Thank you. Thank you very much. Also, to come and stay on on keeping your hair for me rather than the free sandwiches as impoverished kids.
So I'm Al Brown and I work at an organisation called DC DC, which is described as the Moody's Independent think tank.
Because we're an organ of government. Semi-independent is probably here.
It's a trade and our job is to think about stuff and study trends and try and work out what it means for the future
and then provide advice to sort of governments can form strategies and try to work out what to do about these things.
And we do that by plagiarising. We feel that we thing a lot relevant to themselves, which is one of the reasons I'm in the region.
So I will get at least as much from you, from the questions as you think you're going to get from me.
There's a very long list there and you don't get to read all of it.
It's meant to just look impressive as you scan across the people that we steal from.
And I wrote a blurb for this, but Robert and I went back and looked at it and realised it's not my promise to talk about
some strongly held certainties that we have about the future and why they might not be safe,
in which they might not be accurate, let's put it that way, and I'll put them on here.
And at the end of that, if you recognise anyone has any of these before then that's good.
I hear these all the time. And then on I ask, What do you mean by that?
Frequently the conversation sort of gets a bit awkward and embarrassing as we start the times,
and hopefully this will sort of skim over some of these bits because there's a lot to cover.
So we have to get quite a little bit, not terribly deep on everything. I just thought it was just natural.
Do you want to say something about caveats? Not you misconceived speak for.
Oh, we have a good point. So I don't mind lecturing here. I am not an organ of state delivering government policy.
You are picking the brains of the black out rounds. That's just what I'm looking expecting.
But they're not here so happy with it as you're talking.
So it's always good to start with a definition in economic thinking.
So in this case, I would say that artificial intelligence is something that sits at the top of the Gartner Gartner point curve.
So the peak of inflated expectations, but simultaneously manages to be something that is frequently underestimated and surprises us.
It's quite an achievement. So something that's always better and simultaneously worse than we think is kind of worth understanding.
And I use that here as a sort of bucket because you can find a million different definitions and that equals terribly well put together.
But the useful way I found in thinking about this it is consists of three things really.
One is algorithms, which is essentially just maths, much of which has been around since the fifties or sixties,
but we didn't actually have other components that allow us to do anything with it.
And then in 2012, we had a revolution in sort of the next leg, which is the processing power, particularly graphics processing chips.
And they allowed us to use some of those algorithms, but they also only became useful once we have the data to exploit.
And this is where the internet comes in and of itself to be enormously valuable.
Not only is it a tool for finding data, but it's also become a system which generates data which makes a difference.
So one of the things we do discover is pretty trends, which means all of our presentations in the polygraph system.
So I going to tell you some things that you don't know already, but that's fine because Olympic athletes use priming by listening to like
particularly inspiring music or watching films before they go and do an event.
So I'm going to do the same to your Olympic level audience in this room and show you some of the things we know already to get you thinking in a way.
So this is Moore's Law, so people talk about it running out.
It kind of depends on how you choose to measure it. So there is a point where the size of a chip versus the size of the atom becomes a problem.
But if you measure it in terms of calculations per thousand dollars, it continues to go strong strongly.
And if you look at data generation, that is increasing exponentially.
And if you look at why that is, again, going back to the Internet connectivity, the Internet matters.
So I think we had 1 million devices in 1992 connected to the Internet whereabouts billion now.
And the 200 billion is the estimate by 2050, probably slightly off on these figures, but kind of doesn't matter.
It's a huge growth. I think it 50 billion. But if I tell you those things that make up the box, they weren't sticking your brain.
So if I show you another image to do that sort of framing thing,
you will instinctively recognise these on almost an emotional as well as an intellectual level.
So we talk about data being generated and occurring automatically.
That's kind of why I'm and that presents us with the sort of the first problem,
which is there's an idea somehow that in all warfare you will just be able to alter and that has its
place in things like you stood the line from home and if you were to be a successful terrorist,
behaving like a 12th century man makes you quite difficult to find on the Internet, but that doesn't necessarily work for warfare.
In an example I give you, is that a typical 99?
The sortie generates between 20 to 40 laptops worth of data, one that comes back and it's presented to a human analyst.
He then sits there and tries to make sense of all that data that necessarily means in the tempo that warfare occurs.
The vast majority of that information then gathered on the cutting room floor.
And if you're making information or making decisions on fractions of the data available to you,
you are necessarily taking risk that you are not making the best decisions that you could do.
So automation and how we ultimately how we process information is going to be fundamental warfare,
but also in all other aspects of life, I would say too.
So those graphs you saw about the increase in rates of data availability are true for all elements of the economy, economics and society as well.
In 2015 we had an interesting development which kind of helps response of the response to that.
And then it goes back to where Office Intelligence plays into this pretty much the first time on a standard benchmark test,
the machines became better than humans at recognising faces and attributes.
And you can see the error rates going down. And unsurprisingly, the error rate for humans has not changed because we're not evolving at that pace.
So our ability to automate processing is going to necessarily depend on some of those technologies.
And I described before how those three constituent elements I'd say there's two underpinning elements that make a difference as well.
The first one, the image of the guy on the left of a subject matter, experts who are able to do this.
And at moment they are a relatively low population figure and that makes them highly sought after.
Now, in the future, there are going to be economic drivers,
which means that everybody is going to be interested in doing more education, more research and creating more specialists.
But right now they are a rare breed. And that ties into the second picture, which is to do with investment.
And this is probably best described by a statistic in time, Wendy Hall's report for the government's view of economics and technology.
When they were looking at artificial standards for machine learning and they highlighted the fact that there's a process called factory hiring,
which is where you go out as a large company and you acquire in order to hire
a small start ups or specialists on the average person is 5 to £10 million,
which is a very significant figure. And you all have heard of sort of the amazing amounts the tiny little company DeepMind
were built for from things that didn't necessarily means this ferocious competition.
It's interesting as well, because if you are Google,
you won't necessarily just go out and buy the Start-Up that has a gap that you need to fill in your stable.
You will buy things that you may already have because you can deny it to your competition.
So, so quite often there are lots of routes to achieve the same end.
And if you own all of them, you start out towards being a natural monopoly.
And the reason I put up a picture here of the Manhattan Project is I think this tends to lead towards an interesting phenomenon,
which I call the Manhattan problem.
So in the Manhattan Project, Oppenheimer is running a program where the state has a monopoly on all the constituent ingredients,
can't go to the shops and buy uranium. You don't have big stables of scientists wandering around in the fray today.
And Fred Oppenheimer and a number of people are all working in a very closely confined environment with
walls of secrecy built around because they don't want to be counted on to be good at building atomic bomb,
you need to build one. You need to bounce the ideas around those small groups of specialists in order to then accelerate the program to get further.
And the state had a monopoly on that, which is one of the reasons, of course, that the U.S. have one.
And then there's a gap of years before other major world powers start to catch up.
In this instance, the reason I call a problem is that the constituent ingredients are no longer really owned by states,
but they are owned by technology giants. So the data say 90% of all searches on the Internet were done through Google.
The nearest competitor, Bing, I think, has the same number of searches as all conducted just through Google Maps.
So you really have start hoovering up that sort of data and controlling it becomes important and then it starts to belong in the commercial sector.
And indeed, there are regulations that prevent states and certain countries from holding or acquiring data.
So you'll have had a GDPR and how that is radically altering what people are allowed to own, particularly state institutions.
And there was this piece of regulation, which is this treaty now in the UK called RIP,
which was the Regulation of Investigatory Powers Act, which stops the states acquiring large amounts of data just because its citizens.
And I think that sort of purchasing power doesn't really tend to belong to the states as well.
It largely belongs to commercial organisations because they can exploit it better as well.
And you would have seen it in Zuckerberg in front of the Senate recently.
You would have seen press statements on Cambridge Analytica and part of how society feels is kind of emerging in that field.
But one thing, and this goes back to one of the least certainties I would be slightly cautious of is if.
You ever hear someone say other people will be prepared to do things that we really wouldn't?
Well, that's always true. That's kind of us having a different moral framework to those around us just in this room.
It's always a fact of life, and different social groups having different ethical frameworks is a natural space as well.
I'll be very careful about any exceptionalism,
assuming that you will always have some sort of moral standing which you sits on which precludes you from doing something the other guys.
Would you tend to alienate the enemy at it, but they will have different views.
I would suggest that if you were in North Korea, for example, your comfort with the concept of autonomy is very different to in a Western state.
So if you live in an organisation where autonomy isn't socially acceptable and yet you can control where people's families are,
every piece of education they receive and what kind of behaviours you expect.
The idea that you would then expect in machines vastly disproportionate level
also to meet with none of those same levers or institutions of competence,
I think is perhaps to paint the opponents in a negative light rather than actually looking to them for power.
And we also you have to be careful as well about the idea of some sort of fixed ethical framework.
We change our ethics all the time, and you can go back and look at things like in-vitro fertilisation,
genetic screening for things like Down's syndrome.
And you go back and you will find that a lot of a furore tends to disappear in retrospect as we change our ethical framework,
as we become comfortable with changes in society and technology, and indeed we're not even that stable now.
So one third of Britons surveyed in this newspaper report said they feared the rise of the robot,
but nearly a fifth were prepared to have sex with an android. That's not a very coherent position, emotionally speaking, because not intellectually.
And one of the reasons we tend to have these images is because we're trying to do we do it here mistake substitution.
So we're not familiar. We can't see you do either of them at work.
It's just a call and response effect on computers mean anything to us on an intellectual or emotional basis.
So we substitute in what we do now, which is sci fi.
And this particular picture and I mean this particular picture,
it's the most common image that people have for the rise of robots, because this is the one that is most licensed by newspapers.
So The Daily Telegraph printed full stories in four months using the same picture and the same story.
So to find that your population then starts to develop a mental image of what the future looks like, and it looks a bit like this.
So it's for us, there are more new exposure to, say, the Zuckerberg and sort of fear of big data is one form.
This is from vehicle slaughter bots. Let's get to my institution of life, which is from the context of killer robots.
And this is if you want to have an influence, campaigns that exist in Britain, this is definitely how to change public opinion.
And it's really good. And the idea is at the beginning, you have a sort of Steve Jobs like military to get he hands up.
He's created quadcopters with little shape charges in. They go out to some people who are nearby.
Well it's like it's never really clear who they are.
And then they end up going out in assassinating thousands of students based on things like Facebook
profiles and leading all the students along because they seem to be right or left wing enough.
And it's a terrifying image and it's quite emotionally powerful.
But one of the reasons it hasn't led to sort of it's frustrated the company,
the robots and others in the UN who are looking at it as an institution called The Gate,
which is looking at whether to ban lethal some of US weapon systems.
Are they really struggling? And they're not struggling because that evil people trying to dissuade it,
they're struggling because of the challenge, which is trying to work out what it is you write about.
So nobody particularly wants to get rid of a heat seeking missile because we know
those have been more firmly front to back and nobody wants the silver version of Army.
It's got some bad storms around them, scores of thousands of people. But you can't write a law about you need to find a boundary.
And all of the things that are in that video kind of exist in the real world already.
So there's very little in that. And you couldn't pick any one of those and say, this is the thing that we hate.
You can say facial recognition technology.
She'll never be in service again because otherwise it will keep getting longer and your friends would recommend you on Facebook and things like this.
So there are all also it's there's no constituency single policy that technology and autonomy is kind of an emergent term.
You tend to think of it as being a an ability to leave a system alone to get along with its job without being supervised.
The problem is that that's contextual and it's based on your assessment of risk,
and it's based on familiarity and the predictability that we've seen in our system in the past, which makes generating a law particularly difficult.
There are issues that are very pertinent, so things like assurance and trust, how that is going to be collective training,
which is more they're more like how sort of car assurance or airline assurance works in some ways.
And those are probably going to get tackled first and will probably be the way that
this starts to resolve itself in the same way as you have legislation around airlines,
which we now would agree. So I would be very wary of fixed conceits or views.
And and there's another problem, I'd say. So this is a system that sits on a North South Korean border northwards.
It's made by. Subsidiary of Samsung. It's a remote weapons station.
And what it does is it has the capability to do automatic detection and targeting.
There are various as well, which can do voice recognition and facial recognition.
And right now, it's it's laid back to a guy in a control room. So there's a bloke with a joystick deciding whether to shoot or not.
But fundamentally, it's actually a software patch away from not needing that.
And it kind of does everything that a century does. So it's great because he's not just sitting in a little box getting rained on,
but he's also more vigilant so he doesn't get distracted by pulling on his iPhone all the time.
Of course, because he's tired and had a big lunch. So none of those things happen.
And yet this is something created by a man in America who doesn't like to kick out his lawn.
This is one of the fundamental problems with the idea of the banner that this guy has put this together,
and it's essentially the same system more or less, and yet he's running it off a laptop.
No difference in the thing over there running it. And he's used some actual systems of service and some software.
It's downloaded from the Internet. So one of the problems that we've got is that this is not, you know,
in the idea that there's a Steve Jobs like figure out that leading all this technology is not the case.
So going back to that kind of Manhattan problem idea, this is very much being developed in the civil commercial.
These technologies that are accelerating in that world first and it's not like any here is somebody who's got a really old breakwater.
And yet if you were to say they're going to put a sighting system on, it has the world's most advanced military accelerometers.
It's links to G.P.S. mapping system.
It's also a communication system that also does azimuth and has data for compass and can get light to mental updates on weather environment.
It sounds really cool. And then you say, I'm talking about knowing that bunch of stuff on the side.
You're able to, you know, generate some quite complex effects or quite complex technological things by using quite simple off the shelf technologies.
The U.S. Marine Corps, I've got a great phrase for this. They call it the democratisation of precision.
And I think it's going to lead to a lower cost of entry to, well, you know,
precision warfare used to be a first world nations force, and it's not going to be for very long.
But don't just think about it in terms of technology as well.
Think instead of old actors being able to do new things.
I would say that you have to think about it in social structures as well.
So that is a self-driving car would have millions that are out there. If I was a member of the Animal Liberation Front,
one of the things I really like doing is forming Huntington like scientists and trying to kill people out.
And I have in my family Animal Liberation Front, I know no tolerance for being a martyr or even being harmed or even being arrested.
And right now, that limits me to sending crappy pass the bombs in the mail to try and get someone to continue the life sciences which funny.
I think they're really stupid, you know, and it does limit what you can say if in the future I can move my bomb to target the target,
I no longer needs to have somebody pressed into service behind will have access to what has been described as the poor man's cruise missile.
So the nature of violence is going to change and it's almost impossible to regulate out all of these things, obviously.
Now, I wouldn't say also some people think that this just leads to a completely flat playing field and everybody's going to have other things.
You will have an economic divergence. So again, it's a bit of footage from now rather than future, say the world's cheapest retailer,
which is basically a small grenade which has been attached to this silly blue like tail structure.
And some of these flying, of course, comes out bombing some of their opponents.
And the ability to do that is not the same as the ability that is shown here.
And there are other variants. So you can see locust or vertical on the internet, which is generated by the U.S. It's pretty similar.
So this is developed by Chinese university in consultation with the Defence Forces.
And the idea here is to build a swarm of systems which are able to interact.
So, you know, there isn't going to be a one size fits all solution either.
And I would also say that some of them are things of the simplicity of the idea and also natural compromise.
So this is a little remote controlled aircraft that can land itself on a wall and then take off again and fly somewhere else.
Maybe Chevron or Chevron domestic, forget which. And their idea here is to actually if I'm trying to look for people in an aircraft, say,
I'm sorry, that my time, my ability to survey the area is limited by battery life.
So if I can park, then I can extend the right the duration of my sensor.
But actually parking on the floor is difficult because everything is fallen over and it says earthquake here.
But, you know, you could easily imagine that would be severe somewhat today.
But walls are self-cleaning if it doesn't stay there. So you're ready to talk?
Actually, it's quite useful. But the battery life issue could be extended by another university,
which you're looking at sending the propeller that sits on the top into a wind farm that regenerates the battery.
As it sits and hangs on more. So you start to change some of the signs.
The parishes. Some of these ideas are also farms from other variations say, you know, you will see stuff that's biomimetic.
So again, one of the ideas I present to you is it's going to be a plethora of different things.
We tend to think about robots back again in that sort of only picture, but they'll be all sorts of shapes and sizes and they won't all be fixed.
So if not all solid.
I also like to put this one up because then as I look across the audience, I can find work one in five, all from that newspaper report.
Some people think that the information of these systems across the future tobacco space means that there isn't going to be any more surprise.
Everything will be seen and anticipated in future. But I'm sorry I've missed a bit.
One of the other things that I think artificial intelligence enables is going back to what's right now.
The other tool for power that's quite significant is you find permanently broadcasting a signal like drain my batteries quite quickly.
And in a military sense, being noisy has always been that and it's tuning the electromagnetic spectrum is what is walking around in the dark.
So the ability to compress that data imagery, so I'm not sending you a full motion video, but I'm now actually sending you the words crank games.
I can send a lot less data and I can be a lot more quiet and I can consider my battery life.
So the ability to develop these algorithms, stick them on those systems, is going to change how the communication works,
which indeed will help with that sort of volume of data, not just getting to the individual, but being processed across the airwaves masses.
The electromagnetic spectrum is already too full and we have to give it to the person as well.
So this isn't just going to be how it gets into the robots, it's going to be how it gets into the human events decisions.
And this is an example from Arrow Glass.
So the idea here was that rather than pilots having to sort of read or memorise their folder before they take off,
they can actually have all of that data put in that the very cool set of glasses that you'll see the operator using the face on it.
And it presents the data in a way that's fast and intuitive because speed of decision matters in all forms of life, but definitely in warfare.
And you can see how the immediate read across to how people conduct warfare and could
make better decisions with better information is going to be not just a robotics thing,
but a how you integrate with the people. I'm going back to the idea of those little senses being everywhere, meaning there's no more surprising war.
I don't think that's true. And the first example I would give you is just so chess.
You have a completely glass bottle, you have perfect situational awareness.
You know where everything is. You know all of the rules and capabilities, bases, and you also know the tempo as well.
And you can. So your opponent takes a turn.
You take it to an example, but if you play against a better player, you will still find yourself being surprised.
The other thing I would say to you and without a quiz is just to be thinking, why does anyone know who that is?
Well, Google's face in that algorithm is absolutely certain that that is military jobs.
And the reason that is certain is because it is slightly semantic like glasses.
That, again, star doesn't seem to be a feature in this presentation. And what it's doing is it's something called up a serial example.
So the best description of it is on the top left here.
So the picture on the top left of the panda is an original source picture, a crazy static looking box.
Awesome changes to the colours of that picture that are really subtle.
Now let's do that first, which it's create a picture on the right.
What it's doing is it's increasing the signals that a computer sees that give it a different outcome.
So you can see the scores below that again from Google in this recognition system.
So it's 57% confident that it's a panda.
And then you and I see what looks like the same picture, and yet it is 93% confident that it is a given.
And that's sort of the subtle pictures there of the subtle pixel changes of water, actually,
and that so the glasses that make the computer think about this military challenge, it's not just linked to that site, that star as well.
There are more simple versions that are out there. So the crazy looking picture on the bottom left a slightly is a fractal pattern developed by an
artist called Army and who has sort of a big brother theme to a lot of his designs for fashion.
And it's a fabric pattern. And what it does is that is the the set of shadows, gaps, distances, the facial recognition software is looking for.
And as you change the folds of your clothes, as you move around, it changes those distances.
And when you walk past the facial recognition camera, it doesn't just get too far.
That's wrong. It says that Robyn Rodney is buried in a list of thousands of other names and if there are enough on the computer isn't good enough,
it just has a little over 500 shots that otherwise it has to sift through to find robots of all of those other names,
which probably correlate to real people as well. So deception is like, well, I like this one.
So this sees it in like real time for something that's 3D. So you can see this live picture there.
It is being so largely recognised as a total 3D printed and.
Some of those adversarial patterns in it and you can see it largely consistently being recognised as a.
So the prospect of surprise disappearing or indeed machines being in front of them, that judgement seems to be extreme.
Of these four people are all interesting. Does anyone know how they what they have in common?
Well, the answer is that none of them exist. One would have.
And so these are all generated by computers that we're trying to create from their own imagination.
If you would like a better sense of what a human looks like and you can see it increases quite radically.
So the prospect of deception isn't just that we can form machines,
but it's increasingly going to be machines will allow us to fool each other as well.
And another example I'll give you is live, real time video stitching.
Now, somebody will know this from slightly more insane resources on leave up to your own morals.
But you can see here somebody is able to in real time change the expressions.
And indeed, there are other videos of people changing horses. And if you stare at George Bush's mouth on this, it looks a little bit grainy.
But it's worth remembering that it will never be this bad again. This is only going to get better.
So our ability to say seeing is believing on the Internet is going to diminish.
And this is an area that is rapidly increasing and capable and an object that I'm thinking of, things that are ongoing.
So DeepMind is is a fascinating organisation been speaking to and a lot of this.
One of the things that is interesting I think is in the world of cybersecurity,
one of the things that professional cybersecurity analysts are worried about is how some of these things can transpose.
So Deepmind's next trick is to try and win a game called Starcraft.
The interesting thing about Starcraft from a machine learning perspective is that you don't get to see over the battlefield.
The other side is shrouded in fog until you get there and look, they aren't the same as you.
They don't necessarily have the same features. You play real time.
So there is a deterrence thing going on and there's a long term as a short term strategy component.
So if I take a my guys have moved over and punched all your guys in the face of the very beginning,
do I do that or do I go a little stunt by going off to build an arms factory so that we're coming back,
makes a big takes versus maybe you're doing something in an intermediate term.
So there's a series of judgements that need to be made. And the thing that is interesting to the cybersecurity world is that this could
be readily transposed to what's called advanced persistent threat techniques.
And how that works in cybersecurity is if you have a shield ball in front of you protecting yourself.
And I am an incredible hacker. What I would do is I came up and I just tried your share war until I find a gap.
So I'm looking for a weakness in your system. But more than that, I might do something which causes you to move one of your shields or responders.
So I'm not trying to solve both.
But if I keep pressing here, I notice you move this to here and I think that expresses weakness and then I can get here.
And right now that requires very skilled hackers and a lot of time and expertise because then manually reading code we can get into the game.
But if you're doing that, the cyclical rate of minute machine learning, your tempo becomes vastly faster.
And so this means that not only in defence in terms of trying to hack in, but in terms of defence,
you are going to find that systems like this are probably going to dominate the online version of cyber attack and cyber defence.
And the other thing as well as you don't have to understand it from the ground up in the same
way as the pilots does not have to be able to go to an aeroplane to be able to operate it.
You just need star to fly. You don't need to be able to code this for first principles.
If you can take it real, write it to a target and send it off on its way so it lowers the threshold for some of those sorts of attacks.
Another factor that's going to change, I think the economics of war is,
is the idea of huge headquarters and massive logistics supply base is not background or capacity.
And the reason I say that is the idea of what's called parasitic warheads. So if you can really accurately target something more accurately,
you can pick your target the less explosives, and that's what you need to take to be targets.
And so if I can fly, if I can send a small unmanned drone to fly to Camp Bastion and it's looking
for the wings of aeroplanes and it just puts bomb the whole street diamonds.
I'm starting to make some really expensive aircraft, unreliable, relatively low risk.
And there's a great quote which says, lining up all these aircraft on the runway might be the next version of lining up the ships at the moment.
So there are some old people will have to reload. So in the Cold War, a whole bunch of an army wasn't just finding some big carrier somewhere.
It was to move and hide in forests. And one of the lessons I think is coming out of the Ukraine, where both sides are using hard to detect these,
is that both sides have to behave as if the other side has kind of superiority because whoever is fighter is dominant,
doesn't determine whether somebody is looking down from the sky. And if you are seeing seen, obviously you can be targeted with alternative.
So the concept of a benevolent sky, I think is going to disappear and it opens up other tactics as well.
So that's the idea of pilot autonomy. So a great example here is David Perkins.
The U.S. general pointed out that a U.S. ally in the Middle East and fighting $3 million paycheques on to destroy a drone have been fired,
which costs $200 for hours. And it was successful.
But Frank Hoffman describes this as a strategy set, by my defence, to measure cost vastly more than your productive measure.
And Perkins went on to say. And if I was the guy who applied to join,
I'd be delighted because I'd now be going out and spending is decreasing because many of these children go to training as I can,
because I know it costs you 300 or $300 every time I do that.
So the prospect of overwhelming things is going to change. And you know, this the one of the other organs from his people tend to be swarms.
I don't think it's as simple as that again. So there are environments where it makes loads of sense to be a swarm.
Say, for example, one missile equals one subset of this radar field does disappeared.
But there are real world signs waiting parachutes to come in. And the reason that we have things like AWACS is for early warnings.
And if you are looking over a border, you need a really big radar dish to look at one on the way.
So the concepts, if you will, of a swarm suddenly starts to become not early warning.
It just tells you what's crossing the border.
So it's going to be a mix of the big and the things we recognise now, and especially along with new systems.
And there are other practical prospects that people cross over.
And so some people have described the advantage of having an autonomous armoured vehicles like tanks.
I don't think necessarily swapping the humans out for robots is going to be the way that this thing plays out.
Since and the example I'll give you here is this a robot tank has been designed and created and it could be part of a platoon of,
let's say, four tanks where three of them, a robot, takes number six to the back of the mountain.
Well, that sounds fantastic. The robots can go forward and do the dangerous stuff and the humans in the back making sort of tactical judgement
until one of those tanks has a moment where the equivalent of an arm and a flat tire is throwing a truck.
And now the only people who can get out and put the truck back on the tank of the crew and you only have one crew.
So I've now gone from having four times to having no tanks, whereas when they were all gone,
I went from having four tanks to having three tanks, and sometimes those small real world practices moments.
Again, there was a prominent guy who was showing this tank driver, but he hated the idea.
And it wasn't the tank truck thing, it was the weapon being because his whole prospect was at the end of the battle.
They're going to spend the next two days exhausting themselves, just feeding the weapons.
So the small, pragmatic realities are going to make a difference and understanding how these things play out.
And I'll run a video here. First of all, see, that's the Tom Cruise dialogue, which it was fun.
So this is from a film called The Edge of Tomorrow. And this goes back to this idea that they're going to be super brilliant.
It's worth understanding that Tom Cruise has the same effect in his films,
that he starts out rubbish fighting this war in the same way as Alphazero was rubbish in fighting for war playing chess the first time.
And yet in 4 hours you've got the grandmaster level. But like Tom Cruise, it's fighting the same battle again and again and again and again.
And there are changes within it, but it's kind of always the same battle.
One of the reasons we always say that we're ill prepared for the next war is because we've never fought the same war twice.
That always different. And so your ability to sort of plug a machine in and it be optimised kind of depends on your
ability to generate the data you need or have a repetition rate that allows you to learn with you.
And another example I give you is fruit flies intelligence.
So people study genetics and fruit flies because they breed fast, mutate fast and die fast.
If we had studied genetics in elephants, the experiments would die at a faster rate than the experiments.
So there's a fairly good reason we pick those once.
And I think the same sort of understanding of what artificial intelligence is trying to learn is getting smarter.
So the types of problems are going to shape what we can actually study these things and it plays itself out in funny ways.
We also tend to think we talk about economics and mass unemployment, but which jobs are automatable is interesting.
So there is quite often an assumption that the lowest paid jobs will be the ones that come
in droves because they can't be that hard because we don't pay people to do them that much.
But it's wrong. An example I'm giving you is on the left of the auto pilot.
He was able to find a747 because it's doing things we think are complicated but are just maths and you can automate that.
Whereas the lady on the right that has to do all sorts of things which we find really difficult to codify,
like dealing with troublesome customers, you know, trying to find who really get ordered vegetarian meals, all of that stuff.
Yeah, very poor situations for computers, which means that, you know, there are counterintuitive examples.
So in legal, legal case study reviews and in diagnostics, there's loads of automation happening which is affecting people.
God is the brightest and best the society's adopted at a glance. And yet the fruit pickers and office cleaners are brilliant.
Remarkably difficult to automate, at least economically. And at the top of the picture.
I'd say going back to that things that you codify idea.
So if the odyssey is describing stay or going to be on an interest to being central to strategy.
We struggle to articulate what those things are to ourselves.
The possibility we can reduce those to maths seems a very distant prospect.
So the odds of machine learning doing what we would be happy with a strategy seems quite far away.
Although they will affect strategy because there'll be tools that matter. So understanding relative strengths becomes important.
So machines are faster than us. They are already proving better at detection and recognition.
They are typically more efficient at things that we can focus on and they are better optimising in all sorts of ways,
especially problems which we do we have a lot of data for. We can cycle quickly through how many would say, don't you know,
spending a lot of power getting tired, sleepy or distracted, but they own negative context.
So the ability of a robots to walk into a room and make a determination as to whether that's a child who
has picked up a rifle or a child soldier is extremely difficult and could well just be an intuitive,
almost unconscious act by a soldier walking into a room because the context is important.
Right now, they're pretty bad at doing this big push to try and change that.
But the ability to take lessons in one environment and reapply them in another one is a very powerful YouTube.
And it is no mistake that all of the longest running religions are books of allegory,
because the lessons that were written about worlds that no longer exist can still be applied by humans successfully.
Or that's how that even why they are still found yet facilitated.
This is not within the realm of the machine, but it's new for the obvious reasons that there's no data.
So if it's the first time something has been seen in the machine control, it's versatility.
So you can take a human and you can kind of do all sorts of things moderately well, if not perfectly, but quite often take the machine and put it on.
A new project requires re-engineering and there's a thing called catastrophic forgetting at the moment,
which is if I teach machines to be amazing objects and then I teach it back and it kind of forgets all of the things that it's learned about.
Yes, there are some progress. There's some progress being made to try and put sort of elastic bands in the memory, but it's all pretty nice.
And right now they tend to be as bad as if you started with a completely blank slate machine.
And I'm one of the one with this will probably be one of the first to disappear is going to be complex manual handling and it's if you
want an idea of how difficult something that we have evolved over thousands of years to do really well so we take it for granted,
I would suggest go home, sit on your hand.
The story isn't going where you think it's going to come and try and strike a match and you'll probably go through the
whole box of matches before you get anywhere near successfully doing it because you're relying on unconscious feedback,
pressure modulation. There's a whole raft of things to do.
That right now is why machines are doing squishy fruit rather than actually packaging stuff.
And, you know, firms like a car, if you've managed to automate everything else in that supply chain apart from irregular handling of soft objects.
Oh, and again, the promise of the bottom now, because they still make mistakes that seem completely alien in that we would never make.
So it's going to be a cocktail of human and machine, as one might say, that there isn't just going to be the machines coming out of it.
And how you apply human consciousness as it becomes more economical to have lots of cheap presence means the human consciousness,
naturally and logically must become proportionally scarcer on the battlefield compared to the different points of presence, you know, housing.
And you then start to get to a place where you can't afford to waste it on doing,
filling in Excel spreadsheets or other stuff that is kind of mechanistic.
If done, you're going to have to start to preserve it for doing the context, judgement, things that you know,
the machine is weaker and I could give you an example, it won't just be one black, one machine.
You will have teams that do this probably with some sort of obviously my own symbol is because I had a kind of simple process intelligence
and the brackets are where those people start with their consciousness and their intelligence and what they're thinking about.
And there will be some places. So on the far right there, you have one driver, maybe he's near the French president.
So you really don't want any emergent good ideas for the machine that you weren't expecting.
So that's you sitting there very carefully with one person flying that system.
And if it's complex to do, you'll find multitasking diminishes, which is one of the reasons, you know, have sex when you drive.
So you focus your consciousness that you might find that you are actually controlling one thing, which is, in fact, a swarm.
So you'll do it in concert with a machine to say, take this woman to that which you can give as an instruction,
but the machine needs to coordinate because there's no way you can fly 50 aircraft.
At the same time, it will also be how you put together a package of things.
Save the little bracket on the left.
There might be that we want to have a surveillance system that's not carrying any heavy metal because we wanted to be for a long time.
We want something with a gun on the floor so that we can actually do the shooting and you want them concert.
It's concerted team of. Things that are actually genius to maximise different kinds of strengths.
But you kind of want have one person to join in flight or so that you're minimising your decision, actually.
And then there'll be other things where, for example, the AWACS in the top left you you don't want to have to spend any time thinking about something.
You just wanted to sit there and say. Right.
Their only point is now I'm not going to pay attention to that because looking at a blank space waste of that consciousness,
you will go there, you will fly around in this area and you will shout when something happens.
But otherwise, just let me know you're going to run out of fuel.
So it's going to be how you balance those things and it isn't going to all arrive overnight.
And I think the way it's going to arrive is going to be the kind of series of overlapping phases.
And I couldn't give you a timeframe. I'll tell you, in ten years it'll be this.
And I think if anyone does a very, very, very clever war room, and it's going to probably play out in the same way as air power did.
So the beginning used to have battleships, which were the kings of the sea.
And in the First World War that was you know, that was in control of the oceans and seas.
Between the sea wars, you added enormous amounts of money to get incremental improvements in what were essentially the same systems.
So by the time the Second World War starts, you know, that got they've got better armour,
they can shoot better and they're more accurate, but they've paid a fortune to do that.
And you have the little box type things that are very likely to fall out of
the sky in the First World War to kill the pilots as the other guy was like, to shoot down.
I've had tens or hundreds of dollars for much bigger jumps in technology.
And the three pictures you see, there were a string of battleships, which are tiny little planes flung up and catapulted into a suicide bomber.
The middle picture is a software terminal taking off from HMS Pegasus, which is a World War One aircraft carrier.
And then by 1942, the battleship is still the most amazing thing in close combat.
But it's kind of irrelevant because you can never get that because the aircraft carrier was able to just a bit range.
So I think we'll see the same sort of thing.
So the first things will be stuff to augment what we already do because we're not in a position to understand those
technologies well enough to know which are definitely going to change stuff and then still operate in parallel.
And then I think eventually much of it will just be some things which become redundant because they're no longer.
But I also don't think it's going to just be one box. And the reason is the picture of a radio.
That is because it's kind of more of a component level technology in some ways.
So in the same way as the French probably have the best time to start of World War two,
it was actually the combination of all the artillery and transportation and people all melded together
through the medium of the radio to allow Blitzkrieg to be as successful as it was at the start of my life.
And I have given us a number of people who tend to get very depressed because they see all of the threats of this.
I don't think there is one natural automatic winner. They do have to cross that my next target.
But I think the future is neutral. I don't think any one person naturally owns this.
There are places that have natural advantages because of research that is going on.
If you do things to the cynical science and metrics,
which is the study of patents and the three leading countries of the world finding patents of currently the US and China.
And depending on how you measure it, the order changes there and then the UK.
So being in that bracket is certainly very healthy. But understanding how these things will play out for the fact that it's going to be driven by
the commercial sector largely means that quite a lot of it's going to be available for everyone.
So it's going to be proactive experimenters who make the most of this.
I think in the same way as was true for the emergence of Echo. And speaking of proactive, inventive people,
this is so I I'm going to show you this that's a test from yesterday I was doing and I saw a preview of what's a robot.
Now, if I'm labelling these pages, I think that is volume two on Google.
They were trying to build some driving cars and I'm like really proving, no, I'm not a robot or am I a slave doing data labelling,
all of it, especially since the old CAPTCHA process was beaten in, I think 2017 by a robot.
So there are robots out there that can be other forms of this test.
So it's all going to be about how your clever in getting everything you need. And on that one, I'll get back and see how much this will.