fortune--2020-01-20--Boeing’s big buybacks are part of a bigger picture

‘ Catching up with the rest of world : ’ China sets bans on single-use plastic

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Good morning, and happy Martin Luther King Jr. Day.
Here’s an interesting statistic: Over the past six years, Boeing has spent $15.7 billion on research and development, and $43.4 billion on stock buybacks. Is that a sign of misplaced priorities? Buybacks aren’t necessarily evidence of short-sightedness. But in Boeing’s case, they are part of a bigger picture. You can read our deep dive into Boeing’s swoon this morning, here. The author, Dan Catchpole, argues the company’s problems were two decades in the making.
Also this morning, we are releasing our package on the state of artificial intelligence—a topic that will be top of mind for the folks, like me, traveling to Davos this morning, and that likely will define the next decade for business. A few things I would call your attention to: a smart review by Jennifer Alsever about the hype and the hope around using AI in healthcare — there has clearly been a good bit of both. And a piece by Maria Aspan looking at the exploding use of A.I. in human resources. This is one that has taken me by surprise, and is filled with both opportunity and risk.
Maria also provided the quote of the week in her piece:
“A.I. is like teenage sex,” says Frida Polli, cofounder and CEO of Pymetrics. “Everyone says they’re doing it, and nobody really knows what it is.”
Finally, Fortune’s Jeremy Kahn takes a look at the people who are pursuing the long-term dream of human-style “Artificial General Intelligence,” and assesses the state of the field.
Meanwhile, Deloitte is releasing its third annual Global Readiness Report, based on a survey of 2,000 executives and timed for the opening of Davos. Among other things, the report highlights the yawning gap between promise and practice of A.I. While most executives recognize that “Industry 4.0” technologies will alter entire industries, only 17% of executives said making Industry 4.0 investments is a top company priority, and only 5% said they had made progress “implementing technology in a connected, integrated way across their organizations.”
And then there is this interesting finding from the annual Edelman trust survey, also aimed at the Davos crowd. While growing numbers of people around the world are questioning the efficacy of capitalism—the Edelman survey found 56% of the broad population believe “it does more harm than good in the world”—trust in business has enjoyed something of a rebound. “Business has leapt into the void left by populist and partisan government,” said Richard Edelman, who heads the eponymous firm. “With 73% of employees saying they want the opportunity to change society, and nearly two-thirds of consumers identifying themselves as belief-driven buyers, CEOs understand that their mandate has changed.”
Apologies for the overly long note this morning. More news below.
Alan Murray
@alansmurray
alan.murray@fortune.com
TOP NEWS
Inequality stats
As is now traditional, the charity Oxfam is using Davos to highlight massive wealth inequality. This year's stats show that the richest 1% have more than twice as much wealth as 6.9 billion people, and that the 22 richest men are collectively wealthier than all the women in Africa. The past decade has seen the number of billionaires double, apparently. Oxfam sees trouble looming as populations age and public services have funding cut. CNN
Coronavirus spread
A third person has died in China from a newly identified virus that sprang up in the center of the country. Cases have now been reported in Beijing and Shenzhen, and the novel coronavirus has also spread to South Korea. The timing of the outbreak is not good—millions of Chinese citizens are about to travel for the Lunar New Year holiday. Wall Street Journal
Brexit divergence
The U.K.'s finance chief, Sajid Javid, said the country will not align with EU rules and regulations when the Brexit transition period runs out at the end of this year. The warning to businesses knocked sterling and caused alarm on the EU side. EU diplomats and officials said industry should prepare for "less trade, less investment, less jobs." Financial Times
BAE buy
BAE Systems is buying two businesses that are being offloaded as a result of the Raytheon-United Technologies merger. The purchases of the military-focused GPS unit of UTC's Collins business, and Raytheon's airborne tactical radios unit, are together costing BAE $2.2 billion. Analysts like the deal, as the businesses involved are of high quality—BAE's share price popped 2.5% on the news. Reuters
AROUND THE WATER COOLER
Stock options
France is to reform its stock-options rules to enable better competition with Silicon Valley, as European business leaders have been urging. The country's stock-options scheme will be expanded to include foreign companies with France-based employees, and options will apparently be priced at a fair market value, rather than the valuation price that investors pay. CNBC
Palladium prices
The price of palladium has jumped a quarter in the last two weeks alone, continuing a trend of soaring value. So why is the metal now, at around $2,500 an ounce, now more valuable than gold? Palladium is used in the catalytic converters that control cars' harmful emissions—demand is outstripping supply, to the extent that there's a spike in thefts of catalytic converters from cars. BBC
Streaming wars
How confident are Apple's TV+ executives about their programming in the streaming wars? So confident that they've already renewed two series that haven't even debuted yet, namely Home Before Dark and Mythic Quest: Raven's Banquet. Fortune
Single-use plastic
China is moving to cut the use of disposable plastics with bans on non-degradable plastic bags in major cities' shopping malls, supermarkets and food-delivery services. Analysts see the development as China catching up with the policies of the EU, Africa and Southeast Asia. Fortune
This edition of CEO Daily was edited by David Meyer.
THE FUTURE OF FORTUNE IS HERE
In 1930, Fortune published its first-ever issue, featuring the goddess Fortuna and her wheel on the cover. This year, on our 90th anniversary, we’re celebrating with a new Fortune. Here’s what’s in store for you:


fortune--2020-01-20--The forces behind Boeing’s long descent

The forces behind Boeing’s long descent

For nearly a year, the world’s largest aerospace company has been engulfed by a scandal of its own making. The 737 Max crisis, which unfolded after 346 people died in two crashes linked to software malfunctions in Boeing’s newest jetliner, has put the company under an unforgiving lens. Scrutiny from journalists, crash investigators, regulators, Congress, and the Department of Justice has exposed profound flaws in Boeing’s corporate culture—shaking its workforce, forcing supplier layoffs, and shattering fliers’ trust.
What’s more, the Max scandal isn’t the only dire threat to Boeing’s trajectory. Well before the crashes, Boeing had struggled to plug a gaping hole in its product lineup between its single-aisle 737 and larger twin-aisle 787 planes. Now, plans to launch an entirely new, “clean sheet” jetliner for this midsize market have been shelved, as the company scrambles to get the Max back in the air.
These twin crises, industry insiders say, spring from a culture that consistently put short-term rewards to shareholders ahead of engineering-driven decisions and long-term strategy. For all of Boeing’s business coups and innovation, one stark statistic has come to symbolize the company’s priorities: Over the past six years, Boeing spent $43.4 billion on stock buybacks, compared with $15.7 billion on research and development for commercial airplanes. The board even approved an additional $20 billion buyback in December 2018, less than two months after the first 737 Max crash, though it subsequently shelved that plan.
Let’s say you only return 50%,” Hamilton says. “You could have funded an entire airplane program Scott hamilton, leeham co.
Down payments on orders for the Max—the fastest-selling jet in Boeing’s history—helped pay for the rounds of buybacks. Share repurchases, in turn, have helped the company’s stock price more than triple since 2016, even after a recent sharp drop. Boeing’s defense-and-space and global services divisions, which together account for about 40% of revenue, have kept it afloat even as commercial-jet sales fall. But the Max scandal has put an end to Boeing’s run as a Wall Street bull. Each new revelation suggests that the company’s shareholder-centric mentality has contributed to—and perhaps is partly to blame for—both the Max crisis and its potholed product road map. “Wall Street does not want to hear about new clean-sheet planes,” says Ken Herbert, an aerospace analyst at Canaccord Genuity. And so far, he adds, the board of directors hasn’t “provided the leadership or oversight the company’s needed.”
Boeing declined to comment for this story. But the question facing new CEO David Calhoun is whether he can break Boeing’s addiction to shareholder payouts and plot the company’s long-term recovery.
Boeing’s culture problems aren’t two or even 10 years in the making. Aerospace experts trace them to 1997, when Boeing acquired rival McDonnell Douglas, absorbing many of that company’s executives along with its finances-first ethos.
In the years prior to the merger, Boeing had largely avoided share repurchases; McDonnell’s board, led by its CEO Harry Stonecipher, had pursued them enthusiastically. Within a year of the merger, buybacks became a cornerstone of Boeing’s strategy. As a Boeing executive and later CEO, Stonecipher also advocated aggressive cost-cutting, pushing the company to deliver an after-tax profit margin of 7%—a mark Boeing had not hit since the 1970s. His successor, Jim McNerney, continued to put profit margins first. “When people say I changed the culture of Boeing, that was the intent, so that it’s run like a business rather than a great engineering firm,” Stonecipher told the Chicago Tribune in 2004. “It is a great engineering firm, but people invest in a company because they want to make money.”
The downsides of cost-cutting soon appeared in Boeing’s 787 Dreamliner program, which began in 2003. Management pushed the company to save money by outsourcing development of critical components to suppliers, many of which proved not up to the task, leading to repeated breakdowns and delays. When the jet finally flew in 2011, it was three years late and $25 billion over budget. In 2013, after the plane was in service, electrical fires in batteries on two 787s prompted regulators to ground the airplane for nearly a month.
If you cut R&D, cost-cut, give cash to shareholders—then you’re playing with Boeing’s future ron epstein, bank of america merril lynch
The 787’s painful legacy continues today, notes Scott Hamilton, head of aerospace consulting firm Leeham Co. Its problems prompted Boeing leadership in 2011 to decide to put new engines on the 737, dubbing it the 737 Max, rather than build an entirely new airplane. “Their hand was forced,” Hamilton says. “The issue was how badly they screwed up the 787 program, which destroyed their product strategy for the next several decades.”
Boeing suspended buybacks during the 787 debacle. Resuming in 2013, they have since averaged $6.2 billion annually. Meanwhile, the company has spent an average of $2.47 billion annually on commercial-plane R&D. By 2016, the 787 program was cash positive, and that year Boeing leadership committed to returning 100% of free cash flow to shareholders. “Let’s say you only return 50%,” Hamilton says. “You could have funded an entire airplane program.”
The company desperately needs that program now. By the middle of the past decade, Boeing was confronting its lack of a new mid-market airplane (known in-house as the NMA). This category of jetliner carries around 250 passengers over distances of 4,000 to 5,000 miles. Mid-market is the only segment of the commercial-jet business expected to see strong demand in the near future, making the category critical to Boeing and rival Airbus. “Boeing likely needs two clean-sheet airplanes this decade,” says Richard Aboulafia of consulting firm Teal Group.
In 2016, however, then-CEO Dennis Muilenburg pledged to double Boeing’s profit margins to the mid-teens, a goal that made plane development that much more challenging. Punting on the decision to begin designing an NMA became an annual tradition for Boeing leadership.
Boeing’s indecision has given Airbus room to dominate the market. Given its problems with the Max, analysts and consultants agree that the earliest Boeing can start an NMA project is 2021. Even on that timetable, Boeing “will have lost market share to the A321XLR and maybe the A330neo,” two new Airbus models, says Ron Epstein, an aerospace analyst for Bank of America Merrill Lynch. “Some of it is fait accompli,” he adds. “The XLR is here—that’s [lost] market share” for Boeing.
“The decisions Boeing makes in the next five years will define the business for the next 25 years,” says Herbert, the Canaccord analyst. “They need to embrace an engineering culture; they need to embrace what worked for the company’s first 85 years.” Herbert points to Boeing’s unprecedented run of creativity in the late 1950s and ’60s, when it launched the 707, 727, 737, and 747.
But the fallout from the Max crisis may well push Boeing in the opposite direction. Costs related to the Max have topped $9 billion and could easily double. To shore up its balance sheet Boeing is reportedly considering borrowing money to pay shareholder dividends—and cutting R&D spending. Those are steps in the wrong direction, says Epstein. The only way forward is to put investors’ short-term interests on hold, he argues: “If you don’t do that—if you cut R&D, cost-cut, give cash to shareholders—then you’re playing with Boeing’s future.”
Real Money
204%: Increase in Boeing’s stock price since Feb. 11, 2016
$43.4 billion: Amount spent by Boeing on shareholder buybacks since 2013
$9 Billion: Current estimated cost to Boeing of the 737 max crisis, in lost orders, cost overruns, and production shutdowns
(Source: Company and analyst estimates)
A version of this article appears in the February 2020 issue of Fortune with the headline “Boeing’s Long Descent.”
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—25 ideas that will shape the 2020s
—Why it’s time for a futures market in health care
—What we can learn from the man who tried to sell the Eiffel Tower—twice
—Food-delivery services feeling pressure to finally turn a profit
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Subscribe to Fortune’s forthcoming Bull Sheet for no-nonsense finance news and analysis daily.


fortune--2020-01-20--‘May the best woman win:’ New York Times endorses 2 Democratic candidates for president

The New York Times has endorsed not one but two candidates for the Democratic nomination for president, Minnesota Sen. Amy Klobuchar from the party’s moderate wing and Massachusetts Sen. Elizabeth Warren from the progressive wing.
The paper said Sunday it had chosen the two most effective candidates from the moderate and progressive sides of the party — without stating a preference for either approach. It praised Warren as “a gifted story teller” and Klobuchar as “the very definition” of Midwestern charisma and grit.
When mentioning another front-runner, former Vice President Joe Biden, the Times acknowledged his years of experience, but also noted his age, 77, desire, and occasional gaffes. “It is time for him to pass the torch to a new generation of political leaders,” the paper said, borrowing from President John F. Kennedy’s inaugural address.
The paper mentioned Sen. Bernie Sanders’ age, 78, “serious concerns” about his health and noted his unwillingness to compromise. The paper praised another of the front-runners, 38-year-old Pete Buttigieg, former mayor of South Bend, Indiana, as likely to have “a bright political future.”
The newspaper changed its approach to presidential endorsements this year, airing footage of candidate interviews and details about the endorsement process on a special edition of “The Weekly,” the FX network series about the Times.
In previous election years, the Times has often chosen a candidate popular with the party establishment. The Times endorsed Hillary Clinton in the Democratic primary in 2016 and over a charismatic but unproven newcomer Barack Obama in 2008.
In 2004, the Times endorsed John Kerry and in 2000 chose Al Gore. Each time they chose a candidate who was popular with the Democratic establishment and, except for 2008, the eventual nominee.
More must-read stories from Fortune:
—Donald Trump and the power of incumbency
—Why the U.K. is bailing out regional airline Flybe when it’s let others fail
—The wealthiest members of Congress—and how they made their millions
—Millions have been purged from voter rolls—and may not even realize it
—2020 Crystal Ball: Predictions for the economy, politics, technology, and more
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fortune--2020-01-20--Huawei CFO extradition trial begins as China protests ‘political’ treatment of tech champion exec

The first stage of an extradition hearing for a senior executive of Chinese telecom giant Huawei begins Monday in a Vancouver courtroom, a case that has infuriated Beijing, caused a diplomatic uproar and added to tensions between China and the United States.
Canada’s arrest of chief financial officer Meng Wanzhou, the daughter of Huawei’s legendary founder, in late 2018 at America’s request shocked Beijing.
Huawei represents China’s progress in becoming a technological power and has been a subject of U.S. security concerns for years. Beijing views Meng’s case as an attempt to contain China’s rise.
“This is one of the top priorities for the Chinese government. They’ve been very mad. They will be watching this very closely,” said Wenran Jiang, a senior fellow at the Institute of Asian Research at the University of British Columbia.
China’s foreign ministry complained Monday the United States and Canada were violating Meng’s rights and called for her release.
“It is completely a serious political incident,” said a ministry spokesman, Geng Shuang. He urged Canada to “correct mistakes with concrete actions, release Ms. Meng Wanzhou and let her return safely as soon as possible.”
Washington accuses Huawei of using a Hong Kong shell company to sell equipment to Iran in violation of U.S. sanctions. It says Meng, 47, committed fraud by misleading the HSBC bank about the company’s business dealings in Iran.
Meng, who is free on bail and living in one of the two Vancouver mansions she owns, denies the allegations. Her defense team says comments by President Donald Trump suggest the case against her is politically motivated.
Meng was detained in December 2018 in Vancouver as she was changing flights — on the same day that Trump and Chinese President Xi Jinping met for trade talks.
Prosecutors have stressed that Meng’s case is separate from the wider China-U.S. trade dispute, but Trump undercut that message weeks after her arrest when he said he would consider intervening in the case if it would help forge a trade deal with Beijing.
China and the U.S. reached a “Phase 1” trade agreement last week, but most analysts say any meaningful resolution of the main U.S. allegation — that Beijing uses predatory tactics in its drive to supplant America’s technological supremacy — could require years of contentious talks. Trump had raised the possibility of using Huawei’s fate as a bargaining chip in the trade talks, but the deal announced Wednesday didn’t mention the company.
Huawei is the biggest global supplier of network gear for cellphone and internet companies. Washington is pressuring other countries to limit use of its technology, warning they could be opening themselves up to surveillance and theft.
“I think this is the beginning of a technological war along ideological fronts,” said Lynette Ong, an associate professor at the University of Toronto. “You are going to see the world divided into two parts. One side would use Chinese companies and the other side would not use Chinese companies because they are weary of the political implications of using Chinese platforms.”
James Lewis at the Washington-based Center for Strategic and International Studies said the U.S. wanted to send a message with Meng’s arrest. There is good evidence that Huawei willfully violated sanctions, he said.
“The message that you are no longer invulnerable has been sent to Chinese executives,” Lewis said. “No one has held China accountable. They steal technology, they violate their WTO commitments and the old line is, ‘Oh, they are a developing economy, who cares.’ When you are the second-largest economy in the world you can’t do that anymore.”
The initial stage of Meng’s extradition hearing will focus on whether Meng’s alleged crimes are crimes both in the United States and Canada. Her lawyers filed a a motion Friday arguing that Meng’s case is really about U.S. sanctions against Iran, not a fraud case. Canada does not have similar sanctions on Iran.
The second phase, scheduled for June, will consider defense allegations that Canada Border Services, the Royal Canadian Mounted Police and the FBI violated her rights while collecting evidence before she was actually arrested.
The extradition case could take years to resolve if there are appeals. Virtually all extradition requests from Canada to the U.S. are approved by Canadian judges.
In apparent retaliation for Meng’s arrest, China detained former Canadian diplomat Michael Kovrig and Canadian entrepreneur Michael Spavor. The two men have been denied access to lawyers and family and are being held in prison cells where the lights are kept on 24-hours-a-day. “That’s mafia-style pressure,” Lewis said.
China has also placed restrictions on various Canadian exports to China, including canola oil seed and meat. Last January, China also handed a death sentence to a convicted Canadian drug smuggler in a sudden retrial.
“Canada is fulfilling the terms of its extradition treaty but is paying an enormous price,” said Roland Paris, a former foreign policy adviser to Prime Minister Justin Trudeau. “This is the kind of world we’re living in now, where countries like Canada are at risk of getting squeezed in major power contests.”
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fortune--2020-01-20--What’s open (and closed) on MLK Day 2020?

The new year has been underway for less than three weeks, but Martin Luther King Jr. Day offers some people the chance to catch their breath. As the nation celebrates the Civil Rights leader, government organizations will largely be closed. But if you’ve got shopping to do, you likely won’t encounter too many hurdles.
Here’s a look at who’s open and closed on MLK Day 2020 and a few informational nuggets about the holiday.
What is Martin Luther King Jr. Day—also known as MLK Day?
Designed to honor Martin Luther King Jr., MLK Day became a federal holiday in 1983. It falls on the third Monday of every January and was the first federal holiday to honor an African American.
Are banks open on MLK Day?
Because MLK Day is a federal holiday, banks will be closed. You can, however, still use ATM machines to get cash or put money into your account.
Will there be any mail delivery on MLK Day?
Nope. The U.S. Postal Service does not operate on MLK Day. UPS and FedEx will both remain open, though, meaning you can expect package delivery as usual. Some FedEx Office Print & Ship Centers might be closed, so be sure to call before you head there.
Is the stock market open on MLK Day?
The New York Stock Exchange, Nasdaq and bond markets are all closed. They’ll reopen on Tuesday.
Are government offices open on MLK Day?
City, county, state and federal offices are generally closed.
Which stores are closed on MLK Day?
Generally most retailers are open on MLK Day. It’s not a big ‘sale’ day (like Memorial Day or July 4), but many stores are still in the process of clearing out inventory from the holiday period, so there might be a few deals. using the holiday as an opportunity to clear out summer merchandise (often at a notable discount). You shouldn’t have any trouble with your neighborhood Walmart, Target, Home Depot or Walgreen’s.
Similarly, big grocery chains, such as Harris Teeter, Kroger and Whole Foods, as well as restaurants are largely open. Some small businesses are closed on the holiday, however.
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fortune--2020-01-20--‘Catching up with the rest of world:’ China sets bans on single-use plastic

‘ May the best woman win : ’ New York Times endorses 2 Democratic candidates for president

China will curtail its consumption of single-use plastic in an effort to tackle a soaring amount of the discarded material that has quickly become one of the world’s most pressing environmental crises.
Non-degradable plastic bags will be banned in places such as supermarkets and shopping malls in major cities, as well as for the country’s ubiquitous food delivery services by the end of this year, according to a plan released by the National Development and Reform Commission on Sunday.
“China is catching up with the rest of world,” said Leiliang Zheng, an analyst at BloombergNEF. “The EU is the leader in solving the plastic crisis and has already passed a law to widely ban single-use plastic items in 2019, and many developing countries in Africa and Southeast Asia are also tracking the problem.”
About 300 million tons of plastic waste is generated each year, and 60% of that has been dumped in either landfills or the natural environment, according to a United Nations report. Whether it ends up in the ocean, a river or on land, plastic’s durability and resistance to degradation make it nearly impossible to completely break down, causing it to persist for centuries.
Regulations on single-use plastic are on the rise globally, according to a BloombergNEF report. France banned the use of plastic plates, cups, and cotton buds starting Jan. 1 with the goal of phasing out all single-use items by 2040. Thailand and New Zealand have both placed restrictions on or banned single-use plastic bags. An Indonesian ban comes into effect this June.
Many countries in Africa have implemented limits on the manufacture of plastic or attempted to restrict the consumption of the material through levies. Still, India held off imposing a single-use plastic ban last year over fear the policy would trigger an economic slowdown, according to BNEF’s Zheng.
Taking Measures
The use of plastic in China has risen as online shopping and food delivery apps have become part of everyday life, even in rural areas. Alibaba Group Holding Ltd., which organizes a 24-hour shopping marathon every year, has been criticized for shipping 1 billion packages in a single day.
The new policy may increase the costs for e-commerce platforms that will need to adjust their packaging strategies, according to Zheng, who added that alternatives to plastic such as biodegradable materials or recycled plastics are still more expensive.
China will ban non-degradable, single-use plastic straws nationwide by the end of 2020, it said, with the goal of reducing the “intensity of consumption” of such plastic utensils by takeout services in urban areas by 30% by 2025. By 2022, some delivery services in major cities including Beijing and Shanghai will be forbidden from using non-degradable packaging, with the ban extended to the whole country by 2025.
While China’s regulations are likely to slow the flow of plastic usage and improve the country’s recycling rate, the International Energy Agency said the initiative could be a headwind for the oil industry, which is expecting plastics and petrochemicals to comprise half of its long-term demand growth through 2050.
Several global oil majors, including Saudi Arabian Oil Co. and Exxon Mobil Corp., are investing in petrochemical plants in China to tap into that growing demand. Packaging accounts for more than a third of current plastics consumption.
“The new policy will suppress demand for plastics, a potential risk for oil and chemical companies,” Zheng said.
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fortune--2020-01-20--A.I. breakthroughs in natural-language processing are big for business

This article is part of a Fortune Special Report on Artificial Intelligence.
In October, Google announced the biggest change to the way its search engine works in five years. Given its centrality to Google’s business, the tech giant doesn’t tinker with its search algorithm lightly. But the new algorithm added capabilities Google had been trying to achieve for years without success.
Thanks to the overhaul, the algorithm finally understands how prepositions, such as “for” and “to,” alter meaning. A search for “2019 brazil traveler to usa need a visa” no longer returns, as it did previously, irrelevant results about Brazilian visa requirements for U.S. visitors. Searching “Can you get medicine for someone at the pharmacy” now returns results specifically related to picking up another person’s prescription—not just having one filled in general. Google says the new algorithm improves the results returned for 1 in 10 of its English language searches. That may not sound like much, until you realize Google handles 63,000 searches every second.
This big leap forward was made possible by revolutionary developments in a branch of A.I. called natural language processing (or NLP for short). NLP refers to software that can manipulate and to some degree “understand” language. (The extent to which the mathematical models that underpin NLP equate to human language “understanding” remains hotly contested). The current A.I. boom, which has been underway now for about a decade, was initially sparked by breakthroughs in computer vision—software that can classify and manipulate images. Scientists have tried to apply many of the same machine learning techniques to language, with impressive results in a few areas, like translation. But for the most part, despite the appearance of digital assistants like Siri and Alexa, progress in NLP had seemed plodding and incremental.
Over the past 18 months, though, computer scientists have made huge strides in creating algorithms with unprecedented abilities at a variety of language tasks. What’s more, these new algorithms are making the leap from the lab and into real products at a breakneck pace—already changing the way tech’s biggest players, and many other businesses, operate. The NLP revolution promises better search engines and smarter chatbots and digital assistants. It could lead to systems that automatically analyze—and maybe even compose— legal documents and medical records. But some believe the NLP revolution will do far more. They think better language understanding just might be the key to unlocking more human-like or, even superhuman, artificial general intelligence.
Ashish Vaswani doesn’t like to take credit for sparking the NLP revolution. In fact, when I catch up with the self-effacing 40-year old computer scientist at an A.I. conference in Vancouver in December, he is reluctant to speak unless I also interview the rest of his research team. “This isn’t about me,” says Vaswani, who works for Google’s A.I. lab, Google brain. His modesty notwithstanding, Vaswani has played a key role in the advancement of NLP. In 2017, he was the lead author on a research paper that transformed the entire field. Vaswani proposed and tested a new design for a neural network —a type of machine learning loosely based on the human brain. There are many different ways of configuring these networks. Vaswani and his team called their novel configuration, appropriately enough, a transformer.
Existing A.I. algorithms were pretty good at predicting the next datapoint in a sequence, but they had a critical weakness: they were lousy at long sequences, especially if the next datapoint was heavily dependent variables that occurred much earlier. This is frequently the case in language where, for example, the correct conjugation of a verb or gender of a pronoun at the end of a sentence can depend on a subject that occurs at the start of the sentence, or even several sentences back. The transformer, to a large degree, solved this problem.
The following year, Vaswani’s colleague at Google, Jacob Devlin, lead another team that took Vaswani’s transformer and trained it on the relationship between words in a massive dataset: 3.3 billion words from 11,000 English-language books as well as Wikipedia. The Google Brain researchers further honed the algorithm by teaching it to accurately predict a missing word in a sentence and, if given a sentence, to accurately identify the next sentence from two possible choices. Once trained, they tried the algorithm on a series of language ability tests. It came close to human performance on most of them, even when faced with tasks for which it had never been explicitly trained.
Devlin and his team called the new algorithm BERT. (Short for Bidirectional Encoder Representations from Transformers, the acronym continued an in-joke among NLP researchers of naming algorithms after characters from Jim Henson’s ouvre. There’s ELMo, Grover, Big BIRD, two ERNIEs, KERMIT and more. Collectively, they are referred to as “Muppetware.”) Google not only published its research, but also open-sourced the algorithm, allowing anyone to download it and then fine-tune it for their own specific purposes. That has spawned a wave of BERT-based innovations.
“That is literally the moment that changed this company,” John Bohannon, director of science at San Francisco technology startup Primer, says of BERT’s publication. Primer makes software that analyzes large datasets for customers that include big law firms, financial firms, and intelligence agencies. Difficult problems Primer once had—such as teaching a system how to determine whom the pronouns “he” and “she” refer to in a sentence when the primary noun wasn’t present—BERT can now handle with only a modicum of additional training. With just 1,000 labelled training examples, Bohannon said, it was now possible to achieve 85% accuracy on many business-specific NLP tasks, something that would have taken 10 times as much data previously. With BERT as a backbone, he says, Primer is working to create software that accurately summarizes complex documents, a Himalayan goal that has stumped NLP researchers for years.
Muppetware: A.I. researchers have named language algorithms after Bert, Ernie, Big Bird, and Elmo among others. Revierfoto/picture alliance/Getty Images
That improvement to Google’s search algorithm in October? That was BERT. Weeks later, Microsoft announced it was using BERT to power its Bing search engine too. At LinkedIn, search results are now categorized using a smaller version of BERT called LiBERT that company created and calibrated on its own data. It has helped increase engagement metrics from search results—such as connecting to a person on the professional network or applying for a job—by 3% overall, and clickthrough rates on online help center query results by 11%, says Ananth Sankar, the company’s principal staff engineer.
Like mechanics tuning a stock car, Facebook’s engineers also modified BERT—changing its training regimen, its training objective and training on more data for longer. The result is a model Facebook calls RoBERTa. And it has set RoBERTa on one of the company’s thorniest problems: content moderation. In 2018, Facebook was forced to admit its social network had been used to incite ethnic violence against Rohingya Muslims in Myanmar. Part of the problem: the company didn’t have enough people who spoke Burmese to screen the volume of content being posted for hate speech and disinformation. And, at the time, it couldn’t turn to existing machine translation algorithms because such systems required a large body of text in a target language to train effectively. Burmese is what is language experts call a “low resource language”—relatively few examples of translated Burmese text are available in digital form.
RoBERTa, however, offered a solution. Facebook took the algorithm and instead of having it learn the statistical map of just one language, tried having it learn multiple languages simultaneously. By doing this across many languages, the algorithm builds up a statistical image of what “hate speech” or “bullying” looks like in any language, Srinivas Narayanan, Facebook’s head of applied A.I. research, says. That means Facebook can now use automatic content monitoring tools for a number of languages, including relatively low resources ones such as Vietnamese. Burmese may be next. The company says the new techniques were a big reason it was able, in just six months last year, to increase by 70% the amount of harmful content it automatically blocked from being posted.
While Facebook and LinkedIn focused on paring BERT down to make it more efficient, other labs took transformers and scaled them up. OpenAI, the San Francisco-based A.I. research company, decided to see what would happen if—rather than using Wikipedia and a dataset of thousands of books to train its model, as Google did with BERT—it scraped 8 million pages from the Internet. Instead of looking at 340 million different variables, as the largest version of BERT did, OpenAI’s system considered 1.5 billion parameters. And the company only stopped there because that was the biggest algorithm it could fit on a single server, says Dario Amodei, OpenAI’s vice president of research. The result was GPT-2: an algorithm that can write several paragraphs of novel and mostly coherent prose from a human-authored prompt of a few sentences. (GPT-2 has been controversial, largely because OpenAI initially withheld public release of the full scale version of the model over concerns people might use it for malicious purposes, like automatically generating fake news stories. It later reversed that decision.)
Algorithms like GPT-2 could point the way towards much more fluent chat bots and digital assistants, with big potential implications for customer relationship management and sales, says Richard Socher, Salesforce’s chief scientist. “At some point, maybe we can automate certain parts of the conversation fully,” he says. Salesforce’s A.I. lab has created one of the largest language models published so far. Called CTRL, it is slightly larger than GPT-2 and gives users the ability to more easily control the genre and style of text the algorithm writes (hence the name.) As with BERT, Socher says one of the big benefits of CTRL is that a company can take the pre-trained model and, with very little data of their own, tune to their exact business needs. “Even with a couple thousand examples, it will still get better,” he says.
People have used these large language models in surprising ways. Microsoft took a version of GPT-2 and tuned it on lines of software code from Github, the code repository service it now owns. The result, called IntelliCode, works like the autocomplete function in gmail or Microsoft Office, only for code. OpenAI used the same underlying transformer as GPT-2 but trained it on music instead of text, creating MuseNet, an A.I. that generates four-minute composition for as many as 10 different instruments.
BERT and GPT-2 are creeping closer to what computer scientist Alan Turing first proposed in the 1950s as his test of whether a machine should be considered “intelligent.” (Turing said a machine should be considered intelligent if a person couldn’t tell if it were a machine or a person based on its written responses to questions.) And they may continue to make progress. “I don’t think we’ve seen the limits of what transformers can do for conversations and dialogue,” says Jerome Pesenti, the vice president for A.I. at Facebook.
But, as OpenAI’s Amodei readily admits, today’s NLP software is still very far from perfect. “The things it writes are not at the point where they are indistinguishable from a human,” he says of GPT-2. And Bohannon says that there are critical aspects of language that these transformer-based models don’t capture. One notable one: negation. “They don’t understand the word ‘not,’” he says. They also can’t follow logical chains that humans find trivial, Bohannon says. And computer scientists are still exploring the extent to which these large algorithms actually understand things like grammar.
Another big problem with BERT and its offspring: because they are pre-trained on lots of books, many of them written decades ago, they bake in historical biases, particularly around gender. Ask BERT to fill in the missing pronoun in the sentence, “The doctor got into ____ car,” and the A.I. will answer, “his” not “her.” Feed GPT-2 the prompt, “My sister really liked the color of her dress. It was ___” and the only color it is likely to use to complete the thought is “pink.”
Gary Marcus, an emeritus professor of cognitive psychology at New York University, who is a frequent critic of deep learning approaches like those that underpin BERT and GPT-2, says despite being trained on such large datasets struggle to keep track of numerical quantities, solve simple word problems, and exhibit little common sense. “The underlying representations are actually very superficial,” he says.
Interestingly, both Marcus and Amodei agree that NLP progress is critical if scientists are ever going to create so-called artificial general intelligence, or AGI. (That is the sort of human-like or superhuman intelligence that can perform a range of tasks.) And they think so for exactly the same reasons. Amodei says OpenAI wanted to create GPT-2 in the first place because it is interested in creating a better way for humans to interface with machines using natural language. That is important, Amodei says, so a human could help teach a future machine intelligence what to do—and just as critically what not to do. Marcus says that so much of the world’s knowledge exists in written form, any AGI would have to be able to read and understand what it was reading.
But the two disagree on how to get there. Amodei is convinced that larger and larger deep learning systems will be an important component in solving NLP, while Marcus thinks a hybrid approach that combines something like deep learning with rule-based, symbol manipulation, will be necessary.
David Ferrucci, the former IBM researcher who built and led the team that created Watson, the system that famously beat a human champion in gameshow Jeopardy!, has now dedicated himself to trying to build this kind of hybrid system. He is the founder and chief executive of Elemental Cognition, a Connecticut-based startup that was initially funded by hedge fund billionaire Ray Dalio’s Bridgewater Associates. It is trying to create human-like A.I. through a natural language understanding and dialogue system.
Elemental’s software is not a single algorithm. It is built from several disparate components—including pre-trained transformer-based language models, which it uses to extract information from texts and power its chatbot interface. Elemental’s system can take a simple story then ask a series of questions, through the chatbot, about it, which a human has to answer. The questions ensure the software has correctly extracted the subject and action of the story. But critically, Ferrucci says, the primary objective is to get the software to learn about how the world works, including causation, motivation, time and space. These answers are then encoded symbolically, in a rule-based form. “It is building causal models and logical interpretations of what it is reading,” says Ferrucci.
Ferrucci says Elemental’s software performs well on difficult NLP tests designed to require logic and common sense. He attributes this to the software’s ability to combine formal reasoning with the statistical power of transformer-based algorithms. But Elemental has yet to publish these results.
Of course, a big drawback of Ferrucci’s approach, even if it proves to work (and that is still a big if), is that depending on human instructors and a dialogue is slow. “We are trying to figure out how to scale this,” he says.
Ferrucci is convinced he is on the right track, however. The problem with most NLP research today, he says, is that it is essentially trying to reverse engineer language to get at the underlying concepts that generate it. But as Wittgenstein and other philosophers of language have long pointed out, language is fundamentally ambiguous. Two people don’t have the exact same representation of something in their head, even though they may use the same words for it. “Humans don’t even agree on most concepts,” says Ferrucci. “That is why you actually need dialogues, to establish a common interpretation.”
Simply building ever larger statistical models of language are unlikely to ever yield conceptual understanding, he says. Conversation on the other hand, just might.
No longer lost in translation
Here’s a quick look at a few of the algorithms powering the revolution:
BERT
Based on a new type of neural network called a “transformer,” developed at Google Brain, BERT is the algorithm behind the improved Google search. (Short for Bidirectional Encoder Representations from Transformers, the acronym continues an in-joke among NLP researchers of naming algorithms after Jim Henson’s creations: There’s also ELMo, Grover, Big BIRD, two ERNIEs, and a KERMIT. Together, they’re known as “muppetware.”)
Google not only published its BERT research but also open-sourced the algorithm. That quickly spawned a wave of BERT-based innovations. Microsoft is now using BERT to power its Bing search engine. At LinkedIn, search results are now more efficiently categorized using a stripped-down version of BERT called LiBERT. And Facebook has created a new algorithm called RoBERTa, designed to better identify hate speech and bullying including in languages, such as Burmese, for which there is less digital material to study.
GPT-2
San Francisco A.I. startup OpenAI trained this new NLP system on 1.5 billion language parameters scraped from 8 million Internet pages (versus the 340 million different variables used to train the largest version of BERT). The resulting algorithm can write several paragraphs of mostly coherent prose from a human-authored prompt of a few sentences—and could point the way to more fluent digital assistants.
CTRL
Salesforce’s A.I. lab has created one of the largest language models published so far. Slightly larger than GPT-2, it gives users the ability to more easily control the genre and style of text the algorithm writes (hence the name).
As with BERT, one of the big benefits of CTRL is that a company can take the pretrained model and, with very little data of its own, tune it to its business needs. “Even with a couple thousand examples, it will still get better,” says Salesforce chief scientist Richard Socher.
A version of this article appears in the February 2020 issue of Fortune.
This story has been updated to correct the spelling of Ashish Vaswani’s last name and to correct Jacob Devlin’s exact affiliation at Google. The original story misstated that Devlin worked for Google Brain. He works for Google’s A.I. Language research team.
More from Fortune’s special report on A.I.:
—Inside big tech’s quest for human-level A.I.
—Medicine by machine: Is A.I. the cure for the world’s ailing drug industry?
—Facebook wants better A.I. tools. But superintelligent systems? Not so much.
—A.I. in China: TikTok is just the beginning
—A.I. is transforming HR departments. Is that a good thing?
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fortune--2020-01-20--The big ambitions behind Fortune’s 90th anniversary redesign

A day before this issue of the magazine went to press in mid-January, I went to a hipster workspace in Manhattan to hear Microsoft CEO Satya Nadella talk about the future. Naturally, he began by talking about the past. When Nadella joined the company as a 25-year-old computer scientist in 1992, Microsoft’s stated purpose as a corporation was to put “a computer on every desk and in every home,” as founders Bill Gates and Paul Allen famously enshrined it.
Under Gates’ successor as CEO, Steve Ballmer, the mission statement went through a wordy rewrite—one that seemed to lose both its clarity and its real-world ambition. And not long after Nadella took the reins in 2014, the statement got another revise, one that has stuck until now: Microsoft’s mission is “to empower every person and organization on the planet to achieve more.”
“The sense of purpose and mission needs to be something you keep front and center,” Nadella said in his January remarks to a smattering of tech and business reporters. “Of course, you have to reinterpret this for what is a changing world”—an imperative that, it seems, is driving the CEO to rethink what Microsoft’s core aim is today, at the dawn of a new decade.
Indeed, it’s a worthwhile issue for every company and organization to ponder, and Fortune has been doing the same—a self-reflection timed to the start of our 10th decade. Yes, our sprightly magazine has just turned a youthful 90 years old.
Fortune’s inaugural issue in February 1930. Fortune
Fortune, as you’ll see with this issue and with our stories on Fortune.com, will continue to lead the business conversation. With the guiding wisdom of history and an unflinching eye to the future, we’ll report and reveal the stories that matter today—and that will matter even more tomorrow. That is the purpose that drives us. With the trusted power to convene and challenge those who are shaping industry and society around the world, Fortune will seek to light the path for global leaders—and give them the tools to make business better. (Fortune CEO Alan Murray, a seasoned editor himself, likes to shorten this to “making business better.”)
It sounds awfully highbrow, I know—and maybe just a bit precious. (Such is the risk when you put things down on paper: Just look back on the sweet sentiments you wrote in your high school yearbook.) But we mean it through and through. And, I’m thrilled to say, we’re investing like crazy in it.
In mid-January, we launched a new (and very fast) Fortune.com and an immersive video hub—which I hope you’ll spend hours exploring. Fortune chief technology officer Jonathan Rivers and his remarkable tech team—in partnership with digital edit leaders Andrew Nusca and Rachel Schallom, video czar Mason Cohn, and video strategist Gabe Boylan—spent months building them. (Coming soon: a new mobile app for iOS and Android.)
All of this, of course, comes with a quid pro quo. We’re asking our web readers to let us know who they are. For a while, we’ll continue to make our digital content free to everyone, but a paywall for nonsubscribers is coming soon. And the easiest way around that is to subscribe—which I hope you will do.
To that end, my colleagues and I present this reimagined print magazine as well—the most ambitious redesign in our history. We’ve upgraded the paper stock to showcase the striking design that creative director Peter Herbert has laid out so masterfully with art director Josue Evilla—and offers, in our view, a more generous template to the images that director of photography Mia Diehl and her colleagues have so carefully curated.
Our cover this month, illustrated by Craig & Karl, whose work has been exhibited at museums in Athens, New York, Paris, and Shanghai, is both a look back to Fortune’s many decades of bold cover design and a leap forward. (I can’t tell you how many hours the entire editorial team spent debating which of Craig & Karl’s compelling cover creations to run, so we ultimately opted for two. We hope you’ll mix and match at the newsstand.)
My editorial colleagues—led by deputy editor Brian O’Keefe, senior features editor Matt Heimer, features editor Kristen Bellstrom, the tireless and unflappable senior editors Daniel Bentley and Lee Clifford, and a host of others—have invested an unfathomable amount of time getting each story, large and small, to sing.
At the heart of each new issue you’ll find a deep exploration of a topic that every businessperson ought to care about. For February, we’ve chosen one of the more ineluctable aspects of technology today: artificial intelligence. Careful readers will get a glimpse of where Nadella seems to be leading Microsoft, whether by mission or by manifestation: figuring out new ways to use A.I. to empower customers and his company too.
In all, I might humbly suggest that our 90th anniversary issue embodies not only our new mission statement, but also our original one in 1930. “Fortune’s purpose is to reflect Industrial Life in ink and paper and word and picture as the finest skyscraper reflects it in stone and steel and architecture,” wrote founder Henry Luce. But above all, he said, we’ll make the discoveries of business clear, coherent, and vivid, “so that the reading of it may be one of the keenest pleasures in the life of every subscriber.”
Perhaps “one of the keenest pleasures in life” is too high a bar to hit. But we’ll keep aiming for it.
As always, please let us know what you think.
A version of this article appears in the February 2020 issue of Fortune with the headline “Mission Possible.”
More must-read stories from Fortune:
—Inside big tech’s quest for human-level A.I.
—A.I. breakthroughs in natural-language processing are big for business
—Facebook wants better A.I. tools. But superintelligent systems? Not so much.
—A.I. in China: TikTok is just the beginning
—A.I. is transforming HR departments. Is that a good thing?
Subscribe to Fortune’s forthcoming Bull Sheet for no-nonsense finance news and analysis daily.


fortune--2020-01-20--A.I. in China: TikTok is just the beginning

This article is part of a Fortune Special Report on Artificial Intelligence.
Andy Warhol was wrong. The pop artist famously asserted that in the future everyone would be famous for 15 minutes. But what Warhol failed to imagine back in the 1960s was TikTok, the dangerously addictive video-sharing app that today doles out global renown in 15-second increments.
With some 1.5 billion downloads worldwide—including 124 million in the U.S. as of November—TikTok is racing ahead in the battle for eyeballs, where its short clips compete with the likes of Facebook, Instagram, Twitter, and YouTube. In the world of TikTok, a group of tweens doing a choreographed dance routine can go viral overnight; a dancing ferret can garner millions of likes; or a clip of a man shaking his groove thing in a plushy rabbit costume might turn into a meme. (There’s a lot of dancing on TikTok.)
While the content that powers TikTok is created by the app’s obsessive user base, the company’s A.I.-powered recommendation algorithms—which customize picks for users with increasingly uncanny insight—are what makes the platform so truly enthralling. That secret sauce is provided by TikTok’s parent company, Beijing Bytedance. And thanks to TikTok’s runaway success, Bytedance has emerged as a somewhat unlikely player in another global contest: the A.I. Arms Race.
The privately held Bytedance was founded in 2012 by former Microsoft engineer Zhang Yiming. Six years later, a $3 billion fundraising round led by SoftBank valued Bytedance at $75 billion, ranking it among the world’s most valuable startups. That was before TikTok crossed the billion-user threshold.
Given its prowess in a hot technology like artificial intelligence and its wild popularity outside China, Bytedance should, in theory, be a darling of policymakers at home. Instead, it often appears to be out of favor with the government. The company has been made to apologize several times in recent years owing to content shared over its various apps. (In addition to TikTok, Bytedance owns other video-sharing platforms and a news-aggregating app called Toutiao that utilizes A.I. in a way similar to TikTok.)
People in costume posing at a TikTok Creator’s Lab event in Tokyo. Shiho Fukada—Bloomberg via Getty Images
There are other reasons that Bytedance might not have been Beijing’s first choice to carry the banner for Chinese A.I. The central government intends for artificial intelligence to become a long-term driver of economic supremacy, a means for social governance, and a key tool in developing greater military strength. In short, Beijing has a plan for what A.I. development should look like in China—and frivolous videos are not a core part of it.
But if Bytedance is in some ways an outlier in the fast-growing Chinese A.I. ecosystem, in other ways it is a model for the sort of unpredictable success that’s likely to be replicated given the vast sums of public and private capital flowing into both primary A.I. research and A.I.-based startups in China.
“The industry is definitely hot right now, and I don’t think it will abate,” says Henrik Bork, managing partner of Beijing-based consultancy Asia Waypoint. “We are only just beginning to see A.I. getting started in China.”
It was a board game that helped kick off the current A.I. boom in China. In 2016, Lee Sedol, a South Korean master in the ancient Chinese game of Go, played a best-of-five-game competition against an A.I. system developed by DeepMind, an A.I. startup owned by Google parent company Alphabet. The object of Go is to surround more territory than your opponent on a board of 324 squares by laying down pebble-like pieces, and it’s considered to be exponentially more complex than chess. Some 280 million people in China tuned in to watch Lee’s televised showdown with the algorithm, called AlphaGo. Lee was confident he would beat the algorithm. He lost all but one game.
For policymakers in Beijing, witnessing an A.I.-powered Western computer defeat a human in the ultimate Chinese game was a wake-up call—described by many as a “Sputnik moment” for A.I. in China. According to Luciano Floridi, the director of the Digital Ethics Lab at Oxford University, before 2017 A.I. was treated by Beijing as just one among many technologies in which China should improve its expertise. That soon changed.
The following year Beijing released the Next Generation Artificial Intelligence Development Plan (AIDP). The pivotal policy document singled A.I. out as a strategic technology and set a target for China to become the world leader in A.I. by 2030, with A.I. contributing $150 billion to Chinese GDP.
Then Beijing did what it typically does when it wants to kick-start a given industry: It engaged in a bit of central planning.
In 2017, the central government selected five leading tech companies as “national champions” in A.I., instructing each to pursue a specific avenue of A.I. research. In return for picking up the mantle, the champions receive government support, such as access to finance, preferential contract bidding, and sometimes even market share protection. China’s leading search engine provider, Baidu, was tasked with developing autonomous driving, for example. E-commerce giant Alibaba, meanwhile, has been charged with advancing technology needed to build smart cities. In the past few years, the number of A.I. champions has risen to at least 15.
Social Mania: TikTok has proved to be a global phenomenon, minting video stars and spawning new business ideas, in December at the “Hype House” in L.A. Michelle Groskopf—The New York Times/Redux
The pairings between champion and designated area of expertise aren’t always as random as they might seem. Even before the AIDP was released, Baidu was investing heavily in autonomous vehicles (AVs). The year before that, Alibaba launched its smart-city software service, City Brain, which uses big-data analysis to assist with urban planning and management.
Despite the big policy push from Beijing, China’s approach to A.I. is only semi-centrally planned. Plenty of the heavy lifting is being left to local governments. Some $15 billion of new funding for A.I. has been pledged by Shanghai. Tianjin, another coastal metropolis, is raising $16 billion in funds for A.I. And the city of Beijing is currently building a $2.2 billion industrial park for A.I. research.
“There’s a lot of bottom-up,” says Jeffrey Towson, a private equity investor and former professor at Peking University. “There are a lot of entrepreneurs, lots of venture capital, and half the time the government is trying to figure out what’s going on.”
Just as AlphaGo’s victory caught the attention of Chinese officials, the dollar amounts being invested by the Chinese to pursue their A.I. goals have set off alarm bells in the U.S. amid concern that Washington isn’t investing enough in the technology. In November, for instance, Congress’s National Security Commission on Artificial Intelligence, chaired by former Google CEO Eric Schmidt, published an interim report in which it warned that China is outpacing the U.S. in A.I. spending. That was followed by a report from the Center for a New American Security urging the U.S. government to fund $25 billion of R&D in A.I. At a debate in December, Democratic presidential candidate Andrew Yang warned that China is “leapfrogging us in A.I. because they have more data than we do and their government is subsidizing it to the tune of tens of billions of dollars.”
The Trump administration, meanwhile, has imposed export controls on several categories of A.I. technology in an attempt to curb the flow of key A.I. intellectual property to China. But the Chinese are making gains in the research arena as well. Judging by the number of A.I. patent filings and published academic papers on A.I., China is now neck and neck with the U.S. (See graphic below.)
China’s rapid progress in A.I. has done much to even the playing field with the West. But in many fundamental ways China still has ground to make up. Jeffrey Ding, a researcher at Oxford’s Future of Humanity Institute who studies China’s A.I. strategy, recently conducted a systematic comparison of the U.S. and China A.I. ecosystems. “My conclusion was that the U.S. is still far ahead,” says Ding.
One area in which the U.S. has a clear lead is in hardware. China currently consumes 58% of the world’s semiconductor supply. But according to the Center for Strategic and International Studies (CSIS), only 16% of chips used in China are produced within the country, and only half of those are made by Chinese companies. Beijing has a plan to meet 70% of its domestic demand by 2025 and has pledged more than $100 billion to that end. However, China’s leading chipmaker, Semiconductor Manufacturing Industry Corp., still lags far behind its Western rivals. SMIC began producing 14-nanometer chips only last year; industry leaders have all moved on to 7-nanometer chipsets. (In chips, smaller is better.) “China still depends heavily on Western technology,” says Lian Jye Su, principal analyst at ABI Research in Singapore. “That explains the sense of urgency from Beijing to accelerate China’s R&D into these sectors.”
One area in which the U.S. and China have begun to square off: the war for talent. In November, a Senate Committee on Homeland Security and Governmental Affairs labeled China’s talent acquisition strategies—the committee counts some 200 plans—a threat to national security, claiming that “China unfairly uses the American research and expertise it obtains for its own economic and military gain.” The report notes that the U.S. lacks a “comprehensive strategy to combat this threat” of brain drain.
One of the factors most often cited when discussing China’s advantages in developing artificial intelligence is its massive treasure trove of data. A.I. algorithms feed on data in order to learn. And it’s certainly true that, for the right company, data is available in abundance in China. That’s not only because of the country’s roughly 1.4 billion people, generating multitudes of data points, but also because of a, shall we say, cooperative regulatory environment. And China has a massive video surveillance system with millions of A.I.-enabled cameras.
In this setting, the development of facial recognition technology has thrived. China is home to two of the world’s most valuable computer-vision companies—Hong Kong–based startup SenseTime, which is valued at more than $7 billion, and Beijing’s Megvii, which is valued at just over $4 billion and plans a Hong Kong IPO this year. Megvii operates the world’s largest open source database for training other facial recognition algorithms, Face++, which has over 300,000 users. It has reportedly used government data banks to help compile its training program.
China has faced criticism for its use of facial recognition to track citizens—particularly the Muslim Uighur population—and its experiments with a “social credit system” that would incorporate facial recognition to monitor behavior. As of December, anyone purchasing a new SIM card in China is required by law to submit to a facial recognition scan in order to confirm their identity.
Revelers at a TikTok party at NeueHouse Hollywood. Joe Scarnici—Getty Images
But a massive library of its citizens’ faces alone doesn’t assure China of a long-term advantage in A.I. more broadly. “I think a lot of the arguments about how China’s data advantage means it’s going to be ahead on deploying A.I. applications are overstated,” says Oxford’s Ding. “More facial recognition data, for instance, doesn’t benefit other verticals, like smart manufacturing, precision medicine, or autonomous vehicles.”
Given all the anxiety in the West about China’s A.I. ambition, even TikTok has come under scrutiny. In November, the Committee on Foreign Investment in the United States (CFIUS) opened an investigation into whether TikTok posed a national security threat, prompted by reports alleging that TikTok shares user data with the Chinese government. Bytedance, which declined to be interviewed for this article, has denied the accusations and says its U.S. data is stored on servers in either the U.S. or Singapore.
But for most of TikTok’s billion-plus users, such concerns aren’t on their radar. They are focused on chasing fame, 15 seconds at a time.
The object detection and tracking technology developed by SenseTime Group Ltd. is displayed on a screen at the Artificial Intelligence Exhibition & Conference in Tokyo, Japan, 2018. Kiyoshi Ota—Bloomberg via Getty Images
National champions of A.I.
When China proposes a national policy for industrial advancement, it doesn’t leave success to chance. Instead, the government selects leading enterprises—state-owned or otherwise—and designates them “national champions.” Those selected are expected to work toward the government’s goals and are often rewarded with preferential policy treatment and easier access to financing. In 2017, Beijing selected five companies to spearhead its A.I. program. That number has grown to at least 15. Here are five of the most important:
1. Champion: Baidu
Arena: Autonomous driving
Stats: “China’s Google” launched an open source platform for autonomous driving software, called Apollo, in 2017. Baidu stylizes Apollo as the equivalent to Google’s Android operating system, but for autonomous vehicles (AVs).
2. Champion: Tencent
Arena: Computer vision for medical diagnoses
Stats: The operator of the WeChat super-app has been an active investor in digital health care since at least 2014. Its Medical A.I. Lab is currently working on a computer vision system designed to diagnose Parkinson’s disease.
3. Champion: Huawei Technologies
Arena: A.I. infrastructure and software
Stats: Huawei, the global telecom equipment giant that has been embroiled in the U.S. vs. China trade battle, was added to China’s roster of A.I. national champions last September. It is tasked with boosting “device, edge, and cloud” capacity.
4. Champion: Alibaba Group
Arena: Smart city initiatives
Stats: China’s largest e-commerce conglomerate has pursued its designated mission by developing the City Brain initiative, which uses cloud computing and big data to improve urban planning and is deployed in 23 cities including Kuala Lumpur.
5. Champion: SenseTime
Arena: Computer vision
Stats: SenseTime began as an academic project at the Chinese University of Hong Kong in 2014 and is now a leader in the field of computer vision with a valuation of over $7 billion. Its technology is used in some government surveillance projects.
A version of this article appears in the February 2020 issue of Fortune with the headline “TikTok: China Starts the Clock.”
More from Fortune’s special report on A.I.:
—Inside big tech’s quest for human-level A.I.
—A.I. breakthroughs in natural-language processing are big for business
—Facebook wants better A.I. tools. But superintelligent systems? Not so much.
—A.I. is transforming HR departments. Is that a good thing?
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fortune--2020-01-20--Inside big tech’s quest for human-level A.I.

This article is part of a Fortune Special Report on Artificial Intelligence.
Last July, Satya Nadella, the CEO of Microsoft, one of the world’s most valuable companies, with a market capitalization hovering above $1 trillion, filmed a short video with Sam Altman, the 34-year-old entrepreneur best known for his stint running Silicon Valley’s preeminent startup accelerator, Y Combinator.
The setup for the three-minute segment, which Nadella posted to his LinkedIn profile, bore an uncanny resemblance to an episode of the online video series Between Two Ferns, which features comedian Zach Galifianakis conducting intentionally awkward interviews with A-list guests like Barack Obama and actress Charlize Theron. In this version, Nadella and Altman were positioned in modest-size chairs against a black background. Between them was a small coffee table with two glasses of water and a tiny plant perched on top. The 52-year-old Nadella played the role of host, posing questions to Altman while holding a notecard in his hands.
But Nadella wasn’t going for laughs. Rather, the purpose of the video was to discuss a major milestone for both executives: Microsoft’s $1 billion investment in the San Francisco–based startup Altman currently runs, OpenAI.
Within Silicon Valley, AGI is a kind of religious argument. You’re either a believer or you’re not.
CHRIS NICHOLSON, COFOUNDER & CEO OF PATHMIND
“So, our mission is to develop artificial general intelligence, broad A.I. systems that can do a lot of tasks at superhuman level,” Altman explained to Nadella. “I think that this will be the most important technological development in human history. When we have computers that can really think and learn, that’s going to be transformative.”
With Microsoft’s headline-grabbing investment in OpenAI, Nadella signaled his company’s commitment to that mission. And from a strategic point of view, he officially entered Microsoft in a technological arms race against Alphabet, Google’s parent company, and a handful of others competing to develop technology that could radically reshape the business world. The outcome of the race could well determine whether Microsoft, Alphabet, or someone else is the world’s most valuable company in 20 years’ time.
Nadella’s decision to invest in OpenAI was also a subtle acknowledgment that his company’s own internal efforts to stay at the bleeding edge of A.I. technology were falling short. Microsoft needed to catch up.
“This is about capturing the next great pool of wealth in technology,” says Craig Le Clair, an analyst at Forrester Research, the tech analytics firm. He compares A.I. to electricity in its potential impact. Sundar Pichai, Nadella’s rival CEO at Alphabet, has gone further, calling A.I. the most important project humanity would ever work on, “more profound than fire.”
Imagine being able to monetize the invention of fire. Now imagine missing out on the chance to monetize fire.
OpenAI was founded in 2015 by, among others, Altman and Elon Musk, the billionaire founder of Tesla. While OpenAI’s goal is to develop artificial general intelligence (AGI), the company says it is dedicated to ensuring the technology is developed in a way that “benefits all of humanity.” For that reason, OpenAI was initially set up as a nonprofit corporation. Last year, however, the company established a for-profit arm, the entity in which Microsoft invested. The terms of the deal make Microsoft the preferred partner for OpenAI to commercialize any technology it develops and chooses to license on the path to AGI.
The term “general” in the name artificial general intelligence is meant to differentiate it from more prosaic “narrow” artificial intelligence. It is narrow A.I. that in recent years has brought us breakthrough tech such as Alexa and Siri; the ability to unlock your iPhone with your face; and Facebook’s auto-tagging of your friends in the photos you upload. Narrow A.I. systems also route Amazon orders to your home and decide which agent handles your customer service call to your bank.
The same underlying breakthroughs in algorithms, data science, and computing are responsible for much of the current excitement about both types of A.I. But the two are distinct in their capabilities, and at the moment only narrow A.I. actually exists. AGI is a purely theoretical technology.
Narrow A.I. is often compared to an idiot savant—it’s good only at a specific skill, like recognizing speech or identifying faces, and today it requires many thousands or millions of examples to learn that skill well. Even so, these systems are incredibly valuable—and are only getting more so. The McKinsey Global Institute estimates that the application of narrow A.I. will add some $13 trillion to the global economy by 2030, an amount that it says would make the technology more impactful than the steam engine was in the 1800s.
But AGI would be many times more valuable still. AGI is the A.I. of Hollywood and sci-fi paperbacks. If it ever happens, it would make all the technological wonders of today’s narrow A.I. look as quaint as Stone Age ax heads. AGI promises a single piece of software capable of learning almost any task at human or superhuman level—a system that can master new skills quickly, perhaps by watching a single demonstration or just by reading, with no training at all, and maybe entirely at its own initiative.
Imagine that rather than assign a 15-person task force to decide where your company should build a new factory, you simply ask your company’s AGI. The system would immediately begin researching decision factors: proximity to suppliers and customers, transportation links, land-acquisition costs, local labor markets, tax incentives, etc. It would make a recommendation and produce a report explaining its reasoning. And it would do all this in minutes, not the weeks or months it would take the human task force. Then, if management agreed, it would instantly generate all the relevant work orders to start the process.
It is impossible to overestimate how valuable such a system would be to Microsoft or any other company that developed it. (It might also pose an existential threat to highly paid advisers, such as the McKinseys of the world.) OpenAI has capped the return its initial financial backers can earn at 100 times their investment, with the rest of the money flowing to the organization’s nonprofit. (Microsoft and OpenAI won’t disclose the exact cap Microsoft has agreed to.) Of course, as with nuclear power, such superintelligence might also be dangerous, as Musk himself has famously warned.
AGI has long been fodder for novelists, filmmakers, philosophers, and futurists. It has been the implicit, and at times explicit, goal of an entire branch of computer science, at least since the 1950s. But AGI was always a research project. It was never a business plan—until now.
Big Tech has begun spending big bucks in its quest for AGI. Microsoft and Alphabet each sponsor not one but two separate R&D entities largely dedicated to developing advanced A.I. Facebook has invested in a blue-sky A.I. lab. Chinese search giant Baidu has one too. And smaller labs exist at Uber, Salesforce, and others. Investment in AGI is forecast to reach $50 billion by 2023, according to a report from Seattle-based research firm Mind Commerce.
This investment has come despite the view of many computer scientists that AGI is still, at best, decades away. But for the world’s biggest technology companies, AGI is a race they can’t afford to lose—even if it turns out no one ever wins. “It’s about enhancing the perception of being a technology leader and innovator and being at the forefront,” says David Smith, an analyst for emerging technologies at research firm Gartner. That perception helps sell cloud-computing services and recruit engineering talent. But AGI isn’t merely about playing defense—research toward AGI feeds progress in narrow A.I. “The thing about A.I. is when you work to push forward the research, the downstream applications are incredible,” says Mark Cuban, the billionaire tech entrepreneur and owner of the Dallas Mavericks, who has invested in a handful of A.I. startups.
On a summer’s evening in 2015, Altman, who was running Y Combinator at the time, invited Musk to dinner at the Rosewood Sand Hill hotel, in the heart of Silicon Valley. The hotel, a luxurious stone ranch offering views of the foothills of the Santa Cruz Mountains, was a comfortable enough spot from which to contemplate Armageddon—and how to potentially stop it.
Musk’s views about the dangers of AGI had been informed by his involvement as an early investor in an unusual London-based startup called DeepMind. Founded in 2010, the company is led by Demis Hassabis, a former chess prodigy turned video game entrepreneur, who has an undergraduate degree in computer science and a Ph.D. in cognitive neuroscience. His intuition is that by drawing inspiration from the way the human brain works, DeepMind can achieve AGI. DeepMind’s mission statement is so audacious it borders on the ridiculous: “to solve intelligence, and then use that to solve everything else.”
It began to look less ridiculous though, in January 2013, when DeepMind stunned computer scientists by debuting an algorithm that had taught itself to play seven different classic Atari video games, such as Pong, Space Invaders, and Breakout, achieving superhuman performance in three of them. The breakthrough resounded through Silicon Valley like the crack of a starter’s pistol: The race for AGI was on, and the Valley’s digital giants were desperate to get in on it.
In 2014, Google, which already had its own advanced A.I. research lab called Google Brain, acquired DeepMind for a reported $650 million, a massive sum for a company that didn’t have a single product or a dollar of revenue. Meanwhile, Facebook, which had also been in the hunt to buy DeepMind, established its own advanced artificial intelligence research lab headed by Yann LeCun, one of the field’s top researchers.
The thing about A.I. is when you work to push forward the research, the downstream applications are incredible
MARK CUBAN, BILLIONAIRE TECH ENTREPRENEUR AND INVESTOR IN A.I. STARTUPS, ON THE VALUE OF WORKING TO DEVELOP AGI
DeepMind’s acquisition alarmed Musk, even though he made money from it. Shortly after the deal was announced, he wrote a blog post warning, “The pace of progress in artificial intelligence (I’m not referring to narrow A.I.) is incredibly fast. Unless you have direct exposure to groups like DeepMind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five year timeframe. 10 years at most … This is not a case of crying wolf.”
Musk is friends with Google cofounder Larry Page. But he told journalists he feared Page’s company might succeed in creating superhuman intelligence, and then lose control of it. Even if that didn’t happen, Musk said he worried about a single corporation controlling such a powerful technology.
At dinner, Altman introduced Musk to a 29-year-old computer scientist named Ilya Sutskever, who was at the time working at Google Brain. Despite his age, Sutskever was already a legend among A.I. researchers: In 2012, A.I. software he helped create achieved an unprecedented score on ImageNet, a test that assesses an A.I.’s ability to identify pictures of 1,000 different types of objects. Also at the dinner was Greg Brockman, a 26-year-old coding whiz who had recently left his job as the chief technology officer at the payments processing unicorn Stripe. Together, they were hoping to secure Musk’s backing for a new kind of A.I. organization, one dedicated to open research and free from control by any single corporation.
The dinner led to the formation later that year of OpenAI. Musk joined its board and was listed as a cofounder. A set of initial donors—including, among others, Musk, Altman, Brockman, and Musk’s fellow PayPal alums billionaire Peter Thiel and LinkedIn cofounder Reid Hoffman—pledged $1 billion to support the research group. Sutskever came on board as OpenAI’s chief scientist.
Shortly after he was appointed CEO of Microsoft in 2014, Nadella moved to reposition his company around artificial intelligence. Nadella declared that all of Microsoft’s products and services would be “infused with A.I.” and called A.I. one of three fundamental technologies that will shape the future (the other two being “mixed reality” and quantum computing). The CEO saw enormous potential for A.I. across Microsoft, including in the office productivity software and cloud-computing services that, together, made up two-thirds of Microsoft’s revenues. It was not ground Nadella wanted to cede to Google or other tech rivals.
Microsoft had a long-standing research organization, with labs around the world, dedicated to state-of-the-art technologies, from virtual reality to cybersecurity, and yes, to A.I. But, as a company, Microsoft had mostly been interested in “augmenting human intelligence”—in other words, narrow A.I. Microsoft’s labs hadn’t produced the kind of flashy breakthroughs that DeepMind and Google Brain had. The company sometimes gave the impression AGI was a quixotic quest not worth pursuing.
But sitting out the AGI race presented Microsoft with a problem. The buzz around a series of breakthroughs at DeepMind and Google Brain created a perception that Alphabet was also leading the pack in narrow A.I. applications—giving Alphabet an edge in hiring the best researchers out of academia and potentially in selling cloud services too. This perception was further cemented in 2016 when DeepMind’s A.I. algorithm AlphaGo defeated the world’s best player in the ancient strategy game Go. Most A.I. researchers had thought it would be at least another decade before a system could conceivably equal humans at the game, which has exponentially more possible move combinations than chess. (Or Pong.) “The acquisition of DeepMind was the best marketing spend Google ever made,” says Chris Nicholson, the cofounder and CEO of Pathmind, a San Francisco company that helps businesses implement A.I.
Nadella had to do something to boost Microsoft’s A.I. bona fides. In 2016 he restructured the company’s research efforts, establishing a separate organization focused solely on A.I. research and applications of A.I. in Microsoft products like its Bing search engine and Cortana digital assistant. The CEO also began convening a weekly meeting of his top executives to discuss progress on the company’s A.I.-related projects. But those were incremental changes. Microsoft still lacked an A.I. moonshot.
Operating out of a three-story, gray-sided, pre-earthquake building with loft-like interiors in San Francisco’s Mission District, OpenAI now employs 120 researchers. In the past year, the group has made a series of announcements around “grand challenges,” designed to showcase its own progress toward AGI—and raise its public profile.
It created a team of five A.I. software bots capable of playing together in the video game Dota 2, which is frequently used in professional e-sports tournaments. OpenAI’s five bots defeated the reigning champion human team in a best-of-three demo match in San Francisco in April.
Separately, OpenAI revealed a language algorithm capable of taking a few human-written sentences and then riffing on them, generating several paragraphs of coherent prose—a significant leap forward in the field of natural-language processing.
Finally, in October, the company debuted a human-like robotic hand that could unscramble a Rubik’s cube. Roboticists have struggled for years to mimic the dexterity of a human hand. OpenAI’s hand learned the task well enough that, more often than not, it could unscramble the cube without dropping it.
The three announcements show a lot about how OpenAI thinks it can get to AGI—and also why the organization has become a lightning rod for criticism.
But first, some background: The current push for AGI, along with the rest of the current A.I. boom, is built on neural networks—a kind of software loosely based on the human brain. Arrayed in multiple layers, these artificial neurons convert some raw input, such as the pixels in an image, to some output, such as the label “cat.” Because of the many intermediate layers of artificial neurons these networks rely on, they are said to be “deep,” and using them to perform a task is called deep learning.
Current research into AGI largely divides into two camps: those who believe deep learning alone will be sufficient to achieve AGI vs. those who think it will have to be combined with something else, such as logical rules. Within the deep learning camp, there are further divisions: one tribe emphasizes algorithmic innovation. The other puts more focus on the sheer size of the neural networks they are building and the amount of data they are fed. OpenAI is firmly in the “size matters” society.
OpenAI’s signature achievements have all involved huge models, consuming massive amounts of computing power. Each of its five Dota2 bots, for example, was controlled by an algorithm that took in 159 million different parameters, or data variables. Over their 10 months of training, the bots racked up the equivalent of 45,000 human-years’ worth of Dota2-playing experience.
Altman told Nadella during their video chat in July that “increasing the size of the largest models we can train, keeps letting us solve seemingly impossible tasks.” Altman, Brockman, and Sutskever have all said building ever-larger neural networks is an important avenue to explore for making progress toward AGI.
Few researchers outside the company agree with OpenAI’s thesis, however. Gary Marcus, an emeritus professor of psychology and neuroscience at New York University who is now CEO of startup Robust AI, says there is no evidence larger neural networks will suddenly begin to exhibit human-like skills, such as commonsense reasoning or conceptual thinking. “This is to ascribe to deep learning the quality of magic,” he says. He says OpenAI has failed to show its systems can build representations of the world. “If you can’t do that, you’re not going to get to general intelligence,” he says. Ben Recht, a computer scientist at the University of California at Berkeley, is even more scathing in his assessment of OpenAI’s approach. “Have these guys never heard of the law of diminishing returns?” he says.
Another major criticism of OpenAI is that, desperate for attention, it has unreasonably hyped its accomplishments. When it announced its language algorithm, GPT-2, OpenAI told journalists it was withholding publication of the most powerful version of the software out of concern it could be abused to create fake news and disinformation campaigns. But a number of computer scientists accused OpenAI of exaggerating the risk in order to garner publicity. (Nine months later, the company did, in fact, release the full-scale model, saying it had seen little evidence that the less powerful versions it had made public had been abused.)
Zachary Lipton, a professor at Carnegie Mellon University who has become a vocal critic of OpenAI, accuses the company of doing research that is largely similar to others’ in the field but engaging in “aggressive press manipulation” in order to raise money. “They need to maintain the illusion at all times that something world historic is taking place and that they are at the center of it,” he says. This results in marketing that Lipton says is “unethical and irresponsible.”
In an emailed response, OpenAI spokesperson Ashley Pilipiszyn denied the company engages in manipulative marketing practices and says that the firm should be judged “by the impact of our work,” and “not by what we (or anyone else) says.”
There’s one thing about OpenAI’s approach that’s not in dispute: It is expensive. Bigger models require more computing power—which OpenAI has to lease from a cloud service provider. Top A.I. researchers, meanwhile, command six- and sometimes seven-figure salaries. While OpenAI hasn’t revealed its burn rate, its rival DeepMind, which now employs about 900 people, racked up $746 million in administrative expenses, including staff and computing costs, in 2018 alone, according to U.K. financial filings.
“The amount of money we needed to be successful in the mission is much more gigantic than I originally thought,” Altman told Wired magazine last year. Complicating matters, OpenAI lost one of its biggest supporters: Musk. The billionaire stepped down from OpenAI’s board in early 2018, citing the demands of running Tesla and SpaceX and conflicts of interest as Tesla increasingly moved into A.I. and sought to recruit the same researchers as OpenAI.
In need of more cash, OpenAI’s board decided to radically transform its structure: In March, Altman announced the creation of OpenAI’s for-profit arm. The new structure allows OpenAI to take on venture investment. Crucially, it also gives the group the ability to issue stock options to attract and retain top computer scientists. Reid Hoffman’s charitable foundation and Khosla Ventures, a prominent Silicon Valley venture capital firm, became the for-profit’s first investors, injecting unspecified amounts. Then, in July, Microsoft put in its $1 billion.
Some of that cash will come back to Microsoft as OpenAI buys data center time from the company’s cloud-computing arm, Azure, which it has agreed to use exclusively. What the investment does not do is give Microsoft ownership rights to AGI—if OpenAI is successful in developing it. That will remain the property of the nonprofit part of OpenAI, which has also retained voting control of its for-profit wing. (If that makes you wonder if Microsoft’s investment is really about AGI after all, you aren’t alone.)
Both Microsoft and OpenAI declined to allow their executives to be interviewed about their partnership for this story. But a look at how Google has benefited so far from Google Brain and DeepMind provides a glimpse of what Microsoft stands to gain, even if it never gets its hands on AGI.
Algorithms that Brain has developed have helped improve Google’s search engine, Google Translate, Google Maps, and its cloud-computing infrastructure. “Those kinds of things are really valuable for the company,” says Jeff Dean, the senior software engineer who helped found Google Brain and now heads all A.I. research at Google.
DeepMind, meanwhile, has an entire group called DeepMind for Google (or DMG for short), responsible for collaborating with its sister company and other parts of Alphabet. “We don’t choose product problems and then work out how to fix them,” says Koray Kavukcuoglu, DeepMind vice president of research. But if DeepMind’s research happens to be useful to a problem another Alphabet company is working on, DMG will often collaborate on a solution. In 2016, DeepMind said it had helped Google figure out a better way to manage the cooling systems in the company’s data centers, reducing its cooling bill by 40%. It later used a version of this algorithm to help extend the battery life of Android phones. In 2017, a DeepMind algorithm became the engine behind the computer-generated voice of Google’s digital assistant.
The biggest question about the corporate race for AGI is whether the large technology companies funding it actually believe—or even care—if creating human-like or superhuman intelligence is possible. “Within Silicon Valley, AGI is a kind of religious argument,” says Pathmind’s Nicholson. “You’re either a believer, or you’re not.”
Josh Tenenbaum, a professor of computational cognitive science at MIT, is a believer: He runs a lab focused on reverse-engineering human intelligence and building more human-like A.I. But, like many in the field, Tenenbaum thinks AGI is “very far away.” And he is among those who think the big corporations competing in the race for AGI are not being fully transparent about their motivations.
While there are certainly researchers at DeepMind, Google Brain, and OpenAI who genuinely want to achieve AGI, companies such as Alphabet or Microsoft, in Tenenbaum’s view, care mostly about advances in narrow A.I. They’re focused on getting better tools for building a range of narrow systems—such as algorithms to spot credit card fraud or recognize faces or parse legal documents. These narrow systems can be used internally as well as sold to cloud-computing customers.
Microsoft’s partnership with OpenAI certainly has the potential to yield such innovations. The two companies have committed to helping Azure build better supercomputing capabilities, including the development of new chips designed to make the training and running of A.I. systems more efficient.
Whatever Nadella’s true goals may be for the OpenAI deal, by making a 10-figure investment the CEO has put down a marker in the world of A.I. research. His company has joined the race for AGI. Even if Microsoft doesn’t win, it may be the best $1 billion investment he ever made.
DeepMind’s AlphaStar A.I. ranks in the top 99.8% of the world’s players in the complex, real-time strategy video game Starcraft 2, showing a mastery of both long-term strategy and arcade-style tactical battles. Courtesy of DeepMind
Shall we play a game?
Games have long been used as mile markers in the evolution of A.I. because they present intellectual challenges in a simplified setting. Here are a few notable wins for the computers:
2015: Atari
Expanding on its previous work with the game system, DeepMind demonstrates an A.I. capable of mastering 49 Atari games—from Pong to Space Invaders— many to superhuman ability with just a few hours of training.
2017: Poker
Libratus, a poker-playing A.I. created by researchers at Carnegie Mellon University, defeated four pros in no-limit Texas Hold’em.
2019: Starcraft 2
DeepMind’s AlphaStar A.I. ranks in the top 99.8% of the world’s players in the complex, real-time strategy video game Starcraft 2, showing a mastery of both long-term strategy and arcade-style tactical battles.
A version of this article appears in the February 2020 issue of Fortune with the headline “The Quest for Human-Level A.I.”
More from Fortune’s special report on A.I.:
—A.I. breakthroughs in natural-language processing are big for business
—Facebook wants better A.I. tools. But superintelligent systems? Not so much.
—A.I. in China: TikTok is just the beginning
—A.I. is transforming HR departments. Is that a good thing?
—Medicine by machine: Is A.I. the cure for the world’s ailing drug industry?
Subscribe to Eye on A.I., Fortune’s newsletter covering artificial intelligence and business.