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on five people on Thursday night in which acid was sprayed | :00:00. | :00:00. | |
on their faces in order to steal their motorbikes. | :00:00. | :00:00. | |
Now on BBC News, it's time for Click. | :00:00. | :00:07. | |
The cyborgs are coming, the eyeborgs are watching, | :00:08. | :00:15. | |
the bar staff are serving, and Lara photographs a banana! | :00:16. | :00:47. | |
This is Adam Jensen, star of the video game | :00:48. | :00:49. | |
Set in 2027, the poor chap has to undergo extensive | :00:50. | :01:01. | |
cybernetic modifications after being severely injured. | :01:02. | :01:02. | |
Well, just ten years before those events might occur, | :01:03. | :01:05. | |
that plot line doesn't seem that far off. | :01:06. | :01:20. | |
For years now people have been body hacking, | :01:21. | :01:22. | |
giving themselves extra abilities and, as our understanding | :01:23. | :01:24. | |
of robotics has advanced, so has our creativity. | :01:25. | :01:26. | |
Meet Rob Spence, like the cyborg in the video game, | :01:27. | :01:29. | |
It doesn't have Terminator vision like this, yet, | :01:30. | :01:36. | |
Inside a prosthetic eye, which is an odd shape, | :01:37. | :01:40. | |
they're not a sphere, a prosthetic eye, they're actually | :01:41. | :01:42. | |
Inside that is a battery, a video camera and a video | :01:43. | :01:48. | |
transmitter all attached to a circuit board so they can | :01:49. | :01:51. | |
The camera is turned on and off with a magnet. | :01:52. | :02:09. | |
It doesn't look at all comfortable, is it in anyway comfortable? | :02:10. | :02:13. | |
The first consideration that looks the most uncomfortable, | :02:14. | :02:20. | |
it looks like a 90s iMac, you can see all the goods inside. | :02:21. | :02:23. | |
Like the battery and the wires, but that's covered by smooth | :02:24. | :02:26. | |
I don't have open wires and batteries, you know. | :02:27. | :02:31. | |
That kind of made my stomach drop a little bit when I saw that. | :02:32. | :02:43. | |
Rob damaged his eye when he was nine and in 2009 began exploring | :02:44. | :02:46. | |
As a film-maker himself, he was fascinated with the idea | :02:47. | :02:50. | |
It's like an absurd toy for a one-eyed film-maker. | :02:51. | :03:06. | |
I used to watch the Bionic Man when I was a kid, The $6 Million Man. | :03:07. | :03:11. | |
I had the action figure, you looked through the back of his head, | :03:12. | :03:14. | |
I was looking at my Nokia flip phone at the time I was like - | :03:15. | :03:19. | |
That's in fact who I called, I called Nokia. | :03:20. | :03:23. | |
They said - well, we'll call the camera module people in China. | :03:24. | :03:26. | |
It's very small, it's very challenging. | :03:27. | :03:30. | |
It does visual dropouts, which is the visual language | :03:31. | :03:34. | |
of all video from the future, including Princess Leia | :03:35. | :03:36. | |
Since the initial prototype, Rob and his engineers have gone | :03:37. | :03:45. | |
He now has one eye that glows red when it films and another camera eye | :03:46. | :03:50. | |
I get calls from and emails from mom's whose kid has just lost | :03:51. | :03:55. | |
an eye because it's some sort of fun thing to show a kid this maniac | :03:56. | :03:59. | |
running around on videos and glowing red eye cameras and stuff. | :04:00. | :04:03. | |
They're now looking working on ways to transfer the technology to other | :04:04. | :04:08. | |
We're doing 3D scans of those now and then that creates a space that | :04:09. | :04:12. | |
you can take into software to map on the technology that we're | :04:13. | :04:16. | |
Some people golf, I like to make fake eye cameras and, you know, | :04:17. | :04:20. | |
Right, that's the eye upgraded - now let's do the rest of the body. | :04:21. | :04:45. | |
MIT's media lab is home to some of the most innovative tech | :04:46. | :04:50. | |
research in the world, but there's one room here I find | :04:51. | :04:53. | |
The mission of this lab is probably one of the most | :04:54. | :04:59. | |
important goals of our time, they're trying to essentially | :05:00. | :05:01. | |
They want to make it so that if you lose a limb, | :05:02. | :05:05. | |
it won't have any impact on your quality of life and they're | :05:06. | :05:09. | |
So we work on everything from creating new motors and designs | :05:10. | :05:25. | |
for ankles and knees and artificial joints, all the way to marrying | :05:26. | :05:28. | |
these biomechatronic devices with the human body through novel | :05:29. | :05:31. | |
Evidence of this work can be seen with people like Ryan Cannon, | :05:32. | :05:35. | |
complications after a broken leg left him needing an amputation. | :05:36. | :05:38. | |
What's special about his new robotic leg is that it's doing something | :05:39. | :05:41. | |
the human body can do instinctively, but it's extremely | :05:42. | :05:44. | |
The motor is able to work in such a way that simulates a real | :05:45. | :05:57. | |
It uses on board sensors to interfere whether the leg is, | :05:58. | :06:00. | |
for example, in the air or on the ground and perform actions | :06:01. | :06:04. | |
that to the person feel much more like real walking | :06:05. | :06:06. | |
than they would get from a passive prothesis. | :06:07. | :06:08. | |
For amputees like Ryan such innovations are life-changing. | :06:09. | :06:14. | |
I can move in a more rhythmic, symmetrical way and being able | :06:15. | :06:18. | |
to move in that manner allows me to walk at a faster pace | :06:19. | :06:21. | |
for a longer distance and to do more activities during the day. | :06:22. | :06:25. | |
This is not relying just on straight physics and mechanical design, | :06:26. | :06:28. | |
Not all of the research here is about solving disability, | :06:29. | :06:41. | |
this exoskeleton project is about augmenting humans. | :06:42. | :06:43. | |
It allows the body to use much less energy when running or walking. | :06:44. | :06:46. | |
It improves your ability to walk by 25%. | :06:47. | :06:49. | |
So what that means is, if you were to walk 100 miles, | :06:50. | :06:52. | |
it would only feel to you that you walked 75. | :06:53. | :06:54. | |
We're able to do that today, right and those are devices that | :06:55. | :06:58. | |
I would expect to see rolling out commercially in the | :06:59. | :07:00. | |
We're already beginning to see this kind of technology deployed | :07:01. | :07:05. | |
US retail chain, Lowes, is experimenting with kitting | :07:06. | :07:08. | |
out its staff with exoskeletons, designed in Virginia, | :07:09. | :07:11. | |
which could give their employees more stamina at work. | :07:12. | :07:13. | |
With this in mind, the lab at MIT is now looking | :07:14. | :07:16. | |
to the next huge question - how close are we to the point | :07:17. | :07:20. | |
where people might actually want these kind of prosthetics instead | :07:21. | :07:23. | |
I definitely think that we are entering an age in which the line | :07:24. | :07:27. | |
between biological systems and synthetic systems | :07:28. | :07:29. | |
But what might be some of the drawbacks of having these | :07:30. | :07:53. | |
As there's widespread uptake, that they might only be available | :07:54. | :07:57. | |
to people who have the financial ability to pay for them. | :07:58. | :08:08. | |
Welcome to the week in Tech. This week saw some interesting activity | :08:09. | :08:24. | |
on Facebook, and saw Wiz Khalifa take over as the most watched | :08:25. | :08:28. | |
YouTube video. It has been viewed a staggering 2.9 million times. Elon | :08:29. | :08:37. | |
Musk launched a new vehicle, it is supposed to be more affordable than | :08:38. | :08:41. | |
the previous efforts, which cost $200,000. Faraday future have | :08:42. | :08:53. | |
scrapped plans to build a plant in Nevada, which leaves questions about | :08:54. | :08:59. | |
their new vehicle. And this is not a digital version of the Ministry of | :09:00. | :09:03. | |
silly walking, but this is artificial intelligence attempting | :09:04. | :09:07. | |
to learn how to walk. So far, research is being led in virtual | :09:08. | :09:14. | |
environments, but it could help robots learn how to navigate | :09:15. | :09:18. | |
unfamiliar spaces. And, finally, a former scientist built a super | :09:19. | :09:28. | |
Psycho which can fire a jet of water at over 200 mph. At least you'll see | :09:29. | :09:31. | |
it coming -- super-soaker. It says - fashion, style, | :09:32. | :09:34. | |
outfit, that's you. Sometimes it's not that easy to put | :09:35. | :09:48. | |
into words what you want to search for online, and that's why companies | :09:49. | :09:53. | |
are working on ways of us being able to take a simple picture and then | :09:54. | :09:56. | |
search using that image. Pinterest is a place | :09:57. | :10:02. | |
all about images and ideas, they've had a form of visual search | :10:03. | :10:05. | |
for a couple of years now, allowing you to focus | :10:06. | :10:08. | |
in on a particular object Through a combination of image | :10:09. | :10:11. | |
recognition and the data points attached to that image, | :10:12. | :10:14. | |
including the hundreds of thousands of boards it may | :10:15. | :10:16. | |
have been pinned to, This January, they upped their game, | :10:17. | :10:19. | |
though, launching Pinterest Lens, a way of being able to search | :10:20. | :10:36. | |
through a photo with no other data And from that search term, | :10:37. | :10:40. | |
it aims to come up with similar There we go, we've got a picture | :10:41. | :10:44. | |
and something is emerging. Right, those are definitely | :10:45. | :10:48. | |
shoes, but they don't Black shoe with blue | :10:49. | :10:51. | |
laces, some men's shoes. So there are two parts | :10:52. | :10:54. | |
to visual search. The first is computer vision, | :10:55. | :10:58. | |
which is a way of translating the information coming | :10:59. | :11:01. | |
in through the camera into words. The second is the data | :11:02. | :11:03. | |
set and the data set So with Pinterest Lens, | :11:04. | :11:06. | |
when you point your camera at something in the real world, | :11:07. | :11:12. | |
the computer inside the phone translates that image into text | :11:13. | :11:15. | |
and then it takes the text and it takes the image and runs a search | :11:16. | :11:19. | |
against 100 billion pins on Pinterest to find the ones that | :11:20. | :11:21. | |
seem the most relevant. OK, it knows it's a lemon, | :11:22. | :11:29. | |
that's definitely a good start. OK, I think you scroll through it | :11:30. | :11:32. | |
and some of the results make sense, which is sort | :11:33. | :11:36. | |
of like when you search with words because often you search then | :11:37. | :11:40. | |
and a lot of the things don't make And having come up with those words, | :11:41. | :11:43. | |
I've got a series of recipes So we've got a lemon drizzle cake, | :11:44. | :11:52. | |
a lemon polenta cake. We've now got some | :11:53. | :11:56. | |
artwork of lemons. It's a lot better than it did | :11:57. | :11:57. | |
on my boots and this's probably because this is a very simple image | :11:58. | :12:01. | |
to recognise and understand. Pinterest Lens is also powering | :12:02. | :12:04. | |
Vision, the image search function in Samsung's Bixby | :12:05. | :12:06. | |
which is currently only available And so today we're announcing | :12:07. | :12:09. | |
a new initiative called Google Lens. Google Lens is also | :12:10. | :12:14. | |
due for release soon. The company says it'll be a new way | :12:15. | :12:16. | |
of the computer being able to see and even act on its surroundings | :12:17. | :12:20. | |
whilst you're talking Also working in this space | :12:21. | :12:30. | |
is a chat bot called Glamix, which is a way of photographing any | :12:31. | :12:34. | |
item that you like, sending it to them via Facebook messenger | :12:35. | :12:38. | |
and receiving a response that should tell you where you can | :12:39. | :12:41. | |
buy a similar item. So let's give this a go | :12:42. | :12:43. | |
on my boots to start with. It works with pictures found | :12:44. | :12:47. | |
on Instagram or your phone, eventually allowing you to narrow | :12:48. | :12:54. | |
down results based on price The bot uses artificial | :12:55. | :12:57. | |
intelligence, machine learning and what it calls 'content based | :12:58. | :13:01. | |
image recognition' to search As well as shopping for individual | :13:02. | :13:04. | |
items, it aims to be able to help Making clicking through to items | :13:05. | :13:13. | |
so easy is of course amazing for retailers, | :13:14. | :13:17. | |
but also if you're So if someone passes | :13:18. | :13:19. | |
you by and they're wearing something you really like, | :13:20. | :13:23. | |
you need to be quick. Having spent a while testing both, | :13:24. | :13:26. | |
the results were sometimes surprisingly accurate, and other | :13:27. | :13:34. | |
times, kind of questionable. But it is early days and the more | :13:35. | :13:36. | |
this sort of technology is used, the more data it collects | :13:37. | :13:40. | |
and the more reliable Well, that explains | :13:41. | :13:42. | |
the weird birthday present Now, earlier we looked | :13:43. | :13:46. | |
at human beings attempts to become more robotic, | :13:47. | :13:56. | |
but there's a whole lot of research that's attempting | :13:57. | :13:59. | |
to make robots more human. It's not actually taking place | :14:00. | :14:01. | |
at a robot art school like this, but it's nice to think it might | :14:02. | :14:04. | |
be, isn't it? There is a long way to go | :14:05. | :14:07. | |
in robotics, just picking up all those weirdly shaped everyday | :14:08. | :14:10. | |
objects is still an enormous challenge, requiring a robot | :14:11. | :14:13. | |
to recognise a given object and to decide how | :14:14. | :14:16. | |
exactly to pick it up. But a team at Berkeley says that | :14:17. | :14:20. | |
Dex-Net here is the most effective When not playing with Lego, | :14:21. | :14:23. | |
it's being taught and building up a huge database of 3D objects | :14:24. | :14:27. | |
by its masters. When something new comes along, | :14:28. | :14:30. | |
it uses its 3D sensor to compare it to this list, | :14:31. | :14:33. | |
it then uses its neural network to figure out the best way to grasp | :14:34. | :14:36. | |
it and it is said to get it right The springy legs of this creature | :14:37. | :14:41. | |
were 3D painted at UC San Diego, they're designed to be able to more | :14:42. | :14:49. | |
easily traverse difficult environments, such | :14:50. | :14:52. | |
as disaster areas. As we know, even walking | :14:53. | :14:56. | |
on flat surfaces is still Well, I say ouch, but of course | :14:57. | :14:59. | |
these things don't feel pain. That said, there are those of us | :15:00. | :15:09. | |
who are asking whether even feelings might one day be part | :15:10. | :15:13. | |
of a robot's mind. At the simplest level it makes | :15:14. | :15:18. | |
sense, robots are pretty expensive, you don't want them to run | :15:19. | :15:23. | |
willy-nilly into fire and acid But at a more complex level, | :15:24. | :15:26. | |
we're looking ahead to a time when robots might interact with us | :15:27. | :15:33. | |
on a more personal level as companion robots for the elderly, | :15:34. | :15:36. | |
for those who are sick or are in pain and perhaps maybe | :15:37. | :15:39. | |
they need to understand the similar sort of experience and perhaps | :15:40. | :15:42. | |
develop something like Pain is not just about us | :15:43. | :15:44. | |
saying ouch, there's an emotional element to this | :15:45. | :15:48. | |
as well, isn't there? So are we actually talking about | :15:49. | :15:50. | |
programming some kind of emotions We don't really understand | :15:51. | :15:56. | |
what emotions are in human beings. Like you say, you might assume | :15:57. | :16:05. | |
there's some sort of phenomenon that So hypothetically if we developed | :16:06. | :16:08. | |
systems that worked like pain, might emotion develop off the back | :16:09. | :16:13. | |
of that as well? There are those robots that | :16:14. | :16:16. | |
do look so life-like, the Boston Dynamics' Big Dog | :16:17. | :16:18. | |
and the walking robots, we actually feel quite sorry | :16:19. | :16:21. | |
for them when they fall over or even When those videos were released | :16:22. | :16:24. | |
online the reaction was like - oh no, you're bullying them, | :16:25. | :16:29. | |
don't hurt them. They don't at this stage have | :16:30. | :16:31. | |
that technology at all. There's no suggestion they do feel | :16:32. | :16:34. | |
pain, but the human reaction So is that going to inform | :16:35. | :16:37. | |
how we behave towards Is that where you're looking | :16:38. | :16:41. | |
at applying our sympathies? I mean, I think science fiction | :16:42. | :16:45. | |
model of a human-like entity There may be more kind | :16:46. | :16:48. | |
of cute models we've seen already of robots that, | :16:49. | :16:53. | |
sort of, pull on our heart strings in a more child-like way and there's | :16:54. | :16:56. | |
those that suggest that we shouldn't have anything that looks human-like | :16:57. | :16:59. | |
at all because it's disingenuous, it's cheating and it's tricking us | :17:00. | :17:02. | |
into treating them like they're The doctor thinks that appearing | :17:03. | :17:05. | |
to feel pain may make us treat Of course what many people | :17:06. | :17:11. | |
are worried about is how much respect the robots will have for us | :17:12. | :17:18. | |
and, most of all, our jobs. Last week Caterpillar invested | :17:19. | :17:22. | |
seven million in this Australian Now robots can build houses | :17:23. | :17:26. | |
at the rate of 1,000 bricks an hour. Ambition in the area | :17:27. | :17:34. | |
is huge and for the first time out of the lab, | :17:35. | :17:36. | |
ETH in Switzerland is working on much more ambitious structures | :17:37. | :17:39. | |
like this undulating wall which has Now an increasingly robotic | :17:40. | :17:42. | |
workforce raises a number of issues and along with the worry | :17:43. | :17:52. | |
of what jobs will actually be left to us in the future, | :17:53. | :17:55. | |
there is another one. Fewer workers earning a wage, | :17:56. | :17:58. | |
means fewer workers paying income tax on their earnings and that means | :17:59. | :18:02. | |
less money going into the economy. Now some tech brains, | :18:03. | :18:09. | |
including that of Bill Gates, are calling for a robot tax | :18:10. | :18:12. | |
to counter that and Cat Hawkins went Almost everyone in the world | :18:13. | :18:15. | |
who works pays tax on the money they earn, but at this restaurant | :18:16. | :18:24. | |
in San Francisco there are no waiting staff | :18:25. | :18:27. | |
and robots plate the food. That work is currently not taxable | :18:28. | :18:32. | |
and politician Jane Kim is now looking into how this is changing | :18:33. | :18:35. | |
the city's economy. So what we're seeing | :18:36. | :18:40. | |
is after automation that you can hire less people in order to deliver | :18:41. | :18:43. | |
products maybe quicker But it's one of the questions | :18:44. | :18:46. | |
that we have, it's true this is really convenient, | :18:47. | :18:52. | |
but at what cost? It's not just restaurants, this | :18:53. | :18:54. | |
picture is now seen across the city, from hotels and hospitals | :18:55. | :18:59. | |
to the latest addition to the autonomous family, | :19:00. | :19:01. | |
self-driving cars. Policy makers have noticed, every | :19:02. | :19:05. | |
time a robot take as human job, The research is showing us that jobs | :19:06. | :19:08. | |
are going to get lost over the next ten years and if before | :19:09. | :19:16. | |
the Great Depression we could have predicted | :19:17. | :19:18. | |
what would come afterwards, if government could have prepared | :19:19. | :19:21. | |
for the job loss that occurred, That is the level at which we are | :19:22. | :19:23. | |
looking at potentially over the next ten years, | :19:24. | :19:28. | |
in terms of job loss Estimations of how many jobs will be | :19:29. | :19:31. | |
wiped out vary widely from study to study, | :19:32. | :19:37. | |
but a recent report especially has It's estimated that robots | :19:38. | :19:42. | |
will replace 37% jobs in the United States | :19:43. | :19:46. | |
by the early 2030s. So the biggest concern | :19:47. | :19:49. | |
is mass job displacement, lack of true, meaningful, | :19:50. | :19:52. | |
high wage work. We are already seeing a decrease | :19:53. | :19:55. | |
of that in San Francisco where we have the fastest growing | :19:56. | :19:59. | |
income gap in the country and a wealth gap that is akin | :20:00. | :20:01. | |
to the country of Rwanda, and so we have a shrinking | :20:02. | :20:08. | |
middle-class and we have this growing imminent threat that | :20:09. | :20:12. | |
many of our meaningful, working-class and even | :20:13. | :20:16. | |
middle-class jobs may go away At Cafe X, again a human worker has | :20:17. | :20:19. | |
been replaced by a robot. An Americano with milk, | :20:20. | :20:25. | |
served by a robot. Now, the human has a different role, | :20:26. | :20:29. | |
advising on coffee beans and showing customers how to use the tablet | :20:30. | :20:33. | |
to operate the robot. The owner is not sure about the idea | :20:34. | :20:37. | |
of a tax on the replacement. I guess I find it a little odd | :20:38. | :20:41. | |
because what robots are supposed That means it allows a shift | :20:42. | :20:44. | |
in labour from doing highly repetitive, low productivity tasks | :20:45. | :20:51. | |
to more useful things. So in order to have this machine | :20:52. | :20:57. | |
operate, there has to be a lot of engineers on software, | :20:58. | :21:06. | |
hardware and manufacturing to build Jobs like this require training | :21:07. | :21:09. | |
and that's what Supervisor Kim wants If you're a childcare worker | :21:10. | :21:12. | |
or you're an in home support services worker, | :21:13. | :21:17. | |
working with a senior or individual with disability, | :21:18. | :21:20. | |
you often work three or four hours So one of the ideas was, | :21:21. | :21:23. | |
why not tax robots and invest in these poverty jobs and make them | :21:24. | :21:30. | |
truly living wage This would mean a robot tax | :21:31. | :21:33. | |
potentially subsidising low paying, but essential jobs, | :21:34. | :21:38. | |
so that the human employees Currently, many people are working | :21:39. | :21:41. | |
but not earning enough to live, leading several politicians around | :21:42. | :21:46. | |
the world to float the idea This would be expensive | :21:47. | :21:49. | |
for governments and Supervisor Kim is suggesting an automation tax | :21:50. | :21:55. | |
could be a solution. If there's one thing that | :21:56. | :21:59. | |
San Francisco is known for, it's leading the conversation | :22:00. | :22:01. | |
on technology and innovation, but as harder and harder questions | :22:02. | :22:06. | |
are asked about automation and what this really means | :22:07. | :22:09. | |
for people's jobs it seems appropriate that this city, | :22:10. | :22:12. | |
which has added so much to the problem, is also grappling | :22:13. | :22:14. | |
with what could be the solution. But the rise of robotic workers | :22:15. | :22:21. | |
is playing out on a global scale and San Francisco is not the only | :22:22. | :22:25. | |
place trying to lead In the EU, a proposal to tax robot | :22:26. | :22:28. | |
was voted down earlier in the year and one of the Commissioners who did | :22:29. | :22:36. | |
so says robots will create more They are worried because they say | :22:37. | :22:40. | |
robots they will take their jobs, Progress always created more jobs | :22:41. | :22:44. | |
than progress used to destroy. The train is moving and speed | :22:45. | :22:50. | |
is high and now it's up to us to be on that train or to stay and to wave | :22:51. | :22:55. | |
to the leaving train. Concerns about automation replacing | :22:56. | :23:05. | |
human jobs has been felt sense the Industrial Revolution and more | :23:06. | :23:08. | |
recently workers in the manufacturing industry | :23:09. | :23:11. | |
have seen jobs disappear As the issue of a robot tax | :23:12. | :23:13. | |
begins to spread further, a fundamental question still needs | :23:14. | :23:20. | |
to be answered - In the context of robots of course | :23:21. | :23:22. | |
automation is much broader They gave this definition | :23:23. | :23:29. | |
more than 100 years ago. Politicians can no longer | :23:30. | :23:36. | |
ignore the robots creeping into the workplace and while many | :23:37. | :23:38. | |
of the big questions are still being thrashed out, | :23:39. | :23:41. | |
it's clear that the issue of robot workers is becoming more | :23:42. | :23:44. | |
and more of a political one. Yeah, really interesting | :23:45. | :23:53. | |
issues, aren't they? That was Cat Hawkins | :23:54. | :23:57. | |
and this's it for this week. You can follow us on Twitter | :23:58. | :24:00. | |
@BBC Click throughout the week and like us | :24:01. | :24:02. | |
on Facebook, too. Thanks for watching | :24:03. | :24:05. | |
and we will see you soon. Some decent, dry, and also for some | :24:06. | :24:33. | |
sunny weather around this weekend. But there will be a lot | :24:34. | :24:37. | |
of cloud around at times, threatening some rain, | :24:38. | :24:40. | |
particularly on Saturday. And throughout Saturday, | :24:41. | :24:42. | |
the air gets warmer and more muggy. | :24:43. | :24:45. |