Season 4, Ep. 10 – TWiTH: Alan Turing is born, with Gun.io

Transcript

(THE FRONTIER THEME PLAYS AND FADES OUT)

Abbey (00:05):

[It’s] bright and sunny out. We’re gonna go ride motorcycles after work.

Deividi (00:08):

Nice. Yeah. That’s living the life!

Abbey (00:12):

I’m pretty excited, yeah. I have two motorcycles, and last week, my husband took my truck, and so I was like, “Oh, I’ll ride my big motorcycle,” which I have not ridden since I bought a little one in October.

Deividi (00:25):

Nice, nice. Do you go on long rides or just short rides around your town?

Abbey (00:34):

We do both. When I went to Ohio a few weeks ago, I was planning on riding my motorcycle up there, and it’s about seven to eight hours each way.

Deividi (00:45):

Wow. One sitting?

Abbey (00:47):

I saw that it was supposed to rain both days that I would’ve been riding. (Deividi: Oh, wow.) Not interested in that. (Deividi: <Laugh>.) That’s a fair weather activity for me.

Deividi (00:56):

Yeah. Wait, eight hours riding a motorcycle, is that one sitting only, or just…?

Abbey (01:02):

Don’t think my butt could handle sitting on a seat that long. (Deividi: Right <laugh>?) I don’t mind the gas issue, yeah.

Deividi (01:09):

Well, some days I can forget that I’m sitting for that long and just (Abbey: Yeah.) keep doing work.

Abbey (01:16):

True, true. Yeah.

Deividi (01:18):

Right? Riding a bike, staying like this for eight hours, like, your arms get tired, I bet.

Abbey (01:24):

And I don’t have cruise control, so you gotta be…

Deividi (01:28):

You’ve gotta be fully aware.

Abbey (01:30):

Well, thanks for wearing a Gun shirt. I like the branded merch on the podcast.

Deividi (01:35):

I’m branded, right?

Abbey (01:37):

I just have my sweatshirt. It’s very…

Deividi (01:40):

This is now my uniform. (Abbey: It is?) Yeah. I had three of these made: a gray one and two black ones. This is now my uniform.

Abbey (01:51):

Nice. I only have my zip-up, and it’s in the eighties here, so…

Deividi (02:00):

I don’t get to wear that enough here in Brazil. Definitely not most of the time. Not the weather to wear those.

Abbey (02:09):

No. Are you in the northern part of the country?

Deividi (02:13):

South, but still. Yeah, it’s pretty warm. We’re getting into winter, but it’s still not cold enough.

Abbey (02:21):

Alright. Well, Deividi, we are here. (RETRO SYNTHESIZER MUSIC FADES IN) This is like, a huge one for This Week in History. I feel like you really can’t talk about computers at all without talking about Alan Turing. (Deividi: Yeah.) I’ll kind of read through this rundown. It was really hard to condense this, because the life this man led was just so big. (Deividi: Right?)

Abbey (02:48):

Yeah. British mathematician, logician, and cryptanalyst, Alan Turing was born in London, England on June 23rd, 1912. From a very young age, he showed aptitude for numbers and logic, eventually landing at King’s College in Cambridge. While he was there in the 1930s, he founded the concept of the universal Turing machine, which is a theoretical mathematical device that could compute anything that is computable. That feels very broad, (Deividi: Yeah.) but that’s kind of like, how else would you describe it? To this day, it’s the most widely used model of computation in computability and complexity theory. Both things I don’t have much knowledge in. For that, he went on to get a PhD in mathematics for Princeton in 1938 and wound up going back to England. During World War II, he worked at Bletchley Park, which was Britain’s secret code breaking center. It was there that he made possibly his most renowned contributions to the war efforts in decrypting German ciphers, including their Lorenz cipher and the previously unbreakable Enigma code, which the German High Command was using for all of their battlefield communications. So once they figured that out, they could always tell where the Germans were. They built the machines used to decrypt the Enigma messages, and the decryption of his German naval messages was vital to the Allied victory in the Battle of the Atlantic, and his work was estimated to shorten the war by two to four years and saving over 14 million lives, which is just like…man. (Deividi: Yeah.) With computers at a time (Deividi: It is amazing.) at a time when, I’m sure, computing wasn’t even…

Deividi (04:34):

Wasn’t even a thing like, really. (Abbey: No.) I don’t think I can imagine any other person in the computer science space that has saved so many lives. That can put that contribution and directly relay that to saving that amount of lives or more. (Abbey: Yeah.) It’s insane.

Abbey (04:57):

And you know, like, even with all of the technology we have today, I don’t know that technology has saved 14 million lives put together.

Deividi (05:07):

Yeah, right?

Abbey (05:09):

So in the late ‘40s and early ‘50s, while he was working at the University of Manchester, he did even more pioneering work that we still use in AI and computing. He was the one who proposed the Turing Test, to test whether a machine could exhibit intelligent behavior comparable to a human. He also wrote papers on computer design, including all computable numbers, as well as descriptions of how to use the universal Turing machines. This is where it gets a little dark. In 1952, Turing was prosecuted for his homosexuality, which is pretty like, one of the most widely known facts about him and probably the most disturbing. (Deividi: Yeah, yeah.) It was illegal in Britain at the time. He opted for chemical castration as opposed to prison, which as we know is not something that makes a change. It just ruins someone’s life. Just two short years later on June 7th, 1954, he died of cyanide poisoning. I listen to a lot of true crime podcasts.

Deividi (06:14):

Controversial, at least. Yeah.

Abbey (06:18):

Controversial at the very least. It was officially ruled a suicide, but could have been accidental poisoning, could have been not accidental poisoning.

Deividi (06:29):

Yeah. His mother says he was happy, friends say he was happy. How happy you can be after going through that horrible chemical castration, but things were happening. He had a to-do list by his bed. Who has that if you’re committing suicide?

Abbey (06:48):

I can barely write a to-do list when I know I have to get up and do stuff. On the upside, in 2009, British Prime Minister Gordon Brown made an official public apology on behalf of the British government for, quote, “the appalling way he was treated.” Queen Elizabeth II granted a posthumous pardon to him in 2013, and the term “Alan Turing Law” is now used informally to refer to a 2017 law that went into effect in the UK that retroactively pardoned they who were cautioned, convicted, arrested, all of that, under the historical legislation that outlawed homosexuality, so…

Deividi (07:26):

Took them a long time to get there, that’s for sure.

Abbey (07:30):

Yeah. The fact that the British monarchy apologized in the first place, I feel is…

Deividi (07:36):

That’s something, yeah.

Abbey (07:37):

I’m sure a lot of people with artifacts in the British Museum would love to see them <unintelligible> too. (Deividi: <Laugh>.) It’s good that that still brought about some positive change in the UK, on top of all he had already contributed.

Deividi (07:53):

Yeah. Imagine how many men had to suffer through that, he being one of the most famous one, but so many people suffered with that insanity, having people be chemically castrated for being homosexual. It’s insane. Insane.

Abbey (08:16):

And to think, you know, I mean he was 41.

Deividi (08:22):

So young.

Abbey (08:24):

Forty-one. I will be 41 in a few months. Yeah, like…

Deividi (08:32):

I’m 37, and well, my whole career is based on the work of this man, but I’m nowhere near, maybe I’ll never make any contribution as relevant as those founding papers for computing, computer science. Imagine at 41, dying, how much more could [he] have contributed? He would be like 111 today, so…I don’t do math in public, but that’s probably it. (Abbey: <Laugh>.) <Laugh>. Imagine if we could get another, I don’t know, another 40 years of contributions from this man? And computers getting more advanced, how many more ideas he would have? Right? And just in the cryptanalysis phase, he lost his, his government clearance was revoked when he was persecuted. So just in that confined space, all of his contributions during the war and all of that, how much more he could have done if it wasn’t for them, right? He didn’t have access anymore. So that’s such a shame. I don’t know, we would probably have a lot more, and computers would be a lot more advanced had this man had more opportunity to go on living his life fully and producing more in the computer science space.

Abbey (10:05):

Do you think that it would’ve prompted, you know, like, the kind of the surge we see in AI right now, do you think that that would’ve happened earlier?

Deividi (10:15):

Possibly, right? ‘Cause he was already thinking about that stuff in the ‘40s and ‘50s, and none of this computing wasn’t thing, actually, right? (Abbey: Yeah. Yeah.) We would have been thinking about these things way earlier, if he was still producing.

Abbey (10:33):

Which is also terrifying, because look at how quickly we went from like, zero to a thousand with AI, just in the last, you know, year or two.

Deividi (10:43):

Right? And that’s what, that’s like, that’s public facing. AI feels like that, but it’s been like, that 10-year, overnight success, (Abbey: Yeah.) Like even if you talk about open AI alone, they’ve been, for a long time, building up to this and getting capital and resources to make the thing that is now a revolution by having that distinct app that works like that and other competitors moving closer to that, trying to compete with. But yeah, a lot more, I think we would get there much sooner, if this man was still being fully capable, working research after 40, 50 [years] and going on with his life.

Abbey (11:43):

I mean, and that’s still like, I think that’s a testament to how important, and how like, intelligent he was that the Turing test is still something that we use. Even if it has, you know, like, it’s controversial in some ways. I don’t know that there’s…I don’t know. My opinion is that it still feels like a good thing.

Deividi (12:04):

It’s still relevant. It’s still a thing.

Abbey (12:05):

It’s still relevant, yeah. And it was developed at a time where there wasn’t a host of external factors, (Deividi: Right.) because those didn’t exist, and that’s for better or worse. But like, would there be another way that doesn’t have outside factors that you could even use?

Deividi (12:24):

I love when people propose like, simple, elegant, kinda solutions for things, and this one feels like, super simple. It’s a very straightforward premise, easy to understand, sets a clear benchmark for what we wanna see as a machine feeling like it is intelligent, being able to mimic human behavior. So I love the fact that it’s simple like that, and it’s focused on the functionality, right? You don’t get too much entangled in philosophical (Abbey: Right.) or psychological aspects of it. It’s purely observable behavior. You’re looking at a machine, talking to a person. Can this machine deceive the person, that it is another person, another human being talking to them. (Abbey: Right.) That’s it. Elegant, simple, right? (Abbey: Yep.) So those are the, as I see, those are the straight points of this task, and why it’s still relevant today. And when you think about, on the other hand, as people criticize that, it has its limitations, and you think about, hey, we’re talking about a test that is about deception. It’s about mimicking a behavior, so it feels kinda weird, so that is the parameter for the test. (Abbey: Yeah.) Right?

Abbey (13:53):

Especially when <unintelligible> you start seeking out to mimic humans in some of the more important parts of life, where like, you know, like an AI can pass a bar exam in the U.S. or pass a medical licensing exam.

Deividi (14:08):

Exactly. It’s [a] narrow definition, right? (Abbey: Yeah.) It’s only focusing on that one aspect of intelligence, (Abbey: Mm-hmm <affirmative>.) so you cannot apply that for more general behavior. So that’s why people get talk[ing] about narrow AI and more general AI and how the field limits those ideas and advances on that. So it lacks understanding, and there are other tests. One that I really like is the, how do you call it, the coffee test, where the test is also a simple premise, where a robot is required to navigate an average American home and make coffee. So how do you find the kitchen? How do you go about finding the coffee machine, and et cetera, and make coffee, right? (Abbey: Yeah.) If you put a human being that knows what coffee is and the process of making coffee inside a house, they will be able to navigate, open drawers, and find the coffee machine, and do the things, (Abbey: Yeah.) but it requires a lot of cognitive knowledge for a computer to do that by itself. So it’s…

Abbey (15:25):

‘Cause there are a lot of nuances of, you know, like, do you know, does it make sense which drawer would have the spoons in it? (Deividi: Exactly.) Where, if you know, it’s gonna be the one that’s closest to where, you know, all the, you know, (Deividi: Yeah.) where the <unintelligible> is or whatever.

Deividi (15:42):

And you find the coffee. Is it the grains, or do you have to grind it? Do you get pounder? Do you need to find a filter, or do you use a coffee machine that is automated? It’s so interesting. I really like that proposal, because it’s so simple, but it feels, ok, if a computer can do that, I think it’s fully aware of its surroundings.

Abbey (16:11):

Yeah. That’s kind of terrifying.

Deividi (16:15):

<Laugh>. We’ll get there.

Abbey (16:17):

The first image that conjures up is like, one of those crazy Boston Dynamics robots…

Deividi (16:24):

Oh yeah. Yeah, those are scary.

Abbey (16:29):

…coming in your house to make coffee <laugh>. (Deividi: <Laugh>.) But it’s also like, how, you know, how far off is that really? And whether that can be used for, obviously, could be used for good to help people with like, mobility limitations and stuff like that, but it could also be exploited (Deividi: Yeah.) pretty easily. I also think it’s interesting like, ’cause the work that he did on like, cryptography like, those basics are things that we still use today in like, security and passwords, and I mean, not to mention the myriad ways of use in, you know, government and military operations. When you look at like, at security, and cryptography, and AI, like, that’s kind of like, the things we’re still going through, we’re still experiencing, we’re still growing. And he was doing these 50, 70 years ago. Fifty…that would’ve been the ‘70s.

Deividi (17:26):

Yeah, when there were no computers around. Can you imagine that?

Abbey (17:31):

Like, it wasn’t even an emerging field; it was a non-existent field. (Deividi: Exactly.) They got as far as like, you know, trying to break code, but it certainly wasn’t being done with computers. I don’t know. Do you think he realized at the time, like, the impact that his work would have or the potential for it?

Deividi (17:56):

It’s impossible to tell if he fully realized the potential, right? I think he had like, looking at papers and the proposals he’s making there, with the like, theoretically on computer science, you can see the end game, how he’s proposing a machine that can solve any problems. (Abbey: Yeah.) So that’s super interesting, but there’s no way he could, no one could have predicted, oh hey, this is gonna give birth to the Internet, and this is going to (Abbey: Right.) allow people to connect all over the world. This is going to allow people to work remotely, like we do, and so much of that, like you can trace back all of the discoveries and all the inventions on computers to his work. Right? So even your smartphone, (Abbey: Yeah.) right? So there’s no way he could have fully realized the extent work, which just, it’s amazing to see, and, I don’t know, I wish there was a way that we could show people that had such influence on the lives of billions of people. If you could show them, “Hey, your work enabled this.” By the time of his death, he was probably fully aware that he saved millions lives with his team on the Enigma machine work, trying to crack that, but not fully realize what would come next, and where we’re today. And with AI, with the AI thing, we’re far from the end game, right? This is just the beginning, what we’re seeing with AI.

Abbey (19:41):

It’s my understanding, too, that like, he wasn’t really allowed to talk much about the work.

Deividi (19:47):

Oh yeah.

Abbey (19:48):

And so I wonder, you know, how would his life have been different, and how might he have been enabled to succeed or to live longer, if the general population understood how, like, seriously, just how fucking important this guy was. (Deividi: <Laugh>. Yeah.) Even at that point, you know, I don’t think it would be hard to overestimate.

Deividi (20:15):

To this day, most people don’t know the influence this man had. (Abbey: Yeah.) Right? I don’t know, maybe, I don’t know how much success the movie had, The Imitation Game movie, when was it, ten years ago? That was fairly successful, but I have no idea how successful it was, but it was a good movie. But people don’t fully understand how much influence this man had on careers, and thousands of people like me, and just the growth of all of these things related to computing. It’s incredible.

Abbey (21:03):

I mean, we wouldn’t be here.

Deividi (21:05):

Literally. Yes. (Abbey: We wouldn’t be <unintelligible>.) We would never work together. (Abbey: Yeah.) No one, no jobs would be posted on Gun.io. Yes. This company exists, (Abbey: If it wasn’t for Alan Turing <laugh>.) Yes.

Abbey (21:19):

I’m sure we could probably keep diving down various Turing-related rabbit holes, but we’ll save that for another time.

Deividi (21:28):

Yeah. There’s so much more trivia.

Abbey (21:30):

Oh yeah.

Deividi (21:32):

He was pretty athletic, you know? He almost made it to the Olympics in 1948.

Abbey (21:37):

No way. I didn’t know that.

Deividi (21:39):

Yeah. He had an injury, and that’s how he left, but he was, yeah. He created the first chess program, and there was no computer around to run the program called Turochamp. It could think two moves ahead, and Kasparov beat it, I don’t know, a few years ago, well, many years ago.

Abbey (22:08):

Before the IBM Blue?

Deividi (22:11):

Yeah, yeah. I think it was, I’m not sure about the dates, but (Abbey: Yeah.) just the fact that someone put that together, a program to play chess, when there was no computer to run this program, and then later, they were able to run it, this chess champion beat it. He beat it in less than 20 moves, but still said that it’s super impressive. It wasn’t a thing when the guy built it, when the guy proposed it.

Abbey (22:43):

There wasn’t even a way to make it work the way that we understand those things work now.

Deividi (22:47):

Yeah. (THE FRONTIER THEME FADES IN) They put together the algorithm, and it was there. “Hey, here’s the algorithm for this thing. Run it, eventually, when we’re capable.”

Abbey (22:57):

Hold onto this. It will be useful later.

Deividi (23:01):

<Laugh>. Yeah.

Abbey (23:01):

Awesome. Well, Deividi, thank you so much for joining me today to talk about Alan Turing, his impact on everything that we do.

Deividi (23:11):

Thank you. This was super fun. Yeah.

Faith, via previous recording (23:15):

Thanks for listening to the Frontier Podcast, powered by Gun.io. We dropped two episodes per week, so if you like this episode, be sure to subscribe on your platform of choice, and come hang out with us again next week, and bring all your internet friends. If you have questions or recommendations, just shoot us a Twitter DM @theFrontierPod, and we’ll see you next week.

(THE FRONTIER THEME ENDS)