I'm very pleased to see you see you out front. I did check with him one of the lads already side by his side of history.
He's been a visiting research fellow with CCW for some time because of the way he's been doing.
And we consider him to be a research fellow, but he is now embedded, if you like, in the sort of thinking of the day.
He has been involved in a number of areas of conceptual development in defence, including Arizona Integrated Review Policy,
the main integration, the integrated operating concept and indeed Multi-domain Battle.
So plenty of things for us to think about and these areas is going to help us with thinking through today.
It's got an interesting title integration goblets factor, so we'll come to that in just a matter of what you call it that.
But just explain that.
You know, like many disruptors in his generation, he's done the usual civil rights, we might say towards the future to which we are grateful.
He has been one of those rare individuals who thinks very deeply about what defence means today and how new technologies are not only disruptive,
but also how they're been understood how we might before we get to the point of application,
how to make she understand what these phenomena are, what implications they carry.
I'm not going to all the list of things because I think you might will pick up on a couple of these various un work,
Ruthie Uninsurance, who I suspect are going to come up in the course of your conception, but I won't spoil it for social leave.
But I'm curious to know your integration, the Goldilocks concept.
Thank you very much for coming. I am amazed to see that many people to talk on integration, and I've done my level best to set myself up for failure.
First of all, by talking about a thing that everybody loves to loathe. Secondly, I know that people like me to talk about artificial intelligence,
particularly weirdly for the last six months about artificial intelligence ethics.
So I'm not going to do that. So what I am talking about is integration, and why is a Goldilocks factor to it?
And I by professional my staff officer, and that means I am powered by two things which are caffeine and hatred.
And there's an array of publications that have appeared in the, you know, in the US, UK, France, any number of nations across the world.
And integration is sort of designed KAIST. And you might ask, well, what are they integrating?
And the answer is everything. So every form of medical, military endeavour.
On the one side and on the right hand side, kind of every agent of every lever that state could hope to pull.
Now, could things be better integrated? Yes, absolutely. And I will get out of the way now the idea that I am somehow attacking the idea that
integration is a bad thing and we should go the other way because that's obviously nonsense. But it's this word.
This is the word that bothered me slightly and therefore resulted in this talk.
It's the idea that more is the answer in the sense that more is always the answer until everything is what it's everything else,
until you have every ounce of data that exists and it leads to visions like this and there's nothing critically wrong with them in the sense.
On the left, you have every platform, which every other platform and sending every ounce of data to every other platform in the country so that you
have this universally informed military force and that is also tied into a state where we see kind of Russia,
for example, but it doesn't really matter. And it's using trade and it's using sort of non-military activities.
And in the space and nuclear weapons and all of it is linked seamlessly together.
And there's a reason that people sort of think that way.
And then you get to this idea, which is that if everything is interconnected, that you have everything monitored,
monitored, measured and metastasised that all linked together and you have two things.
One is this tidal wave of information that risks overwhelming you, which is a realistic threat proposition.
But it also seen as an opportunity to create this sort of environment where you can
know everything and you can therefore orchestrate the perfect world that you want to,
ideally with some sort of godlike move that enables you to orchestrate everything even
before you've started and say I either one is the fragility of the glass battlefield.
Now, it's not unreasonable for lots of people to have formed this idea of perfect
orchestration through massively surveilled or massive surveillance and information,
because this guy had a really, really bright idea about this. So that's well known.
Then if you know anything about computers, the word von Neumann architecture would just mean this is why we have computers to you.
And explicitly one of the things he was seeking to create when he created the first set of computers, amazingly, was whether control.
It's in the list of things that he wants to do. Why? Because von Neumann knew that the tiny changes in initial conditions can massively shift.
How is this system performs in the long run? And therefore he.
The idea was that humans are too simple to be able to cope with all the fluid dynamics that are involved.
OK, we have a computer that does the crunching. We'll find the point to balance those in the military.
We'll think about another such terms. And that lets us tip the system into new modes of our liking.
And then Edward Lorenz comes along and ruins it. Now he was working on one of the first sets of computers, as imagined by by von Neumann,
and he ran a simulation and then went back and re put it in a tiny change in the
initial conditions and very soon the whole way the structure was completely different.
And on that afternoon, he realised that long term weather forecasting was dead.
Now there's another stage to that. It's worth explaining slightly more, which is on the on the screen here.
What do you see a broken imaginary boys on the surface of water? And then we have all the water.
And so if you had the simulation that was able to create, you know, understand all the wave properties,
it would take the readings from those boys and be able to predict into the future what the
waves might look like because it's actually a physics system and it's therefore codified.
Your problem is when something happens in the gap between the senses.
So in my hypothetical example, someone threw a rock between the two. And now you have a big new wave.
Now you and I will know looking at the screen that the path those waves forged forevermore will be different.
But the point is for the system that's linked to these boys. It can never know.
And to its perception, there is no difference between that and this at that moment.
So your ability to orchestrate that perfect battlefield is going to be foiled by the gap in between the senses.
So Lorenz spotted that any area can be the point at which chaos points forth from and out.
Our other problem is that you can't have infinitely arrayed senses of infinitely small space covering all of the situations that you have.
And one of the other reasons we tend to think about orchestration in a particular
way is we have classic examples like AlphaGo here now looking at boats.
What's key about that thought? There are no gaps between where the pieces are.
So you can have systems that are playing something like Starcraft or whatever, where you have hidden elements of data.
But what you don't have in something like AlphaGo is you don't have to go between the gap to the god of chaos between the gaps, one of a description.
And this is another useful idea because it takes you to the the premise that some computer models are doing emulation rather than simulations.
So go is not being simulated here.
Go is being played here. There is fundamentally no difference between what AlphaGo is playing as a game and Lee Sedol.
The fact that a human is moving the counters around is irrelevant. The game is being played by the machine.
Now that will apply for some systems we want to use machine learning tools for, but it won't apply necessarily for some simulations.
And now we talked a bit before about something you'll recognise as chaos theory.
Chaos theory obviously has the butterfly effect, and that's dependent on sensitivity in initial conditions.
Now, most people don't know that the chaos butterfly has a less media savvy cousin, which is the hope of now.
The chaos hawk moth is similar to the butterfly, but the difference between the two is that the hawk moth,
the whole cloth effect is where there's sensitivity in the model that you're creating,
and minor changes in the model produce wildly different outcomes, even where the initial conditions that you're feeding in don't.
So I would strongly recommend looking at Erica L. Thompson from the London Maths Labs.
But the example is you can have exactly the same initial conditions,
but tiny tweaks to the model that you're creating will give you wildly different positions, and they can be infinitesimally small.
We have another problem, which is that not only do we need to put the things for a system that enable it to see,
but we can't see what we've learnt to see now. For those of you who clicked on the little click bait thing that might have got you here.
This is where we get to the cats. So Rob has had me talk about this before,
but this is what Tom Hoffman and he took some cats and he raised them in this very weird
environment where the one set of cats was raised in an event where they only see horizontal lines.
Another set of cats was raised in an environment where they could see vertical lines. And this is the result.
So that cat can see, obviously the rod held horizontally.
But there is nothing in the reward system that allows neurones to form to let it to see in the vertical.
And there's the genres that there's nothing special about. That cat is not, you know, he's been raised in a really weird environment.
But the idea that you have to be trying to see a thing or any system has to be trained to see a thing,
whether it's a human machine learning system or an organisation remains true of
its ability to then detect something later on to show that these things apply.
If you train a machine learning system only on dogs, it will detect no muffins in this picture.
And then there are classic case studies that we look at from an organisational perspective.
So the MacNamara family is a good one. McNamara comes out for it.
He arrives in the in his Defence Department job, and he's all about the mattress organisation and the measurement of.
The Vietnam War and is convinced for ages that victory is coming his way because the metrics let him see it that way.
But and it's beautifully described by a man who said, I always mumble on the left and Coslovich.
It's not the bullet, isn't it?
The third step is to presume that which that which can't be readily measured is isn't really important and is therefore blindness.
So the same problem is true in any organisational system makes sense as well.
Now, that might all seem very distant from you, but it happens to you every day.
This is a drop down menu. You will have run afoul of this sometime in your life.
For example, this computer is incapable of seeing that you want to fly with easyJet.
It's simply not at all that it has that it can get from you in the elective way of seeing your preferences that it has developed.
And I'll keep coming back to why these things keep repeating so that there are systemic issues there,
issues of systemic theory and chaos theory that replicate again and again and again.
And we could see now. So hopefully you'll be able to see once that takes place, the right way up one step.
So I don't give you a second to have a look at that.
And the problem with that is, of course, there's fundamentally no difference whatsoever to the picture.
What's happening to those of you who can start to rise in this won't apply to everyone is that you have what's called a prior.
So you have learnt to see a particular way and your brain has told you that
the shadow belongs on one particular way for a plate that is the right way.
And one way for plate that is the wrong way off. And it might even have told you that the shadow sits on the bottom of the picture.
And so you have learnt to see it. So you actually see it in your brain as upside-down all the way up.
And the way that you can tell that this is a neurological limitation as much as anything else is,
it's not possible to see the plates in both orientations at the same time.
You can't look at one of the pictures and simultaneously see it upside down and the right way up.
At the same time, you'll experience this flick in your brain where it went from one to another.
But that is how you have learnt to see and it codifies what you are capable of seeing.
The same can be said for this one. So most people will look at this and hopefully you'll see three colours, maybe some of the colour blind.
But of course, you know it's a trick because the balls are the same brown colour.
What's happened is your brain has learnt to see what it expects.
The change in the wavelength of light. Striking an object coming back to you is as a representative colour, so it doesn't see what's there.
It sees what it considers to be useful to you. And this is systemically true, but it comes with problems.
So what system is trying to recognise how optimised it is to specific types of information over
others and what its natural focus of attention is often not proactively selected choices,
but accidental defaults? Worse still, because they often no associated prompt, what system is blind to is typically reviewed infrequently.
Sometimes this is only triggered by a post, post-mortem or inquests after the event, which is obviously when it's suboptimal.
So every choice you make about how you or any system your creating perceives the world
is necessarily a choice about what you're going to choose to be blind to as well.
And again, that will sound quite arcane, but attention is an intentional,
unapologetic discriminates that asks what's relevant right now in case you are only to notice that.
And for example, you will be listening to my voice and hopefully doing that rather than listening to the faint drone, you can hear from the lights.
You probably didn't notice that you could hear it until I mentioned the faint drone.
And even if you can't hear the faint drone because your hearing shot away,
you probably didn't notice where the chatter was pressing into your back until I mentioned it just now.
And then when I mentioned just now,
you probably even as you were thinking about that didn't think about kind of I feel the tongue on the roof of my mouth.
You weren't conscious of any of those things,
is my guess until you had a prompt to do so because attention causes you to have a discriminatory effect in how you process information.
And this is always true of systems. And it sounds fairly absurd,
but you can watch somebody here who giving directions and 50 percent of people don't notice when they pull that door trick.
The person they started giving directions to is not the person they finished on, because who they're talking to is not the focus of that.
I strongly recommend Googling Daniel Simmons's website. You can see the monkey business situation and loads of stuff in that.
And again, this is a thing where I have given you biological examples,
but you could go to the world of machine learning and you would find a whole array of research that's
looking at tenses and other forms of attention discriminator in a way of saving processing power,
improving how those systems work, but necessarily come at a cost in terms of what they don't sacrifice to do it.
And I don't think I really need to explain where you've come across an organisation that doesn't have attention.
You can just watch the news. Now there's another way of thinking about the why is that why?
Why does why our system so often like this? And there's a fundamental point about truth and advanced.
And then not necessarily the same thing. In fact, Colonel Nathan Jessop would have screamed at you, you can't handle the truth.
And in the courtroom, he was wrong. But if he was an evolutionary biologist, he would have been right.
So this is the mantis shrimp, the mantis shrimp. It has the most complex vision system in the world.
You and I have long, medium and short wavelength cones in the back of our eyes.
The mantis shrimp has 22 different types of receptors. It can see polarisation.
You can see rotation of polarisation, circular polarisation, infra-red, ultraviolet, anything and everything.
The dog has a much better sense of smell than you, but only to say worse perception than us.
The dog is an animal, which is a predator.
Its ability to detect prey is fundamentally linked to its ability to be a successful species to reproduce, adapt and survive.
So why has the dog not got to these cones?
And it's because there's an energy costs to whatever you do that. That's an example, but actually has been backed up by Mat.
So this is work by Donald Hoffman. And what they were doing here is Don Hoffman worked in game theory,
and you have two different types of truth for the things they put through their game theory programmes.
And then you have something that sacrifice is true, so it accepts less data in order to be more efficient gathering reward.
And you can see the drift in all of the models. And again,
we'll get back to you say it's also worth understanding in terms of blindness and your
inability to see your own blindness or any system's inability to see so blindness.
That's how a look at the disaster. This the human eye. So on the image that you can see it to see, it goes through the lens and the back.
You've got a nice clear mirror that's of mirror image it's replicated, allows it to focus features of the disaster in that sense.
Ah, that is the right focal length. If you come into an angle, you don't have the same focal length.
Your ability to produce the right image around there is poor.
But that's also why most of your cells or the back of your eye and not round here you also have a blind spot.
And so what does that mean? Does your focus or point of focus is you're looking straight down the road?
And that's what you think you see when you're driving, because your mind either fills in the rest or tells you not to pay attention,
you don't notice you're not looking at the pavement because you're not looking at the thing.
That's what a replication of the eyes focal activity looks like as you're looking straight ahead.
And this sort of seems fine, a bit dangerous off to the sides.
And this is why your eyes round all at the place and then off to the side as you glance that car,
suddenly realise that the vision off to the side is straight ahead of you.
As you're looking right, it's relatively terrible,
but it's not your life experience because there's no value in presenting that truth to you as an organising construct,
driving the car so your brain doesn't do that. And here's another bit.
So going back to the blind spots you can see here someone's map,
the left eye and I put where the blind spot is about 10 degrees to the left of your vision,
and you can kind of see where they've mapped the fading out as well.
I would invite you to go home and try and see what your blind spot looks like later.
You'll have to cover one eye and you can find things like this on the internet and be ayro.
This works for because of the distance you're at. But if you look at the bluebird on the left and you cover your left eye and I'll
be interested to see how many people have the balls to try this in the room, whether they look stupid.
You will at some point if you're the right distance away, but RedBird will this pick just Google Birds blind spot and you can find this entire time.
And it's interesting to see what happens as well from your perspective, because you don't suddenly have this black spot that appears.
What happens is the blue line seems to carry on. It's not very clear, and the sky looks like it's what fills it in a bit.
But you're just suddenly become aware of the fact it doesn't work very well and you could do other things as well.
If you want to know how small your area of perceptive vision is, if you stick your hand out and you stare at this Nicole in the middle,
you will find that you can count the lines here without moving your own fingers.
But by the time you get up to these ones at the end, you're not quite sure if it's three or four.
So the area that you actually perceive is really small, and it's largely true for most systems because of the energy cost.
So what the advantage is that helps you be gain competitive advantage is the right balance of abstraction and detail.
You need to know enough detail so that you can tackle your problem properly,
but you need enough abstraction so that you are not wasting energy needlessly.
There's another way of thinking about this so you can think of any agent that makes the decision be that machine, organisation or human.
As an agent that's making a decision, it has a lens through which it sees the world,
which is built up by all the prior or the lessons it has learnt up to this point in how it's constructed.
And through that, it must see the right amount of information in order to gain competitive advantage.
And we'll come back to that model minute to really hammer home a point.
The simplest thoughts and your body involves only three neurones and it doesn't involve your brain.
If you've ever reached out and touch to hot iron,
you'll find your hand snapped back and you probably said to yourself, I'll pull my handbag because I know pain.
But that's not what happened. The simplest thoughts in your body involves three neurones and any goes into your spine.
The first neurone basically works on sort of proteins. Detecting heat sends a message.
Second, you realise it goes to the upper muscles in your arm and then you get a snatch away response.
The reason you have both this and the pain response is to give you two different reward functions.
The. First one is because the longer you leave your hand on the hot plate, the greater the amount of damage.
So speed is the reward function you want,
but any number of times doing that isn't really a very effective treatment to stop you doing it again in the first place because it's stupid.
So you have pain. The pain function actually arrives a lot later than the hand reflex, but I put together in your head in a different way.
So quite often we will end up integrating things that have different reward
responses to different functions that will feel like part of the same system.
So integration isn't mindlessly wiring everything together.
Integration has to be about purpose, taking away from the biological back to the machine learning world.
This is Max Tegmark, who was looking at the theoretical largest artificial intelligence you can have.
And his point was intelligence. Intelligent information processing system.
Going big is a mixed blessing. On the one hand, going bigger lets it contain more particles, which enables more complex thoughts.
On the other hand, this slows down the rate at which you can have truly global thoughts,
since it now takes longer for the relevant information to propagate to all of its parts.
So we've covered the biological. We've covered the mechanical. Now we can cover the people.
There's the UN. That's three people making a decision. I don't really need to point out which of those two is going to be quicker.
But I also don't really need to explain to you that if you wanted a global solution for the globe,
you're going to have to have that level of complexity in the top. What does that mean for integration?
Well, it means that you have to disaggregate for victory.
You have to parcel up the parts of your system to do specific jobs and then feed on the right information to the rest of the system,
not just assume everything we needs to know. Everything and everything has to have the same sorts of cognitive processing.
And this is the art buddy and starch Star Trek the Borg. Arguably, if all of these principles from other bright people that aren't made are true,
these people would have been useless because their whole point is that they're homogenous and that they're all wired together.
So you would have had one lens of seeing all of space and they would have been incredibly slow.
In fact,
they move like zombies because they kind of all space on bodies because they would be incapable of reacting quickly and efficiently to those problems.
And again, that goes back to that sometimes the UN. Yes, it is.
How does it work? Well, when it has subcommittees, when it has experts that do things to it and present back to the group,
then reference it and decide whether there's something in that particular field that's going to cause a different problem.
So we're back to some of those lenses, those decision makers and passing on the right information in terms of passing information correctly.
It's not necessarily just what you think is important to matters, it's what the tacit knowledge of the two actors is.
So if you imagine that sort of lenses that see the world, if they completely overlap,
you can pass on a tiny amount of information on one system will know from that trigger.
I know he's going to know what's I mean when I say this single word is going to promote a whole response,
but against that, it also means that we have a very, very similar way of seeing the world.
And it's why you end up with organisations that are in favour of cognitive diversity, for example, trading funds,
they like to have lots of different viewpoints because it will allow them to tap into wealth through lots of different lenses.
Again, examples always help.
So here on the Parade Square, you can have a single word of command that orchestrates vastly complex manoeuvres of human beings.
But it's because they all have incredibly overlapping, tacit knowledge of a high degree that's been invested in beforehand.
Very celebrity RF, very similar with surgeons. And again, waving a flags and naval vessels goes back to the Times of Europe.
So there's a Goldilocks premise as to how integration works.
You have to find the optimum and you might start wondering what that sort of means.
And again, there are other ideas that are really useful. So this is Ross Ashby.
He came up with this theory of requisite variety, and it's it sounds incredibly complicated,
but in its simplest form, you can think of it as if you want a system to control another system.
This system has to be at least as complex as how it interlock with the thing its target is.
Now that sounds really difficult and abstract, and I probably lost half of you,
but I can show you a picture and you will kind of instinctively understand. So early computer games, somebody runs, jumps left, right.
Maybe I should eat something. And because they're very simple, you end up with a very simple controller.
You couldn't play most of the modern games on that kind of controller without having some sort of weird.
I need to go up three times down, twice left. Quite right four times.
So you end up sacrificing speed on all sorts of things,
so you end up with more complex control systems when you're trying to control something inherently more complex
that might lead you to think that the right answer is just to have as many control levers as you possibly can.
I just need to look up a bit of text here in order that you don't use them until actually now I make this leap.
But that in itself has problems, and this is the p38. Was extremely successful in the Pacific, they said to the European theatre.
I was escorting bombers over long range, and it did really badly compared to what they were expecting.
And there was a series of angry letters of increasing rage that went from the U.S. to the pilots who were flying these things,
which is a bit harsh because that being shot down asking Why is this all going so badly wrong?
Are you just inept? What the [INAUDIBLE]? And eventually?
How does a real leader, the 20th fighter ring, wrote this back.
And he describes the changing conditions the plane was able to cope with because it had such
an array of different control levers that meant it could now operate in high altitude,
long range cold conditions, which is what it was, wasn't what it was doing in the Pacific. But the consequence of that?
And he said, as soon as p38 pilot comes into a situation where he must engage in dogfighting,
he must increase power to get rid of the external fuel tanks that's on his main fuel.
So he reaches down turns to stiff, difficult gas, which is to make turns on the drop tanks,
which suppresses the release button puts the mixture to also reach two separate and clumsy actions.
Increases his r.p.m., increases his manifold pressure.
It turns on his gun heater switch, which he's had to do to save the battery because otherwise the plane will still operate through the flight,
which he must feel full and cannot possibly see.
And he turns on his combat side switch, and he's now ready to fight, at which point he's invariably been shot down.
So every time you out of control leave it, you had a training burden, and every time that controlled lever must be used,
you're adding it's high burden if that's a significant deviation from normal action.
And so this looks like a slightly ridiculous picture, but this is I strongly recommend going watching David Silva's talks on reinforcement learning.
So David Silva behind AlphaGo wins for the best of DeepMind. And his point really is simply that you have observation, but you need to build it.
It goes into an agent that makes a decision and does an action,
and the control system will have to have the right level of complexity to be able to cope with the environment correctly.
And then from that, you will gain a reward. And what the reward then does if it's an adaptive system is it changes both of these.
So not only do you need to think about how you're integrating the system in the process of doing it,
you need to examine what your real reward functions are, not necessarily what you just say your reward functions are.
And they kind of paper out, which again, I would recommend. And his premise, which got later take in the artificial intelligence community,
was that you can reach any form of intelligence simply through the correct application of reward.
And it's interesting to watch people argue about architectures and all those sorts of stuff,
which silver and co argue can be reached through having trivial functions which generate those architectures because it's not really a new idea.
If you think about all the intelligences we experience anyway in the world,
they all arrived here by the reward function of natural selection in the first place.
So definitely go watch David Silva's lectures and kind of think about the simplicity
of symptoms and then all the chaos that just boils down to this simple idea.
Now I'm showing you this picture a few times, but if you're integrating anything, let's imagine this isn't necessarily a person,
but it could be a technology, can be an organisation, can be any of those things.
They all exist within systems or systems, so it's always going to be inherently complicated.
So your reward system matters in the sense that whatever subsystem is doing,
if it's adapting or you are adapting it, you're going to change it with respect to reward some function.
But that reward function has to serve the rest of the organisation as a whole.
But he didn't think that was complicated enough. You now have to throw in time as well.
And I'll give you one last idea to play with, and this is the idea of pace layering.
This is really interesting. I think it's one of the places this was first dreamed up independently was Brian Eno's music studio of all places.
But you will find it in architecture, you will find it in neurology, you'll find it in a bunch of different places.
One is a fun one, so we'll take that. And his idea is that things run a different paces that they're interlinked.
The idea that you can have a system, everything works on the same clock.
The second is the same for everything, and all systems work on that pace. It doesn't really work.
So fashion with the argument that it changes constantly. It's this chaotic thing.
It's turbulence, probably. But there's no way that fashion can exist without the commerce to support it.
You can't buy clothes that nobody makes. You can only buy them from shops that exist.
And in that sense, fashion does provide information which changes how commerce works.
So if you are aware of fashion in your a clothing line, you won't ever have a commercial business to last very long.
So there's an intellect between the two. So you kind of think of it as the turbulent interface. And then commerce also drives infrastructure,
and we're seeing that now you're seeing the closing of the streets because commerce has changed
and it's changing where people are buying things and that infrastructure ties into governance,
which then leads back to culture. And then fundamentally, nature. And you can imagine each of these layers moving at a different price.
Now these are indicative layers, and you will find all sorts of things when you peel back the onion of whatever organisation you're looking at.
But just going back to kind of the idea of infrastructure and governance, provenance relationship.
This is a picture of Boston in 1860 on the left, and here it is in 1981, and only one building is the same.
But what is noticeable or notable about the picture is that every single road is the same.
So the infrastructure changes, but the thing that determines where the roads go isn't an infrastructure problem, it's a governance problem.
So because it's different, right? So you can think about these things.
I picked that one there, but you could also think about it in the biological way, so here we have priors.
So what you have learnt previously has shaped priors that determine what your vision system is.
And now when you see this, you will know more about the picture, so you will tend to interpret it differently in the future.
And it occurs to the brain at a different level as well.
So as I tell you new things, your neurones will fire differently to recruit and record that information,
but that isn't the sum total of how the brain adapts. So this is a terrible picture, but it's from a live wired by David Eagleman,
which I recommend and this is scans of the change in brain structure of different musicians.
So as your brains are learning things electronically, they're also released in clinical trials that tell you where to grow new neurones.
And you can see it in violinists who end up with this sort of omega shape twist here on one side,
because that finger is significantly busier than this one.
But pianists, because they have equally busy fingers, end up with a wholly different brain structure.
So the idea here is that you actually end up with these things occurring in organisational systems.
Biological systems exist everywhere. So I would leave you with four things to take away the next time someone starts talking to you about integration,
which will just annoy whoever you're talking to because they'll want a nice, simple answer.
But you have to decide what is your problem in your environment? Is it amenable to that glass battlefield?
Just like God move, and there will be lots of things that erupt at all.
And in those instances, you should absolutely use deterministic planning structures.
You should absolutely use machine learning with a high degree of confidence.
You should actually accidentally use a highly procedural organisational structure.
But if your system is prone to complexity or chaos, or there are a number of reasons why that first model doesn't fit,
you will need to have the Goldilocks form of integration. You need to have the right amount of truth going through your system.
You need to balance the amount of tacit knowledge in the different systems that communicating.
You need to balance how much information is processed from one to another.
You need to have the right diversity as well, so you need to have enough lenses looking at your problem that are suitable to your problem.
If you are not, if your job, for example, is automatic, no recognition for the police.
You don't need neurological diversity and how the no play system is read because that's one lens is great.
It works really well if you're starting to think about more complex things like how we organise social policy for government.
You absolutely need an array of different ways of looking at the same problem.
And the final one is how we look at and I think this is probably the thing that we across all organisations
tend to do worse how we look at the different tempo of feedback that occurs within a situation.
Quite often, we will tend to do things which we tell ourselves are about time, but aren't in awe about the design of time.
So if presented with a complex problem, it is common for people to plan for the length of time they have been given to do the planning,
realise that time is up and announce I have a solution. But what they don't have is I have a solution that I'm confident in and I have.
This is the solution I have reached in the available timeframe and then they might be working
within a structure that says not only do understanding now I do orchestration not only direction.
And then later I will come back and I will do some sort of post after action review and then I will start again.
And if that is really monolithic, you aren't necessarily thinking in the course of analysing the problem which bits of the system,
which bits of this problem going to change really fast.
If the thing changes really fast, faster than my ability to turn around, then that's not great.
If you start thinking I have to do everything at the speed of the fastest component in the target,
you're going to be stuck in this cycle that finds itself choosing to be blind to long term effects.
You will have said, I'm really interested in how my fly's move, but you will never work.
I may fly's evolve because the timeframe over which they look isn't the timeframe in which you're looking at them.
So there's things going on that I'm going to do two plugs.
One, if you're interested in any of this batshit craziness and once get farther, I would strongly recommend to.
Colin Williams is doing a colloquium on 76 81 Democrats.
A history of this and no more on the 23rd. He's. His e-mail is the bottom there.
Either take a photo now or come and find me. And one other thing which you should see.
It's not out now, but coming out, hopefully in the next six months is definitely the most anticipated book and affecting more in the future,
which will be out, probably available through CCW and definitely available through me in about six or eight months time.
Will there be lots of other bright people saying more intelligent things happening right now?
Thank you very much. So, you know, my brain, my brain is teeny.
I also want to get you guys to a class that, you know, once you're taught something new physiologically,
you damage somebody in perspective because actually that brain has changed so much it what people thought was going to just steal an opportunity.
Yes, I pick up a one one sort of idea because you tell us a little bit about how one framing is,
how important diversity of experience is actually on a more diverse groups.
We have to solve complex problems, has a better, even though you oppose the idea.
But one thing that really struck me is that as a species, we're a bit rubbish learning from mistakes and that troubles me.
So if I give you, let me give you a solid in part because because that's a bit abstract.
So when the Mongols were busy inventing sort of Central Asia,
they come across Kiva and then you, the team had a really decent sized military force mobile,
actually, and they could move this mobile force out from the city along the river,
and they could basically hold, you know, this rhythm quite successfully.
The models figured out, but they just needed to use a particular device that could slow, slow, quick, quick.
Those of you know, your racial history, we know what happened. They moved in from the northern direction very, very, very slowly.
They knew the key ones would pick up on it. They were doing this perception thing. They could observe it.
I was doing this thing again. They drew away, even congratulate themselves about how wonderful they'd seen of the Mongols.
Then, a few months later, the monkeys can be very, very slowly from the south and the Cubans getting concussions long.
They moved on, you know, in a rain and the cold was moved away.
And so while the Cubans were now sort of committed to these two different
locations and weeks have gone by and they were busy feasting and writing poetry,
which are wonderful they've done, then the Mongols kicked them in under 24 hours.
They crossed the river in an unexpected location and did a saw two stroke double taps of heads and over three kiefer destroyed.
Now, everyone else in the Middle East saw what had happened. Nobody learnt the lesson.
So the Mongols did it again and again and again. Now that's the existential catastrophic.
You can see examples of those were very personal,
and they all sorts of repeated the same mistake several times over what has gone wrong with us as a species
that we seem to be unable sometimes to learn from our own experiences or from these sort of things.
So I would hope my instinctive response would be, I think that there are two key factors that relate to that.
One is that when it all goes well, it tends to be unremarkable and therefore isn't recorded.
So every time the Mongols had sort of turned off and know there was a force that they'd probably gone well, this was one of the slow ones.
Don't tell anyone. And that and it would also not appear to be as remarkable as the destruction of Cuba.
And so there's this tendency whereby catastrophic mistakes are retold his title more adequately and more interestingly,
say we, we have a kind of a sampling bias problem, and that's to an extent.
I think the the other thing that quite often happens and goes back to the idea of reward functions and say,
Oh, Robert Miles is a guy who's got a YouTube channel by air safety.
And he talks about matter and meta objectives and reward functions.
One of the classic problems is it's that kind of monkeys, poor thing or the sorcerer's apprentice.
I've asked it to do something about it. That's what it's done. And so you can get that in systems where you, you tell somebody you want something.
And again, if you were to jump across to an organisational perspective, you'd get to things like Godwin's Law, where, you know,
we said to someone that you had with the Minister of Health said that they
didn't want to see anybody wait longer than 24 hours for potential appointment.
And the only way they could measure that was by counting the number of appointments that occurred 24 hours after the booking.
What that actually resulted in is a reward function in sight.
Dental centres was receptionist, refusing to take any bookings longer than 24 hours out because now they're going to get a negative reward,
they're going to get punished for it. But that's not actually the same as making more dental appointments available.
So you'll end up. I think sometimes our inability to learn is tethered to an inability to correctly recall what functions are.
And there's there's all sorts of ways you can look at institutions as well and say, you know, that person is engaging and truly appalling politics.
But on the other hand, they have really successful within their own political party.
Yeah. So under what criteria are they judging whether this is a catastrophe or a mistake?
And it might even be to the state where, you know, from a personal advantage point of view, it gets them all the toys.
They want all the platitudes and yet be bad for the system of which that part and they're purporting to act in the benefit,
often quite often they'll will be blind to that mistake that that.
We are not talking about wrongness, and that's where the reward function and selection bias, that's where we're going to focus less.