How does the speed of thought compare for brains and digital computers

From the differences in computer and brain designs discussed above, it is clear that determining their relative speeds of processing cannot be achieved by comparing hardware specifications. The standard method for comparing computing apples and oranges is to determine the relative times for solving benchmark problems, and this is at least partly possible for comparing brains and computers.For various commercial and security reasons, computer systems that can recognize faces automatically are of considerable value, and research in this area has been well supported for the last three decades. Starting in 1993, the National Institute of Standards and Technology has run a series of competitions to evaluate computer face-recognition technology, with the results for the last competition reported in 2007. The first year that fully automatic face recognition was achieved was 1997, at which time target faces were missed about half the time. In the decade that followed, this error rate (missing a face that the system is trying to detect) has dropped to about 1% under the best conditions and is better than 10% over a wide range of conditions. Detecting the face (again, depending on conditions) takes about a second.Humans can detect a familiar face when it is placed within a string of unfamiliar faces presented at 10 Hz. The accuracy of the human and the computers is about the same when it is tested under the same conditions, so that would mean that the human processing speed is about 10-fold greater than the computer's for face recognition. But this comparison is misleading, because the computer face recognition system is optimized just for that job, whereas the human visual system is designed to detect a very large number of objects. One of the most difficult things for a computer to do is to extract objects from a visual scene, but we do this so rapidly and effortlessly that we are not even aware that it is hard. When we look around, we automatically see a world full of objects.Perhaps a more informative way to compare the capabilities of humans and computers is to examine tasks that humans can do easily and that are too hard for computers. At the dawn of the computer age, Alan Turing proposed a test, known as the Turing test, to get at the question of whether or not a computer can think. His test was to have a human judge ask questions of a computer and of a human; if the judge cannot tell from the answers which is the human, the computer has passed the test. Starting about a dozen years ago, being able to tell a human from a computer became commercially important, because internet companies needed to prevent computers from signing up for services, like email accounts that could be used to send spam, intended for humans. The idea was to develop questions that are very easy for humans to answers but that are too hard for computers. A variety of methods, known as ‘completely automated public Turing tests to tell computers and humans apart’ (CAPTCHAs), have been developed to do this. Most of these CAPTCHAs rely on the fact that humans can easily read letters that have been disguised by mixing fonts, distorting the letters or masking them with distracters, but this is a very difficult task for computers. The ease with which CAPTCHAs can be developed exposes obvious gaps between capabilities of computers and the brain.The power of computers has been growing exponentially over the last 60 years, and every year or so we find they can do something — like optical character recognition or speech recognition — that we never imagined they would be able to do. When will the computers catch up with our brains? It has been predicted that this will happen in the next several decades, but we believe the problem is not computer power and ability to program parallel machines, but rather our nearly total ignorance about what computations are actually carried out by the brain. Our view is that computers will never equal our best abilities until we can understand the brain's design principles and the mathematical operations employed by neural circuits well enough to build machines that incorporate them.