The opposite of finishing isn't not starting. It is not finishing. People who give advice often focus on saying start. If you are already running, this isn't enough. Finish, too.
.@RichardSSutton, father of reinforcement learning, doesn’t think LLMs are bitter-lesson-pilled.
My steel man of Richard’s position: we need some new architecture to enable continual (on-the-job) learning.
And if we have continual learning, we don't need a special training…
Logic has flaws when not wielded carefully. One care that is not undertaken carefully in most ancient arguments is accounting for time. Recursion has a leading edge.
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Normalizing cumulative outcomes so as to produce a probability distribution should be talked about more often than it is, because it is a simple algorithm that can lead to better decision making.
If you're trying to solve a very hard problem try solving an easier problem that is akin to it. Then take the solution to the easier problem and leverage it to better solve the hard problem.
Currently, we ask AI for things, and then we carefully check the result. In the future, AI will be responsible for carefully checking the results. Some people are going to disagree with the AI checks; they'll usually either be wrong or judging from a different frame.
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