Dec 15, 2020

Today Lee and Dan bring series 3 to a close with a Quiz covering lots of fun areas from Robocop to Quantum. So brush up on your AI theory and pit yourself against us.

Useful links: 

Dartmouth workshop - Wikipedia

AI winter - Wikipedia

What's the technological singularity? | HowStuffWorks

Quantum Computing Archives - Quantum Physics Made Simple

Westworld (1973) Trailer #1 | Movieclips Classic Trailers

THE LAWNMOWER MAN AT 25: THE GROUNDBREAKING VR FILM - VFX Voice MagazineVFX Voice Magazine

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TRANSCRIPT For this episode of The AI in Education Podcast
Series: 3
Episode: 14

This transcript was auto-generated. If you spot any important errors, do feel free to email the podcast hosts for corrections.

Hi Lee, welcome to the AI education podcast. How are you doing this week?
Hey Dan, I am good. I'm good. You know, you were saying the AI and education podcast, but we're we're a bit of everything these days after our great conversation with ADA last week or last session on the uh on genomics.
Yeah. No, absolutely. And and get up to Christmas now as well. So, I thought we'd we'd have a little bit of a twist today and come up with a trivia time. How do you feel about that?
Uh, look, you know me, I love trivia. I love looking for obscure information on the internet. So, let let's let's do that.
Good. I I thought what we could do as well when we looking through this is to kind of look back on the episodes that we've done by kind of asking some questions and making it a little bit fun. So, let's get stuck into round one. What's the topic of round one going to be, Lee?
Okay, round one. Well, we started a conversation on this one back in very first episode. this season in fact which was the history of AI.
Yes.
We're going to go back in time a bit.
Let's do that.
All righty. All right. Let's get started then. So the way we're going to do this uh folks for those of you listening along trying to answer the questions, we're going to ask you a bunch of questions. We give you some time to think about it and then we're going to answer them and we'll have a little bit of chat about them. So uh so hands on buzzers ready for your first round as it were. So Dan, what was and when was the Dartmouth conference? That's question one. I don't know if you remember that from our first uh episode, but we did touch on it.
Okay, question two. In what decade or what decades, sorry, as there were two. What decades were the AI winters? And of course, bonus points if you know what we mean by the AI winter as well. All right, question three. Uh, this is an easy one, I think, a hard one to describe, but an easy one because I imagine most people know. What was the Turing test named after our great Alan Turing? Uh, fourth question, we'll do five questions per round. Fourth question, what is Alph Go and what did it achieve? And the last question for the history of AI, although oddly enough, probably not looking at the history, probably looking at the future. What is the singularity? There you go, Dan. Those are my first five questions on the history and the future of AI.
Fantastic. Okay, so let's go through some of these then. So the Dartmouth conference, I remember we t talked about these, didn't we? Um, initially really early on when we did the brief history of AI. Um, and when I think Dartmouth, I keep thinking the UK, but it wasn't in the UK, was it?
No, it was not. It was in the US. Yes. Dartmouth College as opposed to Dartmouth a place, which is probably where you're thinking of.
Yeah. Yeah. Absolutely. Absolutely. And and that was where AI was first coined, right?
That's that's the that's the story. Yes. Look, obviously I mean, if you go searching around it, there's some contention around was it mentioned earlier by others, but in what I've understood and what I read, John McCarthy who's the the scientist who who mentioned this used the term artificial intelligence where it would use things like cybernetic intelligence or computer intelligence or other variances of it but it was the first time the word artificial
uh to distinguish this from what was at the time largely the area I mean you think back to the 50s cybernetics robotics was kind of more the thinking so that's
so for those points the Dartmouth conference what was it so it was about the first coining of AI and it was that AI research workshop And what year was it in Lee? Can you remember?
90. Well, I I I don't remember off the top of my head, but I don't know the answer. It was 1956.
Brilliant. Um, so question two was about the decades of the AI winters. And we talked about that in the same uh podcast, didn't we? Um, and those winters were the 70s and 90s, I believe. Right.
That is correct. Now, they didn't last the whole decade, of course, but they were in those two periods. Um, and yes, absolutely. So, I mean, and and for different kinds of reasons in but similar principles you know at the end of the day it was a sort of a if you think about that trough of disillusionment that I think our friends at Gartner often use this idea that it just didn't achieve what we thought it would do you know technology had this great hope
and then of course it all sort of collapsed around them and uh and didn't quite achieve it in the in the same way that um you know that we might say that uh you know the video game bust of the of the early 80s there was great hope for the video game industry and then somebody made a really bad game and the whole thing collapsed. So we have the two two in the win two AI winters. Winters being called because they're the sort of the you know cold dark period of when nothing much has happened.
Yeah. Exactly. So the next question was about the Tan test. Um and I always struggle to describe this. I think I've tried badly in multiple episodes but um
give it a go. Give it a go.
Essentially this was a way that you test the ability of a machine to exhibit an intelligent behavior. So essentially there'd be um somebody interacting with a computer um and essentially chatting to them via text and then they can't distinguish the person at the end whether that's a AI or it's a human being. They don't know which one's which. Um uh so that that's how I describe it. Named after Allan Turing obviously was kind of trying to come up with another thought experiment I suppose around how we can really determine if something is indistinguishable from a real person and AI and And that's the key. I think you're right. I mean, absolutely spot on. It's exactly what it is. And we always think about the idea because it was originally because that was the way in which at the time you would communicate with the computer by sending messages and the assumption was a touring test success was if you couldn't distinguish if the response message back was either a human or computerbased response. But of course over time touring tests could apply to any form of interaction. I think that for me the core of it is can a computer exhibit the emotions and experiences of a human conversation? M
in in whatever form that conversation takes. But you're right, it's it's a difficult thing to characterize, but a simple thing in principle.
Yeah, absolutely. The next question you asked is around Alph Go. Uh what what is Alph Go and what did it achieve? And I I know we mentioned this a couple of times. I even tried to go out and buy a game of this. Um but it was very expensive which shocked me in one of our episodes. So do you want to tell the listeners again? Uh see if they can get any points on that. Alpha Go,
what is it? Yeah. So we tal we talked about this back in what I think was episode six of this season where we talked about is AI created. We probably talked about it a couple of times but so Alph Go was Google's um sort of TensorFlow built uh TTU sorry TensorFlow processing unit built computer that played the game Go widely regarded as the most complex humanbuilt uh board game or or cognitive game if you like ever built because of the complexity of the board and the number of moves and the potential ways in which it could be played. But the potential number of ways in which a move could be done. But what AlphaGo did was playing against Lisa the doll the the world champion master of of that game. Um it played a move I forget the exact details now. I think it was game two move 39 or something of the z. It played a move that had in theory never been played before. And the idea was at this point all that Alph Go had been taught is human games of Alpha Go of Go. It had seen humans play the game.
But it out of that it created a new move that was theoretically possible but logically never played and was it created? Did it create that move? But it's incredible that there was it was a turning point I guess in the idea of AI's ability to learn and evolve.
Yeah, absolutely. So then uh the final question in your round there was singularity. What is a singularity? Now I've heard lots of people talking about the singularity like Ray Kursel and and and folks like that.
Um so what is the singularity? How would you describe This is another two week
lots of different forms of as you say the singularity there's a sort of the technological singularity the the human singularity the the AI singularity um and and Ray Kerszwell and others in fact an employee of Microsoft a very very smart man called Jared Lenier um talks a lot about this as a he's a sort of a smart um thinker on futurist the idea of the singularity is simply that point a hypothetical point because it's not really a proven point it's a hypothetical point where technology grows beyond human control where suddenly we have no longer we are no longer the masters of the machine. The machine is the masters not necessarily the masters of us but the masters of all that it has moved to become the most singular singularly powerful construct is the computer not us and so that's kind of the principle behind the singularity but as you say
yeah
probably more complex
yeah fantastic well I hope everybody scored well on that round there's some really interesting topics there and hopefully reinforce some of the things we've talked about in previous podcasts. So, round two, uh, Lee, we're going to look at AI acronyms and terms. So, this should be, fingers crossed, an easy round. So, round two, question one, what does the acronym ML stand for? Question two, what does the acronym GPU stand for? Question three, what does the acronym CUBIT? Well, it's not really an acronym, but what is a cubit? Uh, question four, DN. What is that an acronym for? That's DN. And then finally, uh, what is AGI an acronym for? Give you some time to think about that.
So, Lee, how do you fair with those? Some of those are easy and some a little bit tricky.
Uh, I think yeah, as you say, some are simple enough in the sense that we we talk about them all the time, but perhaps it might be interesting to talk a bit about kind of why they're important in this in this context. Yeah. Okay. So, so um the first one, ML.
Well, you you you know this one D, you tell me.
Machine learning.
Absolutely. Nice and simple.
Yeah. And it's the way and obviously we've talked about this a lot through our um podcast and the way the machines can learn and there's multiple ways. We did an entire explainer episode on machine learning right back at the beginning of the the um uh the series. But, you know, it's very much around how machines can learn. And you know I'm not going to go into more detail but it's really important for us in in this context because we
and we might have more questions on it later on. So let's
high level.
Yeah.
Excellent one. All right Dan
GPU
GPU graphic processing unit and and that's really important for us in um in uh AI because obviously we've sp spoken to lots of people about you know ADA last week for example in the last episode around some of that kind of calculations he's doing. Lots of people have been using GPUs and they're doing lots of calculations. You know, we know it's used obviously in gaming, but obviously the mathematics that happens in gaming, you know, can be applied to AI and machine learning. So GPUs are used quite a lot when we're looking at AI at the cutting edge and using lots of number crunching and data. So it's an interesting thing because actually I it is GPU graphics processing unit, but of course I think in now it's almost become also an acronym for general processing unit in the way that GPUs as you said have be come because of the way in which they interpret data and manage the the the sort of the architecture of the of the circuit board and the the way in which they work. They're generally good at processing data and they operate actually much better at doing multi-threaded kind of highly complex um uh computational services that make them better than CPUs which of course are central processing units. So it's an interesting one and it really is an almost an enabler of what was as we came out of the second AI winter and into the the new AI GPUs were one of the key drivers for actually how we did that. Uh through the 90s, GPUs couldn't do anything. As we hit the 2000s and GPUs became uh powerful and general purpose in usage, they accelerated us out of that second AI winter. So there you go. Extra bit of trivia.
Good bit of trivia with inside trivia.
Trivia.
The third one you uh which we asked was cubits. So you do a lot of quantum computing. It's not really an acronym, but how would you describe a cubit? Well, yeah, because as as you said it, I was thinking, are you writing that as Qbit sort of the sort or are we writing Qbit, which is the Q bit in itself? Uh, it's a bit it's a quantum bit, isn't it? I mean, that's a QU, I guess. The QU is the quantum and the bit is the bit. Um, but it is exactly that. It's this it's it's the equivalency of what we know about in traditional um, you know, computing terms and binary terms as a bit. You know, a a piece of uh silicon that measures a data set whether a data piece, whether it's a zero or one. binary digit quantum world
binary digits in a quantum world it's the same thing it's a bit but of course what it's measuring is the quantum state of a bit which as we know is is multiple and many and varied it's a what do they call it's in a in a state of uh multiple states at a singular point in time but that's a cubit
fantastic I I suppose this is probably the most tricky um uh trivia quiz to answer you know it's like the these yes
it's not that the answers are simple are they know yeah It was a cubid. Well, uh, so the next one I asked there was DNN.
So what do you do you know that one? So you ask the question deep neural networks, right?
Perfect. Absolutely. Yes.
But what where the what's the significance of those in our context then?
Well, I guess look, I mean we it's again I'm trying to think when we talk about this I think we talked about a whole session on maybe on types of machine learning and we maybe got into types of learning. Then we got into this idea of a neural network. So neural network as you know is that replication of the human uh cognitive model where there are our brain works on synapse connection. So it makes a in theory has a point of reference for a piece of information a label uh piece of um a param parameter as it's called and then we use that parameter to branch off to other thoughts and processes and how we that's how we string together a thought process for a human being and we have this idea of a of neurons.
A deep neural network is exactly the same thing in computing terms, but often because of the power we have in comput and the scale we have, we can go deeper and deeper. So it's essentially like an inception type model where you have a a neural network that spawns a neural network that spawns a neural network and you deeper and deeper into the the thinking process, the synaptic connections between the parameters to determine the outcome of your AI model.
Yes.
So it's a it's a way of it's a it's a machine learning model framework I guess is simplest way.
And then the final one, the that we asked there was AGI.
So that's artificial artificial general intelligence.
That's absolutely right. But yeah, you are. But in my head, I always think we say general AI. So we you talk about general AI or narrow AI, but of course the acronym is the other way around. It's and it's artificial general intelligence
and that's when when the machines got a capacity to understand the intellectual elements and and everything around that human area, right? When when that kind of comes together.
Yeah. Look, and it's not even specifically the human, but it's more about, as you say, it's that general ability to pull together multiple threads of information and create an outcome in the same way that you and I do. So, narrow AI, you know, I've got a AI problem that will tell me whether or not uh, you know, a particular piece of medical data is, you know, healthy or not healthy. Binary in some ways. Maybe that's the best way. It's either binary or non-binary because a general AI doesn't just say that's healthy. It's sort of looks at the big picture and gives you an answer. What's the one you always like? Howal. Howal is your one.
Yeah, that generally that's right. Yeah, talk about that. So, let's go on to round three.
Very good. My turn to throw some questions back to you. So, um so right. So, next J round three general knowledge. So, more kind of just kind of pulling at different threads across the AI world. So, first question for you Dan or for the audience. What is the group of technologies that we uh what do we call them that you that are AI service? that identify faces and other human characteristics. So, you know, think about the ability to speak and listen and learn and behave like a human. What are they called? There's a group of AI services.
Okay. What is the name of the type of computing that allows bits to be in we just talked about this allows bits to be in two states at any one time. And this isn't just the broad brand of computing. I want to know what part of that computing creates that idea of a bit that can be in two states at the same time. Uh, one for you Harry Potter fans. Uh, what programming language used in machine learning? Uh, and we talked about this last week a lot with our with our friend outer could have a connection to Slytherin. You've obviously been watching Harry Potter with your with your family there. Have you done as you thought of these questions? All right. Uh, question four. What is data drift and what does it cause in the process of AI machine learning? And lastly, What is the difference between supervised and unsupervised based machine learning processes?
Give you a second to think about those, but yeah,
again, probably some difficult ones to answer because the answers are broad and wide, but uh I'm sure you'll give it a good go.
Yeah, absolutely. So, let's go through some of the answers then, Lee. So, what's the group of technology uh called that identifies faces and other human characteristics?
Well, I think the best way to describe it is how we think about What is the human process of thinking? We call that cognition. And these services are our cognitive services because they are cognitive. They are how we think and think, see, hear and perceive the world around us.
Great. Well done. That's a point.
One point.
What What's the name of the type of computing that allows bits to be in any two states at any one time?
Yeah. One of one of my favorite things only because I don't understand it. I hear it. I read it. talk about it and then I scratch my head and go but wait what? So quantum being the principle when we talk about cubits in the last round superposition. So this is that idea of quantum a quantum bit can be in a superp position. The bit that always throws me down is this is you have quantum superposition which is the bit in multiple states.
Yes.
But you can't read a quantum bit because when you read a quantum bit if you think sort of shrodic is cat model you set its state.
So you have the addition of this is quantum entanglement where the two bits are infused together at a at a quantum nuclear level then separated and then they share the same superp position. So you can read one bit over here and it will tell you the state of the other bit but because you haven't read that bit it bit stays
and that's the principle of it but beyond that head exploding time
that's really well explained actually I like that um what programming language is what I hate is used in machine learning that could have a connection to Slytherin.
I'm going to make you answer it because you hate it. I know you have a deeper opinion on
I do is Python. Um, you know, actually interesting fact on this one is that we've now embedded that into our make code and Minecraft engines so that kids doing our code this year in 2020, if you're listening to this in 2020, um, uh, can do their code in Python, which is fantastic, but Python's language. Um, and I won't care my throw my toys that the cot talking about Python anymore. What is data drift and what does it cause?
So good one. Um so data drift is uh simple in principle data that drifts over time. So as you start to say we collect any data set. So you know in principle data drives the way in which we do AI. AI uses that data to make a determination and create an outcome that we we act upon. As you feed data into the model, that data defines what the model will decide. So, you know, if all the data has information about young children, the model is going to favor decisions based on young children attributes. If your data then starts to include information, so say you've got a survey out there and started to get older people responding to your survey, their answers are going to drift the data. So, the data drifts in model to a new place and that in effect causes the outcome to drift because the data's changed and the outcomeffect Yeah, got it.
Correct. Yes.
And I know the last question here is about when one was close to your heart because we've talked about this quite a lot. Um we did an entire episode on different types of uh supervision of learning. So what was the difference between supervised and unsupervised learning?
So do you want to have a shot of that one D? What's your Well, not that
I would have mentioned it. I I would have mentioned it last time as well in his in his um in his actual uh uh podcast as well, didn't he? So, supervised is when um we're actually looking at that particular um project. I suppose it's a really high level. So, we actually labeling things and doing that ourselves.
And then unsupervised is where we kind of leave the machine to look at the data and come up with its own results, I suppose, without having a
finite final answer.
Yeah. Look and and it is it's funny because the term implies supervised and you know the your teacher standing over you watching your answers and make which is sort of the right thing but you're absolutely it's the labeling is the core of it. So we in an unsupervised model we give a computer all these images thousands and thousands and thousands of pictures of cats and allow it to label it and it kind of figures out over time and by definition it learns and the theory is that it learns it needs a bigger data set but it learns a better set of sort of broadness of the data because it sees much more and it and it has to think more about it because it has to actually make its own learning
whereas Alternatively, with human labeling, we give it a thousand pictures of cats and label every single one and say, "That's a cat, that's a cat, that's a cat, that's a dog." And the computer instinctively then knows straight away what's what and builds a framework in its mind about what a cat looks like and what a dog looks like,
but it's constrained by the learning that we've given it. It hasn't really learned beyond the bounds of that learning. Yeah, that's it. So, very good. Cool.
Excellent job as you did.
Okay, so the final round. Hopefully, everybody's doing well and they're not having too many arguments in the car and the kids are okay in back. Um, so the the final round is AI in the Movies and I suppose we did that episode on popular culture, so we've included some fun ones here. So question one on AI in the movies. In the movie The Matrix, what is Agent Smith? So what does he represent in the terms of AI? Question two, what was the secret fourth prime directive of Robocop?
I know you mentioned that one when we talked about it, but I'm sure you know.
Number three, who are Ash, Bishop, David, and Walter, and what do they have in common? Question four, who was the gunslinger? And the final question, what was the showcase technology in the Lone Wall Man movie? Brilliant.
Right. Now, now I I know that I wrote down these questions, so I didn't give you any time to think about the answers that I think you did.
I didn't write any answers down. Uh,
cool. So, let's go.
I'm sure you know I know you know the Robocop one. I'm sure you know that.
I need to remember all of these. So, number one, um, in the movie The Matrix, what is Agent Smith and what does he represent? What was Ali?
So, that one, um, look, it's it's essentially Agent Smith is a piece of software.
He's a piece of code uh written by the matrix to essentially exterminate viruses. So in a sense he's a piece of antivirus software but is best thought about as a piece of sentient software. He's obviously you know the theory of the matrix is that software has become aware and self-aware because he goes rogue eventually. You know the whole principle and by the movie three he's actually trying to become self-aware and self-owning the matrix. He wants to take over the matrix. Uh you know replicates himself a billion times. You know fighting with Kiana So he's sentant AI software is what he is.
Yes, exactly. Sentient AI. And we we did a lot of that in the episodes uh previously as well, didn't we?
So the fourth prime directive of Robocop. Do you know that one?
I look I know broadly, but you know I'm going to ask you to do it because you were the one that talked about Robocop.
So do you know we got serve the public trust, protect the descent,
uphold the law, and the fourth one was the classified one, wasn't it?
That's right. I did reading on this this morning so I knew I I knew the specific answer.
What's the specific answer then Lee?
Right.
So the specific answer is the fourth directive which was hidden to Robocop. He didn't know about it and classified
unless it happened unless it became relevant and that is in the course of his duty he cannot harm an OCP uh executive
executive being the company.
Yep. Yes. Correct.
And so he suddenly realized he can't harm the bad guy. What was interesting just a little bit of trivia since we're on trivia session. I didn't realiz but in the other movies I think there's Robocop 2 and three as well I think there's additional it turns out there's about 250 prime directives that he actually has and they're all really bizarre ones like I you must talk through your problems you must communicate with all sides of the citizens it's it's you go a little bit of research it's kind of weird but
fantastic but the fourth one yeah classified
the boss
so they get half a point for saying classified so I'd get half a point and you get a full point if you've got Lee's answer there
so this one here this is interesting Who are Ash, Bishop, David, and Walter, and what do they have in common?
So, do you know this one?
Your head.
I I I'm the only thing that's jumping out to me on this one is robots. So, I know in the in the movie Alien, Bishop was an alien, a robot in in Alien. But then I was trying to think where Ash and David and Walter might be. So, I might be on a completely wrong wrong line here. So, put us out to our misery. No, you you get I'll give you the point for that one. You are absolutely spot on. The thing the thing they have in well what who are they? They are all androids as they're referred to in the movies, not robots. And they were all in the Alien series of movies. So Ash was in Alien, Bishop was in Aliens, David was in Prometheus, and Walter was in Alien Covenant. And they were all in
versions of the android
that uh Whan, who is the secret boss, that was in control of everything built.
Uh, and what's interesting is as I did a bit of research, I'm a bit of an aliens fan, but I did a research because I hadn't really looked into it, is they have an alternating uh journey. So, Ash was uh vicious and violent. Bishop was uh calm and humanistic. David was vicious and um and violent again or sort of, you know, singular in purpose. Walter was uh human and emotional. So, they're kind of this is this journey that Redby was going on as he wrote the wrote the characters. But yes, they're all
uh AI robot androids, I guess. Yeah.
That's brilliant. Okay. It's good to know. I was I was thinking I knew there was a bit of connection there, but yeah, I didn't
Ash Ash or Bishop people probably know, but Daniel Walters a bit.
That's right. That's that's a really good question. Um hopefully you get four points for that people in your cars or listening on your walk if you got if you know one one or more of those. Um so the question four was who was the gunslinger? Now Lee, I know this quite close to your heart because you love the series.
I do. I do. Um but of course this is hearken back to the original 1973 your winner version.
I know it's phenomenal.
It's in fact as I was looking it up I was thinking about AIS and movies and I went and I watched the trailer for the movie because I thought wow that's haven't seen it for a long time. Of course the gunslinger is the robot AI from Westworld who went arai and started killing people. And I think we talked about this in our pop culture episode. We talked about this the the the modern Westworld remake, but of course this is the original. He was called and he's no character name. He was just called the
gunslinger. Yeah. Brilliant. This is iconic though. It was a brilliant role role of his because he was for me you know you're used to seeing him in other movies uh and the western kind of connection. It was just it was like it was brilliant
and watching the trailer again actually really um amazing sort of menacing character to play this you know what would have been difficult thing to play in the 70s a robot humanoid. Yeah, absolutely. Great stuff.
And then the final question of the trivia for today, uh, what was the showcase technology in the lawn mower? Man, I remember when this came out. This was phenomenal.
Remember the movie?
Yeah, I do.
You remember the movie?
Yeah. Yeah. Very well. Yeah.
90s time, I think it was, wasn't it? Mid '90s or something. Early 90s.
Wow. I, you know, I'm even sure which year that that might have been. We'll have to have a look that up. But yeah, the lawn mower man. So, there was lots of different technologies shown in that, but mainly they were going into virtual reality, weren't they?
It was one VR.
Yeah, it was it was really exploring that element of going into those world and being part of those worlds and and the the kind of main character in there. Uh going into those it was very very interesting
and I was at the end at the end they were stuck in there to get out of it somehow. I remember at the end there was some kind of like they had to get through something. It got a little bit evil towards the end if I remember rightly.
I think you're I don't look I don't remember it very well to not as well as you obviously do. Um but it And I don't think it's aged well. I've not seen it for a long time. I hear it's not a great uh not a great movie to watch again. Um but I thought the interesting little bit of trivia that I wanted to to grab on that because you're absolutely right, virtual reality, but in the uh in the movie at the time, the virtual reality headsets that they used to kind of, you know, there's props I guess in it at the time
were from a company um that was I think it was called I want to call Virtuality, but it was um the technology was actually by a guy was one of the early pioneers of virtual reality was a guy called who I mentioned earlier Jared Laneir who is a Microsoft employee now and works deeply on a lot of our sort of futuristic thinking
but he was he is widely credited as the father of virtual reality um long before um Oculus and Facebook sort of made it popularized it and simplified it
um but yeah and and it was so there's a small Microsoft connection back to the lawn mower man it was one of our now employees who who came up with that Uh so yeah, there you go.
I suppose the other interesting part of it, I don't know if you remember too much about the film like you said, but the um there was an element to it as well which is quite socially interesting because there was a time in the 80s or 90s I think it was early 90s the movie but you know lots of people were playing video games they became more powerful you know the graphics in it was a big thing there was a lot of those like high-end almost like amigga graphics on it with like um there was a time where there was a lot of shiny you know, light based graphics happening and and there was there was that element just capturing people and the use of games and in popular culture, but then also there was an element one of the main characters in it was a gardener and he was kind of um
was was sort of uh intellectually um disabled I think they called it in the movie or he was kind of slightly autistic
um and and he was getting into the movie and then become somebody different. Um I think it was based on a story by Stephen King or whatever and and it was very much around like it you know that element of a socially uh person who struggling but then got into this world you know so it's kind of lots of lots of interesting parallels underlying there apart from the technology around the use of technology to make people you know I suppose he was ahead of his time in that way where people nowadays are going online and dating or on you know Facebook and things and you do you know people are putting different personas across to what they are in real life. So, it's kind of like a a forebearer of that in in one respect, I suppose.
May maybe I've uh maybe I've misunderstood it. I need to go back to it. It sounds like you you've had a a good thorough look at it.
It really impacted me the the law man. I remember that. It was one of those movies that the graphics were the things that captured I think people in the '9s, you know, a bit like, you know, uh when uh Pixar started with those movies as well. It was a bit of a groundbreaking kind of connection.
So, well, I just I just checked I just double checked it. It was uh his company was VPL research and that was the first visual uh virtual reality research technology that was used in the movie. So there you go. You can go and look it up.
Fantastic. Well, we shared some really good interesting points there. Also some nice movies to watch over the Christmas break. So we'll be back in in the next series, I suppose, to see uh where the podcast goes and where we going to take it and I think we can interview lots of people um from industry to kind of bring this to life.
Yeah. Well, I think that's a good idea. But look, we love your ideas, but you know, if you're listening in and you've got some thoughts on what we can do, uh we love doing it. We love continuing to do these podcasts. We will pick it back up in the new year. Um and thanks for listening with us and staying with us through the through this uh interesting challenging 2020 year.
Yeah. And the interesting and challenging, very complicated, simple but complicated AI quiz. Cool.
Absolutely.
Thanks, Lee. Fantastic. Thanks, Dan.
Cheers.