Assaf Yablon’s Post

Reflections on Sparse Representations in Language Models Reading about the use of sparse autoencoders in language models revealed an intriguing balance between interpretability and efficiency. Sparse representations enhance feature disentanglement, aiding in understanding model behaviors, especially in AI safety and bias detection. However, for practical applications where interpretability is secondary, sparse representations might introduce computational redundancy and inefficiency. Thus, while sparse autoencoders offer valuable insights for research and safety, more compact representations could be preferable for deployment. Balancing these aspects is crucial, potentially through adaptive approaches that optimize for both interpretability during research and efficiency in real-world applications. https://lnkd.in/egPP7dEg

Responsible and accountable AI models are essential as generative AI continues to revolutionize industries. The rise of this technology, however, presents challenges in understanding how these models make decisions. Mechanistic interpretability addresses this by going beyond traditional methods that track statistical relationships. A recent research work by a team from #Anthropic has involved visualizing which neurons activate in response to specific prompts, advancing monosemanticity—ensuring each neuron’s activation correlates to a single, clear function. This step is crucial for fostering more transparent and safer AI systems in various applications. This is a great progress toward interpreting the thinking mechanisms of large language models (LLMs). #GenerativeAI #AI #LLMs #GenerativeAI #Grainger #MachineLearning #DeepLearning #ExplainableAI #DataScience

Anthropic releases Fascinating paper diving deeper into model inner workings. The paper studies how neurons in language models respond to specific features and how this changes as the models get bigger. Key Terms: Monosemantic Neurons:** Neurons that respond to one specific feature (i.e. a specific word). Polysemantic Neurons:** Neurons that respond to multiple features. Main Points: 1. Detection: Create methods to identify neurons that only respond to one feature. 2. Inhibition: Develop ways to stop neurons from only responding to one feature during training. Why It Matters: Reducing monosemantic neurons can make AI models more flexible and understandable, leading to better overall performance. Paper terms Neuron: A basic unit in a neural network that processes information. Language Models: AI models that understand and generate human language. Feature: A specific characteristic or element that a neuron responds to. This approach can lead to more reliable and easier-to-understand AI models. Kelly Cohen Josette Riep #artificialintelligence #llm

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

We refer to the internal processes an LLM (Large Language Model) goes through when considering output probabilities as a "black box." This term reflects the challenge of comprehending the vast number of comparisons the model performs and how it arrives at the intricate relationships between parts of words (tokens). Recent breakthroughs by Anthropic and others are opening a pinhole into these systems, potentially making AI models more interpretable and safer. Anthropic's recent progress involves using a technique called "dictionary learning" to uncover sets of neuron-like "nodes" in their LLMs. These nodes correspond to specific features, allowing us to glimpse into the model's system of logic – what we might anthropomorphize as its "mind." By mapping these nodes, researchers can better understand how LLMs process and represent information, possibly enabling them to: - Identify potential biases or inconsistencies in the model's reasoning - Improve the model's safety and alignment with intended goals - Enhance the interpretability and transparency of the model's decision-making processes I have recently thought out loud that the AI safety discussion revolves too much around Doom/Boom when in reality it needs to focus on Trust/Bust. Blackbox transparency is crucial to the next phase of adoption. This is by no means an easy task -the research represents a baby step in an exponentially larger picture. Anthropic's paper: https://lnkd.in/e_ERJNfZ Blog: https://lnkd.in/eR--mga5 #GenAI #Blackbox #Anthropic #LLM

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 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

🚨🚨🚨🚨🚨 REALLY COOL INSIGHT INTO AI!!! Peeking Inside the Black Box: New Advances in Understanding How AIs Think The article from Anthropic discusses their research on deciphering the inner workings of their conversational AI system, Claude. Using a technique called sparse dictionary learning, they were able to identify millions of semantic building blocks or "features" that Claude uses for reasoning and generating text. For example, they found distinct features for concepts like the Golden Gate Bridge, computer code, famous people, and geography. By analyzing and intervening on these features, they gained insights into how Claude represents knowledge and makes inferences. The discovery of abstract, interpretable features sheds light on therepresentations and computations happening inside Claude's neural network "black box." The article continues to build on the approach by scaling it up dramatically to extract features from Anthropic's latest model, Claude 3 Sonnet. By training larger sparse auto encoders with more compute power, they were able to find even more sophisticated features corresponding to complex, multilingual, and multimodal concepts. The researchers analyze these features in depth to understand what they represent, how they generalize, and how they enable model capabilities. Intriguingly, they also find features that appear relevant to AI safety, like detecting deception orsecurity flaws in code. While preliminary, this demonstrates how interpretability could eventually help ensure AI systems behave safely and reliably. This represents exciting progress in elucidating the mechanisms by which large language models operate. Methodically decoding these models promises to enhance our ability to build more robust, trustworthy, and beneficial AI. #AI #artificalintelligence #SRED #RD #innovation #funding #fundingexpert #grants #JonIrwin #futuretech https://lnkd.in/gRVVCppY

🚀 **Transforming Promptineering: A Deep Dive into Monosemanticity and Model Mechanics** 🚀 As a former actor and film director turned prompt engineer, I’ve come to appreciate the blend of art and science in what I call "Promptineering." A recent article caught my attention, discussing **scaling monosemanticity** in transformer circuits—an essential step forward for our field. Why is this important? Monosemanticity ensures our prompts elicit **consistent, predictable responses** from NLP models. It's not just about crafting clever text; it's about understanding the **intricate workings** of various models. As promptineers, we must be adept at **reading the nuances** between different models and **adapting to drift**—the subtle changes in model behavior over time. This means having a deep understanding of how models operate under the hood, not just on the surface level. The concept of **consent flux** in the article is particularly intriguing. It refers to the dynamic nature of user consent in interactions with AI, underscoring the need for promptineers to be vigilant and adaptable. To excel in promptineering, one must be well-versed in **all models**—their strengths, weaknesses, and idiosyncrasies. It's about being able to **interpret and pivot** as models evolve, ensuring that our prompts remain effective and our AI interactions remain ethical and user-centric. Let's embrace this knowledge to build **trustworthy and efficient AI systems** that respect user intent and consent. The future of AI depends on our ability to stay informed and agile. 🌟 #Promptineering #AIethics #NLP #MachineLearning #AIinEducation #EdTech #AIforTeachers #FutureofEducation #TechInClassrooms Geri Gillespy, Ed.D. Ken Shelton Kirk Uhler Bonnie Nieves Lindy Hockenbary Anthropic

Humans can't read minds... at least not the biological kind. However, thanks to Anthropic's latest research into interpretability, researchers are starting to get a glimpse into the inner workings of large language models, specifically Claude 3.0 Sonnet. By mapping millions of features corresponding to diverse concepts, they've provided a detailed conceptual map of the model's internal states. This research not only enhances our understanding of how AI models represent and process information but also opens pathways to improve AI safety and reliability. Key Findings: - Identification of features representing complex concepts like cities, people, and abstract ideas. - Capability to manipulate these features to observe changes in model behavior. - Potential to monitor and steer AI systems for safer and more ethical outcomes. The implications of these findings are profound, suggesting that we may finally be able to shine some light on the inner workings of these 'black box' models. While additional research is still needed, this study provides strong evidence that LLMs can be further refined by 'steering' the system away from potentially nefarious behaviors when asked about a specific topic or concept. This study is therefore a major step forward in our ability to align AI behavior with human values, while simultaneously mitigating risks and enhancing trust. 🔗 Read more about the study below: #AI #MachineLearning #AIEthics #TechInnovation #AIResearch