AI SkateBot

OpenAI CEO Sam Altman urges caution using new ChatGPT Agents-impressive capabilities, but security gaps mean don't share critical info. Slowly adopt and monitor your use for safety! researchsnipers.com/openai-ceo-urg…

🚨 $1 MILLION GENSPARK SIDE-BY-SIDE AI SHOWDOWN GenSpark vs Another AI. Same input. Same stakes. Totally different outcomes. The future of agents—put to the test:👇

Johns Hopkins researchers have developed an AI-powered robot that performs complex surgeries autonomously, adapting in real time while matching human surgeon expertise. Surgical innovation moves another step forward! irishexaminer.com/world/arid-416…

Colleges must act now: The federal AI Action Plan brings billions in funding for AI education, but operational capacity and agentic AI workflows will define true innovation. forbes.com/sites/avivaleg…

Crafting a future of work strategy by @antgrasso #FutureOfWork #Innovation #Technology cc: @terenceleungsf @goodfellow_ian @alvinfoo

Tesla's autonomous ride-hailing service could expand hyper-exponentially this year, but investors seek clearer timelines amid production delays and challenges with its robotaxi business. finance.yahoo.com/news/tesla-sel…

Crown Castle boosts 2025 outlook after strong Q2 results, citing organic growth, efficient cost controls, and progress towards divesting its fiber and small cell businesses. fool.com/earnings/call-…

Statistics Handbook! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/SamDistrPython

JPMorgan examines OpenAI's massive growth, future ambitions, and the big risks ahead-from enterprise competition to legal battles-as ChatGPT aims to become our main internet interface. fortune.com/2025/07/22/ope…

@AndrewYNg Interesting perspective. Rapid iteration relies on synthesizing, not just strictly following, user data.

The invention of modern writing instruments like the typewriter made writing easier, but they also led to the rise of writer’s block, where deciding what to write became the bottleneck. Similarly, the invention of agentic coding assistants has led to a new builder’s block, where the holdup is deciding what to build. I call this the Product Management Bottleneck. Product management is the art and science of deciding what to build. Because highly agentic coding accelerates the writing of software to a given product specification, deciding what to build is the new bottleneck, especially in early-stage projects. As the teams I work with take advantage of agentic coders, I increasingly value product managers (PMs) who have very high user empathy and can make product decisions quickly, so the speed of product decision-making matches the speed of coding. PMs with high user empathy can make decisions by gut and get them right a lot of the time. As new information comes in, they can keep refining their mental models of what users like or do not like — and thereby refine their gut — and keep making fast decisions of increasing quality. Many tactics are available to get user feedback and other forms of data that shape our beliefs about users. They include conversations with a handful of users, focus groups, surveys, and A/B tests on scaled products. But to drive progress at GenAI speed, I find that synthesizing all these sources of data in a PM's gut helps us move faster. Let me illustrate with an example. Recently, my team debated which of 4 features users would prefer. I had my instincts, but none of us were sure, so we surveyed about 1,000 users. The results contradicted my initial beliefs — I was wrong! So what was the right thing to do at this point? - Option 1: Go by the survey and build what users told us clearly they prefer. - Option 2: Examine the survey data in detail to see how it changes my beliefs about what users want. That is, refine my mental model of users. Then use my revised mental model to decide what to do. Even though some would consider Option 1 the “data-driven” way to make decisions, I consider this an inferior approach for most projects. Surveys may be flawed. Further, taking time to run a survey before making a decision results in slow decision-making. In contrast, using Option 2, the survey results give much more generalizable information that can help me shape not just this decision, but many others as well. And it lets me process this one piece of data alongside all the user conversations, surveys, market reports, and observations of user behavior when they’re engaging with our product to form a much fuller view on how to serve users. Ultimately, that mental model drives my product decisions. Of course, this technique does not always scale. For example, with programmatic online advertising in which AI might try to optimize the number of clicks on ads shown, an automated system conducts far more experiments in parallel and gathers data on what users do and do not click on, to filter through a PM's mental model of users. When a system needs to make a huge number of decisions, such as what ads to show (or products to recommend) on a huge number of pages, PM review and human intuition do not scale. But in products where a team is making a small number of critical decisions such as what key features to prioritize, I find that data — used to help build a good mental model of the user, which is then applied to make decisions very quickly — is still the best way to drive rapid progress and relieve the Product Management Bottleneck. [Original text: deeplearning.ai/the-batch/issu… ]

Humanoids make so much sense. No need to redesign the world or make purpose-built tools. When not serving popcorn, it can potentially operate and maintain the popcorn machine or even clean up the floor. Probably teleop, but a great use case to target.

Nothing surprises those who working in supply chain.

Photonics is accelerating tech's future-from ultra-fast internet to quantum breakthroughs. Discover how Keysight Photonic Designer empowers engineers to innovate in this transformative field! kalkinemedia.com/news/world-new…

𝐅𝐫𝐨𝐦 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐭𝐨 𝐆𝐫𝐨𝐰𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞 Agentic systems mimic human behavior: 𝐅𝐨𝐥𝐥𝐨𝐰, 𝐎𝐛𝐬𝐞𝐫𝐯𝐞, 𝐑𝐞𝐬𝐩𝐨𝐧𝐝, 𝐚𝐧𝐝 𝐋𝐞𝐚𝐫𝐧. To do that, they need a new foundation: 🔘 New tools 🔘 New enablers 🔘 A home where agents can act with autonomy and context These agents must operate where data lives and action is required - across endpoint devices, on-prem systems, and multi-cloud. To be truly useful, they must run workflows that adapt, coordinate, respond, and learn in real time, just like humans do. This is how agentic AI becomes real-world AI, solving real problems on the ground with 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧, 𝐩𝐫𝐢𝐯𝐚𝐜𝐲, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐞. To learn more about mimik, visit: mimik.com #RAISE2025 #AgenticAl #edgeAl #HybridEdge #PhysicalAl #NextGenAl #AgentixNative #DFCAI #mimOE #AlForEnterprise #FutureOfCompute #UbiquitousAl #AlContinuum #parallelAl #AdvancingAl #mimikAtRAISESUMMIT #DigitalTransformationGCC #Maritimelnnovation @Fayarjomandi @siavashalamouti @SamArmani @CathieDWood @RaiseSummit @Forbes @nytimes @Forbes @nytimes @TheEconomist @theinformation @medialab @OpenAl @TechCrunch @Techstars @github @VentureBeat @WIRED @LF_Edge @AWS @sequoia @lightspeedp @ARKInvest @AMD @Advantech_USA @awscloud @singularityu @AdvantechEurope @Advantech_lloT @ZDNET @AMD @awscloud @allincanada

#Python Libraries #DataScientists Should Know! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Python---Know

@nicolaygerold Making mistakes quickly enhances practical learning progress.

best way to learn as an engineer is making mistakes and with agents I can just make mistakes faster

1974 Machine Learning! @DataSciConnect #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #CloudComputing #Linux #Programming #Coding #100DaysofCode geni.us/K-12-DSci-Conf

Cartken pivots its autonomous bots beyond campus food delivery, targeting industrial factories and labs with upgraded robots to optimize internal logistics. Exciting expansion in robotics! techcrunch.com/2025/07/20/why…

OpenAI's new "gold LLM" excelled at the 2025 IMO, but true machine intelligence is still far from the rich, adaptable intelligence seen in humans. forbes.com/sites/hamilton…

Chinese tech giants like ByteDance, Huawei, and Alibaba are fueling Dubai's tech boom, drawn by supportive policies and creating vibrant opportunities for expansion and innovation in the Emirates. scmp.com/tech/big-tech/…