Today’s Story

• New Research From Anthropic (the maker of AI chatbot Claude) offers the first detailed look inside a modern large language model

• A subfield of AI research: Mechanistic Interpretability Aims to understand how these models work by examining their internal mechanisms

  • Or “Reverse engineer neural networks” - Anthropic

• For the first time, Anthropic made significant strides in interpreting AI models, specifically Claude 3 Sonnet, using a technique called "dictionary learning."

• Finding Patterns: They identified approximately 10 million patterns, or "features," that represent different concepts within the model.

• Examples of Features:

  • San Francisco Feature: Activated when discussing San Francisco.

  • Scientific Terms: Activated for topics like immunology or elements like lithium.

• This is the first step in understanding model output, and tracing LLMs from training data to final output.

The COAI Digest

• Klarna’s AI Customer Service Agents (Read Here)

• The Possibilities of AI – Sam Altman (Read Here)

• The Dawn of AI: Intelligent Tools Reshape Business (Read Here)

• A Pivotal AI Copyright Ruling (Read Here)

• OpenAI’s New Customization Tools (Read Here)

• Meta’s AI Ambitions: 3 Paths (Read Here)

Research

EY’s Chief Technology Officer Reveals AI Future

• Generative AI Creates Endless Opportunities Generative AI like ChatGPT has endless possibilities for enterprises to systematize knowledge, provide better advice to clients, and act as an “intelligent agent” supporting humans.

• Responsible AI Requires Governance There is a need for governance structures and editorial oversight to manage organizational knowledge and prevent issues around bias, ethics, and misinformation. Humans must remain accountable.

• Knowledge Management Will Be Transformed By codifying institutional knowledge, generative AI can make expertise more accessible. This could help with training, free up time for higher-value work, and increase job satisfaction.

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