Rachit Ahuja’s Post

This is an interesting read from Anthropic which can help you understand a bit more about how AI Large Language Models (LLM) work: "Today we report a significant advance in understanding the inner workings of AI models. We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model. This interpretability discovery could, in future, help us make AI models safer."

Do we finally understand the “mind” of AI Language Models? 🤔 Has the #Interpretability problem of #LLMs been finally solved? Much effort in AI nowadays goes towards pushing capabilities by creating ever-larger and more powerful models. While impressive, I believe interpretability research that helps us understand how these systems work is just as crucial. Not only is peering inside the "#blackbox" of these models fascinating from a scientific perspective 🔍 , it also helps us ensure their #safety and #reliability as they become increasingly prevalent across many domains. Two weeks ago, Anthropic published a research that, for the first time, they might have uncovered the "mind" of a production-grade Large Language Model (#LLM). 😮 They extracted patterns of neuron activations of their LLM #Claude, and mapped them to concepts like cities, people, scientific fields, and even abstract ideas. Remarkably, manipulating these conceptual features can alter the model's behavior, validating their causal role in shaping how Claude thinks and reasons (For example, amplifying the "Golden Gate Bridge" 🌉 conceptual feature caused Claude to claim it was the iconic bridge itself when asked about its physical form 😂). Specifically, the conceptual mapping was accomplished through sparse #autoencoder technique, which is a form of dictionary learning. Like the team's previous work (Oct 2023) on a small one-layer #transformer model, sparse autoencoders were trained to decompose the model’s neuron activations into combinations of "features" corresponding to human-interpretable concepts. And this latest work scaled up the dictionary learning approach to Claude 3 Sonnet, Anthropic's medium-sized production language model. The largest autoencoder model resulted in 34 million learned features, which is a significant increase in scale, and exhibited considerably more abstraction, depth and sophistication compared to the toy model. 🚀 While plenty of challenges remain, mapping the mind of a cutting-edge AI language model marks an important milestone. Kudos to the Anthropic researchers pushing the boundaries of what's possible in AI interpretability and safety! 🙌 Link to the post: https://lnkd.in/dxybuQxm Link to the research paper: https://lnkd.in/dYN6uabA #LLM #Interpretability #AI #GenerativeAI #AISafety #Transformer #Autoencoder #DictionaryLearning #Monosemanticity #AIResearch

European Parliament's AI Act has identified a practical list of high-risk AI applications. This Act has been approved by European Council 🇪🇺. 關於 AI 人工智能的一些事實: Some fact findings about AI: https://lnkd.in/gwcPNUPP 其中，用一個簡單的是非題就能顯示我們受著作權保護的中英對照元數據 (metadata) 能做到現今 AI 人工智能無法做到的數據分析工作。 A simple go/no-go test shows that, with our intellectual property (IP), a copyrighted Chinese-English multilingual metadata, we can do what artificial intelligence (AI) can't do in data analytics, NOW. Do you or any contacts of yours need our expertise/IP that can do what AI can't do?

Unveiling Chain-of-Thought Reasoning: Exploring Iterative Algorithms in Language Models Quick read: https://lnkd.in/eEe4EdAN Paper: https://lnkd.in/eb_D4Dk8 AI at Meta

Researchers at Anthropic have made a breakthrough in understanding how large language models like Claude Sonnet "think". They discovered how millions of concepts are represented inside the model, which can help make AI safer and more reliable. By identifying specific features within the model, researchers were able see how these features influence the AI's responses. For example, amplifying a feature related to the Golden Gate Bridge made the AI obsessively mention it in answers. This shows that these features play a crucial role in shaping the model's behaviour. The research found features related to potentially harmful behaviours, like generating scam emails or showing bias. Understanding these features can help develop methods to monitor and steer AI towards safer and more desirable behaviour, ultimately making AI systems more trustworthy and reliable. Original article - https://lnkd.in/e3Ji4VQH

Anthropic has achieved a breakthrough in understanding large language models like Claude 3 Sonnet. They identified how millions of concepts are represented within the model, using dictionary learning to uncover patterns in neuron activation. These features represent a wide array of entities such as cities, people, and scientific fields, and can respond to both text and images in multiple languages. Notably, they also found abstract features related to concepts like inner conflict and logical inconsistencies. By manipulating these features, they could change Claude's behavior, such as making it repeatedly mention the Golden Gate Bridge. This discovery enhances our understanding of AI models and could improve their safety in the future. For more details, visit the articles on Anthropic's website and Transformer Circuits. https://lnkd.in/eAp4TybE #AI #Claude #Anthropic #blackbox

In a recent breakthrough, researchers have developed a new method for interpreting large language models, offering a deeper understanding of how these complex systems represent concepts. This research has the potential to revolutionize the field of artificial intelligence, making AI models safer and more reliable. The article, published by Anthropic, dives into the inner workings of large language models, exploring how these models encode and process information. The researchers identified millions of features within the model that correspond to a vast array of concepts. By manipulating these features, they were able to influence the model's behavior. This groundbreaking discovery holds immense significance for the future of AI. By gaining a deeper understanding of how large language models function, we can develop methods to mitigate potential risks and biases. This will pave the way for the creation of more trustworthy and dependable AI systems. The findings from this research can be applied across various domains, from improving natural language processing tasks to ensuring the safety and security of AI-powered applications. As we continue to explore the potential of large language models, this new approach to interpretation represents a significant leap forward. Key takeaways: - Researchers have developed a new method for interpreting large language models. - This method allows for the identification of millions of features corresponding to various concepts. - By manipulating these features, researchers can influence the model's behavior. This breakthrough has the potential to make AI models safer and more reliable. Read the article here : https://lnkd.in/gVJZtKCk

A few weeks ago, Anthropic published a paper showcasing results that represent significant progress in the field of AI safety, which focuses on ensuring that AI systems operate reliably, ethically, and beneficially, without posing risks to humans or society. Although AI systems are designed and developed by scientists and engineers, their immense size and complex structure often make them operate as "black boxes." This opacity makes it nearly impossible to understand their decision-making processes while in operation. In production-grade Large Language Models (LLMs), it is unclear why a specific input produces a particular output. This issue, known as interpretability, is a crucial concern in the field of AI safety. Without interpretability, it is difficult to determine whether an AI system is being "deceptive" or "hiding real intentions." Addressing interpretability before developing larger and more capable AI systems is essential, in my opinion, to ensure long-term alignment and AI safety. In this paper, Anthropic shows that a method they previously used for the interpretability of small models could scale to their medium-sized LLM, Claude 3 Sonnet. They demonstrated that features (concepts, entities, words, etc.) are represented inside the LLM by patterns of neurons firing together. By mapping millions of features in Claude 3 Sonnet, they were able to understand what the model was "thinking" about at any given moment. The paper showcases many examples of these features, of which sycophancy is my favorite. Great result... that might also inspire progress in neurology and psychology... https://lnkd.in/e-MNASrV https://lnkd.in/e5aQbMic https://lnkd.in/etQDZBaY #artificialintelligence #AI #AISafety #LLM #LLMs #Anthropic #Interpretability #InterpretableAI #Explanations #ExplainableAI #Monosemanticity

This the direction forward: combining (old-fashion) Knowledge Graphs and leveraging the LLM's strengths. Good explainer from Anthony Alcaraz. "The fusion of complementary graph and neural methods will facilitate language models that can reason, explain, and deduce — not just retrieve — powering the next era of capable conversational AI." #ai #artificialintelligence #LLMs #knowledgegraphs

𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗠𝗶𝗻𝗱 𝗼𝗳 𝗮 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 https://lnkd.in/eExvff39 "Today we report a significant advance in understanding the inner workings of AI models. We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model. This interpretability discovery could, in future, help us make AI models safer. We mostly treat AI models as a black box: something goes in and a response comes out, and it's not clear why the model gave that particular response instead of another. This makes it hard to trust that these models are safe: if we don't know how they work, how do we know they won't give harmful, biased, untruthful, or otherwise dangerous responses? How can we trust that they’ll be safe and reliable?"