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.
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. We also highlight an upcoming webinar we are hosting on discussing AI with teenagers.
Mechanistic Interpretability & Memory vs. Margins
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Episode Notes:
Mechanistic Interpretability
An emerging way for us to unravel the "blackbox" of AI models by understanding their internal representations and computations.
Allows for safer and more reliable use of AI, especially in critical applications like healthcare.
Involves conceptualizing models in terms of "features" and "circuits" instead of just neurons.
Anthropic has developed new methods for creating sparse representations to query models.
Costs of Large Language Models: Memory vs. Margins
Costs broken down into prompt (input) and response (output) costs per 1000 tokens from our perspective.
Conversations require sending full context back and forth, rapidly increasing costs.
Assistants that aim to maintain long-term conversational context could get expensive because memory requires processing many more tokens.
Upcoming Webinar on Teens & AI
Free webinar on December 23 at 11am Pacific that we are hosting on talking to teenagers about AI.
Dave Edwards is a Co-Founder of Artificiality. He previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Apple, CRV, Macromedia, Morgan Stanley, Quartz, and ThinkEquity.
Helen Edwards is a Co-Founder of Artificiality. She previously co-founded Intelligentsia.ai (acquired by Atlantic Media) and worked at Meridian Energy, Pacific Gas & Electric, Quartz, and Transpower.
