“Robot Predictions Evolution”, 2004-04 (; similar):
[previous: 1988, 1998; later: 2008]Here’s how my anticipations about the time of arrival of human level robotic intelligence evolved:
In the early 1970s, doing simple computer stereoscopic vision, it became rapidly obvious that the computer power in our mainframe PDP-10 was hugely insufficient to do even that basic function in real time, implying that doing the job of the whole nervous system was even further out of reach. Besides enormously more speed, we needed enormously more memory.
This was contrary to the orthodoxy in AI at the time. My advisor John McCarthy wrote in many essays that existing computers were sufficient for human-intelligent AI, but we needed some theoretical breakthroughs (humorously 2 Newtons and 3 Einsteins) to achieve it.
I tried to quantify the power needed, first by estimating the number of switching operations in the brain and comparing it to switching in computer circuits, and got a rough number of about one trillion (1012) operations per second (ops)
…By the end of the 1970s, it was pretty clear there wouldn’t be an AI Apollo project, but I thought that if people were willing to put as much effort as was expended in the weapons labs, we could have the requisite power in a supercomputer-class machine in about 20 years. That’s the glimpse you got in that TV show [Love Machine 2000?]…Even though our AI and Robotics computers were still stuck at 1 MIPS (but much cheaper—in the 1970s we used million-dollar [>$4.97$11970m] computers, by the mid 1980s equivalent power could be had in workstations for tens of thousands of dollars [>$30,828.01$10,0001985]), I was able to plot the historical decrease in computing cost to predict that we would have 10 trillion ops in a $10,000 computer by about 2020 or 2030 [in Mind Children]…By the early 2000s also there were several supercomputers in existence that could do more than 10 trillion ops, though not available for robotics work. [Now] In 2004, VA Tech connected 1,100 dual-processor Macintosh G5 machines for the record low cost of about $9,550,778.42$6,000,0002004, and benchmarked it at over 10 trillion ops. SEEGRID is building visual self-navigating vehicles using onboard computing of a billion ops or so, about the brainpower of a guppy by my numbers.
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