Why Artificial Intelligence Isn’t Intelligent
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Why Artificial Intelligence Isn’t Intelligent

Some experts in AI think its name fuels confusion and hype of the sort that led to past ‘AI winters’ of disappointment.

By Christopher Mims
Tue, Aug 3, 2021 10:34amGrey Clock 5 min

A funny thing happens among engineers and researchers who build artificial intelligence once they attain a deep level of expertise in their field. Some of them—especially those who understand what actual, biological intelligences are capable of—conclude that there’s nothing “intelligent” about AI at all.

“In a certain sense I think that artificial intelligence is a bad name for what it is we’re doing here,” says Kevin Scott, chief technology officer of Microsoft. “As soon as you utter the words ‘artificial intelligence’ to an intelligent human being, they start making associations about their own intelligence, about what’s easy and hard for them, and they superimpose those expectations onto these software systems.”

This might seem like a purely academic debate. Whatever we call it, surely what matters most about “AI” is the way it is already transforming what can seem like almost every industry on earth? Not to mention the potential it has to displace millions of workers in trades ranging from white to blue-collar, from the back office to trucking?

And yet, across the fields it is disrupting or supposed to disrupt, AI has fallen short of many of the promises made by some of its most vocal advocates—from the disappointment of IBM’s Watson to the forever-moving target date for the arrival of fully self-driving vehicles.

Words have power. And—ask any branding or marketing expert—names, in particular, carry weight. Especially when they describe systems so complicated that, in their particulars at least, they are beyond the comprehension of most people.

Inflated expectations for AI have already led to setbacks for the field. In both the early 1970s and late 1980s, claims similar to the most hyperbolic ones made in the past decade—about how human-level AI will soon arise, for example—were made about systems that would seem primitive by today’s standards. That didn’t stop extremely smart computer scientists from making them, and the disappointing results that followed led to “AI winters” in which funding and support for the field dried up, says Melanie Mitchell, an AI researcher and professor at the Santa Fe Institute with more than a quarter-century of experience in the field.

No one is predicting another AI winter anytime soon. Globally, US$37.9 billion has been invested in AI startups in 2021 so far, on pace to roughly double last year’s amount, according to data from PitchBook. And there have also been a number of exits for investors in companies that use and develop AI, with $14.4 billion in deals for companies that either went public or were acquired.

‘You have test tubes, a computer and your machine learning.’

— Viral Shah, CEO of Julia Computing

But the muddle that the term AI creates fuels a tech-industry drive to claim that every system involving the least bit of machine learning qualifies as AI, and is therefore potentially revolutionary. Calling these piles of complicated math with narrow and limited utility “intelligent” also contributes to wild claims that our “AI” will soon reach human-level intelligence. These claims can spur big rounds of investment and mislead the public and policy makers who must decide how to prepare national economies for new innovations.

Inside and outside the field, people routinely describe AI using terms we typically apply to minds. That’s probably one reason so many are confused about what the technology can actually do, says Dr. Mitchell.

Claims that AI will soon significantly exceed human abilities in multiple domains—not just in very narrow tasks—have been made by, among others, Facebook Chief Executive Mark Zuckerberg in 2015, Tesla CEO Elon Musk in 2020 and OpenAI CEO Sam Altman in 2021.

OpenAI declined to comment or make Mr. Altman available. Tesla did not respond to a request for comment. Facebook’s vice president of AI, Jerome Pesenti, says that his company believes the field of AI is better served by more scientific and realistic goals, rather than fuzzy concepts like creating human-level or even superhuman artificial intelligence. “But,” he adds, “we are making great strides toward learning more like humans do, and creating more general-purpose models that perform well on tasks beyond those they are specifically trained to do.” Eventually, he believes this could lead to AI that possesses “common sense.”

The tendency for CEOs and researchers alike to say that their system “understands” a given input—whether it’s gigabytes of text, images or audio—or that it can “think” about those inputs, or that it has any intention at all, are examples of what Drew McDermott, a computer scientist at Yale, once called “wishful mnemonics.” That he coined this phrase in 1976 makes it no less applicable to the present day.

“I think AI is somewhat of a misnomer,” says Daron Acemoglu, an economist at Massachusetts Institute of Technology whose research on AI’s economic impacts requires a precise definition of the term. What we now call AI doesn’t fulfil the early dreams of the field’s founders—either to create a system that can reason as a person does, or to create tools that can augment our abilities. “Instead, it uses massive amounts of data to turn very, very narrow tasks into prediction problems,” he says.

When AI researchers say that their algorithms are good at “narrow” tasks, what they mean is that, with enough data, it’s possible to “train” their algorithms to, say, identify a cat. But unlike a human toddler, these algorithms tend not to be very adaptable. For example, if they haven’t seen cats in unusual circumstances—say, swimming—they might not be able to identify them in that context. And training an algorithm to identify cats generally doesn’t also increase its ability to identify any other kind of animal or object. Identifying dogs means more or less starting from scratch.

The vast sums of money pouring into companies that use well-established techniques for acquiring and processing large amounts of data shouldn’t be confused with the dawn of an age of “intelligent” machines that aren’t capable of doing much more than narrow tasks, over and over again, says Dr. Mitchell. This doesn’t mean that all of the companies investors are piling into are smoke and mirrors, she adds, just that many of the tasks we assign to machines don’t require that much intelligence, after all.

Mr. Scott describes AI in similarly mundane terms. Whenever computers accomplish things that are hard for humans—like being the best chess or Go player in the world—it’s easy to get the impression that we’ve “solved” intelligence, he says. But all we’ve demonstrated is that in general, things that are hard for humans are easy for computers, and vice versa.

AI algorithms, he points out, are just math. And one of math’s functions is to simplify the world so our brains can tackle its otherwise dizzying complexity. The software we call AI, he continues, is just another way to arrive at complicated mathematical functions that help us do that.

Viral Shah is CEO of Julia Computing, a cloud-software company that makes tools for programmers who build AI and related systems. His customers range from universities working on better batteries for electric vehicles to pharmaceutical companies searching for new drugs.

Dr. Shah says he loves to debate how “AI” should be described and what that means for its future abilities, but he doesn’t think it’s worth getting hung up on semantics. “This is the approach we’re taking,” he says. “Let’s not talk about the philosophical questions.”

For consumers, practical applications of AI include everything from recognizing your voice and face to targeting ads and filtering hate speech from social media. For engineers and scientists, the applications are, arguably, even broader—from drug discovery and treating rare diseases to creating new mathematical tools that are broadly useful in much of science and engineering. Anyplace that advanced mathematics is applied to the real world, machine learning is having an impact.

“There are realistic applications coming out of the current brand of AI and those are unlikely to disappear,” says Dr. Shah. “They are just part of the scientist’s toolbox: You have test tubes, a computer and your machine learning.”

Once we liberate ourselves from the mental cage of thinking of AI as akin to ourselves, we can recognize that it’s just another pile of math that can transform one kind of input into another—that is, software.

In its earliest days, in the mid-1950s, there was a friendly debate about what to call the field of AI. And while pioneering computer scientist John McCarthy proposed the winning name—artificial intelligence—another founder of the discipline suggested a more prosaic one.

“Herbert Simon said we should call it ‘complex information processing,’ ” says Dr. Mitchell. “What would the world be like if it was called that instead?”



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New York Watch Auctions Record Uptick in Sales in the Face of Market Slowdown
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Luxury watch collectors showed ongoing strong demand for Patek Philippe, growing interest in modern watches and a preference for larger case sizes and leather straps at the June watch sales in New York, according to an analysis of the major auctions.

Independent and neo-vintage categories, meanwhile, experienced declines in total sales and average prices, said the report from  EveryWatch, a global online platform for watch information. Overall, the New York auctions achieved total sales of US$52.27 million, a 9.87% increase from the previous year, on the sale of 470 lots, reflecting a 37% increase in volume. Unsold rates ticked down a few points to 5.31%, according to the platform’s analysis.

EveryWatch gathered data from official auction results for sales held in New York from June 5 to 10 at Christie’s, Phillips, and Sotheby’s. Limited to watch sales exclusively, each auction’s data was reviewed and compiled for several categories, including total lots, sales and sold rates, highest prices achieved, performance against estimates, sales trends in case materials and sizes as well as dial colors, and more. The resulting analysis provides a detailed overview of market trends and performance.

The Charles Frodsham Pocket watch sold at Phillips for $433,400.

“We still see a strong thirst for rare, interesting, and exceptional watches, modern and vintage alike, despite a little slow down in the market overall,” says Paul Altieri, founder and CEO of the California-based pre-owned online watch dealer BobsWatches.com, in an email. “The results show that there is still a lot of money floating around out there in the economy looking for quality assets.”

Patek Philippe came out on top with more than US$17.68 million on the sale of 122 lots. It also claimed the top lot: Sylvester Stallone’s Patek Philippe GrandMaster Chime 6300G-010, still in the sealed factory packaging, which sold at Sotheby’s for US$5.4 million, much to the dismay of the brand’s president, Thierry Stern . The London-based industry news website WatchPro estimates the flip made the actor as much as US$2 million in just a few years.

At Christie’s, the top lot was a Richard Mille Limited Edition RM56-02 AO Tourbillon Sapphire
Richard Mille

“As we have seen before and again in the recent Sotheby’s sale, provenance can really drive prices higher than market value with regards to the Sylvester Stallone Panerai watches and his standard Patek Philippe Nautilus 5711/1a offered,” Altieri says.

Patek Philippe claimed half of the top 10 lots, while Rolex and Richard Mille claimed two each, and Philippe Dufour claimed the No. 3 slot with a 1999 Duality, which sold at Phillips for about US$2.1 million.

“In-line with EveryWatch’s observation of the market’s strong preference for strap watches, the top lot of our auction was a Philippe Dufour Duality,” says Paul Boutros, Phillips’ deputy chairman and head of watches, Americas, in an email. “The only known example with two dials and hand sets, and presented on a leather strap, it achieved a result of over US$2 million—well above its high estimate of US$1.6 million.”

In all, four watches surpassed the US$1 million mark, down from seven in 2023. At Christie’s, the top lot was a Richard Mille Limited Edition RM56-02 AO Tourbillon Sapphire, the most expensive watch sold at Christie’s in New York. That sale also saw a Richard Mille Limited Edition RM52-01 CA-FQ Tourbillon Skull Model go for US$1.26 million to an online buyer.

Rolex expert Altieri was surprised one of the brand’s timepieces did not crack the US$1 million threshold but notes that a rare Rolex Daytona 6239 in yellow gold with a “Paul Newman John Player Special” dial came close at US$952,500 in the Phillips sale.

The Crown did rank second in terms of brand clout, achieving sales of US$8.95 million with 110 lots. However, both Patek Philippe and Rolex experienced a sales decline by 8.55% and 2.46%, respectively. The independent brand Richard Mille, with US$6.71 million in sales, marked a 912% increase from the previous year with 15 lots, up from 5 lots in 2023.

The results underscored recent reports of prices falling on the secondary market for specific coveted models from Rolex, Patek Philippe, and Audemars Piguet. The summary points out that five top models produced high sales but with a fall in average prices.

The Rolex Daytona topped the list with 42 appearances, averaging US$132,053, a 41% average price decrease. Patek Philippe’s Nautilus, with two of the top five watches, made 26 appearances with an average price of US$111,198, a 26% average price decrease. Patek Philippe’s Perpetual Calendar followed with 23 appearances and a US$231,877 average price, signifying a fall of 43%, and Audemars Piguet’s Royal Oak had 22 appearances and an average price of US$105,673, a 10% decrease. The Rolex Day Date is the only watch in the top five that tracks an increase in average price, which at US$72,459 clocked a 92% increase over last year.

In terms of categories, modern watches (2005 and newer) led the market with US$30 million in total sales from 226 lots, representing a 53.54% increase in sales and a 3.78% increase in average sales price over 2023. Vintage watches (pre-1985) logged a modest 6.22% increase in total sales and an 89.89% increase in total lots to 169.

However, the average price was down across vintage, independent, and neo-vintage (1990-2005) watches. Independent brands saw sales fall 24.10% to US$8.47 million and average prices falling 42.17%, while neo-vintage watches experienced the largest decline in sales and lots, with total sales falling 44.7% to US$8.25 million, and average sales price falling 35.73% to US$111,000.

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