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Well actually this neural network style of machine learning is not all that application specific. You create a general architecture and throw lots of data at it

Well actually this neural network style of machine learning is not all that application specific. You create a general architecture and throw lots of data at it, could be go, could be recognising pictures. You will need different general architectures, but the point is that this is a fundamentally different approach to the old school chess algos.

studentrob 11y ago
Yeah I get that. I studied and have worked in machine learning. Neural networks are more general than previous approaches but they still need to be customized by humans for different applications. And none of these programs are going off and learning how to play other games on their own. They need to be led.
- pixl97 11y ago
  >They need to be led. We should rejoice in that fact. We are woefully unprepared for true learning programs as a species. Let us hope that between now and the time we do manage to create one that we mature to the point where we don't create these thinking entities for malicious purposes.
  - studentrob 11y ago
    > Let us hope that between now and the time we do manage to create one that we mature to the point where we don't create these thinking entities for malicious purposes That's a long way off and we'll face a lot of other problems before then. For instance, fear mongering of a looming AI. We're better off focusing on teaching kids computer science and allowing them to see for themselves how theoretical and unscary true AI remains.
    - pixl97 11y ago
      >That's a long way off People, up until very recently said that computers being able to beat people at Go were a long way off too.
- piquadrat 11y ago
  > And none of these programs are going off and learning how to play other games on their own With the danger of sounding stupid, isn't that what Deep-Q did? http://arstechnica.com/science/2015/02/ai-masters-49-atari-2600-games-without-instructions/ http://arstechnica.com/science/2015/02/ai-masters-49-atari-2... > Scientists tested Deep Q’s problem-solving abilities on the Atari 2600 gaming platform. Deep-Q learned not only the rules for a variety of games (49 games in total) in a range of different environments, but the behaviors required to maximize scores. It did so with minimal prior knowledge, receiving only visual images (in pixel form) and the game score as inputs. Sure, the problem space is still fairly limited, but the AI did learn new games without much guidance at all.

studentrob 11y ago

Yeah I get that. I studied and have worked in machine learning. Neural networks are more general than previous approaches but they still need to be customized by humans for different applications. And none of these programs are going off and learning how to play other games on their own. They need to be led.

pixl97 11y ago
>They need to be led. We should rejoice in that fact. We are woefully unprepared for true learning programs as a species. Let us hope that between now and the time we do manage to create one that we mature to the point where we don't create these thinking entities for malicious purposes.
- studentrob 11y ago
  > Let us hope that between now and the time we do manage to create one that we mature to the point where we don't create these thinking entities for malicious purposes That's a long way off and we'll face a lot of other problems before then. For instance, fear mongering of a looming AI. We're better off focusing on teaching kids computer science and allowing them to see for themselves how theoretical and unscary true AI remains.
  - pixl97 11y ago
    >That's a long way off People, up until very recently said that computers being able to beat people at Go were a long way off too.
piquadrat 11y ago
> And none of these programs are going off and learning how to play other games on their own With the danger of sounding stupid, isn't that what Deep-Q did? http://arstechnica.com/science/2015/02/ai-masters-49-atari-2600-games-without-instructions/ http://arstechnica.com/science/2015/02/ai-masters-49-atari-2... > Scientists tested Deep Q’s problem-solving abilities on the Atari 2600 gaming platform. Deep-Q learned not only the rules for a variety of games (49 games in total) in a range of different environments, but the behaviors required to maximize scores. It did so with minimal prior knowledge, receiving only visual images (in pixel form) and the game score as inputs. Sure, the problem space is still fairly limited, but the AI did learn new games without much guidance at all.