:00:00. > :00:07.Today, this part of the capital is home to the third largest
:00:08. > :00:10.technology start-up cluster in the world.
:00:11. > :00:15.No wonder they call this place Silicon Roundabout.
:00:16. > :00:21.Invented In London tells the story of the pioneers of tech.
:00:22. > :00:23.We're solving some of the hardest computer science problems today.
:00:24. > :00:25.From the creators at the cutting edge...
:00:26. > :00:27.To regain that control and that ownership over my body
:00:28. > :00:33.I'm trying to create a series of robots that are imitating
:00:34. > :00:39.To the Victorian Countess who started it all.
:00:40. > :00:44.Ada was known as the enchantress of numbers.
:00:45. > :00:46.I meet the inventors and their inventions
:00:47. > :00:59.Join me, Suzi Perry, as I uncover the past,
:01:00. > :01:15.present and future inventors of tech right here in London.
:01:16. > :01:21.I know you will all make really cool, weird, creative stuff.
:01:22. > :01:28.At Goldsmiths university in south-east London,
:01:29. > :01:31.its annual hack-athon Anvil Hack is getting underway.
:01:32. > :01:33.This 24-hour invention marathon sees student hackers
:01:34. > :01:36.from across the country going head-to-head to legally create
:01:37. > :01:43.Anvil Hack is special because there are no limits.
:01:44. > :01:46.Essentially, we give you three categories, audio,
:01:47. > :01:47.visual and hardware and say, do whatever you like.
:01:48. > :01:56.So the crazier, the weirder, the more out there it is, the better.
:01:57. > :01:59.Regardless of how they come to this event, whatever their skill set is,
:02:00. > :02:01.whatever their ability is, everyone here is going to learn
:02:02. > :02:03.new things and build new things together.
:02:04. > :02:05.Pandelis is a computer science student
:02:06. > :02:07.from Birmingham university and a seasoned hacker.
:02:08. > :02:11.The initial idea is we want to kind of take a ball and shove a computer
:02:12. > :02:15.in it so we can detect the motion of the ball and how it's kind
:02:16. > :02:19.of flying around in the air so, by playing around with this ball,
:02:20. > :02:24.you can make cool sounds and maybe potentially cool music.
:02:25. > :02:26.Ph.D student Amy Dickens has travelled
:02:27. > :02:30.This is a device that has cameras and it can see
:02:31. > :02:33.It recognises when I turn them upside down,
:02:34. > :02:37.She hopes to modify an existing product.
:02:38. > :02:39.The possibilities are endless for what you can control.
:02:40. > :02:42.Anything you can control on a computer, it's like having
:02:43. > :02:46.The issue I have when working with users with complex disabilities
:02:47. > :02:49.is sometimes they have closed hands or movements that aren't typical,
:02:50. > :02:52.so that means that the sensor doesn't necessarily even see them,
:02:53. > :02:59.and that's what I'm trying work on today.
:03:00. > :03:01.Now, it may be the case at other hack-athons that
:03:02. > :03:03.hackers build really technically impressive projects.
:03:04. > :03:04.Here, we're more interested in creativity.
:03:05. > :03:07.I'm trying to create a series of robots that are imitating
:03:08. > :03:12.the behaviours of my cat in different ways.
:03:13. > :03:17.So, Leah is building a cushion type object to emulate a cat.
:03:18. > :03:20.She has a cat called Ray and she knows that,
:03:21. > :03:22.when she rubs it the wrong way, it will attack her.
:03:23. > :03:27.Hopefully, once you touch a certain part of the conductive fabric band,
:03:28. > :03:30.it will poke up just enough to hurt you.
:03:31. > :03:33.I'm pretty much completely self-taught.
:03:34. > :03:35.So, if Google doesn't have the answer, then
:03:36. > :03:41.Once the competitors have decided on their inventions,
:03:42. > :03:44.it's time to get hacking, and that is a race
:03:45. > :03:50.My confidence level would be about a six or seven at the moment.
:03:51. > :03:55.12 hours in, it will probably be a one.
:03:56. > :03:58.We'll catch up with the competition at Goldsmiths a little bit
:03:59. > :04:00.later on but, right now, I'm at the headquarters
:04:01. > :04:03.of Deliveroo, one of the most successful brands to come out
:04:04. > :04:06.of London's tech world, and this is the first time they've
:04:07. > :04:14.Food delivery service Deliveroo is barely five years old but already
:04:15. > :04:17.they are in 140 towns and cities worldwide, working with 20,000
:04:18. > :04:25.Their tech team has developed software that solves the complex
:04:26. > :04:34.So today we'll talk about the rider delivery
:04:35. > :04:39.Co-founder and CEO Will Shu started Deliveroo in 2012,
:04:40. > :04:42.but the seed of his idea dates back to a decade earlier
:04:43. > :04:44.to when he was working as an investment banking
:04:45. > :04:48.In New York, back then, this is 2001, we didn't
:04:49. > :04:51.So we would actually call up restaurants,
:04:52. > :04:54.give them our credit card numbers over the phone and then
:04:55. > :04:58.Despite that lack of technology, it was actually a very good experience.
:04:59. > :05:01.But then in 2004 I was transferred over here by my company.
:05:02. > :05:04.I didn't understand why one of the greatest cities in the world
:05:05. > :05:07.didn't have a decent food delivery network.
:05:08. > :05:10.Will reached out to his childhood friend Greg Orlowski.
:05:11. > :05:12.A computer programmer who would eventually
:05:13. > :05:21.But, at that time, before smartphones and apps had caught on,
:05:22. > :05:24.the only solution would have meant relying on fax machines.
:05:25. > :05:27.You almost had to wait for the technology to catch up
:05:28. > :05:30.Absolutely, but anyone could have had this idea.
:05:31. > :05:32.Honestly, the idea isn't the most important thing,
:05:33. > :05:35.because it's a pretty obvious thing to say, I want better food
:05:36. > :05:38.from restaurants to be delivered to my house quickly.
:05:39. > :05:41.The important part around that is the technology and the execution.
:05:42. > :05:45.Our head of data science is from Netflix.
:05:46. > :05:50.Guys like this really helped teach the rest of the team.
:05:51. > :05:57.In 2016, Deliveroo saw orders increase by 650% globally.
:05:58. > :06:01.The tech start-up has received almost half $1 billion in investment
:06:02. > :06:03.to date and it's using data to expand its operation
:06:04. > :06:14.exciting thing that we are working on now.
:06:15. > :06:21.We've created a series of off premise kitchens,
:06:22. > :06:23.whereby a restaurant will staff up chefs in our kitchens and,
:06:24. > :06:26.in this way, the restaurant can focus specifically on delivery.
:06:27. > :06:29.What this allows restaurants to do is go to areas that they otherwise
:06:30. > :06:32.maybe would never dream of opening a restaurant, and they can reach
:06:33. > :06:35.a lot more people, and we give restaurants that information.
:06:36. > :06:39.We say, here is where we have super high demand.
:06:40. > :06:42.Here are some missing cuisines in that neighbourhood.
:06:43. > :06:44.And we say, OK, we think there's a huge opportunity there.
:06:45. > :06:49.So the best way to understand technology is to test it yourself.
:06:50. > :06:53.It's told me that I've got an order and I can accept it,
:06:54. > :07:02.Before I started the business, I tried pretty much every style
:07:03. > :07:04.of pizza you could have, whether it was American,
:07:05. > :07:10.I went out to Napoli to watch how they do it and I think it's just
:07:11. > :07:12.the love of the pizza over there is so apparent,
:07:13. > :07:14.so it's something we really wanted to replicate.
:07:15. > :07:18.We bake it in the oven which is about 500 degrees,
:07:19. > :07:26.so it means it cooks in 60 to 90 seconds.
:07:27. > :07:37.What I found actually, and I've done thousands of deliveries,
:07:38. > :07:40.is that customers never want to talk to you because they are hungry.
:07:41. > :07:44.So in the beginning I would say, hey, I'm Will from Deliveroo
:07:45. > :07:47.and people would just shut the door in my face.
:07:48. > :07:53.You don't really want to sit around and talk to someone.
:07:54. > :07:58.How important would you say that your tech and coding team is to you?
:07:59. > :08:01.Tying all this together is our logistics algorithm.
:08:02. > :08:06.We are solving some of the hardest computer science problems today.
:08:07. > :08:16.How do we make sure that the right driver goes to the right restaurant
:08:17. > :08:18.minimising the wait for the customer, maximising
:08:19. > :08:21.All of these things are incremental changes
:08:22. > :08:22.powered by technology, so absolutely it's
:08:23. > :08:26.Deliveroo are part of the rich history of invention
:08:27. > :08:28.and evolution in tech and, when it comes to computers
:08:29. > :08:32.and programming, it all began right here in London.
:08:33. > :08:34.Ada Lovelace was the world's first ever computer programmer,
:08:35. > :08:39.Her 1843 blueprint for a computerised future
:08:40. > :08:47.She was the only legitimate child of the poet Lord Byron
:08:48. > :08:49.and Anne Millbank but, when Ada was just five weeks
:08:50. > :09:03.Her mother's reaction was to reject the world of art and extol
:09:04. > :09:05.the academic pursuits of science and mathematics, fields
:09:06. > :09:09.But the real catalysts for her work were the designs of the inventor
:09:10. > :09:13.I met Anne-Marie Imafidon, a figurehead today for girls in STEM
:09:14. > :09:19.Ada was known as the enchantress of numbers.
:09:20. > :09:23.She was a great mathematician who loved to play with numbers
:09:24. > :09:27.and understand logic and discover new things, so she would write
:09:28. > :09:29.letters about this, the way that mathematicians communicated
:09:30. > :09:33.And Charles Babbage was one of the people that she struck
:09:34. > :09:35.up a friendship with, who is known as the father
:09:36. > :09:39.of computers, building the analytical engine.
:09:40. > :09:43.She was the first person to write a programme for that computer.
:09:44. > :09:46.Looking back, how important was that algorithm?
:09:47. > :09:54.There's a big change there in building a machine
:09:55. > :09:56.and in programming a machine, giving the machine a set
:09:57. > :09:58.of instructions for it to do independently,
:09:59. > :10:01.which was something that she figured out how to do and she was able
:10:02. > :10:04.Following on from his theoretical difference engine,
:10:05. > :10:06.a machine calculator, Charles Babbage's next designs
:10:07. > :10:11.His steam driven analytical engine would be the world's first computer.
:10:12. > :10:15.And it's here Lovelace's work is crucial.
:10:16. > :10:16.She realised more than the originator
:10:17. > :10:22.Babbage had designed the hardware but Ada Lovelace wanted
:10:23. > :10:29.It seems that she knew she was onto because she said,
:10:30. > :10:31.that brain of mine is something more than merely mortal,
:10:32. > :10:40.Of course, it's something that now we still use today,
:10:41. > :10:43.that translates into our apps and the websites that we use and
:10:44. > :10:49.You talk about people being ahead of their time,
:10:50. > :10:53.I think definitely both of them were almost living today.
:10:54. > :10:55.Ada's layout for the world's first general-purpose
:10:56. > :10:58.computer machine went as far as to envisage its potential
:10:59. > :11:00.for musical composition and graphic design, ultimately the machine
:11:01. > :11:07.However, before her untimely death at just 36, the same
:11:08. > :11:15.age her father had died, Ada Lovelace's theories
:11:16. > :11:16.Ada's layout for the world's first general-purpose
:11:17. > :11:19.computer machine went as far as to envisage its potential
:11:20. > :11:21.for musical composition and graphic design, ultimately the machine
:11:22. > :11:24.However, before her untimely death at just 36, the same
:11:25. > :11:27.age her father had died, Ada Lovelace's theories
:11:28. > :11:30.and algorithms had foreshadowed the future of computing.
:11:31. > :11:33.Is the spirit of Ada Lovelace very much alive today
:11:34. > :11:39.I think it might have been quietened or maybe dampened a little bit over
:11:40. > :11:42.I think we're still seeing women creating and solving big
:11:43. > :11:46.We are seeing women come into the field who haven't had that
:11:47. > :11:49.background and using maybe arts backgrounds and kind of putting that
:11:50. > :11:52.We call that Steam, being the science, technology,
:11:53. > :11:54.engineering, arts and maths all coming in together.
:11:55. > :11:55.Ada Lovelace, the original computer programmer.
:11:56. > :11:58.Back at Goldsmiths, the hack-athon is almost a third of the way
:11:59. > :12:00.through and competitors are preparing for
:12:01. > :12:04.I'm less confident than I was at the beginning of the hack.
:12:05. > :12:08.With 16 hours to go, I still have a lot of work to do.
:12:09. > :12:14.One of Anvil Hack's sponsors is Spotify
:12:15. > :12:17.Along with other companies, they make elements of their product
:12:18. > :12:20.available to be hacked by entrance of events like these.
:12:21. > :12:22.Hugh Rawlinson is a developer advocate engineer.
:12:23. > :12:24.It's really important for us to see what kinds of products
:12:25. > :12:28.people want to build, what kind of interesting new ways
:12:29. > :12:31.of interacting with music that people come up with.
:12:32. > :12:35.Kind of made like a semi-phone pheromone, whereby, based
:12:36. > :12:40.on the rotation of the phone, it kind of makes different noises.
:12:41. > :12:44.Talent scouting is one part of it, for sure.
:12:45. > :12:48.I mean, people who attend hack-athons when they are in
:12:49. > :12:51.university usually are sort of the cream of the crop in terms
:12:52. > :12:53.of they are computer science students, the really creative minds
:12:54. > :13:05.I think one of the biggest challenges for people
:13:06. > :13:08.building hardware that users are going to react with in
:13:09. > :13:11.Sure, this is a hack-athon and whatever's going to be built
:13:12. > :13:14.is probably going to be rough around the edges, but ultimately,
:13:15. > :13:17.when it comes to 12pm tomorrow and people are showing
:13:18. > :13:18.off their projects, it needs to work.
:13:19. > :13:27.Design student Leah's aim of creating a robot
:13:28. > :13:36.to emulate her pet cat isn't quite going to plan.
:13:37. > :13:43.It's like the second you want to show someone...
:13:44. > :13:48.Fingers crossed when you come back in the morning it will work.
:13:49. > :13:51.Tech dreams often start small, and turning those dreams
:13:52. > :13:53.into a marketable reality can require big backing,
:13:54. > :13:59.especially if the idea is as radical as artificial intelligence.
:14:00. > :14:06.From Imperial College's development of the Google DeepMind platform
:14:07. > :14:10.to UCL's predictive keyboard app SwiftKey, now owned by Microsoft.
:14:11. > :14:13.Today, Old Street start-up Emotech are looking to join the bandwagon
:14:14. > :14:22.with their personal assistant, Olly.
:14:23. > :14:25.I'm excited to meet Olly, but you seem to have two of him.
:14:26. > :14:29.So Olly will adapt personality and the life behaviour
:14:30. > :14:32.from the owner, so you have two Ollys, one is from my personality,
:14:33. > :14:34.the other is from one of our developers' personalities.
:14:35. > :14:39.Can we talk to the Olly with your personality?
:14:40. > :14:44.Sure, the one is always more exciting.
:14:45. > :15:00.So, if I ask this Olly the same question, it
:15:01. > :15:10.I feel charming, alarmingly charming.
:15:11. > :15:20.Emotech co-founder Chelsea Chen believes Olly's USP,
:15:21. > :15:23.in a marketplace that includes giants Amazon and Google,
:15:24. > :15:27.is its ability to respond uniquely to each owner,
:15:28. > :15:31.and in time it will even be able to communicate proactively.
:15:32. > :15:41.Currently in London, it's sunny with some clouds,
:15:42. > :16:01.One reason behind Olly is like as your assistant,
:16:02. > :16:04.your friend, so even when you are down, Olly
:16:05. > :16:09.When I cry, Olly will kind of comfort me.
:16:10. > :16:43.Tries to give you a little bit of a surprise.
:16:44. > :16:51.In Silicon roundabout's EC1V postcode, there are over 3000 tech
:16:52. > :16:57.The competition for investment is fierce.
:16:58. > :16:59.Now, with two rounds of investment, the company's current valuation
:17:00. > :17:07.Yes, but I always say it's more than two years,
:17:08. > :17:11.because everyone works over 12 hours per day.
:17:12. > :17:13.So you are fitting two years into one year.
:17:14. > :17:25.We believe how human being learning systems will be changed.
:17:26. > :17:29.Now Olly learns from the owner and, when we have enough Ollys sold out,
:17:30. > :17:36.So where do you see AI going in the future?
:17:37. > :17:58.OK, let's go back to Goldsmiths and see how the hack-athon is going.
:17:59. > :18:01.Morning has broken at Anvil Hack three.
:18:02. > :18:10.For some of our hackers, it's been a long night.
:18:11. > :18:13.I did sleep, for about three hours on the sofa in various
:18:14. > :18:16.uncomfortable positions, because I was sharing it
:18:17. > :18:24.And I'm not feeling so great about that.
:18:25. > :18:29.Unfortunately, I completely slept over my alarm.
:18:30. > :18:34.I just snoozed it about ten times and then went back to speak again.
:18:35. > :18:37.Leah's hopes of building something that behaves like her pet cat
:18:38. > :18:46.I wired this up wrong and managed to burn it out.
:18:47. > :18:52.I'll get something in for the deadline.
:18:53. > :18:59.Whether I'll be happy with it or not is a different question!
:19:00. > :19:02.When you go around and talk to people, you really feel bad,
:19:03. > :19:04.not like you are disturbing them, but they are really
:19:05. > :19:08.Elsewhere, Pandelis and his team are in a more positive position,
:19:09. > :19:14.We kept some audio elements but kind of turned it into more of a game,
:19:15. > :19:18.and then it turned into a phone game and now it's a shouty phone game.
:19:19. > :19:29.For Ph.D student Amy, who works with people
:19:30. > :19:37.there was always rather less to shout about.
:19:38. > :19:40.I don't think it will have the motion controller in there,
:19:41. > :19:43.but what I have been able to do is learn what parts of the software
:19:44. > :19:55.After meeting Chelsea Chen and Olly, I wanted to learn more
:19:56. > :19:57.about how inventors today can fund their ambitions.
:19:58. > :19:59.Anastasia Emmanuelle helps tech start-ups go to market.
:20:00. > :20:04.Crowdfunding is just a way of raising money
:20:05. > :20:12.from a large number of people, and typically small sums of money.
:20:13. > :20:15.So instead of going to a bank or an investor and asking
:20:16. > :20:19.for a loan, for a large cheque, you are asking the Internet, people
:20:20. > :20:22.over the world to potentially help bring your idea to life.
:20:23. > :20:24.That's the point with crowdfunding, that products coming to market
:20:25. > :20:28.You would invest because you like the idea of it, wouldn't you?
:20:29. > :20:30.Essentially, crowdfunding is democratising access
:20:31. > :20:32.to capital because, instead of there being these handful
:20:33. > :20:35.of gatekeepers who decide which ideas come to life or not,
:20:36. > :20:37.there is the crowd who, if they like an idea,
:20:38. > :20:45.It puts the power back into the hands of the people.
:20:46. > :20:47.There is a huge benefit around marketing, in terms of crowdfunding
:20:48. > :20:53.In the case of Olly, which will retail at between 600
:20:54. > :20:55.and 800 US dollars, the crowdfunding campaign is planned for the next
:20:56. > :20:59.To succeed, it will rely on a positive public perception.
:21:00. > :21:02.Where do you think AI fits into the whole scheme of things?
:21:03. > :21:05.AI has become a bit of a buzzword I think people outside the tech
:21:06. > :21:08.world think that AI means that there's going to be robots
:21:09. > :21:10.and supercomputers taking over the world, taking their jobs
:21:11. > :21:16.It's very much just a technology that is in a lot
:21:17. > :21:25.In Amazon Echo or Google Home or Olly, who you met.
:21:26. > :21:27.AI isn't just something that you see in sci-fi movies.
:21:28. > :21:31.It's really about this proactive and continual learning.
:21:32. > :21:34.The DIY spirit of hack-athons lies right at the heart of tech.
:21:35. > :21:36.Britain's garden shed and bedroom inventors really do play
:21:37. > :21:46.Frank Swain is having a new set of hearing aids fitted on a Harley
:21:47. > :21:54.street, but all is not quite as it seems. This is a gateway to an
:21:55. > :22:01.alternate world. How did this idea, round? I had been going deaf since
:22:02. > :22:08.my mid-20s, probably from too many loud concerts and bad genetics.
:22:09. > :22:11.Hearing aids give you an exact replica of the environment, which
:22:12. > :22:15.got me interested in the idea of what other cells could I change. If
:22:16. > :22:20.I listen to an interpretation of the world the rest of my life, I want to
:22:21. > :22:25.play a role in that and change the sound I can hear. Collaborating with
:22:26. > :22:29.the artist Daniel Jones, Frank has hacked his hearing aids so they now
:22:30. > :22:36.detect Wi-Fi networks wherever he goes and translate them into sound.
:22:37. > :22:40.We use a smartphone to gather information. Daniel has written
:22:41. > :22:44.information on the phone to turn it into a continuous stream music. That
:22:45. > :22:51.is sent to my hearing aid to stream. As I walk around, the phone is in my
:22:52. > :22:56.pocket and I hear the Wi-Fi. The project aims to give the user an
:22:57. > :23:00.additional sense or in augmented reality. As well as sound, the data
:23:01. > :23:08.gathered is made into animations that map out the Wi-Fi fields that
:23:09. > :23:12.Frank hears. Walking around, you hear two separate layers, one
:23:13. > :23:15.sounding like a Geiger counter, the density of networks around you, and
:23:16. > :23:21.the other one translating the names of individual networks. This project
:23:22. > :23:26.kind of performed a series of experimentation and research, but
:23:27. > :23:30.the outcome is very much a kind of aesthetic process, immersing you in
:23:31. > :23:34.this kind of uncanny architecture. I'm trying to imagine, listening to
:23:35. > :23:41.it when you are walking along, what does it bring to you that you didn't
:23:42. > :23:44.have before? I think it is being connected to the world, experiencing
:23:45. > :23:50.something directly that you know is there, that you use everyday, but
:23:51. > :23:54.isn't quite tangible. It is simultaneously a very alien sort of
:23:55. > :23:58.landscape, but also something inherently familiar about it. When
:23:59. > :24:01.you heard it properly for the first time and you walked out, wasn't
:24:02. > :24:09.there a part of you that thought, the air is thick with so much stuff
:24:10. > :24:13.that we don't see? Absolutely, we were surprised how much the device
:24:14. > :24:20.could detect. How would you describe this? Is this tech or an art
:24:21. > :24:23.installation? I think of it as sitting at the borderline between
:24:24. > :24:28.art and science. A lot of technologists are moving into the
:24:29. > :24:31.world of art, understanding that the technology they make as expressive
:24:32. > :24:36.powers, and a lot more artists are coming to the technological world,
:24:37. > :24:40.picking up programming almost vibrant collage. Both art and
:24:41. > :24:42.science are concerned with enquiry, about the world around us, showing
:24:43. > :24:50.ourselves things we might be unaware of. Some audiologists would
:24:51. > :24:54.configure hearing aid sometimes and that would be bad. And now we are
:24:55. > :24:58.seeing that the user can change settings and programme them and
:24:59. > :25:06.change the way they work. For me, we gaining that control and ownership
:25:07. > :25:11.of my body is really exciting. At Goldsmiths, Anvil Hack three has
:25:12. > :25:15.entered its closing stages. The final hour. Start getting your
:25:16. > :25:19.presentations ready. The last hour is the most tense bit, but that is
:25:20. > :25:26.the thrill. It gets the adrenaline flowing. That is why people come. As
:25:27. > :25:32.the hackers get ready for final presentations, finishing touches are
:25:33. > :25:36.being made to the inventions. First up is Pandelis's team. We've
:25:37. > :25:42.designed a game with the intention of being loud and fun and
:25:43. > :25:46.visualising things. We've created a phone number people can call and it
:25:47. > :25:49.puts everybody into a conference call and, as people shout, the bird
:25:50. > :25:59.will flap based on how loud people shout.
:26:00. > :26:05.SHOUTING. Next is Amy, whose aim of modifying
:26:06. > :26:08.a device to work for users with complexed disabilities has eluded
:26:09. > :26:15.her, but she has mastered a new function for the system. I also have
:26:16. > :26:18.a pitched one, to shift the pitch wait up for any user. I came to try
:26:19. > :26:25.and pick apart those bleak gestures, but implementing web audio myself
:26:26. > :26:28.for the first time was good, because I have only ever used visual
:26:29. > :26:35.interfaces before. Having it written in code was a great feeling. One
:26:36. > :26:39.person who will not be presenting is master 's student leader. I don't
:26:40. > :26:43.know what happened but flames started erupting from where the
:26:44. > :26:48.relays are. I don't know what went wrong. Five minutes ago, I was on
:26:49. > :26:53.top of the world, it was finally working, and I was like, I've got
:26:54. > :26:57.something to present. And then, I don't know... I keep thinking, if it
:26:58. > :27:03.was a software project, it would be impossible to set on fire! I've
:27:04. > :27:07.already got a collection of things I'd blown up this year. I'm aiming
:27:08. > :27:13.to make them into a necklace. I'm going to wear them at Pride. This
:27:14. > :27:20.will be a nice centrepiece. Wood after 24 long hours, time to
:27:21. > :27:23.announce the winners. And that prize goes to screaming bird or shouting
:27:24. > :27:30.bird, but that shouting at your phone thing. Pandelis and his team
:27:31. > :27:37.have done it. Their audio driven game has won them the price. I might
:27:38. > :27:43.try my hand at making some more games, because that was quite a bit
:27:44. > :27:46.of fun. It's been an emotional roller-coaster, as they usually are.
:27:47. > :27:51.You come to learn something and you've got something to take away. I
:27:52. > :28:00.kind of want to go to the pub and forget about it! From Charles
:28:01. > :28:04.Babbage and Lovelace to hack-athons and artificial intelligence. For
:28:05. > :28:08.centuries London has led the way in artificial intelligence and
:28:09. > :28:12.technology and it is showing no sign of slowing down. Artists and
:28:13. > :28:19.creatives using technology. The number one city of AI in the world.
:28:20. > :28:23.We are pioneers in this service. 40,000 tech companies in this area.
:28:24. > :28:28.We want to make this a global success story. You can find out more
:28:29. > :29:05.about inventions across the UK if you go to the website.
:29:06. > :29:08.Hello, I'm Sarah Campbell, with your 90-second update.
:29:09. > :29:11.Police say the Grenfell fire started in a fridge.
:29:12. > :29:14.We also learned today that the building's cladding