I don't know what he said here specifically, but a good rule of thumb is that you must not agree with Lee Cronin about AI
ETHICS
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AI Adverse Event Reporting Systems: Policy Recommendations
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New issue brief: The most serious risks of advanced AI systems often don’t emerge until after deployment. Our latest brief with scholars from RegLab provides policy recommendations for adverse event reporting systems for AI. https://
hai.stanford.edu/policy/adverse
-event-reporting-for-ai-developing-the-information-infrastructure-government-needs-to-learn-and-act
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How Leaders Can Thrive in the Age of AI
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https://
linkedin.com/pulse/how-lead
ers-can-thrive-age-ai-fabio-moioli-nbabf/
… Published yesterday for @SpencerStuart
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Superintelligence vs AGI: Redefining Long-Term AI Goals
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Artificial Superintelligence has always made sense as an aspiration and long-term goal.
It has always been FAIR's long-term goal (as well as mine). It still is, now more than ever. Artificial General Intelligence never made sense: it's meant to designate "human-level AI", but -
Chaos Creation as Accountability Avoidance: Ethical Concerns
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True, some people can operate in chaos — and that’s valuable.
But I’m talking about those who create chaos to avoid accountability.
That’s a different pattern — and a dangerous one. -
Neural Networks Learn Regardless of Weight Initialization and Hidden Patterns
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it's crazy to me that neural networks learn ~arbitrarily well regardless of initialization. you can even embed patterns in the weights and learning works fine you could encode an image of your face into the layers of a language model and no one would ever know
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Understanding Decision Making Through Incentives and Constraints
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I used to say that the entire world’s decisions could be condensed into tradeoffs and incentives. If you understand the WHY and the resources and options and requirements and limitations and goals a person has, you’re more likely to be able to anticipate their decisions and
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Creating Chaos to Hide: Ethics and Security in Tech
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Some people create chaos and uncertainty, so they can hide in it.
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Fair Use Analysis and AI Model Training Legal Arguments
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Your arguments do not make such a clear distinction. If uses before training all went through a Fair Use analysis, they *must* have been expressive. The fact downstream uses are potentially non-expressive helps the affirmative defense if defendants can provide proof.
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Copyright Verdicts on AI Training and Fair Use Analysis
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"essentially" doing a lot of heavy lifting. All the verdicts fell under Copyright law that's why they did the Fair Use analyses. Thus, it must have been expressive each time. Training often does capture expressions; verdict mentions this as "compressed copies". Keep up!