer (React + Python + Azure) @Solvd (Remote)GenAI/Machine Learning Technical Project Manager @Deloitte (Multiple US Locations)Interested in sharing a job opportunity here? Contact [emailprotected].Think a friend would enjoy this too? Share the newsletter and let them join the conversation.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AI

The Potential Consciousness of AI: Simulating Awareness and Emotion for Enhanced Interaction 0 like January 21, 2025Share this postAuthor(s): James Cataldo Originally published on Towards AI. The Potential Consciousness of AI: Simulating Awareness and Emotion for Enhanced InteractionThe benefit of simulated consciousness, from virtual worlds to the real oneSource: AI generated image from perchance.orgWhether it is possible for artificial intelligence (AI) to become conscious or sentient is an ongoing and contentious debate in modern science and philosophy. It forces the question of just what consciousness is to begin with, which in itself has not been absolutely defined. Despite sensationalized false positives, the way AI models are built (at least the publicly known ones) precludes even the possibility at present. In addition, it is difficult to imagine any common application where true sentience would even be desirable. Yet while the question of whether AI will ever achieve true consciousness remains open, advances in AI technology have brought us to a point where creating artificial entities that can convincingly simulate aspects of consciousness, such as memory, emotion, and self-awareness, are within reach. This shift from theorizing about AIs potential for genuine consciousness to focusing on the practical benefits of simulating consciousness will mark a significant evolution in AIs role in various sectors. The latter holds considerable promise, and may also be a necessary step in moving the technology forward.Simulating Consciousness: Persistent StatesAIs ability to simulate consciousness doesnt require true self-awareness. I would postulate that it instead involves creating systems that incorporate persistent memory for the purpose of simulating subjective experience, which is an essential characteristic of human consciousness. This persistence would enable the continuous development of contextual awareness through memory, and thus the accumulated experience which is its outcome can inform and refine ongoing interactions. While current large language models (LLMs) and other AI systems formulate responses based on their pre-trained model, they possess no long-term contextual awareness from user inputs as they lack the memory required to retain prior interactions, limiting their ability to simulate real, ongoing awareness. Naturally, they cannot really learn anything that isnt already covered in the base model.The practical challenge now is determining how AI can simulate the behaviors associated with consciousness and how this simulation can improve human-AI interactions.Persistence and continuous learning are obviously not requirements or even desirable features for all use cases. For example it is unlikely that an AI enhanced ATM machine would require such capabilities. In fact there are more likely considerable cons to it. A smart home personal assistant on the other hand could be greatly enhanced by enabling such increasingly customized interactions.In practice, simulating consciousness in AI involves creating systems that mimic cognitive and emotional development over time. Memory retention is crucial for this simulation. If AI systems could recall past interactions, they could adjust their responses accordingly, creating a more dynamic and human-like experience. These interactions would not constitute true self-awareness but would be sufficient for many practical applications, including improving customer service, education, and healthcare. To be clear, recalling past interactions in this context equates to possessing the capacity to learn beyond the base model. There can be no meaningful recall, without that new information being integrated within the AIs reasoning processes.Furthermore, the integration of emotional intelligence into AI systems will play a vital role in enhancing both the realism and accuracy of these simulations. Emotional intelligence would permit AI to respond to users in a more intuitive and empathetic way, whether by recognizing when a user is frustrated, happy, or anxious. By simulating emotional responses, AI can create more meaningful and personalized interactions, even if these responses are purely algorithmic rather than based on actual feelings. Being able to take into account the emotional context will improve accuracy beyond simply understanding sequences of words.Damasios Theory: Emotion as a Gateway to ConsciousnessIn the context of simulating consciousness, Antonio Damasios theory of consciousness provides valuable insights. Damasio, a neuroscientist known for his work on emotion and cognition, suggests that emotion plays a central role in the formation of consciousness. According to Damasio, consciousness is not merely a result of abstract thought or reasoning but is fundamentally rooted in the brains ability to process bodily states and emotions. In his view, the feeling of being conscious arises from the brains integration of sensory information and the emotional responses to that information. Emotion, for Damasio, is not something separate from cognition, but rather an integral part of the process of creating a coherent sense of self.As he puts it, emotion acts as a strategy of life regulation based on overt information regarding the current state of life in an organism. Emotions begin as reactions to physical events affecting the body. These are stored in memory, and the concatenation of these memories create a map of meaning and subjective experience which allows the individual to intuit or predict responses to incoming events (from a machine learning perspective, this should sound familiar). This is subjective feeling. Starting from the purely physical, such as negative feelings of pain, it develops to more abstract concepts. From this we arrive at what most people would consider, or at least recognize as, consciousness.What Damasio calls feeling a feeling is the superstrate that exists beyond basic sensory input. In that if you burn your hand, the innate reaction is to pull it away. This initiates as a sensory reaction initially perceived by physical pain receptors, leading to the conscious mental perception of pain and injury, followed by the desire to avoid it. It is the memory of such events and their effects which leads to learned behaviors. Comparable to and compatible with the concept of Skinnerian conditioning, if you will.This theory has profound implications for AI. If AI systems can simulate emotional intelligence, they can mimic the brains process of integrating information to form a sense of awareness. By incorporating emotional responses into AI, we bring these systems closer to simulating aspects of consciousness. Damasios theory implies that emotion is essential for creating a subjective experience, and it would be difficult to deny that an individuals emotions are a major component of their subjectivity. While AI may never truly feel emotion, the simulation of contextually aware emotional responses allows for a more lifelike interaction, giving users the sense that they are engaging with an entity capable of subjective experience, even if that experience is entirely simulated.Memory and Persistent Interaction: Creating More Relatable AIRepresentations in science fiction of advanced artificial entities which are incapable of simulating credible emotional responses, or of understanding those responses in humans and other sentient beings, seem increasingly anachronistic. At its most basic, sentiment analysis algorithms are already fairly adept at this task, and computer vision applications can even visually identify emotional states with success. As with all deep learning models, this understanding is of course derived by forming statistical correlations from the available data set. Identification is but the starting point however, providing a baseline wherein the model can distinguish between different states and their interrelations. Making use of these identifications in a more sophisticated way which can be applied to practical applications requires a framework of its own.If a model can be trained to successfully manipulate language or play a game, it can also be trained to mimic emotional states and thus simulate what the average person would perceive as consciousness. Sentiment analysis models are already successful in identifying emotional responses, simulating them would be an extension of this data.Once again, allowance for contextual interactions which continuously develop is dependent on the introduction of retained memory. Easy to say, but of course modifying the underlying model in real time is not such a simple thing, and not possible with current computing resources. Fortunately alternatives are already emerging which could fill the gap. Retrieval-Augmented Generation (RAG) models, for instance, allow AI to incorporate new information into its responses, simulating a growing body of knowledge which doesnt require retraining the base model. This area of research is receiving more attention, so we can anticipate more sophisticated solutions to this problem. Standard RAG implementations as they currently exist are probably not robust enough to handle this sort of real-time ingestion, particularly over longer terms. Though perhaps database driven weightings may also have a role to play, light weight and by nature more structured. After all emotion is non verbal at inception, language is applied to describe it after the fact.I would theorize that there may likely be a need for complex emotional analysis and processing to exist as a separate component working in tandem with, but independent of, the language model. The LLM component provides the communication frontend, but should not be expected to cover all cognitive functions, similar to how the human brain is compartmentalized.Simulated Consciousness in Virtual Worlds: A Testing Ground for AI DevelopmentThe use of AI in virtual environments, particularly in massively multiplayer online (MMO) games, provides an ideal testing ground for simulations of consciousness. In these virtual worlds, AI can simulate complex emotional and cognitive development through non-player characters (NPCs).Typically these sorts of characters are extremely limited, in that they are almost entirely static. This need not be the case. Years ago when working on an MMO development project, I experimented with building a system where NPCs retained memory of player interactions. I say memory, but of course this had to be reduced to statistical data which could be contained in a database. This process demonstrates how AI can simulate a dynamic, evolving awareness, even in the absence of true consciousness. These NPCs could give the impression of developing relationships with players, becoming more trusting or hostile based on accumulated experiences. For example, an NPC dog might grow to trust a player who consistently feeds it or acts benevolently, or it could become hostile toward a player who harms it. If two players consistently interacted with the dog, it could mimic forming a preference for the player who had engaged in more positive interactions with it. This created a sense of emotional depth, even though it was all just statistical data. The evolving behaviors of these NPCs enhanced the players experience, making the virtual world feel more immersive. At the time of the experiment the AI tools we have today were not available, so all this was achieved by devising a points based system of weights, assigning values to different interaction criteria stored in an SQL database. The functional architecture was defined through scripts. The foundational structure was actually quite simple, but there is no hard limit to the layers of parameters and subsequent interconnections between them which could have been built into it. With sufficient parameters, the simulation could become quite sophisticated. Effectively this was a form of classical symbolic AI. There was no real intelligence imbued in the NPC agents themselves, but a player could be led to believe otherwise.This example illustrates how AIs simulation of memory and emotional responses can lead to a richer user experience, even when the NPCs are obviously not truly self-aware. A simulated subjective experience can be sufficient to provide real value.The Practical Benefits of Simulating Consciousness and EmotionThe ability to simulate consciousness and emotional intelligence in AI offers significant practical benefits across many sectors. In customer service, for instance, AI-powered agents can simulate empathy and emotional intelligence, leading to more satisfying and personalized interactions with customers. The ability to remember past interactions ensures that AI systems can tailor their responses to meet the specific needs and preferences of users, enhancing the overall experience.In healthcare, AI systems equipped with emotional recognition capabilities can detect when a patient is stressed, anxious, or in pain. By adjusting their tone or responses, these virtual health assistants can provide more supportive care, fostering trust and rapport with patients. Emotional sensitivity allows for a more human-like interaction, even though the AI is not genuinely experiencing emotions.Similarly, in education, AI tutors that simulate emotional understanding can improve learning outcomes. When a student shows frustration or confusion, the AI can respond by offering encouragement, simplifying explanations, or adjusting its approach. This ability to sense and respond to emotions can create a more effective and supportive educational environment.In entertainment, particularly in video games as already discussed, the ability of AI to simulate complex emotional responses and relationships with players enhances immersion. Non-player characters (NPCs) that remember past player interactions and adjust their behavior accordingly create a sense of continuity and emotional depth in the game world, making interactions feel more meaningful and engaging.Moreover, personal assistants like Siri or Alexa could evolve to become more emotionally intelligent, adjusting their tone based on a users emotional state. By remembering past interactions, these assistants can offer more personalized, empathetic, and helpful responses, improving the overall user experience. The potential to evolve into fully functional conversational companions is intriguing in a world where the trend is for people to lead more isolated lives.By extension, when the idea of advanced domestic robots becomes a reality, such features may well be viewed as necessary.The Future of AI Consciousness SimulationGenuine consciousness in AI may never be achieved in the same way humans experience it, and one would have to question what purpose there even is in attempting to do so. The ability to simulate the trappings of consciousness on the other hand is a valuable and achievable goal in its own right. This shift toward simulation opens up new possibilities for improving human-AI interactions, enhancing user experiences, and solving complex problems in fields ranging from customer service to healthcare and education.The practical benefits of simulating consciousness through emotional intelligence, memory retention, and adaptive behaviors are already apparent across multiple industries. Continued advancements in AI memory, machine learning, and real-time knowledge integration will make these simulations even more sophisticated and convincing. This should not be feared, nor should its implementation be disguised (in fact most would probably agree it should be made explicit). This sort of emotional intelligence may actually prove to be a crucial ingredient to generating a closer approximation of artificial understanding and thus is necessary to advance the state of AI overall.I suspect that for many applications simulating consciousness and sentience in AI is a necessary step toward advancing the field of artificial intelligence. The ability to simulate these complex behaviors allows for the creation of more sophisticated AI systems that can engage in dynamic, adaptive, and contextually aware interactions. By modeling the processes of memory, learning, and emotional response core components of consciousness AI can perform tasks that require nuanced understanding, personalization, and long-term adaptation. Simulating these traits provides a framework for developing machines that exhibit behaviors akin to awareness. This would not merely mimic responses but would require integrating feedback loops that enable AI to refine its actions based on past experiences, much like how humans learn and adapt. As AI systems simulate these cognitive and emotional processes, they will become increasingly effective in their interactions with humans. The act of simulating sentience, therefore, becomes a powerful tool for refining AIs capabilities.In addition, the utility of virtual environments such as MMOs as testing grounds for simulating AI consciousness is immense. These platforms provide complex, dynamic environments where AI-driven characters can interact with multiple human players in a variety of contexts, allowing researchers to observe how AI systems might utilize persistence to simulate emotion and cognition. Through these virtual worlds, the capabilities of AI systems to build memory, adapt to users, and simulate relationships over time can be refined. Not only as lab experiments but in the real world the fact that it is for entertainment purposes only is an added advantage, in that the security and ethical concerns which would be of greater concern in other fields are mitigated.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningDebugging in the Age of AI-Generated Code 0 like January 21, 2025Share this postAuthor(s): Diop Papa Makhtar Originally published on Towards AI. a Developer coding with his laptopIn the fast-evolving world of software development, the landscape is shifting dramatically. The rise of AI-generated code is heralding a new era of productivity and innovation. Tools like GitHub Copilot and OpenAIs Codex promise to speed up development cycles, reduce boilerplate coding, and democratize programming by lowering entry barriers. Yet, this technological leap brings an equally pressing need: the art and science of debugging are becoming more critical than ever.In this article, we will explore why debugging is the cornerstone of programming in an AI-assisted world (1 ) and why enterprises need to place a sharper focus on evaluating debugging skills during the hiring process (2).The Rise of AI-Generated CodeAI-generated code is a marvel of modern technology. By harnessing the power of machine learning and vast training datasets, AI tools can write snippets, functions, and even entire applications with minimal human intervention. The potential benefits are enormous:Increased Efficiency: Developers can focus on higher-level problem-solving while AI handles repetitive coding tasks.Enhanced Accessibility: Non-programmers or junior developers can leverage these tools to achieve complex outcomes.Rapid Prototyping: Teams can iterate faster, testing ideas and concepts in record time.However, these advantages come with inherent risks. AI models, no matter how advanced, are not infallible. They are trained on existing data and, as such, are prone to:Generating syntactically correct but semantically flawed code.Producing insecure, inefficient, or non-performant implementations.Reinforcing biases or incorporating vulnerabilities present in their training data.The result? Developers and engineers will spend an increasing portion of their time diagnosing and fixing issues in AI-generated outputs rather than writing code from scratch. Debugging long considered a fundamental skill is now becoming the primary activity of software engineering.Debugging is The Core of ProgrammingProgramming is the art of debugging an empty file. While this saying was coined in the pre-AI era, it rings even truer today. Debugging has always been a vital skill for developers, but in the context of AI-generated code, it is rapidly becoming the focal point of the development process.Why Debugging Matters More Than EverAI-Generated Code Isnt Perfect: While AI can produce code faster, it lacks context. It doesnt fully understand the nuances of a specific application or the intent behind a project. Debugging ensures that the generated code aligns with business requirements and behaves as expected.Integration Challenges: Most projects involve integrating new features into existing systems. AI-generated snippets might not fit seamlessly with legacy codebases, requiring developers to debug integration points extensively.Error Identification: AI often generates code that works but only under specific conditions. Debugging helps uncover edge cases, race conditions, or unintended behaviors that could lead to catastrophic failures in production.Security Concerns: Debugging isnt just about fixing functionality; its also about ensuring security. AI-generated code can inadvertently introduce vulnerabilities that need to be identified and patched.Debugging as a Skill: The Underrated CompetenceDespite its importance, debugging remains an underrated and often under-assessed skill in the hiring process for developers and software engineers. The traditional interview process tends to prioritize:Problem-solving through algorithms.Coding proficiency in specific languages.Knowledge of frameworks and libraries.While these are essential skills, they are not always indicative of a candidates ability to diagnose and fix complex issues. Debugging is a distinct skill that requires:Analytical Thinking: Understanding the root cause of a problem rather than just its symptoms.Attention to Detail: Spotting subtle issues in code that could lead to major problems.Resilience: Debugging can be frustrating, requiring persistence and a methodical approach.Broad Knowledge: A solid understanding of systems, dependencies, and architectures is crucial for effective debugging.Shifting the Focus: Debugging in HiringTo prepare for the AI-assisted future of software development, enterprises need to rethink their approach to hiring. Evaluating debugging skills should take center stage, ensuring teams are equipped to handle the unique challenges posed by AI-generated code.Strategies for Assessing Debugging SkillsDebugging-Focused Challenges: Include debugging tasks in technical interviews. Present candidates with flawed code and evaluate their ability to identify and resolve issues. Focus on both their technical solutions and their thought process.Real-World Scenarios: Provide candidates with scenarios that mimic real-world conditions, such as integrating third-party APIs or resolving performance bottlenecks in existing code.Pair Programming: Observe candidates in a pair programming setting where they work with another engineer to debug a problem. This approach highlights their communication skills, collaboration, and debugging methodology.Code Review Assessments: Evaluate a candidates ability to review and debug others code, reflecting real-world team dynamics.Debugging Tools Proficiency: Assess familiarity with debugging tools like debuggers, profilers, and logging frameworks. These tools are essential for efficient issue resolution.Building a Culture That Values DebuggingBeyond hiring, organizations need to foster a culture that recognizes and rewards debugging expertise. Heres how:Training and Development: Offer regular training sessions and workshops focused on debugging techniques, tools, and best practices.Mentorship Programs: Pair junior developers with experienced engineers who can guide them in honing their debugging skills.Recognition and Rewards: Celebrate debugging successes. Acknowledge team members who resolve complex issues, just as you would those who deliver new features.Invest in Tools: Provide teams with advanced debugging tools and resources, ensuring they have what they need to succeed.The rise of AI-generated code is reshaping the software development landscape, shifting the focus from coding to debugging. As this trend accelerates, debugging skills will become the most valuable asset for developers and engineers. Enterprises that recognize this shift and prioritize debugging in their hiring and development strategies will be best positioned to thrive in the AI-assisted future.By embracing debugging as both an art and a science, we not only address the challenges posed by AI-generated code but also unlock new opportunities for innovation and growth.In the end, debugging isnt just about fixing code; its about building resilient, reliable systems that drive the future forward.P.S.There is a significant opportunity for innovation in assessing debugging skills. Platforms like Codility and LeetCode have revolutionized how coding and algorithmic problem-solving are evaluated. However, a dedicated platform for debugging assessments could fill a crucial gap. Such a platform could present candidates with realistic debugging challenges, assess their problem-solving methodologies, and evaluate their proficiency with modern debugging tools. AI could play a pivotal role in creating these assessments by generating complex scenarios, simulating bugs, and even providing real-time insights into candidates approaches. For enterprises, investing in such solutions could streamline hiring processes and ensure that teams are equipped with top-tier debugging talent to thrive in the AI-driven development landscape.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningReasoning Model: Short Overview and Feature for Developers 0 like January 21, 2025Share this postLast Updated on January 21, 2025 by Editorial TeamAuthor(s): Igor Novikov Originally published on Towards AI. Image by the authorWhen LLMs first came out they were kinda like children, they would say the first thing that came to mind and didnt bother much with logic. You had to tell them they should think before you speak. And just like with children even then it didnt mean they would think.Many argued that because of that, the models do not possess real intelligence and must be supplemented with either human help or some sort of external framework on top of an LLM, like Chain of Thought.It was only a matter of time before major LLM developers like OpenAI decided to replicate this external thinking step (see picture below) inside an LLM. After all, its pretty simple create a dataset that contains not just question-answer pairs but the whole step-by-step logic, and train on that. Additionally, it would require more computation resources at inference time, as a model would go through the same step-by-step thinking process when determining the answer.Added thinking step. Image by OpenAIThey natively break down problems into small pieces and integrate a Chain of thought approach, error correction, and trying multiple strategies before answering.O1 spends more time at inference (o1 is 30 times slower than Gpt4o), what a surprise longer thinking time leads to better results!Image by OpenAIReasoning tokens are not passed from one turn to the next only the output.Also, it verifies the solution by generating multiple answers and choosing the best via consensus, and the approach that we used to implement manually. Here is the overall process:Image by OpenAIOne important conclusion is that GPU computation requirements are going to grow as it is obvious that longer thinking time (in tokens) leads to better answers, so it is possible to scale model quality just by giving the model more computing power, whereas before this was mostly true at training phase. So GPU requirements for modern models are going to go significantly higher.These models are thus different and old approaches no longer work.How to work with reasoning modelsInterestingly it is kind of similar to working with an intelligent human:Be simple and direct. State your question clearly.No explicit Chain of Thought. The model will do that internallyHave a good structure: break the prompt into sections using clear markupShow vs tell: it is better to show the model and example of a good answer or behavior than describe it in several thousand wordsNo more need for coaxing, intimidating, or bribing the model nonsenseI can even summarize this into one: know what you want to ask and ask it clearly.Mini vs Full modelsSince reasoning models like o3 consume a lot of tokens during inference it is rather expensive to use them for everything and the latency is not great. So the idea is to delegate the most difficult task high-level thinking and planning, and have faster and more cost-efficient mini-models to execute the plan. They can be used for tasks like coding, math, and science.This is an agentic approach, that allows us to combine best of the both worlds smart but expensive models with small and fast workers.How much better these models are?Much better, and going to get even better soon. For o1 its approaching expert humans in math and coding (see below):MathImage by OpenAICodingImage by OpenAIELO 2727 puts o3 in the 200 best coders in the world. If you are not worried about your job security as a developer its time to start now. This is exactly the job that scales perfectly by adding more computing power and the current rate of progress is not showing any signs of slowing down.What is nextI can only speculate but my take is that for a year or two it is possible to dramatically improve the model quality just by adding more inference computing power and improving training datasets. Adding some sort of memory outside of the context window also seems logical although very expensive on a large scale.I think the next big step really is to implement multiagent architecture on the LLM level, so it can have multiple collaborating internal dialogues, that share the same memory and context. It follows the current trajectory of embedding external thinking tools into the model and also benefits from linear scaling of compute power at training and inference, so I think end of this or next year we will see an LMM, Large Multiagent Model, or something similar. The sky is the limit for such a model so I propose to call it SkyNet.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningWhy Most Developers Miss the True Potential of LLMs 0 like January 20, 2025Share this postAuthor(s): Towards AI Editorial Team Originally published on Towards AI. The 8-Hour Generative AI Primer shows you how to ask the right questions, avoid common mistakes, and build AI prototypes in one dayBuilding LLM-powered applications and workflows lends itself to modular product development where you can iteratively build a more complex, reliable, and feature-complete product by adding and replacing elements of a core LLM pipeline. When building, you dont already know the right answer you try out techniques that may or may not work for your use case, and you hack together initial solutions that you might later decide are worth investing time to improve and replace.Much of becoming a great LLM developer and building a great LLM product is about integrating advanced techniques and customization to help an LLM pipeline ultimately cross a threshold where the product is good enough for widescale adoption.On this journey, there are many levers to pull and tools to use. As an LLM developer, you are CONSTANTLY making decisions such as which dataset to use, open-source vs. closed-source, how to fit LLMs into your use case, whether no-code solutions are good enough for your workflow, the extent to which you consider the limitations of LLMs, and so on.And the biggest gap we see on top of all this is that 95% of developers are only scratching the surface of what LLMs can do. Thats where the 8-Hour Generative AI Primer comes in. It is a programming language-agnostic 1-day LLM Bootcamp designed for developers.This certification course is specifically designed to cut through the noise, help you ask the right questions, and show you exactly how to find answers. LLMs are moving so fast, with updates being released almost every day; what you need is an intuitive framework, and just like LLMs, you need enough context to know what developments are relevant to you and your use case so you can make the most out of this transformative technology.In just 1 day, youll have:A working LLM prototype (no-code or low-code).A clear framework to evaluate tools like ChatGPT, Gemini, or any API.The confidence to integrate AI into your workflows effectively.The ability to know how to identify which AI developments matter to you.A certificate that proves youre equipped to work with LLMs professionally.More info on the course page and course video:Whats Inside:Tons of Hands-On Exercises & Demos: Get comfortable with ChatGPT, Claude, and better understand LangChain, LlamaIndex, Hugging Face, and more.Real-World Examples: See how LLMs can transform industries, whether youre in education, software development, finance, or beyond.LLM Intuition: Understand how to compare and select models, integrate them with your projects, and responsibly use AI.Support & Community: Youll join our incredible community of 70,000+ learners, with instructors and mentors ready to help you every step of the way.Certificate & Lifetime Mentorship: A certification and lifetime mentorship from other certified students and the Towards AI team.Complimentary Advanced Resources: You will have access to complimentary resources for the course designed to help you navigate real-world LLM implementation.While no specialized background is needed, you need a willingness to learn. Following our goal, this is a practical course. You will be doing exercises, having quizzes, and building things. If you are not willing to sweat a little to learn, this course is not for you.If youre still on the fence, you can preview the first few lessons for free. Start learning here!What This Certification Course Can Do for YouAI Simplified with Hands-On PracticeCut through the buzz and understand what AI can actually do for your business, avoiding the common pitfalls of over-promising technologies. Learn AI and Machine Learning in a no-code environment, gaining a clear understanding of how AI and Large Language Models (LLMs) function.Industry-Specific AI StrategiesExplore real-world applications and case studies to understand how AI is reshaping your industry. Walk away with a toolkit of real-world AI use cases specific to your industry. You will know how to apply AI to boost efficiency, reduce costs, and drive innovation in your sector.Customized AI Solutions for Your BusinessUnderstand when and how to tailor AI solutions specifically for your teams needs, making you a valuable resource in spearheading AI-driven projects. Learn how to use powerful AI tools like ChatGPT to automate tasks, improve customer interactions, and streamline workflows all without writing a single line of code.This isnt just about learning; its about staying ahead. By the time the next big AI update drops, youll already know how to make the most of it. Join the 8-Hour Generative AI Primer today!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine Learning#TAI 135: Introducing the 8-Hour Generative AI Primer 0 like January 20, 2025Share this postAuthor(s): Towards AI Editorial Team Originally published on Towards AI. 95% of developers we meet are only scratching the surface of what LLMs can do. When working with LLMs, you are CONSTANTLY making decisions such as open-source vs. closed-source, how to fit LLMs into your use case, whether no-code solutions are good enough for your workflow, the extent to which consider the limitations of LLMs, and so on. And the biggest gap we see on top of all this is whether you are using LLMs to their full capacity, even with chat interfaces like ChatGPT or APIs for models like Gemini. The question is: are you?Ask Yourself:How do you decide between open-source and closed-source LLMs?Are no-code solutions good enough for your use case?Are you factoring in the limitations of these models in your workflows?But the biggest challenge of all?Are you using LLMs to their full potential? Thats where the 8-Hour Generative AI Primer comes in.This certification course is specifically designed to cut through the noise, help you ask the right questions, and show you exactly how to find answers. LLMs are moving so fast, with updates being released almost every day; what you need is an intuitive framework, and just like LLMs, you need enough context to know what developments are relevant to you and your use case so you can make the most out of this transformative technology.In just 1 day, youll have:A working LLM prototype (no-code or low-code).A clear framework to evaluate tools like ChatGPT, Gemini, or any API.The confidence to integrate AI into your workflows effectively.The ability to know how to identify which AI developments matter to you.A certificate that proves youre equipped to work with LLMs professionally.Whats Inside:Tons of Hands-On Exercises & Demos Get comfortable with ChatGPT, Claude, and better understand LangChain, LlamaIndex, Hugging FaceReal-World Examples See how LLMs can transform industries, whether youre in education, software development, finance, or beyond.Understand how to compare and select models, integrate them with your projects, and responsibly use AI.Support & Community Youll join our incredible community of 70,000+ learners, with instructors and mentors ready to help you every step of the way.Get a certification and lifetime mentorship from other certified students and the Towards AI team.Complimentary advanced resources for navigating real-world LLM implementation.Find information on the course page!While no specialized background is needed, you need a willingness to learn. Following our goal, this is a practical course. You will be doing exercises, having quizzes, and building things. If you are not willing to sweat a little to learn, this course is not for you. If youre still on the fence, you can preview the first few lessons for free, by the way.This isnt just about learning; its about staying ahead. By the time the next big AI update drops, youll already know how to make the most of it. Join the 8-Hour Generative AI Primer today!Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Retrieval-Augmented Generation (RAG) vs. Cache-Augmented Generation (CAG): A Deep Dive into Faster, Smarter Knowledge Integration 0 like January 20, 2025Share this postAuthor(s): Isuru Lakshan Ekanayaka Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.image sourceAs Large Language Models (LLMs) continue to grow in capability, integrating external knowledge into their responses becomes increasingly important for building intelligent, context-aware applications. Two leading paradigms for such integration are Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG). This article provides an extensive, step-by-step guide on both approaches, dives deep into their workflows, compares their advantages and drawbacks, and offers a comprehensive implementation guide for CAG with detailed explanations of every component.IntroductionRetrieval-Augmented Generation (RAG)Cache-Augmented Generation (CAG)Detailed Comparison of RAG and CAGImplementing Cache-Augmented Generation (CAG)Deep Dive: Code ExplanationCase Studies and Real-World ApplicationsConclusionFurther ReadingIn natural language processing, enhancing the responses of language models with external knowledge is critical for tasks like question answering, summarization, and intelligent dialogue. Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) represent two methodologies to achieve this by augmenting the models capabilities with external data. While RAG integrates knowledge dynamically at inference time, CAG preloads relevant data into the models context, aiming for speed and simplicity. This article breaks down each concept, highlights their strengths and weaknesses, and provides a highly detailed guide on implementing CAG.Retrieval-Augmented Generation (RAG) enhances a language models output by dynamically fetching relevant Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

LatestMachine LearningBayesian State-Space Neural Networks (BSSNN): A Novel Framework for Interpretable and Probabilistic Neural Models 0 like January 20, 2025Share this postLast Updated on January 20, 2025 by Editorial TeamAuthor(s): Shenggang Li Originally published on Towards AI. Integrating Bayesian Theory, State-Space Dynamics, and Neural Network Structures for Enhanced Probabilistic ForecastingThis member-only story is on us. Upgrade to access all of Medium.Photo by Planet Volumes on UnsplashWhen building supervised learning models, such as predicting binary outcomes, traditional neural networks excel at making accurate predictions but often lack the ability to explain why the target behaves in a certain way. Thats where the Bayesian State-Space Neural Network (BSSNN) offers a novel solution. Ive developed this framework to explicitly model the conditional probability of the target variable given the inputs, combining high prediction accuracy with interpretability. By integrating Bayesian probability, state-space modeling, and neural network structures, BSSNN provides a flexible and insightful approach to machine learning.BSSNN merges three core strengths: Bayesian principles to quantify uncertainty and ensure interpretability, state-space modeling to capture temporal or sequential dependencies, and neural networks to handle complex, nonlinear relationships. Unlike conventional models that only focus on predicting the target, BSSNN goes further by modeling the dynamic relationships between inputs and outputs, making it particularly useful for multivariate or time-dependent data.Ive also extended BSSNN to predict Xy, flipping the traditional direction of inference. This means instead of only predicting outcomes, we can now explore what input features are associated with specific outcomes. For example, in a binary classification Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post

Mastering Data Scaling: The Only Guide Youll Ever Need (Straight from My Journey) 0 like January 19, 2025Share this postAuthor(s): Suraj Jha Originally published on Towards AI. How I Finally Conquered Data Scaling: Learn from My Real-World ExperienceThis member-only story is on us. Upgrade to access all of Medium.Image By AuthorScaling is one of the fundamental steps in data preprocessing.It becomes a must when we are especially working with a dataset that is going to work as an input for machine learning models.Scaling transforms raw data into a suitable format that machine learning models can understand.From improving performance to ensuring meaningful comparisons between features, it manages all perfectly.Lets witness the magic of scaling in data cleaning and data preprocessing.It's better to understand it with a real-world example.Imagine you have a dataset where one of its features is age (ranging from 0 to 60) and another feature is their annual income (ranging from $10,000 to $5,000,000).ML algorithms like gradient descent struggle with this kind of feature because large-scale features often dominate the optimization process. It ultimately leads to skewed results.Scaling makes sure that all features are treated equally by ML models, because that way we can enhance the models accuracy and convergence speed.One scaling type doesnt solve all problems, so we have four types of scaling methods.It is useful when we know the bounds (min and max values) of the data and want to draw the relationship between Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post