Pretty pictures are cool. But raw, scalable intelligence is on its way.
RESEARCH
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Beyond GPT-3: Next Wave of Superhuman AI Systems
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In a couple of years, we’re going to look back at GPT-3 as a fun toy… The coming waves of AI systems will be smarter than humans in many ways. A different kind of intelligence. It’ll feel alien, yet familiar. And more generally capable than any single person alive today.
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AI hype cycle: short-term overvalued, long-term underestimated
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AI is over-hyped in the short-term But massively under-hyped in the medium/long-term
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Robots Gain Sense of Touch: The Next Robotics Innovation
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The next big thing in robotics: building machines with a sense of touch #RuleoftheRobots https://
wsj.com/articles/robot
s-sense-of-touch-11666899973?st=ef9llv7ucjuqrjf
… via @WSJ -
Hyperparameter Optimization Techniques and Practical Implementation Guide
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Check out @subirmansukhani
’s technical blog on how to perform hyperparameter optimization. Then try the free accompanying Domino project yourself. https://
domino.buzz/3h6XxFq -
Radio’s Role in Nazi Rise: Historical Media Amplification Study
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Analysis of radio broadcasts in pre-WW2 Germany finds pro-democracy broadcasts initially slowed the Nazi rise. But after the Nazis gained access to the airwaves, radio boosted Nazi membership & then it accelerated antisemitism (in areas of Germany that were already antisemitic).
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GPT-3 Requires Chain-of-Thought for Problem Solving, Paper Analysis
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Regardless how a human would do it, GPT-3 needs chain-of-thought to perform on these problems. Only skimmed the paper, but I don't see a specific deficit in ToM vs. known inability to do multi-hop inference without CoT.
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Unlocking AI’s Full Potential for Human Intelligence
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We're Not Using AI to Its Fullest Human Potential https://
time.com/6227118/eric-s
chmidt-ai-human-intelligence/
… @TIME #AI #MachineLearning #DataScience #BigData #Analytics #100DaysofCode #IoT #serverless #womenwhocode #DigitalTransformation #Robots #Python #TensorFlow #DeepLearning -
Adaptive Optimizers Should Track True Squared Gradients
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Adaptive optimizers like Adam track the square of the gradient, but what they receive as the gradient is actually the sum of the gradients across the batch. It seems likely that better results at different hyper parameters could be obtained if backward passes emitted true grad^2.
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Detecting Non-Gaussian Distributions in Machine Learning Models
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The easiest way to see this is to fix the mean to be 0 and then observe that the returned value will never be negative. Thus it can't be Gaussian. Definitely not easy to spot.