The Myth of a Superhuman AI

• sample-factory

  6 743 7.9 Python

  High throughput synchronous and asynchronous reinforcement learning

Everything in this reply is wrong.

In AlphaZero for example, there were 44 million training games total for 700,00x0 steps of training for the full 9 hours.

Turning that human-like numbers, 44million games with on average 60 moves, at 1 second thinking time per move,

> 44000000*60/60/60/24/365 = 83,7138508371 years of training experience in 9 hours

The whole field of Reinforcement learning has agents training and playing games for many orders of magnitude more time than a human ever will. In-fact, we can scale this to over 100k of actions per second, in a single machine:

https://github.com/alex-petrenko/sample-factory

Then, there is also distributed Reinforcement Learning, where hundreds of agents can play at different machines and share experience, see AlphaZero, LeelaZero, R2D2 agent, R2D3 agent, Apex, Acer, Asynchronous PPO.

> but the data isn't useful without the context of experience

The experience is the data in Reinforcement Learning.

> and all processing power can do it overfit model without experience.

That is wrong, the agents perform what is called exploration to avoid getting stuck in simple strategies.

> Even if we put AI into an army of robots running around and experiencing things, there are still scaling limits to encoding and communicating knowledge and understanding.

True, but machines scale better because they speak the same language, or they can learn to tune their language to get their message across.

> Human organizations are a great example of the scaling limits of intelligence.

Human organization is a testament to how far we can get with something as limiting as the commonly used language. The language that we use to communicate is subject to misinterpretation due to our subjective experiences, this limitation is not shared by machines.

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