Sublime
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Gary Marcus • Deep Learning Is Hitting a Wall
supervised learning techniques, and a few have used reinforcement learning (for
Andrew McAfee, Erik Brynjolfsson • Machine, Platform, Crowd: Harnessing Our Digital Future
A technique called backpropagation then adjusts the weights to improve the neural network; when an error is spotted, adjustments propagate back through the network to help correct it in the future. Keep doing this, modifying the weights again and again, and you gradually improve the performance of the neural network so that eventually it’s able to
... See moreMustafa Suleyman • The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma
a high-quality domestic robot will constantly need to reevaluate. “Where am I?,” “What is my current status?,” “What risks and opportunities are there in my current situation?,” “What should I be doing, in the near term and the long term?,” and “How should I execute my plans?”*2
Ernest Davis • Rebooting AI: Building Artificial Intelligence We Can Trust
Deep-learning pioneers like Geoffrey Hinton, Yann LeCun, and Yoshua Bengio—the Enrico Fermis of AI—continue to push the boundaries of artificial
Kai-Fu Lee • AI Superpowers: China, Silicon Valley, and the New World Order
the advantages of moving slowly emerge most concretely in a regularization technique known as Early Stopping.
Brian Christian, Tom Griffiths • Algorithms to Live By: The Computer Science of Human Decisions
Perhaps R1’s biggest breakthrough is the confirmation that you no longer need enormous data centers or thousands of labelers to push the limits of LLMs. If you can define what “correctness” means in your domain —whether it’s coding, finance, medical diagnostics, or creative writing— you can apply reasoning-oriented RL to train or fine-tune your own
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