Rodney Brooks: The Seven Deadly Sins of AI Predictions: Weekend Reading
"The principal control mechanism in factories... is based on programmable logic controllers, or PLCs.. introduced in 1968 to replace electromechanical relays. The 'coil' is still the principal abstraction unit used today, and PLCs are programmed as though they were a network of 24-volt electromechanical relays. Still": Rodney Brooks: The Seven Deadly Sins of AI Predictions: "Overestimating and underestimating. Roy Amara was a cofounder of the Institute for the Future, in Palo Alto, the intellectual heart of Silicon Valley. He is best known for his adage now referred to as Amara’s Law: 'We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run'.... A great example... the U.S. Global Positioning System... nearly canceled again and again in the 1980s... first operational use for its intended purpose was in 1991 during Desert Storm; it took several more successes for the military to accept its utility....
Today GPS is in what Amara would call the long term, and the ways it is used were unimagined at first. My Series 2 Apple Watch... the technology synchronizes physics experiments across the globe... synchronizing the U.S. electrical grid... allows the high-frequency traders who really control the stock market to mostly avoid disastrous timing errors.... used by all our airplanes... used to track people out of prison on parole... determines which seed variant will be planted... tracks fleets of trucks and reports on driver performance. GPS started out with one goal, but it was a hard slog to get it working as well as was originally expected. Now it has seeped into so many aspects of our lives that we would not just be lost if it went away; we would be cold, hungry, and quite possibly dead....
...We see a similar pattern with other technologies over the last 30 years. A big promise up front, disappointment, and then slowly growing confidence in results that exceed the original expectations. This is true of computation, genome sequencing, solar power, wind power, and even home delivery of groceries....
Imagining magic... "Any sufficiently advanced technology is indistinguishable from magic."... Imagine we had a time machine and we could transport Isaac Newton from the late 17th century to today, setting him down in a place that would be familiar to him: Trinity College Chapel at the University of Cambridge. Now show Newton an Apple. Pull out an iPhone... play a movie of an English country scene, and then some church music that he would have heard. And then show him a Web page with the 500-plus pages of his personally annotated copy of his masterpiece Principia, teaching him how to use the pinch gesture to zoom in on details. Could Newton begin to explain how this small device did all that? Although he invented calculus and explained both optics and gravity, he was never able to sort out chemistry from alchemy....
If something is magic, it is hard to know its limitations. Suppose we further show Newton how the device can illuminate the dark, how it can take photos and movies and record sound, how it can be used as a magnifying glass and as a mirror. Then we show him how it can be used to carry out arithmetical computations at incredible speed and to many decimal places. We show it counting the steps he has taken as he carries it, and show him that he can use it to talk to people anywhere in the world, immediately, from right there in the chapel. What else might Newton conjecture that the device could do?.... If the iPhone can be a source of light without fire, could it perhaps also transmute lead into gold? This is a problem we all have with imagined future technology. If it is far enough away from the technology we have and understand today, then we do not know its limitations. And if it becomes indistinguishable from magic, anything one says about it is no longer falsifiable.... Nothing in the universe is without limit. Watch out for arguments about future technology that is magical. Such an argument can never be refuted. It is a faith-based argument, not a scientific argument.
Performance versus competence. We all use cues about how people perform some particular task to estimate how well they might perform some different task. In a foreign city we ask a stranger on the street for directions, and she replies with confidence and with directions that seem to make sense, so we figure we can also ask her about the local system for paying when you want to take a bus. Now suppose a person tells us that a particular photo shows people playing Frisbee in the park. We naturally assume that this person can answer questions like What is the shape of a Frisbee? Roughly how far can a person throw a Frisbee? Can a person eat a Frisbee? Roughly how many people play Frisbee at once? Can a three-month-old person play Frisbee? Is today’s weather suitable for playing Frisbee? Computers that can label images like “people playing Frisbee in a park” have no chance of answering those questions. Besides the fact that they can only label more images and cannot answer questions at all, they have no idea what a person is, that parks are usually outside, that people have ages, that weather is anything more than how it makes a photo look, etc. This does not mean that these systems are useless.... But here is what goes wrong. People hear that some robot or some AI system has performed some task. They then generalize from that performance to a competence that a person performing the same task could be expected to have. And they apply that generalization to the robot or AI system....
Suitcase words. Marvin Minsky called words that carry a variety of meanings “suitcase words.” “Learning” is a powerful suitcase word; it can refer to so many different types of experience. Learning to use chopsticks is a very different experience from learning the tune of a new song. And learning to write code is a very different experience from learning your way around a city. When people hear that machine learning is making great strides in some new domain, they tend to use as a mental model the way in which a person would learn that new domain. However, machine learning is very brittle, and it requires lots of preparation by human researchers or engineers, special-purpose coding, special-purpose sets of training data, and a custom learning structure for each new problem domain. Today’s machine learning is not at all the sponge-like learning that humans engage in.... Suitcase words mislead people about how well machines are doing at tasks that people can do....
Exponentials. Many people are suffering from a severe case of “exponentialism.”... The reason Moore’s Law worked is that it applied to a digital abstraction of a true-or-false question. In any given circuit, is there an electrical charge or voltage there or not? The answer remains clear as chip components get smaller and smaller—until a physical limit intervenes, and we get down to components with so few electrons that quantum effects start to dominate. That is where we are now with our silicon-based chip technology. When people are suffering from exponentialism, they may think that the exponentials they use to justify an argument are going to continue apace. But Moore’s Law and other seemingly exponential laws can fail because they were not truly exponential in the first place.... The memory increase on iPods... gigabytes: 2002 10... 2007 160.... Extrapolating through to today, we would expect a $400 iPod to have 160,000 gigabytes of memory. But the top iPhone of today... has only 256 gigabytes of memory.... Exponentials can collapse when a physical limit is hit, or when there is no more economic rationale to continue them. Similarly, we have seen a sudden increase in performance of AI systems thanks to the success of deep learning. Many people seem to think that means we will continue to see AI performance increase by equal multiples on a regular basis. But the deep-learning success was 30 years in the making, and it was an isolated event....
Hollywood scenarios. The plot for many Hollywood science fiction movies is that the world is just as it is today, except for one new twist.... Many AI researchers and AI pundits... are similarly imagination-challenged.... Long before there are evil super-intelligences that want to get rid of us, there will be somewhat less intelligent, less belligerent machines. Before that, there will be really grumpy machines. Before that, quite annoying machines. And before them, arrogant, unpleasant machines. We will change our world along the way, adjusting both the environment for new technologies and the new technologies themselves. I am not saying there may not be challenges. I am saying that they will not be sudden and unexpected....
Speed of deployment. New versions of software are deployed very frequently.... Deploying new hardware, on the other hand, has significant marginal costs.... The building I live in was built in 1904, and it is not nearly the oldest in my neighborhood. Capital costs keep physical hardware around for a long time, even when there are high-tech aspects to it, and even when it has an existential mission. The U.S. Air Force still flies the B-52H variant of the B-52 bomber... introduced in 1961.... The last one was built in 1962.... Currently these planes are expected to keep flying until at least 2040.... I regularly see decades-old equipment in factories around the world. I even see PCs running Windows 3.0—a software version released in 1990. The thinking is “If it ain’t broke, don’t fix it.” Those PCs and their software have been running the same application doing the same task reliably for over two decades.
Almost all innovations in robotics and AI take far, far, longer to be really widely deployed than people in the field and outside the field imagine. The principal control mechanism in factories, including brand-new ones in the U.S., Europe, Japan, Korea, and China, is based on programmable logic controllers, or PLCs. These were introduced in 1968 to replace electromechanical relays. The “coil” is still the principal abstraction unit used today, and PLCs are programmed as though they were a network of 24-volt electromechanical relays. Still.... I just looked on a jobs list, and even today, this very day, Tesla Motors is trying to hire PLC technicians at its factory in Fremont, California. They will use electromagnetic relay emulation in the production of the most AI-enhanced automobile that exists...
#shouldread
#riseoftherobots
#weekendreading
