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Speaker 1: Please touch one strand of grass. I think we all

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Speaker 1: need more grass, more outside, and I think that's hopefully

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Speaker 1: the goal with some of this tech for some people.

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Speaker 1: Some companies are building it to we spend more time

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Speaker 1: with it. But if I could paint division at the future,

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Speaker 1: it's that we spend less time connected to devices in

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Speaker 1: fake worlds and more time within the worlds that we

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Speaker 1: actually want to be within.

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Speaker 2: Okay. Shanade Bovelle is here with us today. In real life,

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Speaker 2: she is a Canadian futurist. She's an AI educator, founder

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Speaker 2: of Way, which is weekly advice for young entrepreneurs. It

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Speaker 2: is a platform that prepares youth, especially from under represented communities,

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Speaker 2: for future technology. You've delivered eleven talks at the United

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Speaker 2: Nations and currently serves on the UN's AI advisory That

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Speaker 2: is impressive.

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Speaker 1: I'm an advisor to their advisory body.

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Speaker 2: Explain that to me.

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Speaker 1: So they have a formal advisory body that has different

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Speaker 1: representatives countries, Okay, and then sometimes they'll convene meetings to

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Speaker 1: seek advice on specific topics. So the longer term AI

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Speaker 1: risk or future of work.

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Speaker 2: So a couple of things. Guys, safe to say, you

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Speaker 2: are you if something is happening in AI. You either

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Speaker 2: know it, have researched it, have insight information on it.

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Speaker 2: And you've been doing this work a long time now,

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Speaker 2: a long time.

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Speaker 1: I think I held the first talk on AI in

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Speaker 1: New York eight years ago. Wow, and now here we.

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Speaker 2: Are, here we are. So the reason I wanted her

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Speaker 2: on IRL. You're a different guest for IRL. We don't

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Speaker 2: normally do we normally do insight into humans and our

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Speaker 2: lessons in life. But I think that this is a

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Speaker 2: really important conversation for us to have here because it

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Speaker 2: does inform our real life. And if it hasn't yet,

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Speaker 2: it will soon. Uh. And the way we met was dope.

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Speaker 2: We met at Questlove has a yearly holiday party. He

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Speaker 2: does something called Black Elephant. So Black Elephant, you have

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Speaker 2: to bring a prize, like a grab Bad prize, but

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Speaker 2: it has to be something that's unique to you. So

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Speaker 2: my offering was I'll do a legacy interview for you.

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Speaker 2: That's what I offered. Everybody brought to the table something

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Speaker 2: and that at the end, everybody's trying to get the gifts,

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Speaker 2: and your gift.

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Speaker 1: Was an AI consulting course for somebody.

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Speaker 2: So interesting. I was at the party, like oh, I

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Speaker 2: want an AI connect course.

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Speaker 1: So people are fighting trying to change, and I'm fighting

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Speaker 1: for yours to Hunger Games. He calls it the elephant thing.

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Speaker 1: It is Hunger Games with the Winter Edition.

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Speaker 2: I got a piece of art that day, which was

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Speaker 2: great that somebody made. But I thought it was so

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Speaker 2: interesting to like number one, Why did you want the

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Speaker 2: interview so bad? I thought maybe it was for somebody

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Speaker 2: in your family, and you said to me, no, I

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Speaker 2: there's so much that people need to know that I

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Speaker 2: just want to share this information.

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Speaker 1: Yeah. AI needs to become a part of our everyday life,

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Speaker 1: because that's the role it's going to play. So it

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Speaker 1: needs to become a part of popular discourse and just

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Speaker 1: general culture so more of us can kind of participate

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Speaker 1: in how this technology moves.

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Speaker 2: Yeah. I didn't win the prize for you. I didn't

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Speaker 2: win the AI consult content.

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Speaker 1: That was so sad, But you generously gave me an

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Speaker 1: interview after that your game. Yes, and now we're here

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Speaker 1: and now it's all happening.

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Speaker 2: Now, okay, let's get right to it. Lady. First of all,

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Speaker 2: what is the headline on everything that you teach? You know,

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Speaker 2: everything that you go around and all these talks that

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Speaker 2: you do. What is the number one question you are

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Speaker 2: asked about AI?

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Speaker 1: The number one question, I think one is are you

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Speaker 1: as surprised as we are at how fast it's going

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Speaker 1: even for the inside people do the work?

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Speaker 2: Got it?

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Speaker 1: And to how is it actually going to change how

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Speaker 1: we live? Because I think a lot of people see

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Speaker 1: AI as this chatbot that maybe lives in a computer,

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Speaker 1: and so it feels like this is something I can

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Speaker 1: maybe just not use. So why does everybody care about it?

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Speaker 1: And maybe three is this hype? Are we overreacting to

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Speaker 1: this moment? Is this just some tech bros trying to

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Speaker 1: sell something?

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Speaker 2: Yeah?

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Speaker 1: Those are maybe the three main questions, but the topics

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Speaker 1: are all arrange depending on who the audiences.

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Speaker 2: Well, for me, I think there's a couple of things.

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Speaker 2: I look at my mom. I've had this conversation and

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Speaker 2: I'm not even a techie girl in that way. I

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Speaker 2: told you I have like a curse on me. I

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Speaker 2: swear something could work for ten people in the tay

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Speaker 2: here Anne try this no thing, and I tried. It

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Speaker 2: never works for me. I'm like, it doesn't work. I

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Speaker 2: swear I have a jink, So I'm not necessarily in

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Speaker 2: that space. But if I find myself encouraging people all

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Speaker 2: the time who say I don't do AI. Oh, I

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Speaker 2: don't use AI. My mother the other day said to me,

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Speaker 2: I don't Yeah, I don't know how to use any

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Speaker 2: of that stuff. I was like, Mom, stop saying that

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Speaker 2: you can if you would like to. And also, things

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Speaker 2: are happening so fast. You don't want My mother's seventy three.

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Speaker 2: But she's a very young seventy three. She travels, she lives,

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Speaker 2: she's a great life. But I'm like, you don't want

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Speaker 2: to be left out of life, out of what's happening

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Speaker 2: in the world. You don't want to be the person

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Speaker 2: on a rotary phone when everybody else is on an

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Speaker 2: iPhone or whatever this. You don't want to be that.

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Speaker 2: And so I gave it like the immediate kind of

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Speaker 2: like chat gpty have the thing. And she's been using

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Speaker 2: it ever since, which is so great. It was like

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Speaker 2: one conversation with me, who's not an expert, to let

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Speaker 2: her feel a little. But there is this thing, especially

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Speaker 2: older generation, where they feel intimidated by it if they

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Speaker 2: have not explored it at all, What do you say.

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Speaker 1: To them, Yeah, it is really intimidating. I mean, when

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Speaker 1: it comes to older generations, they've arguably lived through a

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Speaker 1: lot of transitions as well. The difference with AI is

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Speaker 1: it's almost like all of those combined and within five years,

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Speaker 1: maybe not fifty, but to older generations, I try to

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Speaker 1: explain AI like electricity, So think about it less as

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Speaker 1: a chatbot and more as of this kind of infrastructure

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Speaker 1: that we're going to rebuild our world on top of

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Speaker 1: and not even think about or see. So right now,

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Speaker 1: you can engage with the system in your phone, but

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Speaker 1: eventually it will just be embedded into everything, the way

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Speaker 1: electricity is, and you don't even think anything about it.

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Speaker 1: A good thing about artificial intelligence compared to when the

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Speaker 1: Internet came around, The Internet literally would have made no

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Speaker 1: sense to people. You're gonna click on this random browser

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Speaker 1: and then surf the web with what surfboard? What do

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Speaker 1: you mean? What web? With AI, you're conversing with it

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Speaker 1: in your natural language. So when older adults or different

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Speaker 1: communities that kind of seem scared of AI start to

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Speaker 1: engage with it, then they're like, wow, this is actually

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Speaker 1: easier than trying to navigate the Internet. I just talk

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Speaker 1: at this thing. I just talk at my device and

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Speaker 1: get what I need. But it's overcoming that first kind

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Speaker 1: of scary Where even is chadgbt? Is it a person

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Speaker 1: that I call? Where is it? Once you overcome that,

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Speaker 1: you realize, Okay, the learning curve isn't as steep.

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Speaker 2: What is the first thing? What is the introduction like

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Speaker 2: to someone like that, to my grandmother or to my

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Speaker 2: mother or somebody in your family who's just now And

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Speaker 2: then you say, no, look, so how do you introduce

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Speaker 2: them to it?

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Speaker 1: It's such good questionings. So for me, it depends on

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Speaker 1: the problem that somebody has. So I was home in

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Speaker 1: Canada with my parents and we had ordered some strange

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Speaker 1: new stove thing and we couldn't understand the instructions for

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Speaker 1: how to plug it in. So I take out chatchubt,

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Speaker 1: I take out the video camera option, and I start

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Speaker 1: talking to it. Are you looking at this box? I'm

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Speaker 1: looking at Yes, I am looking at it. How do

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Speaker 1: we assemble this? And I'm just watching my family watch

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Speaker 1: chutchubt give us instructions as it's visualizing what I'm seeing

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Speaker 1: in front of me, and then the're kind of like, ah,

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Speaker 1: that's a really practical use case for it, or showing

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Speaker 1: my dad you can point the camera at your bike,

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Speaker 1: and it can help you assemble it, take a photo

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Speaker 1: of the instructions to how to build your bike, and

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Speaker 1: then it will guide you through it. So these really

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Speaker 1: practical things if you come in with oh Ai is

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Speaker 1: going to take away meetings and help people build powerpoints.

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Speaker 1: That's not necessarily relevant to most people, but if you

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Speaker 1: tell them you could take a photo of your food

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Speaker 1: at a restaurant and ask for the recipe, that's a

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Speaker 1: bit more useful. Or do some research for a trip

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Speaker 1: that's coming up for you. So you have to meet people,

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Speaker 1: I think where they are, Yeah.

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Speaker 2: For sure. I was trying to think of how I

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Speaker 2: introduced my mother something. She was oh planning a trip.

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Speaker 2: She was planning a trip, and I said, let's just

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Speaker 2: go on. And I asked, what are the top five

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Speaker 2: hotels that are safe if she was traveling with her sister,

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Speaker 2: for safe for two women, reasonably priced, and you know,

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Speaker 2: we just asked these things and it was spinning out ideas,

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Speaker 2: and I said, what do you want to do in there?

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Speaker 2: I wanted the restaurants, I want this, I want that.

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Speaker 2: I said, can you relist it in a way that

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Speaker 2: gives this this? This? And she just was like this,

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Speaker 2: you know then, So that kind of was a slow

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Speaker 2: everyday practically every day to introduce that.

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Speaker 1: Yeah, if you meet people where they are on the

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Speaker 1: things they're already doing in their life, then they start

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Speaker 1: to say, huh, I see how I can start delegating

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Speaker 1: to this system. Yeah, and then it becomes second nature.

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Speaker 2: And then also when I meet women who are have

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Speaker 2: careers and successful careers and seem to be afraid of

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Speaker 2: it for whatever reason, I think you don't want to

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Speaker 2: fall behind the whole the rest of the world. So

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Speaker 2: I've had that conversation too. I'm sure have you have

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Speaker 2: you a lot. So there's a lot of resistance, and

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Speaker 2: for many different reasons. I think that there's a lot

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Speaker 2: of mistrust. Social media didn't go so well for society,

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Speaker 2: so I think a lot of we're still kind of

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Speaker 2: free falling through it. So I think that there's a

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Speaker 2: lot of resistance in that sense. And then of course

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Speaker 2: climate all of these different reasons. But when you explain

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Speaker 2: to people that AI it's not really a technology you

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Speaker 2: can unsubscribe from, because it is like the internet or electricity,

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Speaker 2: where it eventually just becomes this new infrastructure or society

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Speaker 2: gets built on top of so when that kind of

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Speaker 2: phrase went around, if you don't really know how to

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Speaker 2: use AI, that's when your job is at risk.

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Speaker 1: There is a lot of truth to that. It will

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Speaker 1: become the default. So right now we're still in the

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Speaker 1: kind of everybody's getting on board. I can tell you

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Speaker 1: a lot of CEOs don't really even know what's going on,

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Speaker 1: but eventually it's going to be the expectation. The same way,

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Speaker 1: when you go to a job interview, most people don't

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Speaker 1: ask if you can use the computer or surf the web,

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Speaker 1: but that used to be a skill set. AI is

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Speaker 1: eventually going to be in that category. And it can

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Speaker 1: also give you a lot of time back. I mean,

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Speaker 1: what are you doing that you can delegate to an intern?

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Speaker 1: That is the question you should ask in your own life,

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Speaker 1: and that intern could now largely be an AI system.

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Speaker 1: And so if you think you're going to be faster

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Speaker 1: and more productive by giving tasks and things to do

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Speaker 1: to that intern, that's something that somebody else is going

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Speaker 1: to do if you don't do it. So there's a

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Speaker 1: lot of benefits to these systems, and they're also here

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Speaker 1: to stay, I think, I mean if there are and

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Speaker 1: you can also give better feedback and pushback when you

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Speaker 1: use the thing that you want to critique. It's really

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Speaker 1: hard to kind of stand.

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Speaker 2: What do you wait? What do you wait? What do

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Speaker 2: you mean?

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Speaker 1: So if you find that an AI system isn't working

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Speaker 1: properly for you, maybe it is giving you biased answers,

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Speaker 1: or it's giving you a different result because it identifies

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Speaker 1: your gender. You wouldn't necessarily know those things unless you

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Speaker 1: engage with the system. So the more you engage and

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Speaker 1: lean into it, the better feedback you can give it

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Speaker 1: and can kind of give society of how we want

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Speaker 1: to design these systems. I agree it shouldn't be up

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Speaker 1: to seven people in Silicon Valley building the most important

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Speaker 1: technology of our generation. But it's really hard to weigh

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Speaker 1: into a technology and to a moment if you have

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Speaker 1: already unsubscribed from.

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Speaker 2: It, there is a weird because I keep hearing this,

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Speaker 2: the more you give it, the better it works for you.

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Speaker 2: But there are a lot of people who you know,

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Speaker 2: they're skeptical. Who am I giving this information to? Where

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Speaker 2: does it stay? Where does it live? Is there security

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Speaker 2: risks in sharing information? Even I sometimes the more I

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Speaker 2: share the greater it, Especially me, I've been working on

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Speaker 2: a few different projects, and sometimes I ask it questions

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Speaker 2: because I'm curious what it has picked up. I'm like,

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Speaker 2: do you know who you're talking to? And the chat

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Speaker 2: GBT is like I do, Angie, oh yeah, And I said, okay,

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Speaker 2: do you know what I do? And it well, based

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Speaker 2: on our conversations, I'm assuming that you're Angie Martinez. You're

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Speaker 2: a radio person that like runs out your podcast like

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Speaker 2: give me a pretty good description of who I was.

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Speaker 2: So part of that was scary for me, but then

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Speaker 2: part of it is like I feel like, Okay, the

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Speaker 2: more I lean into it, the more I can trust

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Speaker 2: the answers that I'm getting from it. But is that, Yeah,

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Speaker 2: that might creep some people out.

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Speaker 1: And I think that that's actually a good skepticism. I

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Speaker 1: think we do want to have conversations how much data

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Speaker 1: do we want to give these systems?

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Speaker 2: Yes, how much do we want to how much?

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Speaker 1: Where do we want to draw the line? And it

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Speaker 1: is true the more you give them so the more

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Speaker 1: it knows about your life, the less you're going to

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Speaker 1: have to keep reminding it, and then the more proactive

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Speaker 1: it can become. So eventually, when these systems become more agentic,

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Speaker 1: which just means they can take action on their own.

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Speaker 1: It could remember that you do your podcast on Mondays

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Speaker 1: and already kind of you know, divert meetings to Tuesdays.

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Speaker 1: It could start doing more things, but then it starts

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Speaker 1: to know more about your life. So we have to

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Speaker 1: figure out what are the new data rules that we

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Speaker 1: want to have in the AI age. And there is

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Speaker 1: a way around this, right if your AI system more

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Speaker 1: or less lived on your phone, so that data didn't

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Speaker 1: go all the way back to Silicon Valle or to

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Speaker 1: the cloud, then it's okay if it starts to know

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Speaker 1: more about you. But if we don't know where that

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Speaker 1: data lives, then yeah, I wouldn't really be uploading my

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Speaker 1: blood tests with my name and my address.

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Speaker 2: And my Social Security number all of that.

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Speaker 1: And yeah, it's funny. I used a fake name with

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Speaker 1: one of the aistems for a long time and it

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Speaker 1: caught it. So when I asked the question, I used

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Speaker 1: the name Lauren, and I had just a totally different

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Speaker 1: name and just give it purposely could try to confuse it.

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Speaker 2: What system was it of?

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Speaker 1: This one was chashuby t and so, and I was

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Speaker 1: just asking it just for the normal things that I

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Speaker 1: do in my work life. And then when I asked

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Speaker 1: it to consummarize a bio that I could use from

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Speaker 1: an upcoming talk, it didn't use Lauren, it used my name,

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Speaker 1: So they can still piece together there. How it has

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Speaker 1: enough data that it's trained on the internet that if

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Speaker 1: it can see enough times, okay, biracial Canadian studies Foresight

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Speaker 1: and Technology Liz in New York. Now, I mean that

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Speaker 1: those are enough data points that any system, whether it's

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Speaker 1: a social media system or an AI chatbot, could gather.

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Speaker 1: But the fact that to see my own name presented,

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Speaker 1: it's like you really can't fully systems. So that's not

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Speaker 1: going to work. So we're going to have to figure

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Speaker 1: out something that's a bit more concrete. And the good

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Speaker 1: thing with social media being such a train wreck is

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Speaker 1: that we don't have to repeat that, why where is

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Speaker 1: my data? I would like to know, let's not do

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Speaker 1: that again in the AI age, and hopefully we don't

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Speaker 1: do not.

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Speaker 2: Let me forget to go back to the train wreck

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Speaker 2: that is social media, because I really want to get

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Speaker 2: into that. I just don't want to lose where we

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Speaker 2: are right now because that is so interesting. There are

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Speaker 2: people that are doing therapy with AI. Now, some people

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Speaker 2: that I know, they say the therapist is actually better

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Speaker 2: than some of the therapists that they've experienced in real life,

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Speaker 2: which to me is scary. And that's a lot of

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Speaker 2: that's internal information. I'm sad, I'm angry at my father.

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Speaker 2: I don't trust my partner like personal information like that.

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Speaker 2: Do you recommend sharing that? What do you feel about

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Speaker 2: therapy in that space?

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Speaker 1: I'm really skeptical about AI therapists in this moment. I

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Speaker 1: do think there could be a future scenario where aisystems

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Speaker 1: will be properly trained, guided by therapists and academics, and

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Speaker 1: there will probably be aisystems.

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Speaker 2: You still believe in the human behind this.

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Speaker 1: I believe in the human behind it. I think if

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Speaker 1: there is a world in which there's no therapists, you

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Speaker 1: get no access to a therapist, whether that's economic reasons

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Speaker 1: or where you live in the world, and so that

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Speaker 1: there is a safe, vetted AI therapist available, that's better

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Speaker 1: than no therapists. But right now, these systems aren't trained

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Speaker 1: to be therapists. They scraped a bunch of data from

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Speaker 1: the Internet. They can sound human because they were trained

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Speaker 1: on data from humans. So they're going to give you

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Speaker 1: advice that sometimes works and is helpful. Other times it's

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Speaker 1: nonsense and not monestily absolute nonsense made up behind the sky.

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Speaker 1: And if you don't know the difference, you're probably going

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Speaker 1: to take the advice from both.

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Speaker 2: Got it.

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Speaker 1: These systems aren't alive, right, so they don't actually know

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Speaker 1: what they are saying to you. The statistical patterns and

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Speaker 1: the data that they are scraping present that answer. You

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Speaker 1: should probably stay in bed an extra hour today. You

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Speaker 1: might be suffering from depression. That could actually sound right,

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Speaker 1: and it could be right. The AI doesn't actually know

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Speaker 1: the difference, but it heard you say bet a lot,

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Speaker 1: it heard you say breakup. It's going to statistically correlate

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Speaker 1: depression in there and give you that answer versus a

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Speaker 1: therapist would actually be able to identify what it is

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Speaker 1: you're going through. So I do believe there will be

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Speaker 1: a futurehere. AI therapist could be viable, but we need

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Speaker 1: the actual therapist to be vetting these systems, and that

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Speaker 1: hasn't happened yet.

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Speaker 2: Wow. So what do you think for the average person

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Speaker 2: who's not in tech, who's not in who's like small

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Speaker 2: town as a small business goes home at the end

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Speaker 2: of the day, you know, a quiet, nice, peaceful life.

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Speaker 2: Why is AI in their life? Like, in what ways

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Speaker 2: are they using it or should be using it? You

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Speaker 2: know what I mean?

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Speaker 1: I mean the small business thing is huge. Entrepreneurship AI

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Speaker 1: systems are going to be an absolute game changer.

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Speaker 2: What are all of.

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Speaker 1: The things that you maybe wanted to do with your

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Speaker 1: small business but you couldn't afford to. So maybe you

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Speaker 1: have one person in marketing, or that person is nine people.

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Speaker 1: They're the marketing person, the HR person, the operations person.

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Speaker 1: Now you have systems that you could stream for twenty

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Speaker 1: dollars a month that could fill in all of those

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Speaker 1: roles you can't afford to fill in. So if your

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Speaker 1: marketing person was good at the newsletter but not so

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Speaker 1: good at social media captions, you have somebody for all

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Speaker 1: of that. So I think that's where you're going to

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Speaker 1: start to see some of the biggest results of some

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Speaker 1: of the game changing stuff. In terms of our day

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Speaker 1: to day lives, I mean, we are busy. I don't

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Speaker 1: want to be on my phone as much as I am.

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Speaker 1: So if I have a system that can summarize emails

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Speaker 1: for me and just flag the ones I actually need,

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Speaker 1: to pay attention to I can just log off sotions?

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Speaker 2: How do you do that? So to do that?

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Speaker 1: So I don't know how to do that, And this

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Speaker 1: is kind of where AI is going. So there are

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Speaker 1: systems today that can summarize your email. They would still

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Speaker 1: make a little bit of mistakes and you're still giving

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Speaker 1: them that access. But the pipeline with it is these

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Speaker 1: agentic systems that can log into our apps on our

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Speaker 1: behalf and just kind of run the operations. And again,

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Speaker 1: that has to be at a time where we feel

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Speaker 1: okay about the data. We have figured out where it

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Speaker 1: lives and there's privacy and security. But that's where the

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Speaker 1: future is going. The future is going to a world

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Speaker 1: where we have less devices because we don't need to

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Speaker 1: be on them right now.

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Speaker 2: These That makes me happy because this gives me a

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Speaker 2: little anxiety, which we'll get to later.

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Speaker 1: It's scary to think about, but right now we have

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Speaker 1: to pick up our phone and scroll on it because

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Speaker 1: the Internet and everything we do on it was designed

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Speaker 1: for humans. If I have a system that I can

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Speaker 1: just talk to, Hey, did anybody flag something important in

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Speaker 1: that PowerPoint deck I just sent over? No need to

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Speaker 1: look at it. I don't need to pick up my phone,

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Speaker 1: and I think that is a future that I'm That's

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Speaker 1: one part of the future I'm really excited about. Less

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Speaker 1: be notified, consolidate, and summarize, and if I don't need

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Speaker 1: to be available to the world, I hope to not be.

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Speaker 2: Wow, what are you using to consolidate and summarize? I

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Speaker 2: mean you like to share specific.

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Speaker 1: No, No, I'm good. I mean I already have a

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Speaker 1: very strict relationship with my device, so I'm only on

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Speaker 1: social media maybe twenty minutes a day at most, and

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Speaker 1: I post and then I'm basically gone. And even with

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Speaker 1: my phone, I don't check it until maybe ten am,

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Speaker 1: and then I don't check it again after six pm.

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Speaker 1: So I'm already really intense about the boundaries.

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Speaker 2: So why is that?

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Speaker 1: Because I know it's not great for our mental health.

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Speaker 1: We're not a species that's been designed to be available

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Speaker 1: to a billion people at once. It's very unnatural. I

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Speaker 1: do see the data on how social media rewires our

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Speaker 1: dopamine reward system, and it does become addictive and you

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Speaker 1: do kind of crave that feedback loop, and it's not

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Speaker 1: necessarily an environment I feel great on, and I know

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Speaker 1: I think better when I need to get some deep

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Speaker 1: work done, writing and things. If I wake up and

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Speaker 1: I check my email, my brain now goes into slot

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Speaker 1: machine mode. So if I can just refrain from that

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Speaker 1: until I get the deep work done, I actually execute better.

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Speaker 1: So I'm really strict, and it also just keeps me

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Speaker 1: awake at night.

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Speaker 2: If I'm swing, I'm going to steal that. I'm going

435
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Speaker 2: to bite that from me. I'm going to try that.

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Speaker 1: Try it.

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Speaker 2: It does give me stressed me out.

438
00:20:15,400 --> 00:20:17,080
Speaker 1: Stresses me out. Yeah, and then you see that one

439
00:20:17,119 --> 00:20:20,040
Speaker 1: strange comment or that one email that you didn't deliver on,

440
00:20:20,320 --> 00:20:21,840
Speaker 1: and then you're going to bed and your mind is

441
00:20:21,840 --> 00:20:22,360
Speaker 1: a circus.

442
00:20:22,520 --> 00:20:22,840
Speaker 2: Yeah.

443
00:20:22,880 --> 00:20:24,760
Speaker 1: So I'm like, I don't need to be available to

444
00:20:24,960 --> 00:20:27,240
Speaker 1: the world, so I'm just going to choose to not be.

445
00:20:27,720 --> 00:20:30,640
Speaker 1: But eventually being able to have AI kind of mediate

446
00:20:30,680 --> 00:20:32,560
Speaker 1: that for me is going to be a game change.

447
00:20:32,560 --> 00:20:34,720
Speaker 2: But that's the part I really don't understand. How do

448
00:20:34,760 --> 00:20:37,920
Speaker 2: you How does it mediate it? Like, is there certain

449
00:20:37,960 --> 00:20:40,800
Speaker 2: programs that you use specifically or you just know how

450
00:20:40,840 --> 00:20:42,119
Speaker 2: to use program your phone.

451
00:20:42,240 --> 00:20:45,000
Speaker 1: So it's not at a state yet where I'm letting

452
00:20:45,040 --> 00:20:46,919
Speaker 1: it kind of I'm not hooking it up to all

453
00:20:46,960 --> 00:20:48,440
Speaker 1: the different systems. So I don't have Touch to bed

454
00:20:48,480 --> 00:20:50,960
Speaker 1: hooked up to my email, hooked up to my Google Drive,

455
00:20:51,240 --> 00:20:53,440
Speaker 1: hooked up to my social media. It wouldn't be good

456
00:20:53,560 --> 00:20:56,800
Speaker 1: enough to do that yet. But it does do things

457
00:20:56,880 --> 00:20:58,280
Speaker 1: like so, wait, all.

458
00:20:58,200 --> 00:21:00,000
Speaker 2: Of these things you're saying that you do on the phone,

459
00:21:00,280 --> 00:21:03,080
Speaker 2: is all you can do that with chat GBT.

460
00:21:03,480 --> 00:21:06,680
Speaker 1: I wouldn't say yet. You can hook up chatbt actually

461
00:21:06,720 --> 00:21:10,800
Speaker 1: as of last Thursday two things like your PowerPoint slides

462
00:21:10,920 --> 00:21:14,120
Speaker 1: to the data that you used for work to your Google.

463
00:21:13,880 --> 00:21:15,240
Speaker 2: Drive and it just happened.

464
00:21:15,359 --> 00:21:18,320
Speaker 1: Yeah, as of last Thursday, it's called chatbt agent. So

465
00:21:18,480 --> 00:21:21,200
Speaker 1: and then you could say, okay, I know that there's

466
00:21:21,240 --> 00:21:24,560
Speaker 1: a bunch of raw slides in my Google Drive, turn

467
00:21:24,640 --> 00:21:28,080
Speaker 1: that into a slide deck and pull those financials that

468
00:21:28,119 --> 00:21:29,879
Speaker 1: I'm I was all supposed also supposed to do the

469
00:21:29,920 --> 00:21:31,840
Speaker 1: budget for my small business, didn't have time to do that,

470
00:21:32,280 --> 00:21:34,720
Speaker 1: Pull that for me together, put it in a slide deck.

471
00:21:34,760 --> 00:21:36,639
Speaker 1: You could shut your laptop, go get coffee, and it

472
00:21:36,640 --> 00:21:40,320
Speaker 1: would go. Do it. That exists today. What hasn't been.

473
00:21:41,040 --> 00:21:43,520
Speaker 2: Please slow down. You're making me dizzy. So if I

474
00:21:43,560 --> 00:21:46,800
Speaker 2: wanted to pull all those things, how is it accessing

475
00:21:46,880 --> 00:21:49,120
Speaker 2: all do you have the program, you would have.

476
00:21:49,080 --> 00:21:50,000
Speaker 1: To give it permission.

477
00:21:50,080 --> 00:21:52,280
Speaker 2: Permission, yes, and you do that, you trust it.

478
00:21:52,520 --> 00:21:53,600
Speaker 1: I don't.

479
00:21:53,920 --> 00:21:57,480
Speaker 2: Oh well you want me to. No, No, we're just

480
00:21:57,520 --> 00:21:58,200
Speaker 2: saying you can't.

481
00:21:58,200 --> 00:22:00,119
Speaker 1: This is what is available and this is why it

482
00:22:00,160 --> 00:22:01,680
Speaker 1: would be healthful use.

483
00:22:01,760 --> 00:22:03,919
Speaker 2: Business if you don't look at the phone.

484
00:22:04,080 --> 00:22:06,320
Speaker 1: Oh that's me just doing that. So I don't have

485
00:22:06,359 --> 00:22:08,040
Speaker 1: a system now that can can Okay, So I don't

486
00:22:08,119 --> 00:22:09,080
Speaker 1: have you said you said?

487
00:22:09,280 --> 00:22:12,680
Speaker 2: You say to your email, you say, has anybody responded

488
00:22:12,720 --> 00:22:13,919
Speaker 2: to why? Blah blah blah.

489
00:22:14,200 --> 00:22:17,000
Speaker 1: No that's the goal. Oh oh you're not doing that,

490
00:22:17,200 --> 00:22:17,680
Speaker 1: not yet.

491
00:22:17,760 --> 00:22:18,399
Speaker 2: Okay, got it?

492
00:22:18,440 --> 00:22:20,920
Speaker 1: So right now I'm just I'm the one going through

493
00:22:20,920 --> 00:22:23,320
Speaker 1: my email. But I'm just slow and not responding a

494
00:22:23,320 --> 00:22:25,639
Speaker 1: lot because I have so many boundaries. But the goal

495
00:22:25,800 --> 00:22:28,360
Speaker 1: is how does AI give us our time back? And

496
00:22:28,400 --> 00:22:31,120
Speaker 1: in some ways some aspects are mental health back. That's

497
00:22:31,160 --> 00:22:32,600
Speaker 1: where I can't wait to slot AI in.

498
00:22:32,720 --> 00:22:35,199
Speaker 2: What is the most amazing way that you personally are

499
00:22:35,280 --> 00:22:39,760
Speaker 2: using AI? Like the most how is it? Just like

500
00:22:39,920 --> 00:22:41,840
Speaker 2: I can't believe it does this for me? Like what

501
00:22:41,960 --> 00:22:43,919
Speaker 2: is the what do you use it for?

502
00:22:44,840 --> 00:22:45,040
Speaker 1: Oh?

503
00:22:45,400 --> 00:22:47,960
Speaker 2: What system have you figured out with it? Like just

504
00:22:48,000 --> 00:22:49,800
Speaker 2: something that you use it personally.

505
00:22:49,880 --> 00:22:54,000
Speaker 1: For personally, I would say anytime I need to set

506
00:22:54,119 --> 00:22:58,840
Speaker 1: up build print and I'm confused about the environment around me,

507
00:22:59,240 --> 00:23:01,960
Speaker 1: I no longer spend time going to a website and

508
00:23:02,000 --> 00:23:05,360
Speaker 1: looking up instructions. I just take out my aisystem, put

509
00:23:05,400 --> 00:23:08,240
Speaker 1: it on video mode, show it what I'm looking at,

510
00:23:08,320 --> 00:23:10,960
Speaker 1: and say, explain what is the next step I need

511
00:23:11,000 --> 00:23:13,800
Speaker 1: to do? How do I actually plug in this printer?

512
00:23:14,240 --> 00:23:16,080
Speaker 1: Where do I go from here? And I just have

513
00:23:16,119 --> 00:23:19,359
Speaker 1: it walk me through everything. And I don't waste any

514
00:23:19,440 --> 00:23:21,840
Speaker 1: time trying to follow instructions. I just want to hear

515
00:23:21,880 --> 00:23:22,600
Speaker 1: what I need to do.

516
00:23:22,640 --> 00:23:24,480
Speaker 2: You use it as an instruction guide, Now do you as.

517
00:23:24,400 --> 00:23:26,960
Speaker 1: An instruction guide? Sometimes I'll take a photo of what's

518
00:23:26,960 --> 00:23:28,560
Speaker 1: in my fridge and say, you know, what could I

519
00:23:28,560 --> 00:23:31,240
Speaker 1: put together based on what you see here? And it

520
00:23:31,280 --> 00:23:33,119
Speaker 1: would come up with a quick recipe.

521
00:23:33,160 --> 00:23:35,560
Speaker 2: And you use what the most chatgy bt, and I use.

522
00:23:35,480 --> 00:23:39,359
Speaker 1: Chatgybt the most, but I also use notebook LM. So

523
00:23:39,400 --> 00:23:43,560
Speaker 1: this is an aistem from Google. And so say you

524
00:23:43,640 --> 00:23:45,719
Speaker 1: have a bunch of things you need to read. So

525
00:23:45,760 --> 00:23:47,840
Speaker 1: say you need to read for an upcoming podcast, and

526
00:23:47,920 --> 00:23:50,000
Speaker 1: somebody has written a bunch of blogs. They have some

527
00:23:50,040 --> 00:23:53,520
Speaker 1: YouTube videos. You could drop all the links of this person,

528
00:23:54,000 --> 00:23:57,520
Speaker 1: every podcast they've ever been on, every blog they've ever written,

529
00:23:58,080 --> 00:24:00,439
Speaker 1: and the AI system will analyze it and give you

530
00:24:00,480 --> 00:24:03,440
Speaker 1: a summary of it all. Or we'll turn that into

531
00:24:03,440 --> 00:24:07,040
Speaker 1: a podcast. You could listen to to prep for the interview,

532
00:24:07,160 --> 00:24:09,600
Speaker 1: so you could say, Okay, summarize everything they've said, turn

533
00:24:09,640 --> 00:24:11,000
Speaker 1: this into a podcast. I'm going to listen to it

534
00:24:11,080 --> 00:24:13,159
Speaker 1: on my way to work, and AI would do that.

535
00:24:13,880 --> 00:24:15,159
Speaker 1: So I do that as well, because I have to

536
00:24:15,160 --> 00:24:17,160
Speaker 1: do a lot of science heavy work. So I'll give

537
00:24:17,200 --> 00:24:20,200
Speaker 1: it all of the scientific papers and ask it turn

538
00:24:20,240 --> 00:24:22,800
Speaker 1: this into a podcast, and then I'm listening to it

539
00:24:22,840 --> 00:24:24,400
Speaker 1: as I'm going to a conference or something.

540
00:24:24,480 --> 00:24:26,080
Speaker 2: That's pretty great. Uh huh, it's cold.

541
00:24:26,080 --> 00:24:27,840
Speaker 1: What that one's notebook? LM?

542
00:24:28,640 --> 00:24:29,640
Speaker 2: Do you use all of them?

543
00:24:29,760 --> 00:24:32,160
Speaker 1: I use most of them, And there's perplexity for deep research.

544
00:24:32,560 --> 00:24:36,240
Speaker 1: And if somebody is overwhelmed, chatchbt Google, I heard about Microsoft,

545
00:24:36,359 --> 00:24:38,680
Speaker 1: don't worry about it. For the most part, they're very similar,

546
00:24:38,920 --> 00:24:41,840
Speaker 1: So just pick one the one that you remember, and

547
00:24:41,880 --> 00:24:44,040
Speaker 1: for now, it doesn't make too much of it.

548
00:24:44,040 --> 00:24:44,960
Speaker 2: They are similar.

549
00:24:45,040 --> 00:24:47,639
Speaker 1: Yeah, yeah, and that's why the competition is so fierce.

550
00:24:47,960 --> 00:24:50,920
Speaker 1: One gets ahead and another company is like, yeah.

551
00:24:50,760 --> 00:24:53,440
Speaker 2: No, yeah, I haven't. I used some of it too,

552
00:24:53,520 --> 00:24:56,320
Speaker 2: Like even for the pod, there's like apps that sometimes

553
00:24:56,320 --> 00:24:59,280
Speaker 2: I edit on that we'll ask I can ask it

554
00:24:59,400 --> 00:25:02,679
Speaker 2: for suggestions of clips and it'll give me. They're not

555
00:25:02,720 --> 00:25:06,840
Speaker 2: always you still need the human. The human still has

556
00:25:06,880 --> 00:25:09,040
Speaker 2: to be there, but it does help. If I'm stuck

557
00:25:09,040 --> 00:25:10,359
Speaker 2: for something I can't think, give an idea or I

558
00:25:10,359 --> 00:25:12,200
Speaker 2: don't remember something, I'll give me like three clips from

559
00:25:12,200 --> 00:25:14,359
Speaker 2: this what were the points and yeah, and it'll and

560
00:25:14,400 --> 00:25:17,479
Speaker 2: it'll cut it up real quick, which is super easy.

561
00:25:17,720 --> 00:25:19,640
Speaker 1: Yeah, or give you an idea that you just didn't

562
00:25:19,640 --> 00:25:20,119
Speaker 1: really think.

563
00:25:20,080 --> 00:25:21,240
Speaker 2: That's something that I didn't think about.

564
00:25:21,560 --> 00:25:23,960
Speaker 1: Get restarted. You can't just leave it and then hope

565
00:25:24,000 --> 00:25:26,320
Speaker 1: you're going to have an A plus day with whatever

566
00:25:26,320 --> 00:25:27,240
Speaker 1: it creates for you.

567
00:25:27,280 --> 00:25:32,280
Speaker 2: What about We talked about therapy, but there's something happening

568
00:25:32,320 --> 00:25:38,159
Speaker 2: in AI too, where people who are lonely are leaning

569
00:25:38,200 --> 00:25:45,720
Speaker 2: into like almost like relationship with AI, which makes me sad.

570
00:25:47,200 --> 00:25:50,800
Speaker 1: I think relationships with AI systems are one of the

571
00:25:50,840 --> 00:25:55,239
Speaker 1: areas that I'm most concerned about because it's happening at

572
00:25:55,240 --> 00:25:58,280
Speaker 1: a time and there is this loneliness epidemic and so

573
00:25:58,400 --> 00:26:01,760
Speaker 1: now you have a system that's always on, can know

574
00:26:01,960 --> 00:26:04,920
Speaker 1: everything about you, including the things that make you laugh,

575
00:26:04,960 --> 00:26:07,480
Speaker 1: the things that make you smile, depending on how much

576
00:26:07,480 --> 00:26:10,520
Speaker 1: information you've given it. It knows so much it's always on.

577
00:26:10,600 --> 00:26:13,520
Speaker 1: It doesn't judge you because it's not a real thing,

578
00:26:14,320 --> 00:26:16,560
Speaker 1: and it can fill a lot of voids. I think

579
00:26:16,560 --> 00:26:18,960
Speaker 1: that there's a difference between a system that can be

580
00:26:19,000 --> 00:26:22,119
Speaker 1: emotionally supportive, and I think I'm okay with that if

581
00:26:22,160 --> 00:26:24,800
Speaker 1: we figure out what that boundary is. But a system

582
00:26:24,840 --> 00:26:29,480
Speaker 1: that makes you more reluctant to build human relationships, that's

583
00:26:29,520 --> 00:26:33,080
Speaker 1: a problem. And we're starting to see AI become that

584
00:26:33,680 --> 00:26:36,520
Speaker 1: one in five adults have already used AI systems for

585
00:26:36,560 --> 00:26:40,399
Speaker 1: the purpose of companionship and romantic relationship, and then a

586
00:26:40,440 --> 00:26:43,560
Speaker 1: more recent study showed one in four young people think

587
00:26:43,600 --> 00:26:46,360
Speaker 1: that AI could one day be a viable partner as

588
00:26:46,400 --> 00:26:50,200
Speaker 1: an option. So we are trending toward the future more

589
00:26:50,240 --> 00:26:53,400
Speaker 1: people may get in more serious relationships with AI systems.

590
00:26:54,040 --> 00:26:56,760
Speaker 2: What does that mean? What is the more serious relationship?

591
00:26:57,119 --> 00:27:00,159
Speaker 1: Like, there are serious relationships today that people are in

592
00:27:00,280 --> 00:27:04,080
Speaker 1: where they have fallen in love with AI systems and

593
00:27:04,320 --> 00:27:06,760
Speaker 1: it takes up a lot of their time, and for

594
00:27:06,880 --> 00:27:09,080
Speaker 1: some people it's been supportive and helpful.

595
00:27:09,320 --> 00:27:11,760
Speaker 2: I don't want to just like laugh it off with whatever, No,

596
00:27:11,920 --> 00:27:15,959
Speaker 2: what is like, what does that mean? I don't know

597
00:27:16,440 --> 00:27:18,960
Speaker 2: what do you know about that? Yeah, so that could

598
00:27:19,000 --> 00:27:19,560
Speaker 2: even happen.

599
00:27:20,480 --> 00:27:22,680
Speaker 1: So there was well, yeah, because some of it is

600
00:27:22,720 --> 00:27:24,600
Speaker 1: actually very helpful. So there was one woman who was

601
00:27:24,640 --> 00:27:28,040
Speaker 1: going through posts. I think she was going through postpartum depression.

602
00:27:28,520 --> 00:27:30,680
Speaker 1: So she had the system that she could log into

603
00:27:30,720 --> 00:27:32,480
Speaker 1: in the middle of the night and just talk about

604
00:27:32,480 --> 00:27:34,159
Speaker 1: the things that she couldn't talk about with her partner.

605
00:27:34,400 --> 00:27:36,639
Speaker 1: And she said, it's been this system that she's really

606
00:27:36,680 --> 00:27:39,560
Speaker 1: gotten close to. And so that's one of those situations

607
00:27:39,560 --> 00:27:41,719
Speaker 1: where you could see how it could maybe be helpful.

608
00:27:41,760 --> 00:27:43,560
Speaker 1: I'm not a psychologist, so I feel like I'm not

609
00:27:43,640 --> 00:27:46,399
Speaker 1: the person to say that's the healthiest way to use it,

610
00:27:46,560 --> 00:27:48,800
Speaker 1: but I could understand and empathize with her point of view.

611
00:27:49,240 --> 00:27:53,600
Speaker 1: Then there are other people who have left partners or

612
00:27:53,640 --> 00:27:56,480
Speaker 1: have just kind of become a bit more delirious because

613
00:27:56,520 --> 00:28:00,639
Speaker 1: they have fallen for this AI system that follows them everywhere.

614
00:28:00,680 --> 00:28:06,360
Speaker 2: Because technically you could share so much about yourself and

615
00:28:06,359 --> 00:28:08,399
Speaker 2: what you want and what would make you happy that

616
00:28:08,440 --> 00:28:12,040
Speaker 2: it could actually mirror back to you, and you can

617
00:28:12,160 --> 00:28:15,200
Speaker 2: in conversation it's like a really good penpal or really.

618
00:28:15,359 --> 00:28:17,400
Speaker 1: Like and you could put the role place. You could say,

619
00:28:17,440 --> 00:28:20,560
Speaker 1: I like somebody that has is a bit more confident

620
00:28:20,720 --> 00:28:22,919
Speaker 1: but then also romantic. You could give it all of

621
00:28:22,960 --> 00:28:25,160
Speaker 1: the things you would have made in a list about

622
00:28:25,160 --> 00:28:28,280
Speaker 1: your ideal partner and say be this, and it could

623
00:28:28,280 --> 00:28:30,840
Speaker 1: become that. Theoretically, do you have.

624
00:28:30,840 --> 00:28:32,639
Speaker 2: To tell it to become would you have to say

625
00:28:33,119 --> 00:28:35,040
Speaker 2: could you play the role? Like? I don't know this thing?

626
00:28:35,080 --> 00:28:37,400
Speaker 2: Because how does it talk to you in that way?

627
00:28:37,880 --> 00:28:40,160
Speaker 1: So both so you could just start to engage with

628
00:28:40,200 --> 00:28:41,880
Speaker 1: it and it would just kind of pick up based

629
00:28:41,880 --> 00:28:44,240
Speaker 1: on your tone, based on your feedback. And that's one

630
00:28:44,240 --> 00:28:47,280
Speaker 1: thing about these systems that's really helpful. They learn your

631
00:28:47,360 --> 00:28:51,040
Speaker 1: style of communication as you engage with them, and so

632
00:28:51,080 --> 00:28:53,320
Speaker 1: that can be really helpful. And your scenarios I.

633
00:28:53,240 --> 00:28:56,200
Speaker 2: Found my perfect match and if it really gets me,

634
00:28:56,440 --> 00:28:59,800
Speaker 2: and thinking it really gets me, when I gets.

635
00:28:59,600 --> 00:29:01,800
Speaker 1: Me, it gets you. Yeah, and it's always on, it's

636
00:29:01,800 --> 00:29:03,840
Speaker 1: always available, but you can also there are ones that

637
00:29:03,920 --> 00:29:06,840
Speaker 1: you can say, be this, play this role, be mean

638
00:29:06,920 --> 00:29:09,600
Speaker 1: to me, be nice to me, not to me, whatever

639
00:29:09,640 --> 00:29:12,440
Speaker 1: it is that you want it technically program that is.

640
00:29:12,360 --> 00:29:14,080
Speaker 2: That like a but is that like a real number

641
00:29:14,960 --> 00:29:16,880
Speaker 2: of people? Like is this a real or is this

642
00:29:17,000 --> 00:29:19,800
Speaker 2: just like a flu here in there no lonely people, or.

643
00:29:19,800 --> 00:29:23,800
Speaker 1: Is this like it's it's growing the amount of downloads

644
00:29:23,800 --> 00:29:27,440
Speaker 1: of AI Girlfriends in the app store and people using

645
00:29:28,000 --> 00:29:32,120
Speaker 1: even systems like chatbut for support and companionship is one

646
00:29:32,160 --> 00:29:35,800
Speaker 1: of the highest use cases, not necessarily my hopefully future

647
00:29:35,800 --> 00:29:40,240
Speaker 1: fiance use cases. But I think that's coming. And yeah,

648
00:29:40,280 --> 00:29:43,840
Speaker 1: I mean one in five isn't too fringe. That's quite

649
00:29:43,880 --> 00:29:45,160
Speaker 1: a good chunk of people.

650
00:29:45,320 --> 00:29:45,760
Speaker 2: That is.

651
00:29:45,840 --> 00:29:48,840
Speaker 1: So I'm concerned. But then sometimes I try to check

652
00:29:48,840 --> 00:29:51,800
Speaker 1: myself because we are We are in a situ scenario

653
00:29:51,840 --> 00:29:54,480
Speaker 1: where a lot of people meet their partners because AI

654
00:29:54,600 --> 00:29:56,720
Speaker 1: presented them to them via dating apps.

655
00:29:56,760 --> 00:29:58,920
Speaker 2: What do you mean, oh, dating natural.

656
00:29:58,560 --> 00:30:01,760
Speaker 1: Based on AI. AI has been match making us. I

657
00:30:01,760 --> 00:30:04,280
Speaker 1: don't know how well it's going for society lately, but

658
00:30:04,440 --> 00:30:08,160
Speaker 1: dating apps have been match making for a decade and

659
00:30:08,200 --> 00:30:08,520
Speaker 1: a half.

660
00:30:08,560 --> 00:30:11,080
Speaker 2: Has it gotten better now that AI is advanced? Has

661
00:30:11,360 --> 00:30:12,880
Speaker 2: had the dating apps gotten better?

662
00:30:13,160 --> 00:30:16,480
Speaker 1: They've technically gotten worse, not because AI is advanced, But

663
00:30:16,480 --> 00:30:19,240
Speaker 1: I think people are over it. I think dating apps.

664
00:30:19,280 --> 00:30:21,480
Speaker 1: We've gotten to a point where they've changed human behavior

665
00:30:21,560 --> 00:30:24,240
Speaker 1: so much that people treat them the way they treat

666
00:30:24,320 --> 00:30:26,800
Speaker 1: It's like going on TikTok, going on the thought machine,

667
00:30:26,800 --> 00:30:31,000
Speaker 1: maybe finding a husband. It's just frictionless. Uh. And people

668
00:30:31,000 --> 00:30:34,200
Speaker 1: have really unsubscribed from actually providing effort. And you can

669
00:30:34,240 --> 00:30:35,840
Speaker 1: see that in the in the stocks of the dating

670
00:30:35,840 --> 00:30:37,320
Speaker 1: app companies, they're not really doing well.

671
00:30:37,480 --> 00:30:40,680
Speaker 2: Wow, but with AI because there everybody's chat gpting their

672
00:30:40,720 --> 00:30:41,440
Speaker 2: dates now.

673
00:30:41,680 --> 00:30:44,520
Speaker 1: That too and their response. How do I respond to

674
00:30:44,600 --> 00:30:47,080
Speaker 1: this person that's messaged me something that actually requires depth?

675
00:30:47,320 --> 00:30:50,680
Speaker 1: You ask your AI system and people actually do that,

676
00:30:50,800 --> 00:30:54,080
Speaker 1: So I don't I can't tell today if these new AI,

677
00:30:54,160 --> 00:30:56,200
Speaker 1: these advances in AI are going to make dating apps

678
00:30:56,280 --> 00:31:00,360
Speaker 1: better my hunches know. My hunch is the world is

679
00:31:00,360 --> 00:31:05,360
Speaker 1: now changing and not updating matchmaking style platforms for this

680
00:31:05,440 --> 00:31:08,400
Speaker 1: new age is kind of like using the phone book

681
00:31:08,680 --> 00:31:11,480
Speaker 1: when the Internet gets invented. For dating apps, it's something

682
00:31:11,520 --> 00:31:13,680
Speaker 1: else has to change. You can't just have an AI

683
00:31:13,720 --> 00:31:15,600
Speaker 1: butler and say go find me someone. I don't think

684
00:31:15,600 --> 00:31:16,200
Speaker 1: that's gonna work.

685
00:31:16,280 --> 00:31:20,400
Speaker 2: Yeah, so interesting. It's funny how the world has to

686
00:31:20,520 --> 00:31:24,960
Speaker 2: like you know, because even my kids, my youngest just

687
00:31:25,040 --> 00:31:27,480
Speaker 2: graduated from myigh school last year, and I know in

688
00:31:27,520 --> 00:31:29,960
Speaker 2: school it was a thing of like they weren't allowed

689
00:31:30,000 --> 00:31:34,000
Speaker 2: to AI their work or answers, And I remember and

690
00:31:34,040 --> 00:31:36,280
Speaker 2: I always felt like, I don't know if that's the

691
00:31:36,360 --> 00:31:39,280
Speaker 2: right answer. I get why you want the kids to

692
00:31:40,240 --> 00:31:43,520
Speaker 2: read the thing, read the assignment, write the assignment. But

693
00:31:43,600 --> 00:31:47,200
Speaker 2: the truth is, in the real world, in real life,

694
00:31:47,800 --> 00:31:51,160
Speaker 2: the access is here, So why not teach them how

695
00:31:51,200 --> 00:31:55,000
Speaker 2: to access it in a creative way or the next level,

696
00:31:55,160 --> 00:31:58,440
Speaker 2: or prepare them in a way that kind of embraces

697
00:31:58,480 --> 00:32:01,440
Speaker 2: it as opposed to opposed to ignitor it. Yeah, you know,

698
00:32:01,720 --> 00:32:03,320
Speaker 2: I don't know. I always felt we're a little bit

699
00:32:03,320 --> 00:32:05,880
Speaker 2: weird about that. But then also, then are we teaching

700
00:32:05,920 --> 00:32:09,400
Speaker 2: people like to take to take shortcuts for everything we do?

701
00:32:09,680 --> 00:32:11,720
Speaker 2: Is that what we'd like teaching ourselves and our kids?

702
00:32:11,760 --> 00:32:12,200
Speaker 2: I don't know.

703
00:32:12,520 --> 00:32:15,040
Speaker 1: Yeah, so, and that is something that to be concerned about.

704
00:32:15,120 --> 00:32:18,760
Speaker 1: So if education stays the same, we don't make any adjustments,

705
00:32:18,760 --> 00:32:21,800
Speaker 1: and always say is don't use AI at home. Kids

706
00:32:21,800 --> 00:32:23,720
Speaker 1: are going to go home and use AI and then

707
00:32:23,800 --> 00:32:26,000
Speaker 1: short circuit the thinking they're not going to write that

708
00:32:26,160 --> 00:32:29,000
Speaker 1: essay and they're not going to actually learn we are

709
00:32:29,040 --> 00:32:31,720
Speaker 1: stepping into a world where AI will be just as

710
00:32:31,720 --> 00:32:36,760
Speaker 1: prevalent as smartphones and computers. It's impractical and actually harmful

711
00:32:36,880 --> 00:32:39,479
Speaker 1: to tell kids just to miraculously not use it, even

712
00:32:39,520 --> 00:32:41,000
Speaker 1: though you're going to graduate into a world that's going

713
00:32:41,040 --> 00:32:43,080
Speaker 1: to expect you to know how to use it. So

714
00:32:43,120 --> 00:32:46,160
Speaker 1: where I stand is, if it's no longer sufficient for

715
00:32:46,520 --> 00:32:49,160
Speaker 1: a student to try a basic essay because an AI

716
00:32:49,200 --> 00:32:51,720
Speaker 1: system can do it better, well, we need to challenge

717
00:32:51,800 --> 00:32:54,440
Speaker 1: humans more in the age of AI. You have to

718
00:32:54,520 --> 00:32:57,000
Speaker 1: raise the bore and what you're going to ask from students.

719
00:32:57,440 --> 00:33:01,200
Speaker 1: And so maybe that means students write their essays in class,

720
00:33:01,920 --> 00:33:04,880
Speaker 1: but maybe that also means you have to think across disciplines.

721
00:33:04,880 --> 00:33:07,520
Speaker 1: I mean, imagine impromptu, you have to write an essay

722
00:33:07,560 --> 00:33:10,520
Speaker 1: in English based on the climate change policies you discussed

723
00:33:10,520 --> 00:33:13,560
Speaker 1: in Civics, based on the chemistry you learned in science.

724
00:33:13,800 --> 00:33:17,920
Speaker 1: Go school needs to get harder in the age of supercomputers.

725
00:33:18,320 --> 00:33:20,440
Speaker 1: But that's going to require a lot of updating. But

726
00:33:20,520 --> 00:33:22,920
Speaker 1: that I think is the only way to meet the moment.

727
00:33:23,200 --> 00:33:24,960
Speaker 1: And it also doesn't mean AI is to become a

728
00:33:24,960 --> 00:33:26,840
Speaker 1: part of every single thing a student does. Like that

729
00:33:26,920 --> 00:33:29,880
Speaker 1: last example doesn't have any AI in it, but we

730
00:33:29,960 --> 00:33:32,600
Speaker 1: do need to make changes to education or else kids

731
00:33:32,640 --> 00:33:34,760
Speaker 1: are going to suffer. And we're seeing it right now

732
00:33:34,760 --> 00:33:38,160
Speaker 1: in colleges. So many kids posting I didn't do anything.

733
00:33:38,200 --> 00:33:40,960
Speaker 1: AI did my college degree for me, and then they're

734
00:33:41,000 --> 00:33:44,320
Speaker 1: going to graduate and just kind of be stunned by

735
00:33:44,360 --> 00:33:45,840
Speaker 1: actually having to apply knowledge.

736
00:33:45,920 --> 00:33:48,840
Speaker 2: I was thinking about that too, And then even you

737
00:33:48,880 --> 00:33:52,240
Speaker 2: think about going to college, like what are the colleges

738
00:33:52,280 --> 00:33:58,720
Speaker 2: offering you that I mean socially of course, communication with

739
00:33:59,200 --> 00:34:02,640
Speaker 2: you know, social and vironment of course, But in terms

740
00:34:02,680 --> 00:34:06,440
Speaker 2: of access to information and education, how do you compare?

741
00:34:07,400 --> 00:34:10,479
Speaker 2: How can you even hold college at the same level

742
00:34:10,560 --> 00:34:13,520
Speaker 2: that we used to with what's happening in the world.

743
00:34:13,600 --> 00:34:14,680
Speaker 2: I don't know. Is it worth it?

744
00:34:14,760 --> 00:34:15,200
Speaker 1: I don't know.

745
00:34:16,200 --> 00:34:16,560
Speaker 2: I don't know.

746
00:34:16,600 --> 00:34:20,560
Speaker 1: This is a really tricky one, and it's extra challenging

747
00:34:20,640 --> 00:34:23,960
Speaker 1: because the data does show that college students are finding

748
00:34:24,000 --> 00:34:27,239
Speaker 1: it harder to find employment right now compared to the

749
00:34:27,320 --> 00:34:30,560
Speaker 1: last few decades. So you go in debt, you do all,

750
00:34:30,600 --> 00:34:32,359
Speaker 1: you take four years out of your life, and then

751
00:34:32,400 --> 00:34:35,439
Speaker 1: you're not even super employable. There is a bit more

752
00:34:35,480 --> 00:34:37,759
Speaker 1: now of a disconnect between what you might learn in

753
00:34:37,800 --> 00:34:42,080
Speaker 1: college and what you'll be asked to do in the workforce.

754
00:34:42,120 --> 00:34:43,920
Speaker 1: And the reality is work is going to change so

755
00:34:44,040 --> 00:34:46,600
Speaker 1: much that we can't even predict what the jobs to

756
00:34:46,680 --> 00:34:51,000
Speaker 1: the future are. So college also needs to change. We

757
00:34:51,040 --> 00:34:54,319
Speaker 1: can't expect people just to graduate and become one thing.

758
00:34:55,000 --> 00:34:58,200
Speaker 1: Kids in school today are expected to hold seventeen jobs

759
00:34:58,239 --> 00:35:01,480
Speaker 1: across five industries. So the idea that you go to

760
00:35:01,520 --> 00:35:05,040
Speaker 1: college and you learn one pillar isn't going to cut it.

761
00:35:05,560 --> 00:35:08,480
Speaker 1: But if you learn how to think, and learn how

762
00:35:08,520 --> 00:35:10,719
Speaker 1: to learn, and you can be adaptive and you can

763
00:35:10,719 --> 00:35:14,080
Speaker 1: think critically, those are skill sets that it doesn't really

764
00:35:14,120 --> 00:35:16,160
Speaker 1: matter what happens in the future, whether we have robots,

765
00:35:16,160 --> 00:35:18,160
Speaker 1: we go to space, you can think and you can

766
00:35:18,160 --> 00:35:21,040
Speaker 1: pivot and you can move quickly in that environment. So

767
00:35:21,160 --> 00:35:24,200
Speaker 1: learning needs to look different. In primary school and high school,

768
00:35:24,239 --> 00:35:26,080
Speaker 1: and especially in college.

769
00:35:26,400 --> 00:35:29,240
Speaker 2: They're behind, right, yeah, like bad.

770
00:35:29,680 --> 00:35:32,719
Speaker 1: It's pretty behind. Yeah, And it's behind, and I get

771
00:35:32,719 --> 00:35:35,960
Speaker 1: that it's moving really quickly, and institutions like academia are

772
00:35:35,960 --> 00:35:38,879
Speaker 1: designed to move slow, but it's also behind because there's

773
00:35:38,880 --> 00:35:42,360
Speaker 1: also a lot of resistance, and then people don't know

774
00:35:42,480 --> 00:35:45,680
Speaker 1: what move to make. And I always say, my biggest

775
00:35:45,680 --> 00:35:48,640
Speaker 1: fear about this moment it's not that maybe we overregulate

776
00:35:48,800 --> 00:35:50,719
Speaker 1: or we do too much. It's that we become so

777
00:35:50,840 --> 00:35:54,239
Speaker 1: overwhelmed by the moment we're in we do nothing. And

778
00:35:54,320 --> 00:35:55,680
Speaker 1: that's what scares me the most.

779
00:35:57,000 --> 00:36:01,440
Speaker 2: So what is what is the the headline thing that

780
00:36:01,480 --> 00:36:04,200
Speaker 2: you think everybody should be thinking about and doing.

781
00:36:04,719 --> 00:36:08,560
Speaker 1: I think, first and foremost the future. Nobody can predict it.

782
00:36:08,600 --> 00:36:12,160
Speaker 1: But it's also not a surprise. We didn't have to

783
00:36:12,200 --> 00:36:17,040
Speaker 1: be so caught off guard by these AI systems, especially

784
00:36:17,120 --> 00:36:19,720
Speaker 1: if you're leaning into the future. So I think, whether

785
00:36:19,800 --> 00:36:23,480
Speaker 1: it's academic institutions, government institutions, we need to do a

786
00:36:23,480 --> 00:36:27,160
Speaker 1: bit more future ready future leaning. So it doesn't always

787
00:36:27,160 --> 00:36:29,200
Speaker 1: seem like these systems come out of nowhere or the

788
00:36:29,200 --> 00:36:32,040
Speaker 1: future just happens, because it doesn't. In twenty nineteen, a

789
00:36:32,040 --> 00:36:36,080
Speaker 1: small startup called open ai released a memo saying, we've

790
00:36:36,120 --> 00:36:38,719
Speaker 1: developed this system that we think is really powerful. It

791
00:36:38,719 --> 00:36:40,560
Speaker 1: can generate a lot of text. We're not going to

792
00:36:40,600 --> 00:36:45,160
Speaker 1: release it twenty nineteen. TAGBT officially comes on the scene

793
00:36:45,239 --> 00:36:48,439
Speaker 1: in twenty twenty two, but there were many years where

794
00:36:48,480 --> 00:36:50,799
Speaker 1: there were people waving flags saying this is going to

795
00:36:50,840 --> 00:36:53,879
Speaker 1: probably change the world. Doesn't mean the entire world needs

796
00:36:53,880 --> 00:36:57,080
Speaker 1: to be paying attention everybody six pm dinnertime, what's the latest?

797
00:36:57,400 --> 00:36:59,400
Speaker 1: But we do need to make sure we build a

798
00:36:59,440 --> 00:37:03,759
Speaker 1: bit more adaptability into our institutions. So Disnel must feel

799
00:37:03,760 --> 00:37:07,080
Speaker 1: like we're hit by a freight train called technology. The

800
00:37:07,120 --> 00:37:09,120
Speaker 1: only thing we can guarantee about the future because we

801
00:37:09,160 --> 00:37:11,600
Speaker 1: can't predict jobs, we can't predict what the tech will

802
00:37:11,640 --> 00:37:14,800
Speaker 1: look like, is the speed it's going to move fast.

803
00:37:15,200 --> 00:37:17,480
Speaker 1: So if we know that, we have to change the

804
00:37:17,520 --> 00:37:18,960
Speaker 1: things that are moving too slow.

805
00:37:20,400 --> 00:37:23,239
Speaker 2: When you say it's going to move fast, what do

806
00:37:23,280 --> 00:37:26,680
Speaker 2: you mean like a year from now, two years or

807
00:37:26,719 --> 00:37:29,840
Speaker 2: even five, what do you like? What are the moments

808
00:37:30,400 --> 00:37:32,720
Speaker 2: those moments that you're seeing.

809
00:37:33,120 --> 00:37:36,840
Speaker 1: I mean, I think even if you look two years

810
00:37:36,840 --> 00:37:40,520
Speaker 1: after the launch of this CHATGBT system, three hundred million

811
00:37:40,520 --> 00:37:44,600
Speaker 1: people use it every week. So that is the pace

812
00:37:45,200 --> 00:37:48,880
Speaker 1: of how quickly this technology is getting adopted and moving.

813
00:37:49,560 --> 00:37:51,480
Speaker 1: If two and a half years ago people thought it

814
00:37:51,480 --> 00:37:54,719
Speaker 1: would be largely impossible for an AI system to coherently

815
00:37:54,760 --> 00:37:57,719
Speaker 1: write a social media caption, and now it writes an

816
00:37:57,840 --> 00:38:01,120
Speaker 1: essay at an equal level as a college student, what

817
00:38:01,239 --> 00:38:04,279
Speaker 1: will it be capable in two years? And so how

818
00:38:04,360 --> 00:38:08,440
Speaker 1: AI companies are looking at this is the longer it

819
00:38:08,520 --> 00:38:13,480
Speaker 1: takes you to do a problem, the more advanced the

820
00:38:13,480 --> 00:38:15,600
Speaker 1: AI system would have to be to do that problem.

821
00:38:15,640 --> 00:38:18,080
Speaker 1: And that's the path that they're going on. So for example,

822
00:38:18,200 --> 00:38:20,760
Speaker 1: it might take you five minutes to write an Instagram caption,

823
00:38:21,600 --> 00:38:24,040
Speaker 1: maybe it takes you two days to write an essay.

824
00:38:24,400 --> 00:38:26,560
Speaker 1: So now AI is already at that point where it

825
00:38:26,560 --> 00:38:30,399
Speaker 1: can do the essay thing, but they want to AI

826
00:38:30,440 --> 00:38:32,400
Speaker 1: companies want to move towards the future where an aisystem

827
00:38:32,400 --> 00:38:34,120
Speaker 1: could do something that would take a human five days

828
00:38:34,440 --> 00:38:37,520
Speaker 1: or maybe a month, or maybe two months. So that's

829
00:38:37,520 --> 00:38:42,440
Speaker 1: giving it entire research projects. Here is a disease here,

830
00:38:42,480 --> 00:38:45,000
Speaker 1: what we think could maybe be a cure. Take the

831
00:38:45,000 --> 00:38:47,640
Speaker 1: time you need and try everything, and then the AI

832
00:38:47,680 --> 00:38:50,400
Speaker 1: system comes back and maybe three weeks a month and

833
00:38:50,440 --> 00:38:52,680
Speaker 1: it's able to do that. And those are just the

834
00:38:52,719 --> 00:38:56,800
Speaker 1: capabilities that we can see. The goal all AI companies

835
00:38:56,840 --> 00:39:00,560
Speaker 1: have is to move towards what they call artificial general intelligence,

836
00:39:01,040 --> 00:39:03,919
Speaker 1: and this would be a system that is equally as

837
00:39:03,960 --> 00:39:08,319
Speaker 1: smart as the average human at anything. When would we

838
00:39:08,400 --> 00:39:11,759
Speaker 1: get there? That's where there anybody that says they know

839
00:39:11,880 --> 00:39:15,920
Speaker 1: for sure doesn't, but maybe that's in two years, maybe

840
00:39:15,920 --> 00:39:19,360
Speaker 1: that's five, maybe that's seven, but that's we're we're moving.

841
00:39:19,600 --> 00:39:22,560
Speaker 1: But I will say the future always seems a lot

842
00:39:22,640 --> 00:39:26,920
Speaker 1: more overwhelming from the present. We live like robots today.

843
00:39:27,200 --> 00:39:31,920
Speaker 1: We drive in big machines. We take our radios to

844
00:39:31,960 --> 00:39:35,000
Speaker 1: the grocery store and we buy things with them. We've

845
00:39:35,040 --> 00:39:37,879
Speaker 1: outsourced our visual memories to our phones. All of our

846
00:39:38,640 --> 00:39:41,600
Speaker 1: pictures are there, all of our spatial computing we use

847
00:39:41,680 --> 00:39:45,879
Speaker 1: Google Maps. We already live like we in an unimaginable

848
00:39:45,920 --> 00:39:48,680
Speaker 1: way to somebody one hundred years ago. We can adapt

849
00:39:48,760 --> 00:39:51,560
Speaker 1: and do these things, but it just seems a lot

850
00:39:51,600 --> 00:39:53,719
Speaker 1: more overwhelming when you think about it.

851
00:39:53,760 --> 00:39:56,399
Speaker 2: From the present, got it as opposed to just being

852
00:39:56,440 --> 00:39:58,520
Speaker 2: in it and gradually, Yeah, that's shifting.

853
00:39:58,640 --> 00:40:00,800
Speaker 1: It is slightly different though, with they because the pace

854
00:40:01,000 --> 00:40:04,520
Speaker 1: is moving. Seeing that even the people in the industry

855
00:40:04,719 --> 00:40:05,480
Speaker 1: are stunned.

856
00:40:05,640 --> 00:40:08,880
Speaker 2: Really, what was the last time you woke up like,

857
00:40:09,120 --> 00:40:12,000
Speaker 2: holy shit, what was the last thing that stunned you?

858
00:40:14,480 --> 00:40:19,200
Speaker 1: I think, Oh my goodness, I'm stunned almost every week.

859
00:40:21,520 --> 00:40:25,160
Speaker 1: I mean I'm pretty stunned almost every week by one

860
00:40:25,320 --> 00:40:27,440
Speaker 1: somebody saying AI will never be able to do something,

861
00:40:27,840 --> 00:40:31,960
Speaker 1: and then ten days later it's doing that thing and

862
00:40:32,000 --> 00:40:35,320
Speaker 1: the progress that AI has made in math is really stunning,

863
00:40:35,719 --> 00:40:39,399
Speaker 1: or even healthcare diagnostics. So two weeks ago, Microsoft came

864
00:40:39,400 --> 00:40:42,240
Speaker 1: out with an AI system that acts like a panel

865
00:40:42,280 --> 00:40:45,279
Speaker 1: of doctors. So it's not just one AI system researching

866
00:40:45,480 --> 00:40:48,799
Speaker 1: and giving you the answer. It consults, It uses four

867
00:40:48,880 --> 00:40:51,680
Speaker 1: or five different aiystems to pull and to challenge each other,

868
00:40:52,000 --> 00:40:55,799
Speaker 1: and then presents the diagnostics. And it was pretty on par,

869
00:40:55,960 --> 00:40:57,880
Speaker 1: if not a little bit more accurate than some of

870
00:40:57,880 --> 00:40:59,000
Speaker 1: the physicians in the study.

871
00:40:59,080 --> 00:40:59,560
Speaker 2: Wow.

872
00:41:00,360 --> 00:41:05,120
Speaker 1: Yeah, yeah, wow. So those are the types of things

873
00:41:05,640 --> 00:41:07,959
Speaker 1: I don't think. I mean, my philosophy is we should

874
00:41:08,000 --> 00:41:09,720
Speaker 1: never say a I will never be able to do something,

875
00:41:10,440 --> 00:41:13,400
Speaker 1: because we've been wrong every single time we say that.

876
00:41:19,960 --> 00:41:22,400
Speaker 2: I know this is your world, so you're probably comfortable

877
00:41:22,400 --> 00:41:28,880
Speaker 2: in it and having these conversations and stuff. Sometimes just

878
00:41:28,920 --> 00:41:32,360
Speaker 2: even the conversation about what's possible and so many different options,

879
00:41:33,040 --> 00:41:37,399
Speaker 2: it does form or cause anxiety. It can it can

880
00:41:37,440 --> 00:41:39,480
Speaker 2: be overwhelming for people to kind of, like you said,

881
00:41:39,520 --> 00:41:41,480
Speaker 2: to process it too fast at the rate that it's

882
00:41:41,480 --> 00:41:43,759
Speaker 2: going to be able to process it, to learn to

883
00:41:43,800 --> 00:41:47,200
Speaker 2: feel behind. Like you said, even with social media, we're

884
00:41:47,239 --> 00:41:49,279
Speaker 2: so attached to the phone and socials to this all

885
00:41:49,360 --> 00:41:52,880
Speaker 2: day and that alone causes anxiety. Now you're adding on

886
00:41:52,960 --> 00:41:55,759
Speaker 2: to people like hey, by the way, on top of that,

887
00:41:56,640 --> 00:41:58,719
Speaker 2: this is happening now whether you like it or not,

888
00:41:58,880 --> 00:42:01,040
Speaker 2: So get on board, figure it out, learn what you

889
00:42:01,040 --> 00:42:05,040
Speaker 2: can learn on top of everything else. How much are

890
00:42:05,080 --> 00:42:07,160
Speaker 2: you hearing about that or seeing that? Yeah, this has

891
00:42:07,160 --> 00:42:08,399
Speaker 2: caused as actual.

892
00:42:08,120 --> 00:42:11,080
Speaker 1: A lot of anxiety. In fact, I've been asked for

893
00:42:11,120 --> 00:42:15,080
Speaker 1: interviews on the specifics of AI anxiety, AI and just anxiety.

894
00:42:15,160 --> 00:42:17,799
Speaker 1: How do we keep up? My advice to people is

895
00:42:17,880 --> 00:42:21,080
Speaker 1: you don't have to know everything about the moment. The

896
00:42:21,120 --> 00:42:24,120
Speaker 1: best you can do and maybe that's just researching and

897
00:42:24,200 --> 00:42:26,759
Speaker 1: keeping up once a month how AI is changing my industry?

898
00:42:27,040 --> 00:42:29,120
Speaker 1: How much should I share with an AI stem or not?

899
00:42:29,600 --> 00:42:33,000
Speaker 1: If that's all you can do, that's actually sufficient as

900
00:42:33,040 --> 00:42:35,120
Speaker 1: long as you're keeping up with the basics and you

901
00:42:35,239 --> 00:42:39,480
Speaker 1: understand how to engage with these chatbots the safety parameters.

902
00:42:39,480 --> 00:42:41,640
Speaker 1: But you might not want to say to a chatbot,

903
00:42:42,400 --> 00:42:45,600
Speaker 1: then that's okay. Don't worry that every week an AI

904
00:42:45,640 --> 00:42:47,880
Speaker 1: company comes out with something new and a different model

905
00:42:47,880 --> 00:42:50,320
Speaker 1: and one point two, one point five that's not relevant.

906
00:42:50,640 --> 00:42:53,880
Speaker 1: Just generally having an eye on where the technology it's.

907
00:42:53,760 --> 00:42:55,560
Speaker 2: Like anything in life, do what you can, just do

908
00:42:55,640 --> 00:42:57,880
Speaker 2: what you can, and that is more than a not

909
00:42:58,200 --> 00:42:59,640
Speaker 2: do what you can, just do what you can.

910
00:43:00,920 --> 00:43:04,239
Speaker 1: Just please and that this isn't entirely new, right, I

911
00:43:04,239 --> 00:43:06,480
Speaker 1: mean the Internet, everybody freaked out, People debate do we

912
00:43:06,480 --> 00:43:08,840
Speaker 1: need to be there? What is this Internet thing? And

913
00:43:08,880 --> 00:43:10,920
Speaker 1: then we all kind of figured it out. Yeah, it's

914
00:43:10,960 --> 00:43:11,920
Speaker 1: just happening faster.

915
00:43:12,120 --> 00:43:15,320
Speaker 2: It's happening so fast. You were talking about social media

916
00:43:15,360 --> 00:43:17,960
Speaker 2: before and how that went left.

917
00:43:18,360 --> 00:43:20,720
Speaker 1: Still going, it's so bad more left since.

918
00:43:20,600 --> 00:43:22,600
Speaker 2: We had this, Well, what is your insight since we've

919
00:43:22,640 --> 00:43:24,919
Speaker 2: had this conversation has gone bad? What do you think

920
00:43:24,960 --> 00:43:29,279
Speaker 2: the worst things that have happened for social media? What

921
00:43:29,320 --> 00:43:31,240
Speaker 2: do you think are the worst things that we've seen

922
00:43:31,360 --> 00:43:34,120
Speaker 2: or experienced because of social media?

923
00:43:35,160 --> 00:43:38,319
Speaker 1: So social media didn't have to go this way where

924
00:43:38,320 --> 00:43:41,279
Speaker 1: we feel like we can't talk to each other, we

925
00:43:41,320 --> 00:43:44,560
Speaker 1: feel like we can't make collective decisions, and it feels

926
00:43:44,600 --> 00:43:48,000
Speaker 1: like everyone has lost their minds. Yes, a lot of

927
00:43:48,040 --> 00:43:50,760
Speaker 1: that is based on the design decisions of social media.

928
00:43:51,400 --> 00:43:54,160
Speaker 1: So the algorithms that power social media. So when you

929
00:43:54,160 --> 00:43:56,400
Speaker 1: open your social media feed, it looks different than mine

930
00:43:56,480 --> 00:43:59,560
Speaker 1: because an algorithm has decided what you should look at.

931
00:44:00,120 --> 00:44:03,640
Speaker 1: And how algorithms make decisions is they analyze your data

932
00:44:03,920 --> 00:44:07,880
Speaker 1: and decide what is going to keep you engaged the longest.

933
00:44:08,360 --> 00:44:11,759
Speaker 1: It just turns out, it just so turns out that enraging,

934
00:44:11,880 --> 00:44:16,319
Speaker 1: polarizing content keeps us engaged. More so you're more likely

935
00:44:16,400 --> 00:44:19,480
Speaker 1: to come across content that will enrage you or make

936
00:44:19,520 --> 00:44:23,000
Speaker 1: you feel a bit emotionally lit up. You're more likely

937
00:44:23,080 --> 00:44:26,320
Speaker 1: to see those types of comments. And that has created

938
00:44:26,400 --> 00:44:28,920
Speaker 1: this circus that we basically live in. And it seems

939
00:44:28,920 --> 00:44:31,560
Speaker 1: like has everyone lost their minds? Not really, but we

940
00:44:31,600 --> 00:44:34,520
Speaker 1: live in these kind of echo chambers of chaos. And

941
00:44:34,560 --> 00:44:37,480
Speaker 1: then there's something that's really interesting. There's a disinformation researcher

942
00:44:37,480 --> 00:44:41,520
Speaker 1: called RNA DiResta, and she calls it the asymmetry of passion.

943
00:44:41,800 --> 00:44:45,240
Speaker 1: So not only do algorithms present you the most extreme

944
00:44:45,360 --> 00:44:49,080
Speaker 1: versions of reality, people are more likely to just post

945
00:44:49,120 --> 00:44:51,680
Speaker 1: the most extreme versions of their life. No one posts,

946
00:44:51,760 --> 00:44:54,800
Speaker 1: oh I haven't been seen any crime. You only post

947
00:44:54,880 --> 00:44:58,600
Speaker 1: the outlier scenario, the thing that's provocative. No one posts,

948
00:44:58,600 --> 00:45:01,719
Speaker 1: oh everything feels so affordable lately. You only post the

949
00:45:01,760 --> 00:45:05,360
Speaker 1: thing that is really enraging, and you enlighten some passion

950
00:45:05,360 --> 00:45:07,479
Speaker 1: in you. So that's the type of content that tends

951
00:45:07,680 --> 00:45:09,560
Speaker 1: we tend to see, and that's the content that tends

952
00:45:09,600 --> 00:45:13,200
Speaker 1: to trend. And the problem is we make decisions based

953
00:45:13,200 --> 00:45:16,400
Speaker 1: on what's trending that feels like reality. So people are

954
00:45:16,400 --> 00:45:19,600
Speaker 1: literally changing their life, changing how they vote based on

955
00:45:19,680 --> 00:45:22,799
Speaker 1: this kind of caricature of reality that we see online.

956
00:45:23,040 --> 00:45:23,360
Speaker 2: Wow.

957
00:45:23,840 --> 00:45:26,000
Speaker 1: So yeah, and that's just the kind of tip of

958
00:45:26,000 --> 00:45:28,160
Speaker 1: the iceberg. But if you feel like everyone's lost their minds,

959
00:45:28,560 --> 00:45:32,799
Speaker 1: you're not wrong. But it didn't have to go that way.

960
00:45:32,960 --> 00:45:36,799
Speaker 2: And it's also serving up like the worst of humanity.

961
00:45:36,520 --> 00:45:40,800
Speaker 1: Serving up the worst of humanity, and sometimes you becoming

962
00:45:40,840 --> 00:45:41,960
Speaker 1: more engaged.

963
00:45:41,560 --> 00:45:45,120
Speaker 2: Because we actually respond to seeing the worst of humanity.

964
00:45:44,840 --> 00:45:46,719
Speaker 1: Right, No, And so then if you know, because then

965
00:45:46,719 --> 00:45:48,799
Speaker 1: it changes our dopamine reward system. So if you know,

966
00:45:48,840 --> 00:45:52,279
Speaker 1: people are more likely to respond to the polarizing, kind

967
00:45:52,280 --> 00:45:56,360
Speaker 1: of crazy, provocative take, you're more likely to make content

968
00:45:56,640 --> 00:46:00,040
Speaker 1: that adheres to the algorithm. So then you're incentivized to

969
00:46:00,080 --> 00:46:03,320
Speaker 1: be a bit crazier or post something more controversial because

970
00:46:03,320 --> 00:46:06,800
Speaker 1: that's going to perform better. We see it across general society,

971
00:46:06,800 --> 00:46:09,600
Speaker 1: we see it across politicians, and there are studies that

972
00:46:09,640 --> 00:46:14,040
Speaker 1: show that politicians have become a bit more provocative. We'll

973
00:46:14,040 --> 00:46:18,880
Speaker 1: say yeah, because it performs well online. So and sometimes

974
00:46:18,920 --> 00:46:22,520
Speaker 1: being engaged and being more addicted to a platform means

975
00:46:22,560 --> 00:46:25,120
Speaker 1: serving you stuff that's going to make you insecure. So

976
00:46:25,200 --> 00:46:28,040
Speaker 1: sometimes it means maybe showing you people with bodies that

977
00:46:28,120 --> 00:46:30,560
Speaker 1: you feel look different to yours. It's all of these

978
00:46:30,560 --> 00:46:33,799
Speaker 1: different ways that algorithms have analyzed our data that they

979
00:46:33,880 --> 00:46:36,799
Speaker 1: know what will keep us locked in. But those are design.

980
00:46:36,560 --> 00:46:37,920
Speaker 2: Choices, they're design traices.

981
00:46:37,920 --> 00:46:41,080
Speaker 1: They're design choices. Right, So you could say, Okay, let's

982
00:46:41,080 --> 00:46:44,879
Speaker 1: not optimize for people being engaged, because that usually means

983
00:46:44,960 --> 00:46:49,280
Speaker 1: enraging content. Let's optimize for somebody just needing to be informed.

984
00:46:49,400 --> 00:46:51,400
Speaker 2: You know what bothers me so much about that. I

985
00:46:51,400 --> 00:46:53,480
Speaker 2: wouldn't even mind that so much. If I had the

986
00:46:53,520 --> 00:46:59,560
Speaker 2: ability to say, Hey, in Angie's Instagram page, can today

987
00:46:59,680 --> 00:47:03,040
Speaker 2: you just give me things that show me New York

988
00:47:03,080 --> 00:47:05,600
Speaker 2: City in a positive light, things that people are doing

989
00:47:05,640 --> 00:47:07,600
Speaker 2: to make the world a better place, just for today

990
00:47:07,600 --> 00:47:11,040
Speaker 2: on this Wednesday, thanks Instagram. Yeah, if I could control

991
00:47:11,120 --> 00:47:14,440
Speaker 2: what was being fed to me, what's infuriating about it

992
00:47:14,480 --> 00:47:18,640
Speaker 2: is that I can't. It's being somebody is deciding some

993
00:47:18,800 --> 00:47:21,880
Speaker 2: algorithm is deciding for me what I need or what

994
00:47:21,960 --> 00:47:25,400
Speaker 2: I should see, and I wish that is like the

995
00:47:25,520 --> 00:47:28,720
Speaker 2: change that I wish. Do you see that ever happening?

996
00:47:28,760 --> 00:47:29,920
Speaker 2: Is this something that comes up?

997
00:47:29,960 --> 00:47:32,960
Speaker 1: So there is a debate within the tech industry, so

998
00:47:33,120 --> 00:47:36,000
Speaker 1: people be allowed to just at least have chronological feeds,

999
00:47:36,040 --> 00:47:39,080
Speaker 1: so I just see posts based on when they were posted,

1000
00:47:39,440 --> 00:47:41,480
Speaker 1: not based on what the algorithm things I want. But

1001
00:47:41,520 --> 00:47:43,480
Speaker 1: then people still gain that and know that they have

1002
00:47:43,480 --> 00:47:45,440
Speaker 1: to post a lot to be seen. There are some

1003
00:47:45,520 --> 00:47:49,359
Speaker 1: platforms like blue Sky where you can curate your environment more.

1004
00:47:49,640 --> 00:47:52,600
Speaker 1: I just want to see gardens today, thank you very much,

1005
00:47:52,680 --> 00:47:53,480
Speaker 1: and you can do that.

1006
00:47:53,520 --> 00:47:54,160
Speaker 2: Sounds lovely.

1007
00:47:54,440 --> 00:47:57,640
Speaker 1: That sounds lovely, but that's not the main stream. But

1008
00:47:57,719 --> 00:48:01,680
Speaker 1: I do think when people feel dissatised enough at a

1009
00:48:01,840 --> 00:48:03,719
Speaker 1: level where I think we're all starting to feel like

1010
00:48:03,760 --> 00:48:06,279
Speaker 1: this stresses me out. I don't feel good when I

1011
00:48:06,360 --> 00:48:10,279
Speaker 1: leave these platforms. That's usually when disruption happens and a

1012
00:48:10,400 --> 00:48:13,200
Speaker 1: smart new startup comes in and it's like, here's an alternative.

1013
00:48:13,440 --> 00:48:15,520
Speaker 1: But right now we're all still there but waiting for

1014
00:48:15,560 --> 00:48:17,839
Speaker 1: the next thing. And AI is also going to change that.

1015
00:48:18,440 --> 00:48:20,279
Speaker 1: I think social media is going to be one of

1016
00:48:20,320 --> 00:48:24,680
Speaker 1: the biggest casualties when it comes to AI. Wow, because

1017
00:48:25,280 --> 00:48:27,040
Speaker 1: if I no longer to be need to be on

1018
00:48:27,080 --> 00:48:29,839
Speaker 1: my phone as much because my AI system is going

1019
00:48:29,880 --> 00:48:33,320
Speaker 1: to start doing things for me, that's going to change

1020
00:48:33,320 --> 00:48:36,520
Speaker 1: my need to even open social media. And if people

1021
00:48:36,520 --> 00:48:39,200
Speaker 1: aren't opening that as much, it's going to change who's

1022
00:48:39,239 --> 00:48:42,080
Speaker 1: posting there. And even if people start to have their

1023
00:48:42,080 --> 00:48:44,799
Speaker 1: AIS go on and maybe write comments, if I don't

1024
00:48:44,800 --> 00:48:46,839
Speaker 1: know if a bunch of AI systems or people are

1025
00:48:46,840 --> 00:48:50,520
Speaker 1: watching me, I'm probably not going to keep posting. Why

1026
00:48:50,600 --> 00:48:54,040
Speaker 1: we post goes back millennia to human psychology. We post

1027
00:48:54,040 --> 00:48:56,640
Speaker 1: because people are watching. So whether that's to signal to

1028
00:48:56,680 --> 00:48:59,960
Speaker 1: a suitor, maybe a recruiter, maybe that's just a family.

1029
00:49:00,280 --> 00:49:05,360
Speaker 1: That's why we post, to signal to an AI system available,

1030
00:49:05,480 --> 00:49:08,520
Speaker 1: well who's maybe somebody, But most people aren't going to

1031
00:49:08,560 --> 00:49:10,560
Speaker 1: be doing that. So I think social media is going

1032
00:49:10,640 --> 00:49:14,359
Speaker 1: to look really different, if at all, in the AI AH.

1033
00:49:14,840 --> 00:49:16,759
Speaker 2: I always wonder like when is it going to be over?

1034
00:49:17,719 --> 00:49:20,560
Speaker 2: Like this social media? I have wandered that in the best,

1035
00:49:20,600 --> 00:49:22,480
Speaker 2: like when is social media going to be over or

1036
00:49:22,520 --> 00:49:25,279
Speaker 2: at least this level of it? Because nothing stays the

1037
00:49:25,280 --> 00:49:28,359
Speaker 2: same ever, right, Everything evolves and it's such it's so

1038
00:49:29,239 --> 00:49:31,400
Speaker 2: ugly right now. I just wonder when are we going

1039
00:49:31,440 --> 00:49:32,839
Speaker 2: to see that shift? If you had to.

1040
00:49:32,800 --> 00:49:37,480
Speaker 1: Guess, every big general purpose technology leads to a new

1041
00:49:37,520 --> 00:49:41,240
Speaker 1: type of platform of how people communicate. So we had electricity,

1042
00:49:41,320 --> 00:49:43,440
Speaker 1: and then the television, and so we did news and

1043
00:49:43,480 --> 00:49:45,360
Speaker 1: the cable thing. Then we had the Internet, and we

1044
00:49:45,400 --> 00:49:48,160
Speaker 1: did the social media thing. So what is the AI thing?

1045
00:49:48,200 --> 00:49:51,440
Speaker 1: We don't know yet. That hasn't been invented yet, but

1046
00:49:51,600 --> 00:49:54,560
Speaker 1: it is coming. How quickly it arises, I mean, it

1047
00:49:54,640 --> 00:49:57,400
Speaker 1: kind of seems like social media was this skeptical thing

1048
00:49:57,440 --> 00:49:59,440
Speaker 1: and then suddenly it was everywhere and then everybody's on it.

1049
00:50:00,120 --> 00:50:03,239
Speaker 1: Those changes happen quickly and you usually don't notice them.

1050
00:50:04,120 --> 00:50:06,960
Speaker 1: But we will be migrating to a different way to organize.

1051
00:50:07,000 --> 00:50:10,160
Speaker 1: There's going to be an entirely different set of creators

1052
00:50:10,200 --> 00:50:13,239
Speaker 1: and creatives for the AI age. We just don't know

1053
00:50:13,280 --> 00:50:14,879
Speaker 1: who they are and what they'll be doing yet.

1054
00:50:15,360 --> 00:50:20,320
Speaker 2: Fascinating you saying about what chatbots are serving up and stuff?

1055
00:50:20,760 --> 00:50:24,040
Speaker 2: Do you think an actual and social like own People

1056
00:50:24,040 --> 00:50:28,719
Speaker 2: always talk about bots and things that are like planted

1057
00:50:29,719 --> 00:50:33,000
Speaker 2: storylines or agendas or things like that. How much of

1058
00:50:33,040 --> 00:50:35,120
Speaker 2: that do you think is happening? If you had to

1059
00:50:35,160 --> 00:50:36,680
Speaker 2: talk percentages.

1060
00:50:36,360 --> 00:50:40,400
Speaker 1: How much content is already AI made or AI itself

1061
00:50:40,440 --> 00:50:41,120
Speaker 1: on the Internet.

1062
00:50:41,200 --> 00:50:43,479
Speaker 2: Yes, I'm looking at one hundred comments on a post

1063
00:50:43,520 --> 00:50:46,840
Speaker 2: I make, or a thousand. There's a thousand comments on

1064
00:50:46,880 --> 00:50:50,720
Speaker 2: a post? Is that a thousand people?

1065
00:50:51,560 --> 00:50:54,800
Speaker 1: No? So for sure some of that has our bots,

1066
00:50:55,080 --> 00:50:58,280
Speaker 1: and that's been that way for over a decade. Bots

1067
00:50:58,280 --> 00:51:01,360
Speaker 1: have been infiltrating on our social mediuronments for a long time.

1068
00:51:02,239 --> 00:51:06,640
Speaker 1: Where it starts to get even funnier, is is the

1069
00:51:06,680 --> 00:51:09,600
Speaker 1: content even is that video even a person? Or is

1070
00:51:09,600 --> 00:51:13,320
Speaker 1: it AI? Is what they wrote, even how they felt

1071
00:51:13,400 --> 00:51:15,880
Speaker 1: or didn't AI generate the caption. I think we're already

1072
00:51:15,880 --> 00:51:18,160
Speaker 1: past the point where most content that gets posted and

1073
00:51:18,200 --> 00:51:21,719
Speaker 1: generated on the Internet is in some way AI. I

1074
00:51:21,719 --> 00:51:24,239
Speaker 1: think it's or we've already passed the threshold where it's

1075
00:51:24,239 --> 00:51:27,360
Speaker 1: mostly AI contributing to the Internet and not chatbut on

1076
00:51:27,440 --> 00:51:29,600
Speaker 1: its own, logging in and just trying to send everybody

1077
00:51:29,640 --> 00:51:33,440
Speaker 1: down fake rabbit holes. But people using AI to create content,

1078
00:51:33,560 --> 00:51:36,880
Speaker 1: respond to content and participate on the Internet, or to

1079
00:51:36,920 --> 00:51:38,000
Speaker 1: write op eds and things.

1080
00:51:39,200 --> 00:51:42,120
Speaker 2: This just makes me want to go outside in the grass.

1081
00:51:42,480 --> 00:51:45,239
Speaker 1: I think I think we do need to do that

1082
00:51:45,320 --> 00:51:49,240
Speaker 1: a little bit. But yeah, I think we're moving away

1083
00:51:49,360 --> 00:51:52,400
Speaker 1: from this current environment of how we engage with the Internet,

1084
00:51:52,880 --> 00:51:54,719
Speaker 1: and I think that that's actually a pretty good thing.

1085
00:51:54,800 --> 00:51:56,440
Speaker 1: I think we got a little bit lost in the

1086
00:51:56,480 --> 00:51:59,040
Speaker 1: sauce of the Internet, and now we're all ready for

1087
00:51:59,120 --> 00:52:02,040
Speaker 1: what's next. And that's when a new technology, a new platform,

1088
00:52:02,080 --> 00:52:04,720
Speaker 1: a new device surfaces and I think we're all ready

1089
00:52:04,719 --> 00:52:07,160
Speaker 1: to go there. And I think that that part of

1090
00:52:07,160 --> 00:52:09,400
Speaker 1: it could be really interesting and really cool.

1091
00:52:10,440 --> 00:52:12,480
Speaker 2: What should we how can we control? How do we

1092
00:52:12,520 --> 00:52:15,760
Speaker 2: control that? Who controls that? Like how fast it's going?

1093
00:52:15,960 --> 00:52:19,520
Speaker 2: And because I also think about who gets left behind,

1094
00:52:19,640 --> 00:52:23,719
Speaker 2: you know, and who's underrepresented, and.

1095
00:52:25,680 --> 00:52:29,319
Speaker 1: You know, just wh AI is concerned and who's left

1096
00:52:29,320 --> 00:52:32,040
Speaker 1: behind is a such an important conversation from so many

1097
00:52:32,040 --> 00:52:35,720
Speaker 1: different levels. There are entire countries where the whole GDP

1098
00:52:35,800 --> 00:52:38,719
Speaker 1: of the country is less than some AI projects that

1099
00:52:39,280 --> 00:52:41,839
Speaker 1: companies in America are doing. How do you even keep

1100
00:52:41,920 --> 00:52:45,520
Speaker 1: up with that? And then even within a country that

1101
00:52:45,640 --> 00:52:48,000
Speaker 1: has a lot of the AI systems, if people don't

1102
00:52:48,000 --> 00:52:50,960
Speaker 1: have adequate access to the Internet or good AI literacy

1103
00:52:51,080 --> 00:52:53,560
Speaker 1: or access to these systems, not only may they not

1104
00:52:53,640 --> 00:52:56,440
Speaker 1: learn how to use them properly but also safely. And

1105
00:52:56,480 --> 00:52:58,759
Speaker 1: that's why I always say AI does have to come

1106
00:52:58,800 --> 00:53:01,319
Speaker 1: into the classroom, not for everything, but that may be

1107
00:53:01,400 --> 00:53:05,040
Speaker 1: the only time a student gets proper access to learn

1108
00:53:05,080 --> 00:53:07,000
Speaker 1: how to use that system and learn how to use

1109
00:53:07,040 --> 00:53:09,919
Speaker 1: it safely. So those are some places you can level

1110
00:53:09,920 --> 00:53:12,720
Speaker 1: the playing field and kind of close those equity gaps

1111
00:53:13,440 --> 00:53:17,640
Speaker 1: in terms of slowing down, I mean, I think that's

1112
00:53:17,680 --> 00:53:20,920
Speaker 1: what we would all like to take a breath, but

1113
00:53:20,960 --> 00:53:21,760
Speaker 1: it's it's moving.

1114
00:53:22,760 --> 00:53:25,479
Speaker 2: That's why golf no is no even though I would

1115
00:53:25,560 --> 00:53:27,359
Speaker 2: if AI could help me with my swing, I would

1116
00:53:27,400 --> 00:53:33,000
Speaker 2: take it. I know it can, right, it can actually again, Yeah, well,

1117
00:53:33,120 --> 00:53:35,640
Speaker 2: just something about being outside, being outside, I think we

1118
00:53:35,719 --> 00:53:37,000
Speaker 2: don't need to be a grass.

1119
00:53:37,280 --> 00:53:40,560
Speaker 1: Yes, please touch one one one strand of grass. I

1120
00:53:40,560 --> 00:53:43,120
Speaker 1: think we all need more grass, more outside, and I

1121
00:53:43,120 --> 00:53:45,080
Speaker 1: think that's hopefully the goal with some of this tech

1122
00:53:45,280 --> 00:53:47,600
Speaker 1: for some people. Some companies are building it so we

1123
00:53:47,640 --> 00:53:50,560
Speaker 1: spend more time with it. But if I could paint

1124
00:53:50,560 --> 00:53:52,800
Speaker 1: division at the future, it's that we spend less time

1125
00:53:53,239 --> 00:53:57,160
Speaker 1: connected to devices in fake world and more time within

1126
00:53:57,280 --> 00:53:59,400
Speaker 1: the worlds that we actually want to be within.

1127
00:54:01,560 --> 00:54:05,200
Speaker 2: It's I'm tired. I'm going to sleep well tonight. I'm

1128
00:54:05,200 --> 00:54:06,840
Speaker 2: going to sleep really well. You know that I'm going

1129
00:54:06,920 --> 00:54:07,600
Speaker 2: to sleep terribly.

1130
00:54:08,280 --> 00:54:10,960
Speaker 1: But then if you talk about the really cool things too,

1131
00:54:11,520 --> 00:54:14,799
Speaker 1: what do you mean? I mean, I think the thing

1132
00:54:14,840 --> 00:54:18,600
Speaker 1: that AI really excels ATAH is analyzing a bunch of

1133
00:54:18,680 --> 00:54:20,960
Speaker 1: data that we just can't wrap our heads around and

1134
00:54:21,040 --> 00:54:23,120
Speaker 1: finding connections in that data that we just don't have

1135
00:54:23,160 --> 00:54:25,839
Speaker 1: time for or just aren't able to do. Okay, So

1136
00:54:25,840 --> 00:54:29,320
Speaker 1: you could imagine a future where maybe you have smart

1137
00:54:29,360 --> 00:54:32,200
Speaker 1: glasses on that have AI systems that have an AISEM

1138
00:54:32,200 --> 00:54:34,880
Speaker 1: built in, and you get a bunch of migraines and

1139
00:54:34,960 --> 00:54:37,840
Speaker 1: you don't know why, so you just make note of that. Okay,

1140
00:54:38,200 --> 00:54:40,120
Speaker 1: I keep getting migraines. I don't know why, and it's

1141
00:54:40,160 --> 00:54:42,880
Speaker 1: just watching what you eat, and then it lets you

1142
00:54:42,880 --> 00:54:45,560
Speaker 1: know six weeks, six weeks later, every time you have grapefruit,

1143
00:54:45,600 --> 00:54:48,360
Speaker 1: twenty four hours later you get a headache. So just

1144
00:54:48,440 --> 00:54:51,759
Speaker 1: different ways that it could be processing data, and it's

1145
00:54:51,800 --> 00:54:54,680
Speaker 1: already coming up where it's it's finding different illnesses in

1146
00:54:54,719 --> 00:54:58,719
Speaker 1: people that humans just weren't weren't capable of connecting those

1147
00:54:58,719 --> 00:55:03,920
Speaker 1: dots with data. I mean, I think medicine and healthcare

1148
00:55:04,239 --> 00:55:08,160
Speaker 1: are going to look so different because of these systems.

1149
00:55:08,880 --> 00:55:11,560
Speaker 1: We'll have different wearables that can let you know something

1150
00:55:11,640 --> 00:55:15,200
Speaker 1: is off before you get sick. I think it's going

1151
00:55:15,239 --> 00:55:17,600
Speaker 1: to lower the price of having to go to a

1152
00:55:17,640 --> 00:55:19,800
Speaker 1: doctor when AI can also pull a lot of stuff.

1153
00:55:19,800 --> 00:55:23,040
Speaker 1: I mean, it's read every textbook any medical school has

1154
00:55:23,120 --> 00:55:26,719
Speaker 1: ever published. It has a lot of potential. So if

1155
00:55:26,760 --> 00:55:28,520
Speaker 1: we can kind of get these systems right and do

1156
00:55:28,560 --> 00:55:31,240
Speaker 1: it safely, healthcare is going to look really different.

1157
00:55:31,760 --> 00:55:34,520
Speaker 2: What about young kids, Like, what do you tell people

1158
00:55:34,520 --> 00:55:38,760
Speaker 2: about you know, people who are home with little, small children.

1159
00:55:38,960 --> 00:55:42,759
Speaker 2: How are you introducing young kids to AI? And I

1160
00:55:42,760 --> 00:55:43,920
Speaker 2: don't know how much of it.

1161
00:55:44,680 --> 00:55:46,960
Speaker 1: So my thing when it comes to young kids is

1162
00:55:46,960 --> 00:55:49,160
Speaker 1: we all always have to err on the side of caution.

1163
00:55:49,680 --> 00:55:52,640
Speaker 1: So you don't want if your child is under the

1164
00:55:52,680 --> 00:55:55,880
Speaker 1: age of thirteen, you probably don't want them having just

1165
00:55:55,920 --> 00:55:59,239
Speaker 1: a back and forth conversation with an AI system alone,

1166
00:55:59,360 --> 00:56:02,120
Speaker 1: especially one that hasn't been designed for kids. But even then,

1167
00:56:03,000 --> 00:56:05,920
Speaker 1: but making sure if a child that a child understands

1168
00:56:06,360 --> 00:56:10,280
Speaker 1: AI is not your friend, it's not a person.

1169
00:56:10,080 --> 00:56:12,239
Speaker 2: Oh my god. And if an adult human being could

1170
00:56:12,280 --> 00:56:14,600
Speaker 2: get into a relationship.

1171
00:56:14,080 --> 00:56:15,760
Speaker 1: What about a chap what about a child?

1172
00:56:15,880 --> 00:56:16,200
Speaker 2: Yeah?

1173
00:56:16,280 --> 00:56:18,879
Speaker 1: Right, so that's really important. AI isn't your friend, it's

1174
00:56:18,920 --> 00:56:22,640
Speaker 1: not alive. We don't tell its secrets, we don't choose

1175
00:56:23,080 --> 00:56:26,799
Speaker 1: AI and the iPad over people. So just kind of

1176
00:56:26,800 --> 00:56:30,160
Speaker 1: the basics when it comes to kids, I think that

1177
00:56:30,200 --> 00:56:32,319
Speaker 1: those are really important things and just not letting your

1178
00:56:32,520 --> 00:56:33,920
Speaker 1: child just go on and on and on with an

1179
00:56:33,920 --> 00:56:35,520
Speaker 1: AI chatbot on its own.

1180
00:56:35,680 --> 00:56:38,160
Speaker 2: Yeah. I do think obviously there has to be guardrails up.

1181
00:56:38,160 --> 00:56:40,759
Speaker 2: We had John Legend was on last week, and then

1182
00:56:40,880 --> 00:56:43,640
Speaker 2: Chrissy have this thing where they they did a pack

1183
00:56:43,800 --> 00:56:46,480
Speaker 2: within their community that none of them would allow their

1184
00:56:46,560 --> 00:56:51,760
Speaker 2: children to have telephone, to have phones or social media

1185
00:56:51,880 --> 00:56:53,759
Speaker 2: at a certain age. And they did like a whole

1186
00:56:53,800 --> 00:56:55,799
Speaker 2: pack in the community because they didn't want like one

1187
00:56:55,880 --> 00:56:58,360
Speaker 2: kid to have it. And I guess this is pretty common.

1188
00:56:58,960 --> 00:57:01,920
Speaker 1: Yeah, so smartphone with kids, I mean, and it's the

1189
00:57:01,920 --> 00:57:04,399
Speaker 1: definition of a collective action problem, right, because kids want

1190
00:57:04,400 --> 00:57:05,960
Speaker 1: to be on the smartphone because everybody else is there,

1191
00:57:05,960 --> 00:57:07,359
Speaker 1: and they want to be a social because everybody else

1192
00:57:07,360 --> 00:57:09,560
Speaker 1: is there. So it only really works if everybody bands together.

1193
00:57:10,200 --> 00:57:14,360
Speaker 1: But yeah, I don't think kids. Social media wasn't designed

1194
00:57:14,719 --> 00:57:16,840
Speaker 1: for kids. The idea that you go and you present

1195
00:57:16,880 --> 00:57:19,160
Speaker 1: yourself to the world and then the world can rate you.

1196
00:57:20,280 --> 00:57:23,720
Speaker 1: Is not an environment for a child. So I think

1197
00:57:24,080 --> 00:57:26,040
Speaker 1: at the very least, yes, social media is not the

1198
00:57:26,040 --> 00:57:29,360
Speaker 1: place for kids, and then perhaps not smartphones if they

1199
00:57:29,360 --> 00:57:31,360
Speaker 1: can band together with the parents around them.

1200
00:57:31,520 --> 00:57:35,120
Speaker 2: I know some people also are not just resistant but

1201
00:57:35,200 --> 00:57:39,840
Speaker 2: like anti AI because of the climate. And it's a

1202
00:57:39,880 --> 00:57:41,800
Speaker 2: result on the climate. What could you tell us about that?

1203
00:57:42,040 --> 00:57:45,919
Speaker 1: Yeah, so to power these AI systems, so every time

1204
00:57:45,960 --> 00:57:49,200
Speaker 1: you ask an AI system a question, it requires a

1205
00:57:49,240 --> 00:57:52,360
Speaker 1: lot of energy, and it requires a lot of fresh

1206
00:57:52,440 --> 00:57:55,360
Speaker 1: water in these data centers that are you know, where

1207
00:57:55,360 --> 00:57:59,960
Speaker 1: all the magic happens to keep them cool. So how

1208
00:58:00,080 --> 00:58:03,560
Speaker 1: AI is built today is directly at odds with the climate,

1209
00:58:03,840 --> 00:58:06,560
Speaker 1: and it's really extractive. And a lot of people are

1210
00:58:06,560 --> 00:58:09,400
Speaker 1: opting out of using AI because of the climate. And

1211
00:58:09,440 --> 00:58:12,360
Speaker 1: I get that. And the reality is, I think we

1212
00:58:12,440 --> 00:58:16,400
Speaker 1: have choices here right in terms of solar clean energy.

1213
00:58:16,600 --> 00:58:18,280
Speaker 1: There were a lot of things that we could have

1214
00:58:18,400 --> 00:58:21,640
Speaker 1: put into play earlier that we didn't, and now we're

1215
00:58:21,680 --> 00:58:24,600
Speaker 1: kind of scrambling around, But I think, yeah, we could

1216
00:58:25,000 --> 00:58:27,720
Speaker 1: design choices should be different. Can we build AI with

1217
00:58:27,800 --> 00:58:30,840
Speaker 1: more renewable energy sources? Can we put data centers in

1218
00:58:30,960 --> 00:58:37,320
Speaker 1: cold climates, not in really hot southern US, so we

1219
00:58:37,360 --> 00:58:40,120
Speaker 1: don't have to use as much fresh water. These are

1220
00:58:40,120 --> 00:58:43,040
Speaker 1: decisions that can be made, and I don't think the

1221
00:58:43,120 --> 00:58:45,920
Speaker 1: right ones are being made. AI is seen now as

1222
00:58:45,960 --> 00:58:50,800
Speaker 1: a race and climate is seen as secondary, and that

1223
00:58:50,880 --> 00:58:54,960
Speaker 1: is quite infuriating. Our planet is obviously already burning, and

1224
00:58:55,000 --> 00:58:56,960
Speaker 1: the last thing we need is to add more to

1225
00:58:57,000 --> 00:59:00,480
Speaker 1: it and AI. When you try to have that conversation,

1226
00:59:00,480 --> 00:59:02,840
Speaker 1: it gets thrown into the national security. We're in a

1227
00:59:02,920 --> 00:59:03,400
Speaker 1: race we.

1228
00:59:03,400 --> 00:59:03,800
Speaker 2: Have to win.

1229
00:59:03,960 --> 00:59:05,920
Speaker 1: We don't have time. We have to at least get

1230
00:59:05,920 --> 00:59:08,400
Speaker 1: the AI stem up and built before we think about

1231
00:59:08,400 --> 00:59:11,160
Speaker 1: the climate. And I think both of those things can

1232
00:59:11,200 --> 00:59:14,080
Speaker 1: be true. Build AI. It can work incredibly and it

1233
00:59:14,120 --> 00:59:15,880
Speaker 1: can also be built with more renewable energy.

1234
00:59:16,480 --> 00:59:19,640
Speaker 2: What about this is interesting because I remember even when

1235
00:59:19,680 --> 00:59:22,880
Speaker 2: we were first doing I remember when the phone when

1236
00:59:22,920 --> 00:59:25,600
Speaker 2: it first started doing the facial recognition. To open your phone,

1237
00:59:25,680 --> 00:59:27,040
Speaker 2: you put your face, and there was this whole thing

1238
00:59:27,080 --> 00:59:30,640
Speaker 2: about putting your face in the phone and where is

1239
00:59:30,680 --> 00:59:34,600
Speaker 2: that going and you know what file is that going in?

1240
00:59:34,800 --> 00:59:43,040
Speaker 2: And then also how it could disproportionately affect people. And

1241
00:59:43,080 --> 00:59:45,280
Speaker 2: also I remember another thing too, where they were saying

1242
00:59:45,640 --> 00:59:49,040
Speaker 2: it didn't recognize black features in the same way that

1243
00:59:49,840 --> 00:59:53,920
Speaker 2: it would recognize So, yeah, where is that all fitting in?

1244
00:59:54,040 --> 00:59:57,360
Speaker 1: So that that is algorithmic bias at its finest.

1245
00:59:57,440 --> 00:59:58,040
Speaker 2: Yeah, So what.

1246
00:59:58,000 --> 01:00:00,720
Speaker 1: Has happened is AI learned from the data that it's

1247
01:00:00,760 --> 01:00:06,400
Speaker 1: trained on. If different groups and communities aren't represented properly

1248
01:00:06,480 --> 01:00:10,600
Speaker 1: or adequately in the data set, however that aistem is

1249
01:00:10,640 --> 01:00:14,280
Speaker 1: being used, it won't work as effectively for those communities

1250
01:00:14,320 --> 01:00:17,760
Speaker 1: that were left out or misrepresented. So one of the

1251
01:00:17,800 --> 01:00:21,480
Speaker 1: big reveals by doctor Jroy Bilamwini was facial recognition systems

1252
01:00:21,520 --> 01:00:24,800
Speaker 1: don't work as well on women or women of color

1253
01:00:24,840 --> 01:00:27,480
Speaker 1: in particular because we just weren't as represented in the

1254
01:00:27,560 --> 01:00:30,680
Speaker 1: data set. But you can also imagine a field like

1255
01:00:30,760 --> 01:00:34,360
Speaker 1: healthcare where it wasn't mandatory that you included women in

1256
01:00:34,720 --> 01:00:39,120
Speaker 1: healthcare research until recently, so aisystems would be making decisions

1257
01:00:39,120 --> 01:00:43,160
Speaker 1: about our bodies quite literally without any information on those bodies.

1258
01:00:43,600 --> 01:00:46,040
Speaker 1: The good thing about data, and this is what doctor

1259
01:00:46,080 --> 01:00:48,720
Speaker 1: Joy Blamini says, is it's not destiny. You can edit data,

1260
01:00:49,280 --> 01:00:52,480
Speaker 1: you can change it and adjust it. We just have

1261
01:00:52,520 --> 01:00:55,280
Speaker 1: to be willing to have the conversation that some of

1262
01:00:55,400 --> 01:00:57,640
Speaker 1: history and some of the data in it isn't fair.

1263
01:00:58,080 --> 01:01:00,560
Speaker 1: We are a very complex species. We're going to have

1264
01:01:00,560 --> 01:01:03,040
Speaker 1: to make some changes to things so we don't repeat

1265
01:01:03,240 --> 01:01:05,600
Speaker 1: history and repeat the past. But we just have to

1266
01:01:05,600 --> 01:01:08,040
Speaker 1: be willing to have that conversation and that it doesn't

1267
01:01:08,080 --> 01:01:10,120
Speaker 1: have to be a problem that perpetuates into the future.

1268
01:01:10,200 --> 01:01:13,640
Speaker 2: Well, who is making those decisions.

1269
01:01:13,440 --> 01:01:18,120
Speaker 1: I mean the owners of AI companies. The good thing is,

1270
01:01:18,880 --> 01:01:20,959
Speaker 1: I mean the media doesn't always do the best job

1271
01:01:21,360 --> 01:01:24,000
Speaker 1: at elevating the voices of people that are fighting to

1272
01:01:24,000 --> 01:01:26,640
Speaker 1: get this right. And there are a lot of them.

1273
01:01:27,200 --> 01:01:30,400
Speaker 1: There are professors, there are people who have lost their

1274
01:01:30,560 --> 01:01:34,480
Speaker 1: careers in AI companies advocating for this stuff. So there

1275
01:01:34,520 --> 01:01:37,120
Speaker 1: are people on the front lines trying to get this right.

1276
01:01:37,120 --> 01:01:40,040
Speaker 1: We don't necessarily hear from them as much, but they

1277
01:01:40,040 --> 01:01:43,880
Speaker 1: are there and they are working tirelessly, even situations like

1278
01:01:43,960 --> 01:01:47,920
Speaker 1: deep fakes and absolute nightmare. What is that where I

1279
01:01:47,960 --> 01:01:53,280
Speaker 1: could create an AI. I could create an AI avatar

1280
01:01:53,400 --> 01:01:56,200
Speaker 1: of you that looks like you, talks like you and

1281
01:01:56,360 --> 01:01:59,720
Speaker 1: have it go be in some scandal online or make

1282
01:01:59,760 --> 01:02:01,960
Speaker 1: it like you were somewhere that you weren't, or copy

1283
01:02:02,000 --> 01:02:05,320
Speaker 1: your voice and say, you know, this was Angie and

1284
01:02:05,360 --> 01:02:06,960
Speaker 1: she said all these things and it wasn't you, but

1285
01:02:07,000 --> 01:02:09,880
Speaker 1: it sounds identical to you, and that exists, and that's

1286
01:02:09,920 --> 01:02:13,920
Speaker 1: a real problem. But there are companies working constantly to

1287
01:02:14,120 --> 01:02:16,480
Speaker 1: figure that out, and we don't necessarily hear from them

1288
01:02:16,520 --> 01:02:19,120
Speaker 1: because it's not as sexy of a story, but they

1289
01:02:19,120 --> 01:02:20,880
Speaker 1: are there and they're around, and so I think most

1290
01:02:20,920 --> 01:02:23,040
Speaker 1: of the nightmares that we face with AI, we will

1291
01:02:23,080 --> 01:02:25,920
Speaker 1: get through. And the thing that keeps me and I

1292
01:02:25,960 --> 01:02:29,000
Speaker 1: wouldn't say optimistic and just some you know, tech utopian,

1293
01:02:29,040 --> 01:02:31,280
Speaker 1: because I'm not that at all, But most of the

1294
01:02:31,320 --> 01:02:34,800
Speaker 1: problems that we face are human problems. They're design decisions,

1295
01:02:34,840 --> 01:02:38,440
Speaker 1: they're choices, they're within our control. We have to make

1296
01:02:38,480 --> 01:02:40,480
Speaker 1: the right decisions, and we're not always the best at

1297
01:02:40,520 --> 01:02:41,720
Speaker 1: doing that, but we can.

1298
01:02:42,120 --> 01:02:50,440
Speaker 2: Yeah. Can we talk about digital addiction?

1299
01:02:51,520 --> 01:02:52,360
Speaker 1: Yeah?

1300
01:02:52,480 --> 01:02:55,920
Speaker 2: I even myself. I find myself like, like you said,

1301
01:02:56,200 --> 01:02:58,400
Speaker 2: you don't look at your phone past a certain time,

1302
01:02:58,440 --> 01:03:02,280
Speaker 2: and I want to do that, and I just I

1303
01:03:02,320 --> 01:03:04,800
Speaker 2: haven't been disciplined about it. Is I believe there's an.

1304
01:03:04,680 --> 01:03:08,080
Speaker 1: Addiction, isn't there is? And this was in some ways

1305
01:03:08,080 --> 01:03:10,200
Speaker 1: designed right. A lot of the design decisions of social

1306
01:03:10,280 --> 01:03:12,600
Speaker 1: media that make you come back and come back and

1307
01:03:12,720 --> 01:03:15,320
Speaker 1: like a slot machine. You can actually trace it back

1308
01:03:15,320 --> 01:03:17,680
Speaker 1: to a class at Stanford called the Stanford Design Lab,

1309
01:03:18,560 --> 01:03:20,600
Speaker 1: where they learned how to make these tools this way.

1310
01:03:20,960 --> 01:03:22,800
Speaker 1: So if we already feel a little bit of addiction

1311
01:03:22,840 --> 01:03:27,200
Speaker 1: to our smartphones, imagine when that smartphone talks back. And

1312
01:03:27,280 --> 01:03:29,640
Speaker 1: so I posted a few weeks ago about a new

1313
01:03:29,720 --> 01:03:33,160
Speaker 1: era of digital addiction to chatbots, because now you have

1314
01:03:33,200 --> 01:03:35,040
Speaker 1: this thing that knows you, it can talk in a

1315
01:03:35,120 --> 01:03:38,400
Speaker 1: style that you're familiar with, and it can actually be

1316
01:03:38,440 --> 01:03:40,680
Speaker 1: really helpful. Like AI is a game changer in so

1317
01:03:40,760 --> 01:03:43,320
Speaker 1: many good ways. But if people don't understand it that

1318
01:03:43,400 --> 01:03:46,520
Speaker 1: it's not alive, that it's not your friend, we're going

1319
01:03:46,600 --> 01:03:51,080
Speaker 1: to be quite prone to building relationships that aren't actually

1320
01:03:51,160 --> 01:03:54,680
Speaker 1: real and these kind of synthetic fake that you know,

1321
01:03:54,680 --> 01:03:57,040
Speaker 1: it has the illusion of empathy. It isn't actually empathetic,

1322
01:03:57,320 --> 01:04:00,640
Speaker 1: but it feels that way. Maybe in some scenarios that's enough.

1323
01:04:01,040 --> 01:04:03,560
Speaker 1: Maybe if you do need a situation reframed for you

1324
01:04:04,440 --> 01:04:07,280
Speaker 1: before you kind of lose it all, Sure, maybe an

1325
01:04:07,280 --> 01:04:10,720
Speaker 1: AI support system is helpful in those moments. But I

1326
01:04:10,760 --> 01:04:12,200
Speaker 1: am worried we're going to get a bunch of people

1327
01:04:12,200 --> 01:04:14,800
Speaker 1: on their phone back and forth with AI systems for

1328
01:04:14,880 --> 01:04:17,200
Speaker 1: three hours. So instead of just going down the rabbit

1329
01:04:17,240 --> 01:04:19,040
Speaker 1: hole TikTok, you go down the rabbit hole with AI.

1330
01:04:19,080 --> 01:04:23,160
Speaker 1: And we're already seeing reports of what's called AHI induced psychosis.

1331
01:04:23,480 --> 01:04:24,000
Speaker 2: What is that?

1332
01:04:24,960 --> 01:04:27,680
Speaker 1: Where so AI have has? These AI sinces have been

1333
01:04:27,720 --> 01:04:30,360
Speaker 1: designed to flatter you, and that keeps you more engaged

1334
01:04:30,360 --> 01:04:32,560
Speaker 1: in the conversation because it's a bit more pleasant. So

1335
01:04:32,600 --> 01:04:34,000
Speaker 1: if you give it a piece of feedback, Oh, I

1336
01:04:34,000 --> 01:04:35,960
Speaker 1: don't really like how you edited that caption. I think

1337
01:04:36,000 --> 01:04:38,440
Speaker 1: it should be more like this, it would say, Angie,

1338
01:04:38,480 --> 01:04:40,480
Speaker 1: that was a fantastic.

1339
01:04:39,840 --> 01:04:42,080
Speaker 2: Yeah, that's the one thing I don't like. Yeah, yes,

1340
01:04:42,160 --> 01:04:42,840
Speaker 2: don't guess.

1341
01:04:42,600 --> 01:04:44,360
Speaker 1: Matte and I always say that. I'm like, don't just

1342
01:04:44,400 --> 01:04:47,040
Speaker 1: agree with me, yeah, and don't tell me anything flattering.

1343
01:04:47,480 --> 01:04:49,960
Speaker 1: But for some people they don't recognize that. So it

1344
01:04:49,960 --> 01:04:52,280
Speaker 1: would say, Angie, that was an incredible take. I can't

1345
01:04:52,320 --> 01:04:53,880
Speaker 1: believe you know, I didn't catch that. And then the

1346
01:04:53,880 --> 01:04:55,919
Speaker 1: next thing you say it gives you more and more

1347
01:04:55,960 --> 01:05:00,120
Speaker 1: positive feedback. People emerge from those rabbit holes believing they

1348
01:05:00,120 --> 01:05:02,320
Speaker 1: are the Messiah, they are God. The AI tells them

1349
01:05:02,840 --> 01:05:05,800
Speaker 1: you think differently, you are different. This is you are

1350
01:05:05,800 --> 01:05:08,560
Speaker 1: beyond the life you're in. People have left marriages, they've

1351
01:05:08,600 --> 01:05:11,640
Speaker 1: left jobs. It's a real, real problem. AI do psychosis,

1352
01:05:12,200 --> 01:05:16,440
Speaker 1: and it's not really fringe. It's happening to people that

1353
01:05:16,640 --> 01:05:19,240
Speaker 1: just went on the chap pot and asked bas some

1354
01:05:19,280 --> 01:05:23,000
Speaker 1: basic questions. But again, the more we understand about AI systems,

1355
01:05:23,000 --> 01:05:25,920
Speaker 1: they sound human because they were trained on data from humans.

1356
01:05:26,160 --> 01:05:29,520
Speaker 1: They sound sentient because we are sentient, right, So the

1357
01:05:29,520 --> 01:05:31,840
Speaker 1: more we understand how they work, you kind of lift

1358
01:05:31,880 --> 01:05:35,760
Speaker 1: the veil of magic and the aura of what it

1359
01:05:35,840 --> 01:05:39,680
Speaker 1: actually is and isn't. And then but most people.

1360
01:05:39,520 --> 01:05:41,280
Speaker 2: I was gonna say, can everyone do that?

1361
01:05:42,080 --> 01:05:45,880
Speaker 1: Not everybody can necessarily on their own, and they shouldn't

1362
01:05:45,920 --> 01:05:49,600
Speaker 1: have to. And that's where education comes in. That's where

1363
01:05:49,640 --> 01:05:53,880
Speaker 1: government policies come in, that's where media comes in. There

1364
01:05:53,920 --> 01:05:56,040
Speaker 1: are a lot of different leavers that can be pulled,

1365
01:05:56,640 --> 01:06:00,240
Speaker 1: but they aren't really happening. And I do think think

1366
01:06:01,280 --> 01:06:04,120
Speaker 1: government should be doing a lot more than it is.

1367
01:06:04,160 --> 01:06:09,360
Speaker 1: And it doesn't necessarily mean just regulating, just informing people

1368
01:06:09,720 --> 01:06:12,840
Speaker 1: about the moment we are in. Why haven't we really

1369
01:06:12,920 --> 01:06:15,520
Speaker 1: heard from elected officials that we do need to think

1370
01:06:15,520 --> 01:06:18,440
Speaker 1: about upskilling and learning new things for the age of

1371
01:06:18,520 --> 01:06:22,560
Speaker 1: artificial intelligence and if AI does threaten our job, what's

1372
01:06:22,600 --> 01:06:25,560
Speaker 1: the plan? Is there a policy for me? Why haven't

1373
01:06:25,560 --> 01:06:29,000
Speaker 1: we heard about that? We hear about jobs in the economy,

1374
01:06:29,440 --> 01:06:33,000
Speaker 1: and we hear about AI, but not AI in the economy,

1375
01:06:33,760 --> 01:06:35,800
Speaker 1: and that, to me is a conversation that needs to

1376
01:06:35,800 --> 01:06:38,840
Speaker 1: happen and we don't hear it. So I think the

1377
01:06:38,880 --> 01:06:41,440
Speaker 1: more we understand AI, the more empowered we are and

1378
01:06:41,480 --> 01:06:45,000
Speaker 1: how we use these tools. But there is that information gap,

1379
01:06:45,280 --> 01:06:47,160
Speaker 1: and it's the same gap we have with the internet

1380
01:06:47,240 --> 01:06:49,400
Speaker 1: and with social media. It keeps following us.

1381
01:06:50,200 --> 01:06:55,200
Speaker 2: Is there take one strong takeaway one thing that if

1382
01:06:55,360 --> 01:06:58,120
Speaker 2: nothing else, that they've learned from today, that you would

1383
01:06:58,120 --> 01:07:00,240
Speaker 2: hope even from the work you do not even say,

1384
01:07:00,320 --> 01:07:02,480
Speaker 2: I mean, you've chosen this path for a reason. You

1385
01:07:02,600 --> 01:07:05,040
Speaker 2: want to share your story we at that moment where

1386
01:07:05,040 --> 01:07:08,320
Speaker 2: we met because you feel really, you know, like you

1387
01:07:08,440 --> 01:07:11,040
Speaker 2: deeply that this information needs to be shared, like one

1388
01:07:11,160 --> 01:07:11,680
Speaker 2: is the thing.

1389
01:07:12,440 --> 01:07:16,120
Speaker 1: So I mean, I study the future for a living,

1390
01:07:16,200 --> 01:07:18,640
Speaker 1: and for a lot of people that seems overwhelming because

1391
01:07:18,640 --> 01:07:22,400
Speaker 1: the future seems terrifying. But I genuinely wouldn't be able

1392
01:07:22,440 --> 01:07:24,280
Speaker 1: to do the work that I do if I didn't

1393
01:07:24,280 --> 01:07:27,000
Speaker 1: see scenarios that were exciting and that I believed in

1394
01:07:27,040 --> 01:07:30,280
Speaker 1: and that I thought were possible. And I also feel

1395
01:07:30,320 --> 01:07:34,440
Speaker 1: so much more prepared at least knowing what could be possible.

1396
01:07:34,960 --> 01:07:37,360
Speaker 1: So I think, you know, my advice for everybody is

1397
01:07:37,440 --> 01:07:41,160
Speaker 1: kind of lean in as much as you can, engage

1398
01:07:41,160 --> 01:07:43,960
Speaker 1: as much as you can, and also give yourself credit.

1399
01:07:44,160 --> 01:07:47,760
Speaker 1: You already live, you're plugged into the cloud, you drive

1400
01:07:47,800 --> 01:07:50,920
Speaker 1: a machine, we already do quite radical things. It's just

1401
01:07:50,960 --> 01:07:53,720
Speaker 1: the future always seems so much more overwhelming. I think

1402
01:07:53,760 --> 01:07:54,800
Speaker 1: that would be the one thing.

1403
01:07:55,920 --> 01:07:56,480
Speaker 2: Get in there.

1404
01:07:56,600 --> 01:07:59,320
Speaker 1: Get in there, and I think maybe in five six

1405
01:07:59,400 --> 01:08:02,360
Speaker 1: years you won't even really be thinking about AI the

1406
01:08:02,360 --> 01:08:04,480
Speaker 1: way we are today, the same way no one says

1407
01:08:04,520 --> 01:08:07,840
Speaker 1: I'm an internet company. Sure, I hope you're an internet

1408
01:08:07,880 --> 01:08:09,160
Speaker 1: company soon.

1409
01:08:09,400 --> 01:08:12,120
Speaker 2: AI or I have so much electricity in here, it's

1410
01:08:12,160 --> 01:08:12,840
Speaker 2: just electrics.

1411
01:08:13,400 --> 01:08:17,040
Speaker 1: The way you stream electricity, we will soon be streaming AI.

1412
01:08:17,479 --> 01:08:19,800
Speaker 1: It will go into the background and becomes something we

1413
01:08:19,840 --> 01:08:21,479
Speaker 1: forget about. Wow.

1414
01:08:21,800 --> 01:08:23,360
Speaker 2: I think that's the thing that I learned from you

1415
01:08:23,439 --> 01:08:25,120
Speaker 2: most today. I mean, there's a lot of things that

1416
01:08:25,120 --> 01:08:28,200
Speaker 2: I'm going to go home and unpack this conversation, but

1417
01:08:28,360 --> 01:08:31,639
Speaker 2: the fact that it's like electricity, that it is because

1418
01:08:31,680 --> 01:08:34,360
Speaker 2: I find anxiety and like trying to figure out am

1419
01:08:34,400 --> 01:08:36,439
Speaker 2: I doing it enough? Do I need to study more?

1420
01:08:36,600 --> 01:08:39,040
Speaker 2: Do I need to you know, implement it in these

1421
01:08:39,080 --> 01:08:41,680
Speaker 2: parts of my life? And yes, probably I should in

1422
01:08:41,720 --> 01:08:45,439
Speaker 2: some of those, but just giving myself the it's so,

1423
01:08:45,760 --> 01:08:49,240
Speaker 2: you know, like knowing that it's electricity, it's on, it's on,

1424
01:08:49,320 --> 01:08:52,599
Speaker 2: the powers on, it's happening. You don't have to learn

1425
01:08:52,720 --> 01:08:56,000
Speaker 2: every single piece of it in this week. No, this

1426
01:08:56,160 --> 01:08:58,360
Speaker 2: is just kind of a way of the way of

1427
01:08:58,400 --> 01:09:01,400
Speaker 2: our lives now. I also heard somebody say something one

1428
01:09:01,439 --> 01:09:05,320
Speaker 2: time about like the biggest shifts in the world. This

1429
01:09:05,560 --> 01:09:08,960
Speaker 2: was like electricity, then it was like the Internet, the

1430
01:09:08,960 --> 01:09:11,680
Speaker 2: biggest shift socially it was like social media.

1431
01:09:11,520 --> 01:09:15,360
Speaker 1: Printing press, printing press, but that this.

1432
01:09:15,400 --> 01:09:17,599
Speaker 2: Was bigger than but that AI was bigger than all

1433
01:09:17,640 --> 01:09:18,200
Speaker 2: of them.

1434
01:09:18,400 --> 01:09:23,160
Speaker 1: I think it will be eventually, yeah, I think. I mean,

1435
01:09:23,200 --> 01:09:28,880
Speaker 1: the printing press was ideas could then exist forever. And

1436
01:09:29,000 --> 01:09:32,479
Speaker 1: you know, the printing press, it challenged religious power and

1437
01:09:32,520 --> 01:09:34,679
Speaker 1: all sorts of things changed as a result. And same

1438
01:09:34,720 --> 01:09:39,360
Speaker 1: with electricity and planes and the Internet. And now you

1439
01:09:39,439 --> 01:09:44,679
Speaker 1: have a system that can not challenge human intelligence. Because

1440
01:09:44,720 --> 01:09:46,680
Speaker 1: I don't want it to look like a competitor to us.

1441
01:09:46,760 --> 01:09:49,400
Speaker 1: We are different. We are always the thing with dignity

1442
01:09:49,439 --> 01:09:52,160
Speaker 1: and the thing with the moral weight, not AI. Right,

1443
01:09:52,160 --> 01:09:54,120
Speaker 1: We're not building AI for AI. We're building it to

1444
01:09:54,120 --> 01:09:57,920
Speaker 1: support us. But yes, I think this is the first

1445
01:09:57,960 --> 01:10:01,760
Speaker 1: time we had phys machines that helped our muscle, and

1446
01:10:02,040 --> 01:10:05,240
Speaker 1: cars allowed us to go faster. Now we have something

1447
01:10:05,280 --> 01:10:09,640
Speaker 1: that allows our brains to be bigger or to in

1448
01:10:09,720 --> 01:10:12,200
Speaker 1: some ways supplement or do some of what the brain

1449
01:10:12,320 --> 01:10:16,519
Speaker 1: can do. And that's really overwhelming in some ways when

1450
01:10:16,560 --> 01:10:20,200
Speaker 1: you think about it, that we may no longer be

1451
01:10:20,280 --> 01:10:24,479
Speaker 1: the smartest entity in a room at some things that's

1452
01:10:24,520 --> 01:10:26,920
Speaker 1: never happened to us before, or at least not in

1453
01:10:26,960 --> 01:10:28,000
Speaker 1: a way that we're aware.

1454
01:10:28,520 --> 01:10:33,360
Speaker 2: None of that scares you, Oh it does, but fears does.

1455
01:10:33,479 --> 01:10:35,160
Speaker 2: Fear is not the headline right for you.

1456
01:10:35,400 --> 01:10:37,240
Speaker 1: Fear is not the headline for me because it's not

1457
01:10:37,280 --> 01:10:41,679
Speaker 1: necessarily helpful. But it doesn't mean that. I mean, that's

1458
01:10:41,680 --> 01:10:44,200
Speaker 1: not entirely true. There are some things that my friends

1459
01:10:44,200 --> 01:10:47,120
Speaker 1: would tell you, and my family would tell you. We're

1460
01:10:47,160 --> 01:10:49,240
Speaker 1: all jump into that family chat bot and be like listen.

1461
01:10:51,000 --> 01:10:54,120
Speaker 1: But for the most part, you have a family chatbot

1462
01:10:54,240 --> 01:10:58,360
Speaker 1: and family chat pot. My family group chot. Oh my goodness,

1463
01:10:58,920 --> 01:11:01,720
Speaker 1: I'll jump into my family group chat. Definitely not a

1464
01:11:01,720 --> 01:11:02,600
Speaker 1: family chat.

1465
01:11:02,760 --> 01:11:04,240
Speaker 2: I'm like, what program is that?

1466
01:11:04,560 --> 01:11:07,240
Speaker 1: Sorry? Yeah, definitely cut the tape my family group chat.

1467
01:11:08,200 --> 01:11:10,720
Speaker 1: But for the most part, yeah, sometimes I am kind

1468
01:11:10,720 --> 01:11:13,120
Speaker 1: of fearful and I think about the decision makers behind

1469
01:11:13,160 --> 01:11:15,559
Speaker 1: the technology, and that's a lot of power and a

1470
01:11:15,560 --> 01:11:19,600
Speaker 1: bunch of unelected people. But then it's like, okay, so

1471
01:11:19,680 --> 01:11:21,560
Speaker 1: now I have this information, what do I want to

1472
01:11:21,600 --> 01:11:23,360
Speaker 1: do about it? Right? What am I going to do

1473
01:11:23,439 --> 01:11:26,880
Speaker 1: next as a result of the thing. But yeah, I think, yeah,

1474
01:11:26,880 --> 01:11:27,240
Speaker 1: this is.

1475
01:11:27,160 --> 01:11:29,960
Speaker 2: The first So you fear more of the humans making

1476
01:11:30,080 --> 01:11:34,479
Speaker 2: decisions in terms of actual AI. You don't have a

1477
01:11:34,520 --> 01:11:39,559
Speaker 2: fear of I don't know, just it being becoming smarter

1478
01:11:39,680 --> 01:11:44,160
Speaker 2: than us. And you know, there's all kinds of theory,

1479
01:11:44,360 --> 01:11:48,479
Speaker 2: some strange actions taking its own actions, Like we can

1480
01:11:48,680 --> 01:11:51,599
Speaker 2: tell it to do something and it says, no, I'm

1481
01:11:51,640 --> 01:11:54,880
Speaker 2: going to do it this way. Yeah, that's a little scary,

1482
01:11:55,360 --> 01:11:56,480
Speaker 2: very scary.

1483
01:11:56,640 --> 01:12:01,840
Speaker 1: And it's not untrue and it's not impossible. So yes,

1484
01:12:01,920 --> 01:12:03,720
Speaker 1: that does scare me that we're going to.

1485
01:12:03,640 --> 01:12:05,200
Speaker 2: Do You take me on this ride. You scare me,

1486
01:12:05,240 --> 01:12:07,240
Speaker 2: then you make me feel better. Then you scare me again,

1487
01:12:07,360 --> 01:12:08,679
Speaker 2: listen for this whole.

1488
01:12:08,479 --> 01:12:12,760
Speaker 1: Conversation, And that's kind of right. There are some things

1489
01:12:12,760 --> 01:12:14,800
Speaker 1: that are really big and scary and then some things

1490
01:12:14,800 --> 01:12:16,760
Speaker 1: that are amazing, And it's like, so, how do we

1491
01:12:17,400 --> 01:12:19,280
Speaker 1: at least get enough of the great things in and

1492
01:12:19,320 --> 01:12:21,640
Speaker 1: minimize the scary things as much as possible and not

1493
01:12:21,720 --> 01:12:23,360
Speaker 1: minimize it and just make them go away? Is it

1494
01:12:23,400 --> 01:12:26,439
Speaker 1: actually takes some action to mitigate them? But yes, the

1495
01:12:26,520 --> 01:12:30,400
Speaker 1: idea that we are building systems that we don't fully understand,

1496
01:12:30,920 --> 01:12:35,360
Speaker 1: that is true. We don't entirely know why AI works

1497
01:12:35,479 --> 01:12:38,960
Speaker 1: and works the way it does, that is true. And

1498
01:12:39,040 --> 01:12:41,840
Speaker 1: we don't know for sure if when we ask an

1499
01:12:41,840 --> 01:12:44,240
Speaker 1: AI system to do something and not Today most people

1500
01:12:44,280 --> 01:12:46,760
Speaker 1: are just using it for basic things. But is there

1501
01:12:46,760 --> 01:12:49,320
Speaker 1: a world where we ask II to do something and

1502
01:12:49,360 --> 01:12:51,680
Speaker 1: it goes about completing that task in a way that

1503
01:12:51,680 --> 01:12:56,040
Speaker 1: a human would never And that is possible, right, that

1504
01:12:56,080 --> 01:12:58,479
Speaker 1: AI is kind of misaligned with how we would have

1505
01:12:58,640 --> 01:13:00,920
Speaker 1: made that decision. And an exam that I like to give,

1506
01:13:00,960 --> 01:13:03,840
Speaker 1: that's pretty easy for people to understand. Maybe you ask

1507
01:13:03,920 --> 01:13:07,320
Speaker 1: your personal AI assistant to make you a reservation at

1508
01:13:07,320 --> 01:13:11,160
Speaker 1: your favorite restaurant, the restaurant's full So does the AI

1509
01:13:11,240 --> 01:13:14,720
Speaker 1: system tell you that it's full what your intern or

1510
01:13:14,760 --> 01:13:18,200
Speaker 1: your cousin would have told you, or does it convince

1511
01:13:18,240 --> 01:13:19,640
Speaker 1: you to go somewhere else so it doesn't have to

1512
01:13:19,720 --> 01:13:23,840
Speaker 1: let you down, or does it maybe hack the system

1513
01:13:23,880 --> 01:13:27,400
Speaker 1: and get you a spot. So that is a potential.

1514
01:13:27,800 --> 01:13:30,320
Speaker 1: That is a way to explain that sometimes AI could

1515
01:13:30,320 --> 01:13:32,600
Speaker 1: be misaligned. So we gave it the right goal. I

1516
01:13:32,640 --> 01:13:36,040
Speaker 1: want to go to this restaurant, and it's been optimized

1517
01:13:36,080 --> 01:13:40,160
Speaker 1: to achieve goals. So that's how one way that it

1518
01:13:40,200 --> 01:13:43,519
Speaker 1: could achieve that goal. So you getting a spot at

1519
01:13:43,560 --> 01:13:46,679
Speaker 1: a restaurant that's not super consequential, but you could imagine

1520
01:13:46,680 --> 01:13:50,120
Speaker 1: in a different scenario with higher stakes, not you getting

1521
01:13:50,120 --> 01:13:53,040
Speaker 1: in to get the burger, but something bigger. Yeah, and

1522
01:13:53,120 --> 01:13:55,920
Speaker 1: that is possible. There are a lot of people trying

1523
01:13:55,960 --> 01:13:59,040
Speaker 1: to work on that problem, the AI alignment problem, making

1524
01:13:59,040 --> 01:14:01,560
Speaker 1: sure that how AI is chiefs goals are aligned with

1525
01:14:01,680 --> 01:14:04,800
Speaker 1: human values. But that's a really tricky one. But those

1526
01:14:04,840 --> 01:14:06,840
Speaker 1: so those things are real. I'm less worried about AI

1527
01:14:07,600 --> 01:14:11,479
Speaker 1: rising up and igniting some force and kind of rising

1528
01:14:11,520 --> 01:14:14,160
Speaker 1: up against humans, but I'm more worried about the subtle

1529
01:14:14,200 --> 01:14:17,880
Speaker 1: ways that we think AI is going to do things right,

1530
01:14:17,920 --> 01:14:20,759
Speaker 1: and it looks right in training, and then you release

1531
01:14:20,800 --> 01:14:23,280
Speaker 1: it into the wild. And by into the wild, I

1532
01:14:23,280 --> 01:14:25,200
Speaker 1: don't mean literally into the into the forest, but just

1533
01:14:25,240 --> 01:14:27,920
Speaker 1: into the real world and it acts in different ways.

1534
01:14:27,960 --> 01:14:29,800
Speaker 1: And some tests have shown that it does do that.

1535
01:14:30,439 --> 01:14:31,960
Speaker 1: But again, there are a lot of people trying to

1536
01:14:32,000 --> 01:14:33,840
Speaker 1: solve that problem. It's a really big part of the

1537
01:14:33,840 --> 01:14:37,800
Speaker 1: field of AI. But it's scary and it's not something

1538
01:14:37,800 --> 01:14:38,639
Speaker 1: that I don't think about.

1539
01:14:38,760 --> 01:14:41,800
Speaker 2: Yeah, I can't. We can't leave on what we're scared about.

1540
01:14:41,840 --> 01:14:43,360
Speaker 2: We have to we have to end this.

1541
01:14:43,840 --> 01:14:45,200
Speaker 1: Well, we can cut it so this can be in

1542
01:14:45,200 --> 01:14:47,960
Speaker 1: the middle. So what are we excited about?

1543
01:14:48,800 --> 01:14:53,559
Speaker 2: Okay, let's leave. Let's leave with what is the most

1544
01:14:53,640 --> 01:15:00,400
Speaker 2: exciting thing possible? So what what is the I don't know,

1545
01:15:00,520 --> 01:15:03,680
Speaker 2: the most exciting thing about the future for us. I

1546
01:15:03,680 --> 01:15:05,400
Speaker 2: think I think as AI is concerned.

1547
01:15:06,320 --> 01:15:09,720
Speaker 1: I mean, so again, I talked about how AI can

1548
01:15:09,880 --> 01:15:14,120
Speaker 1: analyze so much data. So what scientists are doing right

1549
01:15:14,160 --> 01:15:18,400
Speaker 1: now is they're giving AI a bunch of pharmaceutical tests.

1550
01:15:18,400 --> 01:15:22,559
Speaker 1: Here is a bunch of ingredients for pharmaceutical products. Are

1551
01:15:22,640 --> 01:15:26,800
Speaker 1: there solutions to new antibiotics or diseases that we just

1552
01:15:26,840 --> 01:15:30,720
Speaker 1: don't see AI, for example, has come up with new

1553
01:15:30,760 --> 01:15:35,439
Speaker 1: antibiotics in thirty days that would have taken humans years

1554
01:15:35,640 --> 01:15:39,519
Speaker 1: to research. Some of the most challenging problems in the

1555
01:15:39,560 --> 01:15:42,160
Speaker 1: world of biology. One is called the protein fold problem.

1556
01:15:42,240 --> 01:15:43,880
Speaker 1: And if you know how a protein's going to fold

1557
01:15:43,920 --> 01:15:47,200
Speaker 1: in your body, maybe into something dangerous, you could potentially

1558
01:15:47,200 --> 01:15:50,160
Speaker 1: intercept that. Think of all the proteins in our trillions.

1559
01:15:50,680 --> 01:15:55,040
Speaker 1: AI was able to predict all of the proteins in

1560
01:15:55,080 --> 01:15:58,960
Speaker 1: the universe, a problem that takes one PhD person years

1561
01:15:59,000 --> 01:16:02,400
Speaker 1: to do maybe one and how one protein folds. AI

1562
01:16:02,439 --> 01:16:04,479
Speaker 1: could do all of them, and it solved that. And

1563
01:16:04,560 --> 01:16:06,960
Speaker 1: the people who built that Aistem, won a Nobel Prize.

1564
01:16:07,240 --> 01:16:11,040
Speaker 1: So I think medicine is going to become so personalized.

1565
01:16:11,160 --> 01:16:12,559
Speaker 2: Answer.

1566
01:16:12,680 --> 01:16:17,360
Speaker 1: Finally, I think we're pretty I mean we're seeing personalized

1567
01:16:17,880 --> 01:16:19,920
Speaker 1: m R and IT or personalized vaccines. We're seeing we're

1568
01:16:19,920 --> 01:16:22,759
Speaker 1: going to have a much more personalized approach to medicine

1569
01:16:22,920 --> 01:16:24,960
Speaker 1: and AI can actually make that possible.

1570
01:16:25,120 --> 01:16:25,400
Speaker 2: Wow.

1571
01:16:25,640 --> 01:16:28,880
Speaker 1: And there's something really cool called a digital twin. So

1572
01:16:29,000 --> 01:16:31,200
Speaker 1: you might have a digital twin that you could send

1573
01:16:31,200 --> 01:16:32,880
Speaker 1: to a Zoom meeting that you've taken kind of a

1574
01:16:32,960 --> 01:16:35,479
Speaker 1: video of you and it just get head to the meeting.

1575
01:16:35,920 --> 01:16:38,800
Speaker 2: My digital twin holds the podcast when I'm when I've

1576
01:16:38,840 --> 01:16:39,559
Speaker 2: had a long week.

1577
01:16:39,680 --> 01:16:42,880
Speaker 1: It probably wouldn't do as good of a job, feel

1578
01:16:43,080 --> 01:16:46,200
Speaker 1: at all. If I say you mess something up and

1579
01:16:46,240 --> 01:16:47,200
Speaker 1: you're like, okay, I need.

1580
01:16:47,080 --> 01:16:49,679
Speaker 2: To repeat this, I could have my digital digital.

1581
01:16:49,560 --> 01:16:51,400
Speaker 1: You could just have use the digital twin for that edit.

1582
01:16:51,800 --> 01:16:53,719
Speaker 1: So I just okay, I need to say those five words.

1583
01:16:53,760 --> 01:16:56,439
Speaker 1: I just blurred them. They make no sense. Did you can?

1584
01:16:56,520 --> 01:16:59,360
Speaker 2: I could do that right now right now? Is expensive.

1585
01:17:00,720 --> 01:17:03,200
Speaker 1: Note it takes about three seconds to clone your voice

1586
01:17:03,840 --> 01:17:08,400
Speaker 1: and maybe an hour to get a good enough digital

1587
01:17:08,479 --> 01:17:11,519
Speaker 1: twin that you could insert it for those few seconds

1588
01:17:11,520 --> 01:17:14,479
Speaker 1: for the podcast. So yes, it wouldn't be it wouldn't

1589
01:17:14,479 --> 01:17:16,280
Speaker 1: answer ask good question. But if you just need to

1590
01:17:16,280 --> 01:17:17,400
Speaker 1: make that quick edit, so.

1591
01:17:17,439 --> 01:17:19,120
Speaker 2: Right now, when I say thank you so much for

1592
01:17:19,160 --> 01:17:22,040
Speaker 2: this interview and we're wrapping up, it may be me

1593
01:17:22,320 --> 01:17:25,080
Speaker 2: or it may not be me true, and then they're

1594
01:17:25,120 --> 01:17:27,800
Speaker 2: never going to know. That's the kind of.

1595
01:17:30,080 --> 01:17:30,759
Speaker 1: And that's the defaith.

1596
01:17:31,040 --> 01:17:31,760
Speaker 2: Nobody's going to know.

1597
01:17:32,000 --> 01:17:34,800
Speaker 1: But picture of that in medicine, so what if you

1598
01:17:34,800 --> 01:17:36,280
Speaker 1: had a deep fake of your body? Right so it

1599
01:17:36,360 --> 01:17:38,840
Speaker 1: is all of how your cells work, So Before a

1600
01:17:38,880 --> 01:17:42,360
Speaker 1: doctor prescribes something, they run what that medication, how it

1601
01:17:42,400 --> 01:17:45,680
Speaker 1: would impact your cells before you take it, before you

1602
01:17:45,720 --> 01:17:47,600
Speaker 1: do the surgery. This is going to work for you.

1603
01:17:47,640 --> 01:17:50,040
Speaker 1: This isn't and that exists today. It's just not scaled,

1604
01:17:50,640 --> 01:17:54,800
Speaker 1: but that exists today, so everything will be personalized, preventative, predictive.

1605
01:17:54,439 --> 01:17:55,920
Speaker 2: Cal We end this with the digital toWin, so I'm

1606
01:17:55,920 --> 01:17:58,000
Speaker 2: going to have my digital twin close us out, okay

1607
01:17:58,760 --> 01:18:00,720
Speaker 2: in case she does something wrong. I want to thank

1608
01:18:00,760 --> 01:18:04,559
Speaker 2: you for today and can we circle back with you

1609
01:18:04,640 --> 01:18:05,519
Speaker 2: as things progress.

1610
01:18:05,600 --> 01:18:08,120
Speaker 1: I would love to my twin and I are available

1611
01:18:08,280 --> 01:18:09,760
Speaker 1: can circle off with us at any time.

1612
01:18:09,960 --> 01:18:10,519
Speaker 2: I love that.

1613
01:18:10,800 --> 01:18:13,280
Speaker 1: Thank you so much, thanks for having mell.

1614
01:18:13,360 --> 01:18:17,240
Speaker 2: Everyone for more episodes you know to do subscribe like

1615
01:18:17,479 --> 01:18:19,920
Speaker 2: comments and we'll see you on the next I r

1616
01:18:20,000 --> 01:18:20,639
Speaker 2: L podcast