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🎉 Thrilled by the incredible enthusiasm for our LLM Agents MOOC—12K+ registered learners & 5K+ Discord members!  📣 Excited to launch today the LLM Agents MOOC Hackathon, open to all, with $200K+ in prizes & credits! 🔗 Sign up now: https://lnkd.in/edPK6h9p & join us virtually or in person @UCBerkeley!  Huge thanks to our sponsors: OpenAI Google AMD Lambda Intel Corporation Sierra Orby AI (and more to come) 🚀 Explore 5 exciting tracks: 1️⃣ Applications: Build cutting-edge LLM agents!   2️⃣ Benchmarks: Create innovative AI agent evaluation benchmarks!   3️⃣ Fundamentals: Strengthen core agent capabilities!   4️⃣ Safety: Address critical safety challenges in AI!   5️⃣ Decentralized & Multi-Agents: Push the boundaries of multi-agent systems! Special thanks to my co-instructor Xinyun Chen Google DeepMind & our amazing guest speakers for making this a great MOOC: Denny Zhou Google DeepMind; Percy Liang Stanford University; Benjamin Mann Anthropic; Shunyu Yao OpenAI; Chi Wang Google DeepMind; Jerry Liu LlamaIndex; Omar Khattab Databricks; Graham Neubig Carnegie Mellon University; Nicolas Chapados ServiceNow; Yuandong Tian Meta; Jim Fan NVIDIA; Burak Gokturk Google Join us to shape the future of LLM Agents! 🤖✨ #AI  #Hackathon #LLMAgents #UCberkeley

“I believe NEAR will be a powerhouse for AI” - Illia Polosukhin But we won’t get there alone. Eight teams. Twelve weeks. One AI incubator from NEAR Horizon & Delphi Digital. Learn all about it here: https://buff.ly/3Y4F79T

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” This year’s two Nobel Prize laureates in physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures. When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through connections that can be likened to synapses and which can be made stronger or weaker. The network is trained, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward. John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with. Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning. Learn more Press release: https://bit.ly/4gCTwm9 Popular information: https://bit.ly/3Bnhr9d Advanced information: https://bit.ly/3TKk1MM