Historical 'turning point' for AI - Now showing human-like intuition
DeepMind's AlphaZero now showing human-like intuition in
historical 'turning point' for AI
By Sarah Knapton The Telegraph • December 6, 2018
DeepMind’s artificial intelligence programme AlphaZero is
now showing signs of human-like intuition and creativity, in what developers
have hailed as ‘turning point’ in history.
The computer system amazed the world last year when it
mastered the game of chess from scratch within just four hours, despite not
being programmed how to win.
But now, after a year of testing and analysis by chess
grandmasters, the machine has developed a new style of play unlike anything
ever seen before, suggesting the programme is now improvising like a human.
Unlike the world’s best chess machine - Stockfish - which
calculates millions of possible outcomes as it plays, AlphaZero learns from its
past successes and failures, making its moves based on, a ‘nebulous sense that
it is all going to work out in the long run,’ according to experts at DeepMind.
When AlphaZero was pitted against Stockfish in 1,000
games, it lost just six, winning convincingly 155 times, and drawing the
remaining bouts.
Yet it was the way that it played that has amazed
developers. While chess computers predominately like to hold on to their
pieces, AlphaZero readily sacrificed its soldiers for a better position in the
skirmish.
Speaking to The Telegraph, Prof David Silver, who leads
the reinforcement learning research group at DeepMind said: “It’s got a very
subtle sense of intuition which helps it balance out all the different factors.
“It’s got a neural network with millions of different
tunable parameters, each learning its own rules of what is good in chess, and
when you put them all together you have something that expresses, in quite a
brain-like way, our human ability to glance at a position and say ‘ah ha this
is the right thing to do'.
“My personal belief is that we’ve seen something of
turning point where we’re starting to understand that many abilities, like
intuition and creativity, that we previously thought were in the domain only of
the human mind are actually accessible to machine intelligence as well. And I
think that’s a really exciting moment in history.”
AlphaZero started as a ‘tabula rasa’ or blank slate
system, programmed with only the basic rules of chess and learned to win by
playing millions of games against itself in a process of trial and error known
as reinforcement learning.
It is the same way the human brain learns, adjusting
tactics based on a previous win or loss, which allows it to searching just 60
thousand positions per second, compared to the roughly 60 million of Stockfish.
Within just a few hours the programme had independently
discovered and played common human openings and strategies before moving on to
develop its own ideas, such as quickly swarming around the opponent’s king and
placing far less value on individual pieces.
The new style of play has been analysed Chess Grandmaster
Matthew Sadler and Women’s International Master Natasha Regan, who say it is
unlike any traditional chess engine.
”It’s like discovering the secret notebooks of some great
player from the past,” said Sadler.
Regan added: “It was fascinating to see how AlphaZero's
analysis differed from that of top chess engines and even top Grandmaster play.
AlphaZero could be a powerful teaching tool for the whole community.
Garry Kasparov, former World Chess Champion, who famously
lost to chess machine Deep Blue in 1997, said: “Instead of processing human
instructions and knowledge at tremendous speed, as all previous chess machines,
AlphaZero generates its own knowledge.
“It plays with a very dynamic style, much like my own.
The implications go far beyond my beloved chessboard."
The new analysis was published yesterday in the journal
Science, and the DeepMind team and now hoping to use their system to help solve
real world problems, such as why proteins become misfolded in diseases such as
Parkinson’s and Alzheimer’s.
The new results suggest that it could come up with new
solutions that humans might miss or take far longer to discover.
DeepMind CEO and co-founder Demis Hassabis said: “The
reason that tabula rasa was important is because we want this to be as general
as possible. The more general it is across the games the more likely it will be
able to transfer to real world problems. Like protein folding.
“Protein folding has always been our number one target.
I’ve had that in mind for a long time, because its a huge problem in biology
and it will unlock a lot of other things like drug discovery.“
"In chess AlphaZero works not because it’s looking
further ahead but because it understands the position better. It’s generalising
from past experience. It’s almost like intuition in the same way a human
grandmaster would think about it, its evaluation of the current situation is
better. And if you’re evolution is
better then you don’t have to do as much calculation.”
Prof Silver added: “Historically there has been this
amazing mismatch between the things that humans can do and the things that
computers can do.
“With the advent of powerful machine learning techniques
we’ve seen that the scales have started to tip and now we have computer
algorithms that are able to do these very human-like activities really well.”
https://news.yahoo.com/deepmind-apos-alphazero-now-showing-190000147.html
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