How To Build AI That Actually Works For Your Business

Artificial intelligence might one day be used to power genuinely human-like cyborgs or other figments of humanity’s fertile imagination. For now, Ingo Stork is using the technology to help restaurant chains waste less food and do more with fewer workers.

Dr. Stork is co-founder of PreciTaste, a startup that uses AI-based sensors and algorithms to accomplish one fairly specific task: predict how much food people will order at any given moment, and make sure that it’s being prepared in a timely fashion.

The idea—to reduce waste—came in part from a visit Dr. Stork made to a quick-service kitchen one afternoon a few years ago, where he watched a cook fire up 30 burger patties, and then throw them all away when no one showed up to eat them. Why, he wondered, should this cook have to follow that day’s schedule, written in anticipation of a normal day at the restaurant, instead of the slow one it turned out to be?

“Each of those burgers is a 50-mile car ride in terms of CO2 emissions,” he says, referring to the energy required to raise the cows and eventually transform them into burgers. “Think of all the logistics just to get them there, all just to go to waste and be discarded.”

Using AI tools to reduce waste and increase productivity in fast-food joints is hardly the stuff of science fiction. It isn’t as flashy as some of the artificial intelligences that have been getting wider attention lately, such as DALL-E, which can create clever images based on text suggestions, or GPT-3, text-generation software good enough to write scientific papers about itself. And it’s not as likely to make headlines as Google’s LaMDA chatbot, which can produce such humanlike conversation that one of the company’s engineers declared it to be sentient—a notion the company flatly rejected.

But, with a few exceptions, these headline-grabbing systems aren’t having a material impact on anyone’s bottom line yet.

The AI systems that currently matter the most to companies tend to be far more humble. Were they human, they would probably be wearing hard hats and making cameos on the reality show “Dirty Jobs.”

Ruthlessly simplify

When entrepreneur Phuc Vinh Truong found himself holed up in his Massachusetts home because of Covid-19 lockdowns, he hit on a simple idea. What if you could see contaminants in a stream of liquid, and suck them out one by one?

That led to Phuc Labs, a startup working on a new way to use AI to make recycling electronic waste more efficient.

The system starts with the chopped-up debris left after recyclers of batteries and other e-waste crush old electronics. Typically, this waste is processed with a variety of techniques, including chemical separation. Instead, Phuc Labs suspends the particles in water, then channels the resulting slurry through tiny tubes, where a camera captures its passage at 100 frames per second.

Each frame is analyzed by a computer running a machine-vision algorithm that has been trained to tell the difference between the metal particles valuable to recyclers, and everything else. When a particle travels to the end of the tube, a tiny, powerful jet of air fires at the stream, redirecting the “slice” of water containing the particle into a reservoir. The water is recirculated through the system repeatedly until nearly all the valuable bits of metal have been separated out.

Phuc Labs’ “vision valve” technology is still in its early stages, but the company is working on a pilot program with IRI, one of the biggest recyclers of e-waste in the Philippines, says IRI President Lee Echiverri.

This novel kind of filtration would be impossible without AI, but it’s not fancy AI. Machine-vision systems are probably the best-studied flavour of AI, and have been refined for decades. They’re used in everything from the face-recognizing camera in your phone to autonomous-driving systems to the missiles taking out Russian tanks in Ukraine.

Mr. Truong’s team was able to build one of the first versions of their system using an off-the-shelf computer-vision system called Roboflow. They trained it by manually identifying a few hundred images of particles—drawing boxes around particles and labelling them accordingly—and Roboflow’s software did the rest.

While AI is a unique enabler of Phuc Labs’ filtration system, it works because the system asks so little of the AI at its heart—just “is this a piece of metal or not?” In essence, his engineers are creating a simple game for their AI to learn, and games like chess and Go are things AI has already proved to be excellent at, says Mr. Truong.

In many other real-world applications of AI, engineers have found that trying to do less with AI is what ultimately leads to success. A prime example of this is autonomous driving systems, which have consistently failed to deliver on earlier promises of full autonomy, but are finding success in navigating some vehicles in more limited and forgiving environments, such as the ones traversed by trains, oceangoing ships and long-haul trucks.

Specialisation trumps flexibility

Every fast-food restaurant chain that Dr. Stork’s New York City-based company, PreciTaste, works with presents a new set of challenges for his engineers and the AI-powered restaurant-management systems they build.

“Each food chain has its own menu, operations, equipment and way of handling things,” he says. The array of wall-mounted cameras equipped with machine vision that can track an order from the moment its raw ingredients leave a refrigerator until it’s ready to be handed to a customer may have to be laid out differently, for example. And the number of preparation steps can vary greatly by restaurant.

PreciTaste says it can’t disclose which chains are considering its technology. But it’s working with the commercial-kitchen fabrication giant Franke to pilot its tech in a handful of national fast-food and fast-casual restaurants, says Greg Richards, vice president of business development at the company. (Franke has since the 1970s been a supplier to McDonald’s.)

To make its system work, depth-sensing cameras must be trained to recognize how much of an ingredient—say, rice—remains in a prep tray. Knowing when to replenish it depends on what will happen to demand, which in turn depends on factors including weather and local holidays that might determine whether people will go out to eat and what they’ll order. All of this and more is fed into the same kind of prediction algorithms that help retailers like Amazon manage their logistics networks.

Today’s AI systems lack common sense, can behave erratically when faced with unexpected events, and have minimal ability to transfer knowledge “learned” from one task to analogous situations. In this way, it could be said that today’s artificial intelligence possesses no intelligence at all—it is, as one AI pioneer put it, just “complex information processing.”

The result is that engineers and data scientists have to do a lot of hand-holding for their fragile AIs, including planning, hardware engineering, and writing software. All that to build a scaffolding within which an AI can be trained to accomplish a set of tasks that have been defined as narrowly as possible.

It might not always be like this

AIs like DALL-E, GPT-3, and LaMDA are known as “foundation models,” says Oren Etzioni, chief executive of the Allen Institute for AI. For now, they are mostly research projects. But someday systems like these might be flexible enough to throw at problems that today remain solely the domain of human intelligence, he adds.

Already, these AIs are starting to diversify and take on a wider range of tasks. One way this is happening is that foundation models have so much data stuffed into them that they are equally capable of, say, crafting an essay or writing code. For example, genre-fiction writers are using software based on GPT-3 to help them churn out straight-to-Kindle novels faster. And programmers who use Microsoft’s Copilot system can become more productive when it autocompletes lines of code they are writing. Copilot has a shared lineage with GPT-3, and like its cousin that writes marketing copy, fiction and essays, it’s far from perfect.

While we wait for these foundation models to find more applications outside R&D labs, such research into related systems that get us part way there is proving useful.

Gong is a cloud-based system, from a San Francisco-based startup of the same name, that records and analyzes every channel of communication used by a sales team. That includes phone calls, Zoom meetings, emails, chat transcripts and more. It then analyzes all that communication, and makes suggestions, so salespeople can close more deals. These suggestions range from words and phrases that tend to come up in successful sales pitches to how much to talk during a pitch—usually less.

Gong works in dozens of languages. For years this meant that every time the company wanted to update any of its AI models to make them better at transcribing or analyzing speech, it had to do it separately for each language, and sometimes even dialect. It was an enormous task, says Gong CEO Amit Bendov.

Then, in 2019, Meta AI, a research division of Facebook parent Meta Platforms, released a system called Wav2vec that uses a novel algorithm to quickly teach itself any language. Using this open-source code allowed Gong’s engineers to build a single system able to parse all of the languages and dialects Gong supports, says Mr. Bendov. Gong now uses one single polyglot AI model, constantly updated, to understand everything the company’s system works for.

Even with this leg up from the researchers at Meta, Gong still uses a custom-built speech-recognition system trained on thousands of hours of recorded audio and human-written transcripts. (This includes recordings of customer phone calls, “Seinfeld” episodes and fan-transcribed scripts for them.)

Gong’s use of AI for relatively narrow tasks, like speech recognition, and the way its engineers built custom systems to accomplish it, embody the same principles of workaday AI as Phuc Labs’ waste-filtering tech and PreciTaste’s restaurant-management systems.

Someday, the big, fancy models that garner attention might apply to the work of this company and others—but not yet. Getting there may take big leaps, such as giving AI common sense, including knowledge about the real world, so that it can derive meaning from all the data it ingests.

“The funny thing is, Gong doesn’t know what an iPad is or anything about our customers’ business,” says Mr. Bendov. “It just knows ‘this is what is said when you are successful.’”

TAIPEI—For years, Beijing hoped to win control of Taiwan by convincing its people their economic futures were inextricably tied to China.

Instead, more Taiwanese businesses are pivoting to the U.S. and other markets, reducing the island democracy’s dependence on China and angering Beijing as it sees its economic leverage over Taiwan ebb.

In one sign of the shift, the U.S. replaced mainland China as the top buyer of Taiwanese agricultural products for the first time last year.

Electronics firms such as chip maker Taiwan Semiconductor Manufacturing Co. are also selling more goods to American and other non-Chinese buyers, thanks in part to Washington’s chip restrictions and Apple’s bets on Taiwanese chips.

Overall, Taiwanese exports to the U.S. in the first 10 months of 2023 were more than 80% higher than in the same period of 2018, Taiwanese government data shows. Taiwanese exports to the mainland were 1% lower—a major change from a decade or so ago when China’s and Taiwan’s economies were rapidly integrating.

Taiwan’s outbound investment has also shifted. After flowing mostly to mainland China in the early 2000s, it has now moved decisively toward other destinations, including Southeast Asia, India and the U.S.

Taiwanese electronics giant Foxconn, which assembles iPhones in mainland China, is expanding in India and Vietnam after Apple began pushing its suppliers to diversify.

Chinese state media recently reported that China had opened tax and land-use probes into Foxconn. Though Taiwanese officials and analysts interpreted the probes as a sign that China wants Foxconn founder Terry Gou to drop plans to run in Taiwan’s presidential election in January, some have said Beijing may also be trying to pressure Foxconn into resisting decoupling with China.

“Any attempt to ‘talk down’ the mainland’s economy or to seek ‘decoupling’ is driven by ulterior motives and will be futile,” said a spokeswoman for Beijing’s Taiwan Affairs Office in September. “The mainland is always the best choice for Taiwanese compatriots and businesses.”

Fully decoupling from mainland China’s economy likely isn’t possible, and would be disastrous for Taiwan, not to mention China, even if it were.

Foxconn and other major Taiwanese companies depend heavily on China for parts, testing and buyers. Some 25% of Taiwan’s electronic-parts imports still come from the mainland.

If China’s weakened economy returns to strong growth, it could shift the calculus back in favor of the mainland, where the Communist Party claims Taiwan despite never having ruled it. About 21% of Taiwan’s total goods trade this year has been with mainland China, versus 14% for the U.S., though the U.S. share has risen from 11% in 2018.

“My hunch is that the large manufacturing sectors will try to stay in the Chinese market, even with harsh conditions,” said Alexander Huang, director of the international affairs department of the opposition Kuomintang Party, whose supporters include business people with mainland ties. “If you talk to those business owners, they say, ‘Nah, no way will I give it to my competitors.’”

Even so, many forces are pushing Taiwan to rewire its economic relationship with China.

Trump-era tariffs and Biden administration export controls have raised the cost of sourcing from China, and in some cases prohibited it. U.S. firms are pushing their Taiwanese suppliers to diversify sourcing, and rising wages in China have made it less attractive than before.

Long-running shifts in Taiwanese sentiment toward China—and China’s own efforts to punish the island using its economic leverage—are also factors. China has banned Taiwanese agricultural products such as pineapple and, in 2022, grouper fish, and restricted outbound tourism to Taiwan.

Those restrictions to some degree have backfired, pushing Taiwanese businesses to look elsewhere.

Casting for new markets

Chang Chia-sheng, who runs a fish farming operation in Taiwan, said his main export target a decade ago was mainland China. But as geopolitical tensions climbed, he looked elsewhere. Sales to Americans have jumped fivefold since 2018, he said. “In the U.S., things just seem to work out more easily,” Chang said.

The U.S. and Taiwan reached an agreement in May on a number of trade and investment measures to deepen ties, though the deal stopped short of reducing tariffs.

In the June quarter of 2023, 63% of revenue at TSMC, which makes most of the world’s most cutting-edge logic chips, came from the U.S., up from 54% in the same period in 2018, according to S&P Global data. Just 12% of TSMC’s revenue now comes from Chinese buyers, down from 22% in the second quarter of 2018.

Taiwan’s government is also encouraging closer economic links with Southeast Asia, South Asia, Australia and New Zealand. Its “New Southbound Policy,” rolled out in 2016, has been the subject of fierce debate in Taiwan, with the Kuomintang Party saying steps to boost relations—like handing out scholarships—aren’t worth the cost.

Exports to “New Southbound” partners have risen, however, to $66 billion in the first nine months of 2023, about 50% higher than the same period in 2016.

“Frankly speaking, we’re responding reactively” to the need for more diverse trading partners, Taiwan’s Economic Minister Wang Mei-hua said. “Taiwan needs to manage the risks on its own, but we also need our allies to join us more in mitigating these risks.”

Together, the U.S. and the six largest Southeast Asian economies accounted for 36% of Taiwanese exports in the third quarter of 2023, according to data from CEIC, surpassing the percentage sent to mainland China and Hong Kong on a quarterly basis for the first time since 2002.

In September, Taiwan sent less than 21% of its exports to the mainland, the lowest percentage since the global financial crisis.

Taiwanese foreign investment into mainland China, steady at around $10 billion a year for most of the early 2010s, plummeted in late 2018 and has since been running at about half that level, according to Taiwanese government data. In 2023 so far, just 13% of Taiwan’s investment went to mainland China; 25% went to other Asian locations, and nearly half went to the U.S.

A survey of Taiwanese businesses conducted last year on behalf of the Center for Strategic and International Studies, a Washington think tank, found that nearly 60% had moved or were considering moving some production or sourcing out of China—a significantly higher rate than European or American firms.

Jay Yen, chief executive of Yen and Brothers, a Taiwanese frozen-food processing company, said his firm received a government subsidy of around $75,000 to market his products to American consumers. China now only accounts for about 3% of its revenue, he said.

That said, “if you really have to consider the risks of a war between the U.S. and China and its potential impact on Taiwan, you might want to place your bets on a third country—neither China nor the U.S.,” Yen added.

Reversing the tide

After China began to open up its economy in the late 1970s, Taiwanese businesses were among the first investors.

By the 2000s, China seemed to be succeeding in its strategy of integrating the two economies, with more than 28% of Taiwan’s exports going to the mainland in 2010, from less than 4% a decade earlier.

Direct flights between the two sides were normalised for the first time in decades. Mainland tourists were allowed to visit Taiwan on their own.

By 2014, the tide was turning as more Taiwanese grew worried about over dependence on China. Student demonstrators protested against a trade pact, later abandoned, that would have deepened ties with China. President Tsai Ing-wen, who took office in 2016, has pushed to diversify Taiwan’s economy.

China has responded by moving trade issues more into the spotlight.

In April, it opened an investigation into Taiwanese trade restrictions that it says limit exports of more than 2,400 items from the mainland to the island in violation of World Trade Organization rules. In October, China’s Ministry of Commerce announced the probe would be extended until Jan. 12—the day before Taiwan’s coming election.

Taiwan’s government has called the probe politically motivated.

Chinese officials have implied that Beijing could suspend preferential tariff rates for some Taiwanese goods in China under a 2010 deal signed when Kuomintang’s Ma Ying-jeou was president. Beijing has also reacted angrily to Taiwan’s recent trade agreement with the U.S.

For Taiwanese companies, building and operating new factories in places other than China isn’t cheap or easy. Protests have at times disrupted operations at Indian plants operated by Foxconn and Wistron, another Apple supplier. In September, a fire halted production at a Taiwanese facility in Tamil Nadu.

Still, some Taiwanese businesspeople have clearly soured on China.

“The electronics industry has already become a Chinese empire, not a Taiwanese one,” says Leo Chiu, who worked in mainland China in quality control for an electronics manufacturer for 14 years before concluding he couldn’t move up further there and returning to Taiwan in 2019. Many of his old colleagues have left, he said.

“If Xi Jinping steps down, there’s still a chance it could change,” says Chiu. “But I think it’s very hard.”