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I don't think machines will ever achieve human like intelligence in the current paradigm. A very basic property of the human intelligence is that there's just o

I don't think machines will ever achieve human like intelligence in the current paradigm. A very basic property of the human intelligence is that there's just one program (learning algorithm, if you please) that is capable of learning all activities, such as playing chess, playing the guitar, solving math problems etc. We are born with this program and our life is a learning experience. On the other hand, computers have separate programs for different activities. A chess playing computer can only play chess. A machine capable of verbal reasoning (as in this article) is capable of only verbal reasoning. I do not think human like intelligence can be achieved by simply assembling these separate programs together, but this is just my humble opinion or intuition.

sgk284 11y ago
Your general sentiment was true with old-school AI that was just exploring trees of options and intelligently pruning branches. However, for quite some time now we've moved beyond that and into areas that are promising for generalization. You may want to explore the work that DeepMind[1] has done. They're starting with simple universes (Atari games), but have developed a single program that can learn to play dozens of different games without having ever been told the rules. They learn by trial and error (specifically, Q-learning[2]) and rely only on reading pixels and knowing the current score. They learn fairly sophisticated behaviors and ultimately learn to play these games at levels far superior to what humans can achieve. Many people are now trying to generalize these results to more realistic worlds, such as 3D games. And ultimately to agents that interact with the real world. [1] http://deepmind.com/ http://deepmind.com/ [2] https://en.wikipedia.org/wiki/Q-learning https://en.wikipedia.org/wiki/Q-learning
- steinsgate 11y ago
  I am aware of DeepMind. It is surely a step in the right direction. However, while model independent reinforcement learning is one part of the puzzle, the other (and equally important) part is transfer of knowledge between agents (most human learning happens via interaction between a teacher and a student). I would really love to see some progress in that area.

sgk284 11y ago

Your general sentiment was true with old-school AI that was just exploring trees of options and intelligently pruning branches. However, for quite some time now we've moved beyond that and into areas that are promising for generalization. You may want to explore the work that DeepMind[1] has done. They're starting with simple universes (Atari games), but have developed a single program that can learn to play dozens of different games without having ever been told the rules. They learn by trial and error (specifically, Q-learning[2]) and rely only on reading pixels and knowing the current score. They learn fairly sophisticated behaviors and ultimately learn to play these games at levels far superior to what humans can achieve. Many people are now trying to generalize these results to more realistic worlds, such as 3D games. And ultimately to agents that interact with the real world. [1] http://deepmind.com/ http://deepmind.com/ [2] https://en.wikipedia.org/wiki/Q-learning https://en.wikipedia.org/wiki/Q-learning

steinsgate 11y ago
I am aware of DeepMind. It is surely a step in the right direction. However, while model independent reinforcement learning is one part of the puzzle, the other (and equally important) part is transfer of knowledge between agents (most human learning happens via interaction between a teacher and a student). I would really love to see some progress in that area.