[Computer-go] Facebook Go AI

[Detlef Schmicker]

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Am 06.12.2015 um 16:24 schrieb Petr Baudis:
> On Sat, Dec 05, 2015 at 02:47:50PM +0100, Detlef Schmicker wrote:
>> I understand the idea, that long term prediction might lead to a 
>> different optimum (but it should not lead to one with a higher
>> one step prediction rate: it might result in a stronger player
>> with the same prediction rate...)
> 
> I think the whole idea is that it should improve raw prediction
> rate on unseen samples too.

This would mean, we are overfitting, which should be seen by a bigger
difference between unseen and seen samples, which is normaly checked
by using a test database and compare the results with the train
database and small ?!


The motivation of the increased suprevision is
> to improve the hidden representation in the network, making it
> more suitable for longer-term tactical predictions and therefore
> "stronger" and better encompassing the board situation.  This
> should result in better one-move predictions in a situation where
> the followup is also important.
> 
> It sounds rather reasonable to me...?
> 

Yes, it sounds reasonable, but this not always helps in computer go :)

Detlef
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