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Now, in a short while it'll be time for Newswatch. | :00:00. | :00:00. | |
The Design Museum in London has moved into a new home, | :00:00. | :00:29. | |
I have come to see Fear And Love, an exhibition of 11 designers' | :00:30. | :00:40. | |
reactions to our increasingly complex world. | :00:41. | :00:45. | |
The most animated star on show has to be an industrial robot arm, | :00:46. | :00:49. | |
which its owner hopes will present a more friendly face to robotics, | :00:50. | :00:55. | |
and even maybe help us empathise with mechanoids of the future. | :00:56. | :01:00. | |
It senses where you are and comes bounding over to see you, | :01:01. | :01:03. | |
but if it gets bored, it will turn its attention | :01:04. | :01:06. | |
It is a bit like an excitable puppy, actually. | :01:07. | :01:12. | |
Who knows, installations like this may help to allay our fears | :01:13. | :01:15. | |
of being around giant machines like this. | :01:16. | :01:19. | |
I have to say, it will still be a while before I trust this thing | :01:20. | :01:23. | |
That said, computers are increasingly being used | :01:24. | :01:27. | |
There is plenty of research into how artificial intelligence can help | :01:28. | :01:33. | |
doctors to better look after patients. | :01:34. | :01:37. | |
Jen has been taking a look at some of the latest developments. | :01:38. | :01:42. | |
Around the world, hospitals are facing a backlog of patients, | :01:43. | :01:45. | |
ageing populations and a shortage of specialist staff. | :01:46. | :01:50. | |
Some hospitals are teaming up with artificial intelligence | :01:51. | :01:52. | |
research teams to see if there are ways that high-tech | :01:53. | :01:55. | |
solutions can supplement or even enhance healthcare in the face | :01:56. | :01:58. | |
Its health minister says they will need more than 30,000 | :01:59. | :02:06. | |
new nurses before 2020, and completely rethink the way it | :02:07. | :02:10. | |
So when the CEO of one of its largest private hospital | :02:11. | :02:17. | |
networks approached IBM's Watson team, they came up with a pilot | :02:18. | :02:20. | |
project to try to help nurses working with the most | :02:21. | :02:23. | |
This is the intensive care unit at Mount Elizabeth Novena Hospital. | :02:24. | :02:30. | |
It's where four beds are conducted to IBM's artificially | :02:31. | :02:32. | |
Collecting all the vital signs from the patients in the beds, | :02:33. | :02:38. | |
it gives the nurses a more complete picture of who needs the most care. | :02:39. | :02:43. | |
In one of the first trials of its kind in the world, | :02:44. | :02:46. | |
the AI is constantly monitoring output and making connections | :02:47. | :02:48. | |
on a vast range of data, including a commonly used scale. | :02:49. | :02:54. | |
Higher scores correspond to a higher incidence of death, | :02:55. | :02:57. | |
and it is particularly important in the first 24 | :02:58. | :02:59. | |
This patient has four alarms, so if you don't see anything | :03:00. | :03:07. | |
flashing here, it means it has been acknowledged already. | :03:08. | :03:11. | |
One of the patients in the ward is at the high end of the alert, | :03:12. | :03:15. | |
and nurses can quickly access the information in real-time | :03:16. | :03:18. | |
and look at patterns in their vital signs to see if they are at greater | :03:19. | :03:22. | |
Here in the UK, it's the help AI could provide in imaging | :03:23. | :03:28. | |
between the NHS and Google's DeepMind. | :03:29. | :03:33. | |
The UK's Royal College of Radiologists says 99% | :03:34. | :03:36. | |
of hospitals are struggling to keep up with demand, | :03:37. | :03:39. | |
and the UK has the third lowest numbers of specialists who can | :03:40. | :03:42. | |
The large amount of data is overwhelming a health service | :03:43. | :03:50. | |
If you can use algorithms or machine learning or artificial intelligence | :03:51. | :03:58. | |
to set an alert for you, to trigger to say something has | :03:59. | :04:01. | |
happened, you need to go and see this, this is urgent and you need | :04:02. | :04:05. | |
to deal with it, in the next hour or so when you may have not | :04:06. | :04:09. | |
I think it will improve quality of care and actually improve equity | :04:10. | :04:13. | |
One of the first areas where the NHS is testing artificial intelligence | :04:14. | :04:21. | |
is at Moorfields, one of the busiest eye hospitals in the world. | :04:22. | :04:26. | |
DeepMind is applying the same machine learning technology | :04:27. | :04:29. | |
behind its winning AlphaGo computer programme. | :04:30. | :04:32. | |
It beat the world's best human player by computing tens | :04:33. | :04:36. | |
of thousands of positions per second. | :04:37. | :04:40. | |
We started DeepMind to develop general-purpose learning algorithms | :04:41. | :04:43. | |
and use those tools and systems to make the world a better place. | :04:44. | :04:46. | |
It was obvious to us a few years ago that there is a massive opportunity | :04:47. | :04:50. | |
to deliver really meaningful and proved benefits to many patients | :04:51. | :04:53. | |
and people across the world using our sort of techniques | :04:54. | :04:56. | |
to try to improve the way we diagnose and treat patients | :04:57. | :04:59. | |
The Moorfields Hospital research is using scans from this OCT, | :05:00. | :05:08. | |
or optical coherence tomography machine, | :05:09. | :05:10. | |
which creates a 3-dimensional retinal image. | :05:11. | :05:14. | |
It is used to diagnose diseases like age-related macular | :05:15. | :05:18. | |
degeneration, and diabetic retinopathy, two leading causes | :05:19. | :05:20. | |
DeepMind is trying to develop a computer algorithm | :05:21. | :05:26. | |
which will identify scans of concern. | :05:27. | :05:29. | |
OCT scans were chosen because of the high rate | :05:30. | :05:32. | |
of information included in them and the way | :05:33. | :05:34. | |
they can be broken down into pixels showing areas | :05:35. | :05:37. | |
I was particularly attracted to speaking to DeepMind | :05:38. | :05:41. | |
because I thought their algorithms would have the best ability to deal | :05:42. | :05:44. | |
with 3-D imaging of an extremely high resolution form, | :05:45. | :05:47. | |
This is such a delicate area of the eye that any sort | :05:48. | :05:55. | |
of disruption of the normal architecture has really amazingly | :05:56. | :05:57. | |
So I believe health career could be at a pivotal moment in history | :05:58. | :06:06. | |
where these advances in technology, such as artificial intelligence, | :06:07. | :06:09. | |
will fundamentally change the way medicine is practised, | :06:10. | :06:13. | |
If you think about it, the best humans in the world | :06:14. | :06:20. | |
will have seen only a fraction of the number of cases that we can | :06:21. | :06:24. | |
Imagine that we took all of the cases that | :06:25. | :06:28. | |
many of the top ophthalmologists in the world have seen themselves, | :06:29. | :06:31. | |
Now the algorithm can sample from all of the case studies | :06:32. | :06:37. | |
that our various different humans have seen, and try to deliver a much | :06:38. | :06:41. | |
higher standard, more consistently, when making a diagnosis. | :06:42. | :06:46. | |
All these projects are still in the research or pilot stage, | :06:47. | :06:49. | |
but it's fascinating to see how artificial intelligence | :06:50. | :06:51. | |
could transform healthcare and perhaps lead to faster | :06:52. | :06:53. | |
Meanwhile, back here at the Design Museum in London, | :06:54. | :07:11. | |
some of the most beautiful 3D printing I think I've ever seen. | :07:12. | :07:14. | |
These are one artist's suggestion about how we might revive | :07:15. | :07:17. | |
the ancient culture of making death masks. | :07:18. | :07:26. | |
I wouldn't mind one because it would make me look like I was in the film | :07:27. | :07:30. | |
Next, we're going to ask - what would happen if you scaled that | :07:31. | :07:35. | |
What if you were to let it loose on our homes, | :07:36. | :07:39. | |
The buildings around us don't look the way they do by accident. | :07:40. | :07:47. | |
The design, the shape and the structure are | :07:48. | :07:52. | |
all the result of the mix between the desire of designers, | :07:53. | :07:55. | |
what we need the buildings to do and the practical limitations | :07:56. | :07:58. | |
of the materials and building techniques we've discovered. | :07:59. | :08:01. | |
This is very much the age of concrete, steel and glass. | :08:02. | :08:05. | |
But with new technology and techniques, what could the next | :08:06. | :08:08. | |
The building industry is still in 19th century technology. | :08:09. | :08:16. | |
It hasn't really evolved like other disciplines and if you look now | :08:17. | :08:22. | |
at the speed at which cities are growing, so many people | :08:23. | :08:25. | |
are moving to the cities, but our technology | :08:26. | :08:28. | |
Industrial scale 3D printing has already been put to use to print | :08:29. | :08:32. | |
full-scale buildings, like this housing project in China. | :08:33. | :08:34. | |
But researchers are now turning to computers to not just create | :08:35. | :08:37. | |
This is a prototype column that's been 3D printed here at the school | :08:38. | :08:47. | |
of architecture at the University College London. | :08:48. | :08:51. | |
We basically use a computer and use algorithms to generate | :08:52. | :08:53. | |
They may look very alien and strange, but actually | :08:54. | :09:00. | |
So these forms attempt to save material and become more | :09:01. | :09:04. | |
efficient, but at the same time they produce a sort of aesthetic | :09:05. | :09:07. | |
that is very appealing to us as architects and that really | :09:08. | :09:11. | |
doesn't look what a normal building any more. | :09:12. | :09:15. | |
Normal 3D printing creates objects by building up thousands | :09:16. | :09:17. | |
of very thin layers, which you can imagine takes | :09:18. | :09:20. | |
The idea here, though, is to save time by printing just | :09:21. | :09:29. | |
what you need, which means rather than printing | :09:30. | :09:32. | |
flat layers, instead built with shapes, like pyramids. | :09:33. | :09:34. | |
The software they've created can take this a step further, | :09:35. | :09:37. | |
by figuring out which bits are structurally | :09:38. | :09:38. | |
essential and then getting rid of the rest. | :09:39. | :09:43. | |
Before computers we had to build by hand, right? | :09:44. | :09:45. | |
And now we can create algorithms that make these | :09:46. | :09:47. | |
calculations for us, but that doesn't mean that we don't | :09:48. | :09:50. | |
design, we just optimise the process more and we can create things | :09:51. | :09:53. | |
that we couldn't ever think of before. | :09:54. | :09:55. | |
3D printing will allow architecture to be much more detailed, | :09:56. | :09:58. | |
much more fine, and also much more efficient. | :09:59. | :10:04. | |
Like, if you can 3D print exactly the material that you need | :10:05. | :10:07. | |
in a specific part of the building, it will make it perform | :10:08. | :10:11. | |
Before these new techniques can be put to use, they first need to be | :10:12. | :10:19. | |
Case in point, this MX3D Bridge project aims to 3D print a usable | :10:20. | :10:26. | |
steel bridge right in the centre of Amsterdam. | :10:27. | :10:30. | |
Created using similar generative algorithms, | :10:31. | :10:32. | |
the project has been held up while the company proves | :10:33. | :10:34. | |
to regulators that the design is structurally sound. | :10:35. | :10:43. | |
The actual bridge now isn't slated to appear until next year. | :10:44. | :10:46. | |
Techniques like these certainly promise to spice up our city's | :10:47. | :10:49. | |
skylines, but it could still be a while before we see 3D printers | :10:50. | :10:52. | |
That was it for the short edition of Click at the design Museum. You can | :10:53. | :11:10. | |
catch up on our player. Next time, it is the Click Christmas party. Be | :11:11. | :11:16. | |
prepared for anything. Plus a look at our best bits third 2016. Follow | :11:17. | :11:23. | |
us any time on Twitter. Thank you for watching ours. See you soon. | :11:24. | :11:26. |