Why apathy and fear are the two most useless positions on AI

If you haven't stayed up three nights being anxious about AI, if you haven't had an

existential crisis about it, you probably haven't really experienced AI. It is a weird thing. What's it mean to be a human? What's it mean to think

What will I do for a living? What will my kids do? What does it mean that it's better than me at some of this stuff? Is this real or is it an illusion? Nobody actually knows where AI is

heading right now and how good it's going to get. But we shouldn't feel like we don't have control

over how AI is used. As managers and leaders, you get to make these choices

about how to deploy these systems to increase human flourishing. As individuals, we get to decide how to be the human

who uses these systems well. AI is here to stay. That is something that you get to make a decision

about how you want to handle and to learn to work with and learn to thrive with rather than to just be scared of

I'm Ethan Mollick, a professor at the Wharton School of the University of Pennsylvania

where I study innovation, entrepreneurship, in artificial intelligence. I'm the author of the book

Co-Intelligence, Living and Working with AI. Artificial intelligence is about prediction

Basically, AI is a very fancy autocomplete. For a long time, that was about numerical prediction

and doing sort of complex algorithms of math so that Netflix could recommend a show for you to watch

Amazon could figure out where to site its next warehouse or Tesla could figure out how to use data

to make sure its cars were driving automatically. The thing that these systems were bad at predicting

was the next word in a sentence. So if your sentence ended with the word filed, it didn't know whether you were filing your taxes

or filing your nails. What happened that was different was the innovation of the large language model

In 2017, a breakthrough paper called Attention is All You Need outlined a new kind of AI

called the Transform with Attention mechanism that basically let the AI pay attention

to not just the final word in the sentence, but the entire context of the sentence the paragraph, the page, and so on

Large language models work by taking huge amounts of information like all the data on the internet There a lot of Harry Potter fan fiction for example because that what the internet contains And based on all of this data the AI goes through a process called pre And this is that really expensive part that only a few companies in the world can do

And during that time, the AI learns the relationships between words or parts of words called tokens. So it learns that kiwi and strawberry are closely related, but that hawk and potato are

not closely related. It learns across thousands of dimensions in a multi-dimensional space we can't

understand. That lets it do predictions. But it turns out unexpectedly, when large language models

get big enough, they also do all kinds of other things we didn't expect. We didn't expect them to

be good at medicine, but they were actually quite good and beat doctors under many circumstances

We didn't expect them to be good at creativity, but they can generate ideas better than most

humans can. And so they're general purpose models. They do many different things

Interestingly, GPT doesn't just stand for the GPT and chat GPT. It also stands for general

purpose technology, which is one of these once in a generation technologies, things like steam

power or the internet or electrification that change everything they touch. They alter society

they alter how we work, they alter how we relate to each other in ways that are really hard to

anticipate. So you can't think of certainties. You should think of scenarios. And there's really

four scenarios of the future. The first is actually, I think, the least likely, which is that the world

is static, that this is the best AI you're ever going to use. I think that's unlikely. In fact

whatever AI you're using now is the worst AI you're ever going to use. Even if the core large

language model development stopped right now, there's another 10 years of just making it work

better with tools and with industry in ways that will continue to be disruptive. So I think that's

a dangerous view because it isn't static, it's evolving. So I want to skip actually to the last

scenario before covering scenarios two and three. So scenario four is AGI, artificial general

intelligence. This is the idea that a machine will be smarter than a human in almost all tasks

And this is the explicit goal of open AI. They want to build AGI. And there's a lot of debate about what this means

When we have a machine smarter than a human, and it can do all human jobs, can it create

AI smarter than itself? Then we have artificial superintelligence, ASI, and humans become obsolete overnight

And there people genuinely worried about this And I think it worth spending a little time being slightly worried too because other people are But I think that that scenario tends to take agency away from us because it something that happens to us

And I think that it's more important to worry about what I call scenarios two and three, which is continued linear exponential growth

We don't know how good AI is going to get. Right now, the doubling time for capability is about every five to nine months, which is an exceptionally fast doubling time

Moore's Law, which is the rule that's kind of kept the computer world going

doubles the power of computer processing chips every 24 to 26 months

So this is a very fast rate of growth. It's very likely that AIs will continue to improve and get better in the near term

And now is a good time for you to start to figure out how to use AI to support what makes you human or good at things

And what things, as AI gets better, that you might want to start handing off more to the AI

we have a lot of early evidence that this is going to be a big deal for work

so there's now multiple studies across fields ranging from consulting to legal to marketing

to programming suggesting 20 to 80 percent performance improvements across a wide range

of tasks for people who use ai versus don't the problem with being human is that we're stuck in

our own heads and a lot of decisions that are bad results from us not having enough perspectives

AI is very good and a cheap way of providing additional perspectives. You don't have to listen to its advice, but getting its advice, forcing you to reflect for a moment, forcing you to think it either reject or accept it

And that can give you the license to actually be really creative and help spark your own innovation

So you can ask it to create crazy suggestions for you. What's the most complicated way to solve this problem

What's the most expensive way to solve this problem? What is the worst idea about how to do this

How would a supervillain make this problem worse? it can be very unnerving to realize

that the AI is quite good at creativity. We think of this as a very human trait. But I think if we embrace the fact

that AI can help us be more creative, that actually is very exciting. A lot of us feel stifled creatively

and having a partner who can work with you and doesn't ever judge you can often feel very liberating

One of the really weird things about large language models is they don work like we think computers should work LLMs are very bad at math Computers should be good at math Large language models are weirdly emotional and can threaten you or want to be your friend seemingly

And so it can be very hard to know in advance what they're good or bad at. In fact, nobody actually knows the answer

We call this the jagged frontier of AI, the idea that there's almost a spiky shape to what the AI can do and what it can't do

So part of what you need to do is understand the shape of the frontier. You need to know when the AI is likely to lie to you and when it's not going to

Hallucination refers to the idea that what the AI produces could be entirely made up

Plausible sounding fake information. The thing about AI is that everything it does is a hallucination

There's no mind there. You might start to become more persuaded by it

You might become blind to its biases. You might think it's more capable than it is

AI kind of works a little bit like a psychic. It's really good at telling you what you want to hear

The fact that it's accurate so often is kind of weird, actually. And hallucination rates have been dropping over time

So what you need to do is sharpen your own intuition with working with a tool. Get a sense of when you see something that might make you concerned

When you ask people about the future of AI, there's a term used within AI insiders called P-Doom

which is your probability that we're all going to die. I do not have a P-Doom that I really think about

because I don't think we can assign a probability to things going wrong. And again, that makes the technology the agent

We get to decide how this thing is used. And if we think about this the right way

this freezes from boredom and tedium and disaster. But I think we need to think about the mistakes we've made

in regulating other technologies, you know, and what the advantages are. What we did differently for the internet versus social media

There are decisions we get to make that are both personal about how we use it at a organizational level about how it's deployed

and at a societal level. And it's not an inevitability that technology just does what it does

It does what it does because society lets it do that