Thanks for believing that I can do this.

On Sat, May 11, 2019, 18:27 Matt Mahoney <[email protected]> wrote:

> "Conventional" AI was coding all the knowledge in Lisp. Then came rule
> based systems and the AI winter. Neural networks have produced the best
> results in language, vision, and robotics, now that we have enough
> computing power to implement them. Now we have real progress in AI. It will
> be interesting if you come up with something better.
>
> On Sat, May 11, 2019 at 12:10 PM Stefan Reich via AGI <
> [email protected]> wrote:
>
>> I think you are stuck in the "conventional theory". The time is right for
>> new theories. I'm still on the path to show that there are better ways to
>> actual AI than neural networks.
>>
>> On Sat, May 11, 2019, 08:46 YKY (Yan King Yin, 甄景贤) <
>> [email protected]> wrote:
>>
>>> Also, the control theoretic stuff was removed because I am unable to
>>> define the reward based on the current state in a *differentiable*
>>> way.  For example, in the game of chess, the reward comes only when
>>> checkmate occurs (according to the game's official rules), but not when you
>>> capture a piece of high value (eg  xQueen).  This problem is known as
>>> "sparse reward" vs "dense reward" in reinforcement learning:
>>> [image: Screenshot from 2019-05-07 20-49-22.png]
>>> The actual reward is a delta-function occurring at the end of the game.
>>> "Classical" control theory is applicable only when the reward is something
>>> like the dotted line.
>>>
>>>
>
> --
> -- Matt Mahoney, [email protected]
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