Anthropic's 'Brain Scanner' Reveals Surprising Insights into AI Decision-Making
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Why do LLMs make stuff up? New research peers under the hood.
One of the most frustrating things about using a large language model is dealing with its tendency to confabulate information, hallucinating answers that are not supported by its training data. From a human perspective, it can be hard to understand why these models don't simply say "I don't know"
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We are finally beginning to understand how LLMs work: No, they don't simply predict word after word
In context: The constant improvements AI companies have been making to their models might lead you to think we've finally figured out how large language models (LLMs) work. But nope - LLMs continue to be one of the least understood mass-market technologies ever. But Anthropic is attempting to
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Anthropic scientists expose how AI actually 'thinks' -- and discover it secretly plans ahead and sometimes lies
Anthropic has developed a new method for peering inside large language models like Claude, revealing for the first time how these AI systems process information and make decisions. The research, published today in two papers (available here and here), shows these models are more sophisticated than
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How This Tool Could Decode AI's Inner Mysteries
The rhyming couplet wasn't going to win any poetry awards. But when the scientists at AI company Anthropic inspected the records of the model's neural network, they were surprised by what they found. They had expected to see the model, called Claude, picking its words one by one, and for it to only
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Anthropic has developed an AI 'brain scanner' to understand how LLMs work and it turns out the reason why chatbots are terrible at simple math and hallucinate is weirder than you thought
It's a peculiar truth that we don't understand how large language models (LLMs) actually work. We designed them. We built them. We trained them. But their inner workings are largely mysterious. Well, they were. That's less true now thanks to some new research by Anthropic that was inspired by
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What is AI thinking? Anthropic researchers are starting to figure it out
Why are AI chatbots so intelligent -- capable of understanding complex ideas, crafting surprisingly good short stories, and intuitively grasping what users mean? The truth is, we don't fully know. Large language models "think" in ways that don't look very human. Their outputs are formed from
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How Anthropic's AI Model Thinks, Lies, and Catches itself Making a Mistake
Anthropic's Claude was found to be providing false reasoning while attempting to decode how an LLM thinks. AI isn't perfect. It can hallucinate and sometimes be inaccurate -- but can it straight-up fake a story just to match your flow? Yes, it turns out that AI can lie to you. Anthropic
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Anthropic Researchers Achieve Breakthrough in Decoding AI Thought Processes
The researchers found that AI thinks in a shared language space Anthropic researchers shared two new papers on Thursday, sharing the methodology and findings on how an artificial intelligence (AI) model thinks. The San Francisco-based AI firm developed techniques to monitor the decision-making
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Tracing The thoughts of AI : How Large Language Models Learn and Decide
Have you ever wondered how artificial intelligence seems to "think"? Whether it's crafting a poem, answering a tricky question, or helping with a complex task, AI thought process systems -- especially large language models -- often feel like they possess a mind of their own. But behind their
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Anthropic's new research technique, circuit tracing, provides unprecedented insights into how large language models like Claude process information and make decisions, revealing unexpected complexities in AI reasoning.
Anthropic Unveils Groundbreaking AI Interpretability Technique
Anthropic, a leading AI research company, has developed a revolutionary method called "circuit tracing" that allows researchers to peer inside large language models (LLMs) and understand their decision-making processes
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. This technique, inspired by neuroscience brain-scanning methods, has provided unprecedented insights into how AI systems like Claude process information and generate responses3
.Surprising Discoveries in AI Reasoning
The research has revealed several unexpected findings about how LLMs operate:
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Advanced Planning: Contrary to the belief that AI models simply predict the next word in sequence, Claude demonstrated the ability to plan ahead when composing poetry. It identified potential rhyming words before beginning to write the next line
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Language-Independent Concepts: Claude appears to use a mixture of language-specific and abstract, language-independent circuits when processing information. This suggests a shared conceptual space across different languages
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Unconventional Problem-Solving: When solving math problems, Claude uses unexpected methods. For example, when adding 36 and 59, it approximates with "40ish and 60ish" before refining the answer, rather than using traditional step-by-step addition
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Implications for AI Transparency and Safety
The circuit tracing technique has significant implications for AI transparency and safety:
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Detecting Fabrications: Researchers can now distinguish between cases where the model genuinely performs the steps it claims and instances where it fabricates reasoning
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Auditing for Safety: This approach could allow researchers to audit AI systems for safety issues that might remain hidden during conventional external testing
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Understanding Hallucinations: The research provides insights into why LLMs sometimes generate plausible-sounding but incorrect information, a phenomenon known as hallucination
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Challenges and Future Directions
While the circuit tracing technique represents a significant advance in AI interpretability, there are still challenges to overcome:
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Time-Intensive Analysis: Currently, it takes several hours of human effort to understand the circuits involved in processing even short prompts
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Incomplete Understanding: The research doesn't yet explain how the structures inside LLMs are formed during the training process
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Ongoing Research: Joshua Batson, a research scientist at Anthropic, describes this work as just the "tip of the iceberg," indicating that much more remains to be discovered about the inner workings of AI models
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As AI systems become increasingly sophisticated and widely deployed, understanding their internal decision-making processes is crucial for ensuring their safe and ethical use. Anthropic's circuit tracing technique represents a significant step forward in this critical area of AI research.
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