The chess program on a cell phone can beat all but the best human players in the world. It does this by considering every possible move on the board, looking forward perhaps seven to ten turns. Using the balance of pieces on the board, the algorithm works back to the move most likely to yield an advantage as the game develops.
These algorithms are hugely expensive in energetic terms. The human brain solves the same problem in a far more efficient fashion. A human chess player understands that there are certain combinations of pieces that provide leverage over the opposing forces. As opportunities arise to create those configurations, they focus their attention on those pieces, largely ignoring the rest of the board. That means that the human player considers only a small sub-set of the moves considered by the average chess program.
This advantage is the target of recent research using computerized neural networks. A neural net is inspired by the structure of the human brain itself. Each digital “node” is a type of artificial neuron. The nodes are arranged in ranks. Each node receives input values from the nodes in the prior rank, and generates a signal to be processed by the neurons in the next rank. This models the web of dendrites used by a human neuron to receive stimulus and the axon by which it transmits the signal to the dendrites of other neurons.
In the case of the human neuron, activation of the synapse (the gap separating axon and dendrite) causes it to become more sensitive, particularly when that action is reinforced by positive signals from the rest of the body (increased energy and nutrients). In the computerized neural network, a mathematical formula is used to calculate the strength of the signal produced by a neuron. The effect of the received signals and the strength of the generated signal is controlled by parameters – often simple scaling factors – that can be adjusted, node by node, to tune the behavior of the network.
To train an artificial neural network, we proceed much as we would with a human child. We provide them experiences (a configuration of pieces on a chess board) and give feedback (a type of grade on the test) that evaluates their moves. For human players, that experience often comes from actual matches. To train a computerized neural network, many researchers draw upon the large databases of game play that have been established for study by human players. The encoding of the piece positions is provided to the network as “sensory input” (much as our eyes do when looking at a chess board), and the output is the new configuration. Using an evaluative function to determine the strength of each final position, the training program adjusts the scaling factors until the desired result (“winning the game”) is achieved as “often as possible.”
In the final configuration, the computerized neural network is far more efficient than its brute-force predecessors. But consider what is going on here: the energetic expenditure has merely been front-loaded. It took an enormous amount of energy to create the database used for the training, and to conduct the training itself. Furthermore, the training is not done just once, because a neural network that is too large does not stabilize its output (too much flexibility) and a network that is too small cannot span the possibilities of the game. Finding a successful network design is a process of trial-and-error controlled by human researchers, and until they get the design right, the training must be performed again and again on each iteration of the network.
But note that human chess experts engage in similar strategies. Sitting down at a chess board, the starting position allows an enormous number of possibilities, too many to contemplate. What happens is that the first few moves determine an “opening” that may run to ten or twenty moves performed almost by rote. These openings are studied and committed to memory by master players. They represent the aggregate wisdom of centuries of chess players about how to avoid crashing and burning early in the game. At the end of the game, when the pieces are whittled down, players employ “closings”, techniques for achieving checkmate that can be committed to memory. It is only in the middle of the game, in the actual cut-and-thrust of conflict, that much creative thinking is done.
So which of the “brains” is more intelligent: the computer network or the human brain? When my son was building a chess program in high school, I was impressed by the board and piece designs that he put together. They made playing the game more engaging. I began thinking that a freemium play strategy would be to add animations to the pieces. But what about if the players were able to change the rules themselves? For example, allow the queen to move as a knight for one turn. Or modify the game board itself: select a square and modify it to allow passage only on the diagonal or in one direction. I would assert that a human player would find this to be a real creative stimulus, while the neural network would just collapse in confusion. The training set didn’t include configurations with three knights on the board, or restrictions on moves.
This was the point I made when considering the mental faculties out at http://www.everdeepening.org. Intelligence is not determined by our ability to succeed under systems of fixed rules. Intelligence is the measure of our ability to adapt our behaviors when the rules change. In the case of the human mind, we recruit additional neurons to the problem. This is evident in the brains of blind people, in which the neurons of the visual cortex are repurposed for processing of other sensory input (touch, hearing and smell), allowing the blind to become far more “intelligent” decision makers when outcomes are determined by those qualities of our experience.
This discussion, involving a game without much concrete consequence, appears to be largely academic. But there have been situations in which this limitation of artificial intelligence have been enormously destructive. It turns out that the targeting systems of drones employ neural networks trained against radar and visual observations of friendly and enemy aircraft. Those drones have misidentified friendly aircraft in live-fire incidents, firing their air-to-air missile and destroying the target.
So proclamations by some that we are on the cusp of true artificial intelligence are, in my mind, a little overblown. What we are near is a shift in the power allocated to machines that operate with a fixed set of rules, away from biological mechanisms that adapt their thinking when they encounter unexpected conditions. That balance must be carefully managed, lest we find ourselves without the power to adapt.