Findings patterns in data is easy. Finding meaningful patterns that have a logical basis and can be used to make accurate predictions is elusive. We can see this from 18th-century attempts to cure scurvy through 21st century claims about the stock market or history.
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In an unsanitized, politically incorrect (but factual) history, Selmer Bringsjord talks about how the tormented genius Kurt Gödel took up a quest that dated back a thousand years to prove the existence of God by formal logic. His original version didn’t quite work but his editor’s version passed an important logic test.
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Like Excel, AI assists programmers in their design work. AI can search through trillions of possibilities, using data from a million sources, to find a successful design. But the structure of the search and the source of the data is the choice of the programmer. A look at how famous inventors developed products that changed the world sheds some light on the process.
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The basic problem is that accepting on faith what we can’t ever hope to understand is not a traditional stance of science. Thus it’s a good question whether science could survive such a transition and still be recognizable to scientists. But does turning things over to incomprehensible algorithms, as Krakauer proposes, really work anyway? Current results from a variety of areas give pause for thought.
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Jonathan Bartlett agrees with Valley pioneers Davidow and Malone, authors of The Autonomous Revolution (2020) that there are real problems with the misuse of AI. But, he says, that’s because we treat it as powerful. It is a servant but we make it a master.
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AI sifts enormous amounts of accumulated data. But successful military strategy often depends on creating a new approach to a problem, one that lies outside the historical data available to the opposing forces. Muhammad Ali and Hannibal were famous for using such strategies.
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An issue left untouched is whether AI can, in principle, be creative in any event. There is no real evidence for that. The big worry, perhaps, is not being replaced by one’s tools but being ruled by them.
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Experts list various problems, including the fact that AI is vulnerable to failure due to unforeseen problems, including problems with data (too sparse, too noisy, too many outliers, etc.). It also doesn’t learn as well from experience as humans do.
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According to researchers, Ian Burkhart, whose hands and legs were paralyzed in a diving accident in 2010, has regained the sense of touch,, through a brain implant, as opposed to simply the ability to move a hand: The breakthrough came from analysis of years of data collected from NeuroLifeTM program study participant Ian Burkhart, who suffered a spinal cord injury in 2010 when diving into the ocean, and now lives with paralysis in his hands and legs. “When the chip was placed on the surface of Ian’s motor cortex in 2014, it was not known that the signals related to object touch could be observed because of the paralysis,” said lead author and Battelle Principal Research Scientist Patrick Ganzer. “Furthermore, Read More ›
If Alphabet’s Deep Mind or Microsoft had successfully data mined the 29,000 papers and found useful coronavirus information, that would be pretty impressive. But they appear to be giving others a chance to try instead, raising issues once again about the value of data mining in medicine.
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The underlying problem with creating immortality by uploading our minds to computers is that people are conscious and even the most sophisticated foreseeable computers are not. And we are not at all sure what consciousness even IS.
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At first, “shirts without stripes” might not seem like much of an issue but it turns out that many important and interesting problems for computers fundamentally reduce to this “halting problem.” And understanding human language is one of these problems.
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Computer scientist Selmer Bringsjord recalls, “I remember asking James Moor, the Dartmouth professor who’s written quite a bit on AI: “You know. Jim, you really are a true believer in this stuff but can you tell me how much time you’re willing to give these AI people? I mean, if we give them another thousand years, and we still don’t have cognition as I’ve characterized it… Are you going to be skeptical now?” He was, I suppose, as an academic, predictably clever and evasive, but the bottom line is, we don’t have this cognition captured.
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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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