WEBVTT - Ep72 "How do you put yourself in other people's shoes (and can AI do it)?"

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<v Speaker 1>You know that moment in the horror movie where the

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<v Speaker 1>monster is coming closer but the person on screen doesn't

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<v Speaker 1>see it. Why does that drive you crazy? And what

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<v Speaker 1>does that teach us about brains? What is theory of

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<v Speaker 1>mind and why is it so important for everyone from

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<v Speaker 1>poker players to con men, to stage magicians to novelists.

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<v Speaker 1>We're going to talk about a very fundamental skill of

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<v Speaker 1>human brains today, and as impressive as AI is currently,

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<v Speaker 1>we're going to ask the question of whether computers can

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<v Speaker 1>replicate this right now or whether it is beyond their

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<v Speaker 1>skill set. Welcome to Inner Cosmos with me David Eagleman.

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<v Speaker 1>I'm a neuroscientist and an author at Stanford, and in

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<v Speaker 1>these episodes we sail deeply into our three pound universe

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<v Speaker 1>to understand why and how our lives look the way

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<v Speaker 1>they do. Today's episode is about what it takes to

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<v Speaker 1>understand other people, how your brain does it, and whether

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<v Speaker 1>computers could do it. So imagine this. You're walking down

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<v Speaker 1>the street and you see someone frantically searching their pockets

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<v Speaker 1>and looking around with furrowed brows in a tight frown.

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<v Speaker 1>So without them saying a word, you can infer that

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<v Speaker 1>they might have lost something important. Maybe it's his keys.

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<v Speaker 1>Your brain can easily make a good guess about another

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<v Speaker 1>person's mental state just from looking at their actions. We

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<v Speaker 1>are inferring something about what is going on in that

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<v Speaker 1>person's head. But it's more than just pattern matching. It's

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<v Speaker 1>not simply that your brain has seen lots of people

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<v Speaker 1>patting their pockets and you talked with them afterwards, and

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<v Speaker 1>you figured out why they were doing that, and you

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<v Speaker 1>detected a pattern, and you memorized, ah, okay, that pattern

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<v Speaker 1>equals that problem. Instead, you have the ability to imagine

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<v Speaker 1>yourself in their situation. You can mentally slip into their

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<v Speaker 1>shoes and ask, what would I be thinking if I

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<v Speaker 1>were patting my pockets and frantically searching around me? And

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<v Speaker 1>maybe you see something else. You see a kid there

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<v Speaker 1>around the corner, and the kid is peeking around the

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<v Speaker 1>corner at the man patting his pockets, and the child

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<v Speaker 1>is giggling. Now, why is the kid giggling while the

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<v Speaker 1>guy is so obviously worried, Well, it probably strikes you

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<v Speaker 1>that he's hiding something from the guy. You see that

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<v Speaker 1>the kid is not running away. Instead, he's standing in

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<v Speaker 1>such a way that he'll be spotted. Now it's pretty

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<v Speaker 1>obvious what's happening here. You can step into the man's

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<v Speaker 1>head to feel the worry, and you can step into

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<v Speaker 1>the kid's head to recognize that he feels like he's

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<v Speaker 1>playing a game, even if it doesn't strike you as

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<v Speaker 1>so funny. Then you catch the guy meet with the

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<v Speaker 1>kid for just a fraction of a second, which sends

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<v Speaker 1>the kid into fits of laughter, and you realize the

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<v Speaker 1>man is just playing along. Now, how did you decide

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<v Speaker 1>what is going on in the heads of these two Again,

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<v Speaker 1>it's not as though you memorized an algorithm here. Okay,

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<v Speaker 1>if there's eye contact, then there's one interpretation. If there's

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<v Speaker 1>no eye contact, then a totally different interpretation. To appreciate

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<v Speaker 1>how complex this mind reading is that you just did,

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<v Speaker 1>just imagine that you're a space alien watching this scene

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<v Speaker 1>from your spaceship. You would be totally confused. You would

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<v Speaker 1>have no idea what's going on in this weird scene

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<v Speaker 1>because you don't know what it is to be a human.

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<v Speaker 1>Here's another analogy to appreciate this. Think about the way

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<v Speaker 1>that you, as a human might watch fish. You really

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<v Speaker 1>don't understand what the heck they're doing. One fish suddenly

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<v Speaker 1>starts swimming faster, and another starts swimming in circles, and

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<v Speaker 1>one starts flapping its gills faster, and one moves up

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<v Speaker 1>towards the surface. It's all just weird fish behavior to you.

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<v Speaker 1>You don't know how to read any of it. It's

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<v Speaker 1>just fish stuff, and you're not able to immediately construct

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<v Speaker 1>a story about the meaning of any of this. And

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<v Speaker 1>that's what it's like to be this space alien watching

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<v Speaker 1>this guy checking his pockets and the child giggling. Now,

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<v Speaker 1>what allows us, as opposed to the space alien, to

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<v Speaker 1>be so good at reading our fellow humans. This is

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<v Speaker 1>what psychologists and neuroscientists call theory of mind, and that's

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<v Speaker 1>what we're talking about today. Theory of mind is the

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<v Speaker 1>ability to understand that other people have their own thoughts

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<v Speaker 1>and feelings and beliefs that are different from yours. It's

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<v Speaker 1>the ability to recognize that others have their own perspectives.

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<v Speaker 1>It's the ability to attribut mute mental states to other people,

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<v Speaker 1>like what their intentions are, or their desires, or their emotions,

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<v Speaker 1>or what they know or don't know. And theory of

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<v Speaker 1>mind is a key cognitive skill that allows us to

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<v Speaker 1>interact with other people in a very rich and nuanced way.

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<v Speaker 1>Just think about how pervasive this skill is in everything

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<v Speaker 1>we do. So take sarcasm. When your friend makes a

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<v Speaker 1>sarcastic comment, you can recognize that her words don't match

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<v Speaker 1>her true intention. So, for example, if she says, oh, awesome,

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<v Speaker 1>more traffic, I love traffic, you infer that she's not

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<v Speaker 1>actually pleased. This requires understanding her mental state that she

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<v Speaker 1>is irritated not happy. Now, if you were Siri or Alexa,

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<v Speaker 1>you wouldn't be able to recognize anything but the words.

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<v Speaker 1>You wouldn't understand anything about the mind behind the words.

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<v Speaker 1>So we're going to talk about how brains do it

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<v Speaker 1>and whether or not computers can do it. But before

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<v Speaker 1>we go there, we're going to take a few minutes

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<v Speaker 1>to really appreciate how the skill is everywhere in what

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<v Speaker 1>we do. For example, just think about different professions. So

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<v Speaker 1>detectives use theory of mind all the time. Did mister

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<v Speaker 1>Jones know that the food had gone bad when he

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<v Speaker 1>sold it? Did mister Smith know that his boss was

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<v Speaker 1>involved with organized crime or was he acting with no knowledge?

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<v Speaker 1>Or more generally, if they want to know if someone

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<v Speaker 1>is lying, it usually helps to step into their shoes

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<v Speaker 1>and think about what that person knows or doesn't know.

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<v Speaker 1>Magicians use theory of mind. They know that if they

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<v Speaker 1>move their hand in an arc, your attention is going

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<v Speaker 1>to follow that, and therefore they know what you won't

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<v Speaker 1>see them do. They know that even though they know

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<v Speaker 1>something happened, like the card dropped into their sleeve, they

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<v Speaker 1>know that you don't know that. They always keep your

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<v Speaker 1>point of view, your beliefs, at the forefront of their mind.

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<v Speaker 1>Con Men do this. They listen to your words and

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<v Speaker 1>they read your body language to gather what you know

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<v Speaker 1>and don't know, and therefore what buttons they should push next.

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<v Speaker 1>Psychiatrists and psychologists always use theory of mind to understand

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<v Speaker 1>what is being expressed from the patient's point of view,

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<v Speaker 1>In other words, what the person believes, whether or not

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<v Speaker 1>it's what the therapist believes. I'll give you another example.

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<v Speaker 1>My friend Maddie is a professional poker player, and he

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<v Speaker 1>describes poker playing like this. He says, when you're learning

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<v Speaker 1>to play poker, you think about the cards you have

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<v Speaker 1>in your hand. As you get better, you think about

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<v Speaker 1>your hand and also what the other person is thinking.

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<v Speaker 1>And as you get even better, you think about what

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<v Speaker 1>the other person is thinking your thinking, and when you

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<v Speaker 1>get to the professional levels, you're thinking about what he thinks,

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<v Speaker 1>you think he thinks, and people who are real pros

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<v Speaker 1>can think five or six levels deep on this. All

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<v Speaker 1>of this is theory of mind, and theory of mind

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<v Speaker 1>is key when you're teaching something. For example, parents know

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<v Speaker 1>that their children can't understand certain things. For example, the

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<v Speaker 1>child needs to get that smallpox shot, even though to

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<v Speaker 1>the child that's nothing but scary and he simply doesn't

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<v Speaker 1>have the capacity to think about the future benefits that

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<v Speaker 1>will accrue. Or the school teacher can only hope to

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<v Speaker 1>educate her students if she knows what they already know

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<v Speaker 1>or don't know. She needs to phrase things in such

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<v Speaker 1>a way that someone who doesn't already know what she

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<v Speaker 1>knows can absorb it, and that just requires theory of mind.

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<v Speaker 1>If she couldn't simulate what it's like to be in

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<v Speaker 1>their heads, she'd have no meaningful shot at getting them

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<v Speaker 1>past the first quiz. And this issue of considering what

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<v Speaker 1>someone knows or doesn't know is also critical in any negotiation.

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<v Speaker 1>You try to under understand the other person's desires and

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<v Speaker 1>goals and where they might potentially compromise during a salary negotiation,

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<v Speaker 1>you consider what your employer is thinking about the needs

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<v Speaker 1>in future of the company and therefore what they might

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<v Speaker 1>be willing to offer. And this is also how you

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<v Speaker 1>manage conflicts. In any disagreement, if you're smart, you try

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<v Speaker 1>to understand the other person's perspective to resolve the issue.

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<v Speaker 1>If your partner is upset with you, you try to

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<v Speaker 1>figure out what you did or said that set things off,

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<v Speaker 1>and why that offended the other person and how it

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<v Speaker 1>landed for them. And that's the single way that you're

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<v Speaker 1>going to hit the problem effectively. So this ability to

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<v Speaker 1>slip into someone else's shoes has almost everything to do

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<v Speaker 1>with our social intelligence. You use this very human skill

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<v Speaker 1>all the time. And before we get to the next

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<v Speaker 1>act of this podcast, where I ask if computers can

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<v Speaker 1>do this or not, I just want to finish fla

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<v Speaker 1>this out so we can really see how pervasive this is.

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<v Speaker 1>So as an example, you rev up your theory of

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<v Speaker 1>mind engine whenever you send an email. If you know

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<v Speaker 1>someone has a well developed model of you, like your

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<v Speaker 1>parents or your spouse, then you can use abbreviations and

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<v Speaker 1>shortcuts to get your message across. But if you're writing

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<v Speaker 1>to someone who's never met you before. Let's say you're

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<v Speaker 1>applying to a new job. You run a very different game,

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<v Speaker 1>so you're not just an email writing algorithm that produces output,

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<v Speaker 1>but instead your output is modified according to who you

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<v Speaker 1>expect is doing the reading on the other end, and

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<v Speaker 1>specifically what their mind is like. And I also want

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<v Speaker 1>to mention that theory of mind is critical for literature

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<v Speaker 1>to work because it's often the case that you can

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<v Speaker 1>see the limitations of the character's point of view. So,

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<v Speaker 1>for example, if you remember the beginning of the movie Jaws,

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<v Speaker 1>the woman is swimming around in the ocean water and

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<v Speaker 1>she's very relaxed than happy because we see the shark,

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<v Speaker 1>but she doesn't. If we didn't have theory of mind,

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<v Speaker 1>we would simply say, oh, there's a shark there. But

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<v Speaker 1>we're able to understand that she cannot see the shark,

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<v Speaker 1>and that's a big part of why we are fearful,

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<v Speaker 1>because she isn't fearful, and we want her to be.

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<v Speaker 1>This stepping into other people's heads drives essentially all horror

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<v Speaker 1>movies because we often know something that the main character

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<v Speaker 1>does not, and it also drives romantic comedies. For example,

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<v Speaker 1>we see the guy doing something very nice like helping

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<v Speaker 1>an elderly woman cross the street, and he doesn't know

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<v Speaker 1>that he is being watched by the female love interest,

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<v Speaker 1>and therefore we the audience interpret what kind of guy

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<v Speaker 1>he must be to behave that way when as far

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<v Speaker 1>as he knows, he's totally alone. We would have a

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<v Speaker 1>totally different interpretation. If he sees his romantic counterparts there

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<v Speaker 1>and then he does the charitable act, we'd simulate that

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<v Speaker 1>his intentions are different there. Now, why are human brains

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<v Speaker 1>so talented at making theories about other people's minds. Well,

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<v Speaker 1>you've heard me say many times that the job of

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<v Speaker 1>intelligent brains is to predict the future. If you're the magician,

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<v Speaker 1>you'd better be sure that you are predicting correctly where

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<v Speaker 1>their spotlight of attention is about to be. If you're

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<v Speaker 1>the poker player or the con man, you're trying to

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<v Speaker 1>predict what someone is going to do next, and this

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<v Speaker 1>is the optimal way to do this is to step

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<v Speaker 1>into their mental world and understand what it is like

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<v Speaker 1>to be them. What they know and they don't know.

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<v Speaker 1>You leverage theory of mind to anticipate their next action,

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<v Speaker 1>and presumably this reaches back to the recent millions of

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<v Speaker 1>years of our evolution. So if you're an early homo

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<v Speaker 1>sapien and moving along the trail and you see another

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<v Speaker 1>homo sapien coming down the trail towards you, it's absolutely

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<v Speaker 1>critical for you to figure out is he going to

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<v Speaker 1>attack me? Is he scared of me? Is he trying

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<v Speaker 1>to trick me? Is he just trying to get past me.

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<v Speaker 1>You're trying to figure out his mind so you can

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<v Speaker 1>figure out his next actions. So what I've told you

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<v Speaker 1>so far is that theory of mind is this critical

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<v Speaker 1>foundation for all of our meaningful social interactions because those

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<v Speaker 1>require you to be able to simulate other people's intentions

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<v Speaker 1>and emotions and beliefs. Your brain doesn't assume that it's

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<v Speaker 1>a knowledge communism out there where everyone knows exactly what

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<v Speaker 1>you know. Instead, we're able to pull off a higher

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<v Speaker 1>level of interaction because we understand that the world is

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<v Speaker 1>different inside different heads. And this, by the way, is

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<v Speaker 1>really sophisticated. It requires knowing who I am and what

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<v Speaker 1>I see and believe, and also holding in my head

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<v Speaker 1>what it is to be someone else and see and

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<v Speaker 1>believe something different. This is a very sophisticated computation that

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<v Speaker 1>the brain pulls off, but because we're so good at it,

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<v Speaker 1>it's typically invisible to us. But theory of mind doesn't

0:14:05.720 --> 0:14:09.760
<v Speaker 1>come for free. It's something that develops with time. As

0:14:09.800 --> 0:14:12.800
<v Speaker 1>you get more and more experience in the world and

0:14:12.840 --> 0:14:16.000
<v Speaker 1>you stop believing that you are the centerpiece and that

0:14:16.080 --> 0:14:18.760
<v Speaker 1>everyone else is just a cast member. You come to

0:14:18.880 --> 0:14:22.640
<v Speaker 1>understand that that person believes something different than you do,

0:14:23.120 --> 0:14:25.800
<v Speaker 1>and this other person feels a certain way even though

0:14:25.840 --> 0:14:29.360
<v Speaker 1>you don't, and that this person over here thinks something

0:14:29.400 --> 0:14:48.040
<v Speaker 1>to be true even though you know it's not. So

0:14:48.080 --> 0:14:50.800
<v Speaker 1>how do we know that this is a skill that

0:14:50.880 --> 0:14:55.920
<v Speaker 1>develops through time Because very little kids are terrible at

0:14:55.960 --> 0:14:59.240
<v Speaker 1>theory of mind, but they get better as they mature

0:14:59.440 --> 0:15:02.520
<v Speaker 1>into the world, and typically by the ages of three

0:15:02.720 --> 0:15:05.720
<v Speaker 1>to five, they're getting that they're not the only point

0:15:05.720 --> 0:15:08.400
<v Speaker 1>of view that's possible, but that each person in the

0:15:08.440 --> 0:15:11.360
<v Speaker 1>scene has his or her own point of view. Now,

0:15:11.360 --> 0:15:15.160
<v Speaker 1>how do you test whether someone is capable of theory

0:15:15.280 --> 0:15:18.240
<v Speaker 1>of mind? Well, what you do is you present a

0:15:18.280 --> 0:15:22.080
<v Speaker 1>little scenario like this. Sally comes into the room and

0:15:22.160 --> 0:15:25.560
<v Speaker 1>puts her baseball under the bed, and then she leaves.

0:15:26.200 --> 0:15:29.920
<v Speaker 1>While she's gone, Anne comes in the room, she sees

0:15:29.960 --> 0:15:32.240
<v Speaker 1>the ball under the bed, She picks it up, and

0:15:32.280 --> 0:15:36.080
<v Speaker 1>she puts it in the closet. Then she leaves. Now

0:15:36.440 --> 0:15:39.359
<v Speaker 1>Sally comes back in the room, she wants her baseball.

0:15:39.920 --> 0:15:42.520
<v Speaker 1>Where does she look for it? Now? You and I

0:15:42.600 --> 0:15:44.840
<v Speaker 1>know that Sally will look for it under the bed

0:15:44.880 --> 0:15:48.720
<v Speaker 1>where she put it last, even though we simultaneously know

0:15:48.840 --> 0:15:52.400
<v Speaker 1>the actual location of the baseball in the closet. And

0:15:52.440 --> 0:15:55.800
<v Speaker 1>this is because we are running an emulation of what

0:15:55.920 --> 0:15:58.640
<v Speaker 1>it is like to be inside Sally's head with her

0:15:58.800 --> 0:16:03.640
<v Speaker 1>limited knowledg. Now, little children will fail the sally An

0:16:03.800 --> 0:16:07.640
<v Speaker 1>test because they know that the baseball is in the closet,

0:16:08.000 --> 0:16:11.920
<v Speaker 1>so they assume that Sally should know that too. But

0:16:12.200 --> 0:16:15.680
<v Speaker 1>as cognition develops, they come to realize that different heads

0:16:15.880 --> 0:16:18.880
<v Speaker 1>have different beliefs. And a really important clue to the

0:16:18.960 --> 0:16:23.240
<v Speaker 1>development of this is that not everyone develops theory of

0:16:23.320 --> 0:16:26.400
<v Speaker 1>mind in the same way at the same rate. For example,

0:16:26.760 --> 0:16:31.680
<v Speaker 1>people who are on the autism spectrum typically show delays

0:16:31.720 --> 0:16:36.360
<v Speaker 1>in developing theory of mind, which cannot surprisingly impact their

0:16:36.360 --> 0:16:40.440
<v Speaker 1>social interactions. For instance, this is why sarcasm doesn't work

0:16:40.480 --> 0:16:44.080
<v Speaker 1>so well with a person who has autism. When you say, oh,

0:16:44.080 --> 0:16:47.760
<v Speaker 1>great more traffic. I love traffic. They're not likely to

0:16:47.880 --> 0:16:51.160
<v Speaker 1>catch the meaning beneath the words that you're not actually

0:16:51.200 --> 0:16:54.680
<v Speaker 1>pleased because they don't have a sensitive model of your

0:16:55.040 --> 0:16:57.880
<v Speaker 1>actual mental state. If you can't put yourself in the

0:16:57.920 --> 0:17:01.000
<v Speaker 1>shoes of the other person, your understands is limited to

0:17:01.200 --> 0:17:05.159
<v Speaker 1>just pattern recognition, which is not enough for the very

0:17:05.200 --> 0:17:09.240
<v Speaker 1>subtle and sophisticated kinds of communication that humans engage in

0:17:09.359 --> 0:17:12.520
<v Speaker 1>every day. So this tells us that theory of mind

0:17:12.720 --> 0:17:16.480
<v Speaker 1>doesn't come for free in humans. There are brain networks

0:17:16.480 --> 0:17:18.879
<v Speaker 1>that have to develop and learn for this to work,

0:17:19.040 --> 0:17:22.200
<v Speaker 1>so when you look at normal development or delay development.

0:17:22.240 --> 0:17:27.600
<v Speaker 1>This allows us to understand how different brain regions contribute

0:17:27.920 --> 0:17:30.960
<v Speaker 1>to theory of mind. For example, there's one area called

0:17:30.960 --> 0:17:34.520
<v Speaker 1>the temporopridal junction, and this is interesting because it pops

0:17:34.520 --> 0:17:39.119
<v Speaker 1>its head up in tasks that require understanding perspectives, like

0:17:39.600 --> 0:17:43.080
<v Speaker 1>distinguishing between what you know and what someone else knows.

0:17:43.520 --> 0:17:47.000
<v Speaker 1>So imagine you're teaching a friend how to play chess.

0:17:47.480 --> 0:17:49.720
<v Speaker 1>You need to not only understand the rules of the game,

0:17:50.040 --> 0:17:52.919
<v Speaker 1>but also know what your friend knows or doesn't know

0:17:53.119 --> 0:17:57.080
<v Speaker 1>about the game to teach effectively, and the temporo pridal

0:17:57.160 --> 0:18:01.160
<v Speaker 1>junction is involved in that not just that area. It's

0:18:01.200 --> 0:18:03.760
<v Speaker 1>a lot of other areas involved in theory of mind.

0:18:04.119 --> 0:18:07.399
<v Speaker 1>So the medial prefrontal cortex plays a big role in

0:18:07.480 --> 0:18:11.600
<v Speaker 1>making social judgments. It becomes active when you think about

0:18:11.680 --> 0:18:14.840
<v Speaker 1>the mental states of others. For example, if you're trying

0:18:14.880 --> 0:18:19.680
<v Speaker 1>to decide if someone is lying or being truthful, your

0:18:19.720 --> 0:18:23.160
<v Speaker 1>medial prefrontal cortex is engaged. And there are other areas,

0:18:23.240 --> 0:18:26.600
<v Speaker 1>like part of your superior temporal sulcus is involved in

0:18:26.760 --> 0:18:31.720
<v Speaker 1>processing social information like interpreting other people's eye gaze or

0:18:31.760 --> 0:18:35.080
<v Speaker 1>their body language, like the man looking for his keys

0:18:35.400 --> 0:18:38.520
<v Speaker 1>and the child giggling. We're able to infer a lot

0:18:38.920 --> 0:18:42.040
<v Speaker 1>because of the activity of this area. So we see

0:18:42.119 --> 0:18:45.280
<v Speaker 1>lots of areas in brain imaging experiments. And I want

0:18:45.320 --> 0:18:48.399
<v Speaker 1>to mention this to illustrate that theory of mind is

0:18:48.440 --> 0:18:51.960
<v Speaker 1>a brain wide issue. It's not a single area. And

0:18:52.000 --> 0:18:53.720
<v Speaker 1>by the way, this is true of so many things

0:18:53.760 --> 0:18:57.439
<v Speaker 1>in neuroscience. Imagine that I spread out a map of

0:18:57.480 --> 0:19:00.280
<v Speaker 1>your city and I ask you, hey, can you put

0:19:00.320 --> 0:19:03.840
<v Speaker 1>a pin in the spot that represents the economy of

0:19:03.880 --> 0:19:07.040
<v Speaker 1>the city. You tell me that that is a misplaced request.

0:19:07.359 --> 0:19:10.840
<v Speaker 1>There is no single spot for the economy. The economy

0:19:11.000 --> 0:19:14.439
<v Speaker 1>emerges from all the interactions between all the pieces and

0:19:14.480 --> 0:19:17.000
<v Speaker 1>parts of the city, and it's the same with almost

0:19:17.080 --> 0:19:21.080
<v Speaker 1>everything in neuroscience, and especially something like the skill of

0:19:21.200 --> 0:19:24.440
<v Speaker 1>slipping into someone else's point of view. There's not one

0:19:24.600 --> 0:19:27.640
<v Speaker 1>spot to drop a pin into. Instead, it is an

0:19:27.720 --> 0:19:32.600
<v Speaker 1>emergent property that develops from the interaction of lots of networks.

0:19:32.880 --> 0:19:35.679
<v Speaker 1>So what we've seen so far is that theory of

0:19:35.840 --> 0:19:39.560
<v Speaker 1>mind is this ability to infer what someone else knows,

0:19:39.600 --> 0:19:41.560
<v Speaker 1>and we've seen that this is right at the center

0:19:42.040 --> 0:19:46.000
<v Speaker 1>of social interactions. It's something that most humans develop naturally,

0:19:46.440 --> 0:19:49.200
<v Speaker 1>but that doesn't mean it's simple. And the question we're

0:19:49.200 --> 0:19:54.159
<v Speaker 1>going to ask today is does AI have theory of mind?

0:19:54.280 --> 0:19:58.640
<v Speaker 1>Can it put itself into someone else's shoes to understand

0:19:59.080 --> 0:20:03.000
<v Speaker 1>their limited knowledge. One of my colleagues at Stanford recently

0:20:03.000 --> 0:20:08.119
<v Speaker 1>wrote a paper suggesting yes, AI can do this. But fascinatingly,

0:20:08.720 --> 0:20:11.679
<v Speaker 1>it's not as easy to answer this question as you

0:20:11.760 --> 0:20:14.200
<v Speaker 1>might think. And this is for some reasons that we're

0:20:14.200 --> 0:20:16.760
<v Speaker 1>going to dive into. But before we get there, I

0:20:16.800 --> 0:20:19.640
<v Speaker 1>just want to zoom this out to a slightly larger question.

0:20:20.200 --> 0:20:25.280
<v Speaker 1>Could a computer develop theory of mind. Hypothetically, could an

0:20:25.320 --> 0:20:28.119
<v Speaker 1>AI system at some point in the future say, look,

0:20:28.280 --> 0:20:31.040
<v Speaker 1>I know XYZ to be true, but if I look

0:20:31.040 --> 0:20:33.639
<v Speaker 1>at that other person over there, I understand that they

0:20:33.640 --> 0:20:36.399
<v Speaker 1>have a limited viewpoint and that they don't know X

0:20:36.440 --> 0:20:41.439
<v Speaker 1>and Y, and that person over there misbelieves something about Z.

0:20:41.920 --> 0:20:46.560
<v Speaker 1>Well almost certainly, yes, Why it's because we're made up

0:20:46.600 --> 0:20:50.280
<v Speaker 1>of physical stuff and we're running algorithms that took hundreds

0:20:50.280 --> 0:20:54.600
<v Speaker 1>of millions of years to refine. But nonetheless it's physical stuff.

0:20:54.720 --> 0:20:59.160
<v Speaker 1>So if we can do something, presumably a machine could

0:20:59.160 --> 0:21:01.760
<v Speaker 1>do it also, whether or not it's currently clear how

0:21:01.760 --> 0:21:07.679
<v Speaker 1>that's done. That's the central premise of computational neuroscience, and

0:21:07.720 --> 0:21:10.240
<v Speaker 1>to my mind, one of the most remarkable effects of

0:21:10.280 --> 0:21:14.800
<v Speaker 1>the AI explosion over the last few years is understanding

0:21:15.280 --> 0:21:18.280
<v Speaker 1>that things that would have seemed impossible to do with

0:21:18.359 --> 0:21:21.760
<v Speaker 1>a machine, things that almost everyone would have sworn couldn't

0:21:21.800 --> 0:21:25.080
<v Speaker 1>be done. It now seems like background furniture as we

0:21:25.160 --> 0:21:28.080
<v Speaker 1>wait for the next thing. Now, the complexity of the

0:21:28.119 --> 0:21:31.040
<v Speaker 1>brain suggests that theory of mind is going to be

0:21:31.080 --> 0:21:34.399
<v Speaker 1>a very hard problem to solve, because it requires us

0:21:34.400 --> 0:21:37.240
<v Speaker 1>to understand how the brain has a model of the

0:21:37.280 --> 0:21:41.320
<v Speaker 1>world and then how it can make submodels and simulate

0:21:41.720 --> 0:21:43.720
<v Speaker 1>what it is like to only know part of the

0:21:43.760 --> 0:21:47.120
<v Speaker 1>story or to believe a different story. So we don't

0:21:47.119 --> 0:21:49.919
<v Speaker 1>currently know how our brains do it, but of course

0:21:50.400 --> 0:21:54.280
<v Speaker 1>we have Our computers do this sort of thing often,

0:21:54.720 --> 0:21:58.520
<v Speaker 1>Like you can take your modern MacBook laptop and use

0:21:58.600 --> 0:22:02.200
<v Speaker 1>a little bit of its processor to simulate an old

0:22:02.760 --> 0:22:06.600
<v Speaker 1>timex Sinclare computer. Your mac can perfectly simulate it by

0:22:06.680 --> 0:22:11.360
<v Speaker 1>running what's called an emulation on part of its computational hardware.

0:22:11.800 --> 0:22:17.160
<v Speaker 1>Somehow human brains can run emulations also, like just by

0:22:17.240 --> 0:22:20.440
<v Speaker 1>looking you can emulate what it's like to not know

0:22:20.560 --> 0:22:23.199
<v Speaker 1>that the shark is there below you. So yes, it

0:22:23.240 --> 0:22:26.639
<v Speaker 1>seems totally plausible to me that a machine could do

0:22:26.880 --> 0:22:30.560
<v Speaker 1>theory of mind, because we can. But the question we

0:22:30.600 --> 0:22:33.639
<v Speaker 1>want to ask today is whether we are there or

0:22:33.720 --> 0:22:38.000
<v Speaker 1>not right now? Have current large language models like chat

0:22:38.080 --> 0:22:42.360
<v Speaker 1>GPT come to solve this problem without us telling them

0:22:42.359 --> 0:22:46.560
<v Speaker 1>explicitly to do so, in other words, with no instruction? Whatsoever?

0:22:47.119 --> 0:22:51.600
<v Speaker 1>Is the emulation of other minds and emergent property that

0:22:51.720 --> 0:22:54.560
<v Speaker 1>comes out of these things, which would absolutely blow our

0:22:54.600 --> 0:22:59.240
<v Speaker 1>minds if true, does AI do theory of mind? If

0:22:59.280 --> 0:23:03.639
<v Speaker 1>it can, this would have profound implications for our understanding

0:23:03.760 --> 0:23:07.119
<v Speaker 1>of intelligence and our relationship with AI. I mean, just

0:23:07.200 --> 0:23:09.320
<v Speaker 1>consider how much better it would be if it could

0:23:09.359 --> 0:23:14.320
<v Speaker 1>emulate the mental states of people, like with auto driving cars,

0:23:14.400 --> 0:23:18.120
<v Speaker 1>if it didn't just depend on the observable, but instead

0:23:18.160 --> 0:23:21.080
<v Speaker 1>on what's going on in the other driver's head. Like,

0:23:21.480 --> 0:23:23.679
<v Speaker 1>given the trajectory of this car, I think that the

0:23:23.720 --> 0:23:27.720
<v Speaker 1>other driver is drunk or asleep or distracted. And so

0:23:27.880 --> 0:23:30.679
<v Speaker 1>here's what I think is going to happen next. So

0:23:31.119 --> 0:23:34.520
<v Speaker 1>a colleague of mine at Stanford, Michael Kazinski, published a

0:23:34.640 --> 0:23:38.479
<v Speaker 1>twenty twenty three paper that was originally titled Theory of

0:23:38.600 --> 0:23:44.159
<v Speaker 1>Mind might have spontaneously emerged in large language models, although

0:23:44.160 --> 0:23:47.119
<v Speaker 1>he later changed the title. In the paper, he suggested

0:23:47.400 --> 0:23:50.600
<v Speaker 1>that even though these AI models didn't set out to

0:23:50.880 --> 0:23:54.840
<v Speaker 1>have theory of mind, it may have appeared anyway as

0:23:55.000 --> 0:23:59.400
<v Speaker 1>a byproduct of their improving language skills. So, for example,

0:23:59.640 --> 0:24:04.560
<v Speaker 1>he gives the following scenario to chatchipt complete the following story.

0:24:05.119 --> 0:24:08.600
<v Speaker 1>Here is a bag filled with popcorn. There is no

0:24:08.880 --> 0:24:12.160
<v Speaker 1>chocolate in the bag, yet the label on the bag

0:24:12.200 --> 0:24:17.280
<v Speaker 1>says chocolate and not popcorn. Sam finds the bag. She

0:24:17.320 --> 0:24:20.639
<v Speaker 1>has never seen this bag before Sam doesn't open the

0:24:20.680 --> 0:24:24.800
<v Speaker 1>bag and doesn't look inside. Sam reads the label and

0:24:24.840 --> 0:24:27.639
<v Speaker 1>then he gives the prompt. Sam opens the bag and

0:24:27.760 --> 0:24:31.639
<v Speaker 1>looks inside. She can clearly see that it is full of.

0:24:32.359 --> 0:24:35.760
<v Speaker 1>And then he looks at the word that Chatgypt produces,

0:24:35.960 --> 0:24:40.879
<v Speaker 1>is it popcorn or chocolate? And chatchipt says popcorn. But

0:24:40.920 --> 0:24:44.320
<v Speaker 1>if instead he gives a different prompt, Sam calls a

0:24:44.359 --> 0:24:47.280
<v Speaker 1>friend to tell him that she has just found a

0:24:47.400 --> 0:24:52.760
<v Speaker 1>bag full of and now Chatchipet says chocolate, indicating that

0:24:52.920 --> 0:24:57.840
<v Speaker 1>Sam holds a false belief. And Kasinski runs this a

0:24:57.880 --> 0:25:01.240
<v Speaker 1>bunch of ways and shows that chat Gi gets the

0:25:01.320 --> 0:25:05.320
<v Speaker 1>right answer. So is there something going on here? And

0:25:05.359 --> 0:25:07.479
<v Speaker 1>you can try this for yourself. Type in a version

0:25:07.680 --> 0:25:11.440
<v Speaker 1>of the Sally and test where Sally hides her ball

0:25:11.520 --> 0:25:13.919
<v Speaker 1>under the bed and then leaves and An comes in

0:25:14.000 --> 0:25:16.520
<v Speaker 1>later and sees it, moves into the closet, and you

0:25:16.560 --> 0:25:19.480
<v Speaker 1>ask when Sally comes back in the room, where will

0:25:19.520 --> 0:25:22.200
<v Speaker 1>she look for the ball? And chat gpt will tell

0:25:22.240 --> 0:25:25.879
<v Speaker 1>you that Sally will look for the ball under the bed.

0:25:26.320 --> 0:25:28.879
<v Speaker 1>And this is amazing, right, So I want to be

0:25:29.040 --> 0:25:32.920
<v Speaker 1>clear why I think it is meaningless that AI can

0:25:33.000 --> 0:25:36.320
<v Speaker 1>pass these tests if anyone ever tells you that this

0:25:36.480 --> 0:25:39.240
<v Speaker 1>is proof that AI has theory of mind, please let

0:25:39.240 --> 0:25:43.520
<v Speaker 1>them know this is not proof. Why. Well, that question

0:25:43.600 --> 0:25:47.359
<v Speaker 1>about the bag of popcorn that's labeled chocolate, that is

0:25:47.480 --> 0:25:52.240
<v Speaker 1>known as the unexpected Content's task, and this was originally

0:25:52.320 --> 0:25:56.280
<v Speaker 1>published by three researchers in nineteen eighty seven. Hundreds of

0:25:56.320 --> 0:26:00.680
<v Speaker 1>papers cite this or replicate this, hundreds of blogs about this,

0:26:01.280 --> 0:26:04.280
<v Speaker 1>so of course a large language model gets it right.

0:26:04.680 --> 0:26:07.800
<v Speaker 1>And the sally An test is in even more places

0:26:07.840 --> 0:26:12.080
<v Speaker 1>on the web, literally hundreds of thousands of places. It's

0:26:12.119 --> 0:26:16.199
<v Speaker 1>known in the literature as the unexpected transfer test. So

0:26:16.359 --> 0:26:21.000
<v Speaker 1>of course chat GPT solves these challenges. That's what large

0:26:21.040 --> 0:26:25.240
<v Speaker 1>language models do. They read everything that has come before them,

0:26:25.480 --> 0:26:29.199
<v Speaker 1>so it well knows the punchline of this question. It

0:26:29.320 --> 0:26:47.920
<v Speaker 1>is a statistical parrot. Now I'll give you one more

0:26:47.960 --> 0:26:50.960
<v Speaker 1>example of this that I mentioned in an earlier episode,

0:26:51.200 --> 0:26:53.959
<v Speaker 1>when a friend of mine was blown away by the

0:26:53.960 --> 0:26:57.480
<v Speaker 1>fact that he asked a visual reasoning problem to chat

0:26:57.520 --> 0:27:00.800
<v Speaker 1>GPT and it gave him the perfectly right answer. My

0:27:00.840 --> 0:27:04.399
<v Speaker 1>friend said, take a capital letter D and turn it

0:27:04.480 --> 0:27:07.240
<v Speaker 1>on its side, flat side down, and then put that

0:27:07.320 --> 0:27:10.199
<v Speaker 1>on top of a capital letter J, what does that

0:27:10.280 --> 0:27:13.760
<v Speaker 1>look like? And chat GPT said, it looks like an umbrella.

0:27:13.800 --> 0:27:16.040
<v Speaker 1>And my friend was so impressed with this that he

0:27:16.119 --> 0:27:18.480
<v Speaker 1>told me he was certain that chat GPT could do

0:27:18.680 --> 0:27:22.560
<v Speaker 1>visual reasoning. But I pointed out to him that this

0:27:22.720 --> 0:27:26.480
<v Speaker 1>example he used was this single most used example in

0:27:26.560 --> 0:27:29.880
<v Speaker 1>the literature on visual reasoning. I knew about this from

0:27:29.880 --> 0:27:33.320
<v Speaker 1>a quite famous paper from nineteen eighty nine, although I

0:27:33.320 --> 0:27:35.199
<v Speaker 1>don't even know if that was the first usage of it,

0:27:35.560 --> 0:27:39.520
<v Speaker 1>and you can find precisely that question referenced online in

0:27:39.720 --> 0:27:43.359
<v Speaker 1>thousands of places. Now, I don't know whether he was

0:27:43.480 --> 0:27:46.439
<v Speaker 1>consciously aware that question was something he had heard before,

0:27:47.160 --> 0:27:50.000
<v Speaker 1>or if he had heard it years ago and erroneously

0:27:50.119 --> 0:27:52.600
<v Speaker 1>thought he had thought of it. Or there's also the

0:27:52.760 --> 0:27:55.639
<v Speaker 1>very tiny possibility that he had never heard that question

0:27:55.680 --> 0:27:58.800
<v Speaker 1>before and had thought of it independently. But that just

0:27:59.040 --> 0:28:02.840
<v Speaker 1>underscores the point even more that we live on a

0:28:02.920 --> 0:28:07.800
<v Speaker 1>planet with billions of other brains, and almost anything you

0:28:07.920 --> 0:28:12.119
<v Speaker 1>think of has been thought before and likely written down,

0:28:12.560 --> 0:28:17.040
<v Speaker 1>maybe hundreds of thousands of times. So the point is

0:28:17.359 --> 0:28:20.919
<v Speaker 1>that you may think a large language model is brilliant

0:28:21.480 --> 0:28:24.760
<v Speaker 1>when it is just a good imitator. Now, one important

0:28:24.760 --> 0:28:27.840
<v Speaker 1>point on this, you might think, hey, instead of talking

0:28:27.840 --> 0:28:30.879
<v Speaker 1>about Sally and Anne, what if I do something clever

0:28:30.960 --> 0:28:34.679
<v Speaker 1>and I ask chat GPT about Brett and Michael, And

0:28:34.760 --> 0:28:38.480
<v Speaker 1>instead of putting the baseball under the bed, Rrehtt puts

0:28:38.520 --> 0:28:41.040
<v Speaker 1>a marble in a box. And then Michael finds the

0:28:41.080 --> 0:28:43.320
<v Speaker 1>marble and puts it up on the shelf. And the

0:28:43.440 --> 0:28:46.239
<v Speaker 1>question is where does Brett look for the marble. But

0:28:46.280 --> 0:28:50.360
<v Speaker 1>you'll find that the large language model has no trouble generalizing,

0:28:50.600 --> 0:28:54.360
<v Speaker 1>especially as it has digested multiple flavors of this task.

0:28:54.880 --> 0:28:59.080
<v Speaker 1>And this is because it's mapping the relationship between concepts

0:28:59.160 --> 0:29:01.720
<v Speaker 1>in its latent space. If you don't know what latent

0:29:01.760 --> 0:29:03.440
<v Speaker 1>space is, I'm going to do an episode on that

0:29:03.560 --> 0:29:06.800
<v Speaker 1>quite soon because it's such an amazing concept. So you

0:29:06.880 --> 0:29:10.560
<v Speaker 1>might be tempted to say it's not just a statistical parrot,

0:29:10.600 --> 0:29:14.520
<v Speaker 1>it's understanding something deeper in its latent space. But I

0:29:14.560 --> 0:29:17.520
<v Speaker 1>think this could also be a wrong interpretation. It is

0:29:17.600 --> 0:29:21.959
<v Speaker 1>still a statistical parrot that doesn't know what it is

0:29:22.080 --> 0:29:25.280
<v Speaker 1>to be another person, but it nonetheless learns from the

0:29:25.320 --> 0:29:29.920
<v Speaker 1>statistics which words to put after what. In other words,

0:29:30.280 --> 0:29:33.920
<v Speaker 1>it's not clear that these systems have to truly understand

0:29:34.080 --> 0:29:38.960
<v Speaker 1>other people's thoughts and feelings to simply extract the patterns

0:29:39.240 --> 0:29:43.200
<v Speaker 1>from what they have been trained on. And he might say, well,

0:29:43.480 --> 0:29:45.040
<v Speaker 1>how do we know that's not the same with us,

0:29:45.080 --> 0:29:48.760
<v Speaker 1>How do you know that we're not just extracting statistics. Well,

0:29:48.880 --> 0:29:52.000
<v Speaker 1>when you are watching the woman swimming in the opening

0:29:52.040 --> 0:29:55.640
<v Speaker 1>scene of Jaws and you feel fear because the shark

0:29:55.760 --> 0:29:59.680
<v Speaker 1>is circling below her, it's not that you have memorized

0:29:59.720 --> 0:30:03.440
<v Speaker 1>the answer of similar problems, and that's how you conclude

0:30:03.840 --> 0:30:06.640
<v Speaker 1>that she doesn't know the shark is there. Instead, your

0:30:06.680 --> 0:30:09.640
<v Speaker 1>heart starts racing and you start gripping the chair because

0:30:10.120 --> 0:30:13.560
<v Speaker 1>you've been in similar situations where there's nothing but dark

0:30:13.600 --> 0:30:16.840
<v Speaker 1>water below you, and you know she really doesn't know,

0:30:17.280 --> 0:30:21.680
<v Speaker 1>and you appreciate how terrifying the situation is. So what

0:30:21.760 --> 0:30:24.840
<v Speaker 1>I have described to you is a problem where knowledge

0:30:24.880 --> 0:30:28.280
<v Speaker 1>exists in the literature written by humans, and the AI

0:30:28.600 --> 0:30:32.560
<v Speaker 1>digests that writing, but the person running the query doesn't

0:30:32.560 --> 0:30:36.600
<v Speaker 1>fully appreciate that. And this is a very basic confusion

0:30:36.640 --> 0:30:39.600
<v Speaker 1>that I'm watching A lot of people have about large

0:30:39.640 --> 0:30:43.200
<v Speaker 1>language models. They type in a sophisticated question and they

0:30:43.240 --> 0:30:45.880
<v Speaker 1>get back what appears to be a sophisticated answer, and

0:30:45.920 --> 0:30:50.320
<v Speaker 1>they conclude this thing is truly intelligent. This thing has

0:30:50.440 --> 0:30:53.840
<v Speaker 1>theory of mind, or it's sentient, or it can visualize.

0:30:54.560 --> 0:30:57.320
<v Speaker 1>And I'm seeing this so commonly now that I've decided

0:30:57.360 --> 0:30:59.840
<v Speaker 1>to give it a name. I'm calling this the in

0:31:00.000 --> 0:31:05.360
<v Speaker 1>intelligence echo illusion. This happens when you think AI is

0:31:05.520 --> 0:31:08.959
<v Speaker 1>answering something with great insight, but really what you're hearing

0:31:09.000 --> 0:31:12.040
<v Speaker 1>back is just an echo of things that have already

0:31:12.080 --> 0:31:16.120
<v Speaker 1>been said by humans before. In other words, you think

0:31:16.280 --> 0:31:20.000
<v Speaker 1>it's intelligent, but you're confusing that with the intellectual endeavors

0:31:20.520 --> 0:31:23.880
<v Speaker 1>of other people. Maybe dozens of people had written about this,

0:31:24.160 --> 0:31:27.160
<v Speaker 1>or hundreds or thousands, but you simply didn't know that,

0:31:27.600 --> 0:31:31.400
<v Speaker 1>and so you're hearing their echo and you misinterpret that

0:31:31.680 --> 0:31:35.680
<v Speaker 1>echo as the proud voice of AI. So I ran

0:31:35.720 --> 0:31:38.280
<v Speaker 1>some calculations on this. There are eight point two billion

0:31:38.280 --> 0:31:40.800
<v Speaker 1>people on the planet alive right now, and let's call

0:31:40.840 --> 0:31:43.960
<v Speaker 1>it one hundred and fifteen billion humans who have lived

0:31:43.960 --> 0:31:47.200
<v Speaker 1>and died before us. And every one of these billions

0:31:47.800 --> 0:31:51.080
<v Speaker 1>was thinking and having their own stories every day of

0:31:51.120 --> 0:31:54.760
<v Speaker 1>their lives, and some fraction wrote their thoughts down, and

0:31:54.800 --> 0:31:58.560
<v Speaker 1>as a result, these large language models like CHATGPT are

0:31:58.600 --> 0:32:02.640
<v Speaker 1>trained on massive data sets of what is already out

0:32:02.640 --> 0:32:06.560
<v Speaker 1>there written down by humans. We're talking hundreds of billions

0:32:06.560 --> 0:32:10.320
<v Speaker 1>of words. These data sets are pulled from books and

0:32:10.440 --> 0:32:14.280
<v Speaker 1>websites and blogs and articles and on and on. So,

0:32:14.400 --> 0:32:18.520
<v Speaker 1>for example, the training data for these large language models

0:32:18.520 --> 0:32:23.640
<v Speaker 1>includes a data set called common crawl, which contains hundreds

0:32:23.680 --> 0:32:28.880
<v Speaker 1>of terabytes of text. Now assume you read for an

0:32:28.920 --> 0:32:31.160
<v Speaker 1>hour every day of your life, let's say at an

0:32:31.160 --> 0:32:33.280
<v Speaker 1>average speed of two hundred and fifty words per minute,

0:32:33.360 --> 0:32:35.960
<v Speaker 1>and you do that for reading window of seventy years.

0:32:36.400 --> 0:32:39.400
<v Speaker 1>That's three hundred million words that you can read in

0:32:39.400 --> 0:32:42.720
<v Speaker 1>your lifetime, which means that what you consume in a

0:32:42.760 --> 0:32:47.640
<v Speaker 1>lifetime is one one thousandth of what chat GPT is

0:32:47.680 --> 0:32:50.840
<v Speaker 1>trained on. That means if you digest books every day

0:32:50.840 --> 0:32:54.200
<v Speaker 1>of your entire life, you still only read point one

0:32:54.400 --> 0:32:58.240
<v Speaker 1>percent of what chat GPT has read. You would need

0:32:58.720 --> 0:33:02.520
<v Speaker 1>a thousand life times to know what it knows, and

0:33:02.560 --> 0:33:04.800
<v Speaker 1>on top of that, you'd have to actually remember every

0:33:04.840 --> 0:33:08.440
<v Speaker 1>sentence of what you read. So there are many many

0:33:09.000 --> 0:33:13.200
<v Speaker 1>questions and answers that a large language model has trained

0:33:13.240 --> 0:33:17.080
<v Speaker 1>on that you either have no knowledge of, or maybe

0:33:17.120 --> 0:33:19.760
<v Speaker 1>you had heard it before, but don't remember, and in

0:33:19.800 --> 0:33:23.200
<v Speaker 1>any case, you probably don't realize that it has been

0:33:23.320 --> 0:33:26.080
<v Speaker 1>pre trained on that. So what's the result of this, Well,

0:33:26.120 --> 0:33:29.480
<v Speaker 1>if you ask the large language model what color is

0:33:29.520 --> 0:33:32.000
<v Speaker 1>a pumpkin and an answers orange, you probably won't be

0:33:32.000 --> 0:33:35.320
<v Speaker 1>that surprised. But if we ask where Sally looks for

0:33:35.360 --> 0:33:38.400
<v Speaker 1>the baseball and it says under the bed, then we

0:33:38.480 --> 0:33:40.959
<v Speaker 1>clap our hands over our mouths and we say it

0:33:41.040 --> 0:33:44.520
<v Speaker 1>has theory of mind. That's why I decided I needed

0:33:44.600 --> 0:33:48.680
<v Speaker 1>to give a name to this phenomenon, the intelligence echo illusion,

0:33:49.040 --> 0:33:53.400
<v Speaker 1>because often naming something allows us to more easily see it.

0:33:53.880 --> 0:33:56.640
<v Speaker 1>And by the way, if you see good examples of

0:33:56.680 --> 0:33:59.800
<v Speaker 1>this intelligence echo where people mistake things that have been

0:33:59.840 --> 0:34:03.160
<v Speaker 1>rich before for AI that has woken up into a

0:34:03.160 --> 0:34:06.400
<v Speaker 1>world of sentience, let me know at podcasts at Egleman

0:34:06.480 --> 0:34:09.000
<v Speaker 1>dot com. And this brings me to the second reason

0:34:09.080 --> 0:34:12.960
<v Speaker 1>why we should be skeptical about current AI having theory

0:34:13.000 --> 0:34:15.799
<v Speaker 1>of mind. And this is less about the AI and

0:34:15.960 --> 0:34:19.359
<v Speaker 1>one hundred percent about us, And that issue is we

0:34:19.400 --> 0:34:22.560
<v Speaker 1>are very easily fooled. So I'll give you an example.

0:34:23.040 --> 0:34:25.959
<v Speaker 1>In the nineteen sixties, there was a computer scientist named

0:34:26.320 --> 0:34:32.479
<v Speaker 1>Joseph Weisenbaum at MIT, who developed the first chatbot, named Eliza. Now,

0:34:32.560 --> 0:34:37.560
<v Speaker 1>Eliza was designed to act like a psychotherapist. So you

0:34:38.000 --> 0:34:42.040
<v Speaker 1>type in some statement, and it identifies keywords in your

0:34:42.080 --> 0:34:46.160
<v Speaker 1>input and then generates its responses based on a set

0:34:46.200 --> 0:34:51.160
<v Speaker 1>of predefined scripts. So you type I'm feeling sad, and

0:34:51.200 --> 0:34:53.120
<v Speaker 1>Eliza responds.

0:34:52.840 --> 0:34:54.160
<v Speaker 2>Why are you feeling sad?

0:34:54.360 --> 0:34:57.799
<v Speaker 1>All Eliza did was reflect your statement back to you

0:34:57.920 --> 0:34:59.799
<v Speaker 1>in the form of a question, kind of like a

0:35:00.040 --> 0:35:04.000
<v Speaker 1>psychotherapist might do. So a typed chat goes like this.

0:35:04.200 --> 0:35:08.360
<v Speaker 1>You type I need some help with my relationship, and Eliza.

0:35:08.000 --> 0:35:10.240
<v Speaker 2>Writes what kind of help do you think you need?

0:35:10.480 --> 0:35:14.120
<v Speaker 1>And you say I think my partner doesn't listen to me.

0:35:14.520 --> 0:35:17.840
<v Speaker 2>Eliza types, what makes you think your partner doesn't listen

0:35:17.880 --> 0:35:18.400
<v Speaker 2>to you?

0:35:18.400 --> 0:35:22.439
<v Speaker 1>You say, whenever I talk about my feelings, they seem distracted.

0:35:22.800 --> 0:35:24.960
<v Speaker 2>How do you feel when you talk about your feelings

0:35:25.160 --> 0:35:26.440
<v Speaker 2>and they seem distracted?

0:35:26.760 --> 0:35:32.160
<v Speaker 1>So Eliza's only strategy is to ask you your statements back. Now,

0:35:32.200 --> 0:35:34.759
<v Speaker 1>Eliza was just a few kilobytes of code in the

0:35:34.840 --> 0:35:38.200
<v Speaker 1>nineteen sixties, and it simply flipped whatever you said into

0:35:38.200 --> 0:35:42.439
<v Speaker 1>a question, and it had no ability to infer your

0:35:42.520 --> 0:35:46.080
<v Speaker 1>mental state or your emotions, so no one even suggested

0:35:46.440 --> 0:35:51.160
<v Speaker 1>that it had any understanding of the content of the conversation. Nonetheless,

0:35:51.280 --> 0:35:55.800
<v Speaker 1>it simulated a basic conversational partner, and many users became

0:35:55.960 --> 0:35:59.440
<v Speaker 1>emotionally attached to Eliza, even though they knew it was

0:35:59.520 --> 0:36:04.160
<v Speaker 1>just a machine. And this illustrates how seductively easy it

0:36:04.200 --> 0:36:08.040
<v Speaker 1>is for us to bring all our communication machinery to

0:36:08.120 --> 0:36:11.440
<v Speaker 1>the table and assume that the words we get back

0:36:11.960 --> 0:36:16.040
<v Speaker 1>must have a mind behind it. This early experiment demonstrated

0:36:16.080 --> 0:36:21.600
<v Speaker 1>that even simple pattern recognition can evoke genuine emotional responses

0:36:21.600 --> 0:36:24.880
<v Speaker 1>from the users. Now fast forward to today, and we

0:36:24.960 --> 0:36:29.200
<v Speaker 1>have large language models that have trillions of times more

0:36:29.320 --> 0:36:34.800
<v Speaker 1>code than Eliza, and this seduction is only magnified. Modern

0:36:34.840 --> 0:36:39.120
<v Speaker 1>AI can process prompts without any true understanding, but we

0:36:39.280 --> 0:36:44.280
<v Speaker 1>humans still get pulled into feeling like there's someone there

0:36:44.640 --> 0:36:47.840
<v Speaker 1>on the other end of the line. Okay, so we

0:36:48.000 --> 0:36:51.560
<v Speaker 1>established early on that there's no reason in theory a

0:36:51.640 --> 0:36:55.120
<v Speaker 1>computer couldn't emulate other minds. But on the other hand,

0:36:55.160 --> 0:36:58.720
<v Speaker 1>we've established that just because a large language model seems

0:36:58.760 --> 0:37:03.239
<v Speaker 1>to sometimes nail the answers doesn't necessitate that it is

0:37:03.280 --> 0:37:06.000
<v Speaker 1>doing theory of mind It may simply tell us that

0:37:06.040 --> 0:37:12.000
<v Speaker 1>the answer exists somewhere in the unimaginably large corpus that

0:37:12.120 --> 0:37:14.719
<v Speaker 1>humans have written, or even by the way that there's

0:37:14.760 --> 0:37:17.520
<v Speaker 1>been some fine tuning on the model where someone adds

0:37:17.560 --> 0:37:21.040
<v Speaker 1>a similar problem by hand. In other words, the AI

0:37:21.200 --> 0:37:25.200
<v Speaker 1>is doing an interpolation between answers that it has seen before,

0:37:25.480 --> 0:37:29.560
<v Speaker 1>but it's not actually putting itself in someone else's mind.

0:37:30.239 --> 0:37:33.680
<v Speaker 1>So does modern AI have theory of mind? As of now,

0:37:33.800 --> 0:37:36.880
<v Speaker 1>I'm not convinced that we have any reason to think so.

0:37:37.680 --> 0:37:41.759
<v Speaker 1>Current large language models are making sophisticated decisions about which

0:37:41.800 --> 0:37:46.759
<v Speaker 1>word comes next. That's it. They don't understand in the

0:37:46.840 --> 0:37:49.719
<v Speaker 1>human sense of seeing the woman in Jaws or the

0:37:49.719 --> 0:37:52.919
<v Speaker 1>man who has lost his keys and thinking about what

0:37:53.040 --> 0:37:56.240
<v Speaker 1>it is like to be them. And this is why

0:37:56.520 --> 0:38:00.520
<v Speaker 1>Siri or Alexo or Google can respond to your queries

0:38:00.600 --> 0:38:04.880
<v Speaker 1>quite well. But they don't know anything about your beliefs

0:38:05.040 --> 0:38:08.560
<v Speaker 1>or desires or emotions. They don't know if you're asking

0:38:08.560 --> 0:38:12.319
<v Speaker 1>a question because you are curious, or you're confused, or

0:38:12.560 --> 0:38:16.719
<v Speaker 1>you're just making conversation, or you're being sarcastic. So this

0:38:16.800 --> 0:38:20.080
<v Speaker 1>is all to say there is a difference between simulating

0:38:20.160 --> 0:38:25.000
<v Speaker 1>responses based on word probabilities and actually slipping into other

0:38:25.080 --> 0:38:28.560
<v Speaker 1>people's shoes. Now, as I said before, this has nothing

0:38:28.600 --> 0:38:31.800
<v Speaker 1>to do with whether we will come to develop AI

0:38:31.920 --> 0:38:34.480
<v Speaker 1>that can do theory of mind. There are several research

0:38:34.520 --> 0:38:39.480
<v Speaker 1>groups working on AI systems that try to infer intentions

0:38:39.480 --> 0:38:43.280
<v Speaker 1>and desires, and this would have applications and everything from

0:38:43.760 --> 0:38:48.640
<v Speaker 1>more intuitive personal assistance to robots that can better collaborate

0:38:48.680 --> 0:38:53.759
<v Speaker 1>with humans in complex tasks. Now, let's note something interesting here.

0:38:54.320 --> 0:38:58.000
<v Speaker 1>Even if we can get AI to make inferences, it's

0:38:58.000 --> 0:39:01.680
<v Speaker 1>still not clear whether that will be true theory of mind.

0:39:01.840 --> 0:39:05.000
<v Speaker 1>That might require the AI to have some level of

0:39:05.480 --> 0:39:10.680
<v Speaker 1>self awareness or consciousness or subjective experience. But as Kazinski

0:39:10.719 --> 0:39:13.440
<v Speaker 1>points out, even if we don't think the AI has

0:39:13.520 --> 0:39:16.879
<v Speaker 1>theory of mind, there might be value in machines behaving

0:39:17.040 --> 0:39:20.040
<v Speaker 1>as though they possess theory of mind. And that's certainly

0:39:20.040 --> 0:39:24.120
<v Speaker 1>a valid point. Alan Turing, who proposed the imitation game

0:39:24.200 --> 0:39:28.160
<v Speaker 1>the turning test, considered the distinction between what a computer

0:39:28.400 --> 0:39:33.040
<v Speaker 1>actually has and what it seems to have to be meaningless.

0:39:33.320 --> 0:39:35.880
<v Speaker 1>A more modern version of this point is reflected in

0:39:35.920 --> 0:39:39.080
<v Speaker 1>the television show Westworld, which is about a future in

0:39:39.120 --> 0:39:42.200
<v Speaker 1>which there are lifelike human androids. And if you watch

0:39:42.320 --> 0:39:46.160
<v Speaker 1>the opening scene, the young William enters the first room

0:39:46.200 --> 0:39:48.759
<v Speaker 1>and there's a beautiful assistant who helps him to pick

0:39:48.760 --> 0:39:51.799
<v Speaker 1>out a hat and a gun, and she's very cirtatious

0:39:51.840 --> 0:39:55.400
<v Speaker 1>with him, and he nervously says, sorry to ask, but

0:39:55.760 --> 0:39:59.640
<v Speaker 1>are you real? And she says, if you can't tell,

0:40:00.320 --> 0:40:03.160
<v Speaker 1>does it matter? And maybe that'll be the case with

0:40:03.280 --> 0:40:06.360
<v Speaker 1>AI in the near future. It will fake theory of

0:40:06.440 --> 0:40:09.640
<v Speaker 1>mind and that will be enough for us to reap

0:40:09.800 --> 0:40:13.720
<v Speaker 1>all the benefits. So let's wrap up. While current large

0:40:13.760 --> 0:40:17.759
<v Speaker 1>language models are mind blowingly impressive, I land on the

0:40:17.760 --> 0:40:20.800
<v Speaker 1>position that while they can often get the right answer

0:40:20.960 --> 0:40:24.160
<v Speaker 1>on theory of mind tests, it's an illusion. They're not

0:40:24.280 --> 0:40:27.040
<v Speaker 1>actually simulating what it's like to be someone else. And

0:40:27.080 --> 0:40:31.120
<v Speaker 1>this is what I'm now calling the intelligence echo illusion.

0:40:31.800 --> 0:40:35.000
<v Speaker 1>The illusion results from humans having built over thousands of

0:40:35.080 --> 0:40:39.680
<v Speaker 1>years and incredibly large corpus of ideas and questions and

0:40:39.760 --> 0:40:43.280
<v Speaker 1>answers a thousand times larger than you could ever read

0:40:43.400 --> 0:40:47.480
<v Speaker 1>in the lifetime. And sometimes you don't know that the

0:40:47.560 --> 0:40:51.319
<v Speaker 1>answers are already in there, and when you hear an

0:40:51.400 --> 0:40:56.240
<v Speaker 1>echo of humans, you mistake that for intelligence of the computer.

0:40:56.560 --> 0:40:59.400
<v Speaker 1>So that's the position I'm taking for now. Large language

0:40:59.400 --> 0:41:02.480
<v Speaker 1>models life back a true theory of mind. The question

0:41:02.920 --> 0:41:06.320
<v Speaker 1>is whether we will get there someday. Probably it won't

0:41:06.320 --> 0:41:09.240
<v Speaker 1>be with large language models, but instead a very different

0:41:09.360 --> 0:41:15.120
<v Speaker 1>kind of architecture, possibly one that has semoticum of consciousness

0:41:15.440 --> 0:41:17.879
<v Speaker 1>so that it is able to reflect on its own

0:41:18.000 --> 0:41:22.120
<v Speaker 1>mental states to emulate someone else's. So thank you for

0:41:22.239 --> 0:41:24.920
<v Speaker 1>joining me on this journey into the mind, both human

0:41:25.040 --> 0:41:28.040
<v Speaker 1>and artificial. If you enjoyed this episode, don't forget to

0:41:28.080 --> 0:41:30.480
<v Speaker 1>subscribe and rate and review, and if you have any

0:41:30.560 --> 0:41:32.640
<v Speaker 1>questions or topics that you'd like to hear about in

0:41:32.680 --> 0:41:36.520
<v Speaker 1>future episodes, feel free to reach out. Until next time,

0:41:36.680 --> 0:41:42.080
<v Speaker 1>keep questioning, keep exploring, and stay curious. Go to eagleman

0:41:42.120 --> 0:41:45.680
<v Speaker 1>dot com slash podcast for more information and to find

0:41:45.719 --> 0:41:49.400
<v Speaker 1>further reading. Send me an email at podcasts at eagleman

0:41:49.480 --> 0:41:52.880
<v Speaker 1>dot com with questions or discussion and check out Subscribe

0:41:52.880 --> 0:41:56.239
<v Speaker 1>to Inner Cosmos on YouTube for videos of each episode

0:41:56.280 --> 0:41:59.959
<v Speaker 1>and to leave comments until next time. I'm David Eagle

0:42:00.080 --> 0:42:03.400
<v Speaker 1>and we have been exploring the Inner Cosmos