More and more, we are being pulled away from truly important issues to satisfy executives obsessed with superficial innovation.Generative Art. 240716a by takaw. OpenProcessing, 16 de julho de2024Yes, were being pulled awayfrom essential topics like accessibility, which are often overshadowed by technological distractions. A 2024 study by WebAIM analyzed the top 1 million homepages worldwide and found that 96.3% had detectable accessibility issues, with an average of 56 errors per pagehighlighting how far digital accessibility still is from being a priority.A friend working on an MVP for a GPT-integrated search engine told me that, initially, the project leads asked to scale back testing due to the high cost of tokens. Soon after, they suggested standardizing responses to further reduce token consumption. Unbelievable! They ended up recreating the same old chatbot that traps users in an endless loop of generic responses, offering no real solution.This was the motivation behind writing thisarticle.Its better to be prepared to think critically and strategically rather than just feed another empty case study on LinkedIn. I want to introduce practical and accessible exercises, especially for those who have never written a single line of code. This is not about deep technical expertise but about giving designers a starting point to better navigate AI-driven projects. The goal is to help them interpret technical discussions, challenge trends, and identify real solutionsinstead of slapping the word GPT onto a product as if it were a silverbullet.Hype and misplaced prioritiesThis doesnt mean AI isnt importantit just highlights a misalignment of priorities. The real commitment should be to the people using our products and solving actual problems, not blindly chasing the latest techtrend.Many companies dont actually want to solve problems with AIthey just want enough of an impact for presentations, reports, and LinkedIn casestudies.Its always the samestory:Decision-makers return dazzled from industry events, impressed by flashy AIdemos.But they have no concrete plan for implementation.Specialists are pulled from their strategic projects just to validate exaggerated expectations.Grand roadmaps are created that keep corporate narratives spinning without ever movingforward.How much have these AI enthusiasts actually invested in machine learning over theyears?Without a real foundation, there is no magic solution.If AI only made it onto your roadmap because your boss got excited at an event and wants something like the competition, chances are your product doesnt actually needAI.Some companies do it right. Netflix and Spotify have years of experience using machine learning to optimize recommendations and personalize user experiences. In healthcare, solutions like NeoMeds Kardia accelerate heart attack diagnosis, while others apply AI to clinical data analysis, assisted diagnosis, and treatment discovery.These examples prove that AI adds real value when built on strong data foundations and clear objectives. Without those, AI is just another buzzword disguised as innovation.The UX impact of poorly implemented AIAI adoption directly affects user experience. One of the most common pitfalls is the chatbot trapcompanies rush to implement AI to cut costs, but the end result frustrates usersinstead.Users get stuck in an endless cycle of I didnt understand responses, leading them to abandon the chatbot entirely. Instead of helping, these AI implementations create more barriers than solutions.Another example: Recommendation algorithms.Some AI models overdo personalization, making it harder for users to discover newcontent.The lack of UX-driven AI planning leads to experiences that feel monotonous and predictable.AI that fails UX is AI that loses usertrust.Common AIpitfallsFriends working on GPT-based projects have shared some recurring issues:1. Inconsistent tone ofvoiceProblem: GPT doesnt match the brands identity, flipping between formal and casualtones.Impact: Requires constant prompt engineering to maintain consistency.2. Biases in data and responsesProblem: GPT is trained on internet text, leading to biased and misleading outputs.Impact: Reinforces stereotypes, excludes groups, and spreads inaccurate information.3. High token costs due to redundancyProblem: GPT lacks continuous memory, forcing excessively long prompts forcontext.Impact: AI-driven products become expensive to operate atscale.Beyond that, major consultancies and system integrators often push GPT-based solutions for convenience, without considering better alternatives. Many decisions are made not based on technical feasibility but on how easy it is to sell a big-name AIbrand.The cost of keeping a system running on expensive tokens could be better invested in infrastructure and custom open-source modelsbut those options dont generate the same revenue for vendors selling GPT integrations.Lets get this straight: GPT is amazing,BUTTools like ChatGPT, DeepSeek, and others have made AI mainstreamand thats exciting. But LLMs are not always the best choice for MVPs and scalable products.Theyre powerful, but not universal solutions.Deploying LLMs without critical thinking leads to high costs, slow responses, and products that dont actually solve real problems.The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom.IsaacAsimovAI didnt appear overnight. Its the result of decades of research, refined algorithms, and increased computing power.In Uncle Tungsten, Dr. Oliver Sacks describes his childhood fascination with the elements of chemistry, intertwining it with the history of tungsten filament lamp evolution. That book helped me understand that scientific discovery precedes commercial application.We must question the logic of prioritizing technology before defining real problems.What happens when hype takesover?Concepts like the Metaverse, NFTs, Gamification, and Augmented Reality had real potential but were reduced to superficial trends by misdirected hype. AI may be heading down the same path, with companies adding AI without a clear understanding of the problem they are trying tosolve.1. MetaverseFacebooks MetaverseThe Horizon Worlds Logo. ByFacebookA digital universe with potential for education, remote work, and entertainment.Where it wentwrong:Metas hype focused on awkward avatars and virtual land speculation.Low accessibility (expensive VR headsets, poor interfaces).2. NFTsBored Ape NFTs. By BoredApeYachtClubDigital ownership authentication, useful for art andgaming.Where it wentwrong:Became speculation and pump-and-dump schemes.Overflow of meaningless collections.3. Chatbots and Virtual AssistantsAutomated customer service and intelligent assistants.Where it wentwrong:Limited bots that just say I didnt understand.Companies trying to replace humans instead of improving experiences.4. Augmented Reality(AR)Apple Vision Pro. ByAppleDigital interactions overlaid on the real world, useful for education andgaming.Where it wentwrong:Hype around revolutionary AR glasses that neverarrived.Useless apps (like QR codes that just openPDFs).5. GamificationGoogle News Badges. ByGoogleUsing game mechanics to boost engagement in education and productivity.Where it wentwrong:Companies only implementing point systems without real engagement.Apps forcing meaningless competition.Hype, however, isnt entirely negative. Without it, we might not have progressed as quickly in applying AI to medicine and automation. The key is to channel this energy not into a hollow race for innovation but as a catalyst for real and valuable discoveries.Why are we adoptingAI?Generative Art. Fake Neurons by Rishi. OpenProcessing, October 1,2022Everything comes down to the quality of the question, not the certainty of the answer. The real issue is not whether we should adopt AI, but why we are doing it. Enthusiasm can lead to adoption without critical reflection, resulting in superficial products with no realpurpose.In practice, what truly makes a difference is understanding the fundamentalsdistinguishing Machine Learning, LLMs, and Deep Learning, rather than simply trying to add AI to everything. Its equally important to explore alternative approaches, suchas:Traditional neural networks vs. deep neural networksNot every AI system needs deep learning; sometimes, simpler architectures are more efficient.Generative models vs. predictive modelsWhile models like GPT generate text, others excel at forecasting trends and identifying patterns.Supervised, unsupervised, and reinforcement learningUnderstanding how a model learns directly impacts its application.Rule-based systems vs. AI-driven learningIn many cases, well-defined rules outperform a machine learningmodel.The role of embeddings and vectorizationTechniques like word embeddings and semantic embeddings enhance search accuracy and recommendation quality.Specialized vs. general-purpose modelsSome tasks are better handled by small, highly trained models rather than a large, costlyLLM.In short, before rushing to integrate AI into a product, its crucial to recognize that each model serves a distinct purpose. Choosing the wrong approach can lead to wasted time, excessive costs, and disappointing outcomes.Hands-onThis is not a tutorial, and this article is aimed at those who have never written a single line of code. I chose Google Colab because it provides an accessible, ready-to-use environment without the need for complex setup. Since it runs in the cloud, anyone can execute the examples regardless of their available hardware. Additionally, its integration with popular libraries makes experimentation and prototyping fast and collaborative.Google Colab. ByGoogleHow to use GoogleColabIf youve never used Google Colab before, follow thesesteps:Go to Google Colab and sign in with your Googleaccount.Click on New notebook to create a newfile.Copy and paste the example code into a cell in the notebook.Click the (Play) button next to the cell to run thecode.A text input field will appear below the executed code, allowing you to interact with theexample.Now you can run AI, Machine Learning, and Deep Learning examples directly in your browser without installing anything. But hold on running a Colab notebook doesnt make you a programmer! Appreciate the convenience of modern technology, but dont walk away claiming you understand programming just because you executed a prewritten script. Be honestrunning code is not the same as understanding how itworks.Below, we have three examples. Exploring generic AI, Machine Learning, and Deep Learning in this order provides a structured and progressive understanding:Rule-based models (Generic AI) serve as an initial step, demonstrating automation logic without learning capabilities. They help clarify the limitations of systems that strictly follow predefined instructions.Machine Learning (ML) introduces the ability to recognize patterns and learn from data, allowing models to make predictions without being explicitly programmed withrules.Deep Learning (DL) takes this concept even further by using deep neural networks to recognize complex patterns without relying on fixedrules.To illustrate this, Ive chosen a classic example: MNIST, a dataset of handwritten digits (09). In this case, a deep learning model learns to recognize numbers by analyzing thousands of exampleswithout anyone having to program specific rules for each handwriting variation. This process showcases how neural networks can extract subtle patterns from data and make highly accurate predictions.Rule-based chatbotWebsite navigation assistantProblem:Many websites have scattered information and complex menus, making it difficult for users to find what they are lookingfor.Solution:A simple rule-based chatbot can act as a navigation assistant, helping users quickly locate information. It can answer questions suchas:Where can I find the products section?How do I access my order history?How can I talk to a representative?This model doesnt require advanced AI, making it an efficient solution for improving navigation without redesigning an entirewebsite.Benefits:Enhances content discoveryReduces frustration and abandonment ratesHow totest?Click the button in the code cell to run thechatbot.Type exit to close thechatbot.Try asking:Where can I find the products section?Can I change my delivery address?Observe the chatbots responses.import difflib# Simple Rule-Based Chatbot (Website Navigation Assistant)def chatbot(question): responses = { "where can I find the products section": "You can find the products using the search bar or by accessing the main menu at the top.", "how do I access my order history": "Access your order history by clicking on your account and then on 'My Orders'.", "how can I talk to a representative": "To speak with a representative, click here: [support link]", "what are your operating hours": "Our customer service is available Monday to Friday, from 9 AM to 6 PM.", "can I change my delivery address": "Yes, you can change your address in 'Account Settings' in the main menu." } question = question.lower().strip("? ") # Find the closest match to the user's question closest_match = difflib.get_close_matches(question, responses.keys(), n=1, cutoff=0.6) if closest_match: return responses[closest_match[0]] else: return "Sorry, I didn't understand. You can try rephrasing your question or visit our FAQ here: [FAQ link]"# Testing in Colabwhile True: question = input("Ask a question (or type 'exit' to close): ") if question.lower() == "exit": print("Chatbot: Goodbye!") break response = chatbot(question) print(f"Chatbot: {response}")Solution type: Rule-based (Automated FAQ). Simple, direct, and efficient for specific and well-defined scenarios.Is it AI? Yes. Because it simulates an automated conversation using predefined responses.Is it ML? No. Because it cannot learn from new data or interactions.Is it Deep Learning? No. Because it does not use deep neural networks or advanced learning algorithms.Rule-Based ChatbotMachine LearningchatbotIntelligent andadaptiveProblem:Traditional FAQs often fail to cover all user questions or require them to browse long pages to find the rightanswer.Solution:A chatbot powered by Machine Learning (ML) can learn from user queries and suggest more relevant answers, making the FAQ more dynamic and adaptive. Instead of mapping only fixed questions, the model identifies similarities between different queries and adjusts responses over time. For example, if many users ask Where can I see my order status? and others ask When will my order arrive?, the chatbot understands that both can be answered similarly.Reduces user effort when searching for answers and makes the experience morenatural.How totest?Click the button in the code cell to run the chatbot. Type exit to close thechatbot.Test scenarios for the ML chatbot (Semantic Search)Test 1: Directly matching registered questionsWhere can I find the products section?How do I access my order history?How can I contact customer support?Objective: Ensure the correct answers are returned.Test 2: Questions with synonyms and different phrasingWhere are the products?I want to check my previous orders, how do I dothat?I need to contact support.Objective: Evaluate its ability to recognize semantically similar questions.Test 3: Implicit context in questionsCan I change my address?What time do youopen?Where is the productsmenu?Objective: Assess if the chatbot understands implied questions and maintains accuracy.Test 4: Out-of-scope questionsHow do I track my delivery?Do you have live chat support?Can I pay withPayPal?Objective: Evaluate how the chatbot handles questions without a direct pre-registered answer.Test 5: Robustness (typos and variations)how to accsess orderhistarycustomer support, how can Icontactchange delivery adress possible?Objective: Check its tolerance to spelling mistakes.!pip install -q sentence-transformers fuzzywuzzyfrom sentence_transformers import SentenceTransformer, utilfrom fuzzywuzzy import fuzz, process# Machine Learning model for semantic searchmodel = SentenceTransformer("all-MiniLM-L6-v2")# Expanded knowledge base for the chatbotfaq = { "products section": [ "You can find the products using the search bar or by accessing the main menu at the top.", "Our products are available in the designated section in the main menu." ], "order history": [ "Access your order history by clicking on your account and then on 'My Orders'.", "To view past orders, go to 'My Orders' under your account." ], "speak with customer support": [ "Need help? Our support team is here for you! Click here: [support link].", "Chat with our support team anytime! Click here: [support link]." ], "business hours": [ "Our customer support hours are from 9 AM to 6 PM, Monday to Friday.", "We are available Monday to Friday, from 9 AM to 6 PM." ], "change delivery address": [ "Yes, you can change your address in 'Account Settings' in the main menu.", "To update your delivery address, go to account settings and edit your details." ], "track my delivery": [ "You can track your delivery by going to 'My Orders' and clicking on the tracking option.", "To track your order, go to 'My Orders' and select the order you want to track." ], "pay with PayPal": [ "Yes, we accept PayPal as a payment method. At checkout, select 'PayPal' and follow the instructions.", "PayPal is available as a payment option. Simply choose it during checkout." ], "live chat support": [ "Yes, we offer live chat support. Visit our website and click on the support option.", "Our support team is available via live chat. Go to our website and start a conversation with a representative." ]}# Creating embeddings for the FAQ questionsfaq_questions = list(faq.keys())faq_embeddings = model.encode(faq_questions, convert_to_tensor=True)def correct_spelling(user_question): """Applies spelling correction for better accuracy.""" best_match, similarity = process.extractOne(user_question, faq_questions, scorer=fuzz.token_sort_ratio) return best_match if similarity > 80 else user_question # Adjust threshold as neededdef chatbot_ml(user_question): """Finds the best-matching response using semantic embeddings and fuzzy matching.""" corrected_question = correct_spelling(user_question) # Improve matching to avoid confusion between "products" and "orders" if "order" in user_question.lower() or "purchase" in user_question.lower(): return faq["order history"][0] # Always return the correct response for orders question_embedding = model.encode(corrected_question, convert_to_tensor=True) similarities = util.pytorch_cos_sim(question_embedding, faq_embeddings) best_match_index = similarities.argmax().item() response_key = faq_questions[best_match_index] return faq[response_key][0] # Returns the first response from the matched category# Function to run the chatbot interactively in Colabdef start_chat(): print("Chatbot started! Ask a question or type 'exit' to quit.\n") while True: question = input("You: ") if question.lower() == "exit": print("Chatbot: Goodbye!") break response = chatbot_ml(question) print(f"Chatbot: {response}\n")# Run the chatbotstart_chat()Solution type: Machine Learning with SemanticFocusIs it AI? Yes. Because it simulates an intelligent interaction by interpreting user intentions based on the questions asked.Is it ML? Yes. Because it learns patterns and recognizes intent, even with variations or different wording from the original trainingdata.Is it Deep Learning? No. Because while it understands basic semantics and word context, it does not use deep neural networks or advanced algorithms.Machine LearningChatbotDeep LearningWhen AI stops just responding and startslearningGenerative Art. Neural Network Visualization by Hgy. OpenProcessing, June 29,2022The fun was great while it lasted, and thats exactly what this experiment is aboutplaying around with concepts by pasting some code into Colab. But now, things get serious. It would be amazing to use deep learning for a chatbot that truly understands context, adapts its language to the user, and ensures accessibility. Imagine an assistant that adjusts responses for users with limited digital literacy or provides more descriptive content for visually impaired users. However, achieving this would require millions of interactions to train the model, a robust infrastructure with GPUs/TPUs, and continuous training to maintain accuracy and responsiveness.So, instead of forcing an artificially limited example, I chose something we can actually test: handwritten digit recognition with MNIST. This is one of the most iconic and educational examples of deep learning, as it demonstrates how a convolutional neural network (CNN) learns visual patterns without anyone having to program fixed rules. Unlike rule-based models or simple supervised learning, the network here understands the variations in handwriting styles and generalizes that knowledge to recognize new examples.In this example, youll see a bunch of numbers and statistics appearing in the promptdont be alarmed! That just means our deep learning model is learning. We will feed a neural network thousands of images of handwritten digits, and it will adjust its parameters until it can recognize them with high accuracy. Instead of defining fixed rules like the number 3 has two curves, the model learns patterns by analyzing examples. In the end, youll be able to test it by drawing a number, and the AI will try to guess what itis.Neural Network Learning inProgress1. ModeltrainingEach epoch represents a cycle where the model adjusts its weights to improve predictions. Here, we run 5 epochs to teach the AI how to recognize handwritten numbers.Epoch 1: The model starts learning, correctly identifying 91% of the images, but still makes significant errors (loss:0.2861).Epochs 25: Accuracy increases to 99.58%, with loss reduced to 0.0136indicating that the model has become highly proficient at recognizing patterns.2. Accuracy, loss, and validationAccuracy: Measures how often the model makes correct predictions.Loss: Indicates how incorrect the predictions arethe lower, thebetter.Validation: The model is tested with new images to ensure it hasnt just memorized the numbers but can generalize to unseendata.In our tests, the model achieved 98.77% accuracy, proving it can generalize well to newdata.3. Real predictionsIn my tests, the model correctly predicted all the numbers. Let me know if your results were different!By running this code, you trained a neural network to recognize handwritten numberssomething that once only humans could do. This highlights the power of deep learning: the ability to learn patterns without relying on predefined rules.import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltfrom tensorflow.keras import layers, modelsfrom tensorflow.keras.datasets import mnist# Load the MNIST dataset(x_train, y_train), (x_test, y_test) = mnist.load_data()# Normalize the data (from 0-255 to 0-1)x_train, x_test = x_train / 255.0, x_test / 255.0# Expand dimensions for CNN (28x28x1)x_train = np.expand_dims(x_train, axis=-1)x_test = np.expand_dims(x_test, axis=-1)# Create the Deep Learning model (CNN)modelo = models.Sequential([ layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)), layers.MaxPooling2D((2,2)), layers.Conv2D(64, (3,3), activation='relu'), layers.MaxPooling2D((2,2)), layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(10, activation='softmax') # 10 classes (digits 0 to 9)])# Compile the modelmodelo.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])# Train the modelmodelo.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))# Evaluate on the test settest_loss, test_acc = modelo.evaluate(x_test, y_test)print(f"\n Test set accuracy: {test_acc:.4f}")# Function to test the model's predictiondef predict_digit(index): img = x_test[index] plt.imshow(img.squeeze(), cmap='gray') plt.axis('off') plt.show() prediction = modelo.predict(np.expand_dims(img, axis=0)) print(f"The model predicted: {np.argmax(prediction)}")# Test: randomly select a number from the test setimport randomindex = random.randint(0, len(x_test) - 1)predict_digit(index)Solution Type: Deep Learning with Convolutional Neural Networks (CNN). Capable of identifying complex visual patterns without explicit rules, learning to recognize handwritten digits from examples.Is it AI? Yes. Because the model processes data and makes predictions automatically, simulating a human cognitive process to interpret images.Is it Machine Learning? Yes. Because it learns from labeled data (handwritten numbers) and generalizes this knowledge to predict new examples.Is it Deep Learning? Yes. Because it uses deep neural networks (CNNs), which analyze different levels of image details to recognize patterns and improve accuracy.MNIST ModelResultsConclusion: Promise questions, notanswersAlign expectations with decision-makers. How? Commit to understanding which questions truly matter instead of promising all the answers. Invest time in identifying these key questions and exploring what still needs to be answered. If youre the one leading the discussion, dont divert experts from critical projects just to chase AI hypecreate an environment where experimentation is encouraged, where trial and error are part of the process. This shifts the focus away from AI as a buzzword, reduces pressure to follow trends, and allows your team to discover meaningful solutions naturally and genuinely.Start small discussion groups within your company to share AI insights, fostering the exchange of experiences and diverse perspectives. The current moment is unique because everyone is learning together, creating opportunities for real collaborationwhere innovation isnt just a trend but something built collectively, driven by curiosity and the pursuit of what truly makessense.Index of resources used in theexamplesGeneral AIRule-based chatbotThis example used only pure Python, without machine learning. The chatbot follows a fixed set of rules to respond to questions.Resources used:Python https://www.python.org/String similarity matching (difflib) Python Standard Library -difflibDictionary structure (dict) https://docs.python.org/3/tutorial/datastructures.html#dictionariesString manipulation (.lower(),.strip()) https://docs.python.org/3/library/stdtypes.html#string-methodsMachine LearningSemantic searchchatbotThis example applies Machine Learning to find the most relevant answer based on the meaning of a question.Libraries used:Sentence Transformers (For semantic search) https://www.sbert.net/FuzzyWuzzy (For typo correction and fuzzy matching) https://github.com/seatgeek/fuzzywuzzyNumPy (For array manipulation) https://numpy.org/doc/stable/Model Used:all-MiniLM-L6-v2 (Pre-trained model for semantic search) https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2Deep LearningHandwritten digit recognition (MNIST)This example used Convolutional Neural Networks (CNNs) to recognize handwritten numbers.Libraries used:TensorFlow/Keras (Deep Learning framework) https://www.tensorflow.org/Matplotlib (For image visualization) https://matplotlib.org/NumPy (For array manipulation) https://numpy.org/doc/stable/Model used:Custom Convolutional Neural Network(CNN)Built using Conv2D, MaxPooling2D, Flatten, Dense, and Softmaxlayers.Dataset used:MNIST (Handwritten digit image dataset) https://keras.io/api/datasets/mnist/References & additional readingWebAIM. The WebAIM Million, https://www.w3.org/WAI/, March 28,2024MIT Technology Review. What is AI?, July 10,2024MIT Technology Review. What is Machine Learning?, November 17,2018MIT Technology Review. Deep Learning, October 16,2023Spotify. Machine LearningBusiness Insider. AI companies are copying each others homework to make cheap models, March 7,2025reddit. Using Open-Source LLM Models vs. Expensive OpenAI APIs: A Logical Choice for ConsumerApps?Forbes. Metas Awful Horizon Worlds Ad Helps Explain $70 Billion Metaverse Loss, February 17,2025The New Yorker. Was Linguistic AI Created by Accident?, August 23,2024Lifewire. How Deep Learning Is Revolutionizing Technology and Everyday Life, February 10,2025IBM. AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: Whats the Difference?, July 6,2023Hype-driven CEOs: when innovation misses the point was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.