Generative AI is not the panacea we’ve been promised

There's kind of an illusion with generative AI

This promises to be the viral sensation that could completely reset how we do things

According to all the headlines, it's on the brink of solving all business problems automatically

with the slight side effect of displacing huge amounts of workforce. It seems so amazing. It's potentially a panacea

No, it's hyperbole. It's hype. What we get with generative AI is extremely impressive

but it's not going to run the world. It does have the ability to create efficiencies, but it's more limited

Whereas predictive AI, which is older, very much still has great amounts of untapped value

I'm Eric Siegel. I'm the co-founder and CEO of Gooder AI, the founder of the Machine Learning Week conference series

and the author of the AI Playbook, Mastering the Rare Art of Machine Learning Deployment

I became fascinated with the concept of artificial intelligence as a kid in the late 70s and then in the early 80s

Eventually, my education led me to machine learning, and I've been in the field since 1991

Now, I was sort of semi-horrified with the AI hype for a few decades, and it just got a lot worse in recent years because of generative AI

It's going to feed that frenzy because it's so seemingly human-like. Generative AI, something like ChatGPT, a large language model, it is capable of communicating about any topic

and often giving responses that seem to understand what you're saying. And I grant that on some level, it has captured understanding

and the meaning of words and phrases and sentences and paragraphs. But I believe that the difference between what it can do and what humans can do is going to become increasingly apparent

Generative AI is sort of correct often only as a side effect

When people say hallucinate, they're like, well, look, it just makes things up. What impresses me is that it actually gets things right sometimes because it's only working on that low level of detail, the per word level

which results in that sort of seemingly human-like capability. There's a big difference between that impressive capability and the potential value

It's certainly valuable for writing first drafts. So it'll produce a first draft of a letter you need to write or a syllabus or something like that

But you can't trust it blindly. You have to proofread everything that it gives you

That actually, in a way, makes it less potentially autonomous. The whole point of computers is to automate, right

It does things really fast. And to the degree that we can actually trust it well enough to do things automatically

that ultimately helps the economy. It helps the efficiencies of the world

Predictive AI, that's the technology you turn to when you want to improve your existing largest scale operation

It does have the potential to enjoy the benefits of autonomy. So predictive AI or enterprise machine learning

That's the technology that learns from data to predict in order to improve any and all

of the millions of decisions that make up large-scale enterprise operations. And these are the things that make the world go round

So predict who's going to buy in order to decide who to contact with marketing, which

transaction is most likely to be fraudulent to decide which transactions to block or audit

which train wheel is most likely to fail in order to decide which one to inspect. It's not just train wheels

The New York Fire Department does that to predict which buildings are at most risk of fire to triage and prioritize inspections

Or which healthcare patient should we take another look at before discharging because they predicted very likely to be readmitted to the hospital All of these predictive applications are a form of prioritization or triage and the computer is systematically making those decisions over and over again real fast fully autonomous So we have data

we give it to machine learning, which is the underlying technology. It generates models that

predict, and those predictions improve all the large-scale operations that we conduct

Predictive AI is so applicable across industries. Let's take the delivery industry

UPS is one of the biggest three delivery companies in the United States

and they actually streamlined the efficiency of their deliveries by predicting tomorrow's deliveries

That makes such a big difference that in combination with another system

that actually prescribes the driving directions, To this day, UPS enjoys savings of $350 million a year and hundreds of thousands of metric tons of emissions

So this is how it works. When they have to start planning and then loading the trucks in the late afternoon or early evening so that it'll be ready the next morning, they have incomplete information

What they don't know is some of the packages that are still coming in later that night

So what they do is they augment the known information, which is that they already have a bunch of packages in hand that they know are meant to go out tomorrow morning for their final deliveries

And they'll augment that with tentatively presumed predicted deliveries by applying a predictive model for each potential delivery address and saying, hey, what are the chances that there'll be a delivery there tomorrow

Now they have a more complete picture of all the deliveries needed for tomorrow

They can do a better job planning and loading the packages overnight

so that when the trucks go out in the morning, they'll have relatively optimal routes that don't take too many miles of driving

too much gasoline, too much time of the drivers. Now, some of those predictions will be wrong

but they're confident enough that the completeness now actually overweighs some of that uncertainty

This is what you need to do if you want to improve existing large operations You need to work with probability Assign a number How likely is this outcome

Here's the thing. It doesn't make a difference how good the number crunching is unless you act on it

It's not intrinsically valuable. The value only comes if you actually deploy it and change your existing operations

We have this incredible, seemingly human-like capability of generative AI, which in one sense I think is the most amazing thing I've ever seen

But underlying the excitement is the idea that we are moving steadily towards and potentially very near AGI, artificial general intelligence, which is a computer that can do anything a person can do

It's this feeling of a computer kind of coming alive like Frankenstein, which we see over and over again in science fiction movies

In the real world, I do not believe we're going to fully replicate humans anytime soon or that we're actively making progress in that direction

That is a recipe for mismanaged expectations, otherwise known as hype. The antidote to hype is simple

Focus on concrete value. Discover whether you're using generative AI or predictive AI

Determine a very specific, concrete, credible use case of exactly how this technology is going to improve some kind of operation in the enterprise and deliver value

If you want to just sort of explore how close it is to the human mind and why you think it might be getting there, that's kind of a philosophical conversation, and that's great

But if you're talking about sort of improving efficiencies of operations that make the world go around, I think we should be a lot more practical and less pie in the sky