Browse content similar to 10/12/2016. Check below for episodes and series from the same categories and more!
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Now on BBC News it is time for Click. | :00:00. | :00:00. | |
This week, mapping the poorest parts of the world. | :00:00. | :00:07. | |
And, hands up who is not flying the plane? | :00:08. | :00:37. | |
There are things happening in artificial intelligence right | :00:38. | :00:46. | |
now that will fundamentally change our world. | :00:47. | :00:51. | |
Soon, machines will learn to do our jobs. | :00:52. | :00:56. | |
And at that point, things get very interesting. | :00:57. | :01:08. | |
We'll talk more about the consequences of an automated | :01:09. | :01:11. | |
society in a few minutes but, after thinking, walking and driving, | :01:12. | :01:14. | |
have you ever wondered how hard it would be for a computer to fly? | :01:15. | :01:24. | |
I'm not talking about drones that can fly between points, follow | :01:25. | :01:28. | |
I'm talking about aircraft that could intelligently | :01:29. | :01:35. | |
decide on a flight plan, just as a human would. | :01:36. | :01:41. | |
And that is what Mark has been hunting down. | :01:42. | :01:48. | |
Here at BAE Systems in Woolton in Lancashire, they are used | :01:49. | :01:52. | |
They built and have tested Eurofighter Typhoons here. | :01:53. | :02:00. | |
Today, however, I am going to take a flight in an aircraft that is much | :02:01. | :02:04. | |
This is a Jetstream 31, a small passenger aircraft. | :02:05. | :02:12. | |
It's a design from the 1980s but it's currently used by BAE | :02:13. | :02:16. | |
Systems as a flying test-bed for technology which could lead | :02:17. | :02:19. | |
Maureen McCue is head of research here. | :02:20. | :02:27. | |
Very well flown and understood aircraft from the outside, | :02:28. | :02:32. | |
but on the inside, it's filled with the latest technology. | :02:33. | :02:37. | |
That technology will eventually allow this aircraft to fly | :02:38. | :02:43. | |
Today, they are testing the plane's ability to detect and avoid | :02:44. | :02:47. | |
clouds as well as testing its satellite communication systems. | :02:48. | :02:52. | |
But take-off and landing will still be handled by human | :02:53. | :02:55. | |
pilots and the plane will be remotely controlled | :02:56. | :02:57. | |
How does this fit into the autonomous equation? | :02:58. | :03:01. | |
At the moment, it's effectively a remotely controlled aircraft. | :03:02. | :03:03. | |
It is, and really, with autonomous operations, | :03:04. | :03:06. | |
you need to progressively expand the boundary. | :03:07. | :03:09. | |
You can't start with such a big bang right out at the full range | :03:10. | :03:13. | |
This humble looking outbuilding houses the grand station. | :03:14. | :03:23. | |
Here, a pilot will remotely fly the plane and he can ensure it | :03:24. | :03:27. | |
will react to instructions from air traffic control. | :03:28. | :03:35. | |
I would expect to see a joystick and images coming | :03:36. | :03:39. | |
through from the cockpit by you're not going to be flying | :03:40. | :03:42. | |
No, everything is through the numbers that you can see there. | :03:43. | :03:47. | |
These flights are taking place in uncongested airspace. | :03:48. | :03:53. | |
Today, we will be flying over the Irish Sea. | :03:54. | :03:59. | |
To help fly itself, this aircraft uses data from satellites | :04:00. | :04:10. | |
as well as identifying radio signals broadcast by other aeroplanes, so it | :04:11. | :04:13. | |
It is also fitted with a camera that can see other air | :04:14. | :04:20. | |
users, even if there are not warning signals. | :04:21. | :04:24. | |
So right now, the pilots aren't actually flying the aircraft. | :04:25. | :04:26. | |
From that 15-year-old Dell laptop that's probably Windows XP. | :04:27. | :04:34. | |
At this point, the aircraft is flying autonomously with a human | :04:35. | :04:38. | |
Handing control of the computing over to the autopilot in the back | :04:39. | :04:46. | |
and once established on the route, I can hand the computing | :04:47. | :04:51. | |
over to Clive by the satellite on the ground. | :04:52. | :04:57. | |
So that little shed-like building we were in earlier with Clive | :04:58. | :05:00. | |
in front of the computer, he is now flying the aircraft. | :05:01. | :05:04. | |
Over the course of the testing of this aircraft, it's going to have | :05:05. | :05:09. | |
to perform a variety of different, complex tasks. | :05:10. | :05:12. | |
For instance, it's going to have to recognise and avoid bad weather. | :05:13. | :05:15. | |
Not just weather, but other aeroplanes, too. | :05:16. | :05:19. | |
It will eventually be able to select a safe landing spot | :05:20. | :05:22. | |
Today, we can't really test its weather detection | :05:23. | :05:30. | |
abilities though as, unusually for the UK, | :05:31. | :05:32. | |
BAE suggests that autonomous aircraft could be used | :05:33. | :05:38. | |
to perform dirty, dangerous or repetitive tasks. | :05:39. | :05:42. | |
But could this technology be introduced into | :05:43. | :05:44. | |
At the moment all commercial aircraft have a set number of crew. | :05:45. | :05:50. | |
There are programmes in existence looking at how you reduce crew | :05:51. | :05:53. | |
either planned from the outset or, in the case of an emergency, | :05:54. | :05:58. | |
you've got the autonomous system as a fallback so that you can | :05:59. | :06:02. | |
still have perhaps a two-crewed aircraft on a certain length | :06:03. | :06:05. | |
of flight but one of the crew happens to be an autonomous helper | :06:06. | :06:09. | |
But what happens when things go wrong? | :06:10. | :06:19. | |
While aerospace manufacturers are exploring the possibilities | :06:20. | :06:31. | |
of fewer cockpit crew, what do commercial pilots think? | :06:32. | :06:34. | |
To find out, we paid the British Airline Pilots Association a visit. | :06:35. | :06:39. | |
Many decades of looking at aviation has brought us to the position | :06:40. | :06:42. | |
where we have come to the conclusion it's best to have two | :06:43. | :06:46. | |
pilots in the cockpit, because if you reduce that to one, | :06:47. | :06:49. | |
the problem you've got then is you've got no one to cross check | :06:50. | :06:52. | |
Take for example, the miracle on the Hudson. | :06:53. | :06:55. | |
When the aircraft lost both its engines, the pilots had | :06:56. | :07:06. | |
to have a discussion and they decided their only course | :07:07. | :07:09. | |
No computer can be programmed to do that. | :07:10. | :07:13. | |
The flight testing of autonomous aircraft continues but the debate | :07:14. | :07:16. | |
about regulating them and how they are going to be used has | :07:17. | :07:19. | |
That was Mark and this is Tim Harford, columnist | :07:20. | :07:23. | |
for the Financial Times and Tim, you've written a book about how | :07:24. | :07:26. | |
the systems that we now rely on can sometimes backfire. | :07:27. | :07:32. | |
What do you make of the idea of planes that might only need one | :07:33. | :07:36. | |
And of course, autopilots have made planes safer | :07:37. | :07:46. | |
but what worries me is, what happens when | :07:47. | :07:48. | |
No system is perfect, including a system where | :07:49. | :07:51. | |
I guess when it goes wrong, it has two hand back | :07:52. | :07:55. | |
Autopilot hands back to the human in the cockpit but then what? | :07:56. | :08:00. | |
The human is out of practice, the human is not used to flying | :08:01. | :08:03. | |
the plane and because the autopilot has failed, it's probably | :08:04. | :08:06. | |
There is a worrying example of this a few years ago. | :08:07. | :08:11. | |
An Air France crash over the Atlantic Ocean. | :08:12. | :08:14. | |
The plane was flying quite high above a storm. | :08:15. | :08:18. | |
The autopilot disconnected and the pilots just weren't used | :08:19. | :08:23. | |
They were only used to operating the plane on take-off and landing | :08:24. | :08:32. | |
and they flew a perfectly good plane into the Atlantic Ocean | :08:33. | :08:35. | |
because they were confused about what was happening. | :08:36. | :08:37. | |
They killed everybody on board, an absolute tragedy, and this | :08:38. | :08:40. | |
The autopilots are normally so safe, so reliable, | :08:41. | :08:45. | |
that when they fail, the pilots find | :08:46. | :08:47. | |
I guess the next question is, what about autonomous cars? | :08:48. | :08:53. | |
We have been talking about how they will blissfully drive us around | :08:54. | :08:57. | |
I suppose for the foreseeable future, they won't be good enough | :08:58. | :09:02. | |
I guess they'll never be 100% reliable but the model | :09:03. | :09:10. | |
where if it's confused, it hands back to the human, | :09:11. | :09:13. | |
You are there with your bagel, your coffee, your newspaper. | :09:14. | :09:22. | |
You look up, there's a bus coming towards you, and the car goes, | :09:23. | :09:25. | |
autopilot disengaged, human take control, it clearly not | :09:26. | :09:28. | |
What makes more sense is for the human to be driving | :09:29. | :09:32. | |
and for the computer to be watching out for a dangerous situation, | :09:33. | :09:36. | |
for the computer to take over if there's a problem. | :09:37. | :09:38. | |
Humans get bored, get distracted, lose their skills. | :09:39. | :09:43. | |
None of these things happened to computers. | :09:44. | :09:49. | |
I guess we are in an extended period of time before the far future | :09:50. | :09:53. | |
happens and computers drive and fly us. | :09:54. | :09:55. | |
We've got possibly decades if not a century of being in this | :09:56. | :09:58. | |
interim period where, if there is a problem, | :09:59. | :10:01. | |
we are going to end up blaming them for this really unusual, | :10:02. | :10:04. | |
weird crash that a human wouldn't have made. | :10:05. | :10:07. | |
Yes, and I think a glimpse of that is where we are asking | :10:08. | :10:10. | |
the computers to make a decision not about planes or cars but about, | :10:11. | :10:14. | |
for example, who'll get a promotion or who gets a special deal in a shop | :10:15. | :10:18. | |
or who gets arrested for shoplifting because the computer | :10:19. | :10:21. | |
We are already asking computers to make this sort of decision | :10:22. | :10:26. | |
and the lesson of the paradox of automation is that we need to be | :10:27. | :10:30. | |
much more savvy about the fact that computers do make mistakes | :10:31. | :10:33. | |
Hello and welcome to The Week In Tech. | :10:34. | :10:45. | |
It was the week that inventor Haiyan Zhang developed a smart | :10:46. | :10:48. | |
wristband to help people with Parkinson's disease | :10:49. | :10:50. | |
The device's in-built motors vibrate to distract | :10:51. | :10:56. | |
It was also the week that we discovered queueing | :10:57. | :11:02. | |
at the shops and using those beyond infuriating self-service | :11:03. | :11:04. | |
checkouts could soon be a thing of the past. | :11:05. | :11:08. | |
Amazon has unveiled a sci-fi store in Seattle that uses your smartphone | :11:09. | :11:11. | |
and advanced technologies like deep learning, computer vision and sensor | :11:12. | :11:16. | |
fusion to automatically detect when products are taken | :11:17. | :11:20. | |
When you're done, you can simply trot off and then wait | :11:21. | :11:26. | |
for that gargantuan virtual receipt to follow. | :11:27. | :11:30. | |
And if you feel like you're forever stuck in traffic, | :11:31. | :11:32. | |
Audi has rolled out an update to make every second count. | :11:33. | :11:36. | |
Its new traffic light information feature tells drivers exactly how | :11:37. | :11:39. | |
long they'll have to wait behind a red signal before it turns | :11:40. | :11:42. | |
green and the length of time it will stay green. | :11:43. | :11:47. | |
It works by connecting directly to the city's | :11:48. | :11:50. | |
And finally, robotic research has reached new heights, literally. | :11:51. | :11:56. | |
This hopping mad bot developed at UC Berkley cannot only jump a meter off | :11:57. | :12:04. | |
the ground but can then again jump off objects to reach | :12:05. | :12:07. | |
Inspired by the agility of bushbabies, researchers hope | :12:08. | :12:12. | |
it can one day identify jumping spots for itself. | :12:13. | :12:21. | |
Which these days isn't guaranteed to be true. | :12:22. | :12:30. | |
In the run-up to the US election, for example, the Speaker | :12:31. | :12:33. | |
of the House of Representatives did not get naked, the Pope did not | :12:34. | :12:37. | |
endorse Donald Trump and he did not win the popular vote, | :12:38. | :12:40. | |
but these stories, from websites posing as real news sites, | :12:41. | :12:45. | |
Of course, it doesn't help that in 2016, the real news sounds | :12:46. | :12:51. | |
But anyway, it's made events like the Trust Hack here in London | :12:52. | :12:57. | |
Here, journalists and technologists from large news organisations | :12:58. | :13:06. | |
are workshopping ways to help readers tell the difference | :13:07. | :13:11. | |
between well-researched journalism, propaganda, advertising, | :13:12. | :13:13. | |
The thought is to provide images like icons back-up materials | :13:14. | :13:20. | |
that the public could see connected to a piece of news and then it | :13:21. | :13:24. | |
would send a signal back to the news distribution platform like Google | :13:25. | :13:27. | |
or Twitter so that they can identify quality news out of the fake news | :13:28. | :13:31. | |
There are already projects afoot to try to flag up stories on sites | :13:32. | :13:48. | |
known to generate fake news like this plug-in but the ideas | :13:49. | :13:50. | |
here are not about blacklisting sites or producing automated | :13:51. | :13:53. | |
Both would be massive undertakings and would themselves provoke cries | :13:54. | :13:56. | |
This is more about letting news organisations prove to their readers | :13:57. | :14:00. | |
The journals of the Washington Post, I work with some amazing people. | :14:01. | :14:05. | |
We produce really great stuff and they remain really committed | :14:06. | :14:08. | |
The tools that we are building here are just a way for us | :14:09. | :14:12. | |
to communicate that we are putting in the effort, where our stuff | :14:13. | :14:16. | |
is coming from, who we are talking to. | :14:17. | :14:18. | |
We are trying to create something that would easily allow audiences | :14:19. | :14:29. | |
to verify for themselves what sources we have used. | :14:30. | :14:32. | |
You'd be able to click and see, who did we talk to, and you'd | :14:33. | :14:36. | |
Readers want to feel like journals are being held accountable to them | :14:37. | :14:42. | |
Other ideas here include ways to fight information bias | :14:43. | :14:49. | |
by surfacing articles that support the opposite side of an argument | :14:50. | :14:52. | |
or to look at the likelihood of truth by finding similar articles | :14:53. | :14:55. | |
Many think the reputation of the journalists themselves plays | :14:56. | :14:58. | |
a big part in the trustworthiness of reporting. | :14:59. | :15:00. | |
Italian newspaper La Stampa is suggesting a system | :15:01. | :15:02. | |
where an author is assigned a unique identifier that shows their piece | :15:03. | :15:06. | |
The best ideas won a small prize at the end of the day but it's | :15:07. | :15:17. | |
The hope is that this part-Google funded initiative may lead | :15:18. | :15:22. | |
to a system that helps news outlet stories rank high up on search | :15:23. | :15:25. | |
That said, I can't help wondering if that is actually something that | :15:26. | :15:38. | |
platforms like Google and Facebook really want. | :15:39. | :15:40. | |
Do you think they care what it is they serve to us or do | :15:41. | :15:43. | |
you think, really, as long as you click on it, that's | :15:44. | :15:47. | |
How do we know the motivations of any company? | :15:48. | :15:51. | |
These companies make their money through people clicking | :15:52. | :15:54. | |
on the adverts, so do you think any of the large companies care | :15:55. | :15:57. | |
Based on my conversations with them, I think they do. | :15:58. | :16:07. | |
The argument would be if they start being perceived as not caring | :16:08. | :16:10. | |
about the information that's sent out there, | :16:11. | :16:12. | |
Yes, it may be that the truth will out, not because of a desire | :16:13. | :16:25. | |
for the facts, but because everyone, readers and news aggregators, | :16:26. | :16:28. | |
More and more people are shopping online but still at this time | :16:29. | :16:48. | |
of year, the high street seems pretty chaotic and the retailers | :16:49. | :16:51. | |
So they are trying to create some more engaging experiences. | :16:52. | :17:01. | |
But do they help us or are they just a distraction? | :17:02. | :17:05. | |
Here in London's Covent Garden, 140 shops and restaurants are taking | :17:06. | :17:08. | |
part in creating one huge augmented reality experience. | :17:09. | :17:13. | |
With the help of AR app Blipper, things come to life. | :17:14. | :17:21. | |
It may not have created the personalised shopping experience | :17:22. | :17:24. | |
I dreamt up, but there were some promotional offers presented | :17:25. | :17:27. | |
as virtual Christmas presents almost around the tree. | :17:28. | :17:32. | |
A reindeer hunt and a giant reindeer you can take a selfie with, | :17:33. | :17:35. | |
I have to say, it wasn't quite as cutting-edge as I'd hoped | :17:36. | :17:41. | |
but I suppose it's a bit of light-hearted fun. | :17:42. | :17:45. | |
Rather more purposefully, the signs in windows can be scanned | :17:46. | :17:48. | |
using image recognition, taking you to online content, | :17:49. | :17:50. | |
partly the sort of stuff you'd be able to look at from your sofa | :17:51. | :17:54. | |
Then came our trip to London's Westfield where augmented | :17:55. | :18:01. | |
We've seen technology like this before but now it's actually | :18:02. | :18:09. | |
on the shop floor here at Charlotte Tilbury. | :18:10. | :18:12. | |
This is what's known as the Magic Mirror and this | :18:13. | :18:14. | |
You choose a lipstick and in real-time, you will see | :18:15. | :18:19. | |
Bright red lips, although it doesn't seem to have any around the edges, | :18:20. | :18:28. | |
I've tried the Rimmel app that does something similar on your phone | :18:29. | :18:38. | |
and you can buy things through it, but here you can do it in the store | :18:39. | :18:42. | |
with assistants all around and a whole shop of products that | :18:43. | :18:46. | |
you can test, smell, and after you see what your face | :18:47. | :18:49. | |
looks like on here, you might want to have a go to check that | :18:50. | :18:52. | |
Meanwhile, here at this eBay event, they are taking things | :18:53. | :18:57. | |
The data on this screen represents what is apparently visitors | :18:58. | :19:02. | |
Using what they call facial coding, the camera looks for reactions | :19:03. | :19:06. | |
which these guys reckon you have when you do online shopping. | :19:07. | :19:09. | |
Nice but don't know who I'd give it to. | :19:10. | :19:12. | |
And I've been told to overact my reactions. | :19:13. | :19:27. | |
Whilst my results bore absolutely no correlation to what I'd liked, | :19:28. | :19:32. | |
maybe they were the ones I contorted my face to the most. | :19:33. | :19:35. | |
Maybe it would have worked better if I'd reacted more naturally, | :19:36. | :19:38. | |
although I struggle to imagine that my face would have shown anything. | :19:39. | :19:41. | |
It left me wondering whether eBay could be developing this if it | :19:42. | :19:44. | |
worked as something more permanent to assess our feelings when online | :19:45. | :19:47. | |
Either way, don't expect me to look too excited about it. | :19:48. | :19:57. | |
Not that I'm sure the tech would have even noticed. | :19:58. | :20:02. | |
Now, in 2015, members of the United Nations adopted a set | :20:03. | :20:05. | |
Number one on that list is to end poverty and to achieve that goal, | :20:06. | :20:10. | |
you first need to work out where poverty exists and how | :20:11. | :20:13. | |
We met up with some scientists at Stamford who have that | :20:14. | :20:23. | |
Marshall Burke is a professor of earth systems science | :20:24. | :20:26. | |
at Stanford University but he spends much of his time in Africa | :20:27. | :20:30. | |
The way this is done is to elicit from the household a listing | :20:31. | :20:38. | |
of everything they've consumed in the last week, | :20:39. | :20:40. | |
So literally everything they've consumed. | :20:41. | :20:47. | |
Every single thing and the value of that item and then you add up | :20:48. | :20:51. | |
all these items for every single person in the household. | :20:52. | :20:53. | |
This can take hours and hours just for one single household. | :20:54. | :20:56. | |
Then you have to do this for thousands of households to get | :20:57. | :21:00. | |
It's painstaking work but Burke has teamed with computer science | :21:01. | :21:06. | |
Using machine learning to predict poverty data | :21:07. | :21:12. | |
But to find out whether the people living in those areas are rich | :21:13. | :21:18. | |
or poor, the researchers used a process called transferred | :21:19. | :21:20. | |
learning and this image of the Earth at night. | :21:21. | :21:26. | |
The parts of the world that are lit up are typically the wealthier parts | :21:27. | :21:30. | |
So basically we use the lower resolution night-time images to help | :21:31. | :21:34. | |
us figure out what in the really high resolution daytime images | :21:35. | :21:37. | |
we should should be using and then we use that to predict poverty | :21:38. | :21:40. | |
Between 300 and 400,000 images were used to train the algorithm. | :21:41. | :21:52. | |
The algorithm will figure out what's important, | :21:53. | :21:53. | |
So some of the things it finds are things that | :21:54. | :21:58. | |
you or I would recognise, things like roads, | :21:59. | :22:00. | |
Based on those features, the algorithm can predict | :22:01. | :22:03. | |
Things like refrigerators, cars, the sum of all those assets. | :22:04. | :22:10. | |
It can also be used to predict incomes. | :22:11. | :22:13. | |
These poverty maps show the team's findings. | :22:14. | :22:15. | |
In areas marked red, people spend as little | :22:16. | :22:17. | |
In green regions like Uganda's capital, Kampala, they spent | :22:18. | :22:21. | |
We are providing a very cheap and scalable alternatives | :22:22. | :22:30. | |
to traditional means of data collection. | :22:31. | :22:31. | |
Traditionally, you have to send people out into the field | :22:32. | :22:34. | |
with clipboards, the surveys aren't always accurate, | :22:35. | :22:36. | |
Like for example lots of governments where they are underperforming, | :22:37. | :22:40. | |
All we need to make our predictions are satellite images. | :22:41. | :22:45. | |
But can you draw conclusions about the economic well-being | :22:46. | :22:48. | |
of communities in Africa when you're thousands of miles away, | :22:49. | :22:50. | |
sitting at a laptop in an office at Stamford? | :22:51. | :22:53. | |
We actually have really good survey information in a few locations. | :22:54. | :22:59. | |
We can use the satellite imagery to make a prediction about poverty | :23:00. | :23:02. | |
and then we can compare that to what the survey says was actually | :23:03. | :23:05. | |
So we used a couple of the really good surveys we had to validate | :23:06. | :23:11. | |
To be a truly useful tool though, the algorithm needs an upgrade. | :23:12. | :23:20. | |
We would also like to use historical imagery so maybe we can figure out | :23:21. | :23:24. | |
how poverty dynamics work overtime and even give us the chance | :23:25. | :23:27. | |
of predicting what's going to happen in the future. | :23:28. | :23:31. | |
If you can pinpoint poverty on a map, aid could be distributed | :23:32. | :23:35. | |
more evenly, policies could be more effective. | :23:36. | :23:37. | |
A picture may be worth a thousand words but combining that picture | :23:38. | :23:45. | |
with artificial intelligence could make a world of difference. | :23:46. | :23:52. | |
That was Sumi and that's it for this week. | :23:53. | :23:54. | |
You can follow us on Twitter @BBCClick for backstage fun | :23:55. | :23:57. | |
and photos and extra technology news throughout the week. | :23:58. | :24:00. | |
Thanks for watching and we'll see you soon. | :24:01. | :24:31. | |
Well, it's still very mild and murky out there. | :24:32. | :24:34. |