Gaming consoles have come a long way from the days of simple joysticks and monochrome screens. As we move further into 2025, the landscape continues to evolve rapidly, with new devices blurring the boundaries between traditional gaming, social connectivity, and creative expression. When choosing the right console, it’s no longer just about specs or exclusive titles. Today’s best devices are about how they fit into your life, how they connect you with others, and how they foster creativity or healthy habits. They’re as much about self-expression and lifestyle as they are about graphics and gameplay. We have curated a collection of five exciting gaming consoles—each with a unique vision for the future of play. Dive in and pick your favorite! 1. Nintendo Wii U Revival Handheld Nintendo’s legacy of innovation finds new life in a modern handheld that pays homage to the underappreciated Wii U gamepad. This concept isn’t about replicating the hybrid appeal of the Switch – instead, it’s a focused portable device built for pure gaming pleasure. The design is refreshingly straightforward—a central, high-resolution screen flanked by familiar symmetrical analog sticks and a thoughtfully arranged button layout. Unlike the original Wii U’s somewhat cumbersome form, this new iteration is compact and portable, making it easy to slip into a bag or pocket. USB-C charging, an authentic D-pad, and a visually striking range of colorways (including a stunning gold Zelda edition) make it clear this is a modern device for devoted Nintendo fans. By reviving the Wii U’s backward compatibility and expanding on it, it offers a treasure trove of classic titles from the NES era to the Game Boy Advance—all playable locally, without subscriptions or cloud dependencies. What we like Access to a massive selection of classic Nintendo titles across multiple generations, playable offline and without ongoing fees. Slim, ergonomic form factor with contemporary features like USB-C charging and a responsive D-pad. What we dislike While nostalgic, the focus on past games may not appeal to those seeking cutting-edge, next-gen exclusives or mainstream AAA releases. Unlike the Switch, this device is a dedicated handheld, with no option to dock for TV play, potentially limiting its appeal for some users. 2. Kimera In an era where screens too often serve as conduits for endless scrolling and digital noise, the Kimera AI handheld introduces a refreshing alternative. This device is engineered for those seeking a more mindful relationship with technology, prioritizing creative expression and personal growth over passive consumption. Kimera’s AI-driven platform encourages users to disconnect from social media and instead invest their time in building unique, story-driven games, short films, animations, or even entirely new interactive worlds. Its sleek, distraction-free design invites you to put down your phone and channel your curiosity into something constructive. What we like Designed to help users break free from social media addiction and replace passive habits with creative pursuits. Offers guided experiences for game design, animation, and storytelling, making creativity accessible to all skill levels. What we dislike Its focus on creativity and self-development may not resonate with gamers seeking action-packed or traditional gameplay experiences. Those unfamiliar with creative software or design principles may need time to fully unlock the device’s potential. 3. Nintendo Niino Imagine a console that fits comfortably on your wrist, ready for gaming whenever and wherever you are. The Nintendo Niino takes inspiration from the portability of smartwatches and the playful essence of classic Nintendo handhelds, resulting in a device that’s both charming and functional. Its design is minimalist yet instantly recognizable – a SIM card-shaped body with a black base, accented by red, blue, and grey buttons, echoing Nintendo’s signature colors. The touchscreen and streamlined three-button layout are perfect for quick microgames and bite-sized fun, while the option to wear it like a gaming smartwatch makes it a conversation starter. It’s small enough to be a constant companion, whether you’re waiting in line, commuting, or taking a break at work. What we like Its compact, wrist-friendly design ensures gaming is always within reach, blurring the line between console and smart device. Quick, microgame-focused gameplay encourages spontaneous play and sharing among friends. What we dislike The small screen and minimal controls restrict the types of games that can be fully enjoyed, making it less suitable for deep or immersive experiences. While its novelty is undeniable, long-term engagement may wane if not supported by an excellent library or meaningful connectivity features. 4. Nex Playground For those who crave movement and shared experiences, the Nex Playground reimagines what it means to play together at home. This innovative cube-shaped console transforms any living room into a hub for active gaming, blending the best aspects of classic motion-based systems like the Wii and Kinect with the convenience of modern technology. The built-in motion-tracking camera captures players’ movements without the need for wearable sensors, making setup effortless and play instantly immersive. The console’s approachable design and family-friendly focus ensure that gamers of all ages can join the fun, whether they’re dancing, kicking, or competing in friendly mini-games. What we like Motion-tracking technology encourages players to move, making gaming a healthier, more dynamic experience. Suitable for all ages and skill levels, fostering family fun and group interaction. What we dislike Focus on motion-based and party games may limit appeal for solo players or fans of traditional genres. To fully enjoy the active gameplay, you’ll need ample room—something not every living space can accommodate. 5. ORBIS The ORBIS handheld is a love letter to personal expression and flexible gaming. Taking inspiration from classic clamshell devices, it brings a modern twist by allowing users to customize the look of their console through removable ABS covers. The ORBIS lets you swap out covers as easily as you change phone cases. RGB lighting around the analog sticks amplifies the gamer vibe but also serves a practical purpose, enhancing visibility in low-light settings. Beyond its appearance, the ORBIS is packed with powerful specs to ensure smooth gameplay across a variety of titles. It’s more than just a fashion statement—it’s a versatile platform that appeals to both serious and casual gamers. What we like Removable covers and RGB lighting allow for unprecedented personalization, making each unit truly one-of-a-kind. The folding design protects the screen and controls during transport, enhancing portability and longevity. What we dislike Customization options and premium features may drive up the price compared to more basic handhelds. The clamshell mechanism and removable parts could make the device thicker or heavier than sleeker competitors. The post Top 5 Gaming Consoles To Consider In May 2025 For Every Gamer first appeared on Yanko Design.

A shared language for product teams to build usable, intelligent and safe GenAI experiences beyond just the modelGenerative AI introduces a new way for humans to interact with systems by focusing on intent-based outcome specification. GenAI introduces novel challenges because its outputs are probabilistic, requires understanding of variability, memory, errors, hallucinations and malicious use which brings an essential need to build principles and design patterns as described by IBM.Moreover, any AI product is a layered system where LLM is just one ingredient and memory, orchestration, tool extensions, UX and agentic user-flows builds the real magic!This article is my research and documentation of evolving GenAI design patterns that provide a shared language for product managers, data scientists, and interaction designers to create products that are human-centred, trustworthy and safe. By applying these patterns, we can bridge the gap between user needs, technical capabilities and product development process.Here are 21 GenAI UX patternsGenAI or no GenAIConvert user needs to data needsAugment or automateDefine level of automationProgressive AI adoptionLeverage mental modelsConvey product limitsDisplay chain of thought (CoT)Leverage multiple outputsProvide data sourcesConvey model confidenceDesign for memory and recallProvide contextual input parametersDesign for coPilot, co-Editing or partial automationDefine user controls for AutomationDesign for user input error statesDesign for AI system error statesDesign to capture user feedbackDesign for model evaluationDesign for AI safety guardrailsCommunicate data privacy and controls1. GenAI or no GenAIEvaluate whether GenAI improves UX or introduces complexity. Often, heuristic-based (IF/Else) solutions are easier to build and maintain.Scenarios when GenAI is beneficialTasks that are open-ended, creative and augments user.E.g., writing prompts, summarizing notes, drafting replies.Creating or transforming complex outputs (e.g., images, video, code).E.g., converting a sketch into website code.Where structured UX fails to capture user intent.Scenarios when GenAI should be avoidedOutcomes that must be precise, auditable or deterministic. E.g., Tax forms or legal contracts.Users expect clear and consistent information.E.g. Open source software documentationHow to use this patternDetermine the friction points in the customer journeyAssess technology feasibility: Determine if AI can address the friction point. Evaluate scale, dataset availability, error risk assessment and economic ROI.Validate user expectations: - Determine if the AI solution erodes user expectations by evaluating whether the system augments human effort or replaces it entirely, as outlined in pattern 3, Augment vs. automate. - Determine if AI solution erodes pattern 6, Mental models2. Convert user needs to data needsThis pattern ensures GenAI development begins with user intent and data model required to achieve that. GenAI systems are only as good as the data they’re trained on. But real users don’t speak in rows and columns, they express goals, frustrations, and behaviours. If teams fail to translate user needs into structured, model-ready inputs, the resulting system or product may optimise for the wrong outcomes and thus user churn.How to use this patternCollaborate as a cross-functional team of PMs, Product designers and Data Scientists and align on user problems worth solving.Define user needs by using triangulated research: Qualitative (Market Reports, Surveys or Questionnaires) + Quantitative (User Interviews, Observational studies) + Emergent (Product reviews, Social listening etc.) and synthesising user insights using JTBD framework, Empathy Map to visualise user emotions and perspectives. Value Proposition Canvas to align user gains and pains with featuresDefine data needs and documentation by selecting a suitable data model, perform gap analysis and iteratively refine data model as needed. Once you understand the why, translate it into the what for the model. What features, labels, examples, and contexts will your AI model need to learn this behaviour? Use structured collaboration to figure out.3. Augment vs automateOne of the critical decisions in GenAI apps is whether to fully automate a task or to augment human capability. Use this pattern to to align with user intent and control preferences with the technology.Automation is best for tasks users prefer to delegate especially when they are tedious, time-consuming or unsafe. E.g., Intercom FinAI automatically summarizes long email threads into internal notes, saving time on repetitive, low-value tasks.Augmentation enhances tasks users want to remain involved in by increasing efficiency, increase creativity and control. E.g., Magenta Studio in Abelton support creative controls to manipulate and create new music.How to use this patternTo select the best approach, evaluate user needs and expectations using research synthesis tools like empathy map (visualise user emotions and perspectives) and value proposition canvas (to understand user gains and pains)Test and validate if the approach erodes user experience or enhances it.4. Define level of automationIn AI systems, automation refers to how much control is delegated to the AI vs user. This is a strategic UX pattern to decide degree of automation based upon user pain-point, context scenarios and expectation from the product.Levels of automationNo automation (AI assists but user decides)The AI system provides assistance and suggestions to the user but requires the user to make all the decisions. E.g., Grammarly highlights grammar issues but the user accepts or rejects corrections.Partial automation/ co-pilot/ co-editor (AI acts with user oversight)The AI initiates actions or generates content, but the user reviews or intervenes as needed. E.g., GitHub Copilot suggest code that developers can accept, modify, or ignore.Full automation (AI acts independently)The AI system performs tasks without user intervention, often based on predefined rules, tools and triggers. Full automation in GenAI are often referred to as Agentic systems. E.g., Ema can autonomously plan and execute multi-step tasks like researching competitors, generating a report and emailing it without user prompts or intervention at each step.How to use this patternEvaluate user pain point to be automated and risk involved: Automating tasks is most effective when the associated risk is low without severe consequences in case of failure. Low-risk tasks such as sending automated reminders, promotional emails, filtering spam emails or processing routine customer queries can be automated with minimal downside while saving time and resources. High-risk tasks such as making medical diagnoses, sending business-critical emails, or executing financial trades requires careful oversight due to the potential for significant harm if errors occur.Evaluate and design for particular automation level: Evaluate if user pain point should fall under — No Automation, Partial Automation or Full Automation based upon user expectations and goals.Define user controls for automation (refer pattern 15)5. Progressive GenAI adoptionWhen users first encounter a product built on new technology, they often wonder what the system can and can’t do, how it works and how they should interact with it.This pattern offers multi-dimensional strategy to help user onboard an AI product or feature, mitigate errors, aligns with user readiness to deliver an informed and human-centered UX.How to use this patternThis pattern is a culmination of many other patternsFocus on communicating benefits from the start: Avoid diving into details about the technology and highlight how the AI brings new value.Simplify the onboarding experience Let users experience the system’s value before asking data-sharing preferences, give instant access to basic AI features first. Encourage users to sign up later to unlock advanced AI features or share more details. E.g., Adobe FireFly progressively onboards user with basic to advance AI featuresDefine level of automation (refer pattern 4) and gradually increase autonomy or complexity.Provide explainability and trust by designing for errors (refer pattern 16 and 17).Communicate data privacy and controls (refer pattern 21) to clearly convey how user data is collected, stored, processed and protected.6. Leverage mental modelsMental models help user predict how a system (web, application or other kind of product) will work and, therefore, influence how they interact with an interface. When a product aligns with a user’s existing mental models, it feels intuitive and easy to adopt. When it clashes, it can cause frustration, confusion, or abandonment​.E.g. Github Copilot builds upon developers’ mental models from traditional code autocomplete, easing the transition to AI-powered code suggestionsE.g. Adobe Photoshop builds upon the familiar approach of extending an image using rectangular controls by integrating its Generative Fill feature, which intelligently fills the newly created space.How to use this patternIdentifying and build upon existing mental models by questioningWhat is the user journey and what is user trying to do?What mental models might already be in place?Does this product break any intuitive patterns of cause and effect?Are you breaking an existing mental model? If yes, clearly explain how and why. Good onboarding, microcopy, and visual cues can help bridge the gap.7. Convey product limitsThis pattern involves clearly conveying what an AI model can and cannot do, including its knowledge boundaries, capabilities and limitations.It is helpful to builds user trust, sets appropriate expectations, prevents misuse, and reduces frustration when the model fails or behaves unexpectedly.How to use this patternExplicitly state model limitations: Show contextual cues for outdated knowledge or lack of real-time data. E.g., Claude states its knowledge cutoff when the question falls outside its knowledge domainProvide fallbacks or escalation options when the model cannot provide a suitable output. E.g., Amazon Rufus when asked about something unrelated to shopping, says “it doesn’t have access to factual information and, I can only assists with shopping related questions and requests”Make limitations visible in product marketing, onboarding, tooltips or response disclaimers.8. Display chain of thought (CoT)In AI systems, chain-of-thought (CoT) prompting technique enhances the model’s ability to solve complex problems by mimicking a more structured, step-by-step thought process like that of a human.CoT display is a UX pattern that improves transparency by revealing how the AI arrived at its conclusions. This fosters user trust, supports interpretability, and opens up space for user feedback especially in high-stakes or ambiguous scenarios.E.g., Perplexity enhances transparency by displaying its processing steps helping users understand the thoughtful process behind the answers.E.g., Khanmigo an AI Tutoring system guides students step-by-step through problems, mimicking human reasoning to enhance understanding and learning.How to use this patternShow status like “researching” and “reasoning to communicate progress, reduce user uncertainty and wait times feel shorter.Use progressive disclosure: Start with a high-level summary, and allow users to expand details as needed.Provide AI tooling transparency: Clearly display external tools and data sources the AI uses to generate recommendations.Show confidence & uncertainty: Indicate AI confidence levels and highlight uncertainties when relevant.9. Leverage multiple outputsGenAI can produce varied responses to the same input due to its probabilistic nature. This pattern exploits variability by presenting multiple outputs side by side. Showing diverse options helps users creatively explore, compare, refine or make better decisions that best aligns with their intent. E.g., Google Gemini provides multiple options to help user explore, refine and make better decisions.How to use this patternExplain the purpose of variation: Help users understand that differences across outputs are intentional and meant to offer choice.Enable edits: Let users rate, select, remix, or edit outputs seamlessly to shape outcomes and provide feedback. E.g., Midjourney helps user adjust prompt and guide your variations and edits using remix10. Provide data sourcesArticulating data sources in a GenAI application is essential for transparency, credibility and user trust. Clearly indicating where the AI derives its knowledge helps users assess the reliability of responses and avoid misinformation.This is especially important in high stakes factual domains like healthcare, finance or legal guidance where decisions must be based on verified data.How to use this patternCite credible sources inline: Display sources as footnotes, tooltips, or collapsible links. E.g., NoteBookLM adds citations to its answers and links each answer directly to the part of user’s uploaded documents.Disclose training data scope clearly: For generative tools (text, images, code), offer a simple explanation of what data the model was trained on and what wasn’t included. E.g., Adobe Firefly discloses that its Generative Fill feature is trained on stock imagery, openly licensed work and public domain content where the copyright has expired.Provide source-level confidence:In cases where multiple sources contribute, visually differentiate higher-confidence or more authoritative sources.11. Convey model confidenceAI-generated outputs are probabilistic and can vary in accuracy. Showing confidence scores communicates how certain the model is about its output. This helps users assess reliability and make better-informed decisions.How to use this patternAssess context and decision stakes: Showing model confidence depends on the context and its impact on user decision-making. In high-stakes scenarios like healthcare, finance or legal advice, displaying confidence scores are crucial. However, in low stake scenarios like AI-generated art or storytelling confidence may not add much value and could even introduce unnecessary confusion.Choose the right visualization: If design research shows that displaying model confidence aids decision-making, the next step is to select the right visualization method. Percentages, progress bars or verbal qualifiers (“likely,” “uncertain”) can communicate confidence effectively. The apt visualisation method depends on the application’s use-case and user familiarity. E.g., Grammarly uses verbal qualifiers like “likely” to the content it generated along with the userGuide user action during low confidence scenarios: Offer paths forward such as asking clarifying questions or offering alternative options.12. Design for memory and recallMemory and recall is an important concept and design pattern that enables the AI product to store and reuse information from past interactions such as user preferences, feedback, goals or task history to improve continuity and context awareness.Enhances personalization by remembering past choices or preferencesReduces user burden by avoiding repeated input requests especially in multi-step or long-form tasksSupports complex tasks like longitudinal workflows like in project planning, learning journeys by referencing or building on past progress.Memory used to access information can be ephemeral (short-term within a session) or persistent (long-term across sessions) and may include conversational context, behavioural signals, or explicit inputs.How to use this patternDefine the user context and choose memory typeChoose memory type like ephemeral or persistent or both based upon use case. A shopping assistant might track interactions in real time without needing to persist data for future sessions whereas personal assistants need long-term memory for personalization.Use memory intelligently in user interactionsBuild base prompts for LLM to recall and communicate information contextually (E.g., “Last time you preferred a lighter tone. Should I continue with that?”).Communicate transparency and provide controlsClearly communicate what’s being saved and let users view, edit or delete stored memory. Make “delete memories” an accessible action. E.g. ChatGPT offers extensive controls across it’s platform to view, update, or delete memories anytime.13. Provide contextual input parametersContextual Input parameters enhance the user experience by streamlining user interactions and gets to user goal faster. By leveraging user-specific data, user preferences or past interactions or even data from other users who have similar preferences, GenAI system can tailor inputs and functionalities to better meet user intent and decision making.How to use this patternLeverage prior interactions: Pre-fill inputs based on what the user has previously entered. Refer pattern 12, Memory and recall.Use auto complete or smart defaults: As users type, offer intelligent, real-time suggestions derived from personal and global usage patterns. E.g., Perplexity offers smart next query suggestions based on your current query thread.Suggest interactive UI widgets: Based upon system prediction, provide tailored input widgets like toasts, sliders, checkboxes to enhance user input. E.g., ElevenLabs allows users to fine-tune voice generation settings by surfacing presets or defaults.14. Design for co-pilot / co-editing / partial automationCo-pilot is an augmentation pattern where AI acts as a collaborative assistant, offering contextual and data-driven insights while the user remains in control. This design pattern is essential in domains like strategy, ideating, writing, designing or coding where outcomes are subjective, users have unique preferences or creative input from the user is critical.Co-pilot speed up workflows, enhance creativity and reduce cognitive load but the human retains authorship and final decision-making.How to use this patternEmbed inline assistance: Place AI suggestions contextually so users can easily accept, reject or modify them. E.g., Notion AI helps you draft, summarise and edit content while you control the final version.Save user intent and creative direction: Let users guide the AI with input like goals, tone, or examples, maintaining authorship and creative direction. E.g., Jasper AI allows users to set brand voice and tone guidelines, helping structure AI output to better match the user’s intent.15. Design user controls for automationBuild UI-level mechanisms that let users manage or override automation based upon user goals, context scenarios or system failure states.No system can anticipate all user contexts. Controls give users agency and keep trust intact even when the AI gets it wrong.How to use this patternUse progressive disclosure: Start with minimal automation and allow users to opt into more complex or autonomous features over time. E.g., Canva Magic Studio starts with simple AI suggestions like text or image generation then gradually reveals advanced tools like Magic Write, AI video scenes and brand voice customisation.Give users automation controls: UI controls like toggles, sliders, or rule-based settings to let users choose when and how automation can be controlled. E.g., Gmail lets users disable Smart Compose.Design for automation error recovery: Give users correction when AI fails (false positives/negatives). Add manual override, undo, or escalate options to human support. E.g., GitHub Copilot suggests code inline, but developers can easily reject, modify or undo suggestions when output is off.16. Design for user input error statesGenAI systems often rely on interpreting human input. When users provide ambiguous, incomplete or erroneous information, the AI may misunderstand their intent or produce low-quality outputs.Input errors often reflect a mismatch between user expectations and system understanding. Addressing these gracefully is essential to maintain trust and ensure smooth interaction.How to use this patternHandle typos with grace: Use spell-checking or fuzzy matching to auto-correct common input errors when confidence is high (e.g., >80%), and subtly surface corrections (“Showing results for…”).Ask clarifying questions: When input is too vague or has multiple interpretations, prompt the user to provide missing context. In Conversation Design, these types of errors occur when the intent is defined but the entity is not clear. Know more about entity and intent. E.g., ChatGPT when given low-context prompts like “What’s the capital?”, it asks follow-up questions rather than guessing.Support quick correction: Make it easy for users to edit or override your interpretation. E.g., ChatGPT displays an edit button beside submitted prompts, enabling users to revise their input17. Design for AI system error statesGenAI outputs are inherently probabilistic and subject to errors ranging from hallucinations and bias to contextual misalignments.Unlike traditional systems, GenAI error states are hard to predict. Designing for these states requires transparency, recovery mechanisms and user agency. A well-designed error state can help users understand AI system boundaries and regain control.A Confusion matrix helps analyse AI system errors and provides insight into how well the model is performing by showing the counts of - True positives (correctly identifying a positive case) - False positives (incorrectly identifying a positive case) - True negatives (correctly identifying a negative case)- False negatives (failing to identify a negative case)Scenarios of AI errors and failure statesSystem failure (wrong output)False positives or false negatives occur due to poor data, biases or model hallucinations. E.g., Citibank financial fraud system displays a message “Unusual transaction. Your card is blocked. If it was you, please verify your identity”System limitation errors (no output)True negatives occur due to untrained use cases or gaps in knowledge. E.g., when an ODQA system is given a user input outside the trained dataset, throws the following error “Sorry, we don’t have enough information. Please try a different query!”Contextual errors (misunderstood output)True positives that confuse users due to poor explanations or conflicts with user expectations comes under contextual errors. E.g., when user logs in from a new device, gets locked out. AI responds: “Your login attempt was flagged for suspicious activity”How to use this patternCommunicate AI errors for various scenarios: Use phrases like “This may not be accurate”, “This seems like…” or surface confidence levels to help calibrate trust.Use pattern convey model confidence for low confidence outputs.Offer error recovery: Incase of System failure or Contextual errors, provide clear paths to override, retry or escalate the issue. E.g., Use way forwards like “Try a different query,” or “Let me refine that.” or “Contact Support”.Enable user feedback: Make it easy to report hallucinations or incorrect outputs. Read more about pattern 19. Design to capture user feedback.18. Design to capture user feedbackReal-world alignment needs direct user feedback to improve the model and thus the product. As people interact with AI systems, their behaviours shape and influence the outputs they receive in the future. Thus, creating a continuous feedback loop where both the system and user behaviour adapt over time. E.g., ChatGPT uses Reaction buttons and Comment boxes to collect user feedback.How to use this patternAccount for implicit feedback: Capture user actions such as skips, dismissals, edits, or interaction frequency. These passive signals provide valuable behavioral cues that can tune recommendations or surface patterns of disinterest.Ask for explicit feedback: Collect direct user input through thumbs-up/down, NPS rating widgets or quick surveys after actions. Use this to improve both model behavior and product fit.Communicate how feedback is used: Let users know how their feedback shapes future experiences. This increases trust and encourages ongoing contribution.19. Design for model evaluationRobust GenAI models require continuous evaluation during training as well as post-deployment. Evaluation ensures the model performs as intended, identify errors and hallucinations and aligns with user goals especially in high-stakes domains.How to use this patternThere are three key evaluation methods to improve ML systems.LLM based evaluations (LLM-as-a-judge) A separate language model acts as an automated judge. It can grade responses, explain its reasoning and assign labels like helpful/harmful or correct/incorrect.E.g., Amazon Bedrock uses the LLM-as-a-Judge approach to evaluate AI model outputs.A separate trusted LLM, like Claude 3 or Amazon Titan, automatically reviews and rates responses based on helpfulness, accuracy, relevance, and safety. For instance, two AI-generated replies to the same prompt are compared, and the judge model selects the better one.This automation reduces evaluation costs by up to 98% and speeds up model selection without relying on slow, expensive human reviews.Enable code-based evaluations: For structured tasks, use test suites or known outputs to validate model performance, especially for data processing, generation, or retrieval.Capture human evaluation: Integrate real-time UI mechanisms for users to label outputs as helpful, harmful, incorrect, or unclear. Read more about it in pattern 19. Design to capture user feedbackA hybrid approach of LLM-as-a-judge and human evaluation drastically boost accuracy to 99%.20. Design for AI guardrailsDesign for AI guardrails means building practises and principles in GenAI models to minimise harm, misinformation, toxic behaviour and biases. It is a critical consideration toProtect users and children from harmful language, made-up facts, biases or false information.Build trust and adoption: When users know the system avoids hate speech and misinformation, they feel safer and show willingness to use it often.Ethical compliance: New rules like the EU AI act demand safe AI design. Teams must meet these standards to stay legal and socially responsible.How to use this patternAnalyse and guide user inputs: If a prompt could lead to unsafe or sensitive content, guide users towards safer interactions. E.g., when Miko robot comes across profanity, it answers“I am not allowed to entertain such language”Filter outputs and moderate content: Use real-time moderation to detect and filter potentially harmful AI outputs, blocking or reframing them before they’re shown to the user. E.g., show a note like: “This response was modified to follow our safety guidelines.Use pro-active warnings: Subtly notify users when they approach sensitive or high stakes information. E.g., “This is informational advice and not a substitute for medical guidance.”Create strong user feedback: Make it easy for users to report unsafe, biased or hallucinated outputs to directly improve the AI over time through active learning loops. E.g., Instagram provides in-app option for users to report harm, bias or misinformation.Cross-validate critical information: For high-stakes domains (like healthcare, law, finance), back up AI-generated outputs with trusted databases to catch hallucinations. Refer pattern 10, Provide data sources.21. Communicate data privacy and controlsThis pattern ensures GenAI applications clearly convey how user data is collected, stored, processed and protected.GenAI systems often rely on sensitive, contextual, or behavioral data. Mishandling this data can lead to user distrust, legal risk or unintended misuse. Clear communication around privacy safeguards helps users feel safe, respected and in control. E.g., Slack AI clearly communicates that customer data remains owned and controlled by the customer and is not used to train Slack’s or any third-party AI modelsHow to use this patternShow transparency: When a GenAI feature accesses user data, display explanation of what’s being accessed and why.Design opt-in and opt-out flows: Allow users to easily toggle data sharing preferences.Enable data review and deletion: Allow users to view, download or delete their data history giving them ongoing control.ConclusionThese GenAI UX patterns are a starting point and represent the outcome of months of research, shaped directly and indirectly with insights from notable designers, researchers, and technologists across leading tech companies and the broader AI communites across Medium and Linkedin. I have done my best to cite and acknowledge contributors along the way but I’m sure I’ve missed many. If you see something that should be credited or expanded, please reach out.Moreover, these patterns are meant to grow and evolve as we learn more about creating AI that’s trustworthy and puts people first. If you’re a designer, researcher, or builder working with AI, take these patterns, challenge them, remix them and contribute your own. Also, please let me know in comments about your suggestions. If you would like to collaborate with me to further refine this, please reach out to me.20+ GenAI UX patterns, examples and implementation tactics was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

Researchers across Taiwan are tackling complex challenges in AI development, climate science and quantum computing. Their work will soon be boosted by a new supercomputer at Taiwan’s National Center for High-Performance Computing that’s set to deliver over 8x more AI performance than the center’s earlier Taiwania 2 system. The AI supercomputer at NCHC is slated to feature NVIDIA HGX H200 systems with over 1,700 GPUs, two NVIDIA GB200 NVL72 rack-scale systems and an NVIDIA HGX B300 system built on the NVIDIA Blackwell Ultra platform — interconnected by NVIDIA Quantum InfiniBand networking. Announced today at COMPUTEX, it’s expected to go live later this year. NCHC also plans to deploy a set of NVIDIA DGX Spark personal AI supercomputers and a cluster of NVIDIA HGX systems in the cloud. Researchers from academic institutions, government agencies and small businesses in Taiwan will be able to apply for access to the new system to accelerate innovative projects. “The new NCHC supercomputer will drive breakthroughs in sovereign AI, quantum computing and advanced scientific computation,” said Chau-Lyan Chang, director general of NCHC. “It’s designed to empower Taiwan’s technological autonomy, fostering cross-domain collaboration and global AI leadership.” Developing Local Language Models for Sovereign AI The new supercomputer will support projects like Taiwan AI RAP, a generative AI application development platform. Taiwan AI RAP aims to support the rapid development of AI products by offering startups, researchers and enterprises access to customized models that reflect local cultural and linguistic nuances. Among the platform’s offerings are models created by Taiwan’s Trustworthy AI Dialogue Engine, or TAIDE — a public sector initiative to build Taiwanese large language models (LLMs) for tasks including natural language processing, intelligent customer service and translation. Collaborators providing text, images, audio and video data for the initiative include local governments, news organizations and public departments such as the Ministry of Education and Ministry of Culture. To support the creation of sovereign AI applications, TAIDE currently offers developers access to a collection of Llama3.1-TAIDE foundation models. The team is building additional sovereign AI LLM services using NVIDIA Nemotron models. A professor at National Tainan University is using the TAIDE model to power a conversational AI robot that speaks Taiwanese and English with elementary and middle school students. It’s been used by over 2,000 students, teachers and parents to date. Another professor tapped the model to generate high-quality educational materials, shortening lesson preparation time for teachers. In healthcare, research teams in Taiwan used the TAIDE model to develop an AI chatbot with retrieval-augmented generation that helps case managers deliver timely, accurate medical information to patients with major injuries and illnesses. And the Epidemic Prevention Center at Taiwan’s Centers for Disease Control is training the model to generate news summaries to support the tracking and prevention of how diseases spread. Speeding Scientific Research in Climate and Beyond In climate research, NCHC supports researchers using the NVIDIA Earth-2 platform to advance atmospheric science. These researchers are tapping Earth-2’s CorrDiff AI model to sharpen the precision of coarse-resolution weather models, and DeepMind’s GraphCast model in NVIDIA PhysicsNeMo for global weather forecasting. They’re also adopting the NVIDIA NIM microservice for FourCastNet, an NVIDIA model that predicts global atmospheric dynamics of weather and climate variables, and using NVIDIA GPUs to accelerate the simulation of numerical weather prediction models. With the new supercomputer, the researchers will be able to run more complex simulations and accelerate the pace of AI training and inference. Advancing Quantum Innovation  NCHC researchers are also advancing quantum research using the NVIDIA CUDA-Q platform and NVIDIA cuQuantum library targeting applications in quantum machine learning, chemistry, finance, cryptography and more. The research institute has developed Quantum Molecular Generator, a tool that generates valid chemical molecules, using quantum circuits and the CUDA-Q platform. It’s also created cuTN-QSVM, an open-source tool built on the cuQuantum library that accelerates large-scale quantum circuit simulations. The tool enables researchers to tackle more complex problems, offering linear scalability and supporting hybrid quantum computing systems to help accelerate the development of large-scale quantum algorithms. NCHC researchers recently used cuTN-QSVM to perform a record-breaking 784-qubit simulation for a quantum machine learning algorithm. The institute also plans to build a hybrid quantum-accelerated computing system by integrating NVIDIA DGX Quantum systems. Learn more about Taiwan’s National Center for High-Performance Computing at NVIDIA GTC Taipei, taking place May 21-22. Watch the COMPUTEX keynote by NVIDIA founder and CEO Jensen Huang.

Anyone asking how to get to Sesame Street will have a new answer as of this morning: Just log on to Netflix. The industry leader in streaming, which topped 300 million global subscribers last December and reported revenue of $10.5 billion for the first quarter in 2025, just picked up Sesame Street ahead of its 56th season. That’s after HBO wound down its deal with the beloved children’s institution last December. Although financial details about Netflix’s new arrangement are not yet publicly available, HBO reportedly paid $30 to $35 million annually for rights to the edutainment lodestar. Whatever price Netflix is paying to add the series to its robust slate of children’s programs, which includes Cocomelon, it’s likely a bargain, considering the intangibles. With this deal, Netflix created a lot more than just a new home for Big Bird, Cookie Monster, and the rest of the gang; by allowing the embattled PBS network to freely air new episodes as they roll out, Netflix just elegantly orchestrated the feel-good corporate PR coup of the year.  We are excited to announce that all new Sesame Street episodes are coming to @netflix worldwide along with library episodes, and new episodes will also release the same day on @PBS Stations and @PBSKIDS platforms in the US, preserving a 50+ year relationship.The support of… pic.twitter.com/B76MxQzrpI— Sesame Street (@sesamestreet) May 19, 2025 Sesame Workshop, the nonprofit that produces Sesame Street, has faced rough headwinds in recent months. After losing its lucrative contract with HBO last December, amid a pronounced reduction in children’s programming on the network, the fate of the show was uncertain. Making matters worse, Trump recently moved to cut federal funding to PBS, which has aired Sesame Street for over half a century—and only did so after his administration’s cuts to the U. S. Agency for International Development abruptly deprived Sesame Workshop of valuable grants. The venerable children’s show needed a champion, and into the void stepped the entertainment company whose branding is the color of Elmo.   “I strongly believe that our educational programming for children is one of the most important aspects of our service to the American people, and Sesame Street has been an integral part of that critical work for more than half a century,” said Paula Kerger, president and CEO of PBS, in a statement. “We’re proud to continue our partnership in the pursuit of having a profound impact on the lives of children for years to come.” The new deal will involve a mild cosmetic overhaul. According to The Washington Post, Sesame Street had intended to start a new format of two 11-minute stories, along with a new five-minute animated segment called “Tales From 123,” but the show will now instead feature a single 11-minute story, followed by “Tales From 123,” and then some revived fan favorites such as “Elmo’s World.” The most profound change in the new deal, though, will have nothing to do with the content, but in how it’s delivered. When HBO began airing episodes of Sesame Street in 2016, the show continued to run on PBS, but new episodes only reached the public channel nine months later. This arrangement continued after 2020, when the show transitioned to HBO Max, and remained in place until HBO announced its intention to drop the show last December. In contrast, Netflix’s deal ensures that new episodes will be available on PBS stations and PBS Kids digital platforms the day they’re released, honoring the creators’ commitment to free, fun educational material for children. “This unique public-private partnership will enable Sesame Workshop to bring our research-based curriculum to young children around the world with Netflix’s global reach, while ensuring children in communities across the U.S. continue to have free access on public television to the Sesame Street they love,” Sesame Workshop CEO Sherri Westin added in the announcement. What makes the deal such a savvy PR move is that it’s a win for the company on several levels. Not only does it provide a sharp contrast between Netflix and the once and future HBO Max, and what might be considered a subtle rebuke to this administration’s attack on publicly funded channels, it also offers a counterpoint to some recent perceptions of Netflix. Over the past couple years, Netflix has made some moves that helped foster an image of the company as more profit-focused and less consumer-friendly. Between the crackdown on password-sharing, multiple price hikes, and sunsetting of the popular Basic ad-free plan, pushing users toward either higher-priced tiers or an ad-based option, the company seemed driven by a shrewd quest to leave no money on the table, no matter who got left behind. By ensuring that American families will be able to access new episodes of Sesame Street for free for the first time in a decade, however, Netflix has demonstrated it has more on its mind than the bottom line. It’s the kind of message that resonates with would-be subscribers, and it should go a long way toward keeping the clouds away from Netflix’s reputation for some time.

Secondo Fuoco collection by TIPSTUDIO (photo courtesy Nicolò Panzeri) Tradition Meets Innovation in Interior and Textile Design at IED Firenze May 19, 2025 Sponsor IED Firenze Istituto Europeo di Design (IED) Firenze expands its academic offerings with the Master of Arts in Interior and Furniture Design—Hacking Tradition: Reinventing Craftsmanship and Materials. This new program redefines the intersection of heritage and innovation, where traditional craftsmanship meets contemporary design. Aligned with IED’s sustainable textile focus, students learn to critically reinterpret cultural and material heritage through a sustainable lens, with design positioned as a transformative force that shapes environments while addressing social and ecological challenges. This Master of Arts program equips students with the skills to “hack” tradition—decoding artisanal methods, rethinking materials, and innovating applications. By fostering a dialogue between past and future, craftsmanship becomes a space for reinvention. The curriculum integrates DesignXCommons, an ecological design philosophy focused on sustainability and resource regeneration. Rooted in Tuscany’s world-renowned manufacturing traditions—marble, textiles, furniture, jewelry, and leather—the program offers hands-on experience. Students engage in workshops, industry collaborations, and real-world projects, turning cultural contexts into design innovation. Secondo Fuoco by TIPSTUDIO (photo courtesy TIPSTUDIO) It complements IED Firenze’s Master of Arts in Textile Design—Eco-Threads: Materials and Circularity, which explores sustainable materials, eco-design, and process innovation across fashion, interiors, and product design. Together, these programs position the school as a leader in sustainable design education. Set in Florence—where history, art, and fashion merge—IED Firenze offers a unique learning environment. From the grandeur of Ponte Vecchio to the heritage of Gucci and Ferragamo, the city itself becomes part of the curriculum. Housed in an architectural landmark, IED Firenze nurtures designers ready to challenge conventions, honor heritage, and shape the future of design. To learn more, visit ied.edu/courses.  Founded in 1966, IED (Istituto Europeo di Design) is an international network of higher education institutions specializing in design, fashion, visual arts, and communication. With campuses in Italy, Spain, and Brazil, IED fosters a multidisciplinary approach that blends creativity, craftsmanship, and technological innovation. Through hands-on training and strong industry connections, it prepares students to become forward-thinking professionals in the global design landscape. Cratere by TIPSTUDIO (photo courtesy TIPSTUDIO) Loto mirror by TIPSTUDIO (photo courtesy TIPSTUDIO) Secondo Fuoco by TIPSTUDIO (photo courtesy TIPSTUDIO) Previous articleNext article

Jump to:Selling your home is often a long and confusing process, especially if you've never done it before. And while homebuyers are subject to a much clearer set of expectations and costs—they're the ones making the offer after all—how much it costs to sell a home is less understood.“There is no set cost to sell a house,” says Amanda Pendleton, Zillow’s home trends expert. “Sellers will want to prepare for a range of 10 to 15 percent of the sale price. An online home sale proceeds calculator can provide a great initial estimate of those costs.” It's also important to know how certain costs may differ.“The overall transaction costs of selling a house include expenses in two main areas: likely costs and potential costs,” Pendleton continues.Likely costs include things like commissions, closing costs, taxes, and attorney fees. On the other hand, potential costs include renovations or remodel costs, home staging, HOA fees, and temporary housing or storage. Think of potential costs as largely optional or on a case-by-case basis. Our guide ahead helps break down the many components to the question, "how much does it cost to sell a home?" and our experts have plenty of insights to share. Read on for everything you need to know.Related StoriesHow Much Does It Cost to Sell a Home?seksan Mongkhonkhamsao//Getty Images“On average, it costs about eight to 10 percent of the home's sale price to sell a home in the U.S. For a $400,000 home, that’s roughly $32,000 to $40,000,” says Jake Krimmel, a senior economist at Realtor.com. “This cost includes items like agent commissions, seller-paid closing costs, repairs and improvements needed to list a house, staging and marketing, and transfer taxes and fees. It is important to note that costs are likely to vary based on the market, property conditions, and buyer/seller negotiations.”Related StoryMain Costs to ConsiderPendleton recommends budgeting 10 to 15 percent of your asking price to cover all selling costs.Real Estate CommissionsThe primary cost to keep in mind when selling your home is the commission rate that you'll pay to your broker or real estate agent. Krimmel estimates that it is “typically five to six percent of the sale price, split between the listing agent and the buyer’s agent. For example: On a $400,000 home with a six percent commission, $24,000 would go to commissions (split 50-50).”Transfer TaxesPendleton estimates that approximately two to four percent of the selling price is spent on transfer taxes and other real estate fees. As the name suggests, this tax is paid to "transfer" the deed of ownership from the seller to the buyer. Think of the transfer tax as a sort of sales tax or transaction fee. Below, Krimmel breaks down the transfer taxes in three major areas of the country. Washington, D.C.: Up to 1.45 percent transfer tax (split buyer/seller or paid fully by one party)New York City: Has both state and city transfer taxes, often totaling 1.4 to 2.075 percentCalifornia: Typically has smaller transfer taxes, but they may also include city-specific feesOn average, it costs about 8–10% of the home's sale price to sell a home in the U.S.Capital Gains TaxesIf you profit from your home sale, you could also be faced with a capital gains tax. “Sellers could be subject to capital gains tax depending on, you know, how long they've owned it, whether it's their principal residence, and how much money they're making on the sale,” says Jared Antin, expert realtor and managing director of Elegran Real Estate.According to Rocket Mortgage, “The capital gains tax can apply to any type of asset that increases in value. Most people encounter this tax when they sell their primary residence. You may be subject to the capital gains tax if your home’s sale price is more than what you initially paid for it.” You won't pay this tax at closing. Instead, this tax is typically paid within the same year you buy your home, or the next time you file your taxes.However, if you meet certain qualifications, you can exempt your home sale from the capital gains tax. For those who file taxes as single, the IRS may allow you to exclude up to $250,000 of gains (profit in the sale), and up to $500,000 if married and filing jointly. The property you're selling must be your primary residence, owned by you for longer than two years, and you must have lived in it for at least two years leading up to the sale. You will be denied this tax exemption if you've given up your USA citizenship, claimed this exemption recently, or bought this property through a like-kind exchange. Related StoryCommon Categories to Budget ForMortgage Pay-OffMost people will sell their home before they've finished paying off the mortgage. Antin explains that it's important for the seller to know how much they need to sell their home for to break even after paying off the mortgage. In most cases, sellers use the profits from their homes to place new offers on property wherever they may be moving to.“In New York City, about 65 percent of our recent transactions have been cash, so New York City tends to have a larger share of the buyers that have never had a mortgage, but generally speaking, unless the person has lived in the house for 30 plus years, if they had a mortgage, they generally still have an amount owed,” Antin continues. Repairs and RenovationsWhile the home buyer will pay for your home to be appraised and inspected, they often negotiate for the seller to cover the costs of any repairs and maintenance needed prior to their move-in date. This could be as simple as trimming an overgrown tree or updating smoke alarms to high-maintenance changes like new garage doors or a new water heater.MovingRobert Daly//Getty ImagesMoving fees are a commonly forgotten cost in the process of selling a home. Whether you're renting a truck and moving yourself or hiring a team to take you cross-country, you'll need to budget for labor, vehicles, and tips for the movers. Moving costs have a wide range, from $400 to about $10,000, depending on the size of your home and how far you're moving. Attorney FeesIf you're in an attorney closing state—Delaware, Georgia, New York, Virginia, North Carolina, South Carolina, West Virginia, and Alabama—you'll be required to have a lawyer oversee the sale of your home. “During this process, the attorney that's representing the seller prepares the purchase contract and then the buyer's attorney goes back and forth with the seller's attorney to review it and align on the language for the contract,” Antin explains.Related StoryFAQs on the Costs of Selling a Homespawns//Getty ImagesWhat will I pay when I sell my house?According to Krimmel, most money that comes out of the seller's pockets is paid from money made in the sale, rather than costs to pay up front. “Realtor commissions (five to six percent) and closing costs ($2,000-10,000) such as title insurance, escrow fees, transfer taxes, and repairs to prep for listing, such as painting, landscaping, and deferred maintenance, are all examples of costs that the seller is responsible for.”Do sellers pay any closing costs?Other than commission rates, Antin explains that most traditional closing costs fall on the home buyer, not the seller. The seller will pay the commission rates, any taxes owed, and the remainder of their mortgage (if needed). What is a common mistake people make when selling a home?“One big mistake is mispricing the home,” says Krimmel. “Overpricing can lead to the home sitting on the market, price cuts, and ultimately selling for less than if it were priced correctly from the start. Why? Because if a home sits on the market for too long and/or the seller keeps dropping the price, buyers will assume something is wrong with the home. Plus, as the home sits unsold, the buyer still has to pay their normal mortgage, taxes, utilities, etc. Even if the home is unoccupied.” Follow House Beautiful on Instagram and TikTok.Expert consulted:Jared AntinReal Estate BrokerJared Antin has built a distinguished career in the real estate industry, excelling in both development and brokerage roles. Rising quickly from a successful agent to Managing Director at Elegran Real Estate, he has been recognized as one of NYC’s “Rising Stars” by Crain’s. As a member of Elegran’s executive leadership team, Jared drives strategy, fosters innovation, and leads the firm’s growth while maintaining its collaborative culture. He is passionate about supporting agents and strengthening relationships within the team and the real estate community. 

Posted on : May 19, 2025 By Tech World Times General  Rate this post The fitness and social media guru Sam Sulek, in all probability most famous for his physique and workout regimen, has raised eyebrows regarding his towering height. The person in consideration is an impressive 6′ 3″ tall and weighing 205 pounds. His stature performs a significant role in Sulek’s online presence as well as influencing his audience. The Impact of Height on Social Media Influence Heights stretch back through time as a yardstick of perception among societies, affecting how individuals are treated. Height in social media has a bearing on engagement, eroding the credibility of potential influencers shorter than average; tall images are perceived as being more credible and charismatic. Influencers like Sam Sulek may also benefit from height as a distinguishing characteristic upon which they could essentially build their entire brand. Heights can speak to certain audiences who identify or relate to their image, creating a rather loyal following. Physical Presence and Intimidation Factor Certainly, Sulek’s height adds to his great physical presence. He stands tall in the fitness industry, where much importance is given to the physicality of a person. His tall stature adds to his muscular build and gives him an imposing physical presence. Physical intimidation, as mentioned earlier, is an advantage in the fitness industry, which generally regards confidence and strength with great value. Visual Content and Social Media Sulek frequently uses his workout videos and body updates on social media sites, especially YouTube and Instagram, to highlight his height. His height adds a visually appealing element to his material by enabling him to perform poses and exercises that accentuate his physical characteristics. By captivating his audience, this visual component can increase the engagement and shareability of his content. Relatability and Admiration Even though not everyone can relate to Sulek’s height, his followers may be inspired and motivated by it. His success in the fitness industry may be attributed to the fact that many people see his size as a benefit. His audience may feel inspired and motivated by this impression and try to accomplish comparable physical objectives. Brand Partnerships and Influencer Opportunities Sulek’s influencer opportunities and brand partnerships may also be impacted by his height and physical presence. His commanding presence and attractive physique may entice companies to collaborate with him. His online reputation and influence may be further cemented if this results in profitable sponsorship agreements and partnerships. Authenticity and Confidence Despite the possible benefits of his height, Sulek’s self-assurance and genuineness are key components of his online persona. His content demonstrates his confidence and self-assurance, which helps his audience relate to and like him more. With his followers, this genuineness can play a significant role in fostering loyalty and trust. Conclusion To sum up, Sam Sulek’s height plays a significant role in his online persona, impacting his physical attributes, visual content, and brand collaborations. Although it might not be the only element influencing his success, it surely helps to mold his persona and captivate his audience. Sulek’s height will probably continue to be a significant component of his identity and brand as he expands his online presence. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

In my town of Bend, Oregon, where the spirit of independence and community thrives, the concept of “Democratic AI” resonates in a uniquely powerful way. In a world increasingly shaped by algorithms and artificial intelligence (AI), the notion of democratizing its creation, access, and governance offers a powerful counterpoint to the centralized control often associated with Big Tech. But what exactly is Democratic AI, and why might it be the best, perhaps even the only truly sustainable path forward for this transformative technology? Buckle up, because we’re about to dive into the digital town hall of the future. Then, we’ll close with my Product of the Week: Slate’s new pickup truck, backed by Jeff Bezos, which could transform the EV market. What Democratic AI Really Means At its core, Democratic AI is a philosophy and set of practices aimed at distributing the power of AI more broadly. It encompasses several key principles: Openness and Transparency: The underlying code, data, and models are often open-source or readily accessible for scrutiny and modification. Think of it as the difference between a proprietary black box and a transparent, well-documented library. Decentralization of Development: Instead of being solely the domain of large corporations with vast resources, Democratic AI encourages contributions from a diverse range of individuals, researchers, smaller organizations, and even governments. It’s the digital equivalent of a community barn-raising. Participatory Governance: The ethical guidelines, development priorities, and deployment strategies are shaped through broader stakeholder involvement rather than top-down mandates. Imagine citizens having a say in how AI is used in their communities. Accessibility and Affordability: The tools and resources needed to develop and utilize AI are made as widely available and affordable as possible, breaking down barriers to entry. It’s about leveling the playing field so that innovation isn’t limited by deep pockets. Data Sovereignty and Privacy: Individuals and communities retain greater control over their data, and privacy is prioritized when developing and deploying AI systems. It’s about ensuring AI serves people, not the other way around. Democratic AI May Be the Best Path So, why is this open and collaborative approach potentially superior to more traditional, often proprietary, AI models? Here are several advantages it brings to the table: Faster and More Diverse Innovation: When you open the floodgates to contributions from a global community, the pace of innovation explodes. Diverse perspectives and skill sets lead to more creative solutions and the exploration of a wider range of ideas, outpacing what any single organization could accomplish. It’s like having a thousand brilliant minds tackling a problem instead of just a handful. Increased Trust and Accountability: Transparency in code and data allows for greater scrutiny, making it easier to identify and address biases, errors, and potential security vulnerabilities. When the workings of an AI are open for all to see, there’s a greater sense of trust and accountability. It’s harder to hide digital shenanigans in broad daylight. Reduced Vendor Lock-In and Monopoly Risk: By promoting open standards and interoperability, Democratic AI reduces reliance on proprietary platforms, fostering a more competitive landscape and mitigating the risks associated with the dominance of a few powerful AI providers. It’s about avoiding digital monopolies where a few companies control core AI resources. Alignment with Public Good: With broader participation in governance and ethical considerations, Democratic AI is more likely to be aligned with the public good and societal values rather than solely driven by corporate profits or narrow interests. It’s about building AI that serves humanity, not just shareholders. Empowerment and Skill Development: Democratizing AI empowers individuals and smaller organizations to become creators and innovators, fostering a broader understanding of the technology and driving the development of local expertise. It’s about turning passive consumers into active participants in the AI revolution. Governments and Companies Advancing Democratic AI While the concept is still evolving, several governments and companies are dipping their toes, or even diving headfirst, into the waters of Democratic AI: The European Union: With its emphasis on digital sovereignty and open-source initiatives, the EU actively promotes a more democratic and human-centric approach to AI development and regulation. Various Open-Source AI Initiatives: Projects like Hugging Face, with its open platform for models and datasets, and initiatives around open data for AI training, embody the spirit of Democratic AI. Decentralized AI Platforms: Emerging projects are exploring blockchain and other decentralized technologies to create more open and community-governed AI infrastructure. Government-Backed Open AI Research: Some governments are supporting open research efforts to promote collaboration and transparency in AI development. For example, Canada funds its CIFAR AI Chairs program, and the U.K. advances similar goals through the Alan Turing Institute. Benefits: A Brighter Algorithmic Future Aggressively pursuing Democratic AI could deliver transformative results: More Ethical and Fair AI Systems: Open scrutiny and diverse participation can help mitigate biases embedded in data and algorithms, leading to fairer and more equitable AI outcomes. AI Tailored to Diverse Needs: A decentralized and collaborative approach can foster the development of AI solutions that address the specific needs and contexts of diverse communities and cultures. Greater Public Trust in AI: Transparency and participatory governance can build greater public trust in AI systems, fostering wider adoption and acceptance. Accelerated Solutions to Global Challenges: By harnessing the collective intelligence of a global community, Democratic AI can accelerate the development of solutions to pressing global challenges, from climate change to health care. Where Democratic AI Stands Today The concept of Democratic AI is still in its relatively early stages. While the principles of open source have a long history in software development, applying them comprehensively to the complex world of AI — including data, models, and governance — is a more recent endeavor. We are likely in the “seed” or early “sapling” phase of this movement. While there are promising initiatives and growing awareness, widespread adoption and the establishment of robust Democratic AI ecosystems will take time, research, and concerted effort from individuals, organizations, and governments. Wrapping Up: A People-Led AI Future Democratic AI offers a compelling vision for the future of artificial intelligence, one where power is distributed, innovation is accelerated through collaboration, and the technology serves humanity’s broader interests. While the path to realizing this vision is still unfolding, the principles of openness, transparency, and participatory governance hold immense promise. As we navigate the transformative power of AI, embracing a democratic approach might not just be the best way forward; it might be the only way to ensure that this powerful technology truly benefits all of us, here in Bend and across the interconnected world. The seeds of a people’s AI are being planted, and it’s up to us to cultivate a future where everyone shares its fruits. Slate Electric Pickup – Bezos-Backed Bargain With a Twist The buzz around the Slate electric pickup truck has been palpable, and after its recent unveiling, it’s clear why. This no-nonsense EV, with ties to Amazon founder Jeff Bezos through his investment in the parent company Re:Build Manufacturing, is making waves with its incredibly aggressive starting price of just $25,000. In a market where electric trucks often flirt with six-figure sums, Slate is aiming for the heart of the value-conscious buyer. What makes the Slate EV truly intriguing is its innovative modular construction. Reportedly, the pickup can be converted into a compact SUV with a relatively inexpensive kit. This transformative capability addresses the reality of how many pickup owners actually use their vehicles. For the vast majority, the truck bed often sits empty or carries lighter loads, while the need for passenger space and enclosed cargo for family and everyday life is more frequent. Slate’s ability to morph into an SUV offers unparalleled versatility, essentially providing two vehicles in one at an exceptionally low total cost of ownership. Slate’s price point makes it especially appealing to buyers focused on practicality and value. At $25,000, it’s positioned to be one of the most affordable EVs on the market, let alone an electric truck. This price point opens electric mobility to buyers who were previously priced out of the EV market. Considering that most pickup truck owners primarily use their vehicles for commuting, errands, and occasional light hauling, Slate’s EV core functionality likely meets their needs without the excess capacity and exorbitant cost of larger, more powerful trucks. Adding to its affordability, Slate’s truck is reportedly still eligible for the U.S. federal tax credit for electric vehicles, currently as much as $7,500. For those who qualify (check with your tax advisor), this effectively brings the starting price down to a mere $17,500, making it an absolute steal and a compelling alternative to many gasoline-powered used vehicles. While specific performance figures are still emerging, early reports suggest a respectable range suitable for daily driving, a powertrain adequate for typical truck duties, and the added weight of its modular components. Its focus on affordability likely means it won’t boast the blistering acceleration of high-end EVs, but it promises a practical and efficient driving experience. The Slate electric pickup, with its Bezos connection, groundbreaking modularity, and incredibly low price, could very well be the value king of the electric truck revolution. The clear value of this Slate pickup — and the fact that it’s likely giving Elon Musk nightmares — makes it my Product of the Week. Credit: The Slate pickup truck images are courtesy of Slate.