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1
Introduction
2
What are foundation models
3
Are foundation models limitless
4
Knowledge of language
5
Linear scaling
6
Bullshit machines
7
Data choice
8
How to make them more sustainable
9
Concerns
10
Realistic
11
Should you want school
12
Black boxes
13
calibrated probabilities
14
conclusion
Description:
Explore the strengths, weaknesses, and societal implications of foundational models like GPT-3 in this 42-minute panel discussion from AI UK 22. Join experts from The Alan Turing Institute, University of Oxford, University of Bristol, and DeepMind as they delve into topics such as the limitations of these models, their knowledge of language, linear scaling, data choice, sustainability concerns, and the concept of "bullshit machines." Gain insights into the future of AI, the challenges of black box systems, and the importance of calibrated probabilities in this thought-provoking conversation about the latest developments in artificial intelligence.

Foundation Models: Strengths, Weaknesses, and Future Implications - Session 4

Alan Turing Institute
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