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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In the book, Baylor professor Marks asks “What if ambitious nations such as China and Iran develop lethal AI military technology but the United States does not?” He argues that “Advanced technology not only wins wars but gives pause to otherwise aggressive adversaries.”
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According to analytical philosopher Richard Johns, we cannot represent ourselves completely mathematically so we cannot generate fundamentally contradictory thoughts about ourselves. Some part of us lies beyond mathematics. An android would not be so lucky, as Captain Kirk realized in an early Star Trek episode.
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Jacob Schatzer identifies three issues in the essay, “The Allure of Transhumanism,” that might prompt some queasy recognitions in all of us, at times.
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A free course at Seattle University addresses the “meaning of ethics in AI.” I’ve signed up for it. One concern that I hope will be addressed is: We must not abdicate to machines the very thing that only we can do: Treat other people fairly.
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To see what I mean about centralization, consider a non-digital tool, say, a shovel. The shovel doesn’t keep track of your shoveling, read your biometrics, and store a file on you-as-shoveler somewhere. It’s a thing, an artifact. So you see, the new digital technology is itself the heart of the surveillance problem. No Matrix could be built with artifacts.
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With AI decision-making, a non-trivial explanation can’t be both accurate and understandable but it can be inaccurate and comprehensible. There is a huge difference between understanding something and almost understanding it.
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Researchers at the Allen Institute for Artificial Intelligence (AI2) published a paper recently, deflating claims of rapid progress toward giving computers common sense.
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As a lawyer with medical training reminded us recently, any one image is a snapshot in time, a brief part of the patient’s whole story. And it’s the whole story that matters, not a single image, perhaps taken out of context.
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