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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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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The last decade has resounded with failed predictions about the future of transportation. I can't speak for other locations, but the Great E-Scooter Revolution of 2019 has both come and gone in the city in which I live. Isn’t the real future of transportation working and shopping at home? Why not just let the electrons do the commute and streamline the grocery run?
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Dr. Smith thinks that the most dangerous error is putting data before theory. Many data-mining algorithms that are now being used to screen job applicants, price car insurance, approve loan applications, and determine prison sentences have significant errors and biases that are not due to programmer mistakes and biases, but to a misplaced belief in data-mining.
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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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The explosion of data has vastly increased the number of coincidental patterns that can be discovered by tenacious researchers. If there are a relatively fixed number of useful patterns and the number of coincidental patterns is growing exponentially, then the ratio of useful patterns to useless patterns must necessarily be getting closer to zero every day.
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Many Millennials yearn for socialism but, according to the Gilder Fellows scholars, socialism is—at best—irrelevant to the challenges of an information society.
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Recovering more precious metals from our departed electronics would help both economic growth and sustainable development at once.
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Maybe both. AI will require more from us, not less, because how we choose to use these tools will make an increasingly stark difference between benefit and ruin.
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AI does pose at least one threat to filmmaking. It could intensify the very tone-deafness that studios hope it can fix: Too much reliance on ever more finely grained analysis of the patterns in past data could blind decision makers to the real risks, volatility, and opportunities in the future. That’s a recipe for losing money and inflicting “Oh, not that again!” on audiences.
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Using your talents to identify and invest in high-quality assets and pull money away from low-quality assets is a benefit to everyone involved in the market and, on the larger scale, the market’s future. If you invest in this way, you will beat the market.
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Cinelytic’s AI does is what every AI does at its most basic level. It collects and sifts large amounts of data to find patterns that can be used to make predictions. AI excels at pattern recognition because that is its only purpose. Humans aren’t so linear or predictable.
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Jay Richards asks: Just imagine if our government had taxed earlier technological innovations because they threatened jobs. Does anyone think a targeted “tractor tax” would have been a good idea in the early twentieth century?
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Computer algorithms are much, much better than humans at discovering statistical patterns but much, much worse than humans at discerning whether the patterns are meaningful. Wise investors can use the resulting blips to their advantage.
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Tim Harford points to the Sanchez tragedy to raise an important question: How do we know when a given technology is really helping us? And when we are taking too great a risk or paying too high a price?
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Both computers and humans can be fooled by patterns that appear significant but really aren’t. But the bigger the computer, the more random patterns it can find in the vast swathes of data processed.
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Some problems experts hope AI can help with may be outside AI's capacities. Some people may simply want to believe doctored images and deepfakes, for example.
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Because Tesla has yet to make a yearly profit in any of its sixteen years of existence, it depends on capital raises of various forms (equity, debt, etc.) to stay in business. Capital raises require big promises and Tesla’s overstatements about its self-driving cars are always good for a few billion.
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Artificial intelligence may do well summarizing data, but the new insights that will lead the economy forward cannot be gleaned that way. What we need is not old data but new truths.
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In reality, without appropriate human supervision, AI is just as likely to find false or unimportant patterns as real ones. Additionally, the overuse of AI in science is actually leading to a reproducibility crisis.
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