From Edge.org:
....MUCH MOREFRANK WILCZEK is the Herman Feshbach Professor of Physics at MIT, recipient of the 2004 Nobel Prize in physics, and author of A Beautiful Question: Finding Nature’s Deep Design. Frank Wilczek's Edge Bio PageI don't think a singularity is imminent, although there has been quite a bit of talk about it. I don't think the prospect of artificial intelligence outstripping human intelligence is imminent because the engineering substrate just isn’t there, and I don't see the immediate prospects of getting there. I haven’t said much about quantum computing, other people will, but if you’re waiting for quantum computing to create a singularity, you’re misguided. That crossover, fortunately, will take decades, if not centuries.
There’s this tremendous drive for intelligence, but there will be a long period of coexistence in which there will be an ecology of intelligence. Humans will become enhanced in different ways and relatively trivial ways with smartphones and access to the Internet, but also the integration will become more intimate as time goes on. Younger people who interact with these devices from childhood will be cyborgs from the very beginning. They will think in different ways than current adults do.
ECOLOGY OF INTELLIGENCEFRANK WILCZEK: I’m a theoretical physicist, but I’m going to be talking about the future of mind and intelligence. It’s not entirely inappropriate to do that because physical platforms are absolutely a fundamental consideration in the future of mind and intelligence. I would think it’s fair to say that the continued success of Moore’s law has been absolutely central to all of the developments in artificial intelligence and the evolution of machines and machine learning, at least as much as any cleverness in algorithms.
First I’ll talk about the in-principle advantages of artificial intelligence with existing engineering principles. Then I will talk about the enormous lead that natural intelligence in the world has, although there are obviously great motivations for having general-purpose artificial intelligence—servants, or soldiers, or other useful kinds of objects that are not out there. Then I’ll talk a little bit about the forces that will drive towards intelligence. Perhaps that’s superfluous here, but we’ve been talking about how improvements in intelligence are an end in themselves, but it’s worth at least saying why that’s going to happen. Finally, I’ll argue for an emphasis on a new form of engineering that is not being vigorously cultivated, and I’ll draw some consequences for what the future of intelligence will be.
One of the advantages of artificial over natural intelligence is that they're extraordinarily powerful quantitatively and qualitatively. Take speed, for instance. Transistors, which are the basic decision-making processes or information processors in modern computers, operate at 10 billion operations per second. If you were to ask how fast human brains notice that movies are a series of still images rather than a continuous image, it's about 40 per second. There’s a factor of a billion there, at least, plus an order of magnitude. Machines are a lot faster. They have much better error freedom and ability to correct errors. They operate digitally. Associated with that, they have the ability to download enormous amounts of information seamlessly and automatically.
Their architecture is known because they were built, so they're modular. You can add abilities to them, you can add programs, but you can also add senses. If you want them to, say, look at scenes in ultraviolet, you'd plug in an ultraviolet camera. They’re ready for quantum mechanics, so if quantum mechanics turns out to be an important way of processing information because it opens up new levels of parallel processing, then, again, you can plug it in as a module. And they have a very good duty cycle. They don’t need care and feeding and, most importantly, they don’t die.
Artificial intelligence has many advantages, so it’s almost paradoxical as to why they aren’t doing better than they are. What advantages does natural intelligence have in the present competition? For one thing, it’s much more compact. It makes use of all three dimensions, whereas existing semiconductor technology is basically two-dimensional. It’s self-repairing, whereas chips are very delicate and have to be made in expensive clean rooms. Lots of things can go wrong with artificial intelligence, and errors frequently make it necessary to shut down and reboot. Brains aren’t that way.
We have integrated input and output facilities—eyes, ears, and so forth—that have been sculpted over millions or billions of years of evolution to match the world we find ourselves in. We also have good muscular control of our bodies and speech. So, we have very good input and output facilities that are seamlessly integrated into our information processing. While impressive, those things are not at all outside the plausible domain of near future engineering. We know how to make things more three-dimensional. We know how to work around defects and maybe make some self-repair. There are clear ways forward in all those things, and there are also clear ways forward in making better input and output modules.
Although the input and output modules for human brains are very impressive, they by no means approach physical limits. Even your intelligent phone can make better images and computers can talk. In some very restricted areas there are physical limits, but we don’t exhaust physical limits, except in a few very exceptional cases. For instance, our resolution in space and time of vision, which is our best sense, is not that good. It only samples a limited part of the spectrum and even in that limited part of the spectrum takes three crude averages. We don’t sense polarization. Machines can do all those things....
