The feats of machines like AlphaGo are due to superior computational power, not to creativity at originating new ideas. Computer scientist Selmer Bringsjord sees the ability to write, say, a novel of ideas as a more realistic test of human vs. computer achievement.
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In the view of Rensselaer philosopher and computer scientist Selmer Bringsjord, the iconic Turing test for human-like intelligence in computers is inadequate and easily gamed. Merely sounding enough like a human to fool people does not establish human-like intelligence. He proposes the much more challenging Lovelace test, based on an observation from computer pioneer Ada Lovelace (1815–1852) that true creativity is what distinguishes humans from machines.
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As we struggle with the COVID-19 crisis, many are beginning to ask hard questions about how our system works, its strengths, weaknesses, and vulnerabilities. One vulnerability might be too heavy reliance on a single source for data modeling and predictions. Considering all the uses to which AI may be put in health care, getting our guidance exclusively from the Institute for Health and Metric Evaluation for modeling is reckless.
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Of 83 studies of the performance of the Deep Learning algorithm on diagnostic images, only two had been randomized, as is recommended, to prevent bias in interpretation.
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The “broken checkerboard” is not the ultimate scientific test for intelligence that we need. But it is a truly scientific test in the sense that it is capable of falsifying the theory that the mind is reducible to computation.
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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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