Antidotes to AI Hype // Money Isn't Everything 1x04

Welcome to Money Isn’t Everything.

I’m Mary Wisniewski, Cornerstone Advisors editor-at-large.

Every other Thursday, we bring on a guest that is shaking up financial services in some way, and we get into the why, how and possibilities.

In this episode, I sit down with Eric Siegel, the author of the book The AI Playbook, which just came out this year.

But his career has long involved predictive analytics.

He taught at Columbia.

He offers consulting to companies that want to deploy machine learning, and he’s a founder of Machine Learning Week, a conference that has a new sister conference taking place in Arizona in early June.

It’s called Generative AI Application Summit.

He thinks of data as a recording of history, but he’s trying to change the mood on AI, make it a little bit less starry-eyed for the business execs out there.

We get into that.

Plus, we chat about the persistent tension of quality experience versus catching fraud.

Eric also offers intel into the music video he made years back, his nickname, Dr. Data.

Here’s our conversation.

Eric, welcome to Money Isn’t Everything.

Thanks so much for joining the show today.

And we’ve got news coming from you, or I guess it’s old news at this point, but you’ve come out with the book The AI Playbook.

And yes, I’m super excited to have you talk about it because this show is for people dreaming up new ideas, experimenting with bank accounts in different ways, which of course will involve predictive analytics and has for a long time.

But your book sort of takes away the starry eyes a bit and says, hey, there’s a lot you need to do to actually make this happen.

So let’s start with, Eric, what made you want to do this book now?

I feel like you hit a perfect moment in time to publish it.

Well, you know, the way you just phrased it, it’s kind of like, oh, we’ve got to get a little sober.

There’s a lot of fanaticism and elation, intoxication around AI hype.

Sure.

This is my second book.

The first book’s about how machine learning works, the value proposition, how it delivers value.

This one’s how to capitalize on it.

So how to make it actually work for your organization, not just the core technology, not just the number crunching, but what gets learned from data.

And data is a recording of history.

It’s a long list of prior events.

It’s experience from which to learn.

And the most valuable, actionable thing you can learn from data is to predict, because predictions directly inform the action taken for each individual case or individual consumer or corporate client.

Where to drill for oil.

Which satellite to investigate is potentially running out of a battery.

All large-scale operations consist of many, many decisions.

And the holy grail for driving decisions more effectively, for improving large-scale operations, is prediction, per-case prediction.

That’s what you get from data.

That’s what you learn to do, is to predict.

But as sound as that number crunching might be, that doesn’t necessarily mean it’ll be valuable to your company unless the company acts on it, unless you actually change the operations and thereby improve them by way of integrating and acting on all those individual per-case predictions.

So, for example, predict who’s going to be a good debtor, then approve their application for a loan.

Which transaction’s most likely to be fraudulent?

Block or audit that transaction.

Who’s going to buy or cancel?

Market accordingly to that individual customer.

So each prediction directly informs the action, which looks great on paper.

But it turns out that the majority of these machine learning enterprise projects actually fail to get that last mile to actual liftoff, to deployment, to integrating what’s been learned into operations.

So that’s what the new book, The AI Playbook, is about.

Well, one of the things I loved about it was you peppered in a bunch of quotes throughout the book to set the stage.

One of them, you were quoting another, well, you were quoting the professor, and the quote was, I wrote this down.

It said, “Do you have an AI strategy?” makes as much sense as asking, “Do we have an Excel strategy?”

Which I think really sets the tone because I feel like there’s a big disconnect there.

And I’m wondering, for an audience of not only fintech entrepreneurs, but the innovators of banks and credit unions, what’s a good starting point, if there is a good starting point, to sort through all the chaos that’s been happening right now in terms of the hype?

No.

The antidote to hype is super straightforward.

It’s to focus on value, a concrete value proposition, the actionable deployment, the way you’re going to actively improve an operation.

Whether we’re talking about predictive AI or predictive analytics, those types of enterprise predictive applications that I’ve mentioned in my little monologue up front here, or we’re talking about generative AI, where it’s still built on the same core technology, machine learning, but it’s generating new content items like graphics or writing or video.

Either way, how exactly are you going to use this?

To what value-driven end?

What’s the actual operation that’s going to be improved in a measurable way?

The antidote to hype is to focus on the value, the concrete value proposition.

The hype kind of says, hey, this is a panacea.

It’s self-evident that it’s valuable.

Of course, the word intelligence is a big problem, and AI is always going to be plagued by the word intelligence because that’s the name of it, right?

But intelligence is this subjective word that doesn’t define anything concrete, any particular value proposition, any particular kind of technology.

It’s very subjective.

And if nothing else, it’s a word that describes something very particular to humans and something very ambitious and hard to try to get a machine to do.

As seemingly human-like as generative AI might be, which is amazing and unprecedented, doesn’t necessarily mean it’s as valuable as everyone seems to be presuming.

So it’s a little bit of a cold shower, right?

Yeah.

But let me put it this way.

If you’re kind of feeling fearful in some way, even if just as far as FOMO or job security, and often it’s a combination, you’re feeling that intoxication of the hype of, this thing is just incredible, what’s coming.

Then you’re almost deifying the technology, which intrinsically the word intelligence actually does.

Because once a computer actually becomes human-level capable in general, then it can conduct AI research and improve itself ad infinitum.

So there’s always that elephant in the room underlying talks, conversations about AI.

Are we really talking about something that’s approaching general human-level capabilities?

That is to say, well, they call it artificial general intelligence, but I like to call it artificial humans.

I say no.

I don’t think that we’re actively headed there.

I don’t mean that it’s theoretically impossible many centuries in the future.

But we need to kind of come down to earth and be like, look, this is where we are now.

There are improvements coming.

But what’s the actual concrete value proposition?

So that kind of fear and/or elation, the antidote is to focus on a concrete value proposition.

Okay.

There’s two things I want to get into from what you just said, but let’s start with fraud because certainly fraud has been soaring in financial services, even, I think, check fraud in particular.

And I know in your book you mention FICO as one of the tools that this industry uses to help get in front of fraud.

But let’s talk about, I mean, well, we can talk about whatever you want to talk about, but we can use FICO as an example.

How is fraud getting so out of control, Eric, and how do we use this technology to maybe get a little bit better with it?

Yeah.

Okay, great.

So the book covers several case studies.

One is FICO for fraud detection.

FICO is very well known, obviously, for its credit scores.

So it kind of identifies strong debtors by day and fights crime by night because the thing that’s lesser known, but is at least as big a business for FICO, is they have the leading payment card fraud detection model that’s used for every single transaction of two-thirds of the world’s payment cards and 90% of the U.S. and the U.K.

Fraud detection is a well-established value proposition of using machine learning, whether it’s for payment card transactions, checks or any other kind of transaction.

So it’s very prevalent.

Fraud is obviously prevalent, and we’re constantly fighting it with these models that say, hey, look at this transaction, everything you know about the transaction, what is the chance that this is not authorized?

That it’s being conducted, it’s a wolf in sheep’s clothing, essentially.

If so, either block and/or audit that transaction.

So that value proposition is really clear, really well established, and it’s a really important part of, you know, the white hats versus the black hats, basically.

This is sort of an ongoing battle between criminals and the above-board world of financial transactions.

And it’s absolutely critical.

But it’s not a magic crystal ball, right?

So you’re not going to be able to know for sure with 100% confidence about every transaction, which one is fraudulent and which one isn’t fraudulent.

In fact, fraud is, the good news is, relatively infrequent compared to the number of legitimate transactions.

Often, also in the case, for example, of payment cards, it may be one in a thousand transactions that are fraudulent.

Now, that still makes up for a lot of fraud, and it’s very, very costly.

But how do you get the computer to automatically, on the fly, in real time, determine which of these transactions are likely enough to be fraud that we want to potentially inconvenience a legitimate transaction and the cardholder, the end consumer, whoever’s trying to conduct that transaction?

That’s the numbers game that you’re playing.

FICO, for payment cards, plays as well as anybody.

They have that leading fraud detection model.

And I cover in the book as an example where they pull the data together from across thousands of banks.

Any bank that wants to use this best-in-class fraud detection model, to be the customer and use FICO’s model, they also have to contribute to the data.

So FICO gets to learn from transactions and data that’s accumulated across a whole bunch of organizations, a bunch of banks.

Whereas with most machine learning projects, you just use the data in-house.

You might augment it with exterior, external data.

But in terms of transactions and how people respond to marketing or which transactions turn out to be fraudulent, this kind of thing, usually it’s from your own customer base or prospects and how they interact with your products and what the outcomes are.

There’s always some behavior or outcome you’re trying to predict for any given machine learning project.

So FICO is a great flagship example.

Another example I cover in the book is UPS, a couple prominent dot-coms, one of my own client base, one of which is a failure.

I kind of lead off the book after the UPS story, which is an amazing one, with my own story, which is a big failure.

Then I go back later with a different success story.

But I think that the failures are just as important because they’re at least as prevalent.

And there may be a really good amount of successes, which really in those case studies and those success stories are the bread and butter of the conference series I’ve been running since 2009, which is called Machine Learning Week.

But there are probably a lot more failures.

So that track record stands to be improved greatly, and it should be.

Yeah.

Well, it definitely should be.

And I wanted to, you know, to that pendulum of inconveniencing the consumer by shutting down a transaction when it’s not fraud versus, oh, you’re getting in front of fraud.

You know, startups are usually criticized for breaking on lots of fraud when they are growing their user base, if you will.

But if you were heading the company, how does one work that pendulum?

Do you really keep it tight at first?

How do you think about that?

Or how would you work that, as far as that balance between false positives and false negatives?

Yes.

Well, I’m so glad you asked.

So this comes down to evaluating how well a predictive model works.

And the evaluation piece is really core to where things break down.

The reason things don’t get to successful deployment in general has to do with a disconnect between business and tech, between the technical data scientists, the number crunchers who are operating machine learning or preparing the data and operating machine learning software, and then their client, the stakeholder, the person who’s running or is in some way in charge of operations meant to be improved with these predictions.

For example, whether transactions should be conducted at all when requested.

So of course, the big question is, how good is machine learning?

How well does it predict?

And that question is almost never answered in a reasonable way.

That’s the disconnect.

Why is that?

Because the data scientists are all trained to focus on technical metrics like precision, recall.

Even accuracy is only a technical metric.

These are numbers that tell you simply the pure predictive performance of the model in comparison to a baseline, the relative performance, a baseline like random guessing.

So the fact is they do predict better than guessing.

That’s generally potentially quite valuable.

That’s the good news.

But that doesn’t directly translate to what the actual business value is.

So for any particular use case like fraud detection, what’s the business value?

The business value is going to be, well, look, every time we allow a fraudulent transaction to go through, we lose.

Usually the bank, for example, in the case of payment cards, is responsible for the full price of that fraudulent transaction.

But there’s also a cost with the other mistake, which is when you inconvenience a cardholder with what’s called a false positive.

You say, hey, this is fraudulent.

Let’s interrupt the transaction.

Then you find out later it was actually legitimate.

Yeah.

Now, that’s generally less costly, but how costly are they?

Well, in the case of payment cards, they’re approximately $100 versus $500.

That tends to be the industry norm.

But you don’t necessarily just want to work with those averages.

It depends on the particular transaction.

It depends on the region.

It depends on a whole bunch of factors.

But one way or another, you need to incorporate those pragmatic business factors to translate the model’s performance into the potential business win.

In the case of fraud detection, the overall savings in comparison, for example, to not doing any fraud detection, based on how you use the model to balance between how aggressive you are stopping fraud versus how lenient you are to avoid inconveniencing cardholders.

The way you turn those knobs depends on those pragmatic factors.

So I’ll tell you now, I’ve actually co-founded an early-stage startup.

We’re almost a year in, and we have a product to do that, to take the performance of the model and actually display it in terms of those business metrics, KPIs, like savings and profit, rather than only the standard technical metrics.

So what happens right now is regularly, systematically, repeatedly, routinely, this is almost always what happens, is the data scientist says, hey, stakeholder, the modeling worked really great.

We’ve got an area under the receiver operating characteristic curve of 0.887.

Isn’t that amazing?

And it is amazing because it’s like, this thing predicts a lot better than guessing, which is cool.

It’s really, really, really cool.

The computers can learn from data to predict a lot better than guessing in a way that’s quite possibly very valuable.

That is to say that they’ve learned from some historical examples and drawn generalizations that hold in general, that hold in new cases, in new circumstances that have never before been seen.

So in that sense, it’s literally learned something about the world, not just about this particular set of examples.

And that’s called induction versus deduction, if you want to put it in abstract terms.

That’s really cool.

But the fact that it predicts better than guessing, and you can put it in terms of this measurement or that measurement, only goes so far in actually speaking in business language.

So it’s lost in translation over and over again.

Well, Eric, one of the things you wrote about was the importance of working backwards.

I think you even quoted something about thinking about the movie script ending before the beginning.

And that’s something I can relate to because I write.

Yeah.

I quoted a scriptwriter who wrote about writing.

It’s like, everything’s backwards planning.

Like in choreography, you’ve got to figure out where you’re going to end up on stage.

And in writing Thelma & Louise, you’ve got to think about the last act before you start writing the first act.

So yeah.

And the same thing is true with any of your enterprise projects.

How’s it going to actually be deployed?

It’s really kind of obvious.

Most people know, hey, you have to have a particular use case.

The reason that gets lost with machine learning and AI, probably more egregiously than you would expect, is because everyone’s so enamored with the core technology.

We’re fetishizing it.

Like I said, it’s so cool to learn from data.

It’s also so cool to start thinking, hey, what would it mean for a computer to be intelligent?

It’s cool philosophically.

Everyone loves the sci-fi.

So next thing we know, we’re incorrectly presuming that this amazing core rocket science is valuable.

It’s not intrinsically valuable.

Only if you use it.

Only depending on how you deploy it.

It’s almost like we’re more excited about the rocket science than the launch of the rocket.

Yeah.

I know.

I mean, I just feel like I relate to this in things I’ve experienced at different corporations.

But you’re pointing to how it is a really cool technology.

And certainly you’ve been in this world for years.

I would call it like a passion career, or it seems that way from an outside perspective.

So you’ve seen it change.

But one of the things we do on this show is “That’s What You Said.”

But this is what I’m pulling, a direct quote in your book, and I picked this one.

It was, “Most people think data is boring. The word data is a deal killer at cocktail parties. I know this from personal experience. I have the data. But data isn’t just an arcane bunch of ones and zeros. It’s a recording of history, a list of prior events. It encodes the collective experience of an organization from which it is possible to learn analytically how to predict.”

That’s a great quote.

It’s a great quote, Eric.

Congratulations on writing that.

Oh, thank you.

That’s actually from my first book, Predictive Analytics.

Oh, well, by the way.

Yeah.

Oh, is it also?

Oh, no, no.

You’re right.

You’re right.

I’m confusing my own books.

It all blends.

A body of work.

You’re right.

I stand corrected.

Well, unpack that, because one of the things, of course, you’re known for is the music video that you put out on predictive analytics.

What year was that, Eric?

Oh, that was 2016.

Yeah, 2016.

It changed my life forever.

My handle is Dr. Data.

Dr. Data.

I’m still waiting for people to start calling me Dr. Data.

The video went, by today’s standard, I’d say slightly viral.

“Netflix top hit that stick. You need predictive analytics.”

“Who’s your data?”

“You call me Big Data.”

Three and a half minutes long, PredictThis.org.

And it’s the best ever genuinely educational music rap video about predictive analytics, which is another word for these predictive enterprise use cases of machine learning.

The problem with the video is that it’s a very campy rendition of what does it mean to be a super ultra nerd, and what does this do to your social life, which may distract you from actually listening to the lyrics.

Which are genuinely educational.

You know, everything you ever wanted to know about predictive analytics but were afraid to ask.

Well, okay.

I want to unpack that a bit too.

But I have to imagine from when you did that to now, now it’s like the cool technology, right?

Do you feel that?

Or do you still feel like you’re going to shut down a party if you’re like, let’s talk about data?

Or do you think people would be like, yeah, let’s talk about data?

You know, I still haven’t really figured it out.

I started programming when I was 10.

So that’s like the late ’70s.

And I was ostracized.

And I still think a lot of people think technology’s cool, but there’s still a level.

And this is a relevant question, this social question, because it speaks directly to that disconnect.

Data scientists, I could get in a room.

I love, like most data scientists are the best kind of nerd.

They’re fun nerds.

They’re funny.

They’re poignant.

That’s why I love running the conference series.

And we could speak for hours about area under the receiver operating characteristic curve.

In fact, just yesterday I listened to a whole podcast episode about it and debating how valuable it is.

And by the end of the podcast episode, they were saying, well, don’t forget about the business metrics.

Yeah.

There’s that, right?

There’s that, unfortunately.

So you do lose the forest for the trees, and that’s sort of the definition of nerd.

It’s not just about being socially different.

It’s about being pragmatically irrelevant after a certain point.

A real rocket scientist very well may be more excited about the cool science itself than the actual launch of the rocket.

And they might be like, look, we could launch the rocket next week or next century.

I don’t really care.

This is just fun technology.

But to the rest of the world, it’s like, come on.

If we don’t launch the rocket, we’re never going to get to Mars.

Right.

No, there’s always, I want to razzle-dazzle, which again, you achieved in the music video.

Well, we tried.

We had the disco people yo-yoing and playing a Rubik’s Cube while they’re dancing in the disco.

That’s my favorite image.

How long did that take to make?

Oh, it was a two-day shoot.

My friends in LA directed it and edited it and stuff.

Oh, okay.

Well, I like it.

So still, still used.

Have a fan base here.

Oh, great.

Well, okay.

So you’ve mentioned you don’t feel like it’s going to take over the whole human.

And one thing that’s been happening in financial services is they’re loving ever more the chatbot deployment.

But it’s also bringing on a fear of, am I going to lose my job as a result of this thing?

But let’s go bigger than that.

Unpack this fear of human versus robot.

You really dashed a dose of reality by saying that you don’t expect the takeover anytime soon.

Why is that?

I guess we’re pointing to how people are always wanting that fantasy, that big storyline, but what’s...

Yeah, we love it and we fear it at the same time.

I mean, there’s a reason the Terminator movies are basically zombie movies, right?

In a good way.

They may be my favorite zombie movies.

The thing is kind of coming at you, unstoppably, maybe sometimes slowly.

So it’s a love-hate thing.

You love to hate it.

You love to fear it, because the fear, those are, it’s criti-hype.

I didn’t coin that word.

You’re saying this stuff is too dangerous.

And that’s just another part of the narrative that’s saying it’s so valuable.

Hey, look, if this thing has potential to cause human extinction, it’d probably also do a pretty good job of targeting my marketing.

Right?

So you can’t get away from that narrative if you believe that it’s actually going to become general human level.

So it’s one thing for it to be better than a human at task A, B, C or D.

It’s a very different thing to achieve what is generally called artificial general intelligence, where it’s basically capable of anything a human could do, including running a Fortune 500 company.

Anything you might want to ask a virtual assistant to do in normal human language.

And that sort of, I believe there’s no evidence that, despite how seemingly human-like and amazing capabilities have emerged, especially more recently in more recent years, I do not believe that they represent concrete steps toward that audacious goal of an artificial human.

So look, these things are tools.

They’re under our control.

They’re potentially very valuable depending on how you use it.

But at the same time, because it’s so seemingly human-like, it makes for an incredible demo.

I don’t mean it’s only a demo.

I don’t mean it’s not valuable.

It is valuable.

Last week I published an article in Forbes about, look, there are some studies that actually measure the concrete enterprise value.

And they’re improving marketing.

They’re speeding up marketing creatives by 30%.

Not threefold improvement.

Thirty percent.

Wow.

Stuff like that.

Many of these projects, of course, they’re not measuring at all.

Sometimes they measure it and it turns out to not be helping at all, unexpectedly.

But that doesn’t mean that they can’t get it to work better.

These are valuable improvements.

But that’s a far cry from just sort of hiring a computer and installing it the same way you would onboard a human employee, unleashing them to operate autonomously.

And ultimately, it comes down to autonomy.

To what degree does a thing operate without human intervention in general?

Not at all.

In fact, the more long-term established, older but not old-school predictive applications I’ve been talking about are more potentially autonomous.

For example, fraud detection is going to automatically, on the fly, decide whether to authorize a transaction.

Generative AI output has got to always be proofread.

You don’t know what it’s going to do.

It does a really good job of being seemingly human-like and sometimes being correct because it’s predicting one word at a time.

So there comes prediction again.

That’s the same underlying core technology.

It’s not literally a word.

It’s a token, but it’s on that level of detail.

But those core language models are not designed or trained to meet higher-order human goals, like being correct.

So that’s a whole different unresolved, open research area.

It’s not just a product design issue.

So you can’t just, it’s not autonomous.

You have to proofread everything it writes.

Eric, that’s a relief.

So we’re going to end the conversation on the relief.

But I do have one more question for you.

Before that, people can get your book on Amazon.

You have a conference coming up in Arizona?

Yeah.

Well, Machine Learning Week, depending on when this drops.

We’re the first week in June in Phoenix.

Perfect.

And then the end of, I think it’s in the fall, in Germany.

The conference is MachineLearningWeek.com.

And the new sister conference, Generative AI Application Summit.

My book is at BizML.

So the business practice paradigm framework playbook that’s espoused in my book is called BizML, a business practice for running machine learning projects.

So BizML.com is the book.

Wonderful.

And for the last question, Dr. Data.

Yeah.

What’s the photo on your lock screen of your iPhone, or whatever phone you have?

I actually updated it pretty recently.

Oh.

Which is saying a lot because you only do it like every two, twice a year or something.

It’s a picture of my wife and our older toddler.

So I’ve got two toddlers, and the toddler’s got chocolate ice cream all over his face.

Which is such a cliché, but I think these things are really a cliché for good reason.

For a very good reason.

Well, Eric, thanks so much for being on the show.

It’s been a pleasure speaking with you today.

Likewise.

Thank you, Mary.

Okay.

The biggest thing I learned today is that I too want a nickname.

And I really like thinking about data as a recording of history, as Eric said.

That’s kind of beautiful.

Also, if you have comments or questions, click the link in this episode’s description.

It lets you text us.

If you like what you hear, please do subscribe.

I don’t want you to miss the next episode with Sophia Goldberg, the CEO and co-founder of Ansa.

We talk digital wallets and one of the busiest TikTok trends that started taking root last summer.

Girl math.

See you then.