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stuck in traffic queues trying to get to the Port of Dover because of | :00:00. | :00:00. | |
extra French security in Calais. Drivers are being warned to expect | :00:00. | :00:11. | |
delays. Now on BBC News, sentence -- Click. | :00:12. | :00:15. | |
With the AI film director, emotional clothes, and robot | :00:16. | :00:19. | |
This is what happens when you let an artificial | :00:20. | :00:46. | |
In a future with mass unemployment, young people | :00:47. | :00:54. | |
Sunspring is a film whose script was written entirely | :00:55. | :01:10. | |
by an AI chatbot, one that's been designed to mimic | :01:11. | :01:12. | |
It's a fun watch but only, I feel, if you're in on the joke. | :01:13. | :01:27. | |
It seems that writing a gripping, real and coherent narrative | :01:28. | :01:32. | |
is beyond AI at the moment, but what if you were to give it | :01:33. | :01:36. | |
the words and let it be the director? | :01:37. | :01:40. | |
What if you gave an AI complete creative control, over | :01:41. | :01:43. | |
That was what Saatchi Saatchi commissioned LA-based | :01:44. | :01:56. | |
It's a real out on a limb kind of experiment for film. | :01:57. | :02:06. | |
They decided not to make a full length feature film, | :02:07. | :02:09. | |
but instead to embark on a more manageable project, a pop video, | :02:10. | :02:14. | |
which is something that many new directors cut their teeth on. | :02:15. | :02:18. | |
A French dance group gave them permission to interpret | :02:19. | :02:20. | |
The lyrics would form the script but the interpretation would be | :02:21. | :02:25. | |
completely down to the machine, or more accurately, the machines. | :02:26. | :02:32. | |
A director's job involves a lot of different disciplines | :02:33. | :02:34. | |
So given that there is no one big artificial brain somewhere | :02:35. | :02:40. | |
that can do all that, we assembled the best | :02:41. | :02:43. | |
and most progressive pieces of artificial intelligence, | :02:44. | :02:47. | |
kind of in a collective, to do that. | :02:48. | :02:52. | |
First up, IBM's Watson was used to analyse the lyrics and make | :02:53. | :02:58. | |
an educated guess at the emotions of each line. | :02:59. | :03:01. | |
Based on these, the humans could ask the next AI questions about | :03:02. | :03:05. | |
For that, take to the director's chair, Ms Rinna. | :03:06. | :03:12. | |
We came up with a series of questions that you might ask | :03:13. | :03:15. | |
somebody like me when you are prepping for a job, | :03:16. | :03:17. | |
I could imagine a creative type coming up with that. | :03:18. | :03:33. | |
Ms Rinna is a Japanese Twitter chatbot that appears to be | :03:34. | :03:36. | |
And it was asked a whole load of questions on who the characters | :03:37. | :03:42. | |
in the lyrics were, what actions they should perform and what outfits | :03:43. | :03:44. | |
Do you have any kind of understanding about how it came | :03:45. | :03:49. | |
up with those answers, day and night, night | :03:50. | :03:53. | |
From what we understand, this particular AI has been | :03:54. | :03:58. | |
programmed by having conversations with fans. | :03:59. | :04:03. | |
It is a Twitter handle, it has millions of conversations | :04:04. | :04:05. | |
It can relate but it is relating in a way that a 17-year-old | :04:06. | :04:11. | |
girl on Twitter would, I guess. | :04:12. | :04:13. | |
So how does a director choose the right actor for a role? | :04:14. | :04:21. | |
Well, it is probably someone who feels emotionally connected | :04:22. | :04:23. | |
to the work and then can transmit those emotions to the audience but | :04:24. | :04:27. | |
And that is something that computers don't have, but what computers can | :04:28. | :04:34. | |
do is measure the performer's brain activity using an EEG like this. | :04:35. | :04:42. | |
In a very unusual casting session, ten unsuspecting actors | :04:43. | :04:46. | |
were asked to perform the song while their facial expressions | :04:47. | :04:49. | |
and brainwaves were compared to those of the song's lead singer | :04:50. | :04:53. | |
The guy with the best emotional match got the gig. | :04:54. | :05:00. | |
And when we asked them to do that, they said, | :05:01. | :05:02. | |
And we said that the director needs that information | :05:03. | :05:06. | |
in order to make a choice, so that whoever he picks has | :05:07. | :05:09. | |
Did the actor know at this point that | :05:10. | :05:13. | |
When did the actor find out the director was an AI? | :05:14. | :05:19. | |
Does the actor know currently that the director is AI? | :05:20. | :05:23. | |
In fact when we cast him, we brought him down to the set | :05:24. | :05:28. | |
We said, it's going to be a little weird because we are going to be | :05:29. | :05:33. | |
giving you direction based on the artificial intelligence's | :05:34. | :05:35. | |
understanding of what this song is and what your actions | :05:36. | :05:37. | |
And unfortunately, I'm just an assistant director in this | :05:38. | :05:42. | |
scenario and I'm not really allowed to do anything other than to execute | :05:43. | :05:45. | |
the vision of the artificial intelligence, or help it along. | :05:46. | :05:50. | |
If it seems weird, it is weird because my nature is to tell | :05:51. | :05:53. | |
I have to go, excuse me, what do you think? | :05:54. | :05:57. | |
On the day of the shoot, even the camera drones followed | :05:58. | :06:01. | |
flight paths that were based on Watson's emotional | :06:02. | :06:04. | |
And when it came to the edit, choosing the shots and cutting it | :06:05. | :06:09. | |
all together, Zoic built a brand new editor AI. | :06:10. | :06:14. | |
This started by creating random assortments of shots and then | :06:15. | :06:17. | |
learned from feedback from the humans which types | :06:18. | :06:20. | |
After many iterations, the finished video was given | :06:21. | :06:26. | |
a visual treatment that was itself a type of mural art. | :06:27. | :06:30. | |
Adding a day and night style to the two characters' scenes. | :06:31. | :06:35. | |
The result is a pop video that is not going to win any awards. | :06:36. | :06:46. | |
In fact, the band, whose videos are usually very highly produced, | :06:47. | :06:50. | |
have distanced themselves from the whole thing. | :06:51. | :06:53. | |
And I'm not really convinced that an AI has actually | :06:54. | :06:56. | |
I mean, what they've done is got a load of different AIs to do | :06:57. | :07:03. | |
Including asking a chatbot what this fellow should wear. | :07:04. | :07:08. | |
But then the people at Zoic aren't really saying otherwise. | :07:09. | :07:13. | |
The exercise we went through provided a lot | :07:14. | :07:19. | |
The output itself of the video, it could be better, | :07:20. | :07:24. | |
But I think for a first attempt, a worthy effort. | :07:25. | :07:31. | |
As the director behind the scenes, I'm like, wow, that is super | :07:32. | :07:34. | |
intimidating because it won't be long before this technology actually | :07:35. | :07:37. | |
evolves where that editor that we created becomes | :07:38. | :07:44. | |
absolutely able to interpret, like, that shot is running and it | :07:45. | :07:47. | |
has emotion and it sweeps, I should definitely use that. | :07:48. | :07:49. | |
And if that cuts to the close-up of this emotional expression, | :07:50. | :07:53. | |
you will feel and it will understand how one influences the other. | :07:54. | :07:56. | |
And all of a sudden we are going to get really emotional cutting | :07:57. | :08:02. | |
And if you might permit just a slight indulgence on my part, | :08:03. | :08:13. | |
the eagle-eyed amongst you may have noticed that Loni was | :08:14. | :08:16. | |
It's actually a door from the ship in Joss Weedon's really rather | :08:17. | :08:25. | |
excellent and criminally cancelled before its time TV show, | :08:26. | :08:28. | |
Loni worked as the series' visual effects supervisor | :08:29. | :08:34. | |
and if you would like to find out why he has the door, as well as how | :08:35. | :08:38. | |
he helped design the ship, head to our YouTube and Facebook | :08:39. | :08:40. | |
pages for the geekiest chat that two grown men can possibly have | :08:41. | :08:43. | |
Time to change gear here in California, a state which is not | :08:44. | :08:57. | |
It also has a long history of innovative aviation | :08:58. | :09:03. | |
and aeronautics companies testing aircraft over its wide open spaces. | :09:04. | :09:09. | |
But there is one outfit here that has worked on projects so secret | :09:10. | :09:13. | |
that the US government has often denied its existence. | :09:14. | :09:17. | |
But Marc Cieslak found it and got exclusive access to the Skunk Works. | :09:18. | :09:27. | |
A Cold War spy plane capable of flying | :09:28. | :09:33. | |
It evolved from a plane commissioned by the CIA in the late '50s, | :09:34. | :09:49. | |
designed at Lockheed Martin Skunk Works. | :09:50. | :09:51. | |
More used to creating secret aircraft, Skunk Works has | :09:52. | :09:53. | |
turned its talents to creating something much slower. | :09:54. | :09:56. | |
We have to be careful about what we film here | :09:57. | :10:02. | |
at Skunk Works because they perform lots of work on classified projects. | :10:03. | :10:07. | |
However, Lockheed Martin have allowed us to film | :10:08. | :10:09. | |
their one third scale model of the airship. | :10:10. | :10:13. | |
It is the biggest scale model I have ever seen although technically this | :10:14. | :10:16. | |
This is a hybrid airship designed for lifting cargo. | :10:17. | :10:22. | |
It has three inflatable hulls, with helium providing 80% | :10:23. | :10:27. | |
It has thrusters which can change direction allowing it to take off | :10:28. | :10:34. | |
They also allow the airship to fly much more like | :10:35. | :10:37. | |
It does away with landing gear, making use of hovercraft technology | :10:38. | :10:43. | |
to get it on the ground or onto water. | :10:44. | :10:47. | |
This demonstrator is actually three times smaller | :10:48. | :10:49. | |
So airships have been around for a long time. | :10:50. | :10:57. | |
Why is it taking so long for governments, for commercial | :10:58. | :10:59. | |
operators to get on board with this sort of aircraft? | :11:00. | :11:03. | |
At the end of the day, for us it is hard to change. | :11:04. | :11:08. | |
And so when you see something like this, so different, | :11:09. | :11:12. | |
and aircraft are expensive, so this is a fairly big risk, | :11:13. | :11:15. | |
but the biggest aspect we bring in is remote cargo. | :11:16. | :11:18. | |
Areas that do not have a road or a rail line or any | :11:19. | :11:23. | |
We would like to do that in a sustainable way so we are not | :11:24. | :11:28. | |
hurting the environment, or at least as little as possible, | :11:29. | :11:30. | |
and without a road or rail line to get to those sites, | :11:31. | :11:33. | |
With the airships, we can take that part away. | :11:34. | :11:36. | |
Lockheed is not the only aerospace outfit working | :11:37. | :11:38. | |
Rival cargo carriers are being developed in the US and UK. | :11:39. | :11:44. | |
However, the boffins at Skunk Works have developed a novel way to keep | :11:45. | :11:48. | |
In order to see how they do that, I've got to get inside | :11:49. | :11:54. | |
So this is how you enter the inside of the airship. | :11:55. | :12:03. | |
You've got to try to make sure you don't lose any of the air | :12:04. | :12:06. | |
As airships are inflatable, they are susceptible to damage | :12:07. | :12:15. | |
which could create holes in its skin. | :12:16. | :12:17. | |
Finding and patching those holes is an important task. | :12:18. | :12:23. | |
Ever wondered what it's like to be inside a bouncy castle? | :12:24. | :12:26. | |
Somewhere in here there should be a spider. | :12:27. | :12:36. | |
Or to be more precise, it is one half of the spider | :12:37. | :12:42. | |
because the other half is on the other side | :12:43. | :12:45. | |
On the other side, there is a light that is shining upwards. | :12:46. | :12:53. | |
Because there will be light shining through the skin of the airship. | :12:54. | :13:00. | |
If it finds a hole, its job is then to patch it up. | :13:01. | :13:08. | |
In fact just under here we can see a patch that has | :13:09. | :13:11. | |
We had the opportunity to go inside the airship. | :13:12. | :13:18. | |
Normally you would not be able to go inside it, a human being would not | :13:19. | :13:22. | |
That's right, it's full of helium so it's hard to breathe in there. | :13:23. | :13:26. | |
They wear oxygen masks to do that kind of thing. | :13:27. | :13:29. | |
Also, it's very hard to plug a hole on the top of the airship | :13:30. | :13:32. | |
from the inside of the airship, there's no way to get scaffolding up | :13:33. | :13:35. | |
Patching it up with a robot, that allows you to patch those holes | :13:36. | :13:39. | |
Lockheed aims to have their hybrid airships operational | :13:40. | :13:43. | |
At which point, swarms of spiders will perform robotic repairs. | :13:44. | :13:57. | |
It was the week that Tinder announced its group function, | :13:58. | :14:02. | |
meaning that the app could have a long-term relationship | :14:03. | :14:05. | |
with you even after you have found love. | :14:06. | :14:08. | |
Tinder Social hopes to help you plan a night out with a chance | :14:09. | :14:11. | |
to make friends or meet up with groups nearby, | :14:12. | :14:15. | |
making connections that are not just about hooking up. | :14:16. | :14:19. | |
Mercedes put its self-driving bus to the test in Amsterdam. | :14:20. | :14:24. | |
The Future Bus successfully took a 20 kilometre trip without the need | :14:25. | :14:27. | |
for a driver, leaving him able to nod knowingly at passengers. | :14:28. | :14:33. | |
As well as camera and radar systems, it recognises traffic lights, | :14:34. | :14:37. | |
The Museum of London is using the video game, Minecraft, | :14:38. | :14:42. | |
350 years on, Great Fire 1666 will allow players to walk down | :14:43. | :14:52. | |
the streets of London, interact with people and combat | :14:53. | :14:55. | |
the plague, whilst also searching for audio clips to tell the story. | :14:56. | :15:01. | |
And finally, children at a Connecticut school have joined | :15:02. | :15:04. | |
forces with a local aquarium to help an injured penguin. | :15:05. | :15:09. | |
Five years after tearing her flexor tendon, she got lucky | :15:10. | :15:13. | |
when the school purchased a 3D printer. | :15:14. | :15:16. | |
They have created her a lightweight book to help her p-p-pick | :15:17. | :15:19. | |
Now back in January, I visited Pier 39 in San Francisco, | :15:20. | :15:33. | |
home to Autodesk's creative workshop, where artists | :15:34. | :15:37. | |
and researchers are let loose on CAD software and 3D printers to come up | :15:38. | :15:42. | |
Well, now it is time to meet one of those artists over | :15:43. | :15:46. | |
at the University of Southern California in Los Angeles. | :15:47. | :15:52. | |
I have trained as an architect and currently I am doing | :15:53. | :15:55. | |
my PhD in media art, and exploring the relationship | :15:56. | :15:58. | |
of human bodies with the space around. | :15:59. | :16:03. | |
I am basically exploring this relationship by using interactive | :16:04. | :16:05. | |
One of them is our near environment, which basically means our | :16:06. | :16:12. | |
And the other, I am exploring that relationship with the space around, | :16:13. | :16:18. | |
I am exploring how architecture can respond to humans, | :16:19. | :16:22. | |
Curse Of The Gaze is an interactive 3D printed outfit. | :16:23. | :16:31. | |
It is basically exploring how our fashion can be | :16:32. | :16:34. | |
I really wanted to create something that becomes | :16:35. | :16:38. | |
It is equipped with a camera, and the camera is located | :16:39. | :16:46. | |
The camera can see your age, gender and where you were looking, | :16:47. | :16:52. | |
and based on what you are looking at, it moves and | :16:53. | :16:55. | |
So if someone is here, they are looking at the cape | :16:56. | :16:59. | |
It is an extension of your brain to your garment, basically. | :17:00. | :17:08. | |
The garment thinks it is real and perceives the world, | :17:09. | :17:13. | |
almost to the most fundamental aspects of social interaction, | :17:14. | :17:16. | |
And by looking at each other, we communicate meanings, | :17:17. | :17:23. | |
and sometimes our gaze can be intrusive. | :17:24. | :17:25. | |
Sometimes our gaze can be reassuring. | :17:26. | :17:28. | |
In other words, if you can feel someone's gaze on your body, | :17:29. | :17:30. | |
your perception of the world is going to be different. | :17:31. | :17:36. | |
Synapse is a 3D-printed interactive helmet that moves | :17:37. | :17:40. | |
When it goes higher, your attention goes higher, | :17:41. | :17:48. | |
and the helmet opens up so you can actually pay attention | :17:49. | :17:51. | |
to the world around you, and as your attention level gets | :17:52. | :17:53. | |
less, basically it creates a cocoon around your head. | :17:54. | :17:57. | |
The brain sensor is basically capturing information, | :17:58. | :18:04. | |
gathering it on various different brain frequencies. | :18:05. | :18:08. | |
And then it sends the information related to your attention level | :18:09. | :18:12. | |
to a microcontroller, which is again embedded inside. | :18:13. | :18:16. | |
And that information can control two small servos located | :18:17. | :18:22. | |
Basically you map your attention level to control servos | :18:23. | :18:30. | |
Aurora is an interactive ceiling application which can see body | :18:31. | :18:41. | |
movement underneath it and respond accordingly. | :18:42. | :18:46. | |
Particularly it uses the ideas on body interaction, in other words | :18:47. | :18:51. | |
how our bodily movement, not just the tips of our fingers | :18:52. | :18:57. | |
on the mobile phone, can inform interaction, | :18:58. | :19:00. | |
but how our movements within the space can inform | :19:01. | :19:03. | |
The ceiling has a computer vision which is basically using a Kinect | :19:04. | :19:12. | |
It sends that information to a series of microcontrollers | :19:13. | :19:20. | |
in which they are controlling 15 different motors. | :19:21. | :19:23. | |
And those motors can inform the motion of each of the notes | :19:24. | :19:29. | |
Like most American cities, LA is a driving city. | :19:30. | :19:47. | |
It can take ages to get across town because of, well, | :19:48. | :19:51. | |
So if you are a parent who has to take their kids somewhere for two | :19:52. | :19:57. | |
or three hours before picking them up again, | :19:58. | :20:00. | |
it might not actually be worth your while coming | :20:01. | :20:02. | |
So what do you do if you are a parent who is too busy to ferry | :20:03. | :20:08. | |
Kate Russell has taken a hop, skip and a drive | :20:09. | :20:13. | |
Lots of homework, hectic after school activity schedules, | :20:14. | :20:25. | |
being ferried around by your parents. | :20:26. | :20:29. | |
I think it is time for you to get ready to go. | :20:30. | :20:32. | |
Hang on a minute, that actually looks pretty good, | :20:33. | :20:34. | |
Juggling work commitments with school runs and extracurricular | :20:35. | :20:40. | |
For some parents, a taxi is the only option, but how do | :20:41. | :20:46. | |
you know your children are safe getting into a car with a stranger? | :20:47. | :20:50. | |
That is the question HopSkipDrive set out to answer. | :20:51. | :20:56. | |
Three of us created the company and we are all working mums. | :20:57. | :20:59. | |
We have eight children between us who go to six different schools | :21:00. | :21:02. | |
and participate in every kid activity known to man. | :21:03. | :21:05. | |
Carly loves to dance and she is pretty good at it, too. | :21:06. | :21:14. | |
We originally lived somewhere different, we lived really close | :21:15. | :21:25. | |
to the freeway and we had good access. | :21:26. | :21:27. | |
And then we moved here and we could not get to class | :21:28. | :21:30. | |
on time, coming to work and then back down. | :21:31. | :21:32. | |
So we put her in a local dance studio which wasn't the same. | :21:33. | :21:35. | |
HopSkipDrive says it runs background checks on all its drivers | :21:36. | :21:37. | |
They also told us that applicants must have at least five | :21:38. | :21:42. | |
Compared to other rideshare services, | :21:43. | :21:46. | |
A minimum fare of around $15 depending on where you are based, | :21:47. | :21:54. | |
and looking at fare calculation comparisons with Uber, | :21:55. | :21:56. | |
it seems to be about 30-50% more expensive over the distance, | :21:57. | :21:58. | |
although there are no surcharges during peak hours with HopSkipDrive. | :21:59. | :22:02. | |
But the founders think what they do is different from a taxi. | :22:03. | :22:06. | |
So for my son Jackson, his care driver will park, | :22:07. | :22:12. | |
go into the school, sign him out, there is a booster seat for him | :22:13. | :22:18. | |
because he's not eight, and I can track the ride | :22:19. | :22:20. | |
Rides are scheduled ahead of time with clearly branded cars | :22:21. | :22:24. | |
and drivers who have a secret password to give to the children | :22:25. | :22:27. | |
on pick-up so everyone can be sure they are getting in the right car. | :22:28. | :22:32. | |
What happens when they come and pick you up? | :22:33. | :22:34. | |
First, I get a text message on my phone and then | :22:35. | :22:37. | |
when they come up to the door, they tell me the password | :22:38. | :22:40. | |
and they go in the car and we have a conversation. | :22:41. | :22:44. | |
For the drivers, many of them mothers themselves, | :22:45. | :22:47. | |
it is a much more rewarding job than just driving a taxi. | :22:48. | :22:51. | |
How is it driving lots of different kids? | :22:52. | :22:55. | |
My main objective is just to obviously be friendly, | :22:56. | :23:05. | |
and some kids just get in and they have their headphones | :23:06. | :23:08. | |
on and they have their music playing, you know, and they just | :23:09. | :23:11. | |
need to chill out because they have just come from school or dance, | :23:12. | :23:17. | |
and other times, you know, some of the kids are really chatty, | :23:18. | :23:21. | |
so I just have to take the emotional temperatures. | :23:22. | :23:25. | |
Now Carly can go to the dance studio she loves, and Jennifer can manage | :23:26. | :23:29. | |
I cannot leave work early every single day, so that is good. | :23:30. | :23:35. | |
So far limited to a few areas in California, | :23:36. | :23:41. | |
HopSkipDrive plans a careful expansion into more | :23:42. | :23:43. | |
They have also recently added carpooling to reduce the cost, | :23:44. | :23:49. | |
and this should help to ease congestion around school | :23:50. | :23:52. | |
That was Kate and that is it from LA for the moment. | :23:53. | :24:02. | |
I have a feeling we will be popping back here every so often | :24:03. | :24:05. | |
Meanwhile, on Twitter we live @bbcclick, online we are: | :24:06. | :24:09. | |
The fortunes weather wise across the UK very from west to east as we look | :24:10. | :24:45. |