Brain vs. Deep Learning (2015)
Long, and fascinating. I can see why this made the front page. I oscillated between 20% strident disagreement and 60% strident agreement. (The rest -- no strong feelings, or I feel that the questions are too ill-formed to answer.)<p>To pick one point of disagreement,<p><i>“We do not need as much computational power as the brain has, because our algorithms are (will be) better than that of the brain.”</i><p><i>I hope you can see after the descriptions in this blog post that this statement is rather arrogant.</i><p>Machines are already better than brains at many cognitive tasks of practical interest. Believing that we&#x27;ll continue to find &quot;tricks&quot; to allow computers to outperform brains on useful cognitive tasks, despite the brains&#x27; much greater complexity, seems like a perfectly sober and conservative prediction.<p>If I had to advance my own pet reasons for discounting the likelihood of a technological singularity, here are my top two:<p>1) It&#x27;s a more challenging case of the general Fermi paradox. Show me the Hubble images of the computronium Dyson swarms. If it takes less than a century to go from the first transistorized computers to superintelligence, and superintelligence is as prone to run amok as Bostrom&#x2F;Yudkowsky think, signs should already be visible from Earth.<p>2) You need experiments to validate scientific models. Even if a machine-intelligence could think a billion times faster than a biological intelligence, it couldn&#x27;t complete experiments a billion times faster. Technologies that act on the material world will improve sublinearly with respect to thinking&#x2F;computing power, for at least this reason and probably others as well.