Will AI Replace Us? with Matt Ginsberg

Is artificial intelligence taking over? Neil deGrasse Tyson and co hosts Chuck Nice and Gary O’Reilly discuss deepfakes, AI hallucinations, and whether AI really is intelligent with software engineer at X, the moonshot factory, Matt Ginsberg.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Neil deGrasse Tyson?

  Coming up on StarTalk Special Edition. Will AI be the end of civilization? But before that happens, will it help us coach sports better? Will it make deep fakes that will destabilize all that we know and trust? Also, will it help us explore space? Stay tuned.

• 0:21Neil deGrasse Tyson?

  Welcome to StarTalk. Your place in the universe where science and pop culture collide. StarTalk begins right now. This is StarTalk Special Edition. An entire show right now devoted to AI. We don't know, is AI good or bad?

• 0:46Chuck Nice?

  It's bad.

• 0:47Neil deGrasse Tyson?

  You heard all about it. Everybody's opining on it. Is it a problem solver? Or does it create problems? How do we use it in space and media and all kinds of places? Here we are back with Chuck Nice. Chuck, how you doing, man? My co-host.

• 1:10Chuck Nice?

  Sorry to tell you, but this is not me. I'm not here. See? This is my AI doppelganger.

• 1:19Neil deGrasse Tyson?

  That's your doppelganger. I have ways to test for that. We'll find out in a minute.

• 1:23Chuck Nice?

  Here's the problem. The AI has me tied up in a closet. Help me, please. Someone help me.

• 1:35Neil deGrasse Tyson?

  Gary, how you doing, man?

• 1:36Gary O'Reilly?

  I'm good. I'm interested in this because it's not a subject I have any real depth of knowledge about, which is most subjects, agreed.

• 1:44Chuck Nice?

  He's lying. That's not Gary. That is not Gary. He's lying.

• 1:50Neil deGrasse Tyson?

  Gary's also tied up in a closet?

• 1:51Chuck Nice?

  Gary is right next to me in the closet, man.

• 1:57Gary O'Reilly?

  All right.

• 1:59Neil deGrasse Tyson?

  All right, so I'll do my best with your two doppelgangers.

• 2:01Gary O'Reilly?

  Mm hmm.

• 2:02Neil deGrasse Tyson?

  Here we go. So Gary, set the scene. What do we have here?

• 2:04Gary O'Reilly?

  All right. Today's guest has a doctorate in mathematics from Oxford. That's in Oxford. Was researching astrophysics and then decided to switch to AI. Author, yes, published author of a book in 2018 called Factor Man. Developed, and I love this thing, developed a computer program called Dr. Phil. That's F I double L that solves crossword puzzles. And Dr. Phil has competed in professional crossword puzzle tournaments successfully. And now this gentleman works for the delightful people at Google. So today's guest, none other than a dear friend, Matt Ginsberg.

• 2:43Neil deGrasse Tyson?

  Who's returning to StarTalk. Matt Ginsberg, welcome back.

• 2:45Speaker 03

  Great. It's great to be talking to you all again. I think at the end, we probably should have a vote on whether we want to let Chuck and Gary out of the closet or whether we prefer the doppelgangers. And we think that's what we have.

• 2:58Neil deGrasse Tyson?

  That's right. Yeah, whether these are better versions, right?

• 3:01Gary O'Reilly?

  Hmm.

• 3:02Neil deGrasse Tyson?

  So Matt, you, what are you doing with Google right now? Or are you on some kind of NDA, non-disclosure agreement?

• 3:10Speaker 03

  So I work for an organization called X. It is part of Alphabet. We are Alphabet's moonshot factory, which means-

• 3:19Neil deGrasse Tyson?

  But just to be clear, Alphabet is the holding company of Google.

• 3:23Speaker 03

  Alphabet is the holding company of Google and other organizations. So Waymo, for example-

• 3:27Neil deGrasse Tyson?

  Is part of Google or X in Alphabet? It sounds like it belongs in Alphabet.

• 3:31Speaker 03

  X is in Alphabet. We're not part of Google, we're in Alphabet.

• 3:35Chuck Nice?

  I gotta tell you something, for a guy who went to Oxford, I ain't so impressed that you know that X is in the alphabet, okay?

• 3:42Speaker 03

  That's fair. That's actually fair.

• 3:47Speaker 03

  We do- The hardest stuff we can think of. It's an unbelievably fun place to work. The people are incredibly smart. We have a project.

• 3:56Neil deGrasse Tyson?

  This is like the X Prize. And that's in that spirit. The X Prize was money for just some, as you say, moonshot, something that who thought you could make this happen? And you do if you put enough smart people funded by enough money. And then there it is.

• 4:12Chuck Nice?

  Is it true that they call it the failure division of of Google? Because they don't care if you fail. It's all about, you know, the discovery of information and advancement through doing stuff that you would never otherwise attempt?

• 4:28Speaker 03

  So we have this project called Tapestry. The goal is to decarbonize the electric grid. So everybody uses renewables and huge climate impact. That is so hard that if we can't do it, nobody's going to be stunned. Any specific project at X is probably more likely to fail than succeed. But there are some amazing successes. So Waymo, which is Alphabet's self-driving car division, came out of X. We have another project called Mineral that just graduated that has these weird vehicles that drive around the tracks between crops on a farm on a farm. And they use cameras and machine vision to figure out

• 5:12Speaker 03

  how the plants are doing and just generally to make farming more efficient. All of these things are things that that were just as hard as Tapestry when they started. But they've actually succeeded. And now they're out as, I mean, we they're bets. They're part of Alphabet, so we call them bets. And these are other divisions.

• 5:29Neil deGrasse Tyson?

  Clever. I like that.

• 5:31Speaker 03

  Like X is. Um, but not it's, I have to tell you, it's great working for a company that expects you to do unbelievably hard things and realizes that when you try and do things that hard, you're not always going to pull it off.

• 5:46Neil deGrasse Tyson?

  Well, of course, science in general has many, many failures. The press only talks about the successes. So you starting out in science, this would not have been a foreign concept to you other than that there's a whole company that's cool with it. Normally, if you don't make the bottom line, you're on the street, you know, the next quarter.

• 6:08Speaker 03

  Exactly. And, you know, I tell my friends who are in projects that eventually get shut down because they didn't work. I say, you know, you always learn more from a failure than from a success. So we should celebrate the failures and we do. We actually, when an effort gets shut down, there's a big meeting, everybody applauds, it's like a party. Because we know that we've learned stuff, we know that we've tried stuff. And we know that now we're going to try something new and enough of it works. That the whole enterprise, you know, is something that continues.

• 6:42Chuck Nice?

  How do you justify that to, you know, the short sighted desires of shareholders? Because honestly, that's

• 6:51Neil deGrasse Tyson?

  No, no, here's how you do it. We ask a different question. So Matt, what percent of Google's annual revenue gets directed towards X?

• 7:03Speaker 03

  So that is covered by an NDA.

• 7:06Neil deGrasse Tyson?

  This is an R and D number, right?

• 7:06Speaker 03

  That is covered by an NDA. It is an R and D effort that tries to do the crazy things.

• 7:15Speaker 03

  All the stuff that we're going to talk about, about generative AI is based on a technology called transformers. It came out of a part of Google called Brain. Brain came out of X.

• 7:27Speaker 03

  So Waymo came out of X. We've done amazingly impactful thing. We have a long timeframe. So when we start a project, it's not, you know, it's common for us to say, this is going to take 10 years. We might kill it in two because we can tell that it's not working, but if it succeeds, it's going to take 10 years. We're okay with that. So the hard part from the shareholders is not arguing that we're adding value. I think we're clearly adding value. We have to get them to be patient enough to see that value materialize.

• 7:58Gary O'Reilly?

  So you're lucky, Matt, because there's a culture there. I saw it once or twice when I was playing. Where if you did lose, defeated, beaten heavily, people went away, licked their wounds, but considered where the things went wrong, came back with solutions. If you build that culture. You can achieve things by using that way, but the pressure to get results, and as Chuck was so rightly pointing out, it's dollars. I don't have time because I'm committing such a phenomenal amount of money to this. And if we lose too many games, my coach gets fired, all sorts of things. So it's a really, really fascinating place if you can develop a sustained culture like that.

• 8:41Speaker 03

  We do have the culture. And Alphabet has been fantastic about recognizing that there is room in an entity as successful as Alphabet to have a bunch of people, to let them take the long view, to let them try and do incredibly hard things and see what happens. And, and obviously, we can't just keep failing. Some stuff eventually has to work, but some stuff does work. And the people at X who decide, what are we going to work on? When are we going to kill it? When are we going to keep pushing it? They seem to be very good about ensuring that net-net, we're a positive.

• 9:21Neil deGrasse Tyson?

  Can you comment, just reflect on a recent AI news story about a John Lennon song, where they sampled John Lennon's voice and then had him finish the song because he died before it was recorded?

• 9:32Speaker 03

  Yeah.

• 9:33Neil deGrasse Tyson?

  Can you just reflect on, is that a good thing or a bad thing?

• 9:36Speaker 03

  That's a complicated thing. I think that, um...

• 9:41Neil deGrasse Tyson?

  Well, instead of asking, what would Jesus do, we say, what would John Lennon do?

• 9:46Speaker 03

  So more...

• 9:47Neil deGrasse Tyson?

  If he were here, would he punch you in the nose? Like, what would he do?

• 9:50Speaker 03

  More Beatles songs, I think, are an undeniably good thing. I think that, that the ability... There are two things I think you want to take away from this. First, look at what they chose to synthesize. They synthesized Lennon's voice.

• 10:08Gary O'Reilly?

  Yep.

• 10:09Speaker 03

  What made the Beatles so magical is, I think, the words. What they chose to say, that's much harder. Synthesizing a person's voice is relatively speaking easy. So the first thing I think we need to think about is... Synthesizing a voice and synthesizing the idea, the essence, different things. And the second thing is the fact that you can synthesize someone's voice is scary. Because it's gonna make deep fakes so much more of a problem than they currently are. Because now, we can make a picture of whoever doing whatever. And we can even attach some voice to it to convince you that it really happened when in fact it didn't really happen. So I think that's a big issue that

• 10:53Chuck Nice?

  Huge.

• 10:54Speaker 03

  That will need to be addressed if we're gonna keep all of society sort of continue to be grounded in reality as opposed to these fabrications.

• 11:04Chuck Nice?

  But that has to be addressed right now. Because if we don't come up with some way to watermark this technology on a digital level. I mean, what is going to, what is going to stop people from utilizing it in the most heinous ways possible? Nothing, nothing.

• 11:22Speaker 03

  So, that's, you're absolutely right. Watermarking is tricky because you can, I mean, you can pass a law saying all digitally created images must be watermarked.

• 11:36Chuck Nice?

  Okay.

• 11:37Speaker 03

  And then somebody creates an image in a country that doesn't have the law. And it's on the internet, and now what do you do? I think the, the way we have to deal with this, I think there, there are a couple of things. One is, we need to develop the technology. So right now, there are programs that can recognize Bard, which is Google's generative AI that you can talk to. And if you give it a text that was written by Bard, they can say, yeah, 95% that was written by Bard, not by a human. We need to use those to understand what was created and what was not. And the same thing can be said of images. There are, there are non-watermark traces that we need to better understand and better make take advantage of.

• 12:22Speaker 03

  And the second thing is, we need to recognize that there are trusted sources. And we need to pay attention to, this came from somewhere that

• 12:36Speaker 03

  I actually am willing to believe if they say they took the picture, they took the picture. It actually happened. So a news agency potentially can serve this role in the way that some guy somewhere far away who just creates a picture might not be a trusted source. And we as a society have to be more suspicious sadly than we have been that just because somebody shows me a picture, it doesn't mean it's true. That is true.

• 13:03Gary O'Reilly?

  Matt, we've discussed some of the positivity and some of the negativity. But if we take the intelligence part of AI, have we actually, since we've been started to play with it, develop it, kind of pointed it in the right direction to do the right things? Or have we kind of just wasted our time with it so far?

• 13:22Speaker 03

  I don't think we've wasted our time.

• 13:24Gary O'Reilly?

  Okay.

• 13:25Speaker 03

  Okay, first of all, so I think there are, there are lots of applications of AI that have been phenomenally successful, incredibly important. The car that you just bought was probably manufactured mostly by robot. The cars have fewer defects coming off the line. Absolutely. They're cheaper. It's a good thing.

• 13:47Gary O'Reilly?

  Aren't they?

• 13:48Speaker 03

  Well, there's inflation, but I think Pathfinder, right? We want to send a robot to Mars because we can. We don't have the technology to send people to Mars yet, but we can send our agents there in the form of these automated devices. They have to be pretty independent because round trip message time to Mars is long. You know, the robot has to avoid a rock all by itself because if you don't talk to it, you can't talk to it so quickly. Or a cliff. Watch out for the cliff. 20 minutes later, it's too late. Or the Marsians. The Marsians, clearly the Marsians. So I think we've done good things. I think that AI is moving very fast at the moment. And when technology moves quickly, it's challenging. It's always been challenging

• 14:31Speaker 03

  for society to keep up. And for society to say, okay, here's what I have to think about. Here's how the world has actually changed. And what

• 14:44Speaker 03

  It's important that society distinguish from the apparent changes, which aren't actually grounded in reality, from what actually has happened that matters. So, for example, all these generative programs, Bard and others, they're not actually smart. They don't actually know what's going on. But it's very easy for people to think they're smart and to ask them for explanations about which they're completely poorly equipped to respond, as opposed to asking them things that they can respond to. So, for example, somebody asked me recently if Bard understood causality. They wanted to understand some causal thing or lack of a causal thing. So he said, "Can Bard understand causality?" And I said, "Okay."

• 15:28Speaker 03

  And I went to Bard and I said, "Is there a correlation between the phase of the moon and the amount of chicken eaten in Denmark?" And it said, "Absolutely." And it quoted a paper that had never actually been written and a survey that had never actually been done. It just made all this stuff up. That was... And I told my son about this and he said, "Dad, why are you asking Bard questions like this? It's not going to answer them." But if I want... I'm a new business and I want to have a website and I can't afford a developer. I can go to Bard and say, "Make me a website that does this." And Bard will do great. So we, all of us, need to understand what these entities can do, what they can't do,

• 16:12Speaker 03

  what they're going to be effective at, what they're going to be ineffective at, and we need to ask them to do what they are good at, which is a lot. It's just not everything. They're not going to, you know, replace all of us.

• 16:24Chuck Nice?

  All of us. Good news? All of us being the operative.

• 16:28Speaker 03

  Well, Chuck, they replaced you apparently fine because you're in the closet and you're... This is just the Chuck avatar.

• 16:35Chuck Nice?

  Well played, Matt. Yes.

• 16:51Gary O'Reilly?

  Matt, siendo así, hablando de robots y Martes, ¿qué tendrías que construir en un programa para que vea el espacio profundo y encuentra cosas que no sabemos buscar todavía? ¿Cómo vas a mejorar la IA para poder lograr eso? ¿O aún no está ahí?

• 17:15Speaker 03

  Creo que en su mayoría aún no está ahí. Así que la forma en que estas cosas funcionan es... Así que aquí es como yo pienso a menudo. El mundo se divide en lo que yo llamo problemas 51-49, donde quieres tener 51% correcto es bueno. Así que si estás jugando al mercado de las acciones y puedes elegir precisamente acciones que suben un 51% de la vez, estás a punto de ser realmente rico.

• 17:44Speaker 03

  Y luego tienes el problema 100, donde el 51% no es suficiente, el 99% no es suficiente. Si estás tratando de cerrar un reactor nuclear en una emergencia, realmente necesitas una respuesta del 100%. Todos estos sistemas de aprendizaje de máquinas son increíblemente buenos en las cosas 51-49 y no son tan buenos en las cosas 100. Así que si quieres ver el espacio profundo y realmente te interesa algo que pensabas que tenía probabilidad cero, un alieno hablando con nosotros, o un nuevo tipo de supernova, o algo que nunca hemos visto antes, ese es un tipo de problema 100. Reconociendo cosas cuyas probabilidades son cero y es una gran sorpresa.

• 18:30Speaker 03

  Los sistemas de aprendizaje de máquinas no son buenos en eso.

• 18:33Neil deGrasse Tyson?

  Pero no, no, no estoy de acuerdo. Lucha nerd en el progreso aquí, no estoy de acuerdo.

• 18:37Speaker 03

  Glove Zone.

• 18:38Neil deGrasse Tyson?

  Nerd fight, okay.

• 18:39Chuck Nice?

  Excellent.

• 18:40Neil deGrasse Tyson?

  Nerd fight, okay.

• 18:43Neil deGrasse Tyson?

  So Matt, I agree with, I in principle agree with you, but there's an important nuance here. Because I can program the computer to show me something.

• 18:54Chuck Nice?

  Right.

• 18:55Neil deGrasse Tyson?

  That I don't recognize, because I have a huge catalog of things I do recognize.

• 19:00Chuck Nice?

  No matter what it is. So it's extremely broad, but it's something that we haven't, we know we haven't seen it because the computer knows our entire catalog.

• 19:10Neil deGrasse Tyson?

  Correct. The computer knows everything that we know, and something shows up that we don't know, and I say, "Show me everything we don't know."

• 19:17Chuck Nice?

  Because that has to be anonymous, because we don't know it.

• 19:20Neil deGrasse Tyson?

  It's going to be an anomalous, one in a gazillion thing.

• 19:22Chuck Nice?

  Right.

• 19:22Neil deGrasse Tyson?

  Yes, or it could just be a glitch in the matrix, but it'll find it though. And so that's why I don't entirely agree it's not good, at least astrophysically, in finding the lone wolf out there.

• 19:37Speaker 03

  So the trick is, what you're asking is, find the one that doesn't belong, basically.

• 19:43Neil deGrasse Tyson?

  Correct.

• 19:43Speaker 03

  And these programs will classify everything. You give them eight things and they'll say, "Oh, this belongs in pot seven."

• 19:51Chuck Nice?

  Oh my God, so whether you want it to or not is what you're saying, is it makes the decision to do so on its own, even if it creates a category that didn't exist and says, "Well, now it's in this category."

• 20:04Speaker 03

  Well, it'll probably put it in an existing category.

• 20:07Chuck Nice?

  Okay.

• 20:07Speaker 03

  Now, what you can do, wait, wait, wait, hang on, hang on, hang on. What you can do is you can-

• 20:12Chuck Nice?

  Don't make me come out there.

• 20:15Chuck Nice?

  Somebody give me some popcorn, this nerd fight's getting good.

• 20:18Speaker 03

  What you can do is you can say, "If the frequency is outside of a frequency I've ever seen, flag it. If the periodicity is shorter than anything I've ever seen, flag it." But what you're doing now is you are actually creating sort of a new category of surprising things that you are defining for the system. And then saying, "What belongs in that category?" Absolutely, you can do that. But if you see a true surprise, something that you actually had no idea was going to exist, so for example, imagine that you find a pulsar.

• 20:57Speaker 03

  And miraculously, the phase of the pulsar is 100% correlated with the phase of another pulsar two light years away. That's amazing. Something totally bizarre is going on, but the machine won't know because it has no idea to look. It just looks.

• 21:17Chuck Nice?

  Because it doesn't have, it doesn't have the parameter.

• 21:19Neil deGrasse Tyson?

  So it's not a new object. It's a, it's a new phenomenon.

• 21:22Speaker 03

  It's a new thing. Right. So these things that are, that are brand new things, it'll just look at that. It'll say, pulsar, next.

• 21:31Chuck Nice?

  Right.

• 21:31Neil deGrasse Tyson?

  Exactly. Okay. So I'll give you that.

• 21:34Chuck Nice?

  Yeah. So what you're saying is it doesn't know to look for it because we don't know to look for it.

• 21:39Speaker 03

  Correct.

• 21:40Neil deGrasse Tyson?

  So I was, I was being blunt about it and saying, objects and phenomena that are sort of singular, that you would just put in a catalog with properties. We use

• 21:51Speaker 03

  Yes, I agree.

• 21:53Neil deGrasse Tyson?

  neural net searching throughout data to find weird stuff all the time. But you are right. If there are two pulsars that are synchronized, we know what pulsars are, we know what their, what their pulses look like, and they're synchronized because aliens are getting ready for the invasion, we would have no idea. Nobody would find that.

• 22:11Speaker 03

  Exactly. But, but what would happen if this happened, people would say, holy cow, these pulsars are synchronized. And then they would define a new thing, which is a synchronized pulsar pair. And then all of a sudden, that would go into our category. And now we would talk about that pulsar pair as an object. And all of a sudden, the neural net would say, oh, pulsar pair. I've seen that before. This is a pulsar pair. But the first time you see one of these fundamentally new phenomena, which is what makes science fun, in all honesty, machines don't know. It's too far outside, you know, what they've been trained to do. Mm hmm. So we've got to retrain them or retrain ourselves.

• 22:55Speaker 03

  I think, so I don't think so.

• 22:57Neil deGrasse Tyson?

  Put a limit on AI's ability to explore for us.

• 23:00Speaker 03

  So it does. So right now, and I've, I've said this before on the show. What we're good at and what machines are good at are different. We are incredibly good at, holy cow, two synchronized pulsars? Who'd have thought it? I gotta pay attention to that. We're amazing at that. Machines are, they're amazing at this thing that you can barely see over here. I looked at it eighteen different ways. It's probably a pulsar. They're better than we are. But right now, we can do more with machines at our side than either of us can do in isolation. And I think that's great. I'm incredibly optimistic because of that.

• 23:41Speaker 03

  I don't see, and the day is coming somewhere way far away. But right now, I don't see that they can get by without us any better than we can get by without them.

• 23:55Speaker 03

  We're good together.

• 23:56Gary O'Reilly?

  Let's flip it into my backyard sports.

• 24:00Gary O'Reilly?

  Could AI become a live in-game play coach, a head coach? Could it react in real time?

• 24:10Speaker 03

  The answer to that is yes. And I have built an NFL play caller. I have run it in simulation against the choices made by actual play callers. And it crushes them. It just annihilates them. And it's easily fast enough. I actually, I played with it before joining X. I played with it and I had it and I actually was watching football games with it. And it would put in real time, this is what you should do. And I would watch the coach do what he did. And then I ran all these simulations. People are, unfortunately, I guess, not terribly good play callers. Play calling is this giant statistics problem. Machines are going to be great at that. And they're great at it fast.

• 24:51Chuck Nice?

  However, does your program take into account the adjustments that are made by quarterbacks who recognize coverage in real time? So the defense plays a call and the offense plays a call. It does?

• 25:06Speaker 03

  The answer is yes.

• 25:06Chuck Nice?

  You're kidding me?

• 25:07Neil deGrasse Tyson?

  No, it's a wrong question. Chuck, that's a wrong question. Does your program deflate the ball?

• 25:14Speaker 03

  No.

• 25:15Neil deGrasse Tyson?

  That's the right question.

• 25:16Speaker 03

  So the answer to Chuck's question is that part of my software was who's the quarterback? Okay. That's enough because the quarterbacks that are effective at adapting to what they see when they come to the line. Exactly. Are going to have slightly different statistics than the quarterbacks who are not effective. Amazing. So when the program decides, do I want to call a run? Do I want to call a pass? Where do I want to call pass to? It does know.

• 25:47Speaker 03

  Damn.

• 25:47Neil deGrasse Tyson?

  But I missed something here. Since you, how do you, what do you mean you do better than the actual call players? Because you don't have an outcome that you can look at. You just say, shouldn't have done that, should have done what I said. Had he done what you said, how do you know what the outcome would have been?

• 26:07Speaker 03

  So there.

• 26:07Neil deGrasse Tyson?

  You don't.

• 26:08Speaker 03

  So there are two ways. First, I, as any, any coach is based on a simulation engine. So I could, I can run that simulation. Now, it's sort of a self-licking ice cream cone because the simulation.

• 26:22Neil deGrasse Tyson?

  Yes, it is.

• 26:23Speaker 03

  Okay, so that's way one. And I think there's still merit there because you can test the accuracy of the simulation. The second thing I can do is I can just go back and look at the game and say, okay, the plays where the actual human coach made my play, A, B, C, D, E. The plays where he made a different play, F, G, H, I, J. And then I can look at them and say, oh, A, B, C worked out pretty well. F, G, H, J, they were duds. And it's easy to look after the fact and see whether a play was a success or a failure.

• 26:55Neil deGrasse Tyson?

  Right. But aren't you still just invoking the statistics of past events?

• 27:01Speaker 03

  I am invoking.

• 27:02Neil deGrasse Tyson?

  To predict a future event?

• 27:03Speaker 03

  But that's reasonable, right? So there's a reason.

• 27:05Neil deGrasse Tyson?

  No, no, I'm not. But if that's the case, isn't that what they're doing every single pitch in baseball today?

• 27:12Speaker 03

  They are.

• 27:12Neil deGrasse Tyson?

  Everything is on a, they're over analyzing it, right? In baseball, why are they not doing that in football?

• 27:19Speaker 03

  So. Maybe they are. I, so that I don't know. I do think that baseball is a little more committed to the statistics of the sport than the other sports are.

• 27:28Neil deGrasse Tyson?

  Nothing else is happening between.

• 27:30Speaker 03

  I also think that.

• 27:34Speaker 03

  At some level, what you're asking is, how hard is it to build a machine learning or other model that tells you how effective a pitch will be, how effective a football play will be, that is able to predict with reasonable accuracy, whatever that is, is able to predict the outcome of a particular sporting choice? The answer is, it's not super hard and it's not super easy.

• 27:58Chuck Nice?

  Right.

• 27:59Speaker 03

  You have to get the data, there's a lot of curation involved, you have to use reasonably modern techniques, that's what I did with the NFL thing, and it worked pretty well. I was able to tell, what was the actual answer? It was a while ago. I think I was able to predict run versus pass with very high accuracy, and I was able to predict exactly what play would be called, something like 20% of the time.

• 28:23Chuck Nice?

  Damn, that's very, very high for football. 20%? That's insane. Could you imagine a football team being on the sidelines, aside from a Belichick team that's stealing the signals? If you could imagine being on the sideline and with high accuracy, knowing 20%, one out of every five plays, you would know that what they're going to do? You would win. You would win a whole game!

• 28:55Speaker 03

  This is part of why the software, why the simulations indicated that a machine coach is really going to do very well against a human coach.

• 29:07Gary O'Reilly?

  I know, but what if you put one against another? You know, my AI is better than your AI.

• 29:12Chuck Nice?

  Oh, I love it! Machine coach against machine coach! Oh, it's the Decepticons versus the Autobots!

• 29:19Gary O'Reilly?

  Do they cancel each other out?

• 29:22Speaker 03

  They sort of cancel each other out. So, you know, if you, let's go to chess, which is sort of less controversial. So you can have, even a relatively poor chess program now is way better than the best human.

• 29:37Chuck Nice?

  Right.

• 29:38Speaker 03

  But there are still better programs and worse programs.

• 29:40Chuck Nice?

  Right.

• 29:41Speaker 03

  So the same thing can happen here. Now you have additional facets, factors in sports because one of the teams may well be simply more physically talented than another team.

• 29:50Chuck Nice?

  True.

• 29:51Speaker 03

  And then the question becomes, can a difference in the quality of the coaches, whether they're ARs or not, overcome the difference in the physical qualities of the team? And do you want to allow that? I don't know. But

• 30:04Chuck Nice?

  Right.

• 30:04Speaker 03

  There will be better AIs and worse AIs. How big those differences are, I don't know, because right now there are no AIs. But that's going to be another facet of what makes a team good. Is how good is their software?

• 30:18Gary O'Reilly?

  What likelihood, Matt, is there that sports organizations, be whichever sport, are already using AI technology for in-game situations?

• 30:28Speaker 03

  I think it's pretty small. Um, when I joined X, I was, I was trying to talk to the NFL about using the software to develop for play calling. And it was, it seemed pretty clear to me that they weren't doing anything like that yet. Now, I've been an extra couple of years, so we're looking back two years.

• 30:46Gary O'Reilly?

  Yeah.

• 30:47Speaker 03

  I think that eventually this is going to happen, but

• 30:51Chuck Nice?

  Of course.

• 30:52Speaker 03

  Not yet.

• 30:53Chuck Nice?

  But it's moving towards that because right now you're using next gen and next gen stats. And what they're use, what they're looking at is a percentage, uh, like likelihood, um, and probabilities for certain plays at certain times. You've never seen more two-point conversions in the NFL than you have right now. That is a direct result of, uh, statistically, you should do it. You've never seen more teams going for fourth and whatever, uh, because statistically, you should do it.

• 31:27Speaker 03

  So hang on.

• 31:28Chuck Nice?

  So we're moving towards that direction.

• 31:29Speaker 03

  So about that specifically, about, I was probably eight years ago now. I did some statistical work for the Oregon Doc.

• 31:39Speaker 03

  And I told the coach at the time, I gave him these huge printouts with what you should do in every situation on fourth down. And I said, just stop punting between the 35 yard lines. That's really what it all says. And he stopped. And that was the year that the Oregon Ducks were the best. They were. And the other college teams noticed and they stopped punting between the 35 yard lines. And you're absolutely right. What has happened at the NFL level is people have noticed. So when I see someone not punting between the 30, not going for it on fourth down, I smile because it's basically my work, the statistical work I did a while ago to get the Oregon Ducks to stop doing it. And then it's propagated out. So.

• 32:20Neil deGrasse Tyson?

  But just so I understand, um, to, to, on fourth down, rather than punt to release the, to, to, um, return the ball to the opposing team, you, they would go for first down. And in some percent of the cases, they don't. But if I'm on my own 35 yard line, I'm handing you the ball at the 35 yard line.

• 32:42Speaker 03

  Right.

• 32:43Neil deGrasse Tyson?

  And you're saying that risk is not as great as just handing over the ball because I might've gotten a first down.

• 32:49Chuck Nice?

  You're only starting 10, 10 yards further than, uh, than you would if I had, if you had made a fair catch.

• 32:55Speaker 03

  Yeah.

• 32:56Chuck Nice?

  So you're only conceding 10 yards. You're conceding 10 yards by going for it. I mean, by not going for it. That's all you're really giving up. You know, unless there's a really good return. Now, does it take that into account? Cause that's fascinating.

• 33:10Speaker 03

  It took everything into account.

• 33:11Chuck Nice?

  Okay. So yeah, if you've got a great return guy, you know.

• 33:14Speaker 03

  It also, it's also a function of how late in the game is it and how much are you ahead or behind by. The actual rules, it turns out, if we're doing sports, is don't punt between the 35 yard lines and always go for it on fourth and one, even from your own 10.

• 33:30Chuck Nice?

  Whoa.

• 33:32Speaker 03

  And I told that to the Oregon Ducks coach and he said, I can't do that. I'll lose my job.

• 33:36Chuck Nice?

  I'll lose my job. Yeah.

• 33:38Speaker 03

  But. Get replaced by. From a straight statistical perspective.

• 33:43Speaker 03

  And the bottom line is if you punt from your own 10, you're still screwed. They're still going to have unbelievably good field position.

• 33:50Chuck Nice?

  Yes.

• 33:50Speaker 03

  And if you go for it on your own 10, fourth and one, you have a reasonably good chance of getting it. And now all of a sudden you're back in it.

• 33:58Chuck Nice?

  Wow.

• 33:59Speaker 03

  But he just said he couldn't do that. He looked, he just looked me in the eye and said, I can't do that. I'll lose my job. So.

• 34:06Chuck Nice?

  Wow.

• 34:07Gary O'Reilly?

  Did he lose his job anyway?

• 34:09Speaker 03

  He went to the NFL. He had a great year for the Oregon Ducks. He went to the NFL.

• 34:13Chuck Nice?

  Well, there you go. He lost his job upwards. That's fantastic.

• 34:32Gary O'Reilly?

  You said earlier on, Matt, about how machine learning programs are getting quicker. Are we going to get to the point where we can really start to predict some of the big natural disasters? The earthquakes, the tsunamis are, you know, or is as good as it gets right now?

• 34:50Neil deGrasse Tyson?

  Well, let me, let me be more precise there. There's certain, I don't know, earthquakes wouldn't be the best example here, but certainly storms. Where we have limits to how many days in advance you can predict the weather. Because there's some, you know, chaos takes over. What, how does AI handle chaos? Any better than we've ever handled it before?

• 35:11Speaker 03

  So those are sort of the same question. And I think that actually comes back to this 5149 versus 100 zero thing. So predicting an earthquake, that's 100 zero thing. How many hurricanes are there going to be this season? That's more like a 5149 thing. Dealing with chaos, very much a 5149 thing. The stock market is sort of chaotic, but I'm wanting, I want to, I don't have to get it right all the time. I just want to get it right most of the time.

• 35:40Neil deGrasse Tyson?

  I should just be clear that my, our audience knows more precisely what we mean by chaos. So what we learned back, I guess in the seventies and eighties, that you can start a system out with certain variables. Having certain values and then you could run a system. And not all systems would behave this way, but some systems you get a result. Okay. And then you can make a tiniest adjustment. In your initial parameters and then set it go, let it go forward and you get a completely different result.

• 36:11Speaker 03

  Exactly.

• 36:12Neil deGrasse Tyson?

  So that small changes in your initial conditions would not lead to small out changes in your outcomes. It led to huge changes in your outcomes, which meant that your ability to predict far into the future for some systems was essentially mathematically impossible.

• 36:29Speaker 03

  So this is.

• 36:30Neil deGrasse Tyson?

  That's what I meant by chaos here.

• 36:31Speaker 03

  A butterfly flaps its wings in East Africa and there's a hurricane six weeks later in the Bahamas. And my, my answer is. You, it's again, it's the hundred zero versus fifty one forty nine. I cannot tell you there will be a hurricane in the Bahamas on October 27. But I can tell you there are going to be more hurricanes this year than average. In general, more hurricane fifty one forty nine. This specific hurricane that depends on that specific butterfly. I don't know any better than I used to.

• 37:11Chuck Nice?

  How about quantum computing? When you add that into the effect, because now you're looking at billions and billions of data points. That are being fed to the AI.

• 37:22Speaker 03

  It is. So quantum computing is good. I know less about it than I want to and that I wish, because I certainly have the background. Right now, conventional machines appear to be able to process the data we need them to process. And quantum computing, I think, will be helpful in other ways. So the quantum stuff appears to be best at sort of doing an almost uncontrollable number of things in parallel. So I want to find an integer that has certain properties. And I can sort of look at hundreds of billions of integers simultaneously using the quantum stuff. That's cool.

• 38:07Speaker 03

  But the machine learning stuff, I'm just trying to look for properties in enormous data sets that involve sort of looking at all the data and seeing how it interacts with each other. And that we seem, at least so far, to be able to do with, takes a lot of computing, ton of computing, but we seem to be able, currently we're keeping up.

• 38:27Neil deGrasse Tyson?

  There might be a way to query that same set of data with the higher performance quantum computing in ways we had not thought to even ask.

• 38:36Speaker 03

  Maybe.

• 38:36Neil deGrasse Tyson?

  Of the data.

• 38:37Speaker 03

  Maybe. Hmm, interesting.

• 38:40Neil deGrasse Tyson?

  By the way, about that butterfly, do you remember that article in the Journal of Irreproducible Results, uh, where, I think I said this on another episode, uh, this is a journal where it's, it's for idle scientists who have some crazy thought that is completely stupid, but they want to publish it anyway. And so it goes into the Journal of Irreproducible Results. So, uh, one of them was the calculation that heaven is hotter than hell, and it looked at the thermodynamics of souls and how many people are heaven-worthy versus hell-worthy.

• 39:13Speaker 03

  Right.

• 39:13Neil deGrasse Tyson?

  And it added up all the energy of the souls going into heaven, and it made heaven much hotter. So stupid calculations like that.

• 39:19Speaker 03

  The problem is, okay, I was just gonna say, the problem is that hell does not have air conditioning.

• 39:25Neil deGrasse Tyson?

  No, that's.

• 39:26Speaker 03

  And heaven does.

• 39:27Neil deGrasse Tyson?

  So, the, the, yeah, this is definitely a bot from, not chucking the closet, uh, no. So, the, so this paper has this photo of a butterfly, and it said, "It captured the butterfly that caused Hurricane Andrew."

• 39:48Speaker 03

  That's.

• 39:49Neil deGrasse Tyson?

  It's in, the one prosecutor.

• 39:53Speaker 03

  The one butterfly that went to hell.

• 39:57Speaker 03

  So I did not read that paper.

• 39:59Neil deGrasse Tyson?

  You missed that, that was a good one. That's fun. So Matt, what I think is most fearful for people is when AI does not just the tasks we give it better than we've ever done it, but when it self learns and achieves what some mild version of what we might call consciousness. And this sort of artificial general intelligence, I think is the scariest part of AI that that gets, that's been discussed in recent months. Could you just comment on where that is today?

• 40:37Speaker 03

  It is scary. What we need to understand is what this technology is actually doing.

• 40:46Speaker 03

  These things that we're dealing with, these generative AI programs, they have no notion of truth. They have no notion of reality. They have no notion of fact. All they're doing-

• 40:55Neil deGrasse Tyson?

  Or morality even, or morality.

• 40:57Speaker 03

  Or anything, right? All they're doing is trying to predict what an expert would say, just what words would come out of his mouth. And as a result, they sort of don't know what they're doing. They just know what someone might say. I don't think we're anywhere near a point where these things exhibit true general intelligence. We need to understand when we're at, when we're interacting with them. These things don't understand that there are, they don't understand there are facts. Not that they don't understand the facts, they don't understand the hard facts.

• 41:29Neil deGrasse Tyson?

  They don't even know what it, right.

• 41:30Speaker 03

  So when I asked about the phase of the moon and chicken in Denmark, and I got back this long study that didn't even exist, it had no idea. And I actually asked it, I said, are you sure? And it responded, it said, well, I'm not really that sure because this was the only study I could find. It's just standing by its non-facts and it has no idea that- This is not how you look at the world. I said, well-

• 41:52Neil deGrasse Tyson?

  Is that what they call mirage?

• 41:54Speaker 03

  It's an hallucination.

• 41:55Neil deGrasse Tyson?

  A hallucination.

• 41:56Speaker 03

  It's hallucination. And it doesn't know it's made something up. Right? When we talk about making something up, just the phrase is identifying a distinction between actual reality and whatever you're saying. These things don't know there is an actual reality.

• 42:14Chuck Nice?

  Okay, so is that a necessary guardrail? Is a necessary guardrail to imbue the digital intelligence with the concept of these things that it will do, you know, like what reality is, what a fact is, what truth is? I mean, is it necessary?

• 42:35Speaker 03

  It would be good, but I don't know how you do it. These things are so divorced from the notion of a reality. You can't just say, hey, there are facts. Remember that. It's just not how they work. It's not how they're architected.

• 42:48Neil deGrasse Tyson?

  Wait, wait, Matt. Could the problem be that these language model AI machines are coming of age at a time where the internet is filled with non-facts? So it's not its fault. We fed it junk food. Had it come around right at the beginning of the internet, where you didn't have QAnon and all the rest of this, might it have performed a little better?

• 43:17Speaker 03

  The fact, and it is a fact, that these things have no notion of truth would still be true. People have tried to curate the information on which they are trained, so they're not trained on nonsense. And they still have this problem with hallucinating. The problem is, they don't understand that there is an abstract, an abject reality of which they are a part. And I think you're right. The problem is not the programs. The problem is us. We need to recognize that these things are divorced from reality. We need to remember, if I want a website created, it's gonna do well. If I want to ask it, is there a correlation between these two crazy things that I pulled out of the air? It's gonna do badly. And we shouldn't pay attention.

• 44:01Speaker 03

  So if somebody, when I told my son, I asked Bard, is there a correlation between the phase of the moon and the amount of chicken eaten in Denmark? His immediate response was, don't ask Bard. That, that's stupid.

• 44:14Speaker 03

  I do think there's gonna be a job here and it's gonna be an important job. Which is, how do I take what I want to know? And it's like prompt programming, what prompt do I give Bard? To get back the most useful answer I can and to avoid all the junk. That's gonna be a thing. There're gonna be people who are good at it, there're gonna be classes that teach you how to do it. It's gonna be a real skill that we are going to need.

• 44:39Chuck Nice?

  Okay, so now, let's make that a given. What do you do? And this may be more philosophical than, you know, than, than you're, you know, qualified to answer. I'm certainly not. What do you do? With the people who purposefully? Use the technology for the end of misinformation, confusion and chaos? Because even if you do everything that you just said? Those agents can still utilize the technology to do some serious harm to society.

• 45:16Speaker 03

  Correct, and I think they will. And I think this gets back to what we were talking about before. I think you need to have trusted sources. Trust is gonna become much more valuable. Because, lack of trust is gonna be such, so much more dangerous. So you need to have trusted sources. You need to the extent you can to have technology that can help identify? Generated images as opposed to real images? So there's, there is both a technical problem there. Can I produce software that tells me? This is fake, this is real? Technical problem. And there's a social problem. How do I get people to care?

• 45:58Speaker 03

  That they're looking at real information as opposed to, to sort of garbage?

• 46:02Neil deGrasse Tyson?

  That's half my life. That's half my life as an educator.

• 46:05Speaker 03

  I believe you. It's a, and it's, I think it's, it is a huge part of what scientists need to do. And our responsibility to do it is even greater now than it's ever been.

• 46:16Gary O'Reilly?

  This is more than one facet. This is more than one facet, I'm guessing now, because yes, scientists and programmers have a responsibility, but the legislation, and I mean, you can't make it governmental, because if I go do something in another country, that government's got no power. So who are you going to get? Space Force to oversee it? No, that won't happen. So who, who will bring to bear legislation and these bad actors keep them in their place? It's going to be a party time for them.

• 46:43Chuck Nice?

  That'll have to be part of what Matt was talking about in the recognition portion. You will have to be able to recognize where these bad actors are. Like, most of the dark web, you know where the people are located. You, you actually know where they are. You know, it's just that they are in a country that's not going to do anything to them. So, you know.

• 47:04Neil deGrasse Tyson?

  I just wonder if, if bad actors here put civilization at risk, that calls for some kind of international oversight over this.

• 47:15Speaker 03

  I think that the, so first of all, the technical problems here are, are hard and they're challenging and they're important. Identifying generated text versus non-generated. And I am, I am thrilled because I'm a technologist and I get to spend my productive time working on technical problems and I don't have to solve the social problems. That's, that's apparently Neil's job.

• 47:38Gary O'Reilly?

  Well, I'm so glad this all worked out for you.

• 47:43Neil deGrasse Tyson?

  Right. You don't have to worry about it.

• 47:45Speaker 03

  I do care about the need to inform people about what the technology can do. I do think that this particular technology is going to take this problem that you've mentioned that we have, right? Truth has become more elusive. And I think that this technology potentially can make it more elusive still. But it is still up to us, and I think it's still possible for us to say, no, enough. We are going to be committed to actually knowing.

• 48:18Speaker 03

  Whether the sky is blue, before we start going telling all of our friends and neighbors what color the sky is, we should check. The sky really is blue. Here's why I'm convinced. Here's my source. Yes, it's trusted. So that's why I'm willing to talk to you about it.

• 48:36Neil deGrasse Tyson?

  Well, part of me thinks that if AI has these hallucinations and it doesn't really know what truth is, there's nothing intelligent about it at all. So it's been misnamed. It's a disruptive force in our culture and in our society. And maybe we should rebrand it as artificial idiocy. It's not gonna like that, Neil. Neil, you were trying to get me killed in this closet. I'll end up back in the closet. Let me just bring some summative remarks here and get your final reaction, Matt.

• 49:11Neil deGrasse Tyson?

  It seems to me if deep fakes become so good that no one can trust them, then that's basically the end of the internet as any source of information. And that has a positive side to it because it means, for example, let's look at QAnon. It means QAnon won't even believe the stuff that's wrong that it thought was true because it doesn't trust it. Because the level of misinformation would be so total that people who were previously misinformed will be worried that they be misinformed.

• 49:52Speaker 03

  I think that the amount of misinformation can go up. I think there will always, I think there's likely to always be an internet. I think that it's likely to always have valuable, factual, accurate-

• 50:11Neil deGrasse Tyson?

  Not if you don't know which is which.

• 50:13Speaker 03

  ...information. And the trick is going to be to find it. And if you think about the stuff that I talked about- On the technical side, we need the ability to find it. And on the social side, we need to have the desire to find it. And I think both of those things.

• 50:27Neil deGrasse Tyson?

  Isn't it a positive outcome if QAnon can't even believe this, the, the stuff that's not true? Isn't that a positive?

• 50:35Speaker 03

  That might be a positive outcome in isolation. But if QAnon has that problem, then so do all the people who are trying to affect positive change.

• 50:41Chuck Nice?

  Well, then that, that brings us to something that we haven't touched upon. And that is our ability as a soc- as a society, where the majority of people are scientifically literate and trained as critical thinkers so that they know exactly where to place their trust.

• 51:03Neil deGrasse Tyson?

  Yeah.

• 51:04Chuck Nice?

  That, that is really where the problem is.

• 51:07Neil deGrasse Tyson?

  Okay, now you put it back on me that I gotta train everybody to think this way.

• 51:09Chuck Nice?

  Come on, Neil, now you, now you know that, that that's your job.

• 51:13Neil deGrasse Tyson?

  I wanted to leave the blame on Matt at the end of the show.

• 51:16Gary O'Reilly?

  No, before we, before we, before we leave the show, Neil, Matt, you saying how AI is learning at quicker speeds and is gonna get there sooner, et cetera, et cetera. Will it not then solve its own, solve this problem for us, therefore itself?

• 51:31Neil deGrasse Tyson?

  Thank you.

• 51:31Speaker 03

  I don't, I don't see a reason for, to expect that it will, it will figure this problem out. I, Chuck, I do agree with you. I think that people don't have to be trained as scientists, but I think they need to be trained as thinkers. They need to be able to understand the information with which they're presented and evaluate it relatively dispassionately. And I think that education broadly, education is gonna become much more important as we need it more. It's also the case that as machines start doing the drudgery, education becomes more important because we've been freed from the drudgery. We get to do the fun, hard stuff.

• 52:16Speaker 03

  People need to understand.

• 52:19Speaker 03

  What that is, how they can contribute and all that, and all that. And I think that's gonna happen.

• 52:25Gary O'Reilly?

  But you mentioned education there, Matt. Sorry to cut across you, but people are handing in their homework or their assignments created by AI. Are we not, are we not heading down a path where generations in the future will not have any desire to self educate?

• 52:42Speaker 03

  I think that, I think the answer to that is no. I think this thing where kids are handing in homework written by, by Bard or what have you. I think this is a relatively, I hope, this is a relatively temporary anomaly.

• 52:53Neil deGrasse Tyson?

  I agree.

• 52:53Chuck Nice?

  I agree too.

• 52:54Speaker 03

  And we'll, we'll sort that out.

• 52:56Chuck Nice?

  Yeah. Honestly.

• 52:57Neil deGrasse Tyson?

  It's not audible. It just means that the school system values your grades more than you value learning.

• 53:04Chuck Nice?

  There you go.

• 53:04Neil deGrasse Tyson?

  And so you give them grades. And so that, that, that'll shift in a good way. How the school systems place their value on what it is to teach you something.

• 53:17Chuck Nice?

  And it may awaken in students, it may awaken in students a reckoning or a recognizing of that value themselves.

• 53:25Neil deGrasse Tyson?

  Yeah, exactly.

• 53:26Chuck Nice?

  You know?

• 53:26Neil deGrasse Tyson?

  Yes.

• 53:26Chuck Nice?

  Yeah.

• 53:27Neil deGrasse Tyson?

  Exactly.

• 53:28Speaker 03

  So I think.

• 53:28Neil deGrasse Tyson?

  So before AI takes us over and, and exterminates us, there are these good sides of what?

• 53:33Speaker 03

  I think there are some, there are some short term, there are some short term bumps. I mean, the fact that there are going to be so many deep fakes is going to be a short term bump. But, you know, problems are always opportunities in disguise. So the commitment to being able to recognize what's true and what isn't, the realization that there are facts. That's something that I can see society embracing more than it has because it has to.

• 54:00Neil deGrasse Tyson?

  Because it has to.

• 54:01Speaker 03

  If you don't believe in reality, you get overrun. So maybe at the end of this, we come out better.

• 54:08Neil deGrasse Tyson?

  All right, that's a good, put them to end this on. Thank you for finally bringing us around so there's some hope. Plus that we gotta get Chuck and Gary out of the closet.

• 54:17Chuck Nice?

  Please.

• 54:20Speaker 03

  It's been great to talk to you.

• 54:22Neil deGrasse Tyson?

  Matt, always good to have you on the show. Thanks for coming, coming around again. And this will not be the last time we reach out to you.

• 54:27Speaker 03

  Cool. Always fun.

• 54:29Neil deGrasse Tyson?

  Always good to have you Chuck, Gary. This has been StarTalk Special Edition. Neil deGrasse Tyson here, your personal astrophysicist. Keep looking.