Chapter 1: What is the main topic discussed in this episode?
The Last Show with David Cooper. Procrastinate your life away with us. I'm sure you've heard the buzz about AI making workers smarter, faster, more productive. But a new study suggests the real secret might be something a little more human. Workers who know what they're good at, who know what their strengths are.
And when it's time to let AI take the wheel, those are the ones that benefit the most from these new tools. I'm here with someone who worked on that study. He is an economics professor at NYU. His name is Andrew Kaplan. Andrew, welcome to the program.
Chapter 2: What does the latest study reveal about AI and self-awareness?
Thank you very much. I think there's a lot of doom and gloom, a lot of fear mongering about AI taking people's jobs and other people being forced to be overly productive. What I like about your study is it frames things like who are these tools good for? What is it about self-awareness at the helm of me using AI that'll make me a better worker with it?
Well, you know, what we were looking for is to find out what skills, this is a wide open question, what skills help people work with an AI? And we found that a way underrated skill is your ability to listen to what the AI tells you. So for example, when you're looking at a medical image, if the AI says it's 95% likely to have a particular condition,
You better be able to say, well, I knew anyway. The doctors will often say, I knew, I'll overrule 100%. That's rubbish.
Chapter 3: How can understanding strengths improve AI utilization at work?
You know, I know the answer. And typically they're wrong. Often they're miscalibrated. They're way overconfident in their beliefs and they can't listen. I do think knowing your own limits and being self-aware is probably a good skill to have independent of AI, isn't it? I don't know. It doesn't pay in many branches that I've been observing. You know, I'm not sure it's been working in politics.
I don't think it works in medicine. You have to appear very confident. And it doesn't work actually in the false art of predicting the future under AI, in which everybody gets to be Nostradamus and tells you what's going to happen. Let's start with this basic idea of calibration. When someone says they're well calibrated, what does that mean?
Why does that make me excellent using AI if I am well calibrated? Well, it means that when you think something is 70% likely, it's roughly objectively 70% likely. When you're very confident that something is true, 95 to 100%, it had better be true 95 to 100% of the time.
Those are the kinds of things that can be checked and the evidence shows that many, many people and in many circumstances are 95% sure and 60% right. And that's not a very comfortable thing. But imagine then you get an AI that tells you something and it's only rationally like 85% confident. And you say, it's just a fool. I see so much better. I won't listen to it.
there's a surprising finding in your study that I love because I'm not a very bright individual. It's that lower skill workers who have a good measure of their own ability benefit the most from AI. Yes, absolutely.
And in the particular thing we studied, it was like the AI is helpful and it's a little bit better, you know, than you if this isn't your great speciality, but you better recognize that and then you can become pretty good yourself. So that's the, that's the root.
So, it's a logical following that the AI is making the less able, the less skilled among us better, as long as they have a good sense of self-awareness? Self-awareness is critical, but no, I would give a big fat caveat to that. Okay. The caveat is, and the thing that I'm currently most interested in, is whether in fact It's about being good at asking questions and listening to answers.
So my own take is that the biggest, generalizing out of that study, the biggest skills are, can you listen? But not only can you listen, in the words with large language models, do you know how to ask questions?
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Chapter 4: What skills enhance collaboration with AI according to the study?
Do you understand the limits and the skills of the party you're talking with? Because with a large language model, that's your job. I mean, I use large language model a lot and I am constantly editing it, but I'm nevertheless listening. So that's the skill. Can you listen, but can you recognize where you're the expert and where the... The LLM is the expert.
And if I were to say a big picture, I would say it helps in more complicated activities. It helps those who plan best. In simpler activities, it helps those with lower skill who understand themselves. It's a bit of a continuum there. No, I think that's a fair way to frame it.
Let's talk about high ability people, people who are highly intelligent, very good at their craft, but overestimate their ability nonetheless. That's a very easy group to hate. That's a very easy group to find annoying. And as it turns, that group, it looks like they're not great at using AI because even when AI is right, they're like, no, no, I know better. Exactly.
The I know better thing is a huge deal. And I mean, I can't think of The biggest professions in the world have I know better written all over them. You know, medicine is a fantastic case where you'll often have doctors saying, think, they've asked doctors to give probabilities in difficult cases. And they say, why should I bother? I know. Yeah. That should never be anyone's answer ever.
Nobody ever knows anything. The only thing that you do know is if they said that, they're a bit of an idiot. So what exactly was the experiment? Like, how do we come to all these conclusions that we're talking about? Well, it's actually quite hard to find cases that work. And there are many, many constraints on doing a good study.
We found judging people of age, whether they're over or under 21, to be a good case study, because even the AI finds that super difficult. And there are sometimes cues that a human might recognize better than an AI. So it's not obvious who's the winner. In fact, the best of the human subjects were better than the AI, but the AI was better than the average subject.
And we offered them both, you know, why don't you try judging this yourself? Then why don't you listen to what an AI has to say? Then why don't you listen to what an AI has to say and then judge yourself? And produce the answer you'd like, which is how likely is this person to be under over 21? Beautiful banqueting, which is very, very stark.
It's not exactly the biggest task in my life, but if you're a bouncer, maybe it is, but that's about it. So that was the task. But it's a precursor for a much bigger set of things that can now be studied, particularly with large language bubbles. So much more where that came from. This idea of calibration, how self-aware I am. Look, I was not born the most self-aware person socially.
And so what I did was I went to therapy. I see a therapist once a week. I know that's not the same as like professional calibration, but I guess my question is, is it trainable? Can it be taught? That's the biggest question. That is the biggest question.
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Chapter 5: Why is self-awareness important for effective AI usage?
We're trying a little bit of a game. I can tell you we are trying to train in a particular set of medical images. We're trying to train people off saying, 99% when they don't have a clue. And we're succeeding a tiny bit. But the much bigger game is to what extent is working well with an AI trainable? And it's my conjecture that it is quite a great deal more than we know.
and that that's where we need to put our energy. I believe it's doable, but I believe you have to be thinking a great deal harder about questions and answers to work that out. And you actually have to start training people to ask good questions and to listen to the answers.
Well, I think folks would be very interested in your research, especially ones that are scared about AI either replacing them or the requirement to become great at AI to keep their jobs. It's an interesting area. And Andrew Kaplan is a economics professor at NYU. Andrew, I've enjoyed this chat. Thanks for coming on the show and sharing your research. Thank you so much. Welcome to Survivor 50.
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