The approval, if made final, opens the door for the new bank to custody cryptocurrencies.
Read more The COVID-19 pandemic has hit the hospitality industry especially hard, and hotels around the world are looking for ways to regain revenue. Today, Marriott International and Grab announced a partnership that will cover the hospitality giant’s dining businesses in six Southeast Asian countries: Singapore, Indonesia, Malaysia, the Philippines, Vietnam and Thailand.
Instead of room bookings, Marriott International deal with Grab focuses on about 600 restaurants and bars at its properties in the six Southeast Asian countries, which will start being added to GrabFood’s on-demand delivery platform in November. A joint announcement from the companies said the deal represents Marriott International’s “first extensive integration with a super app platform in Southeast Asia and Grab’s most comprehensive agreement with a hospitality group to date.”
Marriott International is the world’s largest hotel company. During the second quarter, as the pandemic curtailed travel and in-person events, it reported a loss of $234 million, compared to the profit of $232 million it had recorded a year earlier. Chief executive Arne Sorenson called it “the worst quarter we have ever seen,” even though business is gradually recovering in China.
The Marriott-Grab integration means the two companies will link their loyalty programs, so GrabRewards points can be converted to Marriott Bonvoy points, or vice versa. Marriott International’s restaurants and bars that accept GrabPay will also have access to Grab’s Merchant Discovery platform, which will allow them to ping users about local deals and includes a marketing campaign platform called GrabAds.
Other hospitality businesses that Grab already partners with include Booking.com and Klook. Klook is among several travel-related companies that have recalibrated to focus on “staycations,” or services for people who can’t travel during the pandemic, but still want a break from their regular routines.
Ford plans to reveal in November an all-electric version of its Ford Transit cargo van, the company said Wednesday as part of its broader third-quarter earnings report. The unveiling will showcase an electric van for all of the company’s global addressable markets.
The company has been talking about producing an electric Transit van for more than a year now. Ford announced in April 2019 plans to sell an all-electric Transit for the European market by 2021. Then this spring, Ford said it would also produce and sell an all-electric version of the cargo van for the North American market starting with the 2022 model year.
The electric Transit cargo van is part of Ford’s more than $11.5 billion investment in electrification through 2022, and more specifically, a strategy to go after commercial customers.
The decision to include commercial vans in its EV strategy is linked to sales in North America and the company’s outlook on future growth. The Transit van and the Ford F-150 are the two most important, highest volume commercial vehicles in our industry, CEO Jim Farley said during an earnings call Wednesday with analysts.
“We own ‘work’ at Ford and these electric vehicles will be true work vehicles, extremely capable and with unique digital services and over-the-air capabilities to improve the productivity and uptime of our important commercial customers,” he said. “We believe the addressable market for a fully electric commercial van and pickup — the two largest addressable profit pools and commercial — are going to be massive and we’re going straight at this opportunity.”
The announcement was tucked inside the company’s third-quarter earnings, which crushed Wall Street expectations. Ford reported Wednesday net income of $2.4 billion on $37.5 billion in revenue. Ford said it expects a positive full year 2020 adjusted earnings before interest and taxes, reversing a dimmer outlook it had previously provided.
Today’s Senate hearing on immensely important legal protections for online platforms quickly proved to be little more than an excuse for Senators to accuse the CEOs of Twitter, Facebook and Google of partisan interference with next week’s election. The actual law being considered for revision was mentioned only a handful of times in the nearly four-hour hearing, the balance being taken up by partisan bickering and, ironically, misinformation.
The hearing, assembled and convened with naked haste in order to get ahead of the election, was dominated by Republican bullying and bloviating and Democratic expressions of distaste. Section 230, a law which is under serious and justified consideration for revision, was barely a footnote.
That the hearing, which promised “legislative proposals to modernize the decades-old law,” was thrown together at the last minute was evident from a lack of focus or coordination. When not mispronouncing Alphabet/Google CEO Sundar Pichai’s name, Senators asked redundant questions, presented scant and conflicting evidence of the practices they accused the companies of, and generally used the time to mint sound bites with little substance.
An excellent example of all this was the case, brought up three separate times by Republican senators, of tweets by the Iranian Ayatollah Khameini calling for war and questioning the Holocaust, which were not taken down, while Trump’s tweets regarding COVID-19 had warnings placed on them. Why, they asked again and again, does this not constitute a double standard and a clear example of bias against Trump?
Twitter CEO Jack Dorsey explained what should be a well-known fact by now, especially by legislators who purport to have an interest in this topic: that there is no policy for general misinformation and that world leaders get special consideration anyway, and that the policies that resulted in warnings placed on tweets lately relate specifically to public health and election-related misinformation. This issue has been raised before, you see, and the explanation is quite simple.
The Republican senators avoided Section 230 altogether, using their time to berate Dorsey, Pichai and Facebook CEO Mark Zuckerberg:
- An irritable Sen. Ted Cruz (R-TX) shouted over the hearing’s guests, calling those three specifically “the greatest threat to free speech in America.”
- Sen. John Thune (R-SD) accused the companies of not having sufficient “ideological diversity” in their leadership, and others asked the CEOs to report the party affiliations of their employees. (The CEOs said they don’t ask, though Pichai admitted to Thune’s obvious pleasure that the young, highly educated tech sector skews left.)
- Sen. Marsha Blackburn (R-TN) said Twitter had “censored Donald Trump 65 times,” and Biden zero times, though as Dorsey pointed out none of Trump’s tweets have in fact been removed.
- Sen. Mike Lee (R-UT) asserted that the companies were committing false advertising in saying they were not politically motivated. He then asked the CEOs to provide “examples of censoring liberals.” They bridled at being asked to tacitly admit what they do is censoring, but with that reservation did provide examples — which Lee dismissed as insufficient.
- Sen. Ron Johnson (R-WS) accused the platforms of deliberately exerting influence on elections, citing as misinformation and political bias Twitter declining to take down a tweet that was obviously satirical.
Despite repeatedly claiming that the platforms were biased toward the left, the Republican contingent did not produce any examples of material from Democrats that should, in their estimation, have been taken down but was not. This seems an important part of making the argument, or it leaves open the distinct possibility that Republicans simply break the rules more.
Only Sen. Shelley Moore Capito (R-WV) didn’t get the memo, and proffered constructive, informed questions relating to Section 230. She asked the tech leaders whether they thought the law’s use of the phrase “otherwise objectionable” as a catch-all was too expansive. They replied unanimously (and predictably) that it was not, and that, as Alphabet/Google CEO Sundar Pichai put it, the wording “is the only reason we can intervene with certainty” in cases like the dangerous “Tide pod challenge” and other situations that aren’t covered specifically by the law. Sen. Capito, to all appearances, took their answers seriously.
The Democratic senators, for the most part, cannot be said to have addressed Section 230 substantively either, but a few took the opportunity to address the issue ostensibly at hand.
Sen. Tammy Baldwin (D-WI) asked about the failure of Facebook to take down the Kenosha Guard group, which was actively fomenting violence against protestors, despite hundreds of complaints. She managed to extract from Zuckerberg that Facebook had stopped making group recommendations based on political preferences, while it has worked to clean up its private groups, now notorious for conspiracies and violent militias.
Sen. Maria Cantwell (D-WA) had a timely reminder about what free speech actually is: “Maybe we need to have a history lesson from high school again — yes, free speech means that people can make outrageous statements about their beliefs. What the CEOs are telling us here is what their process is for taking down health care information that’s not true, that is a threat to the public, and information that is a threat to our democracy.”
The others primarily used their time to register their discontent with the obvious election-related motivations of the hearing.
Sen. Brian Schatz (D-HI) led the pack by declaring he would not take part. “I’ve never seen a hearing so close to an election on any topic, let alone on something that is so obviously a violation of our obligation under the law and the rules of the Senate to stay out of electioneering,” he said. “We never do this, and there’s a very good reason that we don’t call people before us to yell at them for not doing our bidding during an election. This hearing is a sham. I will be happy to participate in good faith, bipartisan hearings when the election is over.”
Sen. Ed Markey (D-MA) derided the “false narrative about anti-conservative bias,” saying “the issue is not that the companies before us today are taking too many posts down, the issue is they are leaving too many dangerous posts up, in fact amplifying harmful content.” Out of context this may seem an endorsement of censorship, but it’s clear that he was referring to things like deliberate disinformation campaigns, conspiracy theories and public health hazards.
Though Republicans had tried to downplay the idea that they were “working the refs” by saying that Facebook et al. shouldn’t be refs in the first place, Sen. Tom Udall (D-NM) explained that “when we say ‘work the refs,’ the U.S. government is the referee. The FCC, Congress, the presidency, and the Supreme Court are the referees.” He warned of the danger of federal laws aimed at actions, such as restricting the reach of the NY Post’s highly suspect story, that were in his opinion the right thing to do, if difficult to get exactly right the first time.
Sen. Tester (D-MT), clearly out of patience with his colleagues across the aisle, deplored the double standard he believed he saw: “We’ve heard a lot of information out here today where when you hire someone you’re supposed to ask them their political affiliation. If that business is run by a liberal, we’re gonna regulate ’em different than if they’re run by a conservative outfit,” he said.
“That reminds me a lot of the Supreme Court, where you have two sets of rules, one for a Democrat president, one for a Republican. This is baloney, folks.” If he could have dropped the mic, no doubt he would have.
As for the CEOs themselves, they hardly had a chance to get a word in edgewise except in their opening statements. When they did speak it was mainly to acknowledge that they need to work on transparency, but that they were doing their best in unprecedented circumstances with policies that must be reworked on a daily basis.
Reserve your sympathy for these poor captains of industry, however, until those companies answer for their role in producing the problems of mass disinformation in the first place.
This isn’t the last we’ll hear of this issue by a long shot, but with the election looming, unbelievably, in less than a week, the next time a hearing like this is held it will be under altered circumstances.
New Zealand launch provider Rocket Lab has put its 15th commercial payload into space, delivering 10 Earth observation satellites each to their own orbit. The company is getting back into its stride after an upset in July dampened plans to set a record for launch turnaround time.
Aboard the latest Electron launch vehicle to leave the Earth were nine of Planet’s “SuperDove” satellites, the newer generation of observation craft that allow that company to provide frequently updated imagery of an increasingly large proportion of the surface.
Canon’s CE-SAT-IIB is a demonstration craft, showing off “a middle-size telescope equipped with an ultra-high sensitivity camera to take night images of the Earth,” along with some smaller ones for more ordinary observation. The rideshare with Planet was organized by launch rideshare specialists Spaceflight.
The launch was originally scheduled for last week but stood down at the time because “some sensors are returning data that we want to look into further.” Fortunately there was no shortage of backup launch dates, and today was set for the new attempt.
Everything proceeded nominally and the satellites were on their way and able to be reached about an hour after takeoff.
This is the second launch since Rocket Lab was briefly grounded following the loss of a payload in July — not to any flashy explosion but to a rather graceful shutdown due to an electrical fault before it could reach the desired orbit.
Fortunately the company’s quick investigation meant they were ready to fly less than a month later.
Incidentally, all that and more will be on the table for discussion at TC Sessions: Space 2020 in December, where Rocket Lab founder and CEO Peter Beck will be joining us.
Apple might be building a Google competitor, Audible adds more podcasts and an ad measurement company raises $350 million. This is your Daily Crunch for October 28, 2020.
The big story: Apple seems pretty interested in search
Apple has a growing interest in search technology and might even be working on a product to compete with Google, according to The Financial Times.
The most visible change is the fact that in iOS 14, Apple is now showing its own results when you type queries in the home screen. In addition, there seems to be an increase in activity from Apple’s web crawler.
There may be more of an opportunity here as the U.S. Justice Department has sued Google over what it claims are anticompetitive behaviors around search. However, this doesn’t necessarily mean Apple and Google will soon be going head-to-head in search — it could just be a sign that Apple’s Siri voice assistant is getting more search queries.
The tech giants
Joe Rogan, Alex Jones and Spotify’s illusion of neutrality — Spotify is facing criticism after Joe Rogan brought Alex Jones of InfoWars onto his show.
Audible further expands into podcasts — Audible is adding approximately 100,000 podcasts.
Apple eyes the TikTok generation with an updated version of Clips — The update brings much-needed support for vertical videos, allowing for sharing to TikTok and the “Stories” feature in other social apps.
Startups, funding and venture capital
DoubleVerify, a specialist in brand safety, ad fraud and ad quality, raises $350M — DoubleVerify’s technology can detect fraud, viewability and brand safety.
Outrider raises $65M to bring its autonomous tech to distribution yards — The startup has built a three-part system that includes an autonomous electric yard truck, software to manage the operations and site infrastructure.
Lunchbox raises $20M to help restaurants build their own ordering experiences — CEO Nabeel Alamgir said that if restaurants can handle more online orders themselves (rather than just relying on delivery apps), they’ll make more money while also maintaining a direct relationship with their most loyal customers.
Advice and analysis from Extra Crunch
As venture capital rebounds, what’s going on with venture debt? — While venture capital is back setting new records, it appears that its lesser-known sibling won’t be able to match the past few years’ results.
Current and upcoming trends in Latin America’s mobile growth — Latin America is home to one of the fastest-growing mobile markets in the world.
Dear Sophie: Any upgrade options for E-2 visa holders interested in changing jobs? — Another edition of Sophie Alcorn’s column answering immigration questions about working at technology companies.
(Reminder: Extra Crunch is our membership program, which aims to democratize information about startups. You can sign up here.)
Everything else
Qualtrics CEO Ryan Smith is buying majority stake in the Utah Jazz for $1.6B — Smith sold Qualtrics to SAP for $8 billion in 2018.
US online holiday sales to reach $189B this year, up 33% from 2019 — That’s according to a new forecast from Adobe Analytics.
The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 3pm Pacific, you can subscribe here.
What happened: Less than a week before the US presidential elections, the CEOs of Facebook, Google, and Twitter appeared before the Senate Committee on Commerce, Science, and Transportation.The four-hour hearing was meant to focus on Section 230, the regulation that has shielded internet companies from liability for user content. Most questions, however, had little to do with Section 230, instead following partisan scripts.
Republican senators charged that conservative content was being censored but provided examples of content that was fact-checked, found to be false or misleading, and labeled as such, while Democratic counterparts questioned what the platforms were doing to fight disinformation and voter suppression. Both sides asked numerous questions about posts that they personally disliked. President Trump was not at the hearing but tweeted a call for a repeal of Section 230 while it was in progress.
Twitter CEO Jack Dorsey suggested that current regulations work, but that tech companies need to regain the public’s trust. Facebook’s Mark Zuckerberg made transparency around content moderation his main suggestion for reform. Google CEO Sundar Pichai, making his first congressional appearance since the DOJ filed an antitrust lawsuit against the company last week, faced criticism for his company’s response to the filing.
Why it matters: Given the timing and the lack of substantive questions on Section 230, the reality is that this hearing didn’t matter much. But it was another indicator of the overall impatience and distaste that Americans across the country—and on both sides of the aisle—share for Big Tech. Whoever wins the White House next week, the sense was that further regulation is coming.
What’s next: Enforcement will remain a priority for lawmakers, and this hearing is far from the last time we’ll be seeing these three CEOs. Zuckerberg and Dorsey are already scheduled to appear before another congressional hearing next month on their companies’ content moderation policies. Meanwhile, you are likely to see snippets of the hearing in fundraising videos from certain senators—including a particularly shouty Ted Cruz, who had promoted the hearing as if it were a prize fight. Two places you shouldn’t see such ads, though? Twitter, which banned political ads completely, and Facebook, which started its political ad blackout on October 27.
The fake news: A new weekly satire show from the creators of South Park is using deepfakes, or AI-synthesized media, to poke fun at some of the most important topics of our time. Called Sassy Justice, the show is hosted by the character Fred Sassy, a reporter for the local news station in Cheyenne, Wyoming, who sports a deepfaked face of president Trump, though a completely different voice, hair style, and persona.
Meta commentary: The first episode, released on YouTube on October 26, took on the topic of deepfakes themselves, with Fred Sassy warning his faithful viewers that they shouldn’t believe everything they see. The satirical twist is that all the footage shown as real is, of course, deepfaked, while all the footage labeled fake is either real or played by puppets. The episode features a wide range of highly convincing deepfakes representing people including former vice president Al Gore, Facebook CEO Mark Zuckerberg, and president Trump’s son-in-law Jared Kushner, whose face is deepfaked onto a child. A deepfaked president Trump also makes an appearance.
Deepfake acting: Sassy Justice most likely uses face-swapping, which has grown increasingly popular among artists and filmmakers with the release of the open-source algorithm DeepFaceLab earlier this year. The algorithm works by training on footage of a person and then overlaying a generated version of the person’s face onto a “base actor.” Because the actor’s body, voice, and performance are retained—with the original expressions translated to the deepfaked face—impersonators are usually cast to create the most convincing final product. The process isn’t always seamless, however, so post-production editing is still required to smooth things over.
Deepfake TV: In the last year, a number of other audiovisual productions have made use of professionalized deepfakes. These include a Hulu commercial deepfaking several sports stars, a voters’ rights ad deepfaking dictators Valdimir Putin and Kim Jong-un, and the documentary Welcome to Chechyna, which for the first time used deepfakes to protect the identities of its subjects. Sassy Justice is the first example of a recurring production that will rely on deepfakes as part of its core premise.
Some people might not associate the word “trust” with artificial intelligence (AI). Stefan Jockusch is not one of them. Vice president of strategy at Siemens Digital Industries Software, Jockusch says trusting an algorithm that powers an AI application is a matter of statistics.
This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff.
“If it works right, and if you have enough compute power, then the AI application will give you the right answer in an overwhelming percentage of cases,” says Jockusch, whose business is building “digital twin” software of physical products.
He gives the example of Apple’s iPhones and its facial recognition software—technology that has been tested “millions and millions of times” and produced just a few failures.
“That’s where the trust comes from,” says Jockusch.
In this episode of Business Lab, Jockusch discusses how AI can be used in manufacturing to build better products: by doing the tedious work engineers have traditionally done themselves. The technology can help engineers manage multiple design variations for semiconductors, for example, or sift through routine bug reports that software developers would otherwise have to manually review to figure out what is causing a glitch.
“AI is playing a bigger role to allow engineers to focus more on the real, creative part of their job and less on detail work,” says Jockusch.
Also in the episode, Jockush explains how AI embedded in products themselves have already won over millions of people—think voice assistants like Siri and Alexa—and will someday become such a common component that people will barely talk about the value or the future of AI.
“I mean, how many discussions do you have nowadays about the value of Excel, of cellular calculation, although we use it every day?” says Jockusch. “Everybody uses it every day in something, and it’s so universal that we hardly ever think about it.”
Full transcript
Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma. And this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
Our topic today is artificial intelligence and how it helps companies build products. With highly focused simulations that can be run in countless ways, an enormous amount of data can be collected, analyzed, and then used to make business decisions that help humans build better products. And that will be the difference in a highly competitive market. Two words for you: trust statistics.
My guest is Dr. Stefan Jockusch, who is vice president of strategy for Siemens Digital Industries Software. He is responsible for strategic business planning and market intelligence, and Stefan also coordinates projects across business segments and within Siemens Digital leadership. This episode of Business Lab is produced in association with Siemens Digital Industries. Stefan, welcome to Business Lab.
Stefan Jockusch: Thanks for having me.
Laurel: Could you give us a sketch of your background at Siemens Digital and what you’re working on now?
Stefan: Absolutely. Yeah, so our business is the technical software business in Siemens, and the software we make supports the whole process of the initial idea of the product to all the way through the manufacturing of that product, and then including the mechanical part, the semiconductor, the software running on the device, the sensors, and then also the operation of the product.
So one of our pieces of the portfolio is an IoT [internet of things] platform, where the product then basically feeds back information about its behavior. So, all of this. And what we like to think of is our software really builds a very complete digital twin of what we use every day. And the digital twin, as I said, includes everything from the idea to the design to the manufacturing process to the operation.
Laurel: So your days are very busy. How do you fit into this entire operation?
Stefan: Yeah, my own job is, as you said, for strategy. So in strategy, we, of course, look at the overall business plan for the business. We look at our competitors, we like to understand what they do. We look at the market around us, which is a very big and complex and very dynamic market. Also, of course, we have some initiatives at all times. We look at some aspect of our business, how it will evolve, how we might have to change our business model, how we have to transform our go-to-market model, how we interact with customers.
As you know, in the software space, there’s a lot going on these days, where we move away from having software that you install with a CD-ROM or a flash memory, and you more and more expect now to find yourself around the cloud. So, all these kinds of things are aspects of our environment that keep us busy.
Laurel: In a discussion about AI, it inevitably comes up that people are fearful about it, whether they’ll lose their jobs, whether it’s here to actually help humans or some Terminator situation. But we like to take an optimistic and a forward-thinking look at how artificial intelligence works. So, when we do discuss it, I like to always really set it in a scene thinking about humans and keeping humans in the loop. As AI learns and processes data, how do you then frame human-centric AI versus a more nefarious machine-centric AI?
Stefan: I personally have a huge privilege in that discussion, which is that I did my PhD work about AI and machine learning. And that is a long, long time ago in the mid ’90s. So in the mid ’90s, it was a big topic, all this whole thing of intelligence that’s encoded in these algorithms. And there was probably the same discussion back then, “Is this going to take over? Are we going to be so perfect in automation that we don’t need any humans whatsoever? And aren’t machines becoming not only more intelligent, but only even more creative than humans ever can be?”
So that discussion is at least 25 years old, probably much longer. And nothing of that sort has happened. I would even say, after I was done with my thesis, the interest in all this machine learning stuff probably flattened out, and I would say in the last five to 10 years, it re-emerged.
And basically, that is because the compute power that we have today to do even simple things, very simple things like recognizing language or recognizing a face on a camera picture that this is very doable now. But in terms of computers becoming really more humanlike or dangerous to humans in terms of being able to be creative, I don’t think we have seen any of that. And this is now going on 25 years, so I personally believe we should be safe for another 25 years, at least.
Laurel: People will be very heartened to hear that. But it does bring up a good topic, which is trust. And where are we with AI and trust and what AI can even do today?
Stefan: There are very different opinions, I would say. And one of the reasons why the opinions are also different is that most AI algorithms don’t show you exactly how they reason. Basically, you present AI with tons and tons of data, with so much data that you cover every possibility of what you’re looking at. And if it works right, and if you have enough compute power, then the AI application will give you the right answer in an overwhelming percentage of cases.
So if you look at stuff like face recognition that’s now being used to even unlock your phone or stuff like that, so we just get to a huge reliability. And as I mentioned this example, we start to trust the technology so much that we give it jobs like recognizing identity, which is a very critical application.
So, there is a trust that’s really justified by statistics, if you want. So probably whatever company—I think it was Apple who first came with that face recognition to unlock your phone, they start trusting their technology after they really have been able to test it millions and millions of times and haven’t gotten more than a few misreads. So, that’s where the trust comes from.
Many people are still a little bit worried about it because you never can tell how exactly AI works, because you can say, “Well, it’s the information encoded in about five million parameters. This is how it works,” but you can’t exactly tell.
And I know a few experts who believe more in other learning paradigms that give you a more deterministic way and are a little bit skeptical about the classic machine-learning algorithms that others use. But frankly, my answer is as long as you know your data set and you can test it and you get statistically a hit rate of 18 nines after the decimal point percentage, then you can trust the algorithm.
Laurel: Excellent. So when we’re thinking about a company like Apple, which is probably the best example when thinking about human-centric products, how does AI fit into a product lifecycle now in 2020 compared to five or 10 years ago?
Stefan: Compared to five to 10 years ago, I try to think back myself on all we had and what we didn’t have. Because I would say in a certain modest extent, we probably had AI embedded in a lot of everyday products, again, without knowing that we have them. But, of course, that has increased dramatically, and we just briefly talked about this example of face recognition. You can say that all these smart assistants that we use today, whether they are called Siri or Alexa or Google or whatever their name is, but, of course, that’s a massive application of AI technology that we are actually getting used to.
So yeah, and it’s really becoming more and more of what identifies a product. I think that’s probably the big shift in the last years, where we really go after, what is that experience as a user? How does our product behave in a really smart and helpful and intelligent way? And that’s what ultimately, I think, creates a lot of our desire to have it and our loyalty to the brand.
Laurel: So if you are one of these engineers who are trying to build this smart and unique product, where do you see AI being integrated to help those engineers and product designers make the best product they possibly can?
Stefan: Yeah, that’s getting big, actually. So, AI is basically very good when it comes to taking over heavy-lifting type of work and to allow the engineer to focus on real creative work. And you wouldn’t imagine how much heavy lifting work an engineer has to do every day. One thing that actually we put in our software, which is a feature that watches a user and starts predicting what that user might do next—basically make a recommendation to saying, “Isn’t that what you had in mind of doing next?”
And that, of course, makes it much faster for the user to go through a certain work process. And maybe as for an experienced user, it’s just faster. And for an inexperienced user, it may save a lot of time, where that user isn’t really sure what the next step should be and starts digging through Help menus and the menus on the screen and so on, so doing all this unproductive stuff. So I think in short, there’s a lot of heavy-lifting work that AI is taking over.
Another example is what we use in our semiconductor design side is the semiconductor designers have a lot of boundary conditions and variation of their designs. They have to keep it in mind then when they make a change. So AI is already helping them manage variations and just supporting the engineer here.
Or another example is, when you develop software, you get these bug reports and you get hundreds of them and you have to read them all and manually figure out which component of your software is responsible for that bug. So that’s another function that is now being automated by AI because it’s another piece of work that’s really a lot of tedious, detailed work. So, I think AI is playing a bigger role to allow engineers to focus more on the real, creative part of their job and less on detail work.
Laurel: Yeah. And that’s a bonus and a benefit for everyone, right? More creativity and less tedious work.
Stefan: Absolutely.
Laurel: So when we bring this up a level and we think about sharing data and connecting systems within a modern organization, how does this idea of sharing data and sharing scenarios and simulations and experiences help the organization actually start that evolution?
Stefan: Yeah, I think the simple answer is as everything is becoming digital, so every organization is more dependent on data than it probably was 20 years ago. So we live off data. And as we just started talking about, if you want to take any use out of AI, you need lots of data. You need so much data that ultimately your AI can extract something meaningful out of it.
And the problem is, of course, that historically as every business has become more digital, we have created these islands of data basically because we solved one problem first. So we created product lifecycle management, which is the place where you hold the data for design, but then we have also the ERP system, enterprise resource management, which is like SAP, which holds all the business data. That’s a different data repository.
And if you really look closely into complex manufacturing companies, they have dozens and dozens of data repositories and they are all disconnected. And that’s a challenge.
It’s the next level of what has to happen is that we’ll start bringing together these very disparate, these islands of information, and we start connecting them because ultimately when you hold a product in your hand, all of these data from different sources are in there. So, after we have figured how to put anything we can into an electronic database, the next step is going to be to bring those data sources together.
Laurel: So, in your experience, why is this valuable? Have you seen anything particularly exciting come out of disparate databases brought together for business decisions or just something surprising that helped a client or a customer do something interesting?
Stefan: Yeah. You put it very positively. I think I have a lot of negative examples where a seemingly small change in one of the islands of information has a huge impact, but there’s no chance to see it without knowing the other data. In the automotive industry, like the mechanical design and the electrical design, it basically was born independently, and it’s only right now, automakers are figuring out better and better how to bring these worlds together because they have to.
Just as an example, if you develop the electrical system of a vehicle, you might think that at some point, “I need an extra wire here—I can’t solve it differently.” So I add a wire to my wire harness and just make it a little thicker. So it may look like a fairly modest change where you are sitting, you’re just saying, “We’re changing from a diameter of 0.8 inches to 0.82 inches. That can’t be so dramatic.”
But your mechanical colleague has probably figured out where to put this wire harness in the vehicle, and he might’ve already ordered the tooling to do metal bending and really to build a cable channel that will exactly fit 0.8 inches, but not 0.82. This kind of problem still occurs in that industry.
And the background is really, a lot of the products that we use today, automobiles, but also electronics, cell phones, and so on, they are very highly optimized. If you open the hood of a 20-year-old car, you see a lot of space in there where you could put stuff. If you pop the hood of a modern car, there’s almost no space, there’s no wiggle room.
And because of that lack of wiggle room, it’s really more critical today than ever to understand if I change a little thing in my world, what happens to somebody else’s world? And I think this is where you see I have lots of examples of what can go wrong if you don’t take this into account, but there are, of course, certainly a big potential also of avoiding these mistakes.
Laurel: Yeah, lots of opportunities there eventually, but that challenge is bringing all that data together. So, when we think about this, obviously new, but necessary attention to data, machine learning, and AI, how will it help spur on competition and accelerate a company’s product offerings?
Stefan: Yeah. As I said, I think, as consumers, as most of us who could buy technical products, we more and more, I think, get excited by these very smart types of functionality. And you probably agree, I mean, the day that a really reliable and affordable self-driving car hits the market, we will be very interested. And I think we are already interested if somebody tells us, “OK, this car can actually parallel park without you touching anything.”
That is super exciting. So I think in general, AI-driven functionality will probably have a big part in differentiating what a business can offer, probably be a little, even as exciting or more exciting than the looks of a product or the aesthetics or other parameters like this.
I think also AI and electronics in general is coming into more and more types of products that haven’t been so heavy in electronics before like, imagine running shoes or sports equipment are getting smarter year by year and more and more things have chips in them. So I think overall, it’s becoming more and more of a differentiator and a way to attract people and also build these ecosystems of intelligent applications that get people hooked.
Laurel: Yeah, it’s excellent for the consumer. You can see that. For the person who builds it and the engineer in the production of it, how will AI help keep that human in the loop? How will AI help a person’s job? We talked a little bit about improving creativity, what else helps?
Stefan: Yeah. As I said, my skepticism of humans really being replaced is not so high than it might be for some other people. And I think as we have started talking about the most AI applications that are now basically going into supporting the workplace actively, again, they’re mostly focused on making the human more productive and being an assistant and taking care of detail work of heavy lifting that humans aren’t as good at and they are also not as interested in.
And, of course, you can always make the argument, “Yeah, if I make humans 10 times more productive, doesn’t that mean I can let go nine of 10 workers if I achieve this?” So theoretically, that’s true. I would frankly say, that’s really not what we have been seeing in the past. For some miraculous reason, before covid-19 started, unemployment in the US was continuously going down for, I would say, ever since 2008. So whatever productivity was achieved in that time, did not really lead to job losses.
And if you look at technical professions, I think there’s still a shortage of engineers, which you would think, “OK, if I make engineers more productive, shouldn’t I lose a lot of engineering jobs?” That’s really not what we have been seeing.
And I think one of the explanations is, number one, the more productive we get, the more sophisticated products become and the more there’s at some point consumption growth.
And secondly, you always need experts to deal with the latest technology you come up with. I mean, before we had cars, we didn’t need car repair shops. Now, we do. So it creates a new profession. And I think with AI, you will probably create professions that will be about really making it work, making the application stable. And so to me, it’s very hard to predict if it’s really going to hurt things like job markets. I would say, that’s really not what we have been seeing.
Laurel: Great. So when we think about AI, benefits of it, how can AI be an invisible aid to these people building, designing, producing?
Stefan: I would say, for the most part, it probably already is, because again, I think we had a few examples. Many times you don’t really see where it is in action or not. Of course, if your computer makes a recommendation, what you might do, you will think, “OK, some intelligent instance is there helping me.” But in many other areas, you might not even realize it.
There are of course, some very exciting spaces, where we make it very visible or we get to see it very visible because we are actually getting better and better at completely automating the creative work, for example, of creating structures that are biologically inspired. So as you know, today, there’s a technology in manufacturing called 3D printing that is very flexible in what the shape looks like that you build. And there are technologies that can really take the boundary conditions of a design and then let the computer figure out what the right geometry is. And what comes out of that usually looks like a vegetable or a plant. So, very funny structures get out of this.
So in that case, I have made this intelligence very visible, and I have really taken the whole job of designing out of the hands of the human being, and then the machine comes back with something that you almost think it could grow in your garden.
So those are the very visible things that I think are extremely exciting, because again, if a machine can do it and can do it even in a more elegant way, why should the human then be bothered by it and not think about the more creative aspects?
But on the other hand, I think there’s a lot of functionality already that we really don’t get to realize that AI is supporting it. And I think over time, I almost think 10 years from now, we might even not have so much discussion about the value or the future of AI or how it will evolve because we will be so used to it. I mean, how many discussions do you have nowadays about the value of Excel, of cellular calculation although we use it every day? Everybody uses it every day in something, and it’s so universal that we hardly ever think about it.
So, to me, there are two possibilities 10 years from now: either we are so used to it that we barely ever talk about it, or we hit another wall, like that was hit in the late ’90s, where you figured out there’s so much it can do, but we don’t have strong enough computers to have it do more. So we forget a little bit about it. And then 20 years later, we have yet faster computers and we, again, get all excited.
Laurel: That’s amazing, I love the idea of somehow acquainting AI to be as commonplace as a spreadsheet. You’ll just use it and you won’t even know it, but your life will be better because you have it. Stefan, thank you so much for joining us today in what has been an intriguing on Business Lab.
Stefan: Absolutely. Thanks for having me.
Laurel: That was Stefan Jockusch, a vice president of strategy for Siemens Digital Industries Software, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review, overlooking the Charles River.
That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology. And you can find us in print, on the web, and at dozens of events each year around the world and online.
For more information about us and the show, please check out our website at technologyreview.com. This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Collective Next. Thanks for listening.
It’s a cruel irony that the things that make a restaurant appealing are precisely what currently make it dangerous—the intimacy, the coziness, the groups of people deep in conversation, whiling away the hours over drinks and a meal. Eating in a restaurant is one of the riskiest things you can do during the coronavirus pandemic.
To understand why, you need to think about the latest science around how covid-19 passes from person to person. The official line from the World Health Organization from the start of the pandemic has been that the coronavirus is mostly spread by the droplets we generate as we talk, sneeze, or cough. However, the evidence has been mounting for months now that aerosols—which are smaller than droplets and can hang in the air like smoke—are a significant route for infections, if not the main one. This would explain why virtually every recorded coronavirus outbreak has occurred indoors.
Sadly, the advice to the public still hasn’t caught up. The US Centers for Disease Control and Prevention has only just started to acknowledge the possibility of airborne transmission, and many countries don’t mention it in their official guidance. As a result, many restaurants are still stuck following advice that simply isn’t reflective of the latest science—obsessing over cleaning, wearing visors (which don’t protect you from aerosols), and setting up plastic dividers between tables. Some of these measures might be marginally useful, but they mostly amount to “pandemic theater”—interventions that provide the appearance of safety, but little in the way of real protection.
Why, exactly, are restaurants so risky? First off, they tend to be noisy spaces. People talk loudly, expelling more air than usual—and thus more potentially virus-laden aerosols. Researchers are yet to work out precisely how much virus you have to breathe in, or how long you have to be exposed to someone shedding viral particles, to get infected. The CDC estimates it’s possible to get infected from just 15 minutes of close proximity, but the reported cases of infections in restaurants “all involve an infected and susceptible person sharing the air for a significant amount of time, often 30 minutes up to a few hours,” says Jose-Luis Jimenez, a chemistry professor at the University of Colorado, Boulder, who has studied aerosols for two decades. It’s also possible, theoretically, to catch covid-19 through the aerosols left behind by an infected person who has already left the room—but there aren’t any confirmed cases of this occurring, according to Jimenez. The virus loses infectivity with time, “typically in one to two hours,” he says.
Then there’s the lack of mask-wearing inside restaurants. Diners tend to take them off, because you can’t eat or drink while wearing one. You may have heard that ventilation is pretty important as well—another area in which restaurants typically score poorly. Inadequate ventilation allows tiny virus particles to hang in the air for long periods of time, just waiting to be breathed in.
And of course, for any restaurant to be successful, it needs to be popular enough to attract people from around a neighborhood, city, or even further afield to come and dine under the same roof. It’s hard to imagine a more inviting setting for an airborne pathogen like SARS-CoV-2 to spread (other than perhaps cruise ships). It’s little wonder, then, that restaurants have shown themselves to be the perfect breeding ground for superspreading events, where one person passes the virus to dozens of others. Virtually every documented case of superspreading has taken place in a noisy, poorly ventilated room—many of them restaurants.
At the start of October, Public Health England found that for people who’ve tested positive for the coronavirus in the last two months, “eating out was the most commonly reported activity in the two to seven days prior to symptom onset.” Scotland’s government has consistently found that a quarter of people returning positive tests for covid-19 had been to a restaurant, pub, or cafe in the week before. In September, a CDC study of 802 adults in the US found that people who tested positive for covid-19 were approximately twice as likely to have reported dining at a restaurant than those who tested negative.
“Without a doubt, there’s an association there,” says Nathan Shapiro, a professor of emergency medicine at the Beth Israel Deaconess Medical Center, one of the authors of the CDC study.
With the growing case against dining out, it’s no wonder the pandemic has devastated the restaurant business. While some big-name chain restaurants with drive-through and takeout options have thrived, tens of thousands of dine-in restaurants have been forced to close, potentially taking millions of people’s livelihoods with them.
Making eating out safer
Despite the dire outlook, there are ways to reopen restaurants while minimizing the risk of infection “Any time there are people indoors there is risk,” says William Bahnfleth, a professor of architectural engineering at Pennsylvania State University. But many of the dangers can be mitigated. The crucial thing to remember is that no one measure is enough on its own; increasing safety is about layering as many different efforts on top of one another as possible.
First and foremost, people should eat outdoors whenever possible. “The risk of infection is 20 times higher inside than outside,” says Bjorn Birnir, director of UC Santa Barbara’s Center for Complex and Nonlinear Science. However, some restaurants either can’t get the approval for outdoor seating from their local authorities or don’t have the money for outdoor furniture or the patio heaters that will help make diners comfortable as winter rapidly approaches in the Northern Hemisphere.
If outdoor seating isn’t possible, eateries should focus on simpler stuff. Servers need to wear masks, as should customers while they’re not at their table. Although masks won’t prevent all aerosols from getting through, they will stop some. Tables should be as far away from each other as possible. Again, this isn’t a perfect solution—but the farther away you are from other groups of customers, the less likely you are to inhale a big concentration of their breath. Use the measures you’d take to try to avoid secondhand smoke as an analogy, says Jimenez.
Some adaptations are more inventive. For example, restaurants should turn the music down to discourage customers from talking loudly, says Sam Harrison, who owns a brasserie called Sam’s Riverside in London. And although it might feel unnatural, it’s a good idea for diners to sit diagonally from anyone who isn’t in their household. Simulations generated by the supercomputer Fugaku in Japan found that about 75% fewer droplets will reach you that way than if you sit opposite someone.
It’s difficult to judge how safe a restaurant is just by looking at it. You can’t tell at a glance how many air changes per hour are taking place. Bahnfleth, the architectural engineer, says you want to aim for about six full replacements of the indoor air volume per hour—perhaps achievable by something as simple as opening a window or a door. It’s tricky to measure the air change rate without hiring expensive air quality consultants, but one shortcut could be to use a carbon dioxide monitor (you can buy these for about $150) as a proxy. If your levels stay below about 800-950 parts per million, ventilation is probably sufficient.
Keeping score
Restaurateurs who want to get an idea of how well they’re addressing risk can use one of the free online risk estimators from places like Setty, an engineering firm, experts at Oregon University, or the University of Colorado, Boulder. These models let you input details about your space—size, ceiling height, average occupancy, and so on—and then produce a score that tells you roughly how safe it is. The risk scores are based on modeling of relative aerosol risk, and they require a good basic grasp of numeracy and science, but they can be a useful tool. “These are the best things we have, but they’re still based on a fairly uncertain degree of knowledge about how much virus an infected person sheds, and how much you need to inhale to get infected,” Bahnfleth says. Although they’re based on peer-reviewed science, they should be taken as guides rather than immutable truths, because they rely on many unknowns (they can’t know, for example, if people are wearing their masks correctly).
If open doors aren’t an option, air purifiers can dispose of more than 99% of aerosols in the air stream that passes through them. Some restaurants may already have these installed as part of their overall heating, ventilation, and air conditioning system. For those that don’t, standalone purifiers cost about $100 apiece and can be placed around the dining area.
Finally, there’s a category of interventions that might be marginally useful but verge on pandemic theater. Temperature checks are widespread and highly visible, and can help to weed out some people with symptoms—but they do nothing to prevent asymptomatic people from entering the premises. Dividers between tables, meanwhile, could stop people from sneezing or coughing on each other, but are useless to stop aerosol transmission.
The sad truth is that as long as there are high levels of virus circulating in a community, people are going to be justifiably nervous about eating out. That’s something restaurant owners can’t control. All they can do is adapt—more takeout meals, more outdoor seating—and try to survive. Harrison, the owner of Sam’s Riverside, doesn’t see a return to pre-pandemic levels of profit for the foreseeable future. “It won’t kill us, but it’ll get pretty damn close,” he says.