Edge 406: Inside OpenAI's Recent Breakthroughs in GPT-4 Interpretability
Was this email forwarded to you? Sign up here Edge 406: Inside OpenAI's Recent Breakthroughs in GPT-4 InterpretabilityA new method helps to extract interpretable concepts from large models like GPT-4.
Interpretability is one of the crown jewels of modern generative AI. The workings of large frontier models remain largely mysterious compared to other human-made systems. While previous generations of ML saw a boom in interpretability tools and frameworks, most of those techniques have become impractical when applied to massively large neural network. From that perspective, solving interpretability for generative is going to require new methods and potential breakthroughs. A few weeks ago, Anthropic published some research about their work in identifying concepts in LLMs. More recently, OpenAI published a super interesting paper about their work on identifying interpretable features in GPT-4 using a quite novel technique. To interpret LLMs, identifying useful building blocks for their computations is essential. However, the activations within an LLM often display unpredictable patterns, seemingly representing multiple concepts simultaneously. These activations are also densely packed, meaning each activation is constantly engaged with every input. In reality, concepts are usually sparse, with only a few being relevant in any given context. This reality underpins the use of sparse autoencoders, which help identify a few crucial “features” within the network that contribute to any given output. These features exhibit sparse activation patterns, aligning naturally with concepts that humans can easily understand, even without explicit interpretability incentives... Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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Edge 407: LLMs with Infininite Context Windows? Short-Term Memory and Autonomous Agents
Tuesday, June 25, 2024
The role of context windows in LLMs ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
📽 [Virtual Talk] Powering millions of real-time rankings at GetYourGuide
Monday, June 24, 2024
Hi there, Curious about how GetYourGuide, a leading online marketplace for travel excursions, delivers millions of personalized rankings daily, adapting to users' preferences in real time? Join us
Beyond OpenAI: Apple’s On-Device AI Strategy
Sunday, June 23, 2024
Plus a new super coder model, Meta's new AI releases, DeepMind's video-to-audio models and much more. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 404: Inside Anthropic's Dictionary Learning, A Breakthrough in LLM Interpretability
Thursday, June 20, 2024
Arguably one of the most important papers of 2024 ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
The Sequence Chat: Justin D. Harris - About Building Microsoft CoPilot
Wednesday, June 19, 2024
Quick bio This is your second interview at The Sequence. Please tell us a bit about yourself. Your background, current role and how did you get started in AI? I grew up in the suburbs of Montreal and I
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