Browse content similar to 03/06/2017. Check below for episodes and series from the same categories and more!
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Coming up shortly on BBC News, we will have new Swatch but first it is | :00:00. | :00:00. | |
time for Click. -- news watch. Summer is on the way and, well, | :00:00. | :00:32. | |
it wouldn't be a British summer without a visit to a good | :00:33. | :00:36. | |
old fashioned festival. Known as the Town of Books, | :00:37. | :00:38. | |
Hay-on-Wye, in Wales, It's a literary mecca, | :00:39. | :00:47. | |
an annual gathering of artists, authors, Daleks and, | :00:48. | :00:53. | |
yep, even Royals. It's even been called the Woodstock | :00:54. | :00:58. | |
of the Mind by none other than former US President Bill | :00:59. | :01:02. | |
Clinton. This year it's the 30th Hay Festival | :01:03. | :01:04. | |
and the line-up is pretty stellar. Well, for the second year in a row, | :01:05. | :01:09. | |
we've been invited to share some of our favourite experiences | :01:10. | :01:13. | |
and show off some really good tech, all in front of a real, | :01:14. | :01:16. | |
live audience of actual people. A packed tent waited, | :01:17. | :01:23. | |
all that we had to do was wow them! We have robots falling over, | :01:24. | :01:30. | |
experiments in haptic feedback and demos in binaural sound, | :01:31. | :01:36. | |
but that was nothing compared to the climax - | :01:37. | :01:41. | |
a Click-created wavy, shouty game built using | :01:42. | :01:44. | |
artificial intelligence. In the meantime, it can't have have | :01:45. | :01:50. | |
escaped your attention that around the UK things are getting | :01:51. | :01:53. | |
a touch political. As the general election looms, those | :01:54. | :01:56. | |
politicians are using increasingly sophisticated techniques in order | :01:57. | :01:59. | |
to learn more about us. The advertising reach of Facebook | :02:00. | :02:09. | |
has long been an open secret, but now it's something the political | :02:10. | :02:12. | |
parties are getting in on too. In fact, both the Trump campaign | :02:13. | :02:19. | |
and the Leave.EU groups credited Facebook as being a vital part | :02:20. | :02:22. | |
of their electioneering. We know that the personal details | :02:23. | :02:26. | |
that you give to social networks allow them to send you relevant, | :02:27. | :02:30. | |
targeted content, and it goes much deeper than just | :02:31. | :02:33. | |
your basic demographics. There are now data analytics | :02:34. | :02:37. | |
companies claiming to be able to micro-target and micro-tweak | :02:38. | :02:43. | |
messages for individual readers, If you know the personality | :02:44. | :02:45. | |
of the people you're targeting, you can nuance your messaging | :02:46. | :02:51. | |
to resonate more effectively What's also emerging is that | :02:52. | :02:54. | |
political parties have been using this data to reach potential | :02:55. | :03:02. | |
voters, on a very granular level. So who is being targeted | :03:03. | :03:07. | |
on Facebook and how? Well, until now, there's been | :03:08. | :03:10. | |
nothing around to analyse any of this, but the snap general | :03:11. | :03:16. | |
election galvanised Louis Knight-Webb and Sam Jeffers | :03:17. | :03:18. | |
to develop Who Targets Me, a plug-in to tell each of us how | :03:19. | :03:21. | |
we're being targeted. When you install the plug-in | :03:22. | :03:27. | |
for the first time, it asks for your age, | :03:28. | :03:29. | |
your gender and your location, and then it starts scouring your | :03:30. | :03:32. | |
Facebook feed looking for adverts So once you've installed | :03:33. | :03:35. | |
the plug-in, it works in the background to extract | :03:36. | :03:38. | |
the whole advert that So it pulls out the headline, | :03:39. | :03:41. | |
the subtitle, any related videos, We also get the reaction - | :03:42. | :03:46. | |
so how many likes, how many comments, how many shares - | :03:47. | :03:51. | |
so we can see which messages Are they particularly | :03:52. | :03:54. | |
clandestine messages, are they slightly subversive, | :03:55. | :03:57. | |
are they even fake news? But how do data companies get | :03:58. | :03:59. | |
the information in the first place? A lot of the quizzes | :04:00. | :04:02. | |
you fill out on Facebook or, you know, you open a survey, | :04:03. | :04:05. | |
it asks your Facebook Sometimes you'll notice that there's | :04:06. | :04:12. | |
a lot of permissions attached and as soon as you click yes, | :04:13. | :04:17. | |
all of your data is mined, and it's then sold on to data | :04:18. | :04:20. | |
brokers who then, eventually, sell it to the political parties | :04:21. | :04:23. | |
for use in their campaigns. Although Facebook says it doesn't | :04:24. | :04:26. | |
sell our information on, data brokers can overlay any details | :04:27. | :04:28. | |
they mine from the site with other datasets that they have on people | :04:29. | :04:32. | |
based on their email addresses. The next step after that of course | :04:33. | :04:36. | |
is to find similar users that are using Facebook and then target | :04:37. | :04:40. | |
adverts, from that advertiser that supplied the email | :04:41. | :04:42. | |
addresses, to those users. There are just some people that | :04:43. | :04:46. | |
you don't find on Twitter. The very nature of the fact that | :04:47. | :04:56. | |
I can't see your adverts, you can't see my adverts, | :04:57. | :04:59. | |
means that this approach had to be It's a first of its kind anywhere | :05:00. | :05:02. | |
in the world on this scale, giving us citizens some transparency | :05:03. | :05:11. | |
into what we're being shown, Do you think that people wouldn't | :05:12. | :05:13. | |
know that certain things are advert A lot of the time people | :05:14. | :05:19. | |
are scrolling through Facebook and the adverts fit into this weird | :05:20. | :05:27. | |
intersection of friend It's quite easy to miss | :05:28. | :05:33. | |
the adverts on Facebook. So far, Who Targets Me | :05:34. | :05:36. | |
has some 6,700 users in 620 constituencies, | :05:37. | :05:41. | |
and it's rising as On the down side, it's only | :05:42. | :05:43. | |
as good as the data it's managed to crowd-source, | :05:44. | :05:49. | |
so it isn't necessarily representative, and it also doesn't | :05:50. | :05:51. | |
work with mobile Facebook, So we're seeing | :05:52. | :05:56. | |
a mixture of two things. We're seeing, firstly, A/B testing, | :05:57. | :05:59. | |
which is where I try out two different messages | :06:00. | :06:02. | |
with the same group. I see which one gets | :06:03. | :06:07. | |
the best reaction and then We're also seeing targeting, | :06:08. | :06:10. | |
which is where I pick a particular demographic of people, | :06:11. | :06:16. | |
and then I send a message So, for example, it might be | :06:17. | :06:19. | |
young people targeted The data from Who Targets Me is also | :06:20. | :06:22. | |
being poured over by analysts One aspect of their research | :06:23. | :06:27. | |
is collecting dark posts, ads which are here one day | :06:28. | :06:31. | |
and gone the next. It gives us the ability to create | :06:32. | :06:36. | |
a respository of those dark posts. So if promises are being made | :06:37. | :06:40. | |
on Facebook, in ads which will disappear the day after you use | :06:41. | :06:43. | |
them, we should be able to go back to those after the election, | :06:44. | :06:47. | |
look at them, evaluate them and maybe discuss them | :06:48. | :06:50. | |
in the cold light of day. And the irony is that, | :06:51. | :06:54. | |
as we demand more transparency from public bodies, the whole basis | :06:55. | :06:56. | |
of political propaganda could be on the brink | :06:57. | :06:59. | |
of a revolutionary change. What's interesting, I think, | :07:00. | :07:07. | |
about the new environment is the potential for using paid | :07:08. | :07:09. | |
advertising and other techniques to create individual propaganda | :07:10. | :07:12. | |
bubbles around individual voters. And that's not about controlling | :07:13. | :07:16. | |
the market as a whole, but it's about using smart targeted | :07:17. | :07:21. | |
which, in a sense, creates such a compelling and overarching | :07:22. | :07:25. | |
information environment for individual people that that | :07:26. | :07:27. | |
in some ways constrains what they do I think that's why some academic | :07:28. | :07:30. | |
commentators and others are beginning to think some of this | :07:31. | :07:37. | |
is a bit spooky. But politicians aren't the only ones | :07:38. | :07:41. | |
with Facebook on their minds. The social network was one of many | :07:42. | :07:45. | |
topics on the very large brain of national treasure and tech geek | :07:46. | :07:49. | |
Stephen Fry. I met up with him after he gave | :07:50. | :07:55. | |
a lecture at the Hay Festival highlighting how he thinks the world | :07:56. | :07:59. | |
is being changed by social The very current conversation | :08:00. | :08:02. | |
is whether Facebook and platforms like them should actually | :08:03. | :08:05. | |
be considered publishers? Should they take responsibility | :08:06. | :08:10. | |
for what ends up on the site? They are aware there | :08:11. | :08:14. | |
is a problem, a serious problem. If 80%, some people | :08:15. | :08:16. | |
have said, is the... You know, in proportion of people | :08:17. | :08:18. | |
who get their news from Facebook rather than from mainstream media, | :08:19. | :08:22. | |
then surely it is incumbent upon someone who is providing 80% | :08:23. | :08:24. | |
of their news sources to make sure that those news sources | :08:25. | :08:33. | |
are not defamatory, blatant lies, propaganda, | :08:34. | :08:35. | |
the wrong kind of, I would posit there that a publisher | :08:36. | :08:36. | |
is responsible for all the people They are employed by that publisher | :08:37. | :08:42. | |
and Facebook is clearly not that. So do we need a third | :08:43. | :08:50. | |
definition, a third thing? I think there is a median sort | :08:51. | :08:54. | |
of definition that it's not beyond the wit of lawyers | :08:55. | :09:00. | |
of the right kind to find that. Your presentation was a warning that | :09:01. | :09:08. | |
people should prepare for the changes that are coming, | :09:09. | :09:10. | |
for example, artificial I said that it was a sort | :09:11. | :09:13. | |
of transformation of You know, it's an obsolescence | :09:14. | :09:16. | |
of certain types of job, but that doesn't mean forced | :09:17. | :09:20. | |
redundancy of millions of workers. I mentioned one of the pleasing | :09:21. | :09:23. | |
things about AI and robotics, and that is what's known | :09:24. | :09:26. | |
as Moravec's paradox, that what we're incredibly bad at, | :09:27. | :09:28. | |
as individuals, machines tend to be Complicated sums, rapid | :09:29. | :09:32. | |
and incredible access of memory from a database of a kind | :09:33. | :09:38. | |
that we could never do, sorting and swapping | :09:39. | :09:42. | |
of information and cataloguing But things we can do | :09:43. | :09:44. | |
without even thinking, like walk across the room or pick up | :09:45. | :09:47. | |
a glass and have a sip of water, But that's fine, because we don't | :09:48. | :09:51. | |
want them to do that for us. Where it gets difficult is in medium | :09:52. | :09:56. | |
sort of service jobs, I think. Well, you could go the etymological | :09:57. | :10:00. | |
route, and you could say Legere is read and inter, interleg, | :10:01. | :10:05. | |
and that's pretty good, actually, Just being able to see connections | :10:06. | :10:15. | |
in things and people are talking about the moment that we arrive | :10:16. | :10:23. | |
at AGI, artificial general intelligence, and that's | :10:24. | :10:26. | |
when the various types of pattern recognition, you know, | :10:27. | :10:29. | |
numbers, data, you know, certain faces and things like that, | :10:30. | :10:31. | |
they all come together so that they can be intelligent | :10:32. | :10:33. | |
across these different things. If you've got an artificial | :10:34. | :10:39. | |
intelligence that's good at that and another one that's good at that | :10:40. | :10:41. | |
and another one that's good at that and you get enough of them, | :10:42. | :10:45. | |
and then you put something on top that goes, "Oh, you want to know | :10:46. | :10:49. | |
about a language problem, Surely, just a collection | :10:50. | :10:52. | |
of specialists of intelligences under one umbrella is | :10:53. | :10:58. | |
a generals intelligence. It doesn't have to be | :10:59. | :11:00. | |
a breakthrough, it just has to be I think that's very likely | :11:01. | :11:04. | |
to be the way it goes. It's reward is similar | :11:05. | :11:14. | |
to our reward system It's tryptophan and serotonin | :11:15. | :11:16. | |
and endorphins of various kinds that reward us and then we have a pain | :11:17. | :11:20. | |
system to deter us, and there's nothing to stop us | :11:21. | :11:24. | |
giving that to a machine. Stephen, thank you so | :11:25. | :11:27. | |
much for your time. Thank you for having | :11:28. | :11:29. | |
us at your place. That is it for this short carte of | :11:30. | :11:38. | |
Leg. The full-length version is an eye player. You can follow us on | :11:39. | :11:42. | |
Facebook for loads of extra content as well. | :11:43. | :11:45. |