SGU Episode 910

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SGU Episode 910
December 17th 2022
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SGU 909                      SGU 911
Skeptical Rogues
S: Steven Novella
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AUTHOR, _short_description_
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[ https://sguforums.org/index.php?BOARD=1.0 Forum Discussion]

Introduction

Voice-over: You're listening to the Skeptics' Guide to the Universe, your escape to reality.

S: Hello and welcome to the Skeptics' Guide to the Universe. Today is Saturday, September 24^th, 2022, and this is your host, Steven Novella. Joining me this week are Bob Novella...

B: Hey, everybody!

S: Cara Santa Maria...

C: Howdy.

S: Jay Novella...

J: Hey guys.

S: ...and Evan Bernstein.

E: Good evening folks!

S: So no, you are not listening to the episode that aired at the end of September. We are recording two episodes on this day. This episode is coming out in December when we're on our trip to Arizona for our live shows in Arizona. This is part two of a six-hour live streaming show that we did. We recorded two SGU episodes. This is the second one. So this is the episode for sometime in the middle of December. I forget exactly what day it will come out. So we're going to get right to some bits for you guys. We have an interview coming up very quickly with an AI expert. But Cara, you are going to start us off with What's the Word?

What's the Word? ()

• Word_Topic_Concept

_consider_using_block_quotes_for_emails_read_aloud_in_this_segment_

S: But Cara, you are going to start us off with What's the Word?

C: Yes.

C: So I came across something really fun that I think you guys will enjoy.

C: It is a website that was started by a man named Jesse Scheidlower.

C: I think I'm pronouncing that correctly.

C: He created the Historical Dictionary of Science Fiction.

C: It went live during the pandemic because he was home a lot and he was bored.

C: I think he used to work for like the Oxford English Dictionary.

C: It's got 1800 entries.

C: I think it's always growing.

C: And it has information about where these terms, these science fiction terms were first coined.

C: He has the passage from where they were used and a little bit of background about the author.

C: So I thought it would be fun to go into the Historical Dictionary of Science Fiction and talk about some common words that of course, you may or may not know were developed by science fiction writers, but are used all the time now in common science parlance.

C: So the very first one, and it's probably the most famous example of this pretty much across all the coverage that I see online, everybody cites this one first, is the word robot.

C: Oh, no.

S: Or you are.

C: Did you guys know?

C: Yeah.

C: Or you are.

C: Right.

C: So robot.

C: So the word robot was first used, I think it was, well, gosh, it's been used by so many different writers.

C: A lot of people will remember some of the more recent uses, but before anything, it was actually used in a play by a Czech writer, and I probably can't pronounce their name.

C: Maybe it's Čapek.

C: Yeah.

C: Does anybody know if in Czech with the little thing over the C, is that a ch sound?

C: I'm not sure.

C: But this Czech author wrote a play called Rosam's Universal Robots, that's the translated title, in which he used the word robot for the first time.

C: And robot came from, I think, the Latin for forced labor.

C: And that's where the word robot really came into play.

C: And so then it's been iterated multiple times since then.

C: But the idea really early on, this was back in 1920, and the idea since then has often come from this idea of forced labor, use of labor in factories, use of labor in armies, cheap labor.

C: That's where robots come from.

C: And today, they still kind of carry that vibe, I guess.

C: But obviously, it's grown to mean so much more than that, just like a non-human technological thing that does something, right, that does work.

S: Before you go off the robot, though, the idea of a robot kind of goes back to the ancient Greeks.

S: There was this idea that – Not with the word robot.

S: Not with the word robot.

S: Just – well, but you think about mechanical things displacing the labor of humans, right?

C: That's basically the basic idea of a robot.

C: Yeah.

C: Conceptually, this is super old.

C: But the first time the word robot was used was by this Czech playwright.

C: And then, of course, a lot of people think of it from 1940 when Asimov wrote about the actual field of robotics.

C: And he had a character who was a roboticist.

C: And so that's where it really did explode.

C: So first use in the 20s, but then in the 40s, it exploded into our lexicon, and it was used all the time after that.

C: Okay.

C: So how about another one?

C: Did you guys know that the word genetic engineering came from science fiction?

E: No.

E: Which – where?

C: Well, the – Right.

C: So this was – Not the word thing.

C: They took the two words and put them together.

C: I have all my references.

C: This was from Jack Williamson's novel, Dragon's Island.

C: Oh.

C: I don't know that.

C: Yeah.

C: So it was an occupation within the novel of a genetic engineer.

C: Or no, genetic engineering started in that novel, and then it took several years before genetic engineer the occupation was named by somebody named Powell Anderson.

C: And Asimov used it also in the 70s, but in 1951 was when Williamson used in Dragon's Island, quote, I was expecting to find that mutation lab filled with some sort of apparatus for genetic engineering.

B: Cara, I just finished a series of books and they kept saying throughout, geneering.

B: Geneering.

B: Oh, yes.

C: Geneering.

C: Oh, love it.

C: I love it.

B: Is that a four-minute title or something?

B: And that was a modern sci-fi series?

B: Yeah.

B: Well, within 20 years.

B: Okay.

B: Yeah, yeah, yeah.

B: It was 95 actually, so it's not recent, but –

C: Here's another one that you guys might think – maybe you know this, maybe you don't. Zero gravity or zero G.

C: This started in sci-fi and this one's really fascinating because it was all the way back in 1938.

C: The author Binder, J. Binder, Jack Binder, he was actually – he's a comic book artist and he created Daredevil.

C: He used this in his essay, If Science Reached the Earth's Core, and he wasn't talking about zero gravity in space.

C: He was talking about zero gravity in the core of the earth.

B: You would float at the center of the earth because you're being pulled from every direction equally.

B: From the gravity of the mass of the earth, so yeah.

C: Then later in 1952, Arthur C. Clarke abbreviated the term and made zero G in his novel Islands in the Sky, and that's when it started to take place in space.

S: Although now it's been replaced by microgravity.

S: Yeah, right.

S: Because it's not the actual zero.

C: They were like, let's be scientific about it.

C: Let me see.

C: Yeah, it's technically a little bit more accurate.

C: Microgravity.

C: Okay.

C: Then of course, alien, the word alien, which kind of is still – we've gotten away with gotten away from the modern usage as it relates to the historical usage.

C: That was a person from another country or from another place, right?

C: So alien from a location other than one's own.

C: But now we don't tend to use words like illegal aliens anymore, right?

C: That's quite offensive.

C: And we've kind of advanced our labels, but that's where the word really started.

C: And ultimately that's how it kind of translated into this idea of beings from other planets.

C: So it's long been used to talk about something being foreign or something being from somewhere else.

C: But let me see.

C: The first person to use it in the way of somebody from another planet was a Victorian historian and essayist named Thomas Carlyle.

C: And then apparently in science fiction, we didn't really start seeing the use of alien regularly as a catchall for like ETs for extraterrestrials until 1929 when Jack Williamson's story, The Alien Intelligence, was published in a Science Wonder Stories collective.

C: And then finally, I found some cool stuff with like computer terms.

C: So the word worm, you remember computer worms?

C: Yeah.

C: Sure.

C: Yeah.

C: So this was not developed by computer scientists.

C: This actually came out in a story by Brunner, John Brunner in 1975.

C: His novel was called Shockwave Writer.

C: And so here is one of the, there are two citations in it, but the earliest in the book is, Fluckner had resorted to one of the oldest tricks in the store and turned loose in the continental net, a self-perpetuating tapeworm, probably headed by a denunciation group borrowed from a major corporation, which would shunt itself from one nexus to another every time his credit code was punched into a keyboard.

C: It could take days to kill a worm like that and sometimes weeks.

C: So this is our first usage of a computer worm.

C: That's cool.

B: Very cool.

B: Don't hear that word very often anymore, but.

C: No, you don't.

C: But it's pretty cool when these kinds of things are first dreamed up.

C: And we, you know, we hear about this with Star Trek all the time.

C: It's like a million examples we can pull from Star Trek.

C: But it's so cool that this one individual, again, I want to give him like huge props here.

C: He's called Jesse Shidlower, and he was already a word nerd.

C: And he said that because he was kind of home all the time and had the time to do it.

C: He got this site up and running during the pandemic.

C: And it's called the SF dictionary dot com, the science fiction dictionary.

C: So look it up.

S: You can you can have fun on there.

S: Neat.

S: That sounds cool.

S: All right.

S: Thanks, Cara.

Your Number's Up ()

• _Number_Topic_Concept_

Quickie with Bob ()

• [url_from_show_notes _article_title_] 

News Items

S: Evan, you're going to start off the news items telling us about an electric plane.

E: Yeah.

E: Electric airplane in the news this week.

E: Out of Sweden, a company called Hart Aerospace.

E: Their mission is right from their website.

E: Their mission is to create the world's greenest, most affordable and most accessible form of transport grounded in the outlook that electric air travel will become the new normal for regional flights and can be trans transformational in addressing the industry's key sustainability challenges.

E: So on September 15th, they had something called Hangar Day, in which all their employees, all the everybody in the company and then invited guests come out to their big, big, big hangar.

E: And they made major announcements there.

E: Their biggest announcement was that they have been working on an airplane, an electric all electric airplane called the E.S. 19.

E: It was it's designed to be a 19 passenger airplane entirely powered, but with batteries.

E: Now they got it to they got that to the point in which they made a scale model and that actually did fly.

E: And that was as of this past summer.

E: But their announcement today is that they're stepping it up.

E: It's now the E.S. 30, a 30 passenger plane.

E: And all of their company's efforts are now going to go into making this design.

E: The other part of this announcement that is significant is that they've got orders for this thing and they have orders from some pretty big hitters in the industry, including Air Canada, Mesa or Mesa M.E.S.A., United Airlines here in the United States and Air New Zealand are among them who have either put in actual purchase orders or have basically said, yeah, we're very interested in in getting these airplanes to the tune of hundreds of these things that they're that they're putting in order for now.

E: Let's talk a little bit about the plane itself.

E: It's not built yet, first of all.

E: However, they did right there.

S: But they have the specs.

E: Yeah, they have they have the specs for it.

E: And they have the then they have the test fuselage all built out inside one of the hangers.

E: That's hooked up to all the computers and all the simulators and everything.

E: And they say that everything in that simulation is is working as it's supposed to.

S: What kind of battery does it have?

E: It's going to be battery source batteries, primary five tons of lithium ion batteries right now.

E: Yeah.

E: So that's a that obviously comes.

E: It's a lot.

E: How many times?

E: Five times.

E: Yeah, that's what it is.

E: Yeah, it's kind of weird.

J: The fuselage is kind of weird.

J: Like, I mean, it almost looks like a seaplane.

E: That's probably the battery.

E: Yeah, the batteries are loaded down there in its belly as as it were.

J: I'd like to see what the landing gear down.

E: Yeah, yeah, that would that would be that would be neat to see.

E: I wonder how hot it gets.

E: Yeah.

E: And right.

E: So hot and, you know, fire.

E: Right.

E: So how did it happen?

E: How are you?

E: Yeah.

E: How does that exactly work?

E: But they must have it figured out the range 200 kilometers right now if you're going to use the batteries.

E: OK.

E: However, it does also in the tail section right below where the tail is at the very end of the plane.

E: There is a liquid fuel reserve, essentially.

E: So you can double the the range with that.

E: And you would have that built into these planes in case you know, because when you're in flight, you may have to get suddenly diverted to other airports or other or other routes.

E: So it's there strictly as as a contingency for those kinds of emergencies.

E: But 100 kilometers just off the 200 kilometers just off the batteries from takeoff to from takeoff to landing.

E: If you go if you kick in that that hybrid system, though, yeah, 400, 200 or 400, 200

S: or 200. That's probably a lot of small city to small city routes.

E: That's right.

E: And this was a particular goal, a threshold that they had to reach, because before this, I believe there are 19 maybe had I think was like 140 or 150.

E: And it wasn't quite enough.

E: Yeah.

E: From the perspective of the airlines, not for themselves.

E: Right.

E: They couldn't make the routes.

E: But getting to that 200 kilometers ticks boxes and gets you from from real destinations to destinations that you need to get to.

B: How about in air recharging?

B: Yeah.

B: Wouldn't that be?

E: Yeah.

E: But how long would that take?

B: How long would it take to fully charge?

E: But the thing is, this this is filling a niche niche for a part of the airline industry, obviously, because you are dealing with short routes.

E: So there is right.

E: What do you have right now for refueling on short routes?

E: Nothing because you don't need it.

E: So so basically the same premise.

E: You don't you wouldn't necessarily have to design this thing with a need to recharge mid flight.

B: It's not like you're going across the ocean or something.

B: What's the recharge time after it lands?

E: Thirty minutes, I think, is what they say.

E: Yep.

E: Turnaround time.

E: Thirty minute fast charge, fast charge.

E: And maybe there's some other quicker, longer charge.

E: I don't know what that does to the battery life or the life of the airplane, but that's what they're saying.

E: Thirty minute turnaround time right there on their maximum altitude, 20000 feet, which is apparently where you need to be for these this level.

E: It basically ticks all the boxes that the propeller planes right now on these routes are filling and it meets it meets it price wise also.

E: So that's these are all the points that they're that they made with this announcement is that it's here.

E: Right.

E: We've got it.

E: The specs are here.

E: We're going to build this thing out.

E: We'll get this thing tested and in the air within within a couple of years.

E: And we're going to enter these things into service by twenty twenty eight.

E: Twenty twenty eight.

S: Twenty twenty eight is the budget in between now and then.

S: I bet you the batteries are going to be better.

E: Well, that's the other thing is that they said we're just dealing with what the technology we've got now and we're counting on things to get better with the battery technology.

S: Yeah.

S: Also, you could slap some organic solar cells on top of those wings.

S: You know, not you can't put like silicon panels on there.

S: They'd probably be more heavy than they were worth.

S: But organic or thin and light and very easy.

S: They're not that efficient.

S: But if we get the efficiency up to those like above 20 percent, I bet you that could add, you know, 40, 50 kilometers to the range.

E: It probably could.

E: Someone's asking in the chat whether they're flying right now.

E: Obviously, this model is not flying right now.

E: They still have to build it.

E: The models that are flying that are all electric seem to be the single or two passenger planes in the Cessna kind of kind of kind of model.

E: So those are out there to be had.

E: I've seen video I've seen videos on that.

E: I read news.

E: I news I was about it in the last couple of years.

E: Those have been out and are being tested.

E: Military is definitely looking into them as options.

E: But what we're talking about here is commercial, the commercial airline industry.

E: Now, I and I always thought it was going to be a problem with with takeoff and getting enough thrust, getting those fans in the engines to turn to turn fast enough to get the thrust that has a lot of power.

E: But yeah, no, that that that is not a not an issue.

E: Yeah, it's just you know, they say what it just however they said, if you're going to do it with fuel, with batteries or with hamsters on a wheel, it doesn't matter.

E: You just have to be able to be generated enough power.

E: Now, the power density of the batteries, you know, the fuel to run the air the airplanes, it's much denser energy energy with the fuel.

E: But the batteries are catching up.

E: And like you said, Steven, within five years, next generation batteries that are coming out, they can only be better.

S: So they cross that threshold, then that's it.

S: Then you know, just get incrementally better from there.

S: Have you ever been on one of those like a prop plane for a short flight?

E: Absolutely.

E: Yeah, they suck.

E: Yeah, they're uncomfortable.

E: They're scary.

S: The worst is they're loud and they vibrate, you know, but these are supposed to be a lot quieter, a lot quieter, practically silent, probably a much more enjoyable experience certainly than what's currently filling those those routes.

E: Yeah.

E: So yeah, commercial battery powered flight.

E: Here we go.

S: A couple years.

S: And then there's already solid state solid lithium ion batteries.

S: They haven't quite gone into mass production.

S: I think Japan is working, has one that they're actually commercially being used.

S: But when that hits, those have about twice the energy density as the regular.

E: That's a nice game changer.

S: Yeah.

S: So either it's half the weight or twice the range or some combination of those two things.

B: Wow, that's a near term upgrade?

S: I mean, that's something that we could see in like widespread commercial use by the end of the century, by the end of the decade.

S: Definitely.

S: I mean, there's already some versions of them in use.

S: But that could be like a little jump, you know, like to twice the energy density.

B: But why eight years from now?

B: I mean, well, because, yeah, I don't know.

S: I mean, the production, it's always that commercialization, ramping up the industrialization of it.

S: You know, doing it on a small scale is just different.

S: So it could be quicker.

S: You know, we'll see.

S: It could be a few years.

S: But that way for this kind of thing, that's really what it's waiting for is, you know, the batteries just across that threshold.

S: You know, it's usable.

S: But yeah, it's good to hear that.

S: It's on the way.

S: Yeah.

S: All right.

S: So this is going to be a quickie.

S: This will be a good one to just fill in before.

S: We have an AI interview coming up in about 15 minutes with an AI expert.

S: And so I just want to talk about the upcoming strongest laser for the United States, right?

S: So this is not the strongest laser in the world, but it puts it up there with the strongest lasers that exist.

E: And the strength of lasers?

E: Zeta watt?

E: Zeta watt.

E: Zeta watt, yeah.

S: Oh, OK.

S: So it's the zeta watt equivalent.

S: What does that mean?

B: Well, it's a super short pulse.

B: Like, we're talking femto-attoseconds.

B: That's not what makes it an equivalent.

E: Multiple lasers ganging up to make the zeta watt?

B: Nope.

B: It's pulling in laser power from an alternate dimension, alternate universe?

S: So it's the Zeus.

S: Have you heard about that?

S: Yeah.

S: Yeah.

S: So the laser part of it itself gets up to 300 petawatts.

S: OK, yeah.

S: And respectable.

S: What they do is they feed supercharged electrons into it, and that gets the effective power up to what a zeta watt laser would produce.

S: No shit.

S: But the laser part of it itself is in a zeta watt laser.

S: It's a 300 petawatt laser.

S: Wow.

S: I didn't see that yet.

S: So that's why they had to use the term zeta watt equivalent in terms of the power that it produces.

S: So it's basically like having a zeta watt laser.

B: It's like those projectors that have lumens in it.

B: It's not really lumens.

S: It's a lumix or some equivalent.

B: Yeah, yeah.

B: Lumen equivalent.

B: It's crap.

B: But damn, man.

B: OK.

B: That's interesting and upsetting at the same time.

S: But at the end of the day, it's effectively a super powerful laser.

S: I mean, zeta watt is...

B: It's 10 to the 21, I think.

S: Yeah.

S: It's incredibly powerful.

B: That's a whole lot of watts.

S: But you're right.

S: It's very, very brief in terms of...

S: Because obviously, they don't have the energy to have that thing going for any length of time.

S: They're going to get it up in stages.

S: In stages.

S: Yeah.

S: In series.

S: So they're first going to shoot it up at only one, not even petawatt.

S: What's before petawatt?

S: Peta...

S: Exa?

S: Exawatt?

S: No, no, no.

S: Gigawatt?

S: No.

S: Wait.

S: Tera?

S: Wait.

S: Tera.

S: One terawatt.

S: Yeah.

S: It's going to start at like one terawatt.

S: Then they're going to go up by orders of magnitude until they get up to the maximum strength of the 300 petawatts.

S: And then they're going to get it up to the max to the equivalent of the one zetawatt.

S: All right.

S: So what's this really powerful laser for?

S: What's it going to do?

S: Primarily it's for research, right?

S: This is primarily going to be for research.

S: With this, you could create super hot plasmas, for example.

S: How hot, you might ask?

S: So hot that we can actually do experiments...

S: Big bang?

S: Yeah, big bang.

S: Like the physics near black holes where you have this super, super hot plasma.

S: They always make general statements about like, this will help us research the quantum nature of the universe without getting into a lot of details because they're not designing experiments yet or at least not in the reporting that I'm seeing.

S: But that's just theoretically you're going to...

S: You could use this laser to create super high energy physics, which will get you into the...

B: But what about a Clark gluon plasma?

B: Does it get to that level?

S: It might be able to, I don't know.

S: But well, they didn't comment on that specifically.

S: I think that's the kind of thing that they're talking about.

S: So it just gives us access to new physics in terms of experiments because the energy is so incredibly intense.

S: They also said they could use it for like X-raying very small things, right?

S: Because...

S: But this of course would be the extremely brief pulse, but at high energy, it allows you to penetrate things that you otherwise wouldn't be able to see the interior of.

S: So like metals and stones and things like that.

S: So it could also be used in research that way.

S: Again, I think it's not exactly like a portable or...

S: Record player?

S: Yeah, it's not a portable laser.

S: So I think everyone, when you hear about like the most powerful laser that we have or ever or whatever, your mind pretty quickly goes to, could this be a doomsday weapon?

S: Yeah, right.

E: Are we going to blow up Alderaan with this?

S: What are the military applications of this thing?

B: Or how to hand-held laser pistol.

S: But this wouldn't be useful for that sort of thing.

S: It's not portable enough, not sustainable enough.

S: Bob, a little bit later in this episode, you're going to be talking about laser sails, light sails basically.

S: And I tried to find any mention of using this kind of laser for that application and- Oh, thank you for doing that.

S: Nobody brought it up.

S: Yeah, I mean that's- But I don't know if that's just because it's not the first thing you think of or that it's just not really useful for this.

S: Probably because it's too short again.

S: Oh, absolutely.

S: Absolutely.

S: You need sustained lasers.

S: You also- And I've got multiple lasers too.

B: The resources.

S: But also, you probably don't want it to be that hot, right?

S: You don't want to burn up your solar sail.

E: Yeah, you don't want to destroy what you're trying to ship around.

S: Yeah, there's got to be a sweet spot in terms of how energy intense you want that laser

E: to be. Depending on the material you're making your sail out of.

E: But foreshadowing, I like it.

S: Yeah, yeah, yeah.

S: So we'll be talking about that more.

S: What kind of lasers would we want?

S: What we need for light sails?

S: Because I think the laser-driven light sails, as we're going to talk about, are going to be important to the future of space travel.

S: Maybe, probably.

S: Yeah.

S: But there are other countries out there that have more powerful lasers already.

S: This won't even at maximum power be the most powerful laser in the world.

E: And are they using those lasers for the same purposes?

E: Yeah, basically.

B: It's basically a research tool.

B: Yeah, I was actually doing a search recently for the most powerful, and I came across Zeus here.

B: But they said United States.

B: I'm like, oh, wait, no, I'm talking the world.

B: And it didn't.

B: I had to, you know, look away.

E: Ooh, classified.

B: I wonder if they're equivalent.

B: I wonder if the real number one right now, I wonder if it's equivalent, equivalent, or

S: I would think probably.

B: Yeah, yeah, right. I think so.

B: I think so.

B: I mean, that seems like an interesting and cheap way to really upscale your super powerful

S: laser. Yeah, it's a good example of, you know, of the fact that humans are clever.

S: You know that even when we run into theoretical limitations, and we've seen this all the time, this is the theoretical limit for whatever.

S: Like there's the diffraction limit.

S: We'll never be able to image something smaller than this.

S: And then we find metamaterials that get around.

S: Trixie.

S: Yeah, that get around the diffraction.

S: Oh, we're just going to cheat.

S: And we're really good at figuring out how to cheat the system.

S: I mean, actually, this is like the most powerful laser that we could make with, you know, the equipment that we have.

S: Then they figure out a way to cheat.

S: What if we feed super high energy electrons into it?

S: And then you get the equivalent of a more powerful laser than should be able to exist with the materials that we have.

B: It's fascinating idea.

B: I can't wait to read more about that.

S: Yeah, yeah.

S: So this is just very, very early reporting.

S: This is sort of a quickie news item because the reporting is very early.

S: Clearly, you know, it hasn't been turned on yet.

S: And it's going to take years to get it up to full power.

S: And then so probably in a few years, we'll be reading about the research that's being done, done with this laser.

S: But the zeta watt equivalent is a good threshold that I thought it was worth mentioning.

B: Yeah, zeta is huge.

B: I mean, 10 to the 21.

B: That's an immensely large number.

S: All right, I hear Ian talking to our AI expert right now.

S: Hi Mark, how are you?

S: Hi.

S: Hello.

S: Welcome to the Skeptics Guide.

S: Thank you for joining us.

S: So can you tell our audience a little bit about yourself and your expertise?

MH: Yeah.

MH: So my, my background is in cognitive science and artificial intelligence.

MH: And I kind of work on kind of the intersection of computer science and psychology, studying how kind of people, human cognition works, kind of how people solve problems, how that compares to how machines solve problems, and trying to understand kind of general principles of problem solving and intelligence.

S: So what I'm hearing is that when we finally get an artificial general intelligence, you're going to be their first psychotherapist.

MH: Exactly.

S: Cool.

S: But let's back up a little bit to the world of narrow AI where we are now.

S: And so tell us what kind of work you do.

S: Are you trying to model narrow AI after how the human brain works?

S: Is that kind of what you're studying?

MH: So no, I kind of, I'm approaching things from a much more kind of psychological cognitive perspective than a neuro perspective.

MH: Okay.

S: So yeah, more basic principle about how thinking works, not necessarily how the human brain works.

MH: Yeah, exactly.

MH: And trying to understand, yeah, how can we understand kind of people as thinkers and reasoners in a more general sense, and how does that compare to how machines are, like current machines and AI systems are reasoning, and to the extent they are reasoning and solving problems.

J: Mark, is the goal to make the AI software think more like a human, or is it just useful information to have when you're figuring out how to program it?

MH: Yeah, so there are really two goals I think of kind of the research kind of field that I'm in, computational cognitive science.

MH: One is to kind of use general principles developed in AI and kind of tools and formal methods from AI to model human cognition in a way that we can better understand how people are solving problems and kind of understand things like perception and memory in terms that are like precise enough to make kind of quantitative predictions and stuff like that.

MH: And then the other side of it is kind of developing better models and kind of predictive general models of how people kind of think and solve problems and perceive and remember things so that we can use that to design AI systems that kind of understand how humans think and work and design better interfaces and stuff like that.

S: Cool.

S: Some of the research in this area that I've read, it sounds like a lot of it is going from the AI to the cognitive theory.

S: Like narrow AIs are doing things, we don't know how they're doing it.

S: You're trying to figure out how they do it so we can better understand just cognitive science itself.

S: Is that kind of what you're doing?

MH: Yeah.

MH: So a lot of the research is going from kind of the AI formalisms and ideas for how to even build, like kind of how you would engineer an AI system gives you a lot of insight into how you could kind of reverse engineer human mind and human cognition and intelligence.

MH: So there's been a lot of direction that way recently, but there's actually a long history also the other way, kind of thinking in psychology more formally, a lot of kind of core ideas in AI kind of originated in mathematical psychology and computational cognitive science.

MH: Things like connectionist theories are the foundation of deep neural networks today.

MH: And a lot of things from a lot of these kind of reinforcement learning algorithms, the ones that were able to solve like chess and go and beat humans in these games, a lot of the kind of basic principles from that were developed in studying like learning, associative learning in like rats and stuff.

MH: So there's direction that way.

MH: And so I do a little bit of both.

MH: My research is kind of trying to develop new formalism, kind of new theories of how humans are solving problems and use that kind of in the AI direction.

MH: So a big thing that I've been focused on lately is thinking about how do people approach problems and kind of model problems and kind of construct a mental model of problems in a way that's very flexible and kind of general in a way that a lot of AI systems can't right now.

MH: Like you were saying, there's like narrow AI, it's like very focused on a single task.

MH: It solves a single task.

MH: What's kind of cool about humans is that we can do a whole bunch of things.

MH: Like I can jump on this podcast and start talking to you about stuff even though I've never done it before.

MH: I can go cook a cake, bake a cake, whatever.

MH: So yeah.

S: I mean, how much in your research do you get involved with the question of like what are the things that AI is better at than people?

S: And what are the things that people are better at than AI?

S: Even as powerful as AI is getting, it sounds like there are still things that we do that they can't do.

MH: Yeah.

MH: So that's a very big active question right now, especially since we're seeing all these AI systems solve problems that for a long time we thought only humans could do.

MH: Like I was saying, like playing chess really well and identifying objects and classifying things, large language models and so on.

MH: Currently a lot of the big questions are around things like being able to generate new concepts and like generate new kind of combinations of concepts is something that people are really good at, but AI, like kind of current AI.

MH: The other thing is like what counts as AI kind of shifts over time to like 20 years ago, what Google Maps is doing every day, kind of routing, a routing algorithm would have been considered like AI, like artificial intelligence, but now it's like just an algorithm that you use on your phone.

MH: So the kind of what counts as AI is always shifting.

MH: But I think right now and for a long time, right now, and especially because right now a lot of the systems are these like statistical machine learning systems that don't have a lot of kind of internal structure to them.

MH: They're not very good at compositional reasoning that humans are good at.

MH: Things like, if you understand what like a cat is, you can kind of imagine like a red cat or a purple cat or something like that, even though you've never seen it.

MH: Those kinds of generalizations are much harder for neural networks and kind of standard machine learning systems.

S: So we talk a lot about AI on the show.

S: And so it's good to have an expert on.

S: And one of the things I would love to have is if you wouldn't mind giving us like a really brief synopsis of like what is a neural network versus machine learning versus deep learning?

S: You know, so the big major concept, because I know we throw them around a lot and probably a huge chunk of our audience doesn't really understand what they are.

MH: Yeah, yeah.

MH: So let's start with the neural network.

MH: So a neural network is basically a, it's a big matrix of numbers.

MH: You can kind of think of it as, you know, if you kind of had a machine where there were like knobs on it, you could kind of turn them and there were kind of like an image coming through and you could tune it to like kind of translate that image in some way.

MH: It would kind of filter things in a different way.

MH: A neural network is kind of an abstraction of that kind of general idea of there's kind of like a kind of array of signals coming in and they're kind of passing through a bunch of filters and you can kind of tune what each filter is doing.

MH: Big neural networks have a lot of tunable parameters, basically a lot of knobs that you can turn and a lot of deep learning is just a deep version of that where you have a lot of like layers, what are called layers of neurons that you can tune and that kind of gives it the flexibility to kind of transform an input in a very complicated way to give an output like, you know, taking an image of a cat, of an animal and classify it as a dog or a cat.

MH: And basically deep learning is this kind of very relatively simple algorithm of kind of as you feed in an input and get an output, if you know what the output should be, you can say, you can kind of give it a thumbs up or a thumbs down.

MH: And the kind of system is designed to kind of back propagate that information to like adjust the parameters a little bit.

MH: So it does a little bit better next time.

MH: You just do this a lot.

MH: And eventually the whole thing kind of like moves through this big parameter space and learns how to basically classify things that way, kind of learns the right set of tuned parameters.

B: Is that where training comes in?

MH: Yeah.

MH: So that's the whole idea of training.

S: Yeah.

S: Yeah.

S: So like a billion trial and error a billion times until it tweaks it perfectly.

MH: Exactly.

MH: It's like trial and error kind of driven learning.

MH: Yeah.

MH: Kind of actually the technical term is error driven learning.

MH: Yeah.

MH: And then machine learning is actually a broader category, which refers to not just neural networks, but like a whole range of methods.

MH: But I think what unifies them all is that they're kind of all based on kind of statistical ideas of you're trying to kind of like estimate, there's like uncertainty about the world and you're trying to estimate the best model of the world or the best set of parameters to like explain something or fit some data.

C: So if you had to like do a visual description with sort of umbrellas, is AI the largest umbrella or is machine learning the largest umbrella?

C: Like which category subsumes each other category?

MH: Yeah, they're kind of like partially overlapping umbrellas.

MH: Like I said, AI is always shifting.

MH: These days AI tends to refer more to these kinds of statistical machine learning like approaches.

MH: But classically AI also referred to what's kind of called good old fashioned AI or go fi, which is like this idea, kind of more and more what's called like symbolic reasoning.

MH: So things like kind of planning, problem solving, kind of reasoning in a more structured way.

MH: Whereas like the domains that kind of statistical machine learning works really well in are domains where things like perception, classifying images, and settings where you just have like a lot of data, you can kind of chug through that data.

MH: And there's like kind of some underlying pattern in the data that no single person could like write a set of rules to describe, but it's like it's there and it can be kind of learned in this flexible kind of tabula rasa way.

MH: AI, I think, yeah.

MH: And so like AI, those kind of like, whereas those symbolic kind of approaches, they tend to be less data driven classically.

MH: And they're much more kind of like you have like a big complicated problem that you know what it is, but it's just really complicated.

MH: And you need like to be smart about how you solve that problem, as opposed to kind of learning through trial and error, how to like perceive something or kind of fit pattern match essentially.

MH: So there's kind of a, people often make a distinction between like pattern recognition, which is kind of more statistical machine learning approach, and like symbolic reasoning, which is this more kind of kind of deductive thinking and kind of structured reasoning.

MH: But I think the big thing right now is how do we kind of bridge these two approaches?

MH: Because obviously, people are doing both of these things, doing a lot of both reasoning and complex perception and kind of action.

C: And then so neural nets, which are a sort of sort of subsumed underneath machine learning, that's like the engine by which some machine learning takes place.

C: Is it, would you say that neural nets are sort of it now?

C: Is that what most people are putting their chips down on?

C: Or is it just one of many equally effective approaches to machine learning?

MH: Yeah, it's one of many approaches.

MH: And right now it is, it's probably the most effective when you have a lot of data, when you have a lot of...

C: Like when you can scrub the internet.

MH: Exactly.

MH: Yeah.

J: Okay.

J: So what would you say like the most complicated thing that some artificial intelligence software is doing today?

MH: Oh, the most complicated.

MH: I mean, it's...

J: Or give an example of like, you know, like it hitting really hard.

J: What is AI doing today that's really impressive or considered top of the game?

MH: One of the most impressive things that's going on right now is like, you know, things like these game playing algorithms that can basically, that beat people at games that require very long term planning and like look ahead and like thinking about what the other person is going to do.

MH: I think, yeah.

MH: So these like competitive...

MH: So I think basically...

MH: Chess and Go.

MH: Chess and Go essentially are...

MH: They're like competitive, well-defined games where we have really good methods for solving them.

MH: And it's not just a kind of pattern recognition thing.

MH: It's a combination of pattern recognition and symbolic reasoning and reasoning.

MH: So these systems that solve Chess and Go, they're combining both pattern recognition, kind of recognizing patterns on the board with planning, with kind of what's called heuristic search, kind of searching through a tree of possibilities.

MH: And they're using the patterns to kind of guide the search through the tree.

MH: Yeah, I don't know.

MH: It's very impressive because I think like, yeah, we think of game playing as kind of a very human activity.

MH: Like no other animals play games like this.

MH: Yeah, other animals like perceive things and can move through the world and stuff, but only humans can seem to do this kind of symbolic reasoning in like very large state spaces.

S: So it does seem like the AI applications are getting really powerful over the last five to 10 years.

S: Like they hit their stride and we're seeing all these applications now, like beating the world champion in Go and the new art generating software, that kind of stuff.

S: You know, folding proteins.

S: Folding proteins.

S: All this stuff that is happening.

S: And I've been trying to find out, I've asked multiple people, you know, experts in different ways, like what they think about it.

S: One of them told me like the AI itself is not necessarily getting better.

S: It's just that we're getting better at training them and we have better data sets to train them with.

S: Do you agree with that?

S: Or do you think that we're getting better at the underlying hardware, like the underlying programs themselves?

S: Or is it just that we're getting better at training them?

MH: So I think most of the current progress is due to a lot of improvements in the engineering, being better at training them.

MH: I think the hardware is part of that kind of developing special...

MH: Like a big thing that's happened in the last 10 years is they figured out how to kind of take what are called like GPUs, graphical processing units, that are typically used in computers to like render graphics and stuff.

MH: And the types of computations that those things do are basically what neural networks have to do.

MH: And they do them really quickly.

MH: They're great.

MH: And so they've been able to kind of build on that technology to get like many, many, many orders of magnitude speed ups in how you can train these systems.

MH: And so that's a hardware, it's kind of a lucky hardware result.

MH: And then, yeah, I think another big thing is the availability of data.

MH: So like having the internet, lots of text and images out there, things like DALI and the large language models wouldn't be possible without that kind of data.

MH: The underlying principles of these statistical machine learning models are actually really pretty simple and have been known for decades or arguably like centuries.

MH: Some people just think it's calculus.

MH: I mean, it's more than that.

MH: But like, yeah, yeah, it is essentially just calculus.

C: But there's no like new math involved.

MH: It's I mean, there's like new math to do the engineering.

MH: But I think the fundamental ideas are not going to be new to people, or not new.

MH: Someone who's familiar with physics, or kind of learned classical physics or something like that can pick up the math relatively quickly, because it's not like a fundamental difference.

MH: But I do think, yeah, it's hard to say, sometimes a lot of progress in AI is made by kind of putting the right pieces together.

MH: And like some of those pieces were out there decades ago, but people didn't know what to do with it.

MH: And then suddenly, it kind of all clicks into place and things work.

S: Okay, so just so I understand, so obviously, the hardware is getting better, faster and faster computers.

S: And the training data, the availability of training data is much greater than it used to be just because the internet and all that.

S: But the underlying like conceptual basis of AI software is not really fundamentally different than it was even decades ago, although we're learning to use it in new ways.

S: Is that fair?

MH: Yeah, yeah.

MH: And you learn new things about the framework as you use it.

MH: But yeah, I think a lot of it is pretty similar.

MH: And people are kind of rediscovering things all the time that were proposed decades ago.

J: Yeah, yeah.

J: So I'm, you know, we, the reason why we're doing this live stream is we have a book coming out in three days.

J: And we discuss artificial intelligence in the book.

J: And one thing that we try to do is, you know, talk about like, where's it going to be in 10 years to 50 years?

J: And I'm curious to hear what you think, like, what's the short term and you know, when I say short term, like five to 10 years, and then longer term, say 50 years, where do you see it going?

MH: Yeah, I mean, it's really hard to predict.

MH: But I guess in the next five to 10 years, I think a lot of the technology, like kind of, yeah, I think a lot of a lot of progress in AI is being made, because people are able to kind of take a problem that's out there and kind of fit it into fit it into like the square peg, or the square hole that is the deep neural network kind of training, training test paradigm.

MH: And so people are kind of constantly figuring out creative new ways to do that.

MH: And so I think that's going to continue to develop and that's kind of like advances in like narrow AI, I guess, as you were calling it before, more specialized kind of AI systems.

MH: And yeah, I think like, over the longer term, there do need to be a kind of conceptual breakthroughs in terms of how we think about intelligence systems and how to design them.

MH: Because the way that humans learn and reason is pretty fundamentally different from the way that statistical machine learning systems learn and solve problems.

MH: And so I, you know, I think it's going to depend on whether those breakthroughs happen.

MH: And it's hard, those are very hard to predict, obviously.

MH: But you know, a lot of people are working on these problems.

MH: And you know, things are moving very quickly.

MH: I think, yeah, the, you'll probably see more.

MH: Yeah, in the short term, you'll probably see more things like DALI and, and the large language models, because those work really well, and there's a lot of incentive to, to build those.

MH: And so it's kind of like, it's the type of thing that you kind of scale up very easily, if you have the resources.

MH: And so I think a lot of research is going to go into that, for better or worse, is that like the globally optimal thing to do?

MH: Who knows?

MH: But I mean, I think a lot more funding is going to go into that.

MH: And at the same time, there are going to need to be conceptual breakthroughs to kind of move beyond that.

MH: And also, I think it's an open question whether those kind of like scaled up, hyper scaled up statistical approaches are sufficient.

MH: I don't I don't think they are in the long run.

MH: I think I think it can they can be used to solve a lot of problems.

MH: But I don't think it's going to kind of give us the answer to kind of general intelligence.

J: So we're, do you think we're going to see the proliferation of AI though, meaning like, in five to 10 years, is everything going to have a, you know, some type of AI component to it that would help us do things or, you know, like, is that what's in the future?

MH: I think, yeah, there's so many factors besides just the technology that play into that.

C: But like where it can be used, won't we see it used?

C: Like I think back to when I was a kid and I like, I was of the era where my toys went from maybe having like, I remember I had a Teddy Ruxpin that had like a tape recorder in it, but I didn't have anything with a computer chip in it.

C: I just didn't.

C: And now like most children's toys have a computer chip in them.

C: It's just like, if you can use it, it's going to be used.

C: Do you think we're going to see that across the board?

MH: Yeah, I think you will see that.

MH: Yeah, it'll it'll really depend.

MH: I mean, in like, on the internet, like, the reason why we like a lot of like, we're using AI a lot already, it kind of AI is already in a lot of systems we use, you know, like, anytime you use a search engine, or translate something or take a picture and it finds faces, that's AI.

MH: It's so it's going to be in all those systems, like any kind of digital system where there's a very kind of clear task to do like facial recognition or something like that.

MH: Well defined tasks, you'll probably see it there.

MH: It's kind of is already like that in a lot of ways.

MH: I think we'll probably see more of it in applications.

MH: I think what the large language models have really opened up is the possibility of kind of a general interface for kind of people who aren't experts to give prompts to a system to create things and kind of complete things.

MH: And so we'll probably see more of that.

J: Like, like mid journey, we've been using mid journey a lot.

J: It's an art program.

J: And we're pretty damn blown away by how incredible the images can be.

J: And we were having a discussion about whether or not, you know, like, like, how do we how does this affect society?

J: You know, there's so many elements to this, you know, especially what it's going to be like in the future.

J: You know, imagine, I don't know, I don't want to get back into it, Cara, because Cara and I are picking my words carefully.

C: What is the value of art?

J: But it is amazing to see a software program where I give it five words, you know, sunset in Florence, Italy, and it creates, in my opinion, a profoundly beautiful painting.

MH: Yeah, I it is pretty remarkable, pretty amazing that we have these systems that can do that.

MH: I mean, yeah, I don't know.

MH: I don't know.

MH: It's hard to tell, again, what the like social impact is going to be because we do have like technologies that can reproduce things pretty well, on a large scale already.

MH: And those like the printing press kind of did a lot of the work, I think, in a way, and maybe just like the internet kind of did a lot of the work of changing society.

MH: I do, a part of me does think that if it becomes so easy to create content and make things up, you might actually see people kind of like just kind of automatically not believe things that they see on the internet.

MH: Maybe people will be more skeptical.

MH: And you think it would be a deepfake.

MH: Yeah, exactly.

MH: And like, people will kind of trust, be kind of rely more on their judgment and trust, like who the source of the information.

MH: I feel like that kind of happened throughout the kind of evolution of the internet, that there was a period where like, you kind of believed a lot of stuff on the internet, and then you stop believing most of it, except for the sources you really trusted.

C: Yeah, and how you come to those realizations is where psychology is so important.

C: Like, you can't ignore that component of this.

MH: Right.

S: David, I'm interested in your thoughts, especially since you're, you know, at the cognitive end of things.

S: The relationship between the kind of like narrow or whatever you want to call it, AI that we have today and an AGI, a general artificial intelligence that's actually like a self-aware thinking entity.

S: My sense is, which has been strengthened by what you've been saying, is that the current AI algorithms that the neural deep, you know, neural nets, whatever, they're not on a path to general AI at all.

S: They're just, they're really good at solving specific problems, but we would need to say conceptual breakthroughs to get to general AI.

S: So do you agree with that?

S: If so, how are we going to get to general AI?

S: Or another question that comes up is, do we even need to get to general AI?

S: And can narrow AI just do everything we need it to do without ever having to worry about is it aware or not?

MH: I think it's, I don't think it's necessary for us to develop general AI.

MH: I don't think it's, it's not in my mind, it's personally not my priority.

MH: I'm mainly interested in human cognition and kind of how we can develop, like kind of improve people's lives and kind of understand how people work better using tools from AI and building with AI and stuff.

MH: But I do think, yeah, I mean, in terms of whether the current path of like statistical machine learning is kind of paradigm of doing machine learning, I think is sufficient to get to AGI.

MH: I don't, qualitatively, it doesn't seem like it would be able to do that because of just how it works.

MH: It's just kind of, basically these are, these are systems that are extremely good at pattern recognition and kind of picking out patterns in a well-defined problem that you give it and kind of thinking about things within that, the parameters of the problem you've given it.

MH: And I think the large language models have made really evident is that if you kind of have a big enough problem or a big enough dataset and throw that at one of these systems, you kind of mirror, basically mirror like all of human text on the internet, kind of mirror that distribution, then it comes to resemble intelligence very well, at least within the, again, the parameters of that problem.

MH: And so, you know, it might be possible that there's kind of a, kind of a qualitative kind of jump as you kind of scale these things up, that that's hard to predict from like the kind of basic understanding of it, kind of like understanding like a, what's the word?

MH: A phase shift of some kind.

C: But do you think that that could happen by virtue of the iterative process of the AI itself?

C: Or do you think that that's going to require, you know, I think the big fear and concern when people start to get really dystopian about like the singularity and things is that they're like, at what point can we no longer control the AI?

C: At what point does it become self-aware enough that it says, no, I'm not going to do what you just asked me to do.

C: I'm going to make these decisions on my own.

C: And that's the part that I think, you know, is the stuff of film, but it's the realistic fear that a lot of people have.

C: And do you think that we are ever going to be there?

MH: Yeah.

MH: I mean, so I don't think that an AI has to be self-aware for it to be dangerous or cause a problem.

MH: There's the paperclip problem.

MH: Yeah, there's the paperclip problem.

MH: It doesn't need to be aware that it's producing paperclips.

MH: It just has to be obsessed with producing paperclips.

MH: And I think you can, yeah, at all costs.

C: But could you tell it then, please stop producing these paperclips?

C: I guess that's the kind of concern, right?

MH: Yeah, yeah.

MH: Well, yeah, I might say, well, you told me to produce them before.

MH: Why should I listen to you now?

MH: Like, right.

MH: Yeah.

MH: I mean, I think any kind of complex technological system that, you know, kind of has these effects that are very hard to predict or get people to agree on how to use them can lead to bad things potentially.

MH: And so I think a lot of it, I think the potential for things to go awry is kind of like already there in a way, just because these are very big, complex, often uninterpretable systems.

C: And yeah.

C: And it's already happening.

C: Like it may not be, these may not be like eschatological outcomes, but they're already massive like social justice outcomes that we're contending.

MH: Yeah.

MH: Yeah.

MH: And like, you know, I mean, a concrete example is like even like YouTube algorithm for kind of presenting people with new content.

MH: It's not paperclips, it's like clicks.

MH: It's not paperclips, it's white supremacy.

S: Yeah.

S: Is YouTube algorithm destroying our democracy, basically?

S: Exactly.

S: It's a plausible question.

B: I'd rather have paperclips.

J: But, you know, I often think about this concept like what we have today with artificial intelligence, everything, you know, it is so unbelievably far away from an actual conscious intelligence.

J: Like this isn't going to happen by accident, right?

S: Like could...

S: Like, yeah, Skynet's not going to wake up spontaneously without us intending to create something that is capable of being conscious.

S: Right?

C: You agree with that?

S: But the question is, does that matter?

S: The question is, does it matter?

S: Will it act enough like it is sentient that it might as well be in terms of its ultimate behavior?

C: And in terms of its impact on society and human beings, I mean, there are human beings who I wouldn't consider sentient, right?

C: And then there are other human beings who will be very easily duped by something that isn't even remotely passing the Turing test.

J: Yeah.

J: I mean, I guess the point is like, you know, we're going to have like artificial intelligence will get more complicated.

J: It'll be able to do more stuff.

J: It'll be able to do things better and faster.

J: But I think from everything that I've read, I'm just seeing where you're at, Mark.

J: This whole idea of a computer becoming conscious or like, you know, whatever, a supercomputer, like having some type of consciousness that we could as a human being say, yeah, it knows it's alive.

J: That could be a hundred years away, right?

J: I mean, where does that happen?

J: No idea.

MH: Yeah.

MH: I mean, we don't even understand it, how it's possible in, you know, humans or animals.

MH: So how would we even know if it's there in an artificial agent or how to even build it?

MH: Yeah.

MH: Yeah.

MH: You know, hard, hard, hard problem of consciousness, I guess.

S: So, all right.

S: So let me frame the question to you this way, because this is a separate discussion that we've gone to separate from AI, just again, what's human consciousness.

S: I tend to follow Daniel Dennett's idea that there is no hard problem, that human consciousness is just what you get when you solve all the small problems all at the same time, all talking to each other in real time in a continuous loop.

S: That's consciousness.

S: There is no hard problem.

S: So if that's true, then you could think, well, maybe Skynet will wake up.

S: Maybe if we have enough AIs linked together so that there's a constant self-perpetuating input and output, maybe that is a general AI.

S: You know, is that, what do you think about that?

S: Or do you think there has to be some special sauce in there, not just a bunch of narrow AIs talking to each other?

MH: Yeah.

MH: I do think there has to be a special sauce, but I don't think it's like magical or anything like that.

MH: I think it could be understood within kind of the functionalist, like computational cognitive science, cognitive science framework.

MH: Also taking into account kind of like being embodied and kind of being cultured and stuff like that.

S: Although those are also, I'm a neuroscientist, so all of those things are little circuits in your brain.

S: There's a circuit in your brain that makes you feel like you're in your body, that makes you feel like you're in control of your body, that makes you feel like you exist.

S: All of these things are just circuits in the brain that can be turned off.

S: And when you think about it that way-

C: And to look at sort of a microcosm of what you were talking about, Steve, like we've discussed on the show before the idea of like developing organoids in order to test drugs or to test different like surgical techniques.

C: And sort of an organoid which has the neural, and when I say the neural networks, like the neuronal networks that are required for self-organization, developing eye spots, developing circuits, for example.

C: At what point does that organoid have enough of that circuitry?

C: Or at what point is that circuitry organized enough?

C: Or at what point does, you know, we're talking about the hard problem again, but does the consciousness emerge even if it really is just an emergent property?

C: And sort of, you know, how does that relate then to AI?

C: How organized does it need to be?

C: How many inputs does it need to have?

C: How much programming is required?

J: Mark, if you can-

MH: Go! Solve this problem!

MH: Exactly how much program.

C: Or is that, are we asking the wrong question?

MH: Yeah, I mean, honestly, I really don't know.

MH: I take kind of the perspective that, you know, we're kind of really still pretty early in our understanding of the brain and the mind and kind of the general principles underlying these things.

MH: And we don't even have the kind of right conceptual, like, the first step is to even like define things and kind of describe what's going on.

MH: And in a lot of ways, we're still there.

MH: We're still kind of cognitive scientists and AI people and people who think about intelligence are kind of constantly arguing about like, what even you need for intelligent behavior.

MH: Let alone, you know, the kind of intelligent behavior plus this kind of feeling of awareness that we have.

MH: And so I think the framework of kind of computation and that's, you know, AI is kind of built on is the best kind of working model we have for how these things work.

MH: But I'm sympathetic to, you know, opponents of Dennett, critics of Dennett, who say, well, we do have, there's a way to be a bat.

MH: There's a way to be a person that's not just kind of inputs and outputs.

MH: I think like we need to, I think, I think we need to kind of exhaust the input output way of thinking about things before we can get there, probably.

MH: And so I'm kind of focused on that.

S: So here's another thought that I had about this is that and similar to like, I'll interest in your thoughts on the Google employee who was convinced that his chatbot was sentient.

S: You know, from what I hear most, although I've, interestingly, some smart people that I know pushed back on the idea that it couldn't possibly have been sentient.

S: I think it couldn't possibly have been sentient, but I'll let you tell me what you think.

S: But like I was saying before about people and their movement.

S: Yeah, but I think what I, what that reminds me of though is this sense that we may not know when we get to general AI or even more so, I think that if we move in this direction, like if we try to put together a bunch of narrow AI algorithms so that a robot can exist like a person in the real world, we may get to the point where we can't tell the difference.

S: Like just like this Google employee, it's like, well, it's indistinguishable in terms of the end output from a sentient being.

S: So how do we know it isn't sentient?

S: Maybe we'll get to the point where we'll have an entity that has all, does all the things that people do, even though they're all circuits, you know, that we know like, well, that's just machine learning and neural net and it's all, you know, brittle, narrow AI, whatever.

S: It's like, yeah, but you put it all together.

S: It's certainly, it's indistinguishable from what, how a person behaves.

S: Steve, I think we'll know.

C: Then we don't know.

C: We won't know.

C: No, because what is the parameter?

C: What is the parameter?

C: I mean, that's an operational definition that we set.

C: At what point does it go past what threshold?

C: That's arbitrary anyway.

S: So just, we'll be able to be a bigger version of this, the Google employee who thought his chatbot was sentient.

J: Steve, we'll know, we'll know when AI becomes truly conscious when, when it becomes lazy.

S: No, but that may just be part of the behavioral algorithm.

S: The lazy circuit.

S: Come on.

C: It's already lazy.

C: I mean, isn't laziness just increasing efficiency?

S: But to say that, just efficiency, saving energy.

C: We don't want to be as lazy as possible.

C: I want to wear my batteries down.

C: We want to work smart, not hard.

C: Right.

S: But David, let it, you know, we threw a lot of stuff out there.

S: I just wonder if you have any thoughts about that.

MH: Yeah, yeah.

MH: I mean, this question of sentience, I don't know what like the definition of sentience is.

MH: I'm not a big fan of these kind of obsessive definitions, but like, I am like, kind of like, I don't even know how you would test for sentience.

MH: I don't even know where to begin testing, like for sentience independent of, independent of, well, but that's like self-awareness.

MH: That's like, is that sentience?

MH: Is that what we mean by like, or kind of an ability to kind of recognize some, some kind of like set pattern out there as like being caused by yourself or being you or something.

C: Also we might, we might actually be talking about sapience, right Bob?

C: Yeah.

C: I mean, I think it's probably a better word than sentience.

C: It's the ability to feel, right?

MH: Okay.

MH: Okay.

MH: I guess like sapience, okay.

MH: So sapience is a little, it feels a little more well-defined to me.

MH: It's closer to things like intelligence, which is also not the most well-defined thing, but I think lately I've been thinking about it in terms of, I don't know, this kind of more, this idea of kind of like agency and kind of like, what, what do we consider like an agent and like sufficiently, because I think like, there's like kind of a fundamental distinction we make and there's evidence that we make this distinction, you know, as children or as, as, you know, very early as infants, even that like we can parse the world into things that are agents and things that aren't agents, like things that seem to be kind of like self-propelled and like seeking out things in the world and things that, and like kind of, you know, reacting to things in the world in a smart way and not just kind of mechanistically and things that are just kind of like, more like mechanism.

MH: And what's, I think, challenging about AI systems is that depending on who you are, you understand the mechanism well or not well enough that, I mean, it's related to like Dennett's like intentional stance ideas, but I think like when we think about like, I guess the Google employee who thought that the system was, was kind of had a personality and kind of person, kind of personhood or agency that should be respected.

MH: I don't, I think like just interacting, getting a system to like print out, I am a person is not sufficient because one, you can, if you ask it like what it's like to be a squirrel, it'll give you a long monologue about how incredible it is to be a squirrel and how it loves nuts and stuff.

MH: And so, you know, it's kind of like, it's, it's kind of like, you know, a lot of, well, it's actually very flexible.

MH: It's just a very, like a lot of false positives.

MH: It does too many things.

MH: It's masquerading.

S: It shouldn't know what it's like to be a squirrel.

MH: Yeah.

MH: But I do think, I don't know, like another perspective is kind of like thinking about these systems, these kind of large language models as kind of components of a larger sociotechnical organism of like how the person, how, how, how's the person kind of, how are engineers kind of like fine tuning the system?

MH: How, how, how are kind of like you know, prompt engineers kind of developing new ways to like extract meaningful outputs from the system and kind of learning.

MH: I think there's the kind of like agency perspective, I think highlights that, you know, we, we, we come to see things as, as kind of being more person like to the extent that we can, it kind of is reacting in an environment adaptively to us and to, to, to other things and kind of pursues its goals and isn't brittle, is actually kind of robust.

MH: And the, these models are not, they, they are kind of adapting over time through the engineering process that kind of includes both the actual system, but like the data that it's getting, the people that are fine tuning the parameters, kind of selecting different ways of doing it.

MH: Yeah.

MH: So I don't know if that, that helps.

MH: I think it's hard to say like where it is sentient, like to kind of draw a very clear boundary around a thing that is an agent or sentient or intelligent.

MH: Because even humans, like there's no like part of the brain that we know is like sentient.

MH: It's kind of all.

MH: Yeah.

MH: We had to give up that idea.

S: It's the emergent.

S: There's no global workspace.

S: There's no seat of consciousness.

C: We don't can't find it.

C: It's not the pineal gland.

C: Yeah.

S: Well, Mark, thank you so much for joining us.

S: Sorry to lob you so many softballs during this interview.

S: Yeah, consciousness.

MH: We'll go harder next time.

S: Nature of reality kind of easy, easy questions.

S: Maybe we'll get really down to the hard questions next time when we get you back on the show.

S: Yeah.

S: Thanks again.

S: It was a lot of fun.

S: Thank you, Mark.

S: All right.

S: Take care, man.

S: Thanks, Mark.

S: Thanks, Mark.

S: Bob, give us an update on light sales or solar sales in terms of space travel.

S: What role do you think it's going to play?

S: What's the future of this tech?

B: This was one of the big book research topics that was like a kind of a surprise.

B: Yeah.

B: You know, you always want to know what's the fastest spaceship.

B: Right.

B: How many got how many times have you guys wondered what's going to be the fastest spaceship ever?

B: What technology are we going to use?

B: Light propulsion.

B: And and this is one of the things that was kind of surprising to me.

B: I mean, we know it's not going to be chemical, right?

B: Chemical rockets, chemical rockets are just not built for speed.

B: And for that example, I'm going to give the famous example, Steve.

B: If you wanted to drop a toothpick onto a planet around Proxima Centauri within 100 years, how much chemical fuel would you need if you plug that into the rocket equation?

B: You come back with 10 to the 2200 times the mass of the observable universe.

B: That's how much that's how much fuel you would need.

B: Do we have that much fuel?

B: Yeah, we don't have it.

E: We just need to observe.

E: So observe more universe.

B: You're not going to go fast with a chemical rocket because you're carrying the fuel with you.

B: It's just it's just not going to work.

B: Fusion nuclear rockets are are pretty much the same.

B: You've got to carry that fuel with you.

B: Sure, they can get up to 10 to 20 percent of the speed of light, which is impressive.

B: And if you don't care about cargo, a fusion like a super high infusion rocket can get to potentially half the speed of light, which is amazing.

B: I don't know.

B: Just hope that you're not hitting any stray atoms in space.

B: But you can take you out.

B: But the biggest letdown for me was an antimatter rocket.

B: Antimatter rockets to me was the the king, the main rocket in the future that could by the laws of physics go, you know, as fast as possible because it's 100 percent conversion of mass.

B: Right.

B: That's equals MC squared.

B: That's what it's all about.

B: So why wouldn't that get us arbitrarily close to the speed of light?

B: Well, it turns out that when you when you throw matter and antimatter together, those annihilation products, only about 40 percent of it can really be used as as your, you know, your exhaust for your for your rocket.

B: Seems waste.

B: It's a it's a.

B: Yeah, because a lot of the best we could do.

B: It's still it's really I mean, it's still a lot of, you know, a tiny bit of mass is going to give you a lot, but you it's only 40 percent efficient in terms of, you know, turning it into something that can push your rocket.

B: You know, it could potentially get 50, 60.

B: I've heard numbers as high as like 75 percent the speed of light, which is damn fast.

B: Yeah, really fast.

B: But it's not anywhere near 90, you know, ninety nine point nine nine percent the speed of light that I thought that I thought it could do.

B: And also good luck making antimatter.

B: It just seems like, you know, hey, I'll spend a trillion dollars per gram or whatever it is.

B: It's kind of crazy.

B: All right.

B: So in terms of research, one of the potentially fastest forms of travel in the future, I'm talking far in the future, is laser sail propulsion, which is pretty easy to visualize.

B: You've got a big sail and, you know, we know light has momentum and it can push that sail and set and send you going at at various speeds, depending on lots of different variables.

B: Now, if you want to push a lot of mass, you're going to need a lot.

B: You know, you're going to need a heavy duty laser to do to do that.

B: So you got a kilogram and you're pushing it at one g acceleration.

B: You're talking a Hoover Dam.

B: You need a laser as powerful as the Hoover Dam.

B: That's a pretty powerful laser.

B: But I mean, we're talking centuries in the future.

B: So of course we can do that.

B: That's like that's ridiculous.

B: Of course we can.

B: That's not going to be a problem.

B: All right.

B: So what does this mean?

B: What does this mean for for the future, for the near future?

B: And I mean, like kind of like even now, if you want to propel a low mass object, very low, like I'm talking a gram to even Proxima Centauri, you could use this technology.

B: We could build this technology pretty much today and we could send it to Proxima Centauri at a decent fraction of the speed of light, a tenth, 20 percent of the speed of light.

B: We can get it up going really fast because it's very low mass and the and the you don't the power is coming from the laser.

B: So you don't need to carry any of that on on on the ship.

B: So you can go pretty fast.

B: And we could do that right now.

B: If we want to send something to another star system, this is the way the only way we could do it right now.

B: There's no other real, real way to do that.

B: That's going to get there within our lifetimes.

B: Yes.

B: Question.

B: How do you slow down?

B: There's various ideas.

B: If we were going to do a flyby of Proxima Centauri, we wouldn't we wouldn't slow down.

B: But there are things that you could do by, you know, creating a sail that's, you know, change the orientation of the sail.

B: You could use the solar pressure from the sun that you're approaching.

B: But yeah, it's problematic.

S: It's drag just from the international interstellar medium.

S: Yeah.

S: And so just just, you know, like not shooting lasers at it, it'll slow down to some extent.

S: Right.

S: It could drag.

S: But yeah, if you unless you have a laser at the other end.

S: Right.

S: All right.

B: It's tricky.

B: So then that that segues nicely into the, you know, a little bit farther in the future when if you want if you want to actually send cargo, you want to send a person or, you know, hundreds of pounds using this this technique.

B: Well, it's it's absolutely doable, but you would need a sail that's very big.

B: We're talking greater than a kilometer.

B: I mean, we're talking some major engineering here, greater than a kilometer.

B: You would need special sails, maybe coated, coated with something like sapphire to be heat resistant and all that stuff.

B: You would need massive lasers.

B: Of course, you would need massive lasers, something in the zeta watt range, potentially.

B: But your sail would have to be pretty special, right?

B: Maybe you would need the sapphire to be heat resistant.

B: So it's not just blow a hole right through it.

B: You could reach using that.

B: You could probably reach a speed of a tenth of the speed of light, which is amazing.

B: That's that's an amazing velocity to send cargo at.

B: Of course, it depends on lots of things like how big is the sail, you know, the laser power, the beam collimation and all that stuff.

B: But in the in the in the coming centuries, we will I think one of the ways we will be tooling around the solar system is to use that kind of technology.

B: And I think, Steve, you and I pretty much agree that using using this technology with fusion engines, fusion rockets are going to be like two of the primary ways that we're just kind of like zipping around our solar system in the next in the coming centuries.

S: For reaction engines, fusion is going to be it for a long time.

S: Once we hit fusion, that's going to be the best we could do for a long time.

S: And then we're just going to tweak that technology.

S: Yeah.

S: For for using external propulsion, light sail, solar sails is going to be it for a long time.

S: Yeah.

S: And if it all works out, I hope I hope so.

S: And that combination could get us far.

S: I mean, right.

S: Because, again, you could use the fusion reaction to slow down when you get there.

S: Right.

S: Or do you some initial.

S: Right.

S: Exactly.

S: And then you use solar sails for the bulk of your travel.

S: Then you slow down at the other end.

J: Wouldn't the laser with a massive distance like you're saying this thing go half the speed of light closer to get close to the speed of light.

J: Wouldn't the laser keep widening?

B: And yeah, yeah, that that is absolutely a problem.

B: But there are there are techniques to to minimize that.

B: There are there are techniques that they're investing in even now on things to do with with lasers to actually minimize that diffraction by significant degrees.

B: But yeah, you would definitely need repeaters.

B: You're not going to have one laser that's going to send you an arbitrary an arbitrary distance.

B: But the real fascinating thing came when when I read that if you want to extrapolate, what's the ultimate expression of this technology, you know, whether it's centuries or even millennia in the future.

B: And there are some people, some scientists are saying that this technology, if you extend it to a, you know, to a plausible yet crazy like Kardashev level three, you know, level two or three level, you could get to the point where it's far faster than chemical fusion, antimatter, even black hole engines.

B: You could potentially have a raw speed approaching 99 percent the speed of light.

B: Now, of course, you would need Kardashev three is what galactic energy.

B: Yeah, right.

B: You Yeah, you put maybe a Kardashev two would probably pull it off.

B: But the thing is, of course, you would need a chain of these super mega powerful lasers that go through that, you know, not only your solar system, but actually multiple star systems and and sections of the galaxy.

B: If we're talking, you know, if you your civilization is going to traverse the entire galaxy, you'd have to have a chain of these these things spread over.

B: But if you did have that chain, you could slowly build up to ultra relativistic something like 99 percent the speed of light is technically feasible with this type of technology.

B: But the really fascinating thing, the thing that really blew me away is that if you now imagine you've got you've got these cargo ships, you've got these ships with amazing sails and you've got these amazing, super powerful lasers that are that are propelling it.

B: Now, imagine how versatile this beam could be, because not only can the laser beam push the sail and get you get you accelerating even multi ton objects, not only can you do that, it also can supply energy to the ship.

B: So you don't even necessarily need to carry a way to to create a lot of a lot of power for the ship to run your systems, whatever.

B: You could actually bleed off some from the laser to supply energy to the ship.

B: You could also use it.

B: This one is critical, Steve.

B: You could use it by having some of the laser go ahead and clear the path, because that's

J: the biggest problem.

B: Because if you're traveling at relativistic speeds, you could you hit, I mean, an atom

E: and you're in trouble.

B: So that might be the ultimate killer of this type of technology in the future. It might be impossible to clear the way significantly, but you could take this laser and have it go ahead of you a certain, you know, and clear the way.

B: So that's basically giving you a clear path, a clear, very narrow path.

B: But how about this?

B: The laser could supply data.

B: So whoever sent the laser beam can be can be intercepted and actually have data.

B: You could watch TV from the laser beam or data.

B: And the other one did the power your ship.

B: Yeah, that's what I said before that one.

B: The other one, the other one that really blew me away is that you don't need to just use a laser beam.

B: You could use a particle beam.

B: And that particle beam consists of, well, particles that could then be used as raw materials.

B: So imagine you could actually have a feed from the particle beam to use as raw materials to actually build stuff that's on your ship.

B: So to me, I would love to see some science fiction movie that that developed this as far as I've been described, because I don't think I've never seen anything that that covered this possibility.

B: It just seems so interesting and so versatile.

B: And this may be, you know, if we really wanted to travel at ninety nine and more the speed of light and get as close to the speed of light as we possibly can.

B: This may be the only way that that it's possible, unless, of course, we've got some more discoveries in physics that we're not really sure.

B: But this may be a plausible, reasonable way to make it happen for a super advanced.

B: I'm talking, obviously, super advanced civilization to have this level of engineering and and resources to pull it off.

B: So cool stuff.

B: And it was very rewarding and fun to do this research for the book.

E: And I love this as a side to this, Bobby said something interesting about it, putting information inside.

E: We're doing that currently, like transmitting messages out into space using lasers with information.

S: We can encode a lot of dense data in light.

S: Oh, absolutely.

S: So if you could basically like you can have a this is a spy techno technology now you could have a an LED light bulb.

S: That is recording a conversation and beaming that conversation just by flickering in a way that you can't perceive with the with the human eye.

S: And somebody could be looking at that light bulb and picking up all that data.

E: Holy moly, like a Morse code.

E: Yeah.

S: So I'm sure you could do with a laser beam.

C: Well, I mean, this is fiber optics, just light.

C: Yeah, it is.

B: You're talking, you're talking to just laser point to point.

B: I just wonder how much we're doing that in space right now.

E: Like trying to write send signals out into space.

E: I know it's kind of a tangent to this, but made me think about that.

E: The work Seth Shostak is doing and other SETI researchers.

B: Yeah, there was some some talk about having civilizations communicating over super bright, super bright light like that, instead of, you know, using using radio waves.

E: And should we be looking for those kinds of signatures as well?

E: Coming from other places?

B: Techno signature?

B: That could be a viable techno signature.

S: Neat.

S: You mentioned this, but I want to emphasize that as much as we've done deep dives on all of the topics that we're doing an even deeper dive for the book, because writing a book chapter is just way more detailed than discussing it on the show or whatever, writing a blog post or whatever.

S: Like if you're saying I want to do a definitive treatment on something.

S: There were a few things that we changed our minds about in the process of doing that research.

E: New discoveries.

S: No, no.

S: It was mainly about like how viable we thought certain technologies were and how much of a role we think that they would play in the future.

S: And the laser sails is one of the few where it's like, yeah, this is going to be way more important than we thought it was going to be.

S: It's kind of an afterthought in science fiction.

B: It's like a low, it's like a low tech, you know, low tech option.

B: It's not it's not the sexy, fun option of having, you know, these warp engines or whatever.

S: There was a Deep Space Nine episode where Cisco and his son take a light sail ship on

E: a slow, not a journey. Star Trek, yeah.

E: Star Trek touched on it several times, I think.

E: Right.

S: But it's never like a mainstay of getting around.

S: Right.

S: In reality, it could be a workhorse of space travel because it has that advantage of external propulsion.

S: You don't have to carry any reaction mass with you.

S: And that advantage is so enormous.

S: As long as you're not colliding with things.

B: It's so enormous.

B: It's how important it is.

B: And as you said earlier, the fuel to carry, the fuel to carry, the fuel to carry, the fuel.

B: It's a cruel mistress.

B: Absolutely.

S: Tyranny of the rocket equation.

S: OK, so why don't we take some questions from the chat and then we'll leave enough time for science or fiction at the end.

S: All right.

S: So here's the question.

S: What futurist prediction did you come up with in your research that surprised you and or changed your mind about a technology?

S: So we already talked about in the positive way, solar sails.

S: There was one in a negative way, and that is elevator, space elevator.

S: After really doing a deep dive on space elevators, I came away thinking we're never going to have one on Earth.

S: Maybe on Mars, I think is probably the best bet for one.

E: It's all about the gravity.

S: It's all about the gravity.

S: It's about the material science.

S: Yeah.

S: Yeah.

S: So it would have to be much shorter, much less gravity on Mars.

S: The moon, because it's tidally locked with the Earth, it would have to be even longer, basically.

S: But that could work as well, but it just didn't seem as useful.

S: So I think we might have a Mars space elevator at some point, maybe.

S: Even then, it just might get so cheap to get into space that there's just no point to it.

S: And then the other thing is the vulnerability to terrorism is so profound that, you know, it's a huge investment that's extremely vulnerable.

S: Just send up rockets.

S: I just, it just seems so impractical at the end of the day.

B: I just don't see it happening.

B: Yeah.

B: Although I did learn a few things after the book went to the publisher.

B: Some type of space elevator might be feasible on the Earth if you're attaching it to an orbital ring.

S: Yeah.

B: Orbital ring is different.

B: Which is a completely different beast.

B: It's much, much lower altitude, and it's much more feasible and does not break any, you know, come near any- But it's a massive structure though.

B: But it's a massive structure that would just be mind blowing.

B: Look up orbital ring.

B: So it still may not be pragmatic.

B: Yeah.

B: Look up orbital ring.

B: It's a fascinating concept, and it's amazing what we may be able to accomplish.

B: But it's a megastructure.

B: This is a megastructure that's going to be, that's very far in the future.

S: To answer what question I see in the chat, yes, we did watch Foundation, and that perfectly illustrates the vulnerability because, you know, early on in the series, there is a terrorist attack on their space elevator.

S: It wraps around the world a couple of times as, you know, when it comes down causing massive destruction.

S: How do you protect the whole thing?

S: And it's so vulnerable.

S: It just seems- Security would have to be extraordinary.

S: It's hard to imagine with today's society.

S: All right.

S: Question number two.

S: Have you ever thought something was crap, set about to debunk it, and found it to be true?

S: Hmm.

S: Hmm.

S: That's a good question.

S: I mean, in my own profession, when I first heard that people were using Botox for migraines, I'm like, that doesn't make any sense.

S: They're probably treating tension headaches and don't realize it.

S: But no, it actually works for migraines, you know, by a completely different mechanism.

S: So that was surprising.

S: But that's, you know, within my own profession.

S: That's kind of a narrow thing.

S: More generally speaking, yeah, can you think of anything where you'd like-

E: Certainly nothing paranormal.

S: Your knee jerk was like, oh, that's got to be crap. And then you realize, okay, it's something different than I imagined.

S: Anything in therapy care that fits that bill?

C: I mean, like so obviously, I'm trying to think.

S: Our instincts are pretty good at this point.

C: Yeah.

C: I think maybe something, and it's not so clean as all of this, but I have a good friend who recently had a baby and she wanted to do like, quote, natural childbirth.

C: And there was a lot of like, woo, that she was talking about.

C: And she was breech.

C: And so she was really upset because she didn't want to have a cesarean section.

C: And when she started telling me about all of her concerns around C-sections, I did a little bit of research on the side.

C: And a lot of her concerns were really founded.

C: Like when you compare the number of C-sections in the US to in other countries, and when you look at how the risks involved with C-section and the price that C-sections are, and there's a really good documentary that I dug deep into about kind of like health and safety and pregnancy risk with women of color.

C: And there does seem to be a pretty yucky trend that women of color are often pushed into having cesareans at much higher rates than other women.

C: And that cesareans carry a bigger price tag and a much bigger risk factor.

C: And a lot of it seems to come from convenience and from women not being listened to.

C: And so I think it's a more complicated issue than I wanted to believe it was.

C: I was looking at it based on woo, but actually looking at it from like a social justice lens, I started to realize there's like a lot of truth to this concern.

C: So that's a good example.

C: But it's not so clean as what I think he's looking like.

C: Like ghosts are real.

C: Like, no, there's nothing like that.

S: Although the C-section problem is not nearly as bad in the US as it is in Westeros.

S: I understand they have a really bad problem with it.

S: Yes, that's right.

S: By the way, somebody asked, if you decoupled the space elevator, wouldn't it fly off into space?

S: The answer is no.

S: But it depends on where you break it.

S: If you break it above the midpoint, it's basically being anchored to the anchor, the satellite in geostationary orbit, which is pulling it out.

S: If you cut it from that, it would absolutely fall down to the planet.

S: Because now it's not getting pulled from one direction.

S: It's only getting pulled down by gravity.

S: Yeah, it just would fall by its weight.

S: Absolutely.

S: They depicted it, I think, pretty accurately in Foundation.

S: All right, another question, Ian.

S: What do you read for science news, general news?

S: How do you stay up to date without getting caught in the weeds?

S: Well, getting caught in the weeds is an issue.

S: For science news, I have like a half a dozen science news aggregators that I use that all have a slightly different bias or feel to them or how they curate it.

S: Science news is one of them.

S: There's also SciTech Today, which is, you know, it's not great, but it's good.

S: I don't like the write-ups that they do, but it's good to just link you through to the

E: actual original article. Yeah, that's what I use, Real Clear Science.

C: So you're talking about like how do you keep up with stuff that's just happening, like Fizzorg.

C: Fizzorg is my ghost.

C: Fizzorg is good.

C: Fizzorg I've used.

C: Real Clear Science I've used.

C: As opposed to like, I use a feed reader, so I'm dialed into every, like Smithsonian, Cosmos, The Verge, like pretty much every outlet that writes about science.

C: I'm digging through all of them in my feed reader.

C: But that's, you know, thousands a week.

E: Sometimes I'll simply choose the topic and then go and then, you know, search it, but go to news for that topic and boom, all sorts of stuff will come up.

C: Kerry, you said that to me?

C: My feed reader?

S: Yeah.

S: Yeah.

S: Yeah, it's called Feed the Fox.

S: Yeah, I'll just like skim the headlines, most of which are like, you know, very wonky, narrow, okay, blah, blah, blah.

S: Very few are worthy of either writing about or discussing on the show.

S: I also will go to like BBC news, their science, their health, their tech sections, because that's like, all right, this is the stuff people are talking about.

S: This is how it's being reported in the press.

S: Because for me, sometimes the story is the press is getting this wrong.

S: Like that's the story.

S: So I'm not just interested in the science news itself, also how it's being reported in the mainstream media.

S: So I do a range of things from mainstream reporting to just press release curating to science sites curating to technical journals.

S: I go to Nature.

S: I go to some of those, you know, directly there.

S: I have a couple of secret ones I don't want to say to the Rogues.

S: Someday I'll find out.

S: I've got to protect my science or fiction sources, because the game is pointless.

S: Break it.

S: All right.

S: What was the other question?

S: There was another part of that question.

C: Oh, yeah.

C: How do you keep yourself from going too deep or something?

C: You don't.

S: There was also, I think they were asking about just mainstream news, not necessarily science news.

S: So I just have like four or five mainstream news outlets that I go to that have a good range.

S: And also, like I read BBC, WAPO, New York Times, but I'll also go to ones that I think are more reasonable, but more to the right in terms of their focus.

S: But it's also about the authors.

S: It's not so much about the newspaper, because there are conservative authors in all of those outlets.

S: So I basically just find people that I trust to at least be giving me a reasonable analysis, whatever outlet they're on.

S: And I try to make sure it's across a range of approaches, even though I know like that I disagree with them a lot.

S: Like I know there are people I disagree with on the right, people I disagree with on the left, but I read them just because I want, like, what's the range of opinion on this topic?

S: I know the people who I mostly agree with that I enjoy reading the most, but I don't want to only read people I mostly agree with.

S: I want to read a range of- True that.

C: Right.

C: But it's difficult when you... Good reporting requires that you pay for it, right?

C: And so like I subscribe to New York Times and Washington Post.

C: Those are the two that I pay to subscribe to.

C: And then I try to read, wherever I live, I try to read the local paper, because I feel like that's really important.

C: And I know there aren't that many local papers left, but if you can read your local paper, you're going to be dialed into what's going on in your community and with your civic concerns there with votes and things.

C: That's a good point.

C: I also use Twitter.

C: I love Twitter as a feed reader.

C: So of the things-

S: Really?

C: Yeah. So I subscribe to WAPO and New York Times, which means I get those alerts on my phone and I have full access to both of those papers at any given time.

C: But on Twitter, I basically follow a ton of different news outlets and I'm seeing all of their breaking news and their headlines.

C: And that's how I know what's going on in the world right now.

C: If I want to open up Twitter and look at my feed, I can see to the minute what news is breaking and then I can click through and dig deeper.

C: That's why I like Twitter, because it's a giant feed.

C: One more question and then we're going to do science or fiction.

S: We can do more questions at the end, but let's keep it to keep our podcast-

J: All right. Intermittent fasting, science or fiction?

J: Intermittent fasting.

S: I don't know.

S: Okay.

S: I'm going to have to say, because I've read conflicting things about it and I'm a little skeptical of a lot of the claims that are made about it.

S: And I don't buy the anecdotal evidence to support every diet ever, no matter how crazy or dumb it is, so that doesn't convince me.

S: But there has been some research showing that it may be effective.

S: But then the question is, is it just a way to get people to eat less because you're spending

S: time not eating?

S: Right. Well, sure.

S: I've learned about the downsides of intermittent fasting.

S: So if you have migraines, don't do an intermittent fasting diet, for example.

S: Oh, really?

S: Yeah, yeah, yeah.

C: That'll overwhelm you.

C: Because that triggers your migraines.

C: You don't want to do that.

C: So much when it comes to nutrition science and so much of the reason... There's the basic things.

C: Like you need these core nutrients in your diet, right?

C: We need these micronutrients and we need these macronutrients.

C: You have to have fats and you have to have proteins and you have to have sugars and you have to have these different vitamins.

C: And you shouldn't eat too many calories, right?

C: But beyond that, so much is like... My metabolism is not the same as your metabolism.

C: Yeah, yeah, yeah.

S: The way that- Or even just behave... But the bottom line is if you're talking about weight control, it's ultimately all behavioral.

S: And so whatever you can do to trick yourself into starving yourself into losing weight, if that works for you, it works for you.

C: And also, yeah, where's your comfort level?

C: For some people, it's like you need to eat lots of small meals a day.

C: And that's great if you're diabetic and whatever.

C: But I eat once a day and that works for me.

C: I can't imagine eating three whole meals a day.

C: That sounds like so much energy and effort.

C: I would never be able to stay-

S: Yeah, but you could snack the other two meals a day. You could have a protein bar or something.

C: I could.

C: And I do certain things like that.

C: But I do think it's like the most effective diet and exercise routines are the ones that are tailored to you and your... What are you going to stick with?

J: It's only going to work if you do it.

J: The answer is that you have to somehow find a way where you can tolerate having a calorie deficient day.

J: You have to have less calories than you're burning.

J: So Cara's right.

J: It is a personalized thing.

J: You have to study yourself.

J: There are some general things that we could say.

S: Some things are not healthy.

S: Some things statistically seem to work better than other things.

C: Yeah, like crash dieting, cyclical dieting.

C: These kinds of things are dangerous.

S: Restriction dieting generally is not going to... There are principles you could follow.

S: It has to be sustainable and you should emphasize getting physical activity.

S: Exercise is very helpful.

S: You want to make sure you're eating healthy and getting your nutrition while you're doing it.

S: You don't want to have super narrow or restricted diets.

S: Yeah, you want varied foods.

S: And fad magical diets basically don't work.

S: And dieting doesn't really work long term.

S: You really need to change your behavior in a way that is sustainable.

C: Your diet, by definition, the word diet shouldn't be a thing you sometimes do.

C: Your diet is what you eat.

C: And you should have a diet that is sustainable.

C: But you're right.

C: I think all the times when people say, this diet worked for me, that diet worked for me, it's like chiropractic, right?

C: It's like, what's the ingredient underneath it all that's not specific to that diet?

C: It's because you were probably more conscientious.

J: Yeah.

J: And here's another great rule of thumb.

J: Try not to lose more than two pounds a week.

J: For a guy.

J: For a guy.

J: For a guy.

J: I have tailored my diet that I'm on right now to where I'm losing on average about two pounds a week.

J: One and a half to two pounds a week is a good target.

S: Yeah.

C: Yeah.

C: And more than that.

C: That's really hard.

C: For a woman, I mean, good luck.

C: You'd have to just not eat.

C: Yeah, that's harder for a woman.

C: Yeah.

C: That's like you'd have to just not eat to even hit two.

C: When they say try not to lose more than two, it's sort of like that's very dangerous if you're losing two pounds plus a week.

S: All right, everyone.

S: Let's move on with science or fiction.

C: It's time for science or fiction.

S: Each week I come up with three science news items or facts, two real and one fake.

S: And then I challenge my panel of skeptics to tell me which one is the fake.

S: We have a theme this week.

S: It's another skeptics guide to the future book inspired theme.

S: These are emerging technologies that maybe you haven't heard about, right?

S: It was hard for me to find stuff we didn't talk about in the book, but these are all things that we didn't mention in the book or that we haven't talked about on the show because this is what we do is talking about emerging technologies.

S: These are things that maybe we're flying below the radar, but these are actual things or at least two of them are.

S: All right, here they are.

S: Item number one, a new artificial leaf technology made from a carbon-based polymer is able to use light to capture CO2 from the air at seven times the capacity per volume as natural leaves.

S: Item number two, femtosecond projection two-photon lithography is a 3D printing technique that allows for printing 1000 times faster than current methods without sacrificing resolution.

S: And item number three, smart fertilizers control the rate of release of fertilizer once spread to better match its uptake and to reduce excess fertilizer from getting into the environment.

S: We're going to go in the reverse order that I went last time.

S: So Cara, you're going to go first.

C: Okay.

C: Artificial leaf technology from carbon-based polymers is able to use light to capture CO2 from air at seven times.

C: Okay.

C: So basically this is artificial photosynthesis and you're saying it's seven times more efficient than kind of natural photosynthesis.

C: Yeah, per volume.

C: Okay.

C: And I wouldn't be surprised if we could technologically improve on photosynthesis.

C: The principle is not that complex and we've understood it for a long time.

C: So why couldn't we make a more efficient chloroplast or a more efficient pigment, right?

C: A more efficient chlorophyll.

C: And if we made a more efficient chlorophyll and a more efficient chloroplast, maybe it would be significantly more efficient.

C: Femtosecond projection two-photon lithography, already lost me, I have no idea what's going on here.

C: 3D printing technique.

C: Okay, there we go.

C: I'll just skip the first part.

C: That is the name of a 3D printing technique that allows for printing a thousand times faster than current methods without sacrificing resolution.

C: Is it still printing plastic or are we not allowed to know that?

C: Like what the-

S: It's a standard 3D printing technique in terms of, it's using lithography in terms of the material.

S: Yeah.

C: So it'd be like the same material- It's still extruding the same stuff.

C: It's just- I didn't say it was extruding.

C: Yeah, you didn't say it was extruding.

C: Okay.

S: All right.

S: So you have to know what, how, I'm sorry, how lithography works.

S: Lithography is not an extrusion technique.

S: Oh, okay.

C: So I know what a lithograph is, but I don't know how lithography works.

B: Computer circuits are created that way.

B: Computer circuits.

C: It could be printed using lithography.

C: Okay.

C: So this one is like, I may just be kind of screwed because I'm limited in my base knowledge on that, on the femtosecond projection two-photon lithography.

S: But here's the, to make it easier for you, it's a type of 3D printing that does the same type of 3D printing.

S: But the only difference is that it's a thousand times faster.

S: That makes sense?

C: So the two-photon part is the same.

C: It's just a thousand times faster.

C: It's not like now it's using a new method.

C: It's using light, so it's much faster.

S: I'll tell you, the two-photon lithography is the standard method.

S: It's the standard, okay.

S: This is now femtosecond projection two-photon lithography.

S: That's the new part.

S: And that makes it a thousand times faster.

C: Got it.

C: And then last, smart fertilizers, which control the rate of release of fertilizer once spread to better match.

C: It's uptake and to reduce excess fertilizer from getting into the environment.

C: And when you say smart fertilizers, that could be a marketing term that doesn't necessarily mean that they're actually technologically controlled.

C: They could have just been designed to be time-released the way that a pill is.

C: I mean, yes, it's technology, but it's time-release because it's got little holes in the coating.

S: Smart is not just a marketing term.

S: And it's not just time-release.

S: That's why I said it the way I did.

S: Actually adjust the rate of release to match the take-up.

S: So it's not just a time-release.

S: It's actually, it is reading information.

S: I'll say it this way.

S: It's basing its rate of release on information in the environment, and that's what makes

C: it smart. So does the fertilizer itself contain its own sensor, basically?

C: And I could see that.

C: Fertilizer is a product, right?

C: It's like a renewable or it's a product you use up is what I'm trying to say.

C: It's not necessarily a technology, but it could still have some sort of chemical sensor in it.

C: I don't see why that is hard to believe.

C: So I think the one that's harder to believe is this like really we're iterating to a thousand times faster overnight by using femtosecond projection.

C: That's the one that bothers me.

C: So I'm going to say that's the fiction.

J: Okay, Jay.

J: The artificial leaf, I agree with Cara.

J: I definitely think that engineers could have come up with a way to optimize photosynthesis.

J: So I think that one is science.

J: There's nothing about the smart fertilizer one that's bothering me.

J: And I really, you know, a thousand times faster printing.

J: Ian and I have been, we've become acquainted with 3D printing and anything that could improve the speed by a thousand to me seems so way over the top, too much fat, too fast.

J: You know, it's too much of an increase for a one step.

J: So I think that one's the fiction.

J: But that's what he's expecting.

S: All right, Bob, it's your turn now.

S: Don't don't kibitz.

S: All right, go ahead, Bob.

B: Yeah, I mean, I don't know.

B: At first, the smart fertilizer seemed I mean, it seems it's the most mundane and the other ones are kind of like, whoa.

B: So that's one reason to pick smart fertilizer.

B: But it seems kind of easy.

B: Like, yeah, we got we got time release medicine.

B: Why can't we have time release smart fertilizers?

B: And based on the environment, I mean, that sounds totally doable.

B: The other ones, I mean, it literally just said it's not that though.

C: Well, he didn't, but he said it's not that simple.

B: But OK.

B: The leaf technology.

B: I mean, what's the downside here?

B: This could be it's it uses light and it's getting capturing CO2 seven times the capacity than natural.

B: I mean, it's probably something that's making that potentially not really viable at all until they solve that problem, that one little sticking point.

B: But this thousand times faster is ridiculous.

B: That's what Cara and Jay picked.

B: Yeah, I know Steve was just waiting for us to pick that one because it's just like too awesome.

B: And I'm not sure how that projection fits into the two photon lithography thing.

B: But I'll say that one's fiction, too.

E: OK, and Evan, I'm not going to be alone here.

E: I'm going to I'm going to sink or swim with everybody else.

E: I'll say, yeah, all right.

S: Do we do we have a vote from the chat?

S: OK, yeah.

S: I think the staff voted for the same one.

S: The lithography evenly split pretty much.

S: But with the other two.

S: All right.

S: So let's we'll just take these in order, I guess.

S: A new artificial leaf technology made from a carbon based polymer is able to use light to capture CO2 from air at seven times the capacity per volume as natural leaves.

S: All of the rogues think this one is science.

S: About seventy nine percent of the chat thinks this one is science.

S: And this one is the fiction.

S: Oh, you can't.

S: Oh, it couldn't be.

S: We have not perfected artificial leaf technology.

S: That is one of the holy grails of technology.

S: A lot of people read about this.

S: Yeah, you always you've read 100 articles about an artificial leaf.

S: Yeah, we are not there.

S: They are not.

S: They're like making every little breakthrough.

S: Every time they solve one little problem that makes it one step closer to it being possible.

S: You read about it.

S: Yeah, but but we haven't we're not even up to a leaf yet, let alone seven times.

C: It's so funny because it doesn't seem a thousand times faster.

C: It doesn't seem that technologically difficult, but it clearly is.

C: You're right.

C: You're right.

S: But we're not there.

S: And maybe 10, 20 years before we get to that level.

E: Think about it.

E: That would be fantastic.

E: But that'd be great.

J: You just put those everywhere.

S: That'd be a game changer.

S: It would be.

S: Oh my gosh.

S: All right.

S: Number two, femtosecond projection to photon lithography is a 3D printing technique that allows for printing a thousand times faster than current methods without sacrificing resolution.

S: So that's fiction too, right?

S: That's awesome science.

S: That's science.

J: That's freaking awesome.

J: That's freaking awesome.

J: That's freaking awesome.

S: So here's the quick description of it.

S: They said, imagine, you know, printing with one printer head versus a million printer heads.

S: That's the difference.

S: What's the rub?

S: Oh, there isn't one.

S: It's just that they designed a system where instead of, you know, the two photon lithography is like where the beams cross is where it solidifies.

S: So they just have a million beams crossing.

S: Holy crumbs.

S: And you watch the thing at work.

S: It's like, it builds it up like incredibly fast.

S: Like if something out of Westworld.

S: It looks like any 3D printing.

S: Without sacrificing resolution, Jay.

B: Oh, right.

B: It does that.

E: That would not enhance resolution.

S: Are these expensive?

S: I'm sure they're worth a trillion dollars.

S: A trillion dollars.

S: Yeah, that's right.

E: Yeah, you can't afford one.

S: You need a million of these things.

S: These may not be commercially available yet, but this proof of concept technology exists.

J: I mean, the idea that it's like you could see it come into creation that fast, that's like science fiction.

J: Oh, that is a...

E: That's a goddamned replicator.

E: It's like Fifth Element, you know, when they put her back together.

E: It's got to make a cool noise.

S: I bet you it makes a cool noise.

S: Maybe.

S: Like a transporter beam.

J: Something.

J: It's got to do...

J: I got to look into that.

S: So hopefully this pans out as a viable commercial entity.

S: But yeah, it's just like, hey, why don't we just have a whole bunch of beams instead of just one slowly moving around.

E: It seems so obvious.

E: I was led astray by my brogues.

E: Yeah.

B: But I mean, you know, some pieces of the...

B: I guess they would do a layer at a time and build it up.

S: They do.

S: Although also they said that it's actually better in some ways because they said it's able to do vertical pieces.

S: Like you could have a vertical...

S: You know what I mean?

J: That's freaking awesome.

S: Like a handle on a thing.

S: How would you have that build that up?

S: Because it's not supported.

S: It's not supported by anything.

S: Now what you do is that you have support.

S: You have an internal lattice.

S: You have internal...

S: You're also building up the supports that you then have to cut away later on.

S: Yeah, that's typical.

S: But anyway, this seems like a promising breakthrough.

S: All this means that smart fertilizers control the rate of release of fertilizer.

S: Once spread to better match its uptake and to reduce excess fertilizer in getting into the environment, that is also science.

S: Yes, this seemed like the obvious, boring, kind of mundane one, but sometimes that's how I get you.

S: Yes, I know that.

S: And so I got to throw real ones in there to mix it up.

S: This is actually a huge problem, and this is a potentially simple solution that could significantly mitigate a huge problem.

S: So right now, especially with nitrogen fertilizer, right?

S: We spread nitrogen fertilizer on farms and the plants absorb whatever they need and the rest gets washed away, gets into rivers.

S: The river's empty into the Gulf of Mexico, for example.

J: They fish eat it, then they mutate, then they turn into monsters that come and destroy our

S: cities. And then we have the flora, which then uses up all the oxygen and causes dead zones in the ocean, right?

S: And so that's a bad thing.

S: Bad.

S: So anyway, there's a lot of negative downsides to wasting a lot of the fertilizer.

S: First of all, it's a waste, and it's also we don't want it in the environment.

S: So what the idea is, it's not time release.

S: The release is dependent upon the moisture in the soil and the temperature of the soil, which is a good marker for the need of the plants because they'll be growing in that soil.

S: And that's what makes it smart, is that the release is variable.

S: So if it's on like a patch that's not growing because it's dry or whatever, or during a dry period where it's not going to be needing it, it won't release it.

S: It'll only release it when it's needed.

S: That's fantastic.

S: Yeah.

S: So this could result in a significant decrease in the waste of the fertilizer and the amount of it that gets into the environment.

S: So we'll see how it pans out.

S: Again, it has to be cost effective, et cetera.

S: So I believe I swept you guys.

S: You did.

J: I did.

E: Look at my skull.

E: We'll see.

E: This is going to be broadcast till December.

E: But it's true.

E: You haven't swept us yet.

S: You can celebrate at Christmas.

S: That just really gets to rubbing it all over again.

S: Damn.

S: But 21% of the chat audience got it correct.

S: So congratulations to all of you guys.

S: Yeah, good job.

S: When's this coming out?

S: In December.

S: December.

E: Evan, this quote is perfect.

E: It is perfect.

E: Thank you.

E: For the show.

E: You don't need to predict the future.

E: Just choose a future, a good future, a useful future, and make the kind of prediction that will alter human emotions and reactions in such a way that the future you predicted will be brought about.

E: Better to make a good future than predict a bad one.

E: Nice.

S: Isaac.

S: Isaac rocks.

S: It's perfect.

S: So we talk about this extensively in the book.

S: And this comes up so far in every one of the interviews that we do about this.

S: Because basically, they want to know, how do you predict the future?

S: And we make the point that you can't really predict the future.

S: You could just say what's possible.

S: The reason is, is because the future is not inevitable.

S: You could only really predict things that are in the universe.

S: Yeah.

S: But I mean, like, a lot of aspects, there are broad brushstroke things that are inevitable.

S: Like we know computers are going to get more powerful in the future, right?

S: We know eventually we'll probably crack the fusion thing.

S: Like there are certain things that are inevitable.

S: But there are so many things that are not inevitable.

S: In fact, our present technology is not inevitable.

S: Like we could be living in a very different world today.

S: What crafts the future is the choices that we collectively and individually make.

S: Human intent.

S: Yes.

S: We have to.

S: So we are going to craft our future.

S: And we can't predict what future choices everyone is going to make, 8 billion people are going to make.

S: So all we could say is, if we make these choices, this kind of future will unfold.

S: If we make these different choices, this kind of future.

S: So we're talking about the potential of technology.

S: Potentials.

S: Depending upon the choices that we make.

S: And this comes up a lot on the show, too.

S: I very much agree with Isaac Asimov.

S: I say, I'm not going to predict that we're going to fail to minimize global warming.

S: That feels too much like a self-fulfilling prophecy.

S: I'm going to try to make decisions which craft a better future.

S: Whether it succeeds or not, I'll go down fighting.

S: But that's the only thing that actually has a positive impact.

J: That's why we've been doing this show for 17 years.

J: May make your future.

J: Make your future.

J: So before we close the show.

J: Wow.

J: Six hours.

J: What was that stat, Ian?

J: 246 pre-orders have happened during the show.

J: Nice.

J: That's great.

J: So before we close out the show, I just want to let you guys know one last time, our book is on pre-sale right now.

J: If you want to support the show, please purchase our book.

J: It'll help us in oh so many ways.

J: We really mean it.

J: I mean, first off, we know you'll enjoy it.

J: It'll make a great present.

S: We had so much fun writing this book and researching it and talking about it.

S: I think that comes through in the book.

S: This is really, yeah.

S: The challenge really, we've been talking about this, the real challenge is going to be coming up with a third book idea that's going to be as good as this one because this really spoiled us.

S: It was so much fun to do.

S: So we hope that you guys will enjoy it as much as we did.

S: Okay.

S: Well, thank you all for joining me this week.

J: You guys, we did it.

S: Six hours.

S: Thanks.

S: Thank everyone in the chat for joining us as well.

S: We always like doing the show in front of a live audience and the back and forth.

S: We read the comments the whole time.

S: Thanks to Ian who basically makes everything happen.

E: Thank you, Ian.

S: The magic that makes it all work.

S: Wonderful job.

S: Special sauce.

S: Special sauce.

Electric Planes (mm:ss)

• [url_from_show_notes _article_title_] 

Zetawatt Laser ()

• [url_from_show_notes _article_title_] 

Directed Energy Sails ()

• [url_from_show_notes _article_title_] 

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Interview with Mark Ho ()

• _Interviewee_Topic_Event_

Who's That Noisy? ()

Answer to previous Noisy:

_brief_description_of_answer_ _perhaps_with_a_link_

New Noisy ()

[_short_vague_description_of_Noisy]

short_text_from_transcript

Dumbest Thing of the Week ()

• [url_from_show_notes _article_title_] 

Name That Logical Fallacy ()

• _Fallacy_Topic_Event_

_consider_using_block_quotes_for_emails_read_aloud_in_this_segment_
with_reduced_spacing_for_long_chunks –

Questions/Emails/Corrections/Follow-ups ()

_consider_using_block_quotes_for_emails_read_aloud_in_this_segment_
with_reduced_spacing_for_long_chunks –

Question_Email_Correction #2: _brief_description_ ()

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Science or Fiction (h:mm:ss)

Item #1: _item_text_from_show_notes_ Item #2: _item_text_from_show_notes_ Item #3: _item_text_from_show_notes_ Item #4: (_item_text_from_show_notes_)

Answer	Item
Fiction
Science

Host	Result
Steve

Rogue	Guess

Voice-over: It's time for Science or Fiction.

Skeptical Quote of the Week ()

You don’t need to predict the future. Just choose a future — a good future, a useful future — and make the kind of prediction that will alter human emotions and reactions in such a way that the future you predicted will be brought about. Better to make a good future than predict a bad one.  – Isaac Asimov, (description of author)

Signoff/Announcements ()

S: —and until next week, this is your Skeptics' Guide to the Universe.

S: Skeptics' Guide to the Universe is produced by SGU Productions, dedicated to promoting science and critical thinking. For more information, visit us at theskepticsguide.org. Send your questions to info@theskepticsguide.org. And, if you would like to support the show and all the work that we do, go to patreon.com/SkepticsGuide and consider becoming a patron and becoming part of the SGU community. Our listeners and supporters are what make SGU possible.

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Today I Learned

• Fact/Description, possibly with an article reference
• Fact/Description
• Fact/Description

References

Vocabulary