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THE DAILY INDEX

Technology, AI, software, security, crypto and business

GENERAL

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A NON-ANTHROPOMORPHIZED VIEW OF LLMS

Anthropomorphizing LLMs with concepts like consciousness or ethics is misleading because they are essentially complex mathematical functions generating text. The real challenge lies in quantifying and bounding the probability of LLMs generating undesirable sequences, a task that is difficult because of our inability to define what exactly "undesirable" is. Focusing on LLMs as powerful sequence generators, rather than attributing human-like qualities to them, is necessary for clearer thinking about their impact and potential risks.

THE ERA OF EXPLORATION

Future AI progress will depend more on exploration, acquiring new and informative experiences, rather than just scaling parameters. Pretraining LLMs inadvertently pays an "exploration tax" by learning a rich sampling distribution, allowing smaller models to later distill reasoning abilities. Exploration has two key axes: "world sampling," deciding which problems to learn from, and "path sampling," determining how to gather data within those problems.

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