Comparing deep learning models to human intelligence reminds me of when folks compared computers to brains in the 1950-1980 period. It's simply a category error. Those models have none of the general cognitive abilities of humans, and in reverse, the specialized abilities they
@fchollet
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Regulating Technology: From Data Understanding to Science Fiction Narratives
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It's hard enough to regulate something you understand via data and anecdotes. It's impossible to regulate something you understand purely via pop science fiction narratives.
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Effective regulation addresses demonstrated harms, not hypothetical fears
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Regulation has a shot at being helpful when it is grounded in current issues and addresses harms that have already been demonstrated. But it is likely to be harmful when it is grounded in fear and addresses hypothetical harms.
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OpenCV Seeks Support for Version 5 Development Through Fundraiser
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OpenCV has long been a cornerstone of open-source computer vision. If you're an OpenCV user, you can now support it via this fundraiser that goes towards developing OpenCV 5: https://
indiegogo.com/projects/openc
v-5-support-non-profit-open-source-cv-ai#/
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AI Diagnosis Predictions from 2016 Did Not Come True
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The popular AI wisdom in 2016 was that diagnosis from images was a solved problem and radiologists would be out of a job by 2020.
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Three Essential Deep Learning Lessons: Scale, Data, and Engineering
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Deep learning lessons (known in 2016 but starkly confirmed since): 1. Scalable ideas > clever ideas
2. Improving the dataset > improving the model
3. Engineering chops > academic chops -
Native Language Fluency: Grammar Intuition Over Conscious Thought
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The fact that you do not have to consciously think about grammar and wording when speaking in your native language (though you have to consciously follow the thread of what you *mean*) was always evidence that natural language fluency could be handled by intuitive systems (DL).
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Deep Learning Models vs Human Cognition: Recitation, Intuition, and Reasoning
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DL models do chiefly 2, with some 1 in the mix. Some rare AI systems attempt to do 3. Humans do a combination of 2 and 3, for the most part (recitation is a thing but is les common). By "volume", most cognition is intuition, but reasoning accounts for the most critical bits.
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Recitation, Intuition, and Reasoning: Three Modes of Knowledge
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Recitation is a database lookup. Intuition is interpolative generalization or proximity-based generalization in a continuous space. Reasoning is discrete search and discrete planning.
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Three Categories of Problem Solving: Recitation, Intuition, and Reasoning
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I think there are broadly three categories of problem solving patterns — recitation, intuition, and reasoning. Recitation: you simply recognize a known problem and apply the steps you've learned. Like playing a chess opening. Intuition: in the face of a novel situation, you