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Day 3

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When:
9:00 AM, Wednesday 5 Jun 2024 EDT (8 hours)

Sub Sessions

9:00 AM EDT - 10:30 AM EDT | 1 hour 30 minutes
Large Language Models & Learning

In machine learning, the learner is assumed to be rational, seeking the highest probability, lowest cost, account of the data. This may not be achievable without a tremendous amount of data. Meanwhile, the problem of overfitting remains a formidable challenge: the best hypothesis from one set of data may not generalize well to another. Herbert Simon observed that human learning and decision-making often do not strive for optimal solutions but merely solutions that are good enough ("satisficin...

11:00 AM EDT - 12:30 PM EDT | 1 hour 30 minutes
Large Language Models & Understanding

Several recent publications in cognitive science have made the suggestion that Large Language Models (LLMs) have mastered human linguistic competence and that their doing so challenges arguments that linguists use to support their theories (in particular, the so-called argument from the poverty of the stimulus). Some of this work goes so far as to suggest that LLMs constitute better theories of human linguistic cognition than anything coming out of generative linguistics. Such reactions are m...

1:30 PM EDT - 3:00 PM EDT | 1 hour 30 minutes
Large Language Models & Understanding

The “free energy principle” provides an account of sentience in terms of active inference. Physics studies the properties that self-organising systems require to distinguish themselves from their lived world. Neurobiology studies functional brain architectures. Biological self-organization is an inevitable emergent property of any dynamical system. If a system can be differentiated from its external milieu, its internal and external states must be conditionally independent, inducing a “Markov...

3:30 PM EDT - 5:00 PM EDT | 1 hour 30 minutes
Large Language Models & UnderstandingLarge Language Models & Learning

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