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Podcast: What’s AI doing in your wallet?

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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

Credits

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.

Transcript

[TR ID]

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 its 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.

[SHOW ID]

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 US Treasury. You pull out a dollar bill. It says US 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 US 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 US 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 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&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 COs, 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 AI 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 1% 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 AIs 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.

[MIDROLL]

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—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—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.

[MUSIC IN]

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.

[CREDITS]

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.

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Meet Jennifer Daniel, the woman who decides what emoji we get to use

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Meet Jennifer Daniel, the woman who decides what emoji we get to use


Emoji are now part of our language. If you’re like most people, you pepper your texts, Instagram posts, and TikTok videos with various little images to augment your words—maybe the syringe with a bit of blood dripping from it when you got your vaccination, the prayer (or high-fiving?) hands as a shortcut to “thank you,” a rosy-cheeked smiley face with jazz hands for a covid-safe hug from afar. Today’s emoji catalogue includes nearly 3,000 illustrations representing everything from emotions to food, natural phenomena, flags, and people at various stages of life.

Behind all those symbols is the Unicode Consortium, a nonprofit group of hardware and software companies aiming to make text and emoji readable and accessible to everyone. Part of their goal is to make languages look the same on all devices; a Japanese character should be typographically consistent across all media, for example. But Unicode is probably best known for being the gatekeeper of emoji: releasing them, standardizing them, and approving or rejecting new ones.

Jennifer Daniel is the first woman at the helm of the Emoji Subcommittee for the Unicode Consortium and a fierce advocate for inclusive, thoughtful emoji. She initially rose to prominence for introducing Mx. Claus, a gender-inclusive alternative to Santa and Mrs. Claus; a non-gendered person breastfeeding a non-gendered baby; and a masculine face wearing a bridal veil. 

Now she’s on a mission to bring emoji to a post-pandemic future in which they are as broadly representative as possible. That means taking on an increasingly public role, whether it’s with her popular and delightfully nerdy Substack newsletter, What Would Jennifer Do? (in which she analyzes the design process for upcoming emoji), or inviting the general public to submit concerns about emoji and speak up if they aren’t representative or accurate.

“There isn’t a precedent here,” Daniel says of her job. And to Daniel, that’s exciting not just for her but for the future of human communication.

I spoke to her about how she sees her role and the future of emoji. The interview has been lightly edited and condensed. 

What does it mean to chair the subcommittee on emoji? What do you do?

It’s not sexy. [laughs] A lot of it is managing volunteers [the committee is composed of volunteers who review applications and help in approval and design]. There’s a lot of paperwork. A lot of meetings. We meet twice a week.

I read a lot and talk to a lot of people. I recently talked to a gesture linguist to learn how people use their hands in different cultures. How do we make better hand-gesture emoji? If the image is no good or isn’t clear, it’s a dealbreaker. I’m constantly doing lots of research and consulting with different experts. I’ll be on the phone with a botanical garden about flowers, or a whale expert to get the whale emoji right, or a cardiovascular surgeon so we have the anatomy of the heart down. 

There’s an old essay by Beatrice Warde about typography. She asked if a good typeface is a bedazzled crystal goblet or a transparent one. Some would say the ornate one because it’s so fancy, and others would say the crystal goblet because you can see and appreciate the wine. With emoji, I lend myself more to the “transparent crystal goblet” philosophy. 

Why should we care about how our emoji are designed?

My understanding is that 80% of communication is nonverbal. There’s a parallel in how we communicate. We text how we talk. It’s informal, it’s loose. You’re pausing to take a breath. Emoji are shared alongside words.

When emoji first came around, we had the misconception that they were ruining language. Learning a new language is really hard, and emoji is kind of like a new language. It works with how you already communicate. It evolves as you evolve. How you communicate and present yourself evolves, just like yourself. You can look at the nearly 3,000 emoji and it [their interpretation] changes by age or gender or geographic area. When we talk to someone and are making eye contact, you shift your body language, and that’s an emotional contagion. It builds empathy and connection. It gives you permission to reveal that about yourself. Emoji can do that, all in an image.

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Product design gets an AI makeover

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Product design gets an AI makeover


It’s a tall order, but one that Zapf says artificial intelligence (AI) technology can support by capturing the right data and guiding engineers through product design and development.

No wonder a November 2020 McKinsey survey reveals that more than half of organizations have adopted AI in at least one function, and 22% of respondents report at least 5% of their companywide earnings are attributable to AI. And in manufacturing, 71% of respondents have seen a 5% or more increase in revenue with AI adoption.

But that wasn’t always the case. Once “rarely used in product development,” AI has experienced an evolution over the past few years, Zapf says. Today, tech giants known for their innovations in AI, such as Google, IBM, and Amazon, “have set new standards for the use of AI in other processes,” such as engineering.

“AI is a promising and exploratory area that can significantly improve user experience for designing engineers, as well as gather relevant data in the development process for specific applications,” says Katrien Wyckaert, director of industry solutions for Siemens Industry Software.

The result is a growing appreciation for a technology that promises to simplify complex systems, get products to market faster, and drive product innovation.

Simplifying complex systems

A perfect example of AI’s power to overhaul product development is Renault. In response to increasing consumer demand, the French automaker is equipping a growing number of new vehicle models with an automated manual transmission (AMT)—a system that behaves like an automatic transmission but allows drivers to shift gears electronically using a push-button command.

AMTs are popular among consumers, but designing them can present formidable challenges. That’s because an AMT’s performance depends on the operation of three distinct subsystems: an electro-mechanical actuator that shifts the gears, electronic sensors that monitor vehicle status, and software embedded in the transmission control unit, which controls the engine. Because of this complexity, it can take up to a year of extensive trial and error to define the system’s functional requirements, design the actuator mechanics, develop the necessary software, and validate the overall system.

In an effort to streamline its AMT development process, Renault turned to Simcenter Amesim software from Siemens Digital Industries Software. The simulation technology relies on artificial neural networks, AI “learning” systems loosely modeled on the human brain. Engineers simply drag, drop, and connect icons to graphically create a model. When displayed on a screen as a sketch, the model illustrates the relationship between all the various elements of an AMT system. In turn, engineers can predict the behavior and performance of the AMT and make any necessary refinements early in the development cycle, avoiding late-stage problems and delays. In fact, by using a virtual engine and transmissions as stand-ins while developing hardware, Renault has managed to cut its AMT development time almost in half.

Speed without sacrificing quality

So, too, are emerging environmental standards prompting Renault to rely more heavily on AI. To comply with emerging carbon dioxide emissions standards, Renault has been working on the design and development of hybrid vehicles. But hybrid engines are far more complex to develop than those found in vehicles with a single energy source, such as a conventional car. That’s because hybrid engines require engineers to perform complex feats like balancing the power required from multiple energy sources, choosing from a multitude of architectures, and examining the impact of transmissions and cooling systems on a vehicle’s energy performance.

“To meet new environmental standards for a hybrid engine, we must completely rethink the architecture of gasoline engines,” says Vincent Talon, head of simulation at Renault. The problem, he adds, is that carefully examining “the dozens of different actuators that can influence the final results of fuel consumption and pollutant emissions” is a lengthy and complex process, made by more difficult by rigid timelines.

“Today, we clearly don’t have the time to painstakingly evaluate various hybrid powertrain architectures,” says Talon. “Rather, we needed to use an advanced methodology to manage this new complexity.”

For more on AI in industrial applications, visit www.siemens.com/artificialintelligence.

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

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AI consumes a lot of energy. Hackers could make it consume more.

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AI consumes a lot of energy. Hackers could make it consume more.


The attack: But this kind of neural network means if you change the input, such as the image it’s fed, you can change how much computation it needs to solve it. This opens up a vulnerability that hackers could exploit, as the researchers from the Maryland Cybersecurity Center outlined in a new paper being presented at the International Conference on Learning Representations this week. By adding small amounts of noise to a network’s inputs, they made it perceive the inputs as more difficult and jack up its computation. 

When they assumed the attacker had full information about the neural network, they were able to max out its energy draw. When they assumed the attacker had limited to no information, they were still able to slow down the network’s processing and increase energy usage by 20% to 80%. The reason, as the researchers found, is that the attacks transfer well across different types of neural networks. Designing an attack for one image classification system is enough to disrupt many, says Yiğitcan Kaya, a PhD student and paper coauthor.

The caveat: This kind of attack is still somewhat theoretical. Input-adaptive architectures aren’t yet commonly used in real-world applications. But the researchers believe this will quickly change from the pressures within the industry to deploy lighter weight neural networks, such as for smart home and other IoT devices. Tudor Dumitraş, the professor who advised the research, says more work is needed to understand the extent to which this kind of threat could create damage. But, he adds, this paper is a first step to raising awareness: “What’s important to me is to bring to people’s attention the fact that this is a new threat model, and these kinds of attacks can be done.”

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