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