GMV’s Post

Stanford’s new paper shows AI can now evolve its own intelligence without retraining. Their framework, ACE (Agentic Context Engineering), treats an AI’s context as an ever-growing playbook that learns from every interaction. Instead of compressing or rewriting prompts, ACE helps models build and refine their knowledge over time, enabling self-improvement through context. Backed by Stanford University, University of California, Berkeley, and SambaNova Systems, this approach achieved 10%+ performance gains over state-of-the-art baselines. For more such resources, Join AI Agents Builder Group: bit.ly/aiagentbuilder Nikhil Bhaskaran #Stanford #AI #LLM #AgenticAI #MachineLearning

"How Creativity really emerges in humans". this is Part1, tomorrow i will also post a Part2 about the Creativity as a mechanism, and how to emerge it in AI. Part2: https://lnkd.in/d82PKRpN

In this video, I share my reflections on a recent Toastmasters event that sparked intriguing discussions about artificial intelligence. I delve into the contrasting feelings of disconnection some people experience amid the surge of AI, while also highlighting the importance of human connection through various events. How do you view AI's role in our lives? Join the conversation in the comments! 🔗 🕊 Yanantin Tribe Events: https://yanantintribe.com/ #ArtificialIntelligence #HumanConnection #PersonalDevelopment #CommunityBuilding #EmotionalIntelligence #TechnologyImpact #Toastmasters

Is the 'AI bubble' narrative distracting us from the truly groundbreaking advancements happening in AI? I'm genuinely curious what others are seeing. We're witnessing breakthroughs in **continual learning**, where models can adapt and grow organically, rather than just being static datasets. And the latest on **model introspection** – AI detecting internal 'thoughts' before output – is mind-boggling. Are we prepared for the implications of models that truly learn and self-monitor? #AIethics #TechDebate #AIdevelopment #FutureTech #InnovationMindset

“If we only focus on productivity, our focus is far too narrow to capture the full breadth of value that AI has to offer.” Our Chief Learning Officer Chris Ernst, Ph.D. weighs in on the AI deskilling dilemma: https://w.day/47oxuBb

AI can’t be fair if the data isn’t. So what if fairness started at the dataset? At Sony AI, we’ve spent years examining how bias enters datasets through scraped images, shortcut learning, unchecked assumptions. We’ve published on mitigating bias without group labels. On measuring diversity instead of claiming it. On fairness as a lifecycle, not a checkbox. And now, we’re building something new shaped by everything we’ve learned. Before there’s a model, there’s a dataset. That’s where ethical AI begins. 📖 Read the blog → https://bit.ly/4hKdnAP #SonyAI #EthicalAI #DatasetBias #AIresearch #FairnessInAI #ICML2024 #NeurIPS2024

TIME 100's Professor Dr.Benjamin Rosman of The University of the Witwatersrand asks: 𝗛𝗼𝘄 𝗰𝗮𝗻 𝘄𝗲 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗶𝗻𝘁𝗼 𝗵𝘂𝗺𝗮𝗻-𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝘂𝗿𝘀 𝘄𝗵𝗶𝗹𝗲 𝗲𝗻𝘀𝘂𝗿𝗶𝗻𝗴 𝘀𝗮𝗳𝗲𝘁𝘆? One approach involves creating mechanisms to define desired behaviors in a provable way. This allows us to increase the generalisability of our AI agents while maintaining safety from the outset. By focusing on verifiable conditions, we can build more reliable and trustworthy AI systems. #AI #MachineLearning #AISafety #Algorithms

I’ve always been curious about how intelligent systems actually “think.” Recently, I explored Generative AI, from LLMs built on Generative Pre-Trained Transformers (GPT) with billions of parameters, creating human-like text, to Diffusion Models that transform random noise into realistic images through forward and reverse diffusion. It’s fascinating how these architectures learn, create, and redefine what “intelligence” really means in machines. #GenerativeAI #LLM #DiffusionModels #DeepLearning #AI #ContinuousLearning

Breaking down the complexity of leading through the AI revolution into 6 essential questions. For more context and practical applications, download the full report: https://bit.ly/47uHPeP

AI’s Next Frontier? An Algorithm for Consciousness by Will Knight Some of the world’s most interesting thinkers about thinking think they might’ve cracked machine sentience. And I think they might be onto something. https://ift.tt/aAQ8Io2