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This week - madness in the States with superhuman diving drones, | :00:00. | :00:10. | |
a robot in a hat and four hackers doing the Macarena. | :00:11. | :00:14. | |
So here we are again, the hottest place on earth. | :00:15. | :00:43. | |
Silicon Valley, stretching from San Francisco all | :00:44. | :00:48. | |
The cafes are awash with start-ups, people are brimming | :00:49. | :00:54. | |
with ideas and the streets are paved with silicon. | :00:55. | :00:59. | |
And at the heart of it all is Stanford University. | :01:00. | :01:07. | |
As you would expect, there's tonnes of innovation around this place. | :01:08. | :01:10. | |
In fact, you never know who you're going to bump into next. | :01:11. | :01:20. | |
And actually, he's trying not to bump into you. | :01:21. | :01:26. | |
He's bristling with sensors and trying to learn how the people | :01:27. | :01:28. | |
What social conventions they follow, whether they group together, | :01:29. | :01:34. | |
whether they get out of your way. Or not. | :01:35. | :01:39. | |
The hat and tie are there, apparently, to show | :01:40. | :01:41. | |
The whole look, I have to say, is a little too Westworld for me | :01:42. | :01:47. | |
but the aim, according to his master is to create an algorithm that can | :01:48. | :01:50. | |
help a robot learn how to move through pedestrian environments | :01:51. | :01:53. | |
and how to behave in different parts of the world. | :01:54. | :01:58. | |
We often refer to it as a socially aware AI. | :01:59. | :02:05. | |
Having artificial intelligence that is socially aware | :02:06. | :02:09. | |
of its surroundings, or going beyond IQ, | :02:10. | :02:13. | |
to emotional intelligence. That is our goal. | :02:14. | :02:21. | |
So how is this different from self-driving cars? | :02:22. | :02:25. | |
In self-driving cars, you have well-defined rules. | :02:26. | :02:29. | |
But in social crowds, you have unwritten rules. | :02:30. | :02:37. | |
Humans interact with each other based on social conventions, | :02:38. | :02:40. | |
Nobody has really sat down and started to write that. | :02:41. | :02:48. | |
We want to see if the machine can learn these rules by observation. | :02:49. | :02:53. | |
Have you found that people react differently in different | :02:54. | :02:55. | |
We believe that every culture, every country has its own behaviour, | :02:56. | :03:03. | |
and that is why we have decided to develop an algorithm that | :03:04. | :03:06. | |
can learn on the fly, that can learn from observation how | :03:07. | :03:09. | |
humans interact with each other and simulate it. | :03:10. | :03:15. | |
Just to be clear, Jack is not learning how to move, | :03:16. | :03:19. | |
he is learning how to learn how to move. | :03:20. | :03:24. | |
The resulting algorithm can then be used by any bot, | :03:25. | :03:26. | |
to learn the social conventions of the place it finds itself in, | :03:27. | :03:30. | |
be it an Asian market, a European hotel or an American city. | :03:31. | :03:36. | |
Mind you, not all American cities are the same. | :03:37. | :03:39. | |
Marc Cieslak has been to a place with a slightly different | :03:40. | :03:42. | |
These are the silicon slopes of Utah. | :03:43. | :03:53. | |
Welcome to the streets of Salt Lake City. | :03:54. | :03:58. | |
Salt Lake City in Utah is perhaps most famous as the home | :03:59. | :04:01. | |
of the Church of Jesus Christ of Latter-day Saints, | :04:02. | :04:03. | |
Perhaps less well-known is that in recent years a whole number | :04:04. | :04:10. | |
of established and start-up tech companies have moved | :04:11. | :04:12. | |
into the surrounding areas, and the state of Utah itself. | :04:13. | :04:21. | |
Josh Coates is a veteran tech entrepreneur whose business life | :04:22. | :04:24. | |
And here he is dressed as a panda melting a ribbon | :04:25. | :04:30. | |
with a flame-thrower to open his offices in Utah. | :04:31. | :04:36. | |
He is CEO of Instructure, a company which creates training | :04:37. | :04:39. | |
and learning management software for education and business. | :04:40. | :04:41. | |
He tells me they do things a bit differently around here. | :04:42. | :04:48. | |
When I moved here in 2005 and I was doing a start-up, | :04:49. | :04:51. | |
The first year I was here, I was like, oh my gosh, | :04:52. | :04:55. | |
it's seven o'clock and the parking lot is empty. | :04:56. | :05:00. | |
It's a really hard-working, exciting, driven culture | :05:01. | :05:07. | |
You've got young people but they're married and they have kids. | :05:08. | :05:13. | |
And they don't go out and party, generally speaking. | :05:14. | :05:17. | |
They have good times in more family-friendly kind of ways, | :05:18. | :05:19. | |
and they're not working 80 or 90 hours a week. | :05:20. | :05:25. | |
Utah has a unique culture, it is kind of a weird state. | :05:26. | :05:44. | |
Whenever you get a lot of young people and you sprinkle in some | :05:45. | :05:50. | |
ambition then cool stuff comes out of it. | :05:51. | :05:51. | |
While property in Silicon Valley is at a premium, Utah has | :05:52. | :05:54. | |
With properties commercial and domestic costing less | :05:55. | :05:56. | |
That combined with an abundance of tech talent from local | :05:57. | :06:01. | |
Brigham Young University is the largest private religious | :06:02. | :06:05. | |
99% of the students are members of the Church of Jesus Christ | :06:06. | :06:16. | |
The Mormon faith focuses heavily on family values. | :06:17. | :06:21. | |
It also prohibits the consumption of alcohol, tobacco and caffeine. | :06:22. | :06:28. | |
I think we have a really unique group of students. | :06:29. | :06:30. | |
I remember when I was at college, if I had an 8am class, | :06:31. | :06:33. | |
I would get there and there was about 60 students supposed to be | :06:34. | :06:36. | |
at the class, and there would probably be about three | :06:37. | :06:39. | |
or four of us there at the start of the class. | :06:40. | :06:42. | |
A lot of them tired or hungover from the night before. | :06:43. | :06:45. | |
And it's really different to see them awake and alert at 8:00am | :06:46. | :06:47. | |
in the morning and just ready to absorb and learn information. | :06:48. | :06:52. | |
Professor Rowe runs the University's information technology course. | :06:53. | :06:54. | |
A team of its students recently took part in a national | :06:55. | :06:57. | |
The National Collegiate Cyber Defence Competition is one | :06:58. | :07:03. | |
where teams from around the United States come | :07:04. | :07:05. | |
together and basically we are given a scenario. | :07:06. | :07:10. | |
We're put in individual rooms by team where we have an IT | :07:11. | :07:13. | |
infrastructure that has been given to us and our job is to defend that | :07:14. | :07:17. | |
infrastructure from active attacks that are happening | :07:18. | :07:18. | |
It's kind of two days of anxiety, but it's also incredibly fun. | :07:19. | :07:28. | |
To relieve the stress of the competition, the team | :07:29. | :07:31. | |
The BYU team ultimately took second place in the competition overall. | :07:32. | :07:43. | |
We were the first team that has gone to NCCDC that had | :07:44. | :07:46. | |
an equal amount gender wise, so we had 50% women, 50% men. | :07:47. | :07:49. | |
Not really having any opportunity to have a criminal record of any | :07:50. | :08:00. | |
sort has made it really nice to be able to go up to companies | :08:01. | :08:03. | |
and they can look at your record and say, oh, you look | :08:04. | :08:06. | |
like someone who we can trust with sensitive data. | :08:07. | :08:12. | |
However, there is a perception that the Mormon faith places | :08:13. | :08:14. | |
an emphasis on men working while women stay at | :08:15. | :08:16. | |
One of the reasons that I actually became an IT major was | :08:17. | :08:23. | |
because it is so adaptable to be able to work from home, | :08:24. | :08:29. | |
or I can just work on projects if I would like. | :08:30. | :08:32. | |
And so that was actually a big selling point for me, | :08:33. | :08:35. | |
that it was something that I could do and be with a family. | :08:36. | :08:39. | |
So this is a state with a vibrant tech scene which differentiates | :08:40. | :08:42. | |
itself from Silicon Valley in more ways than one. | :08:43. | :09:02. | |
Welcome to the Whee Kim Tech. It was the week that poor command goal | :09:03. | :09:10. | |
proved controversial. Players move around in the real world to achieve | :09:11. | :09:14. | |
objectives in the game. Some have injured themselves. Some have good | :09:15. | :09:20. | |
players into remote areas to rob them. We might be closer to | :09:21. | :09:27. | |
hypersonic travel. That would mean a plane travelling from London to | :09:28. | :09:32. | |
Sydney in four hours. The European Space Agency has provided the final | :09:33. | :09:40. | |
cash and get -- cash injection for this vehicle. It could jet into | :09:41. | :09:50. | |
space. Land Rover's research could make it tonne is striving possible | :09:51. | :09:55. | |
on any surface or to rain. The camera and sensors should be able to | :09:56. | :10:00. | |
see better than a human. It should allow the vehicle to plan a route. | :10:01. | :10:05. | |
Do you think there is not enough room for everything? Maybe you need | :10:06. | :10:14. | |
some reported furniture. Created using MIT media lab technology, this | :10:15. | :10:18. | |
moves the river around making space in the right place at the right | :10:19. | :10:19. | |
time. This has already been deployed to | :10:20. | :10:53. | |
investigate sea beds. It can doubt be deeper than a human can. The | :10:54. | :11:01. | |
controller sets on a boat. He can feel through feedback in the | :11:02. | :11:08. | |
controllers. Although the robot has eight thrusters and 14 motors, they | :11:09. | :11:14. | |
have managed to make it intuitive to control where you can steer the | :11:15. | :11:18. | |
entire robot using these two controls here. Today and driving the | :11:19. | :11:25. | |
3-D simulation and taking me through my paces is this researcher. You can | :11:26. | :11:35. | |
rotate your wrist. This is the first time I have done this. I got. Why | :11:36. | :11:51. | |
did you decide to make a robot in the shape of a human? Partially it | :11:52. | :11:59. | |
is due to functionality. If it has two arms and two legs than as you | :12:00. | :12:03. | |
control it you feel like you are there and there is one-to-one | :12:04. | :12:09. | |
mapping in terms of manipulation. The robot is meant to work around | :12:10. | :12:13. | |
humans and the human friendly sold by shaping it in a human manner it | :12:14. | :12:20. | |
comes across as a friendly robot and we want to get the right impression, | :12:21. | :12:25. | |
it is a robot that it is safe to be around. Per8-mac I will just pop the | :12:26. | :12:37. | |
stone there. -- I will just put this down here. Per8-mac for controllers | :12:38. | :12:49. | |
with more expertise, Ocean one has already created Treasurer. It found | :12:50. | :12:57. | |
this has lain at the bottom of the Mediterranean for 350 years. | :12:58. | :13:11. | |
After years of talking about drones they go up, it is nice to see one | :13:12. | :13:20. | |
that goes down instead. Dave has travelled to one of the most | :13:21. | :13:24. | |
beautiful places in the United States to see what lies beneath. | :13:25. | :13:49. | |
People have been living here and visiting here for over a century. | :13:50. | :13:56. | |
You couldn't drive around the lakes of the only way to get around it was | :13:57. | :14:00. | |
built. Many ships called the lake their home but only one was referred | :14:01. | :14:10. | |
to by name. She carried the mail and visitors and cargo to all of these | :14:11. | :14:15. | |
places where there is no road connecting them, so the connection | :14:16. | :14:21. | |
was from the steamer. As the boat grew old it was intentionally sunk | :14:22. | :14:25. | |
in an attempt to preserve it, but it did not work. She sank to deep and | :14:26. | :14:34. | |
until now all made the most skilled divers could reach it. There are | :14:35. | :14:42. | |
trucks that let you by sending a live video back to the surface. Our | :14:43. | :14:49. | |
goal is to inspire curiosity. This underwater drone began as a | :14:50. | :14:53. | |
kick-start project. Today it is searching for the ship. We will be | :14:54. | :14:59. | |
doing all our operations from this room, this is Mission control. It | :15:00. | :15:03. | |
has a camera and lights and batteries. There will be sending a | :15:04. | :15:10. | |
signal to the surface. We are operating everything from here. The | :15:11. | :15:15. | |
focus of the project is affordability and accessibility. | :15:16. | :15:21. | |
This is in a multi-million dollar expedition to the deep, it is a | :15:22. | :15:27. | |
community effort. We do not know what is going to happen. We are | :15:28. | :15:31. | |
testing new equipment. Everyone is watching. I guess that is what | :15:32. | :15:34. | |
explanation is. -- exploration. We are 150 metres down on the lake | :15:35. | :16:16. | |
and we're just getting a first look at the ship. | :16:17. | :16:48. | |
No one has been this close to the steamship for more than 70 years. | :16:49. | :17:08. | |
I think what is most exciting about what is happening here is not so | :17:09. | :17:15. | |
much that they're able to go to the boat and look around, more the way | :17:16. | :17:20. | |
they are able to do it. This technology is affordable. That robot | :17:21. | :17:24. | |
was the same cost as a high-end laptop. The first time anyone who | :17:25. | :17:29. | |
wants can explore places that we have not been looking at before and | :17:30. | :17:33. | |
I'm excited to see what they will find. | :17:34. | :17:50. | |
Last year we revealed the latest multi-dollar idea. We were told that | :17:51. | :17:59. | |
we should put all our money into the start-up that does this. Delivering | :18:00. | :18:08. | |
quarters on for your laundry. You pay 15 dollars and you get $10 of | :18:09. | :18:21. | |
quarries. -- quarters. I do not want to spend time going to the store and | :18:22. | :18:25. | |
picking out my groceries and I do not want to wait for my close to | :18:26. | :18:29. | |
watch. I would rather outsource that, pool our pineapple on my phone | :18:30. | :18:36. | |
and have someone do it for me. It is time to put on this same shirt as | :18:37. | :18:42. | |
last time and lets see how those ideas are getting on. Thank you for | :18:43. | :18:49. | |
having this year. Last year we talked about this explosion in | :18:50. | :18:55. | |
start-ups that were servicing other start-ups, doing your laundry and | :18:56. | :18:59. | |
you are earning for you. How's that going? That might not have been the | :19:00. | :19:05. | |
best idea. A lot of start-ups are struggling with that business model. | :19:06. | :19:13. | |
They were charging too little. They started having a problem making | :19:14. | :19:17. | |
money and surviving and some have shut down. You can still find | :19:18. | :19:23. | |
someone to do these things for you, assisted living for the silicon | :19:24. | :19:29. | |
valley start-up worker is still happening. Is there a bubble? I | :19:30. | :19:34. | |
think that there is and it shifted six months ago. There are companies | :19:35. | :19:42. | |
out there we call unicorns, they are meant to be rare, but now there are | :19:43. | :19:45. | |
many companies worth more than $1 billion. Will it be worth that when | :19:46. | :19:54. | |
it goes public? We do not know. The problem is that investors are | :19:55. | :19:57. | |
starting to pull back and really question whether this is a good | :19:58. | :20:01. | |
business idea. Your laundry start-ups were a perfect example. | :20:02. | :20:08. | |
Investors are starting to question the value of these businesses. There | :20:09. | :20:12. | |
is a change in what people are excited about and it is moving | :20:13. | :20:19. | |
towards artificial intelligence and other futuristic technologies and | :20:20. | :20:26. | |
less of these OnDemand, subsidise your life options. Thank you for | :20:27. | :20:34. | |
your time. For all the money that there is this place you do not have | :20:35. | :20:37. | |
to go far in San Francisco to see the opposite side of things. City | :20:38. | :20:44. | |
suffers from chronic homelessness. We met one entrepreneur who is | :20:45. | :20:47. | |
trying to solve one piece of that problem. | :20:48. | :20:58. | |
It's the world's dumbest problem, according to Komal Ahmad. | :20:59. | :21:02. | |
So she is using technology to solve it. | :21:03. | :21:04. | |
The idea sprang while she was at UC Berkeley. | :21:05. | :21:06. | |
She had invited a homeless man to join her for lunch. | :21:07. | :21:08. | |
He said, my name is John, I just came back from my second | :21:09. | :21:12. | |
I have been waiting weeks for my benefits to kick | :21:13. | :21:15. | |
And then to add insult to injury, right across the street, | :21:16. | :21:20. | |
Berkeley's dining hall was throwing away thousands of pounds | :21:21. | :21:22. | |
That lunch led Ahmad to start COPIA, which earlier this | :21:23. | :21:26. | |
Businesses and event organisers can request a pick-up of their excess | :21:27. | :21:30. | |
food and have it delivered to feed communities in need. | :21:31. | :21:35. | |
Click tagged along as COPIA picked up a hefty donation | :21:36. | :21:37. | |
from The Whole Cart, which caters meals for | :21:38. | :21:39. | |
COPIA charges for the pick-up and a per pound fee. | :21:40. | :21:45. | |
For another fee it also provides analytics, an appealing feature | :21:46. | :21:48. | |
Picking up food from donors, delivering it to recipients | :21:49. | :21:58. | |
and making sure it doesn't spoil in the process is, | :21:59. | :22:00. | |
as you might imagine, an enormous logistical puzzle. | :22:01. | :22:02. | |
But it's made simpler with algorithms. | :22:03. | :22:09. | |
When somebody opens up the COPIA app, they will say | :22:10. | :22:12. | |
So they could say, I have 1000 lbs of food, chicken | :22:13. | :22:15. | |
And our algorithm on the back end will now match that exact amount | :22:16. | :22:21. | |
and type of food to the nearest nonprofit that can accept the most | :22:22. | :22:24. | |
amount of food and then it will also match it to a driver | :22:25. | :22:27. | |
who will have the most efficient route to go and drop | :22:28. | :22:30. | |
Within an hour, COPIA arrives at a women's shelter run | :22:31. | :22:34. | |
by the Berkeley Food and Housing Project. | :22:35. | :22:39. | |
The donated food provides a healthier option to the frozen | :22:40. | :22:41. | |
And it frees up money to be spent on other services. | :22:42. | :22:46. | |
It feels good to know that, OK, I won't be hungry, | :22:47. | :22:49. | |
It is cold outside, it's raining, I'm inside. | :22:50. | :22:58. | |
In five years, COPIA has helped feed 700,000 people. | :22:59. | :23:00. | |
An unexpected big donation might require extra staff or vehicles. | :23:01. | :23:06. | |
And finding nonprofit organisations within close proximity of donors | :23:07. | :23:08. | |
The plan is to perfect the model in the San Francisco | :23:09. | :23:16. | |
Other start-ups are also using what would otherwise be wasted. | :23:17. | :23:22. | |
Spoiler Alert doesn't deliver the food but offers a marketplace | :23:23. | :23:24. | |
SIRUM collects access medication from nursing homes or pharmacies | :23:25. | :23:32. | |
and gives it to patients in need at mental health facilities. | :23:33. | :23:37. | |
The prescriptions are typically for chronic conditions, | :23:38. | :23:39. | |
It's the sharing economy with a 'waste not, want not' twist. | :23:40. | :23:55. | |
That was Sumi with a tech idea that seems to be doing some genuine | :23:56. | :23:58. | |
Next week I'm going to be in LA as our US journey continues. | :23:59. | :24:06. | |
There are worse ways to spend your summer. | :24:07. | :24:08. | |
I have been looking for it since they start of June and finally I | :24:09. | :24:43. | |
have found some hot weather. Things are going to warm up next | :24:44. | :24:44. |