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Podcast: What’s AI doing in your wallet?



Podcast: What’s AI doing in your wallet?

Our entire financial system is built on trust. We can exchange otherwise worthless paper bills for fresh groceries, or swipe a piece of plastic for new clothes. But this trust—typically in a central government-backed bank—is changing. As our financial lives are rapidly digitized, the resulting data turns into fodder for AI. Companies like Apple, Facebook and Google see it as an opportunity to disrupt the entire experience of how people think about and engage with their money. But will we as consumers really get more control over our finances? In this first of a series on automation and our wallets, we explore a digital revolution in how we pay for things.

We meet:

  • Umar Farooq, CEO of Onyx by J.P. Morgan Chase
  • Josh Woodward, Director of product management for Google Pay
  • Ed McLaughlin, President of operations and technology for MasterCard
  • Craig Vosburg, Chief product officer for MasterCard


This episode was produced by Anthony Green, with help from Jennifer Strong, Karen Hao, Will Douglas Heaven and Emma Cillekens. We’re edited by Michael Reilly. Special thanks to our events team for recording part of this episode at our AI conference, Emtech Digital.



Strong: For as long as people have needed things, we’ve… also needed a way to pay for them. From bartering and trading… to the invention of money… and eventually, credit cards… which these days we often use through apps on our phones.

Farooq: No one, 10 years ago, no one thought that, you know, you’d be just getting up from a dinner table and using Zelle or Venmo to send five bucks to your friend. And now you do.

Strong: The act of paying for something might seem simple. But trading paper for groceries…or swiping a piece of plastic for new clothes is built on a few powerful ideas that allow us to represent and exchange things of value. 

Our entire financial system is built on this agreement… (and trust). 

But this model is changing… and banks are no longer the only players in town. 

[Sounds from an advertisement for Apple Card] 

[Ad music fades in]

Announcer: This is Apple Card. A credit card created by Apple—not a bank. So it’s simple, transparent, and private. It works with Apple Pay. So buying something as easy as: *iPhone ding*.  

Strong: It’s not just Apple. Many other tech giants are moving into our wallets…  including Google… and Facebook… 

[Sounds from Facebook’s developer conference] 

Mark Zuckerberg: I believe it should be as easy to send money to someone as it is to send a photo. 

Strong: Facebook Pay works through it’s social apps—including Instagram and Whatsapp—and executives hope those payments will one day be made with Facebook’s very own currency. 

And beyond what we use to pay for things, how we pay for things is changing too.

[Sounds from an advertisement for Amazon One]

Announcer: Introducing Amazon One. A free service that lets you use your palm to quickly pay for things, gain access, earn rewards and more.

Strong: This product works by scanning the palm of your hand… and it’s not just for payments. It’s also being marketed as an ID. Something like this could one day be used to unlock the door at the office or to board a plane. 

But letting companies use data from our bodies in this way raises all sorts of questions—especially if it mixes with other personal data. 

Vosburg: We can see in great detail how people, for example, are interacting with their device. We can see the position in which they’re holding it. We can understand the way in which they’re typing. We can understand the pressure that’s being applied on the screen as people are hitting the keystrokes. All of these things can be useful with the combination of artificial intelligence to process the data to create sort of an interaction fingerprint. 

Strong: I’m Jennifer Strong, and in this first of a series on automation and our wallets, we explore a digital revolution in how we pay for things.


Farooq: So, if you think about how we operate today, we primarily operate through central authorities. 

Strong: Omar Farooq is the CEO of Onyx… from J.P. Morgan. It focuses on futuristic payment products. 

Farooq: Frankly, the biggest central authority in some ways is, in the US, for the money purpose, is the US federal reserve and the U S Treasury. You pull out a dollar bill. It says U S Treasury. It’s issued by the, you know, in some ways, quote-unquote, the top of the house. The top of the house guarantees it. And you carry it around with you. But when you give it to someone, you’re ultimately trusting that central authority in how you are transacting.

Strong: This can be a good thing. The value of that otherwise worthless paper bill is guaranteed because it’s issued and backed by the US government. But it can also slow things down. And though we now take for granted being able to transfer money in real time, the ability to do so hasn’t been around that long.   

Farooq: Payments actually do, as a technology, evolve somewhat slowly. Just to give you an example, the U S recently, a couple of years back, launched the real-time payments scheme, which literally was the first new payments, you know, sort of, rails in the U S for decades. As crazy as that sounds. 

Strong: A payment rail is the infrastructure that lets money move from one place to another. And those “real time payments” are a big deal because until recently when money left your account it took time, often days, before it reached its destination.

It’s why we can send money through apps like Venmo and hear the ding that it’s been received on the other person’s phone just a few seconds later. Also, Venmo’s chief competitor, called Zelle, only exists because of unprecedented cooperation between otherwise competing banks.

Farooq: I think where the world is going is towards more open platforms where it’s not just one party’s capabilities, but multiple parties’ capabilities that come together. And the value that is generated is by the ability for anyone to connect to anyone else. So I think what we are seeing is a rapid evolution in the digital sphere where more and more payment types, whether they are wholesale or retail are going into new modes, new rails, 24/7, 365, the ability to pay anyone anywhere in any currency. All those things are basically getting accelerated. 

Strong: This is where cryptocurrencies could come in. Which isn’t just about digital money. 

Farooq: We believe that there’s a path forward where money can be smarter itself. So you can actually program the coin and it can control who it goes to.

Strong: In other words, the trust we usually place in banks or governments would be transferred to an algorithm and a shared ledger. 

Farooq: So you’re almost relying on that decentralized nature of the algorithm and say, “I think I can trust your token coming to me” because there’s, you know, X… X thousand or X hundred-thousand copies of a ledger that shows you as the owner of that token. And then when you give it to me, All those copies get updated. And now this shows me as the owner of that token. 

Strong: And not only could this make payments faster and more seamless. It could also help people who’ve been largely excluded from the banking system.

Farooq: No matter what we do, we cannot really get around this Know Your Customer issue. And I think, you know, our view is that the tech is almost there, but the regulation and the infrastructure around it is not there yet. But, what we do want to do is we want to create these decentralized systems where these people can, over time, be included.

Strong: But sorting out the tech… is just one side of the coin. There’s also a need for better regulation.

Farooq: But I think it’s unfortunately a little bit more than what a bank could do. I think this.. some of these things rise to the level of like, you know, how does a government, or how does a state really enable identity at a global level? And I think that’s why when you look at China or you look at Nordics or some of those countries, I mean, you have national IDs and you have a very standardized method of knowing who someone is.

Strong: And the shift it allows in banking can be transformative…  

Farooq: So if you look at a country like India, India has made dramatic progress in how many people have gone from being unbanked to banked in terms of having a wallet on their mobile phone. So I think these technologies are going to turbocharge people’s ability to come into this ecosystem. What I would hope as someone who grew up in the developing world before migrating here is that you would make those connections so, you know, everyone in those countries has access to markets—to bigger markets. So I mean, whether you’re sitting in Sub-Saharan Africa or you’re sitting in like, you know, a village in India or Pakistan or Bangladesh, wherever, you can actually sell something through Amazon and get paid for it. I mean, you know, those sorts of things. I think there’s tremendous potential human potential that could be unlocked if we could take payments in a digital manner to some of those parts of the world.

Strong: And this vision?… extends not only to connecting anyone, anywhere to a bank… but also anything with an internet connection. 

Farooq: doing some initial R and D work in the IOT space, which is, if, you know, I mean, if one day your fridge had to order milk by itself. Like, does it have to go through your bank or could it just send the money to someone who’ll deliver your milk?

McLaughlin: Every device you use has potential to be a commerce device and our network brings that together.

Strong: Ed McLaughlin is president of operations and technology for MasterCard. He’s speaking at our A-I conference, EmTech Digital.

McLaughlin: So, what all of that connectivity results in? Is.. bringing together pretty much every financial institution in the world, tens of millions of merchants, governments, tech CO’s, and all of that, which results in billions of transactions a year we see. MasterCard across all of those devices and cards is serving about two and a half billion accounts. So we get the data and transactions from a Facebook sized population, if you think about that… And as far as the scope goes, we’ve been probably seeing 20 to 25% of all internet transactions outside of China—since there was an internet.

Strong: But this connectivity creates its own set of new problems. Maybe you’ve had the experience of going out of town and suddenly your card stops working because the change of location triggered a fraud alert. 

McLaughlin: One of the keys in applying AI is how you frame the question and our teams very early on and said it wasn’t to stop transactions. It was to make sure as many good transactions as possible made it through.

Strong: Another key is to have an abundance of data.

McLaughlin: It’s a massive in-memory grid in our network that holds over 2 billion card profiles with about 200 analytical vectors on it. And we make decisions in every transaction that flows through. We have less than 50 milliseconds to make that decision. So in order to do  that, we have 13 different AI technologies that we’ve modeled and experimented over the years that we apply to it.

Strong: Banks are also turning to A-I to look for money laundering. In the physical world, organized crime is often hidden behind the storefronts of real businesses. And in the digital world? Hiding is even easier.

Illegal money can quickly change hands dozens of times and cross borders until there’s no clear trail back to its source. It’s a massive problem. And most of it goes undetected. It’s possible only one percent of the profits earned by criminals gets caught. And the turmoil of the global economy over the last year has only made things worse.

McLaughlin: Our adversary.. They’re using AI too. And if you look online, it’s just bots fighting bots. So you have to pick up things you weren’t looking for before, like low and slow attacks where they stay inside, what looks like acceptable tolerances, but they’re constantly probing or doing a tumbler attack on your systems. Hard to pick up. When COVID hit, you know, the world moved online. Spending patterns shifted dramatically. And what we were able to do because the AI’s are rich enough and look at so many different variables.. We were able to really tell you’re still you and you’re just behaving a little bit differently. 

Strong: And the types of attacks change too…  

McLaughlin: So we saw one attack factor, which was pretty amazing is they thought, okay, people won’t block transactions for personal protective gear. It’s a specific merchant class we have. And we saw the fraudsters pile on in trying to get transactions through because they figured nobody would be blocking. The good news is we look at enough other elements that we could immediately pick that up and block those transactions. 

Strong: They’re building machine learning tools to identify patterns of normal activity. And to flag outliers when they’re detected. Humans can then double check those alerts and approve or reject them.

McLaughlin: We constantly have AIs running also, not just blocking the fraud or looking at it, but I’m just calling it weirdness detection—where we’re constantly predicting what we would expect to see. In fact it’s a great way to step into AI because you have KPIs you’re already tracking. Try to start predicting them. When you see something which is an immediate deviation from it, the first thing we actually do is say, what’s going on here? So we may see something the model hasn’t caught up to, we just throw a rule to block it. And we can do that instantly.

Strong:  The payments industry used to be slow moving… but it’s adapting to a world where any device might one day be connected to a payments network… including self driving cars.

McLaughlin: So whether you’re using your browser to order online, if it’s your iPhone, we’re using an Apple Pay to tap, or Mercedes just announced that, uh, they’re going to be connecting their cars to gas pumps. So you can simply drive up and authorize your transaction, right from your car. And in fact, as things move away from the card and to devices, we’re seeing even more data coming in through the network. 

Strong: We’ll be back… right after this.


Strong: With more and more of our financial lives being documented, tracked and mediated online, that data turns into fodder for AI—which is being enlisted into a whole host of other roles with payments. 

Woodward: People have a really complex relationship with their money. It can be stressful. It’s often boring a lot of the time. 

Strong: Josh Woodward leads the Google Pay team for the US. He sees it as an opportunity to change not just payments…but the entire experience of how people think about…and engage with…their money. 

Woodward: And so what we’re trying to do as a team is think about how can we simplify that relationship with money where people feel in control and they feel confidence when they’re using our app and seeing how their spending is going in and out. 

Strong:  Google Pay began as a peer to peer payment solution—where the main goal was digitizing the plastic cards in your wallet. But over the years, it’s evolved into a tool meant to help you more holistically manage your finances, and relationships with businesses. 

Strong: And it’s taken some cues from social media. Instead of card numbers or accounts, transactions are organized around pictures of people and businesses you’ve recently paid. 

Woodward: We realized that transactions, in some ways, the.. the money, that the digits, the dollars and cents, is secondary. It’s a lot more about the person or the memory around that transaction. So we’ve tried to bring that out. Similarly, we’ve taken that same relationship based design and applied it to businesses. And this is something that’s very different. So when you look today at our home screen, // what you see is actually the icon of the business. And when you tap on that, you are taken to that business page where you can actually. Really see, like your relationship with the business.  If  you have a loyalty card you can see that there, you can see how your points are progressing. So the next time you go buy, you can get 20% off for example. And so we’ve tried to create this… Really almost like a threaded relationship of all your activity with that business inside the Google Pay app a little bit like Gmail, threaded email messages.

Strong: It also lets users sort transactions in a way that mirrors a web search.

Woodward: So you can do things like search for food. And you’ll get all of the transactions at places where you bought food and Google Pay can understand that this restaurant, for example, is a restaurant. You don’t have to go in and manually categorize that. Or you can get more specific and do things like a search for Mexican restaurants. And it’ll just take that subset of Mexican restaurants. There’s no part of that transaction that has the phrase, Mexican restaurant in it. Google Pay’s able to make that connection for you. 

Strong: And using computer vision…it can sort through photos of receipts.

Woodward: What we’ve been able to do in Google Pay, again with someone’s permission, this feature is off by default, is that you can say, I want all the photos I’ve taken of receipts to be searchable in Google Pay. And what that allows you to do is actually search very specifically for individual items that are printed on the receipt. So for example, a couple of months ago, before Christmas, I bought a shirt, uh, it was a Christmas present from Lulu. I can go into Google Pay now and search for “shirt.” And that Lulu receipt comes up. 

Strong: It’s designed to give users a greater sense of control over their spending.

Woodward: It creates a place where you get that full picture. And that’s what we’ve seen. Time and time again, in the research and in talking to people is that different apps have provided different slices of that picture, but being able to bring it all together is really what we aspire to.

[music transition]

Strong: It’s one more way our lives might become a little easier and more efficient with the help of technology… But also where the gathering… filtering… and processing… of vast amounts of personal data raises big questions… even before we get to things like paying with our faces or gestures… or how all of that data… might mix with the rest of our massive data trails.

And longer-term, what would it mean for companies like Facebook to establish their own currencies and take over the global payments system? 

It’s worth asking whether we as consumers really get more control over our finances… or companies get more control over us…


Next episode… 

[SOT: Siri Promo]

Bennett: We couldn’t have imagined something like Siri or Alexa. You know we just thought we were doing just generic phone voice messaging… and so in 2011 when suddenly Siri appeared, it’s like, “I’m WHO??” [laughing]… “WHAT??”… 

Strong: We look at what it takes to make a voice… and how that’s rapidly changing.


Strong: This episode was produced by Anthony Green, with help from Jennifer Strong, Karen Hao, Will Douglas Heaven and Emma Cillekens. We’re edited by Michael Reilly. Special thanks to our events team for recording part of this episode at our AI conference: Emtech Digital.


Optimizing platforms offers customers and stakeholders a better way to bank



Optimizing platforms offers customers and stakeholders a better way to bank

And so, they’ve started to see the benefits of doing things themselves. So, culture change I think has been one of the biggest things that we’ve achieved in the past few years since I joined. Second, we built a whole set of capabilities, we call them common capabilities. Things like how do you configure new workflows? How do you make decisions using spreadsheets and decision models versus coding it into systems? So,  you can configure it, you can modify it, and you can do things more effectively. And then tools like checklists, which can be again put into systems and automated in a few minutes, in many cases. Today, we have millions of tasks and millions of decisions being executed through these capabilities, which has suddenly game-changed our ability to provide automation at scale.

And last but not least, AI and machine learning, it now plays an important role in the underpinnings of everything that we do in operations and client services. For example, we do a lot of process analytics. We do load balancing. So, when a client calls, which agent or which group of people do we direct that client call to so that they can actually service the client most effectively. In the space of payments, we do a lot with machine learning. Fraud detection is another, and I will say that I’m so glad we’ve had the time to invest and think through all of these foundational capabilities. So, we are now poised and ready to take on the next big leap of changes that are right now at our fingertips, especially in the evolving world of AI and machine learning and of course the public cloud.

Laurel: Excellent. Yeah, you’ve certainly outlined the diversity of the firm’s offerings. So, when building new technologies and platforms, what are some of the working methodologies and practices that you employ to build at scale and then optimize those workflows?

Vrinda: Yeah, as I said before, the private bank has a lot of offerings, but then amplify that with all the other offerings that JPMorgan Chase, the franchise has, a commercial bank, a corporate and investment bank, a consumer and community bank, and many of our clients cross all of these lines of business. It brings a lot of benefits, but it also has complexities. And one of the things that I obsess personally over is how do we simplify things, not add to the complexity? Second is a mantra of reuse. Don’t reinvent because it’s easy for technologists to look at a piece of software and say, “That’s great, but I can build something better.” Instead, the three things that I ask people to focus on and our organization collectively with our partners focus on is first of all, look at the business outcome. We coach our teams that success and innovation does not come from rebuilding something that somebody has already built, but instead from leveraging it and taking the next leap with additional features upon it to create high impact business outcomes.

So, focusing on outcome number one. Second, if you are given a problem, try and look at it from a bigger picture to see whether you can solve the pattern instead of that specific problem. So, I’ll give you an example. We built a chatbot called Casey. It’s one of the most loved products in our private bank right now. And Casey doesn’t do anything really complex, but what it does is solves a very common pattern, which is ask a few simple questions, get the inputs, join this with data services and join this with execution services and complete the task. And we have hundreds of thousands of tasks that Casey performs every single day. And one of them, especially a very simple functionality, the client wants a bank reference letter. Casey is called upon to do that thousands of times a month. And what used to take three or four hours to produce now takes like a few seconds.

So, it suddenly changes the outcome, changes productivity, and changes the happiness of people who are doing things that you know they themselves felt was mundane. So, solving the pattern, again, important. And last but not least, focusing on data is the other thing that’s helped us. Nothing can be improved if you don’t measure it. So, to give you an example of processes, the first thing we did was pick the most complex processes and mapped them out. We understood each step in the process, we understood the purpose of each step in the process, the time taken in each step, we started to question, do you really need this approval from this person? We observed that for the past six months, not one single thing has been rejected. So, is that even a meaningful approval to begin with?

Questioning if that process could be enhanced with AI, could AI automatically say, “Yes, please approve,” or “There’s a risk in this do not approve,” or “It’s okay, it needs a human review.” And then making those changes in our systems and flows and then obsessively measuring the impact of those changes. All of these have given us a lot of benefits. And I would say we’ve made significant progress just with these three principles of focus on outcome, focus on solving the pattern and focus on data and measurements in areas like client onboarding, in areas like maintaining client data, et cetera. So, this has been very helpful for us because in a bank like ours, scale is super important.

Laurel: Yeah, that’s a really great explanation. So, when new challenges do come along, like moving to the public cloud, how do you balance the opportunities of that scale, but also computing power and resources within the cost of the actual investment? How do you ensure that the shifts to the cloud are actually both financially and operationally efficient?

Vrinda: Great question. So obviously every technologist in the world is super excited with the advent of the public cloud. It gives us the powers of agility, economies of scale. We at JPMorgan Chase are able to leverage world class evolving capabilities at our fingertips. We have the ability also to partner with talented technologies at the cloud providers and many service providers that we work with that have advanced solutions that are available first on the public cloud. We are eager to get our hands on those. But with that comes a lot of responsibility because as a bank, we have to worry about security, client data, privacy, resilience, how are we going to operate in a multi-cloud environment because some data has to remain on-prem in our private cloud. So, there’s a lot of complexity, and we have engineers across the board who think a lot about this, and their day and night jobs are to try and figure this out.

As we think about moving to the public cloud in my area, I personally spend time thinking in depth about how we could build architectures that are financially efficient. And the reason I bring that up is because traditionally as we think about data centers where our hardware and software has been hosted, developers and architects haven’t had to worry about costs because you start with sizing the infrastructure, you order that infrastructure, it’s captive, it remains in the data center, and you can expand it, but it’s a one-time cost each time that you upgrade. With the cloud, that situation changes dramatically. It’s both an opportunity but also a risk. So, a financial lens then becomes super important right at the outset. Let me give you a couple of examples of what I mean. Developers in the public cloud have a lot of power, and with that power comes responsibility.

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The Download: China’s EV success in Europe, and ClimateTech is coming



The Download: China’s EV success in Europe, and ClimateTech is coming

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Meet Europe’s surprising best-selling Chinese EV maker

China’s electric vehicle sector has been lavished with fame and attention. But its global ambitions hit a roadblock this month when the European Commission launched an investigation into whether Chinese-made EVs benefit from excessive government subsidies. 

If the inquiry finds evidence for this claim, which experts say is very likely, it could result in increased import duties for Chinese-made EVs, which would likely make them less competitive in European markets. 

Many of the Chinese brands that are causing concern are well-known names in China, like the established giant BYD and the promising startup Nio. But there’s one name in the mix you might not expect—former British luxury sports car maker MG. Read the full story.

—Zeyi Yang

Zeyi’s story is from China Report, MIT Technology Review’s weekly newsletter giving you the inside track on all things happening in tech in China. Sign up to receive it in your inbox every Tuesday.

If you’re interested in reading more about China’s car sector, why not check out:

+ Europe is about to crack down on Chinese electric cars. The European Commission is set to launch an anti-subsidy investigation into Chinese automakers. Here’s what you need to know about the likely impact.

+ From generous government subsidies to support for lithium batteries, here’s how China managed to build a world-leading industry in electric vehicles.

+ China’s car companies are turning into tech companies. China has already won the race to electrify its vehicles. Now it’s pushing ahead and adding more features and services to attract new customers. Read the full story.

+ A race for autopilot dominance is giving China the edge in autonomous driving. Electric vehicle makers and AI companies are taking Tesla FSD-like systems to China, but it’s still out of reach for most consumers. Read the full story.

ClimateTech is coming

How can we build a sustainable, greener future? Next week, MIT Technology Review is holding our second annual ClimateTech conference to discuss the innovations accelerating the transition to a green economy.

ClimateTech is taking place at the MIT Media Lab on MIT’s campus in Cambridge, Massachusetts, on October 4-5. You can register for the event and either attend in-person or online, here—before it’s too late!

MIT Technology Review flash sale!

If you haven’t already, you can subscribe to MIT Technology Review to read more of our incisive reporting. We’re holding a flash sale for just 48 hours, allowing you to subscribe from just $8 a month.

Even better, you’ll receive a free print copy of our 10 Breakthrough Technologies of 2023 issue as well. Sign up today and save 17% off the full price.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Amazon is being sued by the FTC in a landmark monopoly case
It’s accused of using illegal tactics to stifle online competition. (Wired $)
+ Head honcho Andy Jassy is facing an uphill climb. (NYT $)
+ The Federal Trade Commission avoided calling to break Amazon up. (Bloomberg $)

2 OpenAI is seeking a new valuation
To the tune of between $80 billion and $90 billion, to be exact. (WSJ $)
+ ChatGPT is about to revolutionize the economy. We need to decide what that looks like. (MIT Technology Review)

3 An astronaut has touched down on Earth after 371 days in space
That’s a new US record. (CBS News)
+ Traveling to space should teach us how to better accommodate disabled people. (Wired $)

4 Linda Yaccarino’s first 100 days at X have been a wild ride
Forget pressure from advertisers: managing Elon Musk is her biggest challenge. (FT $)
+ X appears to have disabled an election misinformation reporting measure. (Reuters)

5 YouTube rewarded a creator who livestreamed attacks on Indian Muslims
Hindu nationalist Monu Manesar has been linked to multiple killings this year. (WP $)

6 Microsoft wants to use nuclear energy to power its AI data centers
It’s looking to nuclear fission to keep those expensive centers ticking over. (CNBC)
+ We were promised smaller nuclear reactors. Where are they? (MIT Technology Review)

7 Maybe we didn’t need to learn to code after all
Generative AI is making it easier than ever to write code, even if it’s far from perfect. (The Atlantic $)
+ Learning to code isn’t enough. (MIT Technology Review)

8 Inside China’s brave online feminist revolution
The country’s burgeoning women’s rights movement is fighting back against a conservative society. (Rest of World)

9 Attempting to reverse your age is the preserve of the ultra-rich
Now they’re competing to win the ‘Rejuvenation Olympics.’ (Vox)
+ Eating fewer calories could help. (Economist $)
+ I just met the founders of a would-be longevity state. (MIT Technology Review)

10 Japan’s female rickshaw pullers are online celebrities 
Social media has helped to drive an influx of female recruits. (Reuters)

Quote of the day

“Sellers pay. Shoppers get lower-quality search results for higher-priced products. Only Amazon wins.”

—The US Federal Trade Commision spells out its case accusing the e-commerce giant of unfair shopping practices, 404 Media reports.

The big story

Novel lithium-metal batteries will drive the switch to electric cars

February 2021

For all the hype and hope around electric vehicles, they still make up only about 2% of new car sales in the US, and just a little more globally. 

For many buyers, they’re simply too expensive, their range is too limited, and charging them isn’t nearly as quick and convenient as refueling at the pump. All these limitations have to do with the lithium-ion batteries that power the vehicles. 

But QuantumScape, a Silicon Valley startup is working on a new type of battery that could finally make electric cars as convenient and cheap as gas ones. Read the full story.

—James Temple

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Are America’s distinctive accents really dying out? Better ask Dolly Parton.
+ Lenny Kravitz’s gigantic scarf is back! 🧣
+ Trying to find the perfect time for a bathroom break during a movie? There’s an app for that.
+ On this day in 1964, the Beach Boys appeared on the Ed Sullivan Show performing this absolute tune.
+ This particular kind of jellyfish may not have a brain, but that doesn’t stop it from learning.

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An inside look at Congress’s first AI regulation forum



An inside look at Congress’s first AI regulation forum

It was leaked that you had an exchange with Elon Musk regarding the risks posed by AI. [Ed note: Musk said he had told the Chinese government that AI might eventually be able to overtake it, and Raji responded by questioning the safety of today’s driverless cars, like the autopilot feature in a Tesla.] Can you tell me more about that?

You know, it wasn’t just Elon. That was the one that got out. There was another CEO that was talking about curing cancer with AI, saying we have to make sure that it’s Americans that do that, and just narratives like that. 

But first of all, we have medical AI technology that is hurting people and not working well for Black and brown patients. It’s disproportionately underprioritizing them in terms of getting a bed at a hospital; it’s disproportionately misdiagnosing them, and misinterpreting lab tests for them. 

I also hope that one day AI will lead to cancer cures, but we need to understand the limitations of the systems that we have today. 

What was it that you really wanted to achieve in the forum, and do you think you had the chance to do that? 

I think we all had substantial opportunities to say what we needed to say. In terms of whether we were all equally heard or equally understood, I think that’s something that I’m still processing. 

My main position coming in was to debunk a lot of the myths that were coming out of these companies around how well these systems are working, especially on marginalized folks. And then also to debunk some of the myths around solving bias and fairness. 

Bias concerns and explainability concerns are just really difficult technical and social challenges. I came in being like, I don’t want people to underestimate the challenge.

So did I get that across? I’m not sure, because the senators loved saying that AI is gonna cure cancer. 

It’s so easy to get caught up in the marketing terms and the sci-fi narratives and completely ignore what’s happening on the ground. I’m coming back from all of this more committed than ever to articulating and demonstrating the reality, because it just seems like there is this huge gap of knowledge between what’s actually happening and the stories that these senators are hearing from these companies.

What else I’m reading

  • I just loved this story from Jessica Bennett at the New York Times about what it’s like to be a teen girl with a cell phone today. Bennett kept in touch with three 13-year-olds over the course of a year to learn about the ins and outs of their digital lives. Highly recommend! 
  • This social reflection on privacy by Charlie Warzel in the Atlantic has stuck with me for a few days. The story gets at the overwhelming questions we—certainly I—have about what we can do to preserve our privacy online. 
  • The United Nations General Assembly convened in New York this past week, and one big topic of discussion was, of course, AI. Will Henshall at Time did a deep dive into what we might expect from the body on AI regulation.

What I learned this week

A Disney director tried to use AI to create a soundtrack reminiscent of the work of symphonist Hans Zimmer—and came up disappointed. Gareth Edwards, director of Rogue One: A Star Wars Storytold my colleague Melissa Heikkilä that he was hoping to use AI to create a soundtrack for his forthcoming movie about … AI, of course! Well, the soundtrack fell flat, and Edwards even shared it with the famous composer, who he says found it amusing. 

Melissa wrote, “Edwards said AI systems lack a fundamentally crucial skill for creating good art: taste. They still don’t understand what humans deem good or bad.”

In the end, the real Zimmer wrote the melodies for Edwards’s upcoming movie, The Creator

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