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Digital transformation is changing banking from the inside out

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Digital transformation is changing banking from the inside out


Companies across all industries are faced with the urgent need to transform the way they do business, including financial services, but changes abound with governance, security, and culture. A shift in mindset and perspective away from “the way things have always been done” is key to a successful digital transformation and to providing the frictionless customer experience banks and other financial services businesses strive to offer.

To stay competitive in the wide-ranging fintech landscape, says Michael Ruttledge, chief information officer and head of technology services at Citizens Financial Group, banks need to become more agile and embrace new technologies. He described the five pillars he has used to guide digital transformations at financial institutions: “The first pillar is moving to agile. Second is moving to a more modern architecture. Third is doubling down on the engineering talent at the bank, and fourth is being more efficient and transforming the technology cost structure. Finally, the fifth pillar is maniacally focusing on security and availability.”

Ruttledge says automation is key to delivering the frictionless experience customers want:

“As we’re developing these platforms, we’re looking at where can we automate. We’re trying to make it frictionless for our customers—for instance, we don’t want it to take a long time for them to open an account because of the amount of information they have to enter. Is there data we can pre-populate? Are there notifications and terms and conditions we can automatically route to the customer, as opposed to them having to send in a document with a signature? We’ve used a lot of robotics technology. For example, we’re using chatbots in our call centers to reduce the call volume to be more efficient.”

By creating the right infrastructure from the bottom up, Ruttledge was able to help Citizens better meet customer needs and expectations. Restructuring has also placed the bank in a good position to take advantage of emerging technologies, says Ruttledge:

“Another area we’ve touched on in the network space is 5G, which is at least 10 times faster than 4G. We’re using it now in some of our branches. As a customer comes into the branch, they’re met by a branch member with an iPad, and they’re able to complete an application together. That wasn’t possible before 5G speeds. Looking further ahead, blockchain is another area where there’s a lot of promise for the future. Whether it’s in contracts or in trading, the fact that it gives you immutability in terms of the data could facilitate a number of future use cases in the financial services industry.”

Show notes and references

Your Company’s Digital Transformation Cannot Wait Until Covid-19 Subsides,” by Michael Ruttledge, CIO, Citizens Financial Group

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 digital transformation and cloud adoption. Companies across all industries are faced with the urgent need to transform the way they do business, including financial services, but changes abound with governance, security, and culture. Two words for you: digital shift.

My guest is Michael Ruttledge, chief information officer and head of technology services at Citizens Financial Group. Prior to this role, he was the group CIO at American Express. Michael is a technology leader with more than 20 years of experience in infrastructure and engineering roles within the financial services industry.

This episode of Business Lab is produced in partnership with Infosys Cobalt.

Welcome, Michael.

Michael Ruttledge: Thank you, it’s great to be here.

Laurel: You joined Citizens Financial Group in 2019. What digital transformation lessons from your former employer were you able to bring to Citizens, and how did you prioritize the bank’s technology strategy from there?

Michael: American Express has been working on technology transformation now for about 10 years. When I came to Citizens, a lot of the same things that we’d adopted at American Express I was able to bring to Citizens to start that journey. Technology transformation centers around five core pillars. The first pillar is moving to agile. Second is moving to a more modern architecture. Third is doubling down on the engineering talent at the bank, and fourth is being more efficient and transforming the technology cost structure. Finally, the fifth pillar is maniacally focusing on security and availability. Basically, we moved to an Agile operating model.

One of the lessons learned when I started at Citizens was that we need to bring the business into the transformation process. Digital transformation is as much a business transformation as it is a technology transformation, and we’ve followed that right from the start.

Laurel: It gets to the point of having tech and business at the same table, making decisions together because one always affects the other.

Michael: Absolutely. And it’s more important in an Agile operating environment, where you have teams ideating closely together, trying to deliver capabilities at speed and iterating. That joint partnership is so critical.

Laurel: There must have been a bit of a culture shock, though, to the business as well instituting a maniacal emphasis on security. Not that there wasn’t one before, but you are now firmly putting a stake in the ground saying, “This is our position from here on out.” Did you find a number of organizational culture changes, and how did that affect how you proceeded?

Michael: Early on, we had to get some quick wins. There was a lot of skepticism with the partnership with technologies. It hadn’t been that successful. Releases were taking on average two to three years to get to market, which is very expensive. We’d outsourced a lot of our core engineering muscle. It was in vendors. We didn’t have it within the core team.

It was a big change to get that support from the business and win that back over time. We had to show them that we could deliver very, very fast. And we did that. We brought in new technology. We moved to more of a build-versus-buy mentality when it came to our engineering. We were building components. We were automating. We were delivering much faster.

The business appreciated that, but to your point, it was a culture change for them, too. No longer were they reliant on hundred-page documents with all of their requirements that they could massage over time. Now they had to iterate, and they had to think through the capabilities and act in a very Agile manner. And that was a learning curve. We call them experience owners in the industry, though really I think they’re known as product owners, but essentially, that was a new role people had to learn. We had to hire those people and we had to show them what success looked like for that role.

Laurel: This is absolutely an example of the concept that every business is a technology business. You’re putting that to the test.Because you can’t have two-to-three-year product cycles. That’s impossible. Your competition would just eat you alive.

Michael: Right, absolutely. In the context of competition for banks, it’s not really banks anymore. It’s fintech organizations and large tech companies like Google, Apple, and Amazon that are delivering at speeds we have to compete with. So, it was imperative that we moved to an Agile operating environment, that we brought in new tools that would allow us to do automation and deliver code much faster than we had previously.

Laurel: In the last two years, we’ve all lived through a global pandemic. How did your teams respond to that? Because here you are, a new person, a new force, and then you’re building an entire tech team, and you’re also building them with a very specific perspective—build first, maybe buy later, maybe not at all—but those teams have to have resilience and they have to be able to problem shoot, troubleshoot, and understand problems as they come up.

Michael: The pandemic was interesting because it crystallized everybody on a key set of outcomes. We had to get 15,000 people working from home in the first few weeks of the pandemic. So, that really crystallized action. We set up war rooms. We were deploying laptops in a rapid pace all around the country. The government rolled out their Payment Protection Program. Within a week, we had capabilities up and running, leveraging some of our API infrastructure to be able to provide loans to our customers.

That wouldn’t have been possible in the old environment. In the old environment, we would’ve gone out to a vendor, bought a package, brought it in, tested it, and implemented. The way we’re now developing code, we already have the platform. So, we’re rebuilding on top of that platform and that’s what’s allowing us to get to market so quickly.

Laurel: And that platform, as well as a modern architecture, has to include cloud, right? So that is another shift, to bring cloud services into the fore.

Michael: Absolutely. In some ways, we were slow to the cloud journey. We only started that about 18 months ago, two years ago, but the advantage of that was the industry has moved on from building private clouds in their own data centers to building public clouds with partners like Amazon and Microsoft.

We were able to leapfrog that whole “let’s develop our own cloud and our own data center” and move straight to the public cloud, which is where the industry’s going. If you look at companies like Capital One, they’re 100% in the public cloud now.

So, that gave us an advantage, in that we were able to build all that infrastructure. And frankly, it’s evolved over the last five to seven years, way beyond where it was before. The level of security that’s provided is much enhanced. And we have a concept we call “above the line and below the line.” Below the line is the security that the provider will provide. So, it’s their data center. They’re protecting the physical location. They’re making sure there’s network redundancy. They’re making sure that there is hardware redundancy, etc., but it’s still our role to make sure that above the line, we have the appropriate firewalls and layers of security in place that protect customer data leaving the bank. So it’s a partnership, but we need clearly defined roles and responsibilities, between the vendor and ourselves.

Laurel: When we discuss that above the line and below the line concept, the potential security and privacy concerns are enormous, obviously, for a financial services institution, as they are for any modern business. With this cloud architecture, how else did you address these concerns internally as well, and what was messaging like to your customers?

Michael: Certainly, we’ve always prided ourselves in putting customers first. We let our customers know that their data is protected. And we have layers of security. We use a variety of monitoring tools to monitor data—we monitor who has access to that data, where it is accessed, and if data is sent externally, we monitor that to see the type of data it is and what’s leaving our premises. So, there are myriad controls there.

We also partner with a number of vendors to prevent cyberattacks. We have sophisticated network technology in place so if we do get a denial of service attack, for example, we’re able to cope with that by diverting traffic to the provider.

Laurel: How do you address the talent gap that so many businesses are facing with digital transformation—the shift to cloud platforms, adopting modern architectures, as well as security? There are only so many folks out there. You mentioned autonomous operations. Is that part of the deal?

Michael: That’s a great question. There’s definitely a war for talent, and I would say this year more than any other, we’re certainly feeling it. During the pandemic and post-pandemic, the need for digital transformation skills, the need for data skills, and skills in security have increased phenomenally. We’ve definitely had to think about how to retain our colleagues, look at practices we can put in place to retain our colleagues, and we’ve doubled down on education.

Because we’ve been transforming the bank in terms of the technology landscape, we’ve brought in tremendous amounts of new technology, and our engineers are enjoying learning that new technology. In fact, as an example, we had 263 engineers who just signed up for our AWS certification. We’ve taken 150 of our colleagues through an academy program we created, which was a nine-week full-time hands-on program where they developed and released code. We drank our own champagne, I like to say, because we leveraged our own automated pipeline, our DevSecOps environment, and our own testing environments to give them those skills.

It has been a huge success. We’ve developed over 40 badging programs. And once again, 400 individuals have gone through those programs where they’re now certified in cloud, in full stack engineering, in data, and in cryptography.

Laurel: That’s no small feat, especially in the two years since you’ve been there. So, DevSecOps is development, security, and operations, which is part of that architecture to get businesses to where they need to be in Agile response. That’s where a lot of the autonomous features are coming in. So, you must also be looking out for opportunities in AI and machine learning as well.

Michael: Absolutely. As we’re developing these platforms, we’re looking at where can we automate. We’re trying to make it frictionless for our customers—for instance, we don’t want it to take a long time for them to open an account because of the amount of information they have to enter. Is there data we can pre-populate? Are there notifications and terms and conditions we can automatically route to the customer, as opposed to them having to send in a document with a signature?

We’ve used a lot of robotics technology. For example, we’re using chatbots in our call centers to reduce the call volume to be more efficient. We’ve implemented Robotic Process Automation (RPA) that allows us to automate some of the back-end processes in our operations groups as well, so they can avoid a lot of the manual work they do today.

Laurel: And these are probably just a handful of the new products that Citizens has been able to launch because of the move to the cloud. Internally, are you also seeing avenues to find efficiencies within the company itself, not just for customers?

Michael: Absolutely. We’re automating a lot of manual processes. For example, we’ve developed a new lending service that allows our customers to apply for loans, which was a very manual process before which required a lot of different handoffs, a lot of different spreadsheets, and we’ve been able to automate a lot of that process. With a few clicks, people are able to apply for a student loan, as an example, and our customers don’t have to go through all the information they have to put in the system, but also our operations groups significantly reduce the time they have to do things manually.

Laurel: You mentioned earlier that because of the pandemic, Citizens was able to speed along certain benefits for customers, like the payment program from the federal government. What other new products has Citizens been able to launch because of the move to the cloud?

Michael: That’s a great question. One of the solutions we used for our Paycheck Protection Program (PPP) loans was Salesforce. It’s a SaaS-based solution in the cloud, and we’ve been able to use that to enhance our lead generation. If you think about our mortgage business, our home equity loans businesses, and our auto business, we’re able to use that platform we developed for PPP loans to enhance our sales leads in those areas.

So, once again, it’s an example where, by taking a platform approach, we’ve been able to extend the capabilities to other areas of the bank and reuse some of those APIs or building blocks that we were able to create early on during the pandemic.

Laurel: Salesforce is certainly a great way to keep track of customers and identify how you’re going to acquire them, but I feel like there’s also a more current story that everyone has in their pocket—the mobile phone. How has banking on your phone changed the way that everyone works these days?

Michael: You’re absolutely right. The expectation is that you can do everything on your phone. We rolled out our new mobile online banking system in January of this year, fully cloud native, and once again, reusing some of the building blocks that we built. It has gotten rave reviews—it received a 4.6 rating in the App Store, which is great.

We’re very proud of it. We’re continuing to enhance it. And in fact, some of the capabilities we launched with that, we’re actually extending now to some of our other products. We have a product called Citizens Pay, for instance, and we’re going to launch the mobile app for that and use some of the same capabilities we developed and launched in January. We have a digital bank, and we’re developing the mobile app for that. Once again, it’ll be on the same platform, the same framework as the core mobile app. Allowing us to service our customers and make sure they always have the bank in their pocket is important to us.

Laurel: So, in terms of customer service and how you think the business is now versus when you first joined, what are some of the greatest competitive advantages you’ve seen with your digital transformation?

Michael: Some of it we covered earlier in terms of reducing customer friction. One of the beauties of being able to use APIs and being able to partner with different fintechs is that we’re able to gather information from them that otherwise we would’ve had to obtain manually. For example, a small business customer used to have to send in their tax returns. Well, now I can go to a provider with their consent, and I can bring that data in automatically so they don’t have to send in that statement. Same with income data, with the appropriate consent from our consumers, I can go to a database, a provider, and bring in that data and not have to ask them to send in their payroll stub to prove that they have that data.

There’s so much data that’s available now in the public domain that you’re able to go after, as opposed to manually either entering it or sending it—or worse, faxing it in.

Laurel: Talk about a security breach.

Michael: Yes.

Laurel: I have to say, one of my favorite innovations is the fact that you can now deposit checks without an envelope. It seems amazing to me.

So, one last question to give this a future forward look: what’s next on the horizon for financial institutions, and what emerging technology (we touched on AI and ML a bit) are you seeing taking a large role in the future of banking?

Michael: We’ve scratched the surface with AI and ML. There’s so much more that we can be doing in that space as computers become more powerful, as storage becomes cheaper, where we’re able to store vast amounts of data and we’re able to crunch through that in real time. I see that being really powerful. There’s a lot of data streaming technology now where, as the data comes in, you’re able to gain insights into that data immediately, in real time. I see that as a real game-changer as we move forward in being able to do much more real-time analytics, which I think is big in the credit and risk decisioning space, in the marketing spaces.

Another area we’ve touched on in the network space is 5G, which is at least 10 times faster than 4G. We’re using it now in some of our branches. As a customer comes into the branch, they’re met by a branch member with an iPad, and they’re able to complete an application together. That wasn’t possible before 5G speeds. Looking further ahead, blockchain is another area where there’s a lot of promise for the future. Whether it’s in contracts or in trading, the fact that it gives you immutability in terms of the data could facilitate a number of future use cases in the financial services industry.

A bit further out is quantum computers. It’s something I’m personally very interested in. We’re now up to over a hundred cubits of performance. I think in the next two years, we’ll get to over a thousand. It really changes the game when it comes to data processing. And once again, some of the use cases will be in combating fraud, and also in the credit space where you want to crunch through very large amounts of data very, very fast. It will also break our current encryption algorithms. It’s something from a threat perspective we’re thinking about and partnering with our security team to make sure we’re thinking through our defenses against that.

Laurel: Michael, thank you so much for joining us today on what has been a fantastic conversation on the Business Lab.

Michael: Thank you.

Laurel: That was Michael Ruttledge, chief information officer at Citizens Financial Group, whom I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review overlooking the Charles River.

This podcast was produced in partnership with Infosys Cobalt. To learn more about cloud-led business transformation, visit technologyreview.com/thecloudhub.

That’s it for this episode of Business Lab, I’m your host, Laurel Ruma. I’m the director of insights of the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you could find us in print, on the web, and at event each year around the world. 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.

This podcast episode 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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Why I became a TechTrekker

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group jumps into the air with snowy mountains in the background


My senior spring in high school, I decided to defer my MIT enrollment by a year. I had always planned to take a gap year, but after receiving the silver tube in the mail and seeing all my college-bound friends plan out their classes and dorm decor, I got cold feet. Every time I mentioned my plans, I was met with questions like “But what about school?” and “MIT is cool with this?”

Yeah. MIT totally is. Postponing your MIT start date is as simple as clicking a checkbox. 

Sofia Pronina (right) was among those who hiked to the Katla Glacier during this year’s TechTrek to Iceland.

COURTESY PHOTO

Now, having finished my first year of classes, I’m really grateful that I stuck with my decision to delay MIT, as I realized that having a full year of unstructured time is a gift. I could let my creative juices run. Pick up hobbies for fun. Do cool things like work at an AI startup and teach myself how to create latte art. My favorite part of the year, however, was backpacking across Europe. I traveled through Austria, Slovakia, Russia, Spain, France, the UK, Greece, Italy, Germany, Poland, Romania, and Hungary. 

Moreover, despite my fear that I’d be losing a valuable year, traveling turned out to be the most productive thing I could have done with my time. I got to explore different cultures, meet new people from all over the world, and gain unique perspectives that I couldn’t have gotten otherwise. My travels throughout Europe allowed me to leave my comfort zone and expand my understanding of the greater human experience. 

“In Iceland there’s less focus on hustle culture, and this relaxed approach to work-life balance ends up fostering creativity. This was a wild revelation to a bunch of MIT students.”

When I became a full-time student last fall, I realized that StartLabs, the premier undergraduate entrepreneurship club on campus, gives MIT undergrads a similar opportunity to expand their horizons and experience new things. I immediately signed up. At StartLabs, we host fireside chats and ideathons throughout the year. But our flagship event is our annual TechTrek over spring break. In previous years, StartLabs has gone on TechTrek trips to Germany, Switzerland, and Israel. On these fully funded trips, StartLabs members have visited and collaborated with industry leaders, incubators, startups, and academic institutions. They take these treks both to connect with the global startup sphere and to build closer relationships within the club itself.

Most important, however, the process of organizing the TechTrek is itself an expedited introduction to entrepreneurship. The trip is entirely planned by StartLabs members; we figure out travel logistics, find sponsors, and then discover ways to optimize our funding. 

two students soaking in a hot spring in Iceland

COURTESY PHOTO

In organizing this year’s trip to Iceland, we had to learn how to delegate roles to all the planners and how to maintain morale when making this trip a reality seemed to be an impossible task. We woke up extra early to take 6 a.m. calls with Icelandic founders and sponsors. We came up with options for different levels of sponsorship, used pattern recognition to deduce the email addresses of hundreds of potential contacts at organizations we wanted to visit, and all got scrappy with utilizing our LinkedIn connections.

And as any good entrepreneur must, we had to learn how to be lean and maximize our resources. To stretch our food budget, we planned all our incubator and company visits around lunchtime in hopes of getting fed, played human Tetris as we fit 16 people into a six-person Airbnb, and emailed grocery stores to get their nearly expired foods for a discount. We even made a deal with the local bus company to give us free tickets in exchange for a story post on our Instagram account. 

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The Download: spying keyboard software, and why boring AI is best

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🧠


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.

How ubiquitous keyboard software puts hundreds of millions of Chinese users at risk

For millions of Chinese people, the first software they download onto devices is always the same: a keyboard app. Yet few of them are aware that it may make everything they type vulnerable to spying eyes. 

QWERTY keyboards are inefficient as many Chinese characters share the same latinized spelling. As a result, many switch to smart, localized keyboard apps to save time and frustration. Today, over 800 million Chinese people use third-party keyboard apps on their PCs, laptops, and mobile phones. 

But a recent report by the Citizen Lab, a University of Toronto–affiliated research group, revealed that Sogou, one of the most popular Chinese keyboard apps, had a massive security loophole. Read the full story. 

—Zeyi Yang

Why we should all be rooting for boring AI

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. It hopes they could improve intelligence and operational planning. 

But those might not be the right use cases, writes our senior AI reporter Melissa Heikkila. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases. 

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. The DoD’s best bet is to apply generative AI to more mundane things like Excel, email, or word processing. Read the full story. 

This story is from The Algorithm, Melissa’s weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The ice cores that will let us look 1.5 million years into the past

To better understand the role atmospheric carbon dioxide plays in Earth’s climate cycles, scientists have long turned to ice cores drilled in Antarctica, where snow layers accumulate and compact over hundreds of thousands of years, trapping samples of ancient air in a lattice of bubbles that serve as tiny time capsules. 

By analyzing those cores, scientists can connect greenhouse-gas concentrations with temperatures going back 800,000 years. Now, a new European-led initiative hopes to eventually retrieve the oldest core yet, dating back 1.5 million years. But that impressive feat is still only the first step. Once they’ve done that, they’ll have to figure out how they’re going to extract the air from the ice. Read the full story.

—Christian Elliott

This story is from the latest edition of our print magazine, set to go live tomorrow. Subscribe today for as low as $8/month to ensure you receive full access to the new Ethics issue and in-depth stories on experimental drugs, AI assisted warfare, microfinance, and more.

The must-reads

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

1 How AI got dragged into the culture wars
Fears about ‘woke’ AI fundamentally misunderstand how it works. Yet they’re gaining traction. (The Guardian
+ Why it’s impossible to build an unbiased AI language model. (MIT Technology Review)
 
2 Researchers are racing to understand a new coronavirus variant 
It’s unlikely to be cause for concern, but it shows this virus still has plenty of tricks up its sleeve. (Nature)
Covid hasn’t entirely gone away—here’s where we stand. (MIT Technology Review)
+ Why we can’t afford to stop monitoring it. (Ars Technica)
 
3 How Hilary became such a monster storm
Much of it is down to unusually hot sea surface temperatures. (Wired $)
+ The era of simultaneous climate disasters is here to stay. (Axios)
People are donning cooling vests so they can work through the heat. (Wired $)
 
4 Brain privacy is set to become important 
Scientists are getting better at decoding our brain data. It’s surely only a matter of time before others want a peek. (The Atlantic $)
How your brain data could be used against you. (MIT Technology Review)
 
5 How Nvidia built such a big competitive advantage in AI chips
Today it accounts for 70% of all AI chip sales—and an even greater share for training generative models. (NYT $)
The chips it’s selling to China are less effective due to US export controls. (Ars Technica)
+ These simple design rules could turn the chip industry on its head. (MIT Technology Review)
 
6 Inside the complex world of dissociative identity disorder on TikTok 
Reducing stigma is great, but doctors fear people are self-diagnosing or even imitating the disorder. (The Verge)
 
7 What TikTok might have to give up to keep operating in the US
This shows just how hollow the authorities’ purported data-collection concerns really are. (Forbes)
 
8 Soldiers in Ukraine are playing World of Tanks on their phones
It’s eerily similar to the war they are themselves fighting, but they say it helps them to dissociate from the horror. (NYT $)
 
9 Conspiracy theorists are sharing mad ideas on what causes wildfires
But it’s all just a convoluted way to try to avoid having to tackle climate change. (Slate $)
 
10 Christie’s accidentally leaked the location of tons of valuable art 🖼📍
Seemingly thanks to the metadata that often automatically attaches to smartphone photos. (WP $)

Quote of the day

“Is it going to take people dying for something to move forward?”

—An anonymous air traffic controller warns that staffing shortages in their industry, plus other factors, are starting to threaten passenger safety, the New York Times reports.

The big story

Inside effective altruism, where the far future counts a lot more than the present

" "

VICTOR KERLOW

October 2022

Since its birth in the late 2000s, effective altruism has aimed to answer the question “How can those with means have the most impact on the world in a quantifiable way?”—and supplied methods for calculating the answer.

It’s no surprise that effective altruisms’ ideas have long faced criticism for reflecting white Western saviorism, alongside an avoidance of structural problems in favor of abstract math. And as believers pour even greater amounts of money into the movement’s increasingly sci-fi ideals, such charges are only intensifying. Read the full story.

—Rebecca Ackermann

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.)

+ Watch Andrew Scott’s electrifying reading of the 1965 commencement address ‘Choose One of Five’ by Edith Sampson.
+ Here’s how Metallica makes sure its live performances ROCK. ($)
+ Cannot deal with this utterly ludicrous wooden vehicle
+ Learn about a weird and wonderful new instrument called a harpejji.



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Why we should all be rooting for boring AI

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Why we should all be rooting for boring AI


This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

I’m back from a wholesome week off picking blueberries in a forest. So this story we published last week about the messy ethics of AI in warfare is just the antidote, bringing my blood pressure right back up again. 

Arthur Holland Michel does a great job looking at the complicated and nuanced ethical questions around warfare and the military’s increasing use of artificial-intelligence tools. There are myriad ways AI could fail catastrophically or be abused in conflict situations, and there don’t seem to be any real rules constraining it yet. Holland Michel’s story illustrates how little there is to hold people accountable when things go wrong.  

Last year I wrote about how the war in Ukraine kick-started a new boom in business for defense AI startups. The latest hype cycle has only added to that, as companies—and now the military too—race to embed generative AI in products and services. 

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. 

The department sees tons of potential to “improve intelligence, operational planning, and administrative and business processes.” 

But Holland Michel’s story highlights why the first two use cases might be a bad idea. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases.  

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. Everyone agrees that humans should make the final call, but that is made harder by technology that acts unpredictably, especially in fast-moving conflict situations. 

Some worry that the people lowest on the hierarchy will pay the highest price when things go wrong: “In the event of an accident—regardless of whether the human was wrong, the computer was wrong, or they were wrong together—the person who made the ‘decision’ will absorb the blame and protect everyone else along the chain of command from the full impact of accountability,” Holland Michel writes. 

The only ones who seem likely to face no consequences when AI fails in war are the companies supplying the technology.

It helps companies when the rules the US has set to govern AI in warfare are mere recommendations, not laws. That makes it really hard to hold anyone accountable. Even the AI Act, the EU’s sweeping upcoming regulation for high-risk AI systems, exempts military uses, which arguably are the highest-risk applications of them all. 

While everyone is looking for exciting new uses for generative AI, I personally can’t wait for it to become boring. 

Amid early signs that people are starting to lose interest in the technology, companies might find that these sorts of tools are better suited for mundane, low-risk applications than solving humanity’s biggest problems.

Applying AI in, for example, productivity software such as Excel, email, or word processing might not be the sexiest idea, but compared to warfare it’s a relatively low-stakes application, and simple enough to have the potential to actually work as advertised. It could help us do the tedious bits of our jobs faster and better.

Boring AI is unlikely to break as easily and, most important, won’t kill anyone. Hopefully, soon we’ll forget we’re interacting with AI at all. (It wasn’t that long ago when machine translation was an exciting new thing in AI. Now most people don’t even think about its role in powering Google Translate.) 

That’s why I’m more confident that organizations like the DoD will find success applying generative AI in administrative and business processes. 

Boring AI is not morally complex. It’s not magic. But it works. 

Deeper Learning

AI isn’t great at decoding human emotions. So why are regulators targeting the tech?

Amid all the chatter about ChatGPT, artificial general intelligence, and the prospect of robots taking people’s jobs, regulators in the EU and the US have been ramping up warnings against AI and emotion recognition. Emotion recognition is the attempt to identify a person’s feelings or state of mind using AI analysis of video, facial images, or audio recordings. 

But why is this a top concern? Western regulators are particularly concerned about China’s use of the technology, and its potential to enable social control. And there’s also evidence that it simply does not work properly. Tate Ryan-Mosley dissected the thorny questions around the technology in last week’s edition of The Technocrat, our weekly newsletter on tech policy.

Bits and Bytes

Meta is preparing to launch free code-generating software
A version of its new LLaMA 2 language model that is able to generate programming code will pose a stiff challenge to similar proprietary code-generating programs from rivals such as OpenAI, Microsoft, and Google. The open-source program is called Code Llama, and its launch is imminent, according to The Information. (The Information

OpenAI is testing GPT-4 for content moderation
Using the language model to moderate online content could really help alleviate the mental toll content moderation takes on humans. OpenAI says it’s seen some promising first results, although the tech does not outperform highly trained humans. A lot of big, open questions remain, such as whether the tool can be attuned to different cultures and pick up context and nuance. (OpenAI)

Google is working on an AI assistant that offers life advice
The generative AI tools could function as a life coach, offering up ideas, planning instructions, and tutoring tips. (The New York Times)

Two tech luminaries have quit their jobs to build AI systems inspired by bees
Sakana, a new AI research lab, draws inspiration from the animal kingdom. Founded by two prominent industry researchers and former Googlers, the company plans to make multiple smaller AI models that work together, the idea being that a “swarm” of programs could be as powerful as a single large AI model. (Bloomberg)

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