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This week, I robot. Robo chef. And some loud, noisy animals meet the | :00:00. | :00:16. | |
locals. The design Museum in London has | :00:17. | :00:48. | |
moved into a new home, and it is suitably stunning. I have come to | :00:49. | :01:00. | |
see Fear And Love, an exhibition of 11 designers reactions to our | :01:01. | :01:05. | |
increase in the context. The most animated star Joe has to be any | :01:06. | :01:10. | |
industrial robot arm it is will present a more friendly face to | :01:11. | :01:16. | |
robotics and maybe help us empathise with the economics of the future. It | :01:17. | :01:20. | |
senses where you are and comes bounding over to see you, but if it | :01:21. | :01:23. | |
gets bored it will turn its attention to someone else. It is | :01:24. | :01:29. | |
like an excitable puppy, actually. Who knows, installations like this | :01:30. | :01:33. | |
may help to allay our fears of being around giant machines like this. I | :01:34. | :01:38. | |
have to say, it will still be a while before I trust this thing with | :01:39. | :01:43. | |
a scalpel, for example. That said, computers are increasingly being | :01:44. | :01:46. | |
used in healthcare around the world. There is plenty of research into how | :01:47. | :01:52. | |
artificial intelligence can help doctors better look after patients. | :01:53. | :01:55. | |
We have been taking a look at some of the latest developments. | :01:56. | :02:00. | |
Around the world, hospitals are facing a backlog of patients, ageing | :02:01. | :02:06. | |
populations and a shortage of specialist staff. Some hospitals are | :02:07. | :02:10. | |
teaming up with artificial intelligence research teams to see | :02:11. | :02:14. | |
if there are ways high-tech solutions can supplement or even | :02:15. | :02:17. | |
enhance healthcare in the face of these challenges. Singapore has a | :02:18. | :02:22. | |
nursing crisis. Its health minister says they will need more than 30,000 | :02:23. | :02:27. | |
new nurses before 2020, and completely rethink the way it cares | :02:28. | :02:32. | |
for its ageing population. So when the CEO of one of its largest | :02:33. | :02:36. | |
private hospital networks approached IBM's Watson team, they come up with | :02:37. | :02:40. | |
a pilot project to try to help nurses working with the most | :02:41. | :02:44. | |
critically ill patients. This is the intensive care unit at Mount | :02:45. | :02:50. | |
Elizabeth Hospital. It is where four beds are conducted to artificial | :02:51. | :02:53. | |
intelligence nursing systems, collecting vital signs from the | :02:54. | :02:56. | |
patients in digging nurses a more complete picture of who needs the | :02:57. | :03:01. | |
most care. -- and giving. In one of the first trials of its kind in the | :03:02. | :03:04. | |
world, the AI is constantly monitoring output and making | :03:05. | :03:08. | |
connections on a vast range of data, including a commonly used scale. The | :03:09. | :03:14. | |
scores correspond to a higher incidence of death, and it is | :03:15. | :03:18. | |
particularly important in the first 24 hours after admission. This | :03:19. | :03:24. | |
patient has four limes, so if you don't see anything flashing, it | :03:25. | :03:31. | |
means it needs monitoring. One of the patient is at the high end of | :03:32. | :03:35. | |
the alert and nurses can quickly access the information in real-time | :03:36. | :03:38. | |
and look at patterns in their vital signs to see if they are at greater | :03:39. | :03:42. | |
risk of infections like sepsis. In the AI could help photo imaging | :03:43. | :03:48. | |
which is the focus of research between Google and the NHS. The | :03:49. | :03:53. | |
Royal College of radiologists says 99% of hospitals are struggling to | :03:54. | :03:57. | |
keep up with demand, and the UK has the third lowest numbers of | :03:58. | :04:00. | |
specialists who can interpret 's gains in Europe. Seven per 100,000 | :04:01. | :04:06. | |
people. The large amount of data is overwhelming a health service | :04:07. | :04:09. | |
stretched to the limit -- scans. If you can use algorithms or machine | :04:10. | :04:15. | |
learning or artificial intelligence to set an alert for you to trigger | :04:16. | :04:19. | |
to say something has happened, you need to go and see this, this is | :04:20. | :04:23. | |
urgent and you need to deal with that, in the next hour or so when | :04:24. | :04:28. | |
you may have not known about that. I think it will improve quality of | :04:29. | :04:32. | |
care and actually improve equity across the system. One of the first | :04:33. | :04:38. | |
areas where the NHS is testing artificial intelligence is at | :04:39. | :04:41. | |
Moorfields, one of the busiest I hospitals in the world. Google is | :04:42. | :04:47. | |
applying the same machinery technology behind its winning Alpha | :04:48. | :04:50. | |
go computer programme. It beat the world's best human player by | :04:51. | :04:55. | |
completing tens of thousands of positions per second. We started it | :04:56. | :05:00. | |
to develop general-purpose burning and use those systems and learning | :05:01. | :05:04. | |
to make the world a better place. It was obvious to us a few years ago | :05:05. | :05:07. | |
that there is a massive opportunity to deliver the lead meaningful and | :05:08. | :05:11. | |
improved benefits to many patients and people across the world using | :05:12. | :05:16. | |
our techniques to try to improve the way we diagnose and treat patients | :05:17. | :05:20. | |
at risk of all sorts of diseases. The Moorfields Hospital research is | :05:21. | :05:26. | |
using scans from this OCT, or optical coherence stenography | :05:27. | :05:30. | |
machine, which gets a 3-dimensional image. It is used to diagnose | :05:31. | :05:34. | |
diseases like macular degeneration and diabetic bred apathy, two | :05:35. | :05:39. | |
leading causes of sight loss. ! Our loss. DeepMind is trying to develop | :05:40. | :05:48. | |
a algorithm to show scans of consent. They were chosen because of | :05:49. | :05:51. | |
the high rate of information on the way they can be broken down into | :05:52. | :05:54. | |
pixels showing areas where damage has occurred. I was especially | :05:55. | :05:59. | |
attracted to speaking to DeepMind because I thought their algorithms | :06:00. | :06:03. | |
would have the best ability to deal with 3-D imaging of an extremely | :06:04. | :06:08. | |
high resolution form such as the city. This is such a delicate area | :06:09. | :06:13. | |
of the eye that any sort of disruption of the normal | :06:14. | :06:18. | |
architecture has really amazingly severe consequences -- OCT. I | :06:19. | :06:23. | |
believe health career could be at a pivotal moment in history by these | :06:24. | :06:28. | |
advances in technology such as artificial intelligence will | :06:29. | :06:30. | |
fundamentally change the way medicine is practised, and have huge | :06:31. | :06:35. | |
benefits for patients. If you think about it, the best humans in the | :06:36. | :06:39. | |
world will have seen only a fraction of the number of cases that we can | :06:40. | :06:43. | |
show to an algorithm. Imagine we took all of the cases that many of | :06:44. | :06:47. | |
the top ophthalmologists in the world have seen themselves, and | :06:48. | :06:51. | |
aggregate them all in one place. Now the algorithm can sample from all of | :06:52. | :06:57. | |
the case studies that are seen by various humans and deliver a much | :06:58. | :07:01. | |
higher standard, more consistently, when making a diagnosis. Are these | :07:02. | :07:08. | |
projects still in the research or project stage, but is fascinating to | :07:09. | :07:11. | |
see how artificial intelligence could transform healthcare and the | :07:12. | :07:14. | |
two better and faster treatment in the future -- all of these projects. | :07:15. | :07:18. | |
Hello, and welcome to the Week In tech. | :07:19. | :07:20. | |
It was the week that Amazon completed its first drone | :07:21. | :07:23. | |
Taking 30 minutes from order to delivery, plus three years | :07:24. | :07:27. | |
if you factor in research and development, the elaborately | :07:28. | :07:29. | |
orchestrated trial involved an Amazon product and a bag | :07:30. | :07:31. | |
It was also the week that Super Mario came to the iPhone, | :07:32. | :07:39. | |
Pokemon Go got an upgrade, and a UK surgeon filmed an operation | :07:40. | :07:43. | |
And mere hours after hitting the road in San Francisco, | :07:44. | :07:53. | |
Uber has been ordered to stop offering passengers | :07:54. | :07:55. | |
Regulators have warned the company required a state of permanent | :07:56. | :08:02. | |
The order comes after footage emerged of a self-driving car | :08:03. | :08:07. | |
And finally, Stanford students put teeny goggles on tiny parrots. | :08:08. | :08:16. | |
But this was to protect the birds' eyes as they were trained to fly | :08:17. | :08:23. | |
The new technique has allowed scientists to gain a greater | :08:24. | :08:27. | |
understanding of how birds fly by analysing the movement | :08:28. | :08:30. | |
of particles around their flight paths. | :08:31. | :08:34. | |
It is hoped the work will improve flying robots of the future. | :08:35. | :08:47. | |
MUSIC PLAYS Where did you love or loathe | :08:48. | :08:55. | |
cooking, sometimes it would be nice to just make it a little bit quicker | :08:56. | :08:59. | |
and easier. So I have been testing some of the latest gadgets that aim | :09:00. | :09:05. | |
to come to the rescue. I have called it a bit of help from a friend. This | :09:06. | :09:15. | |
prototype robotic kitchen is making crab bisque today. It meant the | :09:16. | :09:18. | |
slick moves from a professional chef, whose motions were tracked in | :09:19. | :09:23. | |
the same space, making the same dish, using sensors and cameras. | :09:24. | :09:27. | |
This is actually quite extraordinary to watch, and that is the first drop | :09:28. | :09:32. | |
of mess that I have seen. It seems to be pretty clean and tidy. The | :09:33. | :09:36. | |
only issue is it doesn't do the washing up. That's right, I am not | :09:37. | :09:42. | |
doing it! And no drinking that. Everything needs to be precisely | :09:43. | :09:46. | |
prepared before, although some form of ingredient recognition is claimed | :09:47. | :09:50. | |
to be within its abilities before it goes on sale, which as you might | :09:51. | :09:55. | |
imagine, will be at quite a cost. A figure of around ?100,000 is being | :09:56. | :10:01. | |
thrown around. While Moley gets on with things, I will use my devices | :10:02. | :10:06. | |
to make all of this, and there is nobody to do the troubling to me. I | :10:07. | :10:12. | |
had better get on. First up, the decision could go to make some miso | :10:13. | :10:17. | |
salmon. For anyone who doesn't know what this method is, like me a few | :10:18. | :10:22. | |
weeks ago, it involves serving food in a bad and cooking it in water at | :10:23. | :10:26. | |
a precise temperature for a specific amount of time, so it should end up | :10:27. | :10:31. | |
perfectly and evenly cooked all the way through. This device can connect | :10:32. | :10:36. | |
to a smartphone app where you will find recipes and instructions you | :10:37. | :10:40. | |
need. Once you have the baby food, and that is the salmon in the back, | :10:41. | :10:45. | |
quite literally. -- prepared the food. You pop it in any suitably | :10:46. | :10:50. | |
sized pot with the Anova attached and confirm you are ready to go. | :10:51. | :10:54. | |
Although this model, which is Wi-Fi enabled, you can set it remotely, | :10:55. | :10:58. | |
although you would need to have everything prepared, of course. So | :10:59. | :11:02. | |
that is the main bit of the cooking done. But it does still need ceiling | :11:03. | :11:06. | |
for one minute in a frying pan. This needs to cook for just one minute on | :11:07. | :11:11. | |
each side, so it might heat up! Searing. Now for the moment of | :11:12. | :11:16. | |
truth. The five is great. It feels evenly cooked throughout. -- | :11:17. | :11:20. | |
flavour. I probably missed the fact it is not crispy from the pen. I | :11:21. | :11:24. | |
could have left it in to do that, but followed the instructions. But | :11:25. | :11:28. | |
the taste is fantastic and the flavour is really good. A smart | :11:29. | :11:32. | |
frying pan could have dealt with that issue. And funnily enough, that | :11:33. | :11:39. | |
just what Pantelligent is. I thought the idea it was dark to start with. | :11:40. | :11:43. | |
Who needs a Bluetooth connected frying pan that connects to your | :11:44. | :11:46. | |
mobile to tell you how long to cook things for? I do, it seems, as I | :11:47. | :11:50. | |
perfected some dishes that may otherwise have been compromised. | :11:51. | :11:54. | |
This is great. It tells you how many degrees lower it needs to be. The | :11:55. | :11:59. | |
pen's turbojets jet setter keeps track of the heatsink were regularly | :12:00. | :12:06. | |
reminded to turn it up and down! Pen's temperature setting. You are | :12:07. | :12:09. | |
told register and add other ingredients. That is really good. I | :12:10. | :12:13. | |
was concerned the potato wouldn't be ticked all the way through but if I | :12:14. | :12:17. | |
had done without this might frying pan, that would have been a brisk -- | :12:18. | :12:21. | |
corked. But that was fantastic. Spot-on, I would say. Back to the | :12:22. | :12:28. | |
soup and it seems to be ready. This was the only dish it had on offer | :12:29. | :12:32. | |
for us today, but eventually it should be able to burn as many | :12:33. | :12:37. | |
recipes as it gets taught. -- learn. A great bit of theatre, but I am | :12:38. | :12:41. | |
very irritated by this mark on the bowl. But there is nothing to clean | :12:42. | :12:46. | |
it up with. And the soup needs trying. But I don't eat crab, which | :12:47. | :12:51. | |
is an issue. I am giving it a go. Oh, crab. It's really nice. I will | :12:52. | :13:00. | |
be a while. Do was Lara. Meanwhile, back in at | :13:01. | :13:14. | |
the Design Museum in London, some of the most beautiful 3D printing I | :13:15. | :13:18. | |
think that the scene. -- that was Lara. These are one artist's | :13:19. | :13:24. | |
suggestion about how we might revive the ancient culture of making death | :13:25. | :13:32. | |
masks. I wouldn't mind one because it would make me look like I was in | :13:33. | :13:37. | |
the film Alien.. Next, what would happen if you scaled that technology | :13:38. | :13:42. | |
right up? What if you were to let it loose on our homes, our cities and | :13:43. | :13:50. | |
our architecture? The buildings around us don't look the way they do | :13:51. | :13:55. | |
by accident. The design, the shape and the structure are all results, | :13:56. | :14:02. | |
-- the result of designers, what we need the buildings to do and the | :14:03. | :14:05. | |
practical limitations of the materials and building techniques | :14:06. | :14:09. | |
we've discovered. This is very much the age of concrete, steel and | :14:10. | :14:13. | |
glass. But with new technology and techniques, what could the next wave | :14:14. | :14:18. | |
for our buildings look like? The building industry is still in 19th | :14:19. | :14:23. | |
century technology. It hasn't really evolved like other disciplines and | :14:24. | :14:29. | |
if you look now at the speed at which cities are growing, of | :14:30. | :14:34. | |
technology is really lacking behind. Industrial scale 3D printing has | :14:35. | :14:38. | |
already been put to use the print full-scale buildings, like this | :14:39. | :14:42. | |
housing project in China. But researchers are now turning to | :14:43. | :14:45. | |
computers to not just create buildings but to help design them. | :14:46. | :14:49. | |
And the results? Well, a little unusual. This is a prototype: that's | :14:50. | :14:57. | |
been three -- 3D printed here at the University College London. We | :14:58. | :15:01. | |
basically used a computer and used algorithms to generate these forms | :15:02. | :15:08. | |
for us. They may look strange, but they are highly optimised. So these | :15:09. | :15:12. | |
forms attempt to save material and become more efficient, but at the | :15:13. | :15:16. | |
same time they produce a sort of aesthetic that is very appealing to | :15:17. | :15:20. | |
us as architects and it really doesn't look what the normal | :15:21. | :15:23. | |
building any more. Normal 3D printing creates objects by building | :15:24. | :15:27. | |
up thousands of the layers, which can imagine takes a fair while. The | :15:28. | :15:32. | |
idea here is to save time by printing just what you need, which | :15:33. | :15:35. | |
means rather than printing Flatley is instead with shapes, like | :15:36. | :15:40. | |
pyramids. The software they've created can take this a step further | :15:41. | :15:44. | |
by figuring out which bits are structurally essential and getting | :15:45. | :15:47. | |
rid of the rest. Before computers we had to build with hands and now we | :15:48. | :15:52. | |
can create algorithms that make this calculation is for us, but that | :15:53. | :15:56. | |
doesn't mean we don't design, we does optimise the process and we can | :15:57. | :16:00. | |
create in that we couldn't ever think of before. 3D printing will | :16:01. | :16:05. | |
allow architecture to be much more details, much more fine and also | :16:06. | :16:11. | |
much more efficient. You can 3D printing exactly the material that | :16:12. | :16:17. | |
you need in a specific part of the building. You will make it perform | :16:18. | :16:22. | |
much more efficiently. Before these new techniques can be put to use, | :16:23. | :16:26. | |
they first need to be proven to be strong and safe. Case in point, this | :16:27. | :16:33. | |
bridge project aims to 3D printed usable steel bridge right in the | :16:34. | :16:37. | |
centre of Amsterdam. Created using similar generative algorithms, the | :16:38. | :16:42. | |
project has been held up while the company proves the regulators that | :16:43. | :16:45. | |
the design is structurally sound. The actual bridge now isn't slated | :16:46. | :16:50. | |
to appear on till next year. Techniques like these promised to | :16:51. | :16:55. | |
spice up our city skylines, but it could still be a while before we see | :16:56. | :17:03. | |
3D printed is now building sites. That was Steve. Now, earlier this | :17:04. | :17:09. | |
year we shot an entire programme in 360 degrees. To get these shots we | :17:10. | :17:13. | |
had to use a six oh pro cameras strapped together and let me tell | :17:14. | :17:18. | |
you the postproduction was a nightmare. -- GoPro cameras. But | :17:19. | :17:26. | |
since March more than a dozen much cheaper consumer cameras have gone | :17:27. | :17:30. | |
on sale, so we felt we wanted to see if they were any good, so we sent | :17:31. | :17:36. | |
our top team on a mission. Go to central Africa, see if the cameras | :17:37. | :17:40. | |
can't cope and above all keep calm! It almost went to plan. | :17:41. | :17:58. | |
We're driving through Rwanda. I've come to shoot some of the highlights | :17:59. | :18:07. | |
of this landlocked country in 360, including a beach... We are close to | :18:08. | :18:13. | |
the border with Congo at Rwanda's very own lake. I found my way to the | :18:14. | :18:20. | |
beach and I have to try this first of all. This has two 180 cameras | :18:21. | :18:31. | |
that gets stuck together on the device. It is almost too easy to use | :18:32. | :18:35. | |
and superquick. We actually aren't here to shoot the beach, we are here | :18:36. | :18:42. | |
to capture something quite special. Meet some of this acrobatic squad | :18:43. | :18:46. | |
who have taken an interest in my new camera. I'm not sure this is a good | :18:47. | :18:57. | |
idea. It features the two 180 shops together really well with a few | :18:58. | :19:01. | |
aberrations near the edges of each lens. There is no post, so as soon | :19:02. | :19:07. | |
as it is shot you can watch it back or Sherrock. Time to try something | :19:08. | :19:18. | |
different. We are leading the beach and on our way to the mountains. It | :19:19. | :19:22. | |
is supposed to be a beautiful journey, so we will use this camera | :19:23. | :19:28. | |
to try to capture the beauty of the Rwandan countryside. Dashboard | :19:29. | :19:32. | |
cameras are typically used to record any accident that might happen, but | :19:33. | :19:36. | |
we made use of this super HD wide angles dash cam as a perfect camera, | :19:37. | :19:45. | |
each file has its GPS information attached. Before we set off, we set | :19:46. | :19:51. | |
up another 360 camera just in case we spotted filming opportunity. The | :19:52. | :19:57. | |
LG 360 camera is the cheapest of before we brought with us. It takes | :19:58. | :20:04. | |
a 200 degree shot, two of them, which are then stitched together. | :20:05. | :20:07. | |
Wigan arrived at the volcano mountains, ready for some unexpected | :20:08. | :20:16. | |
guests. -- we arrived. Unlike the Insta360, the LG cam Cannex | :20:17. | :20:22. | |
wirelessly to your iPhones you can leave it in the middle of the action | :20:23. | :20:28. | |
and then sit back and watch. -- connects. The picture wasn't as | :20:29. | :20:32. | |
crisp and colourful as the Insta360. The camera is lightweight and the in | :20:33. | :20:37. | |
battery didn't last long. But the three microphones offered good | :20:38. | :20:40. | |
surround sound, something the will appreciate more if you what your | :20:41. | :20:45. | |
movies through a VR headset. -- something you will. As the light | :20:46. | :20:49. | |
faded, we decided to prepare the series kit that we would be using to | :20:50. | :20:53. | |
fill out for high up on the mountain early the next morning. I brought | :20:54. | :21:00. | |
the 360 Fly, which looks like a golf ball with an eye. That the camera | :21:01. | :21:07. | |
with a 240 degrees superwide lens. That means there is no stitching | :21:08. | :21:12. | |
together of shots and that should mean a smooth and clean picture. She | :21:13. | :21:19. | |
used the Kodak double action camera. The two cameras need to be | :21:20. | :21:24. | |
synchronised, so they are started by a remote-controlled watch so the | :21:25. | :21:28. | |
record the same time. The image from the two cameras need to be stitched | :21:29. | :21:31. | |
together later with Kodak software, if the stitching works well we | :21:32. | :21:39. | |
should get winning results. We've been told Rwanda was stunning so we | :21:40. | :21:46. | |
decided to trek 3000 metres up to take a look. A fellow adventurer at | :21:47. | :21:54. | |
kindly agreed to be our cameraman, which means we strapped the golf | :21:55. | :21:58. | |
ball to his head and it soon became apparent what the limitation of his | :21:59. | :22:02. | |
single lens camera was. A great, lucky black pit at the bottom of the | :22:03. | :22:10. | |
picture. Ones in the jungle it looked awful. To be fair, it can be | :22:11. | :22:15. | |
cropped out later, leaving a better view that actually 360 horizontally | :22:16. | :22:20. | |
but you can't look down. The superwide angle made everything | :22:21. | :22:24. | |
seemed far away. Anything close up looked great, but the sound quality | :22:25. | :22:30. | |
was ruined. As we trudged through the undergrowth, we decided it was | :22:31. | :22:39. | |
time to swap over to the Kodak. It was then the adventure really took | :22:40. | :22:45. | |
off. The air got thinner and this camera looked like it would capture | :22:46. | :22:48. | |
anything we came across. Or anything that came across us. | :22:49. | :23:01. | |
By having two super high-definition cameras we weren't just able to | :23:02. | :23:06. | |
capture this incredible creatures wherever they went, but we have the | :23:07. | :23:12. | |
resolution to zoom in as well. On the downside, the two cameras didn't | :23:13. | :23:16. | |
automatically stitch well together. After fiddling with it using Kodak's | :23:17. | :23:21. | |
on software, we decided on shot was running behind the other. After a | :23:22. | :23:26. | |
calculated week got this much better results. -- tweak. The picture | :23:27. | :23:33. | |
quality was the best of the bunch. The 360 cameras can allow you to | :23:34. | :23:39. | |
capture everything in one go, but finer details still elude even the | :23:40. | :23:43. | |
best of them, meaning it will still be a while before you feel like | :23:44. | :23:47. | |
you're right there. That was Dan Simmons, clearly | :23:48. | :23:52. | |
angling to be the 360 David Attenborough. That's it from the | :23:53. | :23:58. | |
design Museum in London. Next week, it is the Click Christmas party, so | :23:59. | :24:03. | |
be prepared for well, anything! Plus a look back at our best bits of | :24:04. | :24:07. | |
2016. In the meantime, we live on Twitter. Thanks for watching! | :24:08. | :24:33. | |
Friday was another grey day for many parts of the country. Any breaks in | :24:34. | :24:37. | |
the cloud, mist and | :24:38. | :24:38. |