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AI & Scientific Discovery

In science, traditional human search strategies are like wandering through a wilderness with limited visibility, relying on intuition and serendipity. AI, in contrast, can take in the whole landscape, quickly and effectively exploring the vast range of possible combinations.

Reward-Free Curricula for Training Robust World Models

Agentic AI and Self-Driven Exploration

New research on developing AI that builds robust world models shows an AI's ability to seek out surprise, motivation, and novelty, enabling it to navigate and understand the complexities of the world through self-driven exploration rather than just following predetermined reward pathways.

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AI, Intimacy, and Transcendence

Context is everything—whom you're with, where you're going, and why. Machines currently lack the ability to understand this context, but generative AI, especially modern large language models, hold the promise of changing this limitation.

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Our Ideas

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AI and the Extended Mind

The future of the extended mind is not a fixed destination but an ongoing journey that requires our active participation and reflection as we redefine what it means to be human in an age of artificial intelligence.

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AI and Complex Change

Complex change recognizes that change is non-linear, emergent, and deeply interconnected with the system in which it occurs. This is even more important as we adapt to the complexity of generative AI.

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AI and Decision Factories

AI's potential is vast but many applications remain incremental, simply grafting AI onto outdated frameworks. This is why we embrace complexity. We think about designing incentives, understanding self-organization and distributed control, and planning for emergence and adaptation.

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Umwelt

AI could help humans appreciate the diverse perceptual worlds of animals by simulating their senses and thought processes.

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The Science

Research paper on RAG vs fine-tuning

Why RAG Beats Fine-tuning AI

Enterprises face a critical choice in their generative AI adoption strategy: fine-tuning or Retrieval-Augmented Generation (RAG)? While fine-tuning has been the go-to approach for early adopters, a new study suggests that RAG may be the more powerful and sustainable path forward.

Research review of the paper LLM Augmented LLM

AIs that can understand other AIs

By enabling different AI models to 'speak' to each other and combine their strengths, CALM opens up new possibilities for solving complex problems across various domains and tackling tasks with expertise and precision, in a data and compute efficient way.

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Our Research

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Agentic AI

Defined as “the degree to which a system can adaptably achieve complex goals in complex environments with limited direct supervision", agentic AI promises to transform how we make decisions.

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Newsletter

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Context is Everything in Generative AI

There is a lot of chatter about whether companies are realizing value from generative AI. Realizing value from generative AI will take time to prepare, capture, and create the data that allows generative AI to understand the context of our complex human lives.

The Medium is Creating the Message

The Medium is Creating the Message

In traditional media, the role of technology has been largely to convey, distribute, or modify content created by humans. Generative AI, however, is different from any previous technology because it is creating the messages itself.

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Toolkit

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Conversations

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Events

Charter Pro: Making Decisions with Generative AI.

Charter

Artificiality Co-founders, Helen and Dave Edwards, will be speaking on Making Decisions with Generative AI with our friends at Charter on April 10.

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