As soon as AlphaGo – Google´s Deepmind Go player defeated European champion 5-0 many people were celebrating, as a friend of mine who first shared the story. Still pending of a contest against Lee Sedol in South Korea, I would not argue against its merits.
Deepmind team recently published “Mastering the game of Go with deep neural networks and tree search” is indeed in the cutting edge of deep learning algorithm design.
Something is off in the big picture, though, as the (unsettled) argument that followed. If the trend is clear looking back – think of Watson’s feats and you know this was coming. It´s tempting to say same will happen to the future. Increasing complexity in the ways computing appears to beat humans.
Key in this discussion are the boundaries of current approach, precisely for it´s being based in computing. Of course AI beats us in a challenge of speed, memory or brute force strenuous calculation. But this is not all that is. To begin with this is only possible when humans tell AI how to deal with abstract symbols (as bits and code) and how to relate the external reality to such abstractions.
That´s when people start to wonder if it should be the case of having more open minded AIs, as in “Don’t Know Mind: Zen and the Art of AGI Indecision” By Gareth John.
Even accepting that more layers (deeper learning) may imply broader ‘minds’, it’s a different way of looking on what would be next.