How Anthropic's research on Claude 3.0 Sonnet affects AI

🚀 Exciting times in the world of AI! The Anthropic team has made a significant leap in understanding large language models. Their recent investigation sheds light on the intricate workings of these models, allowing us to peek inside their "minds." 🌟 🔍 This groundbreaking research reveals which features, neurons, and neural pathways are activated during specific tasks. It's impressive to see the level of detail and insight that the team has achieved, offering a new dimension to our comprehension of AI behavior and functionality. 👏 Kudos to the Anthropic team for pushing the boundaries of AI research and providing us with these invaluable insights. The future of AI just got a whole lot brighter! 🌐✨ https://lnkd.in/d2J37Bgp #AI #MachineLearning #NeuralNetworks #Research #Innovation #TechNews #atmira

Anthropic is trying to figure out what they actually built. They have made a major breakthrough (or is it???) in AI interpretability with their latest paper "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet". There are a bunch of “first times” mentioned in the paper, like for the first time they have extracted millions of understandable features from the middle layers of a state-of-the-art production language model. The researchers found a wide range of interpretable features corresponding to concepts like famous people, cities, scientific fields, code syntax, and more abstract ideas like security vulnerabilities and gender bias. Remarkably, they were able to manipulate the model's behavior by amplifying or suppressing specific features, like inducing it to self-identify as the Golden Gate Bridge. While there is still much more work to be done, this research lays critical foundations for making AI systems safer and more understandable. Anyhoo I know what I'll be diving into this weekend 😛 #ai #ml #aimusings

Exciting news in the world of AI, with huge implications regarding explainability and transparency. Anthropic interpretability team is working to better understand how AI works. This is a potential game changer and a big step towards secure & responsible AI.

ideas for model risk management for LLMs!! follow ADAO 😀

Validating LLMs?! How about this: modifying certain features to change the behaviour of an LLM. Make it friendlier, more factual, change its context... how can we be in control? Here are two thoughts! - modify certain features/embeddings to determine if their values are appropriate? - define tests that the AI should pass, in order to 'pass' certain validation test. Here is the post qhere these two tiny ideas are based: https://lnkd.in/eaYkgSNM Follow ADAO for more content/info! #validation #ai #modelriskmanagement #llm

One of the biggest challenges in language models today is making them more interpretable. We often treat AI models as black boxes: data goes in, a response comes out, and the reasoning behind that response remains unclear. I remember an interview with Google CEO, where he was asked to explain how Gemini works. He said he didn’t know. This response resonated with the scientific community, as deep learning often similar to the human brain, but the interviewer was shocked! How can a model, released to millions, be so poorly understood? Two weeks ago, Anthropic released an important paper on model interpretability. They used a technique called "dictionary learning," borrowed from classical ML, which isolates patterns of neuron activations that recur across many different contexts. This paper sheds some light on this important challenge, which, if solved, will create more trust in these models and thus ease the integration of AI into our everyday lives. Highly recommend reading: https://lnkd.in/gPzEePx8

I've learned a fantastic paper about LLMs and how to research a group of neurons inside LLM responsible for various objects and how to tune it and put validation / modification to improve model #llm #ai #deeplearning https://lnkd.in/g6m4UZKG

Unlocking AI Interpretability at Scale! https://lnkd.in/dRvHAGUp Anthropic has achieved a significant breakthrough in understanding AI models by using sparse autoencoders. This advancement allows a deeper examination of Claude 3 Sonnet, Anthropic’s medium-sized AI model, contributing to the development of safer and more transparent AI systems. Understanding Features: In AI, a “feature” represents a distinct piece of information or pattern recognized by the model, such as identifying a “cat” in an image or understanding the concept of “happiness” in a text. By observing the inner mechanics of the neural network, Anthropic can determine when and where these semantic contexts are formed, which is key to interpreting neural network behavior. Key Highlights: Work indicates clear and understandable features can be extracted from large AI models, a task previously deemed impossible. Features related to well-known personalities, geographical locations, and coding patterns can be identified. These features remain consistent across different languages and formats, including both text and images. This research enables the detection of features that help identify security risks, biases, deceptive behavior, and harmful content. It remains to be seen if mechanistic interpretability is the key to deeper understanding of complex neural networks but if it works out it for sure opens the door for a much deeper understanding and predictability of these AI models.

Huge progress on LLM Explainability: “The Anthropic team used a technique called "dictionary learning", borrowed from classical machine learning, which isolates patterns of neuron activations that recur across many different contexts. In turn, any internal state of the model can be represented in terms of a few active features instead of many active neurons, which means decomposing the model in features.” #XAI #AISafety #LLM #AI #Anthropic https://lnkd.in/eaYkgSNM

🔬 Exciting Breakthrough in AI Interpretability by Anthropic ! 🔬 Eight months ago, our team embarked on a challenging journey to scale sparse autoencoders for recovering monosemantic features from transformers. Today, we're thrilled to share a significant milestone: we've successfully extracted high-quality features from Claude 3 Sonnet, Anthropic's medium-sized production model! Our research has unveiled a fascinating diversity of highly abstract features that both respond to and influence behaviors. Some key findings include: - Features representing famous people, countries, cities, and type signatures in code. - Multilingual and multimodal features, bridging text and images across languages. - Abstract and concrete instantiations of ideas, such as code vulnerabilities and discussions on security. 📊 Key Results: 1. Interpretable Features: Sparse autoencoders are producing clear, interpretable features even for large models. 2. Scaling Laws: We've utilized scaling laws to guide our training, enhancing efficiency and effectiveness. 3. Abstract Generalization: The features generalize across multilingual, multimodal, concrete, and abstract references. 4. Systematic Relationships: A systematic relationship between concept frequency and dictionary size needed for feature resolution. 5. Influence on Behavior: These features can be used to steer large models, influencing behavior significantly. Importantly, the findings also touch on crucial safety concerns, revealing features related to deception, sycophancy, bias, and dangerous content. This breakthrough marks a significant step forward in AI safety and interpretability, paving the way for safer, more reliable AI systems. Kudos to the Anthropic interpretability team for this remarkable achievement! 🎉 Link to Paper: https://lnkd.in/ehH-eCdr #AISafety #MachineLearning #AIResearch #SparseAutoencoders #Transformers #Anthropic #Claude3Sonnet #TechInnovation #ArtificialIntelligence #AIInterpretability