When will computer hardware match the human brain?

This paper describes how the performance of AI machines tends to improve at the same pace that AI researchers get access to faster hardware. The processing power and memory capacity necessary to match general intellectual performance of the human brain are estimated. Based on extrapolation of past trends and on examination of technologies under development, it is predicted that the required hardware will be available in cheap machines in the 2020s.

Brains, Eyes and Machines
Computers have far to go to match human strengths, and our estimates will depend on analogy and extrapolation. Fortunately, these are grounded in the first bit of the journey, now behind us. Thirty years of computer vision reveals that 1 MIPS can extract simple features from real-time imagery--tracking a white line or a white spot on a mottled background. 10 MIPS can follow complex gray-scale patches--as smart bombs, cruise missiles and early self-driving vans attest. 100 MIPS can follow moderately unpredictable features like roads--as recent long NAVLAB trips demonstrate. 1,000 MIPS will be adequate for coarse-grained three-dimensional spatial awareness--illustrated by several mid-resolution stereoscopic vision programs, including my own. 10,000 MIPS can find three-dimensional objects in clutter--suggested by several "bin-picking" and high-resolution stereo-vision demonstrations, which accomplish the task in an hour or so at 10 MIPS. The data fades there--research careers are too short, and computer memories too small, for significantly more elaborate experiments.

There are considerations other than sheer scale. At 1 MIPS the best results come from finely hand-crafted programs that distill sensor data with utmost efficiency. 100-MIPS processes weigh their inputs against a wide range of hypotheses, with many parameters, that learning programs adjust better than the overburdened programmers. Learning of all sorts will be increasingly important as computer power and robot programs grow. This effect is evident in related areas. At the close of the 1980s, as widely available computers reached 10 MIPS, good optical character reading (OCR) programs, able to read most printed and typewritten text, began to appear. They used hand-constructed "feature detectors" for parts of letter shapes, with very little learning. As computer power passed 100 MIPS, trainable OCR programs appeared that could learn unusual typestyles from examples, and the latest and best programs learn their entire data sets. Handwriting recognizers, used by the Post Office to sort mail, and in computers, notably Apple's Newton, have followed a similar path. Speech recognition also fits the model. Under the direction of Raj Reddy, who began his research at Stanford in the 1960s, Carnegie Mellon has led in computer transcription of continuous spoken speech. In 1992 Reddy's group demonstrated a program called Sphinx II on a 15-MIPS workstation with 100 MIPS of specialized signal-processing circuitry. Sphinx II was able to deal with arbitrary English speakers using a several-thousand-word vocabulary. The system's word detectors, encoded in statistical structures known as Markov tables, were shaped by an automatic learning process that digested hundreds of hours of spoken examples from thousands of Carnegie Mellon volunteers enticed by rewards of pizza and ice cream. Several practical voice-control and dictation systems are sold for personal computers today, and some heavy users are substituting larynx for wrist damage.


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