Protecting Collective Intelligence | Stay Human Chapter 14 | Four New Summit Speakers | Artificial Intimacy
Protecting Collective Intelligence A couple of weeks ago Dave and I were giving a keynote in Venice and someone in
Recent Publications (Page 29)
Research Update: December 2023
In our Artificiality Pro update for December, we covered several key industry updates in AI and introduced mechanistic interpretability and memory vs. margins.
Mechanistic Interpretability & Memory vs. Margins
In this episode, we provide updates from our Artificiality Pro presentation, including key developments in mechanistic interpretability for understanding AI models and considerations around the costs of large language models: aka memory vs margins.
The Paradox of Expertise in the AI Age
Developing expertise now requires fluency in both core disciplines and leveraging AI for insights, posing an uneasy paradox.
Stephen Fleming: Consciousness & AI
In this episode, we speak with cognitive neuroscientist Stephen Fleming about theories of consciousness and how they relate to artificial intelligence.
Integrated Intelligence
Our obsession with intelligence: AI that promotes collective intelligence, not collective stupidity.
The Dawn of Dynamic Design
Of all the interesting parts of Google’s Gemini announcement, one is keeping me up at night wondering about the possibilities for the future: dynamic coding.
Google's new AI model, Gemini
In this episode, we dive into the exciting announcement of Google's new foundation model for AI, Gemini, exploring three key aspects of this important, new technology.
Steven Sloman: LLMs and Deliberative Reasoning
An interview with Steven Sloman, professor of cognitive, linguistic, and psychological sciences at Brown University, about LLMs and deliberative reasoning.
The New Artificiality (including important new log in info!)
After four years, we are relaunching Artificiality with a new site, new focus, and new business model. Woohoo!
GPT Writes Better Prompts for Itself Than Domain Experts Can
New research reveals that large language models can generate superior prompts for themselves through automated techniques, reducing reliance on specialized fine-tuning.