The brain exceeds the most powerful computers in efficiency
The core of this essay is indisputable that the comparison is Apples and Oranges. A computer puts in more effort and gets a better result. However when choosing between more efficient or better results, I&#x27;d prefer better results. The human brain&#x27;s bias towards efficiency is an evolutionary handicap on intelligence. I can provide my body with as much energy as it wants to consume - I&#x27;d rather be able to burn energy learning things than have to put up with a brain that keeps trying to take shortcuts at every turn to conserve energy, and I would love to be able to think deterministicly! It is a stretch to say humans are out thinking the machine with that frame, machine intelligence might simply have firmer foundations.<p>I do have challenges to the method of calculating how much power is put into the human training process. 10^8 might be too many orders of magnitude to breach, but humans:<p>1) Use data picked up visually to provide basic concepts used in playing go (like &quot;connected&quot;, &quot;lines&quot;, &quot;territory&quot;, &quot;me&quot;, &quot;opponent&quot;). Many of these concepts are potentially developed from visual data, and humans process a lot more than 50 bits of visual data in a second. This observation is very hard to quantity respecting how much visual data can be employed to learn about Go - how much of an advantage does the concept of territory give humans in terms of learning? It is probably huge.<p><i>EDIT</i> Thinking about it a bit more, it is very plausible given AlphaGo&#x27;s endgame play that it never developed a concept of &#x27;territory&#x27; as we would understand it. Given that humans judge the state of the game by estimating territory for both players then assuming the winner is the one with more territory, that is a huge deal. It might explain why the learning seems so inefficient vs a human. A human would never make AlphaGo&#x27;s endgame moves because they are bad by the territory heuristic.<p>2) Learn Go in a very communal environment. Game records date back a few centuries and all the patterns that are detected get passed on through the generations of professional players. The practical amount of cached processing power available to humans is higher than first inspection might suggest. Humans don&#x27;t learn mainly through self play, it is closer to supervised learning. A 1900s player would simply lose to modern technique because of this accretion.<p>3) High level Go players are going to be strongly biased to people who &#x27;got lucky&#x27; in how their brains map concepts. An average human probably isn&#x27;t as good at learning Go as a peak professional.