'OpenAI is product development, not AI research,' says Meta's chief AI scientist LeCun | ZDNET
@AndrewYNg
@ylecun
@OpenAI
#AI #artificialintelligence #machinelearning #OpenAI #ChatGPT
MACHINE LEARNING
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Meta’s LeCun Challenges OpenAI’s AI Research Claims
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MIT Intro to Deep Learning 2023 Lecture Videos Available on YouTube
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MIT Intro to Deep Learning 2023 The lecture videos for 2023 iterations and previous iterations are all freely available on YT: https://
youtube.com/playlist?list=
PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
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Robot Learns to Clean with RL and Trajectory Optimization
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Read how we enabled a robot to reliably wipe up crumbs and spills with an approach for robotics applications in complex environments that uses an #RL policy (trained with a stochastic differential equation simulator) followed by a trajectory optimizer. → https://t.co/Iw7pjVBjac pic.twitter.com/DWODDFJRTh
— Google AI (@GoogleAI) 7 avril 2023Read how we enabled a robot to reliably wipe up crumbs and spills with an approach for robotics applications in complex environments that uses an #RL policy (trained with a stochastic differential equation simulator) followed by a trajectory optimizer. → https://
goo.gle/3Mn5lQV -
Deep Models Fail to Generalize on Puzzles Beyond Training Data
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See e.g. https://
arxiv.org/abs/2212.09993 "Our experiments reveal that while powerful deep models offer reasonable performances on puzzles that they are trained on, they are not better than random accuracy when analyzed for generalization." -
Testing AI Performance: Familiarity vs. Novel Questions
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The system is out there. You can just interact with it and test the hypothesis by yourself! You see an immediate correlation between question familiarity & performance, and a fast break down of performance as the questions become more novel — independent of question complexity!
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GPT models fail at genuine generalization on novel problems
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Exactly. Except with the opposite conclusion. Every study so far that tries to test GPT-N for actual generalization has found that it scores no better than random on genuinely new problems — brand new coding problems in particular. This is why it can't do ARC either.
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Training Data Alone Doesn’t Ensure Quality AI Performance
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Even a hashtable can pass the bar exam giving enough training data. But you probably don't want to be represented by a hashtable.
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Skill Acquisition Efficiency as a True Measure of Intelligence
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Skill, on its own, is not a sign of intelligence. But skill acquisition efficiency over arbitrary skillsets is. And that looks very different in the context of human test takers vs. machines.
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General Intelligence vs Specialized Machine Design in Chess
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And so they could have used these 30 years to become great at *any* other human skill. They possess general intelligence. But you cannot make the same intelligence assumption when you see a machine *designed* to play chess, or one that has memorized one billion chess games.
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Language Models Trained on Vastly More Data Than Humans Encounter
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A person is exposed to on the order of a billion words over their entire life through speech and reading. Modern large language models are fed hundreds of times that in the weeks of their training.