Welcome to the Oxford Martin School and to this lecture this evening by Paul Clark.
Yesterday, we had the budget from the government, which is clearly very excited about research and innovation, about infrastructure.
And then layered on top of all of that, we have the corona virus sweeping around the world.
So given all of those things going on to have a tech guru of the Jumah, which Dominic Cummings would obviously get very excited about,
and someone who knows more about logistics and how to keep food on supermarket shelves and showing up to people's houses seems to be extremely timely.
So it's great to have Paul Clark here this evening. Paul is responsible for an incredible operation.
Nineteen hundred engineers in Accardo.
And not only is that transforming retailing, but it's also doing its best to shake up and bust incumbents and disrupt all sorts of other sectors.
Originally, Paul read physics at St. John's here.
He was the duty of so Chris Goldsmith, who's in the audience,
and then went on to do a variety of different software engineering, tech, start-ups and consultancy roles.
And has become an increasingly visible and influential figure in the whole field of tech and logistics.
He's on the government's elite Council on the Robotics Growth Partnership.
He's chair of the CBI Innovation Council and he's on the National Food Strategy Advisory Board.
But the other remarkable thing about Paul is that he's a hands on guy.
The first apps which were created, Accardo, he jointly wrote a list as his past time designing circuit boards.
And he continues to write software as well as during all of the other things that you've just heard about.
So it's a great pleasure to have you here this evening, Paul, in the Oxford Martin School.
Thank you, Jim. So I just kept vaguely set up here. So, yes, it's definitely raised the stakes for me to have my executer here.
And that was definitely not something I was expecting. But anyway, delighted. But unless it takes very little to get me back to Oxford.
So it's it's very exciting to be here. And. No, no.
Sorry. Go away. So the robots are taking over.
Yes. I can't really complain about that. All right.
So, yes, I'm going to talk a little bit about what it says.
Here are recipes for exploring the transformation of food.
But I'm definitely going to stray off onto some other big topics, too,
that are increasingly what I spend my time on because I I'm no longer involved in the
operational side of the businesses and explain I'm off much more on the futures kind of stuff.
So I want to start by talking about the kind of the global context that really underpins
why we need to do significant transformation when it comes to food production.
And of course, we have to start with climate change, which is now irrevocably bolted into the worlds like Geist.
And obviously, food production is at the heart of many of the UN's sustainable development goals.
But, of course, the world population is continuing to grow and there are many more mouths to feed.
And unfortunately, in regions where food is most scarce and we have to also be mindful the fact that the the unintended sort of consequences,
if you like, of agriculture is having on the environment,
whether it be greenhouse gases or biodiversity, soil erosion and flooding,
the kind of stuff that Jim worries about and tries to solve is becoming increasingly well understood.
And depending on who you want to listen to, you know,
these are problems that we either need to solve by 2050 in terms of net zero or perhaps 2030 or maybe some other date.
And in, you know, kind of post Brexit world that we now live in.
The implications for food security, food prices, food import tariffs and food standards are upon us.
And that's, of course, before the crisis that we're now living through,
where food security couldn't be even you know, it's been elevated to another whole level.
And we find ourselves at Acardo slightly in the middle of that game in terms of how will people who can't leave their homes get their food.
And there's a growing demand from from consumers to understand where their food comes from and indeed to reduce the food miles to get it to them.
There's a growing focus on food packaging and food waste, whether it be from regulators, suppliers, retailers or the consumers themselves.
And the NHS is shifting its focus from treatment to prevention.
Food has a significant part to play in our general health and wellbeing, as we know.
And we've seen the impact of things like the shark attacks on products that the FMC G's manufacturer on, on consumer behaviour.
And as one of the many things unfortunate,
I can say this now that my mother used to say to me as a child that I dismissed at the time as a complete nonsense,
but which of course turned out to be true. You are what you eat.
And so she was a little bit ahead of her time on that one. So what are the challenges we face in responding to these issues?
Well, food is a complex, a properly complex, interconnected ecosystem with lots and lots of moving parts, not all of which we fully understand yet.
And we need to be careful where and how we squeeze that toothpaste tube,
because there are real risks of all sorts of unintended consequences as we try to optimise different parts of that system.
And we have to face the fact that it's going to be very hard to transform
traditional agriculture in terms of the impact that it has on on the environment.
The effects of climate change are likely to place more more pressures on conventional agriculture in terms of more extreme weather events,
flooding, droughts, storms and so on.
And furthermore, traditional agriculture is frankly very inefficient at turning photons from the sun into calories and protein,
as we know, and that we then want to consume. And if and if you haven't come across it, I strongly recommend this blogsite by Professor Sir Ian Boyd,
who was previously the CSA at different chief scientific adviser, and he's one of the UK's leading climate scientists.
And in his twin September twenty nineteen blog post that you'll find on their Securing the food of the future.
He shares three really interesting facts or statistics that to meet both demand and net zero targets.
Efficiency in traditional agriculture needs to. Piece by a factor of five to 10 times by 2050.
So that's a 500 percent to a thousand percent increase as compared with the current trend of efficiency gains,
which is typically a few percent each year.
And then if you consider a plant that is subsequently eaten by an animal that we then eat of the sun's energy landing on that plant,
only one to two parts per 10000 will actually be converted to calories within the animal meat.
And finally, roughly 80 percent of UK agricultural output is produced by 20 percent of farmers on 50 percent of the land.
And that means that about half of the UK's agricultural land has very low food productivity.
And so if we start talking about, if you like, the system that gets the food to us,
there's already a lot of food that is shipped to us from far away, as we know.
And here we've listed just some of it. And that comes in these huge kind of containers or or reefers.
And one of the challenges is lowering, increasing lead, competing demands from consumers.
On the one hand, they want variety and availability, but without seasonality.
But on the other hand, they don't want to think that they're in any way contributing to any of these factors.
I'm not going to read through them. You can read them there.
And and that, of course, means that globalisation of food production is somewhat at odds with current consumer trends.
So we're going to need some alternative recipes, if you like, for food production.
We're gonna need find more efficient ways to convert photons into calories and protein.
We're gonna need to minimise those in and unintended consequences of food production and consumption on the environment.
We're gonna need to view the world food production machine as a closed holistic
ecosystem and work out how the UK is food production machine sits within that.
As part of that holistic eco system, we need to consider things like how do we move food around and keep it fresh,
including issues such as food, miles, waste packaging and circular economy models.
How can we better educate consumers and transform their behaviours,
including issues such as quality versus quantity of food, alternative sources of protein, waste, incentives and packaging?
How do we change our relationship with food?
Its impact on our health, including issues such as food, as medicine, personalisation of food, obesity and treatment versus wellness.
We need to build a holistic, validated and fully costed model of our existing food system, a food production system both to help us then optimise it,
but also so that we can do like four like comparisons with alternative food production models.
Of course, we need to continue to look for ways to use science, technology and automation to help transform traditional agriculture.
In terms of its efficiency and impact on the environment, whilst being mindful of those limitations that Professor Enjoy talks about.
But at the same time, we need to explore new forms of farming that offer the possibility of delivering the efficiency
and sustainable get sustainability gains that we require to meet those net zero targets,
including technologies such as vertical farming, insect protein, aquaculture, algae production and others.
And we need to move food production from an agricultural process to a manufacturing process with
improved sustainability whilst preserving or hopefully improving factors such as quality and nutrition.
And all of this is is very much at the heart of the national food strategy that's under development.
So before I talk about some of the puzzle recipes we're engaged in around food production,
I want to introduce you to some of the disruptive ingredients, if you like, that we cook with at.
So as has been said already by Jim Acardo is a world leader in smart logistics.
I used to introduce Acardo as the world's largest online only grocery business, but we aren't that anymore.
We still own 50 percent of one of those.
It's the other half being owned by Marks Spencers, but that as of last year, we've divested that into a joint venture.
But what that means is that it's left what was always there, which is this technology company at our core, owns that.
That's not our core. But we'll get to that.
So our founding vision was to use a huge amount of technology and automation to do online grocery scalable, sustainably and profitably.
And we've done that. And it was also part of that vision that once we had evolved decisions for ourselves,
that we would make it available to other retailers around the world and were
busy doing that at the moment with our platforms and the our business model.
Are these huge automated warehouses that the largest, most sophisticated, their kind in the world?
And as a disruptor, you know, there was no template for them. We had to build them from scratch.
We had to create much of the unblind technology ourselves.
We've always written all the software ourselves, often throwing away the software that came with that hardware and then more recently.
And so and then buying in the hardware, often stealing it from.
All sorts of different industries and adapting it for purposes for which it was never intended.
But then more recently, we started to build the hardware too, including the swarms of thousands of robots that now assemble our customers orders.
And the way to think about this, if you haven't seen it before and film and if you have, is think of it like a giant chessboard.
And on that chessboard, there are robots that a bit like rooks can move along rows and columns.
And under every chess square is a stack of storage bins containing groceries up to 21 bins deep.
And the robots, they can stop on a square. They lower a kind of a grab, a bit like a fairground grab of one,
actually pick something up and they latch onto the top a bin in the stack and they bring it
up into the body of the robot and then they can move to another square and drop it off.
And if that's all they could do, they get very bored and frustrated so they can also bring those bins of cucumbers or sausages, whatever it is,
to a pig station where a robot or another story,
a human or another kind of robot picks groceries from a bin into a customer order or to decant stations where products are coming in from.
Suppliers on pallets are decanted into the bins and then the robots put them away in the grid.
And we call these hives. And in our second generation warehouses, there are typically two of them,
one for ambient products and one for chilled products, frozen products we do in a different way at the moment.
And the robots, they collaborate with one another and be really careful if they're soffer engineers in the room,
because it's not what Soffer engineers call collaboration, because it's not they don't talk to each other.
There is a hive mind that kind of runs the whole process and orchestrates the robots.
And but that pseudo collaborative behaviour means that we can pick a typical 50 item order in about five minutes.
And that's really, really important, given that there is this recipe for immediacy that's going on where everybody wants that order,
you know, within hours or minutes rather than days.
And if a robot, say, wants to get to a bin that's fourth down in the stack,
it just gets three of its friends to move the top three bins out of the way and then it grabs the one that it wants.
And in our latest warehouse, to use this technology, which is in southeast London, which went live in June 2018.
Each of these grids is about the size of three football pitches.
And when it's fully ramped up, it's not at the moment which is turning up the dial at the moment.
It love about 3500 robots roaming around on top of those grids.
And more interestingly, we've got to build 30 of these warehouses around the world over the next three years for our Acardo smart platform customers.
And that's if we don't sign any more deals, which we certainly intend to do.
So the Holy Grail, though, of all of robotics in online grocery is actually the picking and the packing of the customer orders.
And that means for us, picking 55000 different products with multiple different form factors and packing them in the correct sequence.
And in the correct 3D orientation into carrier bags.
So it's unlike welding, you know, the chassis of a of a car and a production line or spray painting it for us.
It's very much about making smart decisions on the fly and dealing with the unpredictable.
So it's all about the kind of the grippers, the machine learning and the vision systems rather than the robotics.
And one of the many robotics research projects that we have are engaged in is about developing these next generation grippers.
This was the Horizon 2020 one called Soma, which finished earlier.
So sometime last year. And it's about building grippers that have capabilities, similar human hands.
And with, you know, it or not, you know, since you were babies,
you've been learning strategies for how to pick stuff up as part of learning how to interact with the world around you.
And if you were gonna pick up a wine bottle here. But if you're going to pick up a wine bottle and put it into a wine rack, you know,
you you would instinctively grab it by the neck because you know that if you grabbed it halfway down, your your hand would be in the way.
I mean, maybe you weren't doing this as babies, but but the point is, you would also automatically and, you know,
cantilever it by putting perhaps two fingers under the neck because you know, that to turn it through 90 degrees, you're gonna have to support it.
Now, nobody taught you how to do that. You know, you either learnt by observing your parents doing it maybe as part of your misspent youth,
but or you you experimented and came up with that strategy yourself.
But, of course, robots have no idea about those strategies.
So they have to either be taught how to do it using learning by demonstration or which is one of the basket of technologies, the AI family or you,
they have to be given the chance to learn those tragedies themselves, either in living labs,
which I'll come and talk about, or using simulation simulated models, if you like, of that physical environment.
But what they don't have time is to learn the strategies on the fly when they're actually picking a real order because that would take too long.
So they have to sort of acquire them in advance and put them on the shelf.
And then when they're faced with a particular situation, they can go, oh, I need strategy number thirty 37.
Taken off the shelf and they apply it. The particular problem that's in front of them. It's one of our other Horizon 2020 funding projects.
Is that still ongoing? Finishes soon. Is about building a humanoid maintenance robot.
And that's not to what to do,
but learns by observing human engineers that work and then uses inference to work out how to collaborate with them on their tasks.
And we've been gently torturing this robot in one of our labs in Hatfield to see what it can do,
a bit like kind of subjecting it to darker type challenges. And.
And the reason why we want to build robots like that is that is, of course, what we learn along the way, which is transferable.
But it's also the fact that we're going to build so many of these warehouses around the world that automating their maintenance is important to us,
but also ultimately automating their construction to the next ingredient is, of course, A.I., a machine learning.
And, you know, A.I. lets you do the really exciting things with other disruptive technologies.
You know, without A.I., a robot is occupied just a pile of electromechanical experiments.
And it is the smart glue in the industry found for zero family of of of, if you like, component technologies.
It can discover or at least unearth knowledge that's been hidden to humans because of factors such as complex in bias.
But most importantly, it's a recursive technology. Shush.
Sorry, you can use one generation of A.I. to help train the next more advanced one.
And that means that if you can call it an intelligence and let me be very careful about that,
but it will certainly evolve its fourth version of intelligence faster than human intelligence.
So when it comes to disruptive technologies in our terms, it is in the Tolkien sense,
the one to rule them all, at least until quantum computing comes along and knocks it off that perch.
But and applications of AI machine learning they completely pervade are our platform.
So things like natural language processing, you know,
customers placing orders with voice and allowing people to talk to robots and things like that, image recognition,
smart machines that we build that route, optimising the routes that our fans drive on a daily basis,
providing monitoring and oversight across the platform many more.
And we're on this kind of slightly crazy journey, if you like, to build an Acardo brain.
So a general A.I. that understands our business model, that understands our data, taxonomies, that has oversight of our N10 platform.
And I either can answer questions about the business that can help us work more efficiently and make less mistakes.
That can act like the third JRA on an aircraft spotting the onset of problems that are about to impactors us that can
help coordinate our suppliers and that can help our customers shop faster with less friction and greater delight.
But obviously, for many of you in the room, we'll know that kind of general A.I. is completely beyond the current state of the art.
So what we're really doing is focussing on building, if you like, the individual pieces that puzzle the individual neurones of that brain.
And then we'll join them up over time as A.I. Technologies, computing power continue to advance.
And we're really not so fast on the current outcomes. They're great,
but we're really far in fussed about is the learnings and the competencies that we
will acquire along what is now firmly for us and A.I. and robotics first journey.
And the next ingredient is the digital twin.
Now I'm gonna explain what system twins are in.
A second friend, he doesn't know, but for me they are the Cinderella of this disruptive family of technologies because they're often overlooked.
But with A.I. and robotics as the big ugly sisters and digital twins combined with robotics let you,
do you risk the physical by learning earlier and faster within virtual worlds and digital twins?
When you couple them with machine learning, they can. You can use them to explore and optimise those virtual worlds.
But unfortunately, the terms is between has been completely hijacked.
And lots and lots of people talk about digital twins that aren't ready, digital twins. But for us,
what differentiates a true digital twin from other digital models or simulations is the fact that your conjoining these digital and physical worlds.
So let's have a real example. Imagine you wanted to optimise the traffic flow around a city like Oxford, which definitely needs it.
The road network and the traffic on it is your physical twin. And you'd probably put cameras in census and junction on the junctions and collect all
the data about the people and bicyclists and dogs and everything else moving around.
And then you'd feed those data into a digital model or simulation of the traffic network.
And then you could use that model to optimise the topology of the road network,
maybe for future things you had built or the timing of the traffic lights and things like that.
And you could then feed those outcomes back into the physical twin and actually update, if you like, the timing of the real traffic lights.
And that's the conjoining of the circle that makes the digital twin.
And another example of that for us is the digital twin that we have that looks after these swarms of robots.
Each of these robots that fly around at about four metres a second, they generate about 5000 data points a thousand times a second and.
That's about a gigabyte of data per robot per day, or about four terabytes of data per sworn per day.
And that's just one warehouse.
And there's no way that human engineers staring at screens could oversee, let alone optimise, such a complex system in near real time.
It's completely beyond human scale. And so what we do is we stream all that data is the cloud and we build a healthcare
system up in the cloud that looks after the swarm by doing predictive maintenance,
trying to spot the onset of problems before they become problems.
And it's a bit like the what we can do with patient data and wearables and sensors in the home,
maybe for humans where we can do remote medicine, if you like, with a similar system.
But what's special about a swarm of identical robots is if any of them does get sick or needs a rest or charge of batteries or have a service,
it can come off into the pits and then any of the others can take its place.
But as well as feeding into the healthcare system, we also feed all that data into our digital twin of the hive.
And that optimises, if you like, the behaviours of us of the swarm.
And then we update the real parameters of the control system of the real time control system that manages the hive.
And this is, if you guessed already is a fish is a video of that digital twin.
And we build simulations of all sorts of parts of our business, like demand forecasting what people are gonna buy in a van,
rooting, you know, the routes that are thousands of fans drive each day.
And what we're really on a journey is to build an end to end flight simulation,
if you like, of our whole e-commerce fulfilment and logistics platform.
And learning with digital twins is powerful, but it has its limits.
It's really quite hard at the moment to simulate more abstract concepts such as public adoption, ethics and privacy concerns in a digital twin.
And this is where living labs come in. And unlike technology, demonstrator's or sandpits, living labs or all about learning by doing.
In the real world environment, delivering real services to real customers day in, day out.
And why that's important is because customers keep you honest.
They give you feedback. Sometimes good, sometimes bad. And they help drive pace and agility.
And that living lab provides a physical environment that is representative of the real world.
But it's initially more constrained to allow you to learn faster and with less risk.
And you can then loosen those constraints over time as your capabilities and competence grow.
And we use living labs to let our robots to learn how to pick those 55000 in products.
But also the whole first warehouse in Hatfield was a living lab because over its kind of 15 years,
when it was being rapidly, innovation is now in a maintenance mode.
It was delivering millions of customer orders, but at the same time, we were ruthlessly inventing new mouse traps,
ripping out the old one and putting the new one in whilst it was kind of in-flight, which is easy to do, say, but not so easy to do.
But the UK needs living labs to accelerate learning towards the future of mobility and smart cities and things like that,
complex environments where we're not going to do it by theory alone.
And we need to learn about how the interplay of different technologies, changes to regulatory models,
stakeholder management, safety, new business models, public adoption and many others will all fit together.
And these are complex challenges to overcome.
And at the moment, I think we believe there's far too much kind of trying to swim the Atlantic by standing around swimming pools.
And so we're embarking on turning the Hatfield Business Park,
where we're based into a living lab to explore the intersection of autonomous vehicles, drones, robots, smart infrastructure and smart services.
And we formed a consortium are now in the midst of our final funding application for that.
But this living lab will just be the first step in a much bigger vision.
Firstly, to turn Hertfordshire into a county scale living lab and then to encourage other business parks and counties around the UK, including Oxford,
to replicate what we've done and what they are doing already in many cases, to create a family of these interconnected living labs.
And we believe that then we might seriously move the dial in terms of things like the future mobility.
And we as a company operate at this intersection of A.I. machine learning, robotics, digital twins, living labs.
We build smart mobile machines which are plugged into the world around them with the Internet of things that stream their data,
exhaust the cloud, and then we use digital twins and living labs to help design and optimise those complex systems and.
With these living lamps along the way, I'm gonna get the food and say we.
We will evolve and test the frameworks and standards for crowdsourcing digital twins as a step towards assembling.
These are the national scale. And this all leads to the idea of creating a national digital twin of the UK,
which is a truly ambitious vision and some might dismiss as unachievable.
But there are people as mad as us who believe in this.
So the centre of digital built Britain, part of Cambridge and University, and the National Infrastructure Commission.
Others do share this vision, and it's all about how you would crowdsource them and glue them together using kind of common frameworks and whatever.
And it's a task that you'll never finish.
You're never gonna build a national twin because there's always going to be more things and higher levels of fidelity that you might as a model.
But the good news is that you don't need to get to the end because each of the digital twins in its own right is useful.
But when you start gluing together, you get combinatorial effects. So, for example,
having additional twin of the UK rail network would be cool and useful for optimising
timetables and minimising delays and risk the impact on maintenance and things like that.
But if you then start combining that with digital trains, with road network airports and seaports,
now you start to get insights across the whole transportation system.
Then you add additional train to the energy network and you might work out where you should put all the E.V. charging points and things like that.
So I think I've said enough participants.
I messed up. Right. Let's carry on. So.
One last bit before we go on to recipe's, which is let's just imagine you are going to erect a building like the Shard.
You know, it's a highly complex building with lots of new technologies. How would you go about that?
Well, obviously, it's you know, what you do is you clear the ground. You get a group of builders down to the site.
You show a picture of the building you want and you say get stuck in.
And now whenever I say that, you know, a true group like you and they don't smile,
it's quite worrying because then I think, oh, my God, that's how you do think you build a shop.
But anyway, you're smiling. So that's okay. But the you wouldn't do that.
It would be an absolute unmitigated disaster. So what would you do? Well, the first thing you might do is build digital twin of the building.
I certainly would. And but the other thing you would definitely do is you create a set of layer drawings.
So architectural engineering, h fact, electrical, plumbing, networking, building management and so on.
And those buildings, boops serve many purposes,
things like transforming the high level vision of the architect into something that can actually be built,
helping drive greater collaboration and communication across stakeholders and contractors,
enforcing building standards and best practise managing complexity.
Because you can turn the layers on and off and just see the ones you're interested in.
And after the building's been built, it acts as a document of.
They act as a document of record aid. The maintenance.
Well, the challenge, I think, is that we're not just trying to build a smart building without a digital twin and a set of ladder drawings.
I believe we're trying to build and operate a smart country without them.
So I believe we need a set of Ladds semantic maps of the UK, including of the food system,
to enable people to understand the bigger vision and how the different pieces fit together,
to enable people to navigate around the institutions and government,
which are often impenetrable from those on the outside to enable people to understand the mesh network of stakeholders,
relationships, technologies and so on, in order to foster better collaboration and foster innovation, to enforce standards and interfaces,
to enable a diverse population of contributors to crowdsource ideas for completing and improving the maps.
And I believe that would even foster a new form of distributed democracy by enabling communities to take ownership of
completing their parts of the map to help people avoid creating the same non-competing technological building blocks.
If you like the common Lego, where the unhealthy diversity at the moment is not going to be good for us.
I'll come back to that to capture via a change control of versioning the historical evolution of the maps as a document of
record and then to provide methods to safely and control me access and share information across and within the semantic maps.
Because I can assure you, if you go and talk to people like she says que as I have about this,
they get quite twitchy about what this in the wrong hands would do.
Anyway, the base layers would be the kind of geospatial layer.
So, you know, environments, buildings, roads, the physical assets where things live.
And that's what organisations like the Geospatial Commission, part of the Cabinet Office and the audit and all that survey are focussed on building.
And then you might have some properly locked down layers for things like the security services, defence, police and so on.
And then as you move up through the layers, you might have layers for institutions such as national government,
local government regulators, NHS still restrictive but visible to more people.
Then there might be layers for more general purposes,
including fostering collaboration and communication between a technology company such as Accardo.
And then as we move up through the layers, we get to more abstract concepts such as relationships, stakeholders, influences.
And this is where we're moving from. Knapp's to semantic knowledge Groff's.
And eventually we get to layers that might be publicly accessible where regions, communities and even interview systems could decorate.
So, for example, the Highlands and Islands of Scotland might want to add rather different information to their part of the map than say,
you know, a city like Oxford. So those are some of the disruptive ingredients that are important to rest in the recipes that were cooking.
But I want to talk a little bit about how how we kind of glue those together in terms of how we approach in innovation.
We've always been this kind of strange blend of a technology business, a retailer and a and a disruptive innovator.
But we've recently now become a platform business, too.
And it's really that innovation factory, which is where the exciting things happen.
I'm going to skip a little bit here because we're going to run out of time otherwise.
And when you're doing a kind of military campaign, there are those areas you want to capture and occupy with your own forces,
the areas where you need to collaborate with your allies in order to make advances in
the areas where you need to stay in touch with what's going on with intelligence,
but where you're not going to be directly involved and the areas where you currently occupy that you can't occupy, which you may need to give up.
Well, how do we like to think about our disruptive landscape, if you like, in a similar kind of way to that campaign?
And in terms of how we map things out,
so the top right hand quadrant is are the things that are truly differentiating where we're going to do it ourselves.
We're going to do the R&D. File the pants, we're going to own the intellectual property and monetise it.
Then the top left hand quadrant is still stuff that's very important, but not quite as strategic.
Where maybe we don't have the skill sets, do it completely ourselves, where we're going to collaborate,
we're gonna find partners, we're going to do joint ventures and we're gonna share the ancestral property.
Bottom left hand corner is where things are now moving towards commodity.
But there's still not you know, vanilla products won't do it for us here.
We want to work with suppliers, get early access to technologies, but also influence the development pipeline of the products that are being produced.
And then the bottom right hand quadrant is where things have now moved to commodity.
They are truly not differentiating. If we ever built them in the past, we should stop building them and we should just buy them off the shelf.
And our research activities are a bit like sending out scouts to explore that top right hand quadrant.
And the patents that we filed are a bit like defensive bunkers to hold land until the main forces arrive.
And sticking with the military analogy are advanced. Research teams are like special forces.
They are multidisciplinary, they are self-sufficient. They operate with different rules of engagement.
They can go behind enemy lines and blow stuff up.
And once we've mapped our destructive landscape, the next step is to identify the technologies and competencies that will be required to implement it.
And this is a bit like, you know, having a complex kind of Lego model that you want to build.
But what you could doing is trying to work out what are all the different shapes of Lego pieces
that you'd need to build it and how many of each of those pieces you'll need to constructed.
And one way to view our history is that really over the last eight years, we've been on this kind of crazy shopping trip of innovation assets.
So data, intellectual property, knowhow, technologies and competencies,
we have to have used online grocery as the vehicle for that trip and all of that,
the competencies that the really valuable part, because although they take time to acquire,
they're a source of competitive advantage, they're not easily displaced.
And once you've acquired them, they can be used all over the places, all over the place.
And some of our key core competencies are things like simulation, A.I., machine learning, optimisation, motion control,
understanding how things move in, search of things, cloud computing, robotics and disruptive innovation itself.
And the reality is that those innovation assets know almost nothing about groceries because they don't need to,
because groceries in our terms are just atoms with certain properties.
They're actually quite troublesome atoms because they have to be kept to the right temperature and, you know,
they'll crush one another if you pack them in the wrong order and they have short shelf life and things like that.
So what that means is if you can do if you can banish food atoms, you can actually manage a bunch of other atoms, too.
And and that's what we're now engaged with. And I used to run a car technology that does the heavy lifting in terms of building our platforms.
But in 80, April 18, 2018, I handed that over and to go off to create a new division called The Office, the CTO Opto.
And our Rippert remit is very much around research, advanced research, IP strategy,
relationships with government, lots going on there at the moment, rhetoricians, the universities,
whether it be collaborative research or internship programmes or Horizon 2020, whatever it is,
stuff with schools and digital literacy, technological future of the business.
But it's also about pursuing these spinner applications.
And here's a slide from a recent are out results announcement that shows some of the spinner applications that we're working on and on here.
You can see things like vertical farming, which I'm about to get to, car parking,
baggage handling parcels, siltation, container ports, rail yards and many others.
And what they have in common is this concept of atoms.
And indeed, a CARDOZ mission is a kind of tag line that you see on our stuff is changing the way the world shops.
But Ottos mission, as it says here, is even more exciting, which is changing the way the world stores sorts assembles moves and sells atoms.
And so we have quite a lot to do. And the first thing we do when we create these spin outs is the far patterns.
And next thing we do is build simulations which eventually will become digital twins.
And here is a very early version of a video for car parking.
I'm afraid it is early one because the later ones have intellectual property that I can't show you.
But you get the idea from this. And these car parks are going to be important within things like smart cities,
not just for conventional cars, but for the storing, car sharing and autonomous vehicles,
including delivery vehicles of different formats so that they can be stored,
charged and serviced and then dispatched on demand close to where they're needed without clogging up our streets.
And this design turns out to be more efficient and cheaper and even more environmentally
friendly because you don't have all the nasty concrete than conventional carhops.
And as you can see from that list, most of these applications are quite far online grocery,
but some of them do have synergies that core business and and the obvious one there is vertical farming as well.
So let's talk about vertical farming. Okay. So people have been trying to do this kind of non conventional farming.
Well, you know, the Aztecs know PanAm and greenhouses have been around for a while, but they all relied on free solar energy.
And of course, people have played with doing vertical farms for certain kind of high value cash crops for a while now.
And indeed, this is quite an interesting one, which we ourselves may get, too.
But but the fact is, we're not going to feed the world by, you know,
sticking farms on top of rooftops in Manhattan, whether people start doing this or even on the side of buildings.
I'm afraid that's just not going to move the dial. So we need to find other ways to do this.
So obviously, there is a revolution going on in terms of lighting at the moment in terms of the cost of that coming down rapidly.
And we we why vertical farming is interesting is because it will help us address food security issues.
It will enable it allow us to be breed plants for taste rather than for pest or drought resistance.
It means that you can pick the plants when they're ripe rather than trying to write them in transport by having multiple cycles.
You know, there is certainly no concept of seasonality. You can use about five percent of the water that you can with commercial agriculture,
significantly lower food, miles, if you build them close to the customer,
which will get to better than organic in terms of food quality demand matched,
which means that you only can grow and bring two, if you like, fruition, what is actually going to be consumed.
And and those go back to those kind of customer trends that I mentioned or desires that I mentioned earlier.
No soil erosion, pesticides or water pollution growing up to up.
If I'm growing these systems using machine learning to optimise them end to end and finally very, very dense and highly automated.
And if you ask yourself, where do you find plenty of sun to generate cheap electricity?
A shortage of water, because, of course, these are closed systems,
which is why you can use so much less water and cheap land that nobody wants to use for anything else.
And of course, the answer is in deserts are now increasingly people are starting to build large vertical farms in desert,
as well as things like large algae production facilities. But the fact is, the world is not going to be fed using leafy greens and basil,
which is kind of like most of the stuff that people grow at the moment, the vertical farms.
So we need new varieties that to be bred suitable for vertical farms.
We need these also for things like exploring not just food that we eat, but food as medicine.
So nutraceuticals and pharmaceuticals and everything that we haven't yet exploited, if you like plants for.
We want to explore the whole area of personalisation of food. If you if we can grow you a. a lycopene enhanced tomato,
you might be able to take eat that rather than taking statins if you've got a particular heart condition.
So lots of exciting things there. Plant based proteins, growing insects, aquaculture and potentially other living things in these dense systems,
exploring new ways to convert green electricity, if you like, into calories, which is the whole purpose of this.
But obviously, vertical farming is only gonna be one part of this new Frudakis ecosystem.
So we have to find all those other ways of doing protein and algae and things like that.
Now, if you remember that grid that I showed you earlier. Hold that in your mind, because I'm going to give you four ingredients.
Here are a few. Angry is kind of it. But now imagine that each of those little cells, each of those boxes was its own separate experiment.
Okay. And imagined that each one is orchestrated as all that whole family of of experiments is orchestrated and
monitored using machine learning and A.I. and and that inside those little boxes you're doing experiments for,
if you like, for creating new kinds of digital growth recipes.
But you're also doing accelerated breeding of new varieties because we have to produce lots of Iraqis that are suitable for farming.
But, you know, we don't necessarily look like the varieties do in the fields at the moment.
And then using maybe even one day, you know,
digital twins of the plants themselves to help steer where we might do the experiments were a little bit way off that at the moment,
generating different growing environments on demand, including potentially ones that don't exist yet.
So whether it be, you know, new environments that will exist because of climate change or if you're Elon Musk,
maybe working out how you're going to do terraforming on Mars by creating that kind of condition and and much more exciting than any of that,
just imagine you could recreate the conditions that were that led to a particular variety or vintage of of wine.
Now, you could do vintage wine as a service. I mean, how good would that be?
But anyway. And. Now, I want to talk about when you start putting all these ingredients together, which is part of our vision.
So the first ingredient is the idea of the vertical farm of different sizes.
You know, these like our warehouse, it come into four sizes. These vertical farms will be the sizes.
Some will be near the customers. Move further out.
If you then glue that onto one of our kind of distribution and logistics machines, that is very, very good at getting the stuff to the customer.
That's that's cool. Then if you add the third ingredient, which is food preparation, but using robotics.
So think about dark kitchens here.
And a company we invested in last year could carry curity is involved in that is building robotics, but very much focussed on on food preparation,
whether it be things like, you know, chilled food, personalised chilled food or recipe boxes or salads or whatever.
And then the last ingredient is one called Acardo Zoome. So we launched this last year.
If you live in West London at the moment, it's the only one that exists at the moment. But we're going to photocopy it soon.
It. You could order from a range of 15000 products.
Bigger than really anybody else who's doing kind of immediacy at the moment.
And we advertise that you can get it in one hour from clicking on your phone app to actually drive your kitchen table.
But it's typically sub half an hour or even some 20 minutes. And indeed, that company, Carrer Curie, when they were doing their launch event,
they told me it got to 10, 30 at night and they ran out of vodka and nibbles.
I mean, that is a proper first world problem. And they they they decided, oh, we're are unless West London, let's use Acardo Zoom's.
They started ordering for carnivals and they came within 20 minutes.
And that's great. And I think they then just carried on ordering folk animals until they really know what they were doing.
But anyway, the. But anyway, so that's all very exciting.
So that's the idea. If you put all that together, vertical farm next to a warehouse, but close to the customer.
Now you've got an ad with automated kind of robotics to prepare the food.
You've now got an extraordinary integrated food machine. And you can go from plant to kitchen table in perhaps two hours.
And unless you live on a farm, that is freshness that you will never know as well as less food, miles and water and all the other benefits.
I went through it again. But you know what?
We need to think way bigger than this, because the transformer racial impact, the technologies like the ones I've talked about robotics,
AI, quantum computing can have on our lives is going to be completely non-linear. It's going to.
And that means that our preparation for that world needs to be non-linear, too.
But one of the challenges I think we really struggle with as individuals, as institutions, as a country, and you as a species is,
as we're talking with Chris before we started, there is insufficient, really big, really long term, really disruptive thinking going on.
And we're not going to solve problems like climate change within a five year democratic term.
And to support more of that big, long term disruptive thinking, we're going to need to structure ourselves to do it.
We're going to need create processes and tools to support it. We're going to need to teach our children how to do it.
And we're going to have to nurture it when it happens. But we're also going to need a compelling vision to drive it,
a holistic vision for a truly smart, equitable, prosperous and sustainable U.K. might look like.
And here, I believe that government has a role not to create that vision, but brackets, God forbid, close brackets,
but rather to convene a diverse group of stakeholders to do that, putting up the Christmas tree for others to decorate,
so to speak, because to signify to successfully compete with some much bigger countries around the world,
we're gonna have to find ways to play the innovation game smarter, more selectively and with greater leverage.
Or, as I like to often put it, we are gonna have to find the asymmetric warfare model of innovation.
And if you've ever sat, as I have in government department waiting areas and watch some of those videos that are playing on the screens,
you would absolutely believe that everything is great. Nothing is broken and that the UK could conquer the world.
Well, as a country, I think we've really got to stop drinking so much of our own Kool-Aid.
We have to get real about what is in the UK is top right hand quadrant like the one I showed you earlier.
And what is the elemental Lego required to implement it?
And then we need to invest heavily in creating all those Lego pieces, whether they be technologies,
institutions, skills, research, programmes, competencies or whatever, because we do not need diversity.
When I was a child, there were five different shaped Lego pieces. Now you go into a toy store and you buy fairy castle thing.
It's got 50 different Lego shapes, you know, and it's great for building fairy castles and it's beautifully pink.
But actually, when you combine that with all the other Terminator's sets, nothing.
A number of Lego pieces just explodes and we just can't afford that.
As a country and the amazing thing is wherever I go.
Around, I find the same recurring patterns at play, whether it be in the airline world,
in extreme environments, the Future Flight Programme, the National Food Strategy and others like fractals.
These these are recurring patterns, though,
things like the need for synthetic environments to speed up innovation and optimise complex systems of systems,
the need for common standards to be able to crowdsource those those models together and build those digital twins.
I was talking about the need for more sophisticated models for sharing assets such as data and digital twin.
So including the passports to do that because free data, open data is great, but it's a massive subset, if you like, of the data.
And you won't get companies like Acardo and others sharing their data unless they know who is going to be shared for and for what purposes.
So it won't be weaponized against them. And if it's not just data for data is needed for doing together digital twins and other assets
to the need for living labs to accelerate learning across lots of different problem domains.
The need for better maps to understand these complex landscapes, because wherever you go,
you find people don't have maps of their areas and they don't really understand how their world fits together,
let alone how their world fits within all the other worlds.
And we need to work out, as I said already, what the elemental Lego is that's needed to implement the four divisions associated with those landscapes.
And we need to think beyond the current challenges and focus on what I would say is the first derivative for the mathematicians.
That room. It's not, for instance, just about where how we deploy robots to solve problems.
It's about how do we create robots that can build and repair robots, how can we build robots that can build factories that build robots and so on.
So it because that's what creates scalability by design, and that's central to what we do.
But I think it's absolute key for the UK to I'm conscious, I'm eating into question time.
So I'm but I'm gonna give you one more two more big ideas.
Okay. So. Right. So I'll leave you with three big ideas, actually.
The first is. Yeah. But by to get one free. So we are a retail aren't we.
So then why the first idea is about how do we move atoms around the UK more efficient and sustainable and scalable ways.
And the fact is that many supply chains are fragmented and inefficient,
including the last mile with everybody kind of doing their own thing with very little coordination and collaboration.
And this is true about freight in general, but it's definitely true about food, which is an extreme case.
And there's a little point about transforming food production.
If we can't get that food to where it's going to be processed, stored, sold and ultimately consumed.
So it's time for another analogy here.
For those of you in the room who use public cloud, and I'm afraid all of you do if you don't know it because your phones do,
you have the advantages of public cloud over on premise? Infrastructure are numerous,
and they include things like foster innovation funded by using their income from lots of customers to drive
your R&D common stance and interfaces shared middleware that no company could afford to build for itself.
Higher levels of security, resilience and robustness.
The ability for users to buy capacity on demand, which means they can scale it up and get it down as they need.
And that leads to faster experimentation and greater scalability is enabling users to start
focussing on where they add real value rather than building operating their own infrastructure.
And so to drive greatest sustainability and efficiency into our supply chains.
What about creating a public internet of freight for the UK? What would that do?
Well, it could provide a common set of data standards, interfaces, governance,
protocols, metrics, analytics and s allays a common set of smart middleware services.
An ecosystem that suppliers, processes, delivery firms, retailers and others could connect to in order to provide and receive services and data.
Operational economies of scale and greater security. Resilience, robustness, improved utilisation of scarce resources, whether that be energy,
land time, transportation, network bandwidth and the rest reduce pollution, waste and congestion.
A single point of integration with post Brexit measures for goods entering leaving the country.
I think extra on steroids, the ability to provide common regulation, quality stands,
analytics and monitoring support for new, cross-sector and complet across competitor models.
If Accardo is delivering a pizza to a hospital, there might be a bag of plasma.
They'd like us to pick up a bit in our refrigerated van.
Well, those are two worlds that will never find each other at the moment because they're completely separated.
Whereas if you actually had a system that is managing that as a holistic model,
we will find those intersections and we need to a high fidelity simulation or digital spin,
of course, of that end to end Internet, afraid to help optimise it and and manage the complexity.
And it isn't just about a digital intranet. Afraid that's where we might start. It's about physical ones, too.
So things like rapid transit networks for freight, not least to get more stuff off our roads.
And here you need to think about things like Hyperloop, which is obviously a must thing.
But for, you know. People. But in vacuum.
But think about the same thing about tubes on the ground with freight moving stuff around.
And how to make better use of our railways and so forth. And the second big idea is about Catalyst's, because as a scientist, the room.
Well, no, you know, Catholics either make reactions occur that weren't naturally occur or they speed up other ones.
And I believe there are a set of national Catholics that will help the UK work smarter,
faster, more efficiently, safer, more sustainably with greater competitive advantage.
And what might be a list on that? Be on that list. National Catholics. Well, a national digital twin and a family Samiti maps the UK,
a national network of living labs, to accelerate learning across lots of proper domains.
A national network of maker labs to balance digital and physical worlds because
the most exciting stuff happens at the intersection that two of those two worlds.
Which is kind of the world that we live in. But it's also to foster the next generation of inventors and tinkerers.
About last but not least, I'm going to leave you with the most important national catalyst, the more.
Which is education.
And I'm mindful of the fact that I'm in a, you know, educational institution and that probably what I'm about to say will ruffle some feathers.
But I'm going to say it anyway when it comes to the uncertainty that we now face
regarding the impact of these disruptive technologies that I've been talking about.
One of the very few insurance policies we have for that future world is education.
But as an employer, as a father, and indeed as a citizen,
I would assert that our current education system is not fit for purpose in terms of preparing the next generation for that smarter,
more automated world that they will inherit. I believe it's broken now, but the cracks are just going to open up wide very soon.
And this is a very big topic. And I look, I'm just going to skim the top of it to finish.
But many of the skills and techniques, I believe,
that we're currently teaching our children are going to become as devalued in the years to come as the encyclopaedia has been by the World Wide Web.
And instead, we need to focus on teaching enduring metter skills, such as learning how to learn collaboration, creative thinking,
problem solving, intersectional thinking, mind mapping, design thinking, goal setting, emotional intelligence, innovation and entrepreneurship.
Because if education is meant to be about preparing the next generation for the smarter foci,
for their future life, and instilling a love of learning because it's going to be all about lifelong learning,
then I believe we're failing at the moment in terms of the structure and curriculum of our current education system,
because the relentless focus on exams, tests and the regurgitation of BARG schemes is consuming almost all of the education oxygen,
leaving teachers with very little time for spontaneity or for just sharing their love of
a subject and and pursuing the curiosity of their students to see where it might lead.
And if we allow education to switch our students off the joy of learning, then we will do them an uncomfortable to service.
But on the other hand, if we are unable our children to leave school,
having learnt how to learn full of curiosity and with a holistic set of future proof skills and with the joy of ongoing learning,
then I believe they will be well equipped for their life ahead.
So I believe we need to completely rethink our education system from the ground up and in so doing, future proof that online curriculum.
So I'm sure some of you are sitting there politely thinking an Internet afraid, a set of national Catalyst's rethink our education system.
He's completely nuts. It's definitely time to sell my Acardo shares. Well, maybe I am.
But then you know what? You do have to dream big.
And I can assure you that many people thought that Google Street View was completely unachievable a man before it just simply got done.
So I inadvertently use that government phrase that I write.
So I want to finish by setting you a challenge, because I believe great leadership is about many things,
but it's definitely about inspiring people to do things they had no idea they could achieve.
And I believe we need to inspire our citizens with a great vision and bigger thinking in order to power our nation to do even more amazing things,
including transforming our food system and including solving climate change and the rest.
So as leaders, technologists, employees, parents and citizens, I believe we need to challenge ourselves to think really big,
really long term and really disruptively about our future and then to challenge our businesses, institutions and governments to do the same.
Thank you very much indeed.