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Now I BBC News, it is time for Click. | :00:00. | :00:00. | |
This week: Swaying votes on Facebook. | :00:00. | :00:07. | |
It's geek versus geek as Spen meets Fry. | :00:08. | :00:12. | |
And lots of people shout in a big tent. | :00:13. | :00:39. | |
Summer is on the way and, well, it wouldn't be a British summer | :00:40. | :00:42. | |
without a visit to a good old fashioned festival. | :00:43. | :00:44. | |
Known as the Town of Books, Hay-on-Wye, in Wales, | :00:45. | :00:56. | |
It's a literary Mecca, an annual gathering of artists, | :00:57. | :01:05. | |
authors, Daleks and, yep, even Royals. | :01:06. | :01:08. | |
It's even been called the Woodstock of the Mind by none other | :01:09. | :01:11. | |
than former US President Bill Clinton. | :01:12. | :01:15. | |
This year it's the 30th Hay Festival and the line-up is pretty stellar. | :01:16. | :01:20. | |
Well, for the second year in a row, we've been invited to share some | :01:21. | :01:24. | |
of our favourite experiences and show off some really good tech, | :01:25. | :01:26. | |
all in front of a real, live audience of actual people. | :01:27. | :01:29. | |
A packed tent waited, all that we had to do was wow them! | :01:30. | :01:44. | |
We have robots falling over, experiments in haptic feedback | :01:45. | :01:56. | |
and demos in binaural sound, but that was nothing | :01:57. | :01:58. | |
compared to the climax - a Click created wavy, | :01:59. | :02:00. | |
shouty game built using artificial intelligence. | :02:01. | :02:02. | |
In the meantime, it can't have have escaped your attention that around | :02:03. | :02:07. | |
the UK things are getting a touch political. | :02:08. | :02:09. | |
As the general election looms, those politicians are using increasingly | :02:10. | :02:12. | |
sophisticated techniques in order to learn more about us. | :02:13. | :02:20. | |
The advertising reach of Facebook has long been an open secret, | :02:21. | :02:27. | |
but now it's something the political parties are getting in on too. | :02:28. | :02:31. | |
In fact, both the Trump campaign and the Leave.EU groups credited | :02:32. | :02:35. | |
Facebook as being a vital part of their electioneering. | :02:36. | :02:37. | |
We know that the personal details that you give to social networks | :02:38. | :02:40. | |
allow them to send you relevant, targeted content, and it goes | :02:41. | :02:43. | |
much deeper than just your basic demographics. | :02:44. | :02:51. | |
There are now data analytics companies claiming to be able | :02:52. | :02:54. | |
to micro-target and micro-tweak messages for individual readers, | :02:55. | :02:56. | |
If you know the personality of the people you're targeting, | :02:57. | :03:07. | |
you can nuance your messaging to resonate more effectively | :03:08. | :03:09. | |
What's also emerging is that political parties have been | :03:10. | :03:14. | |
using this data to reach potential voters, on a very granular level. | :03:15. | :03:17. | |
So who is being targeted on Facebook and how? | :03:18. | :03:26. | |
Well, until now, there's been nothing around to analyse any | :03:27. | :03:28. | |
of this, but the snap general election galvanised | :03:29. | :03:32. | |
Louis Knight-Webb and Sam Jeffers to develop Who Targets Me, | :03:33. | :03:34. | |
a plug-in to tell each of us how we're being targeted. | :03:35. | :03:41. | |
When you install the plug-in for the first time, | :03:42. | :03:43. | |
it asks for your age, your gender and your location, | :03:44. | :03:46. | |
and then it starts scouring your Facebook feed looking for adverts | :03:47. | :03:48. | |
So once you've installed the plug-in, it works | :03:49. | :03:51. | |
in the background to extract the whole advert that | :03:52. | :03:54. | |
So it pulls out the headline, the subtitle, any related videos, | :03:55. | :03:57. | |
We also get the reaction - so how many likes, how many | :03:58. | :04:05. | |
comments, how many shares - so we can see which messages | :04:06. | :04:08. | |
Are they particularly clandestine messages, | :04:09. | :04:10. | |
are they slightly subversive, are they even fake news? | :04:11. | :04:14. | |
But how do data companies get the information in the first place? | :04:15. | :04:16. | |
A lot of the quizzes you fill out on Facebook or, | :04:17. | :04:19. | |
you know, you open a survey, it asks your Facebook | :04:20. | :04:21. | |
Sometimes you'll notice that there's a lot of permissions attached | :04:22. | :04:26. | |
and as soon as you click yes, all of your data is mined, | :04:27. | :04:29. | |
and it's then sold on to data brokers who then, eventually, | :04:30. | :04:31. | |
sell it to the political parties for use in their campaigns. | :04:32. | :04:38. | |
Although Facebook says it doesn't sell our information on, | :04:39. | :04:42. | |
data brokers can overlay any details they mine from the site with other | :04:43. | :04:45. | |
datasets that they have on people based on their email addresses. | :04:46. | :04:51. | |
The next step after that of course is to find similar users that | :04:52. | :04:55. | |
are using Facebook and then target adverts, from that advertiser | :04:56. | :04:57. | |
that supplied the email addresses, to those users. | :04:58. | :05:00. | |
There are just some people that you don't find on Twitter. | :05:01. | :05:09. | |
The very nature of the fact that I can't see your adverts, | :05:10. | :05:12. | |
you can't see my adverts, means that this approach had to be | :05:13. | :05:15. | |
It's a first of its kind anywhere in the world on this scale, | :05:16. | :05:23. | |
giving us citizens some transparency into what we're being shown, | :05:24. | :05:26. | |
Do you think that people wouldn't know that certain things are advert | :05:27. | :05:33. | |
A lot of the time people are scrolling through Facebook | :05:34. | :05:43. | |
and the adverts fit into this weird intersection of friend | :05:44. | :05:45. | |
It's quite easy to miss the adverts on Facebook. | :05:46. | :05:51. | |
So far, Who Targets Me has some 6,700 users | :05:52. | :05:53. | |
in 620 constituencies, and it's rising as | :05:54. | :05:54. | |
On the down side, it's only as good as the data it's | :05:55. | :06:06. | |
managed to crowd-source, so it isn't necessarily | :06:07. | :06:07. | |
representative, and it also doesn't work with mobile Facebook, | :06:08. | :06:09. | |
So we're seeing a mixture of two things. | :06:10. | :06:14. | |
We're seeing, firstly, A/B testing, which is where I try out | :06:15. | :06:16. | |
two different messages with the same group. | :06:17. | :06:18. | |
I see which one gets the best reaction and then | :06:19. | :06:21. | |
We're also seeing targeting, which is where I pick a particular | :06:22. | :06:26. | |
demographic of people, and then I send a message | :06:27. | :06:28. | |
So, for example, it might be young people targeted | :06:29. | :06:35. | |
The data from Who Targets Me is also being poured over by analysts | :06:36. | :06:44. | |
One aspect of their research is collecting dark posts, | :06:45. | :06:47. | |
ads which are here one day and gone the next. | :06:48. | :06:51. | |
It gives us the ability to create a respository of those dark posts. | :06:52. | :06:54. | |
So if promises are being made on Facebook, in ads which will | :06:55. | :06:57. | |
disappear the day after you use them, we should be able to go back | :06:58. | :07:00. | |
to those after the election, look at them, evaluate them | :07:01. | :07:02. | |
and maybe discuss them in the cold light of day. | :07:03. | :07:08. | |
And the irony is that, as we demand more transparency | :07:09. | :07:10. | |
from public bodies, the whole basis of political propaganda | :07:11. | :07:12. | |
could be on the brink of a revolutionary change. | :07:13. | :07:19. | |
What's interesting, I think, about the new environment | :07:20. | :07:21. | |
is the potential for using paid advertising and other techniques | :07:22. | :07:23. | |
to create individual propaganda bubbles around individual voters. | :07:24. | :07:25. | |
And that's not about controlling the market as a whole, | :07:26. | :07:28. | |
but it's about using smart targeted which, in a sense, creates such | :07:29. | :07:31. | |
a compelling and overarching information environment | :07:32. | :07:32. | |
for individual people that that in some ways constrains what they do | :07:33. | :07:35. | |
I think that's why some academic commentators and others | :07:36. | :07:47. | |
are beginning to think some of this is a bit spooky. | :07:48. | :07:59. | |
But politicians aren't the only ones with Facebook on their minds. | :08:00. | :08:03. | |
The social network was one of many topics on the very large brain | :08:04. | :08:07. | |
of national treasure and tech geek Stephen Fry. | :08:08. | :08:08. | |
I met up with him after he gave a lecture at the Hay Festival | :08:09. | :08:12. | |
highlighting how he thinks the world is being changed by social | :08:13. | :08:14. | |
The very current conversation is whether Facebook and platforms | :08:15. | :08:18. | |
like them should actually be considered publishers? | :08:19. | :08:19. | |
Should they take responsibility for what ends up on the site? | :08:20. | :08:31. | |
They are aware there is a problem, a serious problem. | :08:32. | :08:34. | |
If 80%, some people have said, is the... | :08:35. | :08:36. | |
You know, in proportion of people who get their news from Facebook | :08:37. | :08:39. | |
rather than from mainstream media, then surely it is incumbent | :08:40. | :08:41. | |
upon someone who is providing 80% of their news sources to make sure | :08:42. | :08:44. | |
that those news sources are not deflamatory, | :08:45. | :08:46. | |
blatant lies, propaganda, the wrong kind of, | :08:47. | :08:47. | |
I would posit there that a publisher is responsible for all the people | :08:48. | :08:59. | |
They are employed by that publisher and Facebook is clearly not that. | :09:00. | :09:03. | |
So do we need a third definition, a third thing? | :09:04. | :09:07. | |
I think there is a medium sort of definition that it's not | :09:08. | :09:17. | |
beyond the wit of lawyers of the right kind to find that. | :09:18. | :09:20. | |
Everyone knows you are a famous early adopter. | :09:21. | :09:22. | |
You seem to try everything that comes out? | :09:23. | :09:24. | |
Yeah, I'm perpetually driven by curiosity. | :09:25. | :09:26. | |
It may even be a kind of addictive greed, which is a sort of rather | :09:27. | :09:30. | |
graver example of the consumer greed that we all share, really. | :09:31. | :09:33. | |
Maybe I'm just more of a jackdaw, who likes shiny things and I just... | :09:34. | :09:36. | |
I still love the, you know, the first kind of... | :09:37. | :09:51. | |
I get short of breath and I pant slightly. | :09:52. | :09:53. | |
Your presentation was a warning that people should prepare | :09:54. | :09:55. | |
for the changes that are coming, for example, artificial | :09:56. | :09:57. | |
I said that it was a sort of transformation of | :09:58. | :10:01. | |
You know, it's an obsolescence of certain types of job, | :10:02. | :10:04. | |
but that doesn't mean forced redundancy of millions of workers. | :10:05. | :10:06. | |
I mentioned one of the pleasing things about AI and robotics, | :10:07. | :10:09. | |
and that is what's known as Moravec's paradox, | :10:10. | :10:11. | |
that what we're incredibly bad at, as individuals, machines tend to be | :10:12. | :10:14. | |
Complicated sums, rapid and incredible access of memory | :10:15. | :10:20. | |
from a database of a kind that we could never do, | :10:21. | :10:22. | |
sorting and swapping of information and cataloguing | :10:23. | :10:26. | |
But things we can do without even thinking, | :10:27. | :10:41. | |
like walk across the room or pick up a glass and have a sip of water, | :10:42. | :10:45. | |
But that's fine, because we don't want them to do that for us. | :10:46. | :10:49. | |
Where it gets difficult is in medium sort of service jobs, I think. | :10:50. | :10:52. | |
Well, you could go the etymological route, and you could say | :10:53. | :10:56. | |
Legere is read and inter, interleg, and that's pretty good, actually, | :10:57. | :11:03. | |
Just being able to see connections in things and people are talking | :11:04. | :11:14. | |
about the moment that we arrive at AGI, artificial general | :11:15. | :11:18. | |
intelligence, and that's when the various types of pattern | :11:19. | :11:21. | |
recognition, you know, numbers, data, you know, | :11:22. | :11:22. | |
certain faces and things like that, they all come together | :11:23. | :11:25. | |
so that they can be intelligent across these different things. | :11:26. | :11:32. | |
If you've got an artificial intelligence that's good at that | :11:33. | :11:35. | |
and another one that's good at that and another one that's good at that | :11:36. | :11:38. | |
and you get enough of them, and then you put something on top | :11:39. | :11:41. | |
that goes, "Oh, you want to know about a language problem, | :11:42. | :11:44. | |
You want to know about walking and recognising objects, | :11:45. | :11:47. | |
Surely, just a collection of specialists of intelligences | :11:48. | :11:50. | |
under one umbrella is a generals intelligence. | :11:51. | :11:52. | |
It doesn't have to be a breakthrough, it just has to be | :11:53. | :11:55. | |
I think that's very likely to be the way it goes. | :11:56. | :12:01. | |
Part of our intelligence, a huge part, is our emotional intelligence. | :12:02. | :12:04. | |
Einstein would say he did say the same thing. | :12:05. | :12:06. | |
It's not just the cerebral power, it is something to do | :12:07. | :12:08. | |
with our blends of fury, inquisitive desire, | :12:09. | :12:10. | |
These are very, very much more primal and much harder | :12:11. | :12:13. | |
to reduce to a table of calculations and memories. | :12:14. | :12:15. | |
But maybe you give it something else, some other kind | :12:16. | :12:18. | |
It's reward is similar to our reward system | :12:19. | :12:21. | |
It's tryptophan and serotonin and endorphins of various kinds that | :12:22. | :12:25. | |
reward us and then we have a pain system to deter us, | :12:26. | :12:28. | |
and there's nothing to stop us giving that to a machine. | :12:29. | :12:33. | |
Stephen, thank you so much for your time. | :12:34. | :12:35. | |
Thank you for having us at your place. | :12:36. | :12:40. | |
It was the week where Android creator Andy Rubin | :12:41. | :12:49. | |
Co-founder of Google Larry Page had a couple of novices | :12:50. | :12:53. | |
take his Kitty Hawk Flyer for a spin. | :12:54. | :12:55. | |
And Intel popped swing-sensing chips into bats for the ICC Champions Cup. | :12:56. | :12:57. | |
Now, whenever you go into a church you expect to see a man | :12:58. | :13:00. | |
of God or a woman of God, but not a robot of God! | :13:01. | :13:12. | |
A Protestant church in Germany has unveiled a robotic priest, | :13:13. | :13:15. | |
called BlessU-2, to mark 500 years since the reaffirmation. | :13:16. | :13:17. | |
The machine delivers various blessings in eight languages. | :13:18. | :13:28. | |
Researchers at MIT have come up with a unique way to let | :13:29. | :13:30. | |
the visually impaired know more about what's around them. | :13:31. | :13:32. | |
A 3D camera recognises stuff and feeds that detail back | :13:33. | :13:35. | |
using vibrations through a belt and an electronic Braille interface. | :13:36. | :13:37. | |
Microsoft showed off a range of mixed reality headsets, | :13:38. | :13:39. | |
they're designed to blend the real world and virtual content into one | :13:40. | :13:42. | |
where physical and digitally created objects co-exist and interact. | :13:43. | :13:52. | |
Google Maps now doesn't just take you to your destination, | :13:53. | :13:59. | |
Click on an art museum and take a peek around with each artwork | :14:00. | :14:11. | |
displayed with the gallery's notations, plus extra | :14:12. | :14:13. | |
Now, as you've just been hearing, Microsoft may be leading the way | :14:14. | :14:23. | |
in mixed or augmented reality, but they're not the only company | :14:24. | :14:26. | |
this's trying to make you see things that aren't really there. | :14:27. | :14:33. | |
I'm at the Augmented World Expo which has doubled in size since last | :14:34. | :14:36. | |
year, a sign of the excitement this relatively new technology | :14:37. | :14:38. | |
Now you have a hologram in front you. | :14:39. | :14:58. | |
When you open your fist, you release it and then you can look at it. | :14:59. | :15:06. | |
Now you can stick your hand beside it. | :15:07. | :15:10. | |
Place it anywhere in space, anywhere you want. | :15:11. | :15:15. | |
I'm going to put it on top of Cody's head, behind the camera. | :15:16. | :15:20. | |
I feel like this is going to strain my eyes if I use it for any | :15:21. | :15:26. | |
Funny that you say that, we have a team of neuroscientists | :15:27. | :15:31. | |
on staff who actually do user testing with us. | :15:32. | :15:33. | |
We're confirming with our neuroscientists team that it does | :15:34. | :15:35. | |
not cause any eye strain or neck pain or anything when | :15:36. | :15:38. | |
The problem with all of these devices is that most of them require | :15:39. | :15:44. | |
expensive new gadgets, like these $799 glasses or big bulky | :15:45. | :15:46. | |
devices that soon become uncomfortable to wear. | :15:47. | :15:48. | |
But I did find one idea that I think could genuinely make many | :15:49. | :15:51. | |
Project Chalk is an app that uses augmented reality to let | :15:52. | :15:54. | |
you remotely help someone use a gadget or fix an object. | :15:55. | :15:57. | |
So next time your parents ring, you can show them what to do, | :15:58. | :16:00. | |
You can see the clever thing about this app is that, | :16:01. | :16:09. | |
even if I move around the machine, we still have the instructions | :16:10. | :16:12. | |
overlaid in the same place which means it can be much more | :16:13. | :16:15. | |
precise than if it was just drawn on a normal screen. | :16:16. | :16:26. | |
Project Chalk's designed to work on all types of devices - | :16:27. | :16:28. | |
phones, tablets and what we call digital eyewear, which you can also | :16:29. | :16:31. | |
So this will be available on a free basis which means anyone will be | :16:32. | :16:36. | |
able to start by downloading it from the app store and using | :16:37. | :16:39. | |
But once people have to pay, do you think they'll go back to just | :16:40. | :16:45. | |
using Skype or just talking on the phone to get | :16:46. | :16:48. | |
Now last week, the world's best player in the Chinese board game go | :16:49. | :17:03. | |
was pretty soundly beaten by Google's artificial | :17:04. | :17:04. | |
For those following the rapid development of AI, the result | :17:05. | :17:17. | |
20 years ago however, a victory of machine over | :17:18. | :17:20. | |
man shocked the world, prompting apocalyptic | :17:21. | :17:21. | |
warnings about our own role in society and questions | :17:22. | :17:23. | |
about whether humanitarian could survive in a world | :17:24. | :17:25. | |
where we could be out thought by a computer. | :17:26. | :17:27. | |
The man at the centre of that match was chess | :17:28. | :17:30. | |
The machine was IBM's super computer Deep Blue. | :17:31. | :17:38. | |
You talk about the fact that it took you quite a long time to get over | :17:39. | :17:42. | |
Can you explain why it took that long and how | :17:43. | :17:50. | |
It was my first loss and losing this match, | :17:51. | :17:53. | |
I mean, I knew I made mistakes and I didn't play well | :17:54. | :17:57. | |
and I could kick myself in my head for not preparing | :17:58. | :18:00. | |
Since then, you've become an advocate of AI? | :18:01. | :18:08. | |
At the end of the day, the future's a self-fulfilling prophecy. | :18:09. | :18:10. | |
So if we're negative, something wrong will happen. | :18:11. | :18:12. | |
I'm not telling you that if we're positive we can avoid it, | :18:13. | :18:15. | |
but at least we have a chance to turn it into an adventure. | :18:16. | :18:18. | |
Since it's happening anyway, I could see what's happening | :18:19. | :18:20. | |
in the game of chess, I have been looking for ways | :18:21. | :18:23. | |
I believe that there's so many things we can do | :18:24. | :18:32. | |
and when people say, "We don't know what's | :18:33. | :18:34. | |
That's exactly the reason for us to move there because we don't know, | :18:35. | :18:38. | |
that was one of the main driving forces in the history of humanity. | :18:39. | :18:44. | |
The AlphaGo software, that's just won the match with go, | :18:45. | :18:46. | |
works in a very different way, which is teaching itself the rules. | :18:47. | :18:49. | |
How do you feel about that kind of AI? | :18:50. | :18:51. | |
Is that the way that AI should go, learn about the rules of the world? | :18:52. | :18:57. | |
One should say that Deep Blue was anything but intelligent. | :18:58. | :18:59. | |
It was as intelligent as your alarm clock. | :19:00. | :19:01. | |
Very expensive alarm clock, $10 million alarm clock, | :19:02. | :19:03. | |
Very powerful, brute force, with little chess knowledge, | :19:04. | :19:14. | |
but chess proved to be vulnerable to this brute force, | :19:15. | :19:16. | |
But with 200 million positions per second, | :19:17. | :19:21. | |
that's roughly the average speed of Deep Blue, it showed very | :19:22. | :19:23. | |
So it gave us almost no insight into the mysteries | :19:24. | :19:27. | |
AlphaGo is different because it's a self-teaching programme, | :19:28. | :19:34. | |
and if we're looking for AI definition, it's still hard | :19:35. | :19:36. | |
to really understand whether AlphaGo is AI because now we're moving | :19:37. | :19:39. | |
So we still don't understand exactly what human intelligence is. | :19:40. | :19:55. | |
What do you think of the grand challenges | :19:56. | :19:58. | |
What are the things that maybe aren't being talked about enough | :19:59. | :20:02. | |
or that people don't think about enough when it comes | :20:03. | :20:05. | |
to accepting and implementing artificial intelligence? | :20:06. | :20:12. | |
One thing that's important to remember thateverything | :20:13. | :20:14. | |
that we do and we know how to do, machines will do better eventually. | :20:15. | :20:17. | |
Everything we do and we know how we do. | :20:18. | :20:21. | |
But there's so many things that we don't know how we do. | :20:22. | :20:26. | |
Let's concentrate on that because machines have algorithms | :20:27. | :20:28. | |
and they're getting better and better, but machines have no | :20:29. | :20:31. | |
curiosity, no passion and, most importantly, machines | :20:32. | :20:32. | |
Maybe the good thing is that so we know we have purpose, | :20:33. | :20:36. | |
but we don't know exactly what the purpose is. | :20:37. | :20:38. | |
So that guarantees that we'll stay around for quite a while. | :20:39. | :20:51. | |
Now, it's time for artificial intelligence to be put | :20:52. | :20:54. | |
For Click's show at this year's Hay Festival, | :20:55. | :20:58. | |
we challenged Click code monkey Stephen Beckett to create a game | :20:59. | :21:00. | |
We're going to split the audience in two now, right down there. | :21:01. | :21:11. | |
This half is going to play against this half. | :21:12. | :21:13. | |
Our teams have to be the last one to fall off this plank, | :21:14. | :21:24. | |
whether they do that by canny balancing or more boisterous | :21:25. | :21:26. | |
They're in two teams and by each moving their arms left and right, | :21:27. | :21:32. | |
they can altogether control where their player goes. | :21:33. | :21:35. | |
And there's one more surprise feature, by shouting out special key | :21:36. | :21:38. | |
words they can make their player get bigger and therefore heavier. | :21:39. | :21:41. | |
So that's the rules of the game, but how can it see what you're doing | :21:42. | :21:44. | |
Behind the scenes, it's running in a game engine called Unity. | :21:45. | :21:52. | |
You can download this for free, and it's a great way to get started | :21:53. | :21:56. | |
There's a massive community out there offering free | :21:57. | :21:59. | |
I've shared a few of my favourites over on Twitter. | :22:00. | :22:04. | |
Unity is looking after the graphics and rules of the game and it's also | :22:05. | :22:07. | |
hooked into IBM's artificially intelligent Watson system, | :22:08. | :22:09. | |
which is turning the speech from the audience into text | :22:10. | :22:11. | |
Watson runs in the cloud, so my little laptop can | :22:12. | :22:20. | |
have the smarts of a supercomputer just using a Wi-Fi connection. | :22:21. | :22:22. | |
If you've ever used a speech recognition service like Siri, | :22:23. | :22:25. | |
Alexa or Google, you'll know that sometimes they can be | :22:26. | :22:27. | |
IBM's Watson is no exception, things like background noise | :22:28. | :22:36. | |
and music can throw it off, leading to some | :22:37. | :22:39. | |
But to make the game more fun, I've set it up to also look for the most | :22:40. | :22:47. | |
So words like say, stay and may will also count as a shout for hey. | :22:48. | :22:52. | |
Despite this, I didn't anticipate just how loud our | :22:53. | :22:54. | |
They're so loud in fact that the voice recognition struggled | :22:55. | :23:01. | |
to hear all the words, so there wasn't quite as much | :23:02. | :23:04. | |
So that's the voice, but how do we work out | :23:05. | :23:12. | |
Well, we're using the openFrameworks and OpenCV libraries. | :23:13. | :23:15. | |
These are two beefy bits of software to help you build creative | :23:16. | :23:18. | |
OpenFrameworks is more complicated than Unity, | :23:19. | :23:23. | |
but there are lots of great guides out there if you already have some | :23:24. | :23:26. | |
This programme takes the feed from our cameras, | :23:27. | :23:31. | |
looks for edges and then tries to find lines by | :23:32. | :23:33. | |
So if you move your arm left or right, that counts | :23:34. | :23:37. | |
And that is all it takes to get 300 people, in a tent, | :23:38. | :23:41. | |
Give yourself a huge round of applause. | :23:42. | :23:46. | |
Yeah, never a dull moment when you put us in a tent. | :23:47. | :23:50. | |
That's it from us for the Hay Festival for this year. | :23:51. | :23:53. | |
Hopefully, we didn't break too much and they'll have us | :23:54. | :23:55. | |
In the meantime, why don't you follow us on Facebook for loads | :23:56. | :24:05. | |
of extra content and of course on Twitter too @BBCClick. | :24:06. | :24:07. | |
Thanks for watching and, one more time - hey, why. | :24:08. | :24:09. | |
We will do the easy bit first and I will give you the weekend forecast, | :24:10. | :24:41. | |
probably the bit you are after anyway! A mix | :24:42. | :24:42. |