I understand the panic many business leaders experience as they try to stay solvent while customers evaporate. Panic, however, is a poor teacher: AI-based automation will not only not solve all their problems, it may very well add to them. AI is not a magic box into which we can stuff them and make them disappear.
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We often hear that what’s hard for humans is easy for computers. But it turns out that many kinds of problems are exceedingly hard for computers to solve. This class of problems, known as NP-Complete (NPC), was independently discovered by Stephen Cook and Leonid Levin.
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Because AI research is based on a fundamental assumption that has not been scientifically tested—that the human mind can be reduced to a computer—then the research itself cannot be said to be scientific.
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Materialism is a kind of intellectual disability that afflicts even the well-educated. To put it simply, machines don’t and can’t think. Dr. Shallit’s wristwatch doesn’t know what time it is. Dr. Shallit’s iPod doesn’t enjoy the music it plays or listen to his phone calls. His television doesn’t like or dislike movies. And his computer doesn’t, and can’t, think.
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The authors of the paper saying that the virus could not have been designed base their findings on the fact that current software would not have predicted this result. I am not claiming that the virus was designed, only that a researcher need not have relied on this software to design a virus. The paper makes a faulty assumption.
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Starsky Robotics was not just another startup overwhelmed by business realities. In 2019, it was named one of the world’s 100 most promising start-ups (CNBC) and one to watch by FreightWaves, a key trucking industry publication. But the AI breakthroughs did not appear.
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Big social media companies have long wanted to replace human content moderators with AI. COVID-19 quarantines have only intensified that discussion. But AI is far, far from ready to successfully moderate content in an age of where virtual monopolies make single point failure a frequent risk.
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Every human being, whether office worker or high school student, bucks against digital harnesses.
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Building an AI entails moving parts of our intelligence into a machine. We can do that with rules, (simplified) virtual worlds, statistical learning… We’ll likely create other means as well. But, as long as “no one is home”—that is, the machines lack minds—gaps will remain and those gaps, without human oversight, can put us at risk.
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Pundits like Nick Bostrom and Ray Kurzweil worry that smart AI will rule us. But, as the Carnegie Index shows, conventional dictators using conventional AI for mass surveillance are a growing real-world problem while smart AI remains science fiction.
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Here’s the dilemma: The Moral Machine (the Trolley Problem, updated) feels necessary because the rules by which we order our lives are useless with automated vehicles. Laws embody principles that we apply. Machines have no mind by which to apply the rules. Instead researchers must train them with millions of examples and hope the machine extracts the correct message…
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Today’s apocalyptic vision seems now to have moved on from the arrival of the extraterrestrials to uploading ourselves to a supercomputer. Whether it’s possible is really secondary. The main question is whether it answers a cultural need for a vision that mirrors the inner turmoil of the day.
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If Moore’s Law fails, AI may settle in as a part of our lives like the automobile but it will not really be the Ruler of All except for those who choose that lifestyle. Even so, a belief that we will, for example, merge with computers by 2045 (the Singularity) is perhaps immune to the march of mere events. Entire arts and entertainment industries depend on the expression of such beliefs.
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The quest is a curious blend of forlorn hope fueled by half-acknowledged hype and resolute denial of the most serious problems. Also by sometimes systematic confusion as to what, precisely, we are talking about.
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Stanford University’s AI index offers us fanciful measures of the triumph of AI, rivaling the far-fetched metrics of dot-com commerce. The reality has been the opposite. For decades, U.S. productivity grew by about 3% a year. Then, after 1970, it slowed to 1.5% a year, then 1%, now about 0.5%. Perhaps we are spending too much time on our smartphones.
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We might avoid worrying that our artificial intelligence machines are trying to deceive us if we called it “Automated Intelligence rather than “Artificial Intelligence.”
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Have you heard of the Law of the Instrument? It just means, to quote one formulation, “He that is good with a hammer tends to think everything is a nail.” All any given problem needs is a good pounding. This is a risk with AI, as with amateur carpentry. But with AI, it can get you into more serious trouble. Take hiring, for instance.
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Medicine involves many risks, benefits, and tradeoffs. Early diagnosis, for example, can certainly be defended and promoted on a right-to-know basis. But that is not the same thing as saying that it reliably improves outcomes or even enjoyment of life. If a powerful AI method reliably detected the very early onset of Alzheimer, it might ruin a senior's early retirement years without changing the outcome much. Getting the most from AI will include determining the relationship between what it can potentially do and what will provide a medical benefit.
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When no new leads emerge in a murder or missing persons investigation, police must shift their resources to cases that offer new information. Currently, the FBI Uniform Crime Report keeps an estimated 250,000 cold cases on file. Recent developments in AI, however, have shed light on some of these old and cold cases.
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Over time, machine vision will become harder to fool than the one that was recently tricked into rapid acceleration by a defaced sign. But it will still be true that a fooled human makes a better decision than a fooled machine because the fooled human has common sense, awareness, and a mind that reasons.
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