cover of episode The future of AI Chat: Foundation models and responsible innovation

The future of AI Chat: Foundation models and responsible innovation

2023/12/1
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The Future of Everything

Shownotes Transcript

Guest Percy Liang) is an authority on AI who says that we are undergoing a paradigm shift in AI powered by foundation models, which are general-purpose models trained at immense scale, such as ChatGPT. In this episode of Stanford Engineering’s The Future of Everything)* *podcast, Liang) tells host Russ Altman) about how foundation models are built, how to evaluate them, and the growing concerns with lack of openness and transparency.

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Chapters:

(00:00:00) IntroductionHost Russ Altman introduces Percy Liang, who runs the Stanford Center on Foundation Models 

(00:02:26) Defining Foundation Models

Percy Liang explains the concept of foundation models and the paradigm shift they represent. 

(00:04:22) How are Foundation Models Built & Trained?

Explanation of the training data sources and the scale of training data: training on trillions of words. Details on the network architecture, parameters, and the objective function.

(00:10:36) Context Length & Predictive Capabilities

Discussion on context length and its role in predictions. Examples illustrating the influence of context length on predictive accuracy. 

(00:12:28) Understanding Hallucination

Percy Liang explains how foundation models “hallucinate”, and the need for both truth and creative tasks which requires “lying”.

 (00:15:19) Alignment and Reinforcement in Training

The role of alignment and reinforcement learning from human feedback in controlling model outputs. 

(00:18:14) Evaluating Foundation Models

The shift from task-specific evaluations to comprehensive model evaluations, Introduction of HELM & the challenges in evaluation these models. 

(00:25:09) Foundation Models Transparency Index

Percy Liang details the Foundation Models Transparency Index, the initial results and reactions by the companies evaluated by it.

(00:29:42) Open vs. Closed AI Models: Benefits & Risks

The spectrum between open and closed AI models , benefits and security impacts

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