Generative AI is not the panacea we’ve been promised | Eric Siegel for Big Think+

Summary

Eric Siegel, co-founder and CEO of Goodr AI, addresses the hype around generative AI, emphasizing its impressive but limited capabilities compared to humans. He distinguishes it from predictive AI, which offers untapped value in improving large-scale operations through data-driven predictions. Siegel illustrates predictive AI's practical applications in industries like delivery, highlighting UPS's efficiency gains and cost savings. The key takeaway is to focus on concrete value and specific use cases rather than being swayed by hype.

Takeaways

• 🧩 Generative AI is often perceived as a panacea capable of solving all business problems, but this is an overstatement and a form of hype.
• 🤖 Eric Siegel, CEO of Goodr AI, emphasizes that while generative AI is impressive, it is not going to run the world and is more limited in its capabilities compared to predictive AI.
• 📝 Generative AI can create first drafts for documents but requires proofreading and cannot be trusted blindly, indicating a need for human oversight.
• 🔮 Predictive AI, which is older, still has untapped value and is more suited for large-scale operations, unlike generative AI which is often limited to per-word level understanding.
• 🛠 Predictive AI is used for improving existing operations by learning from data to make predictions that can automate and prioritize decisions in various industries.
• 🚚 UPS uses predictive AI to streamline delivery efficiency, saving millions of dollars annually and reducing emissions, demonstrating the practical application of predictive AI.
• 📊 Predictive AI involves working with probabilities to make informed decisions, which is crucial for improving large-scale operations.
• 🔑 The value of AI comes from its deployment and the changes it brings to existing operations, rather than the technology itself.
• 🧐 Siegel expresses skepticism about the hype surrounding the potential of AI to achieve AGI (Artificial General Intelligence), cautioning against overestimating its capabilities.
• 🌟 The seemingly human-like capabilities of generative AI are fascinating, but Siegel advises focusing on concrete value and practical applications rather than philosophical debates.
• 📈 To effectively utilize AI, businesses should identify specific, credible use cases that can deliver tangible improvements to enterprise operations.

Q & A

What is the main illusion associated with generative AI according to the transcript?

The main illusion is that generative AI is on the brink of solving all business problems automatically and is a potential panacea, which is actually hyperbole and hype.

What is Eric Siegel's professional background?

Eric Siegel is the co-founder and CEO of Goodr 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.' He has been in the field of machine learning since 1991.

How does Siegel describe the capabilities of generative AI like chatGPT?

Siegel describes generative AI as capable of communicating about any topic and often giving responses that seem to understand what you're saying, but he emphasizes that its understanding is limited to a low level of detail, the per-word level.

What is the difference Siegel sees between the capabilities of generative AI and human understanding?

Siegel believes that the difference will become increasingly apparent as generative AI operates on a low level of detail and often gets things right as a side effect, whereas human understanding is much deeper and nuanced.

What is the potential use of generative AI in writing according to Siegel?

Generative AI is valuable for writing first drafts, such as letters or syllabi, but it cannot be trusted blindly and requires proofreading.

What is predictive AI and how does it differ from generative AI?

Predictive AI is technology that learns from data to make predictions in order to improve large-scale enterprise operations. It differs from generative AI in that it focuses on improving existing operations through predictions rather than creating content.

How does Siegel view the potential of predictive AI in improving large-scale operations?

Siegel views predictive AI as having great untapped value and potential for autonomy, as it can systematically make decisions over and over again, fully autonomously, leading to significant improvements in operations.

Can you provide an example of how predictive AI is used in the real world as mentioned in the script?

An example given is UPS using predictive AI to streamline the efficiency of their deliveries by predicting tomorrow's deliveries, which results in annual savings of three hundred and fifty million dollars and reduction in emissions.

What is the importance of acting on the predictions made by predictive AI according to Siegel?

Acting on the predictions is crucial because the value of predictive AI comes from deploying it and changing existing operations, not just from the accuracy of the predictions themselves.

What is Siegel's stance on the hype surrounding Artificial General Intelligence (AGI)?

Siegel is skeptical about the hype surrounding AGI, stating that he does not believe we are close to fully replicating human capabilities with computers and that such expectations are mismanaged.

What advice does Siegel give to counter the hype around AI technologies?

Siegel advises to focus on concrete value, determine specific, credible use cases for AI technologies, and be practical rather than overly optimistic about their capabilities.

Outlines

00:00

🤖 Generative AI: Hype vs. Reality

The paragraph addresses the inflated expectations surrounding generative AI, contrasting it with the more grounded and valuable potential of predictive AI. Eric Siegel, CEO of Goodr AI, dispels the myth that generative AI can solve all business problems and replace the workforce, emphasizing that while it's impressive, it's not the panacea it's made out to be. He highlights the difference between generative AI's ability to seemingly understand language and the deeper understanding of humans. Siegel also points out the practical applications of generative AI in creating first drafts but stresses the importance of proofreading due to its limitations. The paragraph concludes by advocating for a focus on the concrete value of AI technologies rather than getting caught up in the hype.

05:00

🚚 Predictive AI in Action: UPS Case Study

This paragraph delves into the practical application of predictive AI in the delivery industry, using UPS as a case study. It explains how UPS leverages predictive models to anticipate next-day deliveries, despite having incomplete information at the planning stage. By augmenting known package data with predicted deliveries, UPS is able to optimize truck loading and route planning, leading to significant annual savings and reduced emissions. The paragraph underscores the importance of probability in improving large-scale operations and the necessity of acting on the insights generated by predictive AI models. It also touches on the broader implications of AI's role in enhancing operational efficiencies across various industries.

Keywords

💡Generative AI

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, or music. In the video, it is discussed as a technology that has the potential to create efficiencies but is not yet capable of running the world or replacing human workforce. The script mentions 'generative AI' like chatGPT, which can communicate about any topic and give responses that seem to understand the context, but it also emphasizes the limitations and the need for human oversight.

💡Efficiency

Efficiency in this context refers to the ability of a system or process to produce the desired outcome without wasting materials, time, or energy. The video script discusses how generative AI can create efficiencies but also highlights that its capabilities are more limited than what some headlines might suggest. For example, it can help in writing first drafts but requires proofreading, indicating a level of efficiency but not full autonomy.

💡Predictive AI

Predictive AI is a type of artificial intelligence that uses data, machine learning, and statistical algorithms to predict future outcomes or trends. The script contrasts generative AI with predictive AI, emphasizing the latter's untapped value and its ability to improve large-scale operations. Predictive AI is presented as a technology that can make autonomous decisions, such as predicting fraudulent transactions or potential equipment failures.

💡Autonomy

Autonomy in the script refers to the ability of a system to function independently without human intervention. It is mentioned in the context of predictive AI's potential to enjoy the benefits of autonomy, where the system can make decisions in real-time based on predictions, such as UPS using predictive AI to optimize delivery routes and save resources.

💡Machine Learning

Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve from experience without being explicitly programmed. The script mentions Eric Siegel's background in machine learning and how it is the underlying technology for both generative and predictive AI, generating models that can predict and improve operations.

💡Hype

Hype in this context refers to the exaggerated promotion or publicity of a product, technology, or concept, often beyond its current capabilities. The video script criticizes the hype surrounding generative AI, stating that while it is impressive, it is not the panacea that some headlines suggest. The term is used to caution against overestimating the current capabilities of AI.

💡Human-like Capability

Human-like capability describes the ability of AI systems to mimic human behavior, understanding, or communication. The script discusses generative AI's seemingly human-like responses, which can sometimes appear to understand and communicate effectively, but also points out the difference between this capability and the full range of human understanding.

💡Proofreading

Proofreading is the process of checking written material to detect and correct errors. In the context of the script, it is mentioned as a necessary step when using generative AI to write first drafts, indicating that while AI can assist in creating content, it cannot yet replace the need for human review and editing.

💡Enterprise Machine Learning

Enterprise machine learning refers to the application of machine learning within an organization to improve business operations, decision-making, and strategic planning. The script positions enterprise machine learning as a technology that learns from data to predict and improve large-scale operations, such as UPS's use of predictive AI to optimize delivery logistics.

💡Artificial General Intelligence (AGI)

Artificial General Intelligence, or AGI, is the hypothetical ability of an AI to understand, learn, and apply knowledge across a wide range of tasks at a level equal to or beyond that of a human. The script addresses the excitement around AGI as a potential outcome of AI development but counters it with a more pragmatic view that we are not close to achieving such a level of AI and should focus on concrete applications instead.

💡Triage

Triage is the process of prioritizing tasks or cases based on their urgency or importance. In the script, predictive AI is described as a form of triage, where the technology systematically makes decisions to prioritize actions, such as predicting which transactions are likely to be fraudulent or which patients are at risk of hospital readmission.

Highlights

Generative AI is often seen as a viral sensation that could revolutionize business practices but is actually overhyped.

Generative AI, while impressive, is limited and not capable of running the world autonomously.

Predictive AI, an older technology, still holds significant untapped value.

Eric Siegel, co-founder and CEO of Goodr AI, discusses the potential and limitations of AI.

Generative AI, like chatGPT, can communicate on various topics but its understanding is limited to a per-word level.

The difference between generative AI's capabilities and human understanding will become more evident over time.

Generative AI's correct responses are often a side effect rather than a result of deep understanding.

Generative AI is valuable for creating first drafts but requires proofreading due to its limitations.

Predictive AI is the technology for improving large-scale operations and has the potential for autonomy.

Predictive AI learns from data to make predictions that can improve decisions in enterprise operations.

Examples of predictive AI include marketing contact strategies, fraud detection, and healthcare patient readmission predictions.

Predictive AI applications involve prioritization and triage, making systematic and fast decisions autonomously.

UPS uses predictive AI to streamline delivery efficiency, saving millions annually and reducing emissions.

Predictive AI in delivery involves augmenting known information with predicted deliveries for better planning.

The value of predictive AI comes from deploying it to change and improve existing operations.

The hype around generative AI feeds into the misconception of approaching AGI or Artificial General Intelligence.

The antidote to AI hype is focusing on concrete value and specific use cases within enterprises.

Practical application and improvement of operational efficiencies should be prioritized over philosophical debates on AI's human-like capabilities.

Transcripts

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 the 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 Goodr 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

seventies and then in the early eighties.

Eventually,

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

ninety-one.

"Whoa,

Kasparov, after the move c4,

has resigned."

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're 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 three hundred and fifty million dollars 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-scale 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 is it 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.