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Aurora Innovation

Uber in talks to sell ATG self-driving unit to Aurora

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Eighteen months ago, Uber’s self-driving car unit, Uber Advanced Technologies Group, was valued at $7.25 billion following a $1 billion investment from Toyota, DENSO and SoftBank’s Vision Fund. Now, it’s up for sale and a competing autonomous vehicle technology startup is in talks with Uber to buy it, according to three sources familiar with the deal.

Aurora Innovation, the startup founded by three veterans of the autonomous vehicle industry who led programs at Google, Tesla and Uber, is in negotiations to buy Uber ATG. Terms of the deal are still unknown, but sources say the two companies have been in talks since October and it is far along in the process.

An Uber spokesperson declined to comment, citing that the company’s general policy is not to comment on these sorts of inquiries. An Aurora spokesperson said it doesn’t comment on speculation.

The talks could falter. But if successful, they have the potential to triple Aurora’s headcount and allow Uber to unload an expensive long-term play that has sustained several controversies in its short life.

Uber has ‘been shopping’

Shedding Uber ATG would follow a string of spin-offs or other deals in recent months that has narrowed Uber’s focus and costs into core areas of ride-hailing and delivery. Two years ago, Uber’s business model could be described as an “all of the above approach,” a bet on generating revenue from all forms of transportation, including ride-hailing, micromobility, logistics, package and food delivery and someday even autonomous robotaxis.

That strategy has changed since Uber went public and has further accelerated as the COVID-19 pandemic has upended the economy and fundamentally changed how people live. In the past 11 months, Uber has dumped shared micromobility unit Jump, sold a stake in its growing but still unprofitable logistics arm, Uber Freight and acquired Postmates. (The Postmates acquisition is expected to close in the fourth quarter of 2020).

Uber leads $170M Lime investment, offloads Jump to Lime

Uber ATG has been the company’s last big, expensive holding. Uber ATG holds a lot of long-term promise and high present-day costs; Uber reported in November that ATG and “other technologies” (which includes Uber Elevate) had a net loss of $303 million in the nine months that ended September 30, 2020. In its S-1 document, Uber said it incurred $457 million of research and development expenses for its ATG and “other Technology Programs” initiatives.

Four sources within the industry told TechCrunch that Uber “has been shopping” ATG to several companies, including automakers this year. Sources have also told TechCrunch that Uber ATG was facing a potential down round, which might have been an additional motivator behind the talks with Aurora.

Aurora, which was founded in 2017, is focused on building the full self-driving stack, the underlying technology that will allow vehicles to navigate highways and city streets without a human driver behind the wheel. Aurora has attracted attention and investment from high-profile venture firms, management firms and corporations such as Greylock Partners, Sequoia Capital, Amazon and T. Rowe Price, in part because of its founders Sterling Anderson, Drew Bagnell and Chris Urmson.

Urmson led the former Google self-driving project before it spun out to become the Alphabet business Waymo. Anderson is best known for leading the development and launch of the Tesla Model X and the automaker’s Autopilot program. Bagnell, an associate professor at Carnegie Mellon, helped launch Uber’s efforts in autonomy, ultimately heading the autonomy and perception team at the Advanced Technologies Center in Pittsburgh.

Aurora has grown from a small upstart to a company with 600 employees and operations in the San Francisco Bay Area, Pittsburgh, Texas and Bozeman, Montana, home of Blackmore, the lidar company it acquired in 2019. About 12% of Aurora’s current workforce previously worked at Uber, according to records on LinkedIn.

Fresh off a $530M round, Aurora acquires lidar startup Blackmore

Despite that growth, Aurora is still dwarfed by Uber ATG, the self-driving subsidiary that is majority owned by Uber. Uber ATG has more than 1,200 employees with operations in several locations, including Pittsburgh, San Francisco and Toronto. Uber holds an 86.2% stake (on a fully diluted basis) in Uber ATG, according to filings with the U.S. Securities and Exchange Commission. Its investors hold a combined stake of 13.8% in Uber ATG.

Uber’s public leap into autonomous vehicle technology began in earnest in early 2015 when the company announced a strategic partnership with Carnegie Mellon University’s National Robotics Center. The agreement to work on developing driverless car technology resulted in Uber poaching dozens of NREC researchers and scientists. A year later, with the beginnings of an in-house AV development program, Uber, then led by co-founder Travis Kalanick, acquired a self-driving truck startup called Otto.

The acquisition was troubled almost from the start. Otto was founded earlier that year by one of Google’s star engineers, Anthony Levandowski, along with three other Google veterans: Lior Ron, Claire Delaunay and Don Burnette. Uber acquired Otto less than eight months later.

Two months after the acquisition, Google made two arbitration demands against Levandowski and Ron. Uber wasn’t a party to either arbitration. While the arbitrations played out, Waymo separately filed a lawsuit against Uber in February 2017 for trade secret theft and patent infringement. Waymo alleged in the suit, which went to trial but ended in a settlement in 2018, that Levandowski stole trade secrets, which were then used by Uber.

Uber settles lawsuit with Waymo

Under the settlement, Uber agreed not to incorporate Waymo’s confidential information into their hardware and software. Uber also agreed to pay a financial settlement that included 0.34% of Uber equity, per its Series G-1 round $72 billion valuation. That was calculated at the time to be about $244.8 million in Uber equity.

In the early days of the Otto acquisition, Uber estimated it could have 75,000 autonomous vehicles on the road by 2019 and be operating driverless taxi services in 13 cities by 2022, according to court documents unsealed and first reported on by TechCrunch. To reach those ambitious goals, the ride-hailing company was spending $20 million a month on developing self-driving technologies.

Uber’s self-driving car unit was burning $20 million a month

Uber never came close to hitting those targets, a mission that was derailed by technical hurdles as well as the lawsuit with Waymo, its troubled relationship with Lewandowski and the fatal crash in March 2018 involving one of its self-driving test vehicles in Tempe, Arizona.

Uber halted all testing following the crash and has been slowly ramping up its more public-facing operations over the past 18 months. The expensive undertaking of developing autonomous vehicles prompted Uber to spin out the company in spring 2019 after it closed $1 billion in funding from Toyota, auto parts maker Denso and SoftBank’s Vision Fund.

The spin-out, which occurred about one month before Uber’s debut as a publicly traded company, had been the subject of speculation for months. It was seen as a way for Uber to share the expensive load with other investors and allow it to focus on its core competencies and nearer-term profit goals.

What Aurora gains

Troubles aside, Uber ATG has two important and critical features that make it attractive to Aurora: talent and Toyota.

The Japanese car giant had already invested $500 million into Uber prior to the 2019 injection of cash. At the time, the two companies announced their intention to bring pilot-scale deployments of automated Toyota Sienna-based ridesharing vehicles to the Uber ridesharing network in 2021, “leveraging the strengths of Uber ATG’s self-driving technology alongside the Toyota Guardian advanced safety support system.”

The 2019 investment into the Uber ATG unit deepened Toyota’s relationship with the company.

“While Uber was facing off against Waymo in the trade secrets lawsuit, Aurora launched with a bang. Within 18 months, Auora had secured several kinds of partnerships with Hyundai, Byton and VW Group. Some have fizzled, while there have been new gains, notably with Fiat Chrysler Automobiles. The musical chair-like changes underscores the sheer number of hopeful players in the self-driving business — a market that is still full of commercial and technical unknowns — and the fickleness of incumbent car makers in search of the best tech and deal.”

VW Group, which had touted its Aurora partnership in January 2018, confirmed to TechCrunch in June 2019 that “activities under our partnership have been concluded.” VW Group ultimately put its capital behind Argo AI, another autonomous vehicle technology developer that had locked up backing and a customer deal with Ford.

While Hyundai does have a minority stake in Aurora, it also went ahead and locked in a joint venture in fall 2019 with autonomous driving technology company Aptiv. Under the deal with Aptiv, both parties took a 50% ownership stake in the new joint company that is now called Motional. The combined investment in Motional from both companies will total $4 billion in aggregate value (including the value of combined engineering services, R&D and IP).

Still, Aurora has had its wins. The company raised $530 million last spring in a Series B round led by Sequoia with “significant investment” from Amazon and T. Rowe Price. Aurora’s post-money valuation at the time was $2.5 billion. More recently, sources in the industry say that Aurora is abuzz with activity, particularly around the office of David Maday, the company’s new vice president of business development who led General Motors’ corporate development and mergers and acquisitions team for 21 years.

Amazon, Sequoia invest in self-driving car startup Aurora

Aurora has always stated that its full driving stack — the combined suite of software and hardware that provides the brains for an AV — would be vehicle-agnostic, but some of its early testing and partnerships suggested it was focused on robotaxi applications, not logistics. Aurora started talking more openly last year about applying its technology to long-haul trucking and has become more bullish on that application, particularly following its Blackmore acquisition.

Aurora announced in July 2020 that it was expanding into Texas and planned to test commercial routes in the Dallas-Fort Worth Area with a mix of Fiat Chrysler Pacifica minivans and Class 8 trucks. A small fleet of Pacificas were expected to arrive first. The trucks will be on the road in Texas by the end of the year, according to the company.

The Jump precedent

What’s unclear is how an acquisition of Uber ATG might be structured; and more importantly, if it will retain any interest in the enterprise. Even with the expected depletion in Uber ATG’s valuation, it would be seemingly out-of-range for Aurora unless it was able to secure additional outside investment or structure the deal in a way that would allow Uber to keep some equity.

There is precedent for the latter. Earlier this year, Uber led a $170 million investment round into Lime. As part of the complex arrangement, Uber offloaded Jump, the bike and scooter-sharing unit, to Lime.

Rumors that Uber CEO Dara Khosrowshahi was keen to get rid of Uber ATG have popped up from time to time in the past year. But as the COVID-19 pandemic took hold, Khosrowshahi and other executives began to focus on its core competency of ride-hailing and double down on delivery. In addition to its micromobility unit and the Uber Freight spin-off, it has divested itself internationally of a number of regional operations that were proving too costly to grow in competition with strong local rivals.

It was on the heels of the Jump deal that interest in selling off Uber ATG ramped up, according to two sources.

One investor in the industry described it as an interesting Plan B for Uber, a deal that would allow the company to take ATG off the books, while potentially getting to benefit from a little upside.

Asheem Chandna

Extra Crunch roundup: Inside DoorDash’s IPO, first-person founder stories, the latest in fintech VC and more

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One of my favorite series of Monty Python sketches is built around the concept of surprise:

Chapman: I didn’t expect a kind of Spanish Inquisition.

[JARRING CHORD]

[Three cardinals burst in]

Cardinal Ximénez: NOBODY expects the Spanish Inquisition!

I was reminded of this today when I needed to reschedule a few stories so we could cover DoorDash’s S-1 filing from multiple angles. First, Managing Editor Danny Crichton looked at how well the company’s co-founders and many investors stand to make out. Alex Wilhelm covered the IPO announcement in depth on TechCrunch before writing an Extra Crunch column that studied the role the COVID-19 pandemic played in the home-delivery platform’s recent growth.

Our all-hands-on-deck coverage of DoorDash’s S-1 is a good illustration of Extra Crunch’s mission: timely analysis of current and future technology trends that serves founders and investors. We have a talented team, and as today’s coverage shows, they’re just as good as they are fast.

The stories that follow are an overview of Extra Crunch from the last five days. The full articles are only available to members, but you can use discount code ECFriday to save 20% off a one or two-year subscription. Details here.

Thanks very much for reading Extra Crunch this week. I hope you have a great weekend!

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist

What I wish I’d known about venture capital when I was a founder

Why I left edtech and got into gaming

Image Credits: Klaus Vedfelt / Getty Images

We frequently run posts by guest contributors, but two stories we published this week were written in the first person, which is a bit of a departure.

In Why I left edtech and got into gaming, Darshan Somashekar brought us inside his decision to pivot away from a sector that’s been growing hotter in 2020.

His post is a unique take on two oft-discussed categories, but it also examines one founder/investor’s thought process when it comes to evaluating new opportunities.

Andy Areitio, a partner at early-stage fund TheVentureCity, wrote What I wish I’d known about venture capital when I was a founder, a reflection on the “classic mistakes” founders tend to make when it’s time to fundraise.

“Error number one (and two) is to raise the wrong amount of money and to do it at the wrong time,” he says. “They can also put all their eggs in one basket too early. I made that mistake.”

You can find business writing that explores best practices anywhere, which is why we hunt down stories that are firmly rooted in data or personal experience (which includes success and failure).

What I wish I’d known about venture capital when I was a founder

Why I left edtech and got into gaming

How COVID-19 accelerated DoorDash’s business

Image Credits: DoorDash

The coronavirus pandemic looms large in DoorDash’s S-1 filing.

According to the food-delivery platform, “58% of all adults and 70% of millennials say that they are more likely to have restaurant food delivered than they were two years ago,” and “the COVID-19 pandemic has further accelerated these trends.”

As in other sectors, the pandemic didn’t wave a magic wand — instead, it hastened trends that were already in play: consumers love convenience, which means DoorDash’s gross order volume and revenue were tracking well before the virus started to shape our lives.

“It’s your call on how to balance the factors and decide whether or not to buy into the IPO, but this one is going to be big,” writes Alex Wilhelm in a supplemental edition of today’s The Exchange.

How COVID-19 accelerated DoorDash’s business

The VC and founder winners of DoorDash’s IPO

SAN FRANCISCO, CA – SEPTEMBER 05: DoorDash CEO Tony Xu speaks onstage during Day 1 of TechCrunch Disrupt SF 2018 at Moscone Center on September 5, 2018 in San Francisco, California. (Photo by Kimberly White/Getty Images for TechCrunch)

None of us knew DoorDash would release its S-1 filing today, but Danny Crichton jumped on the story “so we can see who is raking in the returns on the country’s delivery startup champion.”

After estimating the value of the respective ownership stakes held by DoorDash’s four co-founders, he turned to the investors who participated in rounds seed through Series H.

Some growth funds are about to look very good after this IPO, and each founder is looking at hundreds of millions, he found.

But even so, their diminished haul of about $1.3 billion is “a sign of just how much dilution the co-founders took given the sheer amount of capital the company fundraised over its life.”

The VC and founder winners of DoorDash’s IPO

Fintech VC keeps getting later, larger and more expensive

Image Credits: Nigel Sussman (opens in a new window)

Investors sent stacks of cash to late-stage fintech companies in Q3 2020, but these sizable rounds may also point to shrinking opportunities for early-stage firms, reports Alex Wilhelm in this morning’s edition of The Exchange.

2020 could be a record year for fintech VC in Europe and North America, but are these “huge late-stage dollars” actually “a dampener for new fintech startups trying to get off the ground?”

Fintech VC keeps getting later, larger and more expensive

Accelerators embrace change forced by pandemic

Devin Coldewey interviewed the leaders of three startup accelerators to learn more about the adaptations they’ve made in recent months:

David Brown, founder and CEO, Techstars
Cyril Ebersweiler, founder HAX, venture partner at SOSV
Daniela Fernandez, founder, Ocean Solutions Accelerator

Due to travel bans, shelter-in-place orders and other unknowns, they’ve all shifted to virtual. But accelerators are intensive programs designed to indoctrinate founders and elicit brutally honest feedback in real time.

Despite the sudden shift, that boot-camp mindset is still in effect, Devin reports.

“Cutting out the commute time in a busy city leaves founders with more time for workshops, mentor matchmaking, pitch practice and other important sessions,” said Fernandez. “Everybody just has more flexibility and tranquility.”

Said Ebersweiler: “People are for some reason more participative and have more feedback than physically — it’s pretty strange.”

Accelerators embrace change forced by pandemic

Greylock’s Asheem Chandna on ‘shifting left’ in cybersecurity and the future of enterprise startups

Image Credits: Greylock

In a recent interview with Greylock partner Asheem Chandna, Managing Editor Danny Crichton asked him about the buzz around no-code platforms and what’s happening in early-stage enterprise startups before segueing into a discussion about “shift left” security:

“Every organization today wants to bring software to market faster, but they also want to make software more secure,” said Chandna.

“There is a genuine interest today in making the software more secure, so there’s this concept of shift left — bake security into the software.”

Greylock’s Asheem Chandna on ‘shifting left’ in cybersecurity and the future of enterprise startups

Square and PayPal earnings bring good (and bad) news for fintech startups

Image Credits: Nigel Sussman (opens in a new window)

If you missed Wednesday’s The Exchange, Alex scoured earnings reports from PayPal and Square to see what the near future might hold for several fintech startups currently waiting in the wings.

Using Square and PayPal’s recent numbers for stock purchases, card usage and consumer payment activity as a proxy, he attempts to “see what we can learn, and to which unicorns it might apply.”

Square and PayPal earnings bring good (and bad) news for fintech startups

Conflicts in California’s trade secret laws on customer lists create uncertainty

Concept of knowledge, data and protection. Paper human head with pad lock.

Image Credits: jayk7 (opens in a new window)/ Getty Images

In California, non-competition agreements can’t be enforced and a court has ruled that customer contact lists aren’t trade secrets.

That doesn’t mean salespeople who switch jobs can start soliciting their former customers on their first day at the new gig, however.

Before you jump ship — or hire a salesperson who already has — read this overview of California’s trade secret laws.

“Even without litigation, a former employer can significantly hamper a departing salesperson’s career,” says Nick Saenz, a partner at Lewis & Llewellyn LLP, who focuses on employment and trade secret issues.

Conflicts in California’s trade secret laws on customer lists create uncertainty

As public investors reprice edtech bets, what’s ahead for the hot startup sector?

Image: Bryce Durbin / TechCrunch

News of a highly effective COVID-19 vaccine appeared to drive down prices of the three best-known publicly traded edtech companies: 2U, Chegg and Kahoot saw declines of about 20%, 10% and 9%, respectively after the report.

Are COVID-19 tailwinds dissipating, or did the market make a correction because “edtech has been categorically overhyped in recent months?”

As public investors reprice edtech bets, what’s ahead for the hot startup sector?

Dear Sophie: What does a Biden win for tech immigration?

Image Credits: Sophie Alcorn

What does President-elect Biden’s victory mean for U.S. immigration and immigration reform?

I’m in tech in SF and have a lot of friends who are immigrant founders, along with many international teammates at my tech company. What can we look forward to?

— Anticipation in Albany

Dear Sophie: What does Biden’s win mean for tech immigration?

Election 2020

US election official: Trump’s misinformation is dangerous and lacks any credibility

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On Thursday, President Donald Trump sent an all-caps tweet claiming that voting machines from a company called Dominion Voting Systems had deleted millions of votes for him around the country. The claim isn’t true, but he is the president—so it has had an impact. Election workers say they fear for their safety. They’re receiving death threats from supporters of the president.

Ben Hovland knows voting machines well. He runs the Election Assistance Commission (EAC), an independent federal agency that, among other jobs, tests and certifies this technology. The EAC writes standards for voting systems and tests the machines in labs for security, usability, and safety. And Hovland says there has been no widespread fraud or malfunction that would change the result of the election. Nor has the president—or the lawyers who have unsuccessfully tried challenging the result— produced any actual evidence supporting Trump’s claims.

Hovland and I discussed what’s happened since the election and the extraordinary amount of disinformation coming from the White House. During our conversation, which has been edited for length and clarity, Hovland talked about the president’s legal woes, the future of election security officials, and his message for Donald Trump.

Q: What’s your reaction when the president tweets that Dominion deleted 2.7 million Trump votes?

A: Number one, it’s pretty baffling. Number two, I just wish that if claims like that were going to be made, they would actually be backed up with something credible. I think those types of statements matter. They cause Americans to lose confidence in the process.

That’s really concerning. Look at the president’s litigation. What we see is a very different story in front of a microphone or on Twitter than we see in front of a courtroom or in front of a judge. We see bold statements on Twitter or at the podium, and we see hearsay and we see laughable evidence presented to courts. There’s just not a correlation between those.

This story isn’t new. You look back at the 2016 election: the president made claims that he lost the popular vote because allegedly millions of noncitizens voted. A presidential commission was created to find those millions of noncitizens and prove voter fraud. They didn’t. It was disbanded in embarrassment. We see that time and time again. There has been no evidence anywhere of widespread voter fraud.

Frankly, it’s disrespectful to the people who run elections. It’s disrespectful to their integrity to make these kinds of allegations, particularly when you’re not providing evidence. Anything that has been brought up has been easily refuted because it’s largely conspiracy theories. If there is anything to this, election officials will want to get to the bottom of it more than anyone. They care about the integrity of the process and want to make sure that it was fair and the will of the voters is reflected.

Q: It was recently reported that Chris Krebs, director of the Cybersecurity and Infrastructure Security Agency, is being pressured by the White House to change its Rumor Control page, which combats election misinformation in real time. Krebs now expects to be fired because he refuses to change the facts. What’s your reaction to seeing a well-respected election security official feeling that he’s got a sword over his head for the act of getting the facts out?

A: That alone tells you as much as anything I can say. The reality is Krebs has done a great job. Without his leadership, we would be nowhere close to where we are. I’ve said many times there’s been a sea change in information sharing between state, local, and federal partners on election security. So much of that credit goes to director Krebs and his leadership.

Rumor Control has been a fantastic resource. We really have seen an absurd number of baseless allegations made. None has been rooted in any real fact. It’s important to get the real story out there. Director Krebs has done a great job of empowering his staff and meeting election officials where they are, bipartisan and across the board, recognizing that our elections are decentralized. Each state runs elections in its own unique way. And that means you need to approach the space respecting that and knowing that it’s different states, and different election officials will have different challenges and need different assistance.

He’s done a great job recognizing that and adapting the program. The election infrastructure subsector has been the fastest-growing subsector that the government has ever stood up. Certainly it’s led to the most secure election we’ve ever had.

I was at the CISA operation center on Election Day, and between there and having representatives from election organizations, having representatives there from the manufacturer community, the intelligence community, and having election officials around the country in virtual rooms, we were able to have a level of visibility into what was happening across the country like we never had before.

Look at things that popped up on Election Day. No Election Day is perfect—elections never are—but this was done really well. And the things that popped up were sort of common election problems. There were some machines that didn’t start. There were some issues with the e-poll books. There were some poll workers that didn’t show up—that happens. But we were able to see those pop up and quickly address them. There were regular press background briefings giving the basic “Here’s what we’re seeing; here’s what we know.” Before the e-poll book issue spun up into some grand conspiracy, the facts were ascertained and shared, and we knew it was localized and being resolved and not a major cyber incident.

The ability to have that visibility to be able to keep things from snowballing also made a big difference this year. And so much of that is due to the work that director Krebs has done and his leadership in the space. I hope that he continues in the role for as long as he wants.

Q: Are you worried about further politicization of the election process?

A: I certainly hope that doesn’t happen. What you’re seeing in Rumor Control and in so many of these efforts is a commitment to the oath that we swore to the Constitution.

It’s trying to get the truth out about how our elections run, about the security and integrity of the election, the story of what this election was, about the will of the voters. A record number of Americans cast ballots this year. Ultimately, that is our democracy. And you’ve got to respect the will of the voters.

Q: Do you think the situation is exacerbated by the fact that it’s specifically the president who is putting a megaphone to this misinformation?

A: I think that is alarming, particularly the press conference so many networks cut away from. I think most Americans are not accustomed to seeing the presidential seal at the White House at the podium and hearing accusations like that, where his lawyers and others have failed to come up with any actual evidence or proof.

A lot of Americans listen to the president. They respect the office, or they are supporters of the president. You saw in some ways how that played out in people’s usage of mail-in absentee ballots. Some people have raised questions about how the percentage of absentee ballots going to President-elect Biden was overwhelming. Well, that’s because the president spent months saying you couldn’t trust mail-in ballots.

Certainly there’s a portion of the American people that believe him, and that is very concerning, because we had a free and fair election. The will of the people: they have made their voices heard, and election officials have just put in an unbelievable amount of work to ensure that this was a smooth election and the election has integrity.

Any claims otherwise just are sowing divisiveness amongst the American people. That is what our foreign adversaries want. They want to see these divides. They want to see us lose faith in our democratic process and systems. It’s really unfortunate to be doing anything that would cause Americans to lose faith in the process, particularly one that worked so well this year.

Q: If you could talk face to face with President Trump today about this election, what message would you deliver?

A: More than anything, I would talk about the consequences of these statements for election officials. I’ve heard from election officials personally. I’ve seen them in the media concerned about their own safety, the safety of their staff. These accusations, these conspiracy theories that are flying around, have consequences.

At a minimum, it’s insulting to the professionals that run our elections, and hopefully that’s the worst that comes of it. Our people, they’re doing their jobs, but they don’t feel safe doing it. That is a tragedy. That is awful. These are public servants. This isn’t a job you do for glory or to get rich.

It’s the job you do because you believe in our country, you believe in our democracy, and you want to help Americans. I can think of few callings that are higher. And I think it’s just really unfortunate that in a year we should be singing their praises and giving them credit, instead we’re talking about them receiving threats and being scared. That is unacceptable.

Artificial intelligence

The key to smarter robot collaborators may be more simplicity

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Think of all the subconscious processes you perform while you’re driving. As you take in information about the surrounding vehicles, you’re anticipating how they might move and thinking on the fly about how you’d respond to those maneuvers. You may even be thinking about how you might influence the other drivers based on what they think you might do.

If robots are to integrate seamlessly into our world, they’ll have to do the same. Now researchers from Stanford University and Virginia Tech have proposed a new technique to help robots perform this kind of behavioral modeling, which they will present at the annual international Conference on Robot Learning next week. It involves the robot summarizing only the broad strokes of other agents’ motions rather than capturing them in precise detail. This allows it to nimbly predict their future actions and its own responses without getting bogged down by heavy computation.

A different theory of mind

Traditional methods for helping robots work alongside humans take inspiration from an idea in psychology called theory of mind. It suggests that people engage and empathize with one another by developing an understanding of one another’s beliefs—a skill we develop as young children. Researchers who draw upon this theory focus on getting robots to construct a model of their collaborators’ underlying intent as the basis for predicting their actions.

Dorsa Sadigh, an assistant professor at Stanford, thinks this is inefficient. “If you think about human-human interactions, we don’t really do that,” she says. “If we’re trying to move a table together, we don’t do belief modeling.” Instead, she says, two people moving a table rely on simple signals like the forces they feel from their collaborator pushing or pulling the table: “So I think what is really happening is that when humans are doing a task together, they keep track of something that’s much lower-dimensional.”

Using this idea, a robot could store very simple descriptions of its surrounding agents’ actions. In a game of air hockey, for example, it might store its opponents’ movements with only one word: “right,” “left,” or “center.” It can then use this data to train two separate algorithms: a machine-learning algorithm that predicts where the opponent will move next, and a reinforcement-learning algorithm to determine how it should respond. The latter algorithm also keeps track of how the opponent changes tack on the basis of its own response, so it can learn to influence the opponent’s actions.

The key idea here is the lightweight nature of the training data, which is what allows the robot to perform all this parallel training on the fly. A more traditional approach might store the coordinates for the entire pathway of the opponent’s movements, not just their overarching direction. While it may seem counterintuitive that less is more, it’s worth remembering again Sadigh’s theory about human interaction. We, too, model the people around us only in broad strokes.

The researchers tested this idea in simulation for applications including a self-driving car, and in the real world with a game of robot air hockey. In each of the trials, the new technique outperformed previous methods for teaching robots to adapt to surrounding agents. The robot also effectively learned to influence those around it.

Future work

There are still some issues that future research will have to resolve. The work currently assumes, for example, that every interaction the robot engages in is finite, says Jakob Foerster, an assistant professor at the University of Toronto, who was not involved in the work.

In the self-driving simulation, the researchers assumed that the robot car was experiencing only one clearly bounded interaction with another car during each round of training. But driving, of course, doesn’t work that way. Interactions are often continuous and would require a self-driving car to learn and adapt its behavior within each interaction, not just between them.

Another challenge, Sadigh says, is that the approach assumes knowledge of the best way to describe a collaborator’s behavior. The researchers themselves had to come up with the labels “right,” “left,” and “center” in the air hockey game for the robot to describe its opponent’s actions. Those labels won’t always be so obvious in more complicated interactions.

Nonetheless, Foerster sees promise in the paper’s contribution. “Bridging the gap between multi-agent learning and human-AI interaction is a super important avenue for future research,” he says. “I’m really excited for when these things get put together.”

Biotechnology

Covid-19 vaccines shouldn’t get emergency-use authorization

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I really want a covid-19 vaccine. Like many Americans, I have family members and neighbors who have been sickened and killed by the new coronavirus. My sister is a nurse on a covid-19 ward, and I want her to be able to do her job safely. As a health-care lawyer, I have the utmost confidence in the career scientists at the US Food and Drug Administration who would ultimately determine whether to issue an emergency-use authorization for a covid-19 vaccine. But I am deeply worried about what could happen if they do.

The pace of covid-19 vaccine research has been astonishing: there are more than 200 vaccine candidates in some stage of development, including several that are already in phase 3 clinical trials, mere months after covid-19 became a global public health emergency. In order for the FDA to approve a vaccine, however, not only do these clinical trials need to be completed—a process that typically involves following tens of thousands of participants for at least six months—but the agency also needs to inspect production facilities, review detailed manufacturing plans and data about the product’s stability, and pore over reams of trial data. This review can easily take a year or more.

That’s why, for several months now, the FDA has been considering criteria for initially deploying a covid-19 vaccine under an emergency-use authorization, or EUA, before the FDA has all the information normally required for full approval. At least a few of the manufacturers currently in phase 3 trials have publicly stated their intent to request an EUA. Pfizer plans to do so later this month in light of the exciting preliminary results for its vaccine.

EUAs allow the FDA to make unapproved products available during public health emergencies. While the FDA has issued EUAs sparingly for diagnostics and therapies aimed at other infectious diseases, such as H1N1 and Zika, a vaccine has never been used in civilians under an EUA. Vaccines are different from other medical products in that they are deployed broadly and in healthy people, so the bar for approving one is high.

The FDA’s Vaccines and Related Biological Products Advisory Committee, a group of outside experts who advise the FDA on vaccines, met for the first time to discuss covid-19 vaccines on October 22. Some committee members questioned whether the FDA had set the bar for a vaccine EUA high enough. Members also expressed several important concerns about authorizing a vaccine through an EUA.

One concern is that once a vaccine is authorized in this manner, it may be difficult—for ethical and practical reasons—to complete clinical trials involving that vaccine (and thus to collect additional safety data and population-specific data for groups disproportionately affected by covid-19). It could also hamper scientists’ ability to study other covid-19 vaccine candidates that may be “better” in various ways than the first across the finish line.

But the most important consideration in my view relates to public trust.

Public health experts caution that vaccines don’t protect people; only vaccinations do. A vaccine that hasn’t gained enough public trust will therefore have a limited ability to control the pandemic even if it’s highly effective.

Data from the Pew Research Center show declining trust in a covid-19 vaccine across all genders, racial and ethnic categories, ages, and education levels, with many people citing safety and the pace of approval as key factors in their skepticism. Information presented to the advisory committee by the Reagan-Udall Foundation similarly showed significant distrust in the speed of vaccine development, likely exacerbated by recent political interference with the FDA and the US Centers for Disease Control and Prevention (CDC) and some politicians’ promises that a vaccine would be available before the end of the year. People of color have expressed additional concerns with vaccine research.

Judging from their written and verbal comments to the advisory committee, major vaccine manufacturers recognize the potential disruptions to subsequent clinical trials and are seeking the FDA’s advice to address them. While those considerations are daunting, I suspect that manufacturers and the FDA could create workable responses. But even then, the public trust issues associated with EUAs—which most of the public first heard about through the hydroxychloroquine debacle and again in the context of the convalescent plasma controversy—still make this tool a poor fit for vaccines.

Instead, if vaccine trial data are promising enough to warrant giving some people pre-approval access to a covid-19 vaccine, the FDA should do so using a mechanism called “expanded access.” While the FDA ordinarily uses expanded access to make experimental treatments available to sick patients who have no alternative treatment available, it has been used for vaccines before and could be used now to avoid disrupting ongoing clinical trials or fostering public perceptions that a vaccine was being rushed because of an “emergency.” Expanded-access programs are also overseen by ethics committees and have informed consent requirements for patients that go beyond those associated with products authorized by EUA.

Not only must the public trust a covid-19 vaccine enough to seek out the first wave of authorized vaccines, but that trust must be resilient enough to withstand potential setbacks: protection below 100% (and perhaps below 50%), significant side effects (or rumors of them), and possible recalls. That level of trust takes time to rebuild if it has been eroded. And the stakes here are not just the slowing of this pandemic. As former senior health official Andy Slavitt recently said, “Done right, vaccines end pandemics. Done wrong, pandemics end vaccines.”

Clint Hermes, a former academic medical center general counsel, has advised universities, teaching hospitals, and life sciences companies on global health problems. He has helped set up vaccination, treatment, and surveillance projects for infectious diseases in North and South America, Africa, Asia, and the Middle East. The views expressed here are his own and not those of any organization with which he is affiliated, including his employer. The information presented here should not be construed as legal advice.

Artificial intelligence

How the pandemic readied Alibaba’s AI for the world’s biggest shopping day

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The news: While the US has been hooked on its election, China has been shopping. From November 1 to 11, the country’s top e-commerce giants, Alibaba and JD, generated $115 billion in sales as part of their annual Singles’ Day shopping bonanza. Alibaba, which started the festival in 2009, accounted for $74.1 billion of those sales, a 26% increase over last year. For comparison, Amazon’s 48-hour Prime Day sales only crossed the $10 billion mark this year.

Pandemic stress test: The sheer scale of the event makes it something of a logistical miracle. To pull off the feat, Alibaba and JD invest heavily in AI models and other technology infrastructure to predict shopping demand, optimize the global distribution of goods across warehouses, and streamline worldwide delivery. The systems are usually tested and refined throughout the year before being stretched to their limits during the actual event. This year, however, both companies faced a complication: accounting for changes in shopping behavior due to the pandemic.

Broken models: In the initial weeks after the coronavirus outbreak, both companies saw their AI models behaving oddly. Because the pandemic struck during the Chinese New Year, hundreds of millions of people who would have otherwise been holiday shopping were instead buying lockdown necessities. This behavior made it impossible to rely on historical data. “All of our forecasts were no longer accurate,” says Andrew Huang, general manager of the domestic supply chain at Cainiao, Alibaba’s logistics division.

People were also buying things for different reasons, which was flying in the face of the platforms’ product recommendations. For example, JD’s algorithm assumed people who bought masks were sick and so recommended medicine, when it might have made more sense to recommend hand sanitizer.

Changing tack: The breakdown of their models forced both companies to get creative. Alibaba doubled down on its short-term forecasting strategy. Rather than project shopping patterns based on season, for example, Cainiao refined its models to factor in more immediate variables like the previous week of sales leading up to major promotional events, or external data like the number of covid cases in each province, says Huang. As live-streaming e-commerce (showing off products in real time and answering questions from buyers) exploded in popularity during quarantine, the company’s logistics arm also built a new forecasting model to project what happens when popular live-stream influencers market different products.

And JD retooled its algorithms to consider more external and real-time data signals, like covid case loads, news articles, and public sentiment on social media.

Unexpected boon: Adding these new data sources into their models seems to have worked. Cainiao’s new live-streaming AI model, for example, ended up playing a big role in forecasting sales after Alibaba made live-streaming a core part of its Singles’ Day strategy. For JD, its updates may have also increased overall sales. The company says it saw a 3% increase in click-through rate on its product recommendations after it rolled out its improved algorithm, a pattern that held up during Singles’ Day.

Understanding context: Both companies have learned from the experience. For example, Huang says his team learned that each live-stream influencer mobilizes its fan base to exhibit different purchasing behaviors, so it will continue to create bespoke prediction models for each of its top influencers. Meanwhile, JD says it has realized how much news and current events influence e-commerce patterns and will continue to tweak its product recommendation algorithm accordingly.

Update: The relationship between Alibaba and Cainiao has been clarified.