:00:00. > :00:00.Now, in a short while it'll be time for Newswatch.
:00:00. > :00:29.The Design Museum in London has moved into a new home,
:00:30. > :00:40.I have come to see Fear And Love, an exhibition of 11 designers'
:00:41. > :00:45.reactions to our increasingly complex world.
:00:46. > :00:49.The most animated star on show has to be an industrial robot arm,
:00:50. > :00:55.which its owner hopes will present a more friendly face to robotics,
:00:56. > :01:00.and even maybe help us empathise with mechanoids of the future.
:01:01. > :01:03.It senses where you are and comes bounding over to see you,
:01:04. > :01:06.but if it gets bored, it will turn its attention
:01:07. > :01:12.It is a bit like an excitable puppy, actually.
:01:13. > :01:15.Who knows, installations like this may help to allay our fears
:01:16. > :01:19.of being around giant machines like this.
:01:20. > :01:23.I have to say, it will still be a while before I trust this thing
:01:24. > :01:27.That said, computers are increasingly being used
:01:28. > :01:33.There is plenty of research into how artificial intelligence can help
:01:34. > :01:37.doctors to better look after patients.
:01:38. > :01:42.Jen has been taking a look at some of the latest developments.
:01:43. > :01:45.Around the world, hospitals are facing a backlog of patients,
:01:46. > :01:50.ageing populations and a shortage of specialist staff.
:01:51. > :01:52.Some hospitals are teaming up with artificial intelligence
:01:53. > :01:55.research teams to see if there are ways that high-tech
:01:56. > :01:58.solutions can supplement or even enhance healthcare in the face
:01:59. > :02:06.Its health minister says they will need more than 30,000
:02:07. > :02:10.new nurses before 2020, and completely rethink the way it
:02:11. > :02:17.So when the CEO of one of its largest private hospital
:02:18. > :02:20.networks approached IBM's Watson team, they came up with a pilot
:02:21. > :02:23.project to try to help nurses working with the most
:02:24. > :02:30.This is the intensive care unit at Mount Elizabeth Novena Hospital.
:02:31. > :02:32.It's where four beds are conducted to IBM's artificially
:02:33. > :02:38.Collecting all the vital signs from the patients in the beds,
:02:39. > :02:43.it gives the nurses a more complete picture of who needs the most care.
:02:44. > :02:46.In one of the first trials of its kind in the world,
:02:47. > :02:48.the AI is constantly monitoring output and making connections
:02:49. > :02:54.on a vast range of data, including a commonly used scale.
:02:55. > :02:57.Higher scores correspond to a higher incidence of death,
:02:58. > :02:59.and it is particularly important in the first 24
:03:00. > :03:07.This patient has four alarms, so if you don't see anything
:03:08. > :03:11.flashing here, it means it has been acknowledged already.
:03:12. > :03:15.One of the patients in the ward is at the high end of the alert,
:03:16. > :03:18.and nurses can quickly access the information in real-time
:03:19. > :03:22.and look at patterns in their vital signs to see if they are at greater
:03:23. > :03:28.Here in the UK, it's the help AI could provide in imaging
:03:29. > :03:33.between the NHS and Google's DeepMind.
:03:34. > :03:36.The UK's Royal College of Radiologists says 99%
:03:37. > :03:39.of hospitals are struggling to keep up with demand,
:03:40. > :03:42.and the UK has the third lowest numbers of specialists who can
:03:43. > :03:50.The large amount of data is overwhelming a health service
:03:51. > :03:58.If you can use algorithms or machine learning or artificial intelligence
:03:59. > :04:01.to set an alert for you, to trigger to say something has
:04:02. > :04:05.happened, you need to go and see this, this is urgent and you need
:04:06. > :04:09.to deal with it, in the next hour or so when you may have not
:04:10. > :04:13.I think it will improve quality of care and actually improve equity
:04:14. > :04:21.One of the first areas where the NHS is testing artificial intelligence
:04:22. > :04:26.is at Moorfields, one of the busiest eye hospitals in the world.
:04:27. > :04:29.DeepMind is applying the same machine learning technology
:04:30. > :04:32.behind its winning AlphaGo computer programme.
:04:33. > :04:36.It beat the world's best human player by computing tens
:04:37. > :04:40.of thousands of positions per second.
:04:41. > :04:43.We started DeepMind to develop general-purpose learning algorithms
:04:44. > :04:46.and use those tools and systems to make the world a better place.
:04:47. > :04:50.It was obvious to us a few years ago that there is a massive opportunity
:04:51. > :04:53.to deliver really meaningful and proved benefits to many patients
:04:54. > :04:56.and people across the world using our sort of techniques
:04:57. > :04:59.to try to improve the way we diagnose and treat patients
:05:00. > :05:08.The Moorfields Hospital research is using scans from this OCT,
:05:09. > :05:10.or optical coherence tomography machine,
:05:11. > :05:14.which creates a 3-dimensional retinal image.
:05:15. > :05:18.It is used to diagnose diseases like age-related macular
:05:19. > :05:20.degeneration, and diabetic retinopathy, two leading causes
:05:21. > :05:26.DeepMind is trying to develop a computer algorithm
:05:27. > :05:29.which will identify scans of concern.
:05:30. > :05:32.OCT scans were chosen because of the high rate
:05:33. > :05:34.of information included in them and the way
:05:35. > :05:37.they can be broken down into pixels showing areas
:05:38. > :05:41.I was particularly attracted to speaking to DeepMind
:05:42. > :05:44.because I thought their algorithms would have the best ability to deal
:05:45. > :05:47.with 3-D imaging of an extremely high resolution form,
:05:48. > :05:55.This is such a delicate area of the eye that any sort
:05:56. > :05:57.of disruption of the normal architecture has really amazingly
:05:58. > :06:06.So I believe health career could be at a pivotal moment in history
:06:07. > :06:09.where these advances in technology, such as artificial intelligence,
:06:10. > :06:13.will fundamentally change the way medicine is practised,
:06:14. > :06:20.If you think about it, the best humans in the world
:06:21. > :06:24.will have seen only a fraction of the number of cases that we can
:06:25. > :06:28.Imagine that we took all of the cases that
:06:29. > :06:31.many of the top ophthalmologists in the world have seen themselves,
:06:32. > :06:37.Now the algorithm can sample from all of the case studies
:06:38. > :06:41.that our various different humans have seen, and try to deliver a much
:06:42. > :06:46.higher standard, more consistently, when making a diagnosis.
:06:47. > :06:49.All these projects are still in the research or pilot stage,
:06:50. > :06:51.but it's fascinating to see how artificial intelligence
:06:52. > :06:53.could transform healthcare and perhaps lead to faster
:06:54. > :07:11.Meanwhile, back here at the Design Museum in London,
:07:12. > :07:14.some of the most beautiful 3D printing I think I've ever seen.
:07:15. > :07:17.These are one artist's suggestion about how we might revive
:07:18. > :07:26.the ancient culture of making death masks.
:07:27. > :07:30.I wouldn't mind one because it would make me look like I was in the film
:07:31. > :07:35.Next, we're going to ask - what would happen if you scaled that
:07:36. > :07:39.What if you were to let it loose on our homes,
:07:40. > :07:47.The buildings around us don't look the way they do by accident.
:07:48. > :07:52.The design, the shape and the structure are
:07:53. > :07:55.all the result of the mix between the desire of designers,
:07:56. > :07:58.what we need the buildings to do and the practical limitations
:07:59. > :08:01.of the materials and building techniques we've discovered.
:08:02. > :08:05.This is very much the age of concrete, steel and glass.
:08:06. > :08:08.But with new technology and techniques, what could the next
:08:09. > :08:16.The building industry is still in 19th century technology.
:08:17. > :08:22.It hasn't really evolved like other disciplines and if you look now
:08:23. > :08:25.at the speed at which cities are growing, so many people
:08:26. > :08:28.are moving to the cities, but our technology
:08:29. > :08:32.Industrial scale 3D printing has already been put to use to print
:08:33. > :08:34.full-scale buildings, like this housing project in China.
:08:35. > :08:37.But researchers are now turning to computers to not just create
:08:38. > :08:47.This is a prototype column that's been 3D printed here at the school
:08:48. > :08:51.of architecture at the University College London.
:08:52. > :08:53.We basically use a computer and use algorithms to generate
:08:54. > :09:00.They may look very alien and strange, but actually
:09:01. > :09:04.So these forms attempt to save material and become more
:09:05. > :09:07.efficient, but at the same time they produce a sort of aesthetic
:09:08. > :09:11.that is very appealing to us as architects and that really
:09:12. > :09:15.doesn't look what a normal building any more.
:09:16. > :09:17.Normal 3D printing creates objects by building up thousands
:09:18. > :09:20.of very thin layers, which you can imagine takes
:09:21. > :09:29.The idea here, though, is to save time by printing just
:09:30. > :09:32.what you need, which means rather than printing
:09:33. > :09:34.flat layers, instead built with shapes, like pyramids.
:09:35. > :09:37.The software they've created can take this a step further,
:09:38. > :09:38.by figuring out which bits are structurally
:09:39. > :09:43.essential and then getting rid of the rest.
:09:44. > :09:45.Before computers we had to build by hand, right?
:09:46. > :09:47.And now we can create algorithms that make these
:09:48. > :09:50.calculations for us, but that doesn't mean that we don't
:09:51. > :09:53.design, we just optimise the process more and we can create things
:09:54. > :09:55.that we couldn't ever think of before.
:09:56. > :09:58.3D printing will allow architecture to be much more detailed,
:09:59. > :10:04.much more fine, and also much more efficient.
:10:05. > :10:07.Like, if you can 3D print exactly the material that you need
:10:08. > :10:11.in a specific part of the building, it will make it perform
:10:12. > :10:19.Before these new techniques can be put to use, they first need to be
:10:20. > :10:26.Case in point, this MX3D Bridge project aims to 3D print a usable
:10:27. > :10:30.steel bridge right in the centre of Amsterdam.
:10:31. > :10:32.Created using similar generative algorithms,
:10:33. > :10:34.the project has been held up while the company proves
:10:35. > :10:43.to regulators that the design is structurally sound.
:10:44. > :10:46.The actual bridge now isn't slated to appear until next year.
:10:47. > :10:49.Techniques like these certainly promise to spice up our city's
:10:50. > :10:52.skylines, but it could still be a while before we see 3D printers
:10:53. > :11:10.That was it for the short edition of Click at the design Museum. You can
:11:11. > :11:16.catch up on our player. Next time, it is the Click Christmas party. Be
:11:17. > :11:23.prepared for anything. Plus a look at our best bits third 2016. Follow
:11:24. > :11:26.us any time on Twitter. Thank you for watching ours. See you soon.