0:00:01 > 0:00:04Now on BBC News - Click.
0:00:05 > 0:00:06Coming up:
0:00:06 > 0:00:10Robots build a table, cockroaches go cyborg,
0:00:10 > 0:00:13and I go a little bit crazy.
0:00:13 > 0:00:14Yesss!
0:00:14 > 0:00:19This is the best of Click, 2015.
0:00:41 > 0:00:45It's the end of the year and time to look back on what we
0:00:45 > 0:00:47have learned in the past 12 months.
0:00:47 > 0:00:50And above everything else that has happened in 2015, there is one thing
0:00:50 > 0:00:54that we all agree has been a thing.
0:00:54 > 0:00:592015 has seen the rise of the machines.
0:00:59 > 0:01:03Kind of.
0:01:03 > 0:01:07Yeah, they may not be quite ready to take over just yet but I genuinely
0:01:07 > 0:01:11believe we are starting to see the beginnings of a robot revolution.
0:01:11 > 0:01:13Machines are starting to understand the world around them,
0:01:13 > 0:01:16they are starting to understand what we are talking about,
0:01:16 > 0:01:21and they are starting to be able to build things on their own.
0:01:21 > 0:01:24Welcome to MIT, where these guys are doing something that all humans hope
0:01:24 > 0:01:31we won't have to do in the future.
0:01:31 > 0:01:33They're building furniture.
0:01:33 > 0:01:39Really slowly, but it is doing it.
0:01:39 > 0:01:43It has the screw in, which is better than me for a start.
0:01:43 > 0:01:47The grip is just four rubber bands but as it twists, it manages to
0:01:47 > 0:01:50grip the table leg properly.
0:01:50 > 0:01:52Each piece of the furniture has a unique
0:01:52 > 0:01:54pattern of reflective balls on.
0:01:54 > 0:02:00There is a whole array of infrared sensors around the room.
0:02:00 > 0:02:04The computer system running this demo knows where everything is.
0:02:04 > 0:02:06The Computer Science and Artificial Intelligence Laboratory is the
0:02:06 > 0:02:15largest research lab here at MIT and it is also the weirdest looking.
0:02:15 > 0:02:17Looks like Gaudi has had a go at that one.
0:02:17 > 0:02:21Anyway, it is here that we enrol on our journey.
0:02:21 > 0:02:25The Distributive Robotic Lab looks like this.
0:02:25 > 0:02:27I have no idea what that is.
0:02:27 > 0:02:30This is Baxter, a very famous robot.
0:02:30 > 0:02:34And here is a robotic garden full of programmable moving LED flowers and
0:02:34 > 0:02:38designed to illustrate some less visually interesting
0:02:38 > 0:02:44but nevertheless essential computer science techniques.
0:02:44 > 0:02:47It is difficult to get young students, particularly girls,
0:02:47 > 0:02:50interested in computer science.
0:02:50 > 0:02:52Concepts like fundamental algorithms that every computer scientist needs
0:02:52 > 0:02:55to know, such as how to find the shortest path
0:02:55 > 0:03:05from A to B, demonstrated here by the flowers changing colour.
0:03:05 > 0:03:10One of the main missions of the lab in particular
0:03:10 > 0:03:14is to develop robots that can think for themselves
0:03:14 > 0:03:20and work together to solve increasingly complex problems.
0:03:20 > 0:03:23But to create robots that can do anything,
0:03:23 > 0:03:28you first have to understand how we and other animals use our brains.
0:03:28 > 0:03:30Back in March, we visited researchers at Sheffield
0:03:30 > 0:03:37University, who were working to map out the brain of a bee.
0:03:41 > 0:03:44As you might have guessed, this is not the easiest thing to do,
0:03:44 > 0:03:47which is why they have started with one part of the brain,
0:03:47 > 0:03:50the part that lets the bee see.
0:03:50 > 0:03:53Now, the scientists have plugged this
0:03:53 > 0:03:59simulated bee brain into a drone.
0:03:59 > 0:04:04A computer simulation of a bee's brain is flying this aircraft.
0:04:04 > 0:04:08The bee brain simulation is made up of thousands of virtual neurons,
0:04:08 > 0:04:11each represented by one of these coloured spheres.
0:04:11 > 0:04:14The way they are laid out and wired up is copied directly
0:04:14 > 0:04:17from a real bee and just like with a real bee brain,
0:04:17 > 0:04:20when what the camera sees is filtered through these simulated
0:04:20 > 0:04:24neurons, this is what happens.
0:04:24 > 0:04:27If you look closely, you can see the chessboard pattern forming.
0:04:27 > 0:04:29Amazing.
0:04:29 > 0:04:32Lots of time has been spent training honeybees to fly down tunnels
0:04:32 > 0:04:35and our model reproduces all of the behaviours that real
0:04:35 > 0:04:38honeybees exhibit.
0:04:38 > 0:04:41And you can manipulate the flight behaviour of the model
0:04:41 > 0:04:44in the same way that you can manipulate the flight behaviour
0:04:44 > 0:04:47of a real honeybee that has been trained to fly down a corridor.
0:04:47 > 0:04:50The team here are not the only researchers looking to bees
0:04:50 > 0:04:51for inspiration.
0:04:51 > 0:04:55One team has tried to replicate a bee's sense of smell.
0:04:55 > 0:04:58And across the globe, researchers at Harvard University are trying to
0:04:58 > 0:05:00create tiny bee-sized robots, which they hope could eventually be
0:05:01 > 0:05:06used to pollinate our crops.
0:05:14 > 0:05:17In a tiny basement room at Texas University live hundreds of Central
0:05:17 > 0:05:23American giant cave cockroaches.
0:05:23 > 0:05:25The school is famous for adapting robots for disaster
0:05:25 > 0:05:28zones, but these cockroaches are destined to be cyborgs designed to
0:05:28 > 0:05:31operate in areas difficult for humans to reach, like nuclear
0:05:31 > 0:05:35disaster zones or earthquakes.
0:05:35 > 0:05:38They chose this cockroach for its natural tendency to seek out
0:05:38 > 0:05:43dark spaces and for its size.
0:05:45 > 0:05:47The cockroaches are gassed with carbon dioxide before being
0:05:47 > 0:05:50brought over to be operated on.
0:05:50 > 0:05:54He is fully asleep and he will stay asleep for at least ten minutes.
0:05:54 > 0:05:56The idea is to work pretty quickly on this.
0:05:56 > 0:06:00Why are you using the whiteout?
0:06:00 > 0:06:02Cockroaches have a waxy surface.
0:06:02 > 0:06:09It creates a light adhesive.
0:06:09 > 0:06:13Little hairy legs!
0:06:13 > 0:06:16These acupuncture needles are then set
0:06:16 > 0:06:18into the cockroach's ganglia, an area of neurons responsible
0:06:18 > 0:06:21for involuntary movement.
0:06:21 > 0:06:29It is kind of deceptive.
0:06:29 > 0:06:32And that is the finished product.
0:06:32 > 0:06:34Is that hurting him?
0:06:34 > 0:06:38No, it is just startling because you've picked him up.
0:06:38 > 0:06:40Some of our viewers might think it is cruel
0:06:40 > 0:06:43to put wires into their brain.
0:06:43 > 0:06:45I don't think the cockroaches have any feeling
0:06:45 > 0:06:47for that kind of problem.
0:06:47 > 0:06:49They don't have a big brain to start with.
0:06:49 > 0:06:54They are happy, I have no doubt about that.
0:06:54 > 0:06:59We're not really hurting them in any way, we're not really causing pain.
0:07:00 > 0:07:02The final step in the process is attaching the battery
0:07:02 > 0:07:04so it can work with the controller.
0:07:04 > 0:07:10This is a simple remote that we modified.
0:07:10 > 0:07:13I'm going to try and make the cyborg cockroach go.
0:07:13 > 0:07:16There he goes.
0:07:16 > 0:07:20My goodness!
0:07:21 > 0:07:23Once we have experimented with the cockroaches,
0:07:24 > 0:07:25we put them back in the box.
0:07:25 > 0:07:28The important thing is we don't test them again.
0:07:28 > 0:07:33Once we do the test, they get retired.
0:07:37 > 0:07:40These cyborg cockroaches will be getting ready for field tests
0:07:40 > 0:07:41and the researchers here are already looking
0:07:41 > 0:07:46at other insects they could use.
0:07:51 > 0:07:54Silicon Valley, the centre of the tech world.
0:07:54 > 0:07:56San Francisco and its satellite towns have spawned
0:07:56 > 0:07:59thousands of technology companies over the years, but few have had
0:07:59 > 0:08:04as much impact as this one.
0:08:04 > 0:08:06From its enormous campus in Palo Alto,
0:08:06 > 0:08:10its tentacles now reach everywhere.
0:08:10 > 0:08:13Welcome to the Googleplex.
0:08:14 > 0:08:17Google dominates web search these days.
0:08:17 > 0:08:20Although on this lazy afternoon in the sun, it does appear to be
0:08:20 > 0:08:22taking it easy.
0:08:22 > 0:08:26Well, maybe after years of work that started as just a few
0:08:26 > 0:08:31geeks in a garage, this massive empire feels the need for a break.
0:08:31 > 0:08:33The job of search has been done.
0:08:33 > 0:08:36The web has been indexed.
0:08:36 > 0:08:39But in another sense, there is a whole new job to do
0:08:39 > 0:08:43and that is to understand it.
0:08:43 > 0:08:45After building up a collection of trillions of words,
0:08:45 > 0:08:49Google, amongst others, is trying to connect them all up in meaningful
0:08:49 > 0:08:55ways, maybe even in ways similar to the brains in our heads.
0:08:55 > 0:08:57And this will help Google to work out more precisely what
0:08:57 > 0:09:01we really need to know.
0:09:01 > 0:09:05Here is the Twitter account from BBC Sport.
0:09:05 > 0:09:10They just tweeted that Gareth Edwards has been knighted.
0:09:10 > 0:09:11I wonder how old he is.
0:09:11 > 0:09:16OK, Google, how old is he?
0:09:16 > 0:09:19Gareth Edwards is 67 years old.
0:09:19 > 0:09:21And it has understood the most important thing in that string of
0:09:21 > 0:09:24text and it knows what is the "he."
0:09:24 > 0:09:25Yes.
0:09:25 > 0:09:31It understand the context.
0:09:32 > 0:09:35This is a demo of a function called Now On Tap, which is coming to the
0:09:35 > 0:09:38new version of the Android operating system when it is released.
0:09:38 > 0:09:41It's an extension of Google Now and it offers more information
0:09:41 > 0:09:44on the things that you read about.
0:09:44 > 0:09:46That sounds simple but it requires more understanding
0:09:46 > 0:09:49than you might think.
0:09:49 > 0:09:52If I were to say to you Michelangelo was my favourite Renaissance
0:09:52 > 0:09:57painter, your brain would instantly do loads of things.
0:09:57 > 0:09:59You would know I was talking about Michelangelo the artist,
0:09:59 > 0:10:01not the turtle.
0:10:01 > 0:10:03You would know that the Renaissance was a period of time.
0:10:03 > 0:10:06And you would know there were other artists around then as well,
0:10:06 > 0:10:09including sculptures and musicians.
0:10:09 > 0:10:12But to a computer, that sentence is just a collection of words.
0:10:12 > 0:10:15It doesn't actually mean anything.
0:10:15 > 0:10:18The aim is to make computers understand that these words are
0:10:18 > 0:10:20actually people, places and other things, and crucially,
0:10:20 > 0:10:24that they all interconnect.
0:10:24 > 0:10:28This is what Google calls the Knowledge Graph.
0:10:28 > 0:10:30Think of this as Google's understanding of the world
0:10:30 > 0:10:33and all the things.
0:10:33 > 0:10:35It can be all sorts of things.
0:10:35 > 0:10:38Movies, places, restaurants, cocktail recipes.
0:10:38 > 0:10:43But understanding words is only one part of the equation.
0:10:43 > 0:10:47For a robot to be able to function in the real world, it also needs to
0:10:47 > 0:10:50interpret the deluge of information it gets from its cameras.
0:10:50 > 0:10:54In other words, it needs to understand what it sees.
0:10:54 > 0:10:57Computers find this task incredibly hard because the real world is not
0:10:57 > 0:11:04easily represented by pure data.
0:11:04 > 0:11:07Researchers are working on computer vision.
0:11:07 > 0:11:10For it to be successful, the computer needs to be able to
0:11:10 > 0:11:13distinguish items in a scene, identify what it is looking at,
0:11:13 > 0:11:15and develop an understanding of its circumstances
0:11:15 > 0:11:21so it can complete its task.
0:11:21 > 0:11:23The researchers are working on a neural network
0:11:23 > 0:11:26that can identify 20 objects at a time.
0:11:26 > 0:11:29That does not sound like many but if they get this right,
0:11:29 > 0:11:34they can apply the same method to millions of objects.
0:11:34 > 0:11:37The network is fed manually separated images.
0:11:37 > 0:11:40As it scans the features of an object, it develops
0:11:40 > 0:11:42an understanding, learning from its mistakes and getting better
0:11:42 > 0:11:47at recognising other instances.
0:11:47 > 0:11:53Most importantly, it gets more efficient at it every time.
0:11:53 > 0:11:57But for it to be of any use, it needs to get it right as often
0:11:57 > 0:12:01as humans do and very quickly, no matter how tricky the scene.
0:12:01 > 0:12:08This is where some human help can come in handy.
0:12:08 > 0:12:11Here is a room with some objects inside.
0:12:11 > 0:12:18I'm using a 3D infrared camera to scan my surroundings.
0:12:20 > 0:12:25Now, I'm going to hand the camera to Stuart while I label the scene.
0:12:25 > 0:12:29And you do that like this.
0:12:29 > 0:12:31As I go around touching the items, I'm quickly identifying
0:12:31 > 0:12:35the different classes of objects in my environment.
0:12:35 > 0:12:39One day, we may all be able to help our machines to recognise our
0:12:39 > 0:12:43stuff no matter how unique it is.
0:12:43 > 0:12:46The point of this research is that someday we will have robots
0:12:46 > 0:12:49that can perform lots of tasks to help us in our daily lives.
0:12:49 > 0:12:51But we're already seeing this technology being used
0:12:51 > 0:12:52out in the real world,
0:12:52 > 0:12:54whether it is to help nurses assist surgeons
0:12:54 > 0:12:58or to find a cure for cancer.
0:13:01 > 0:13:03I was working as a currency trader.
0:13:03 > 0:13:06I got a call one day from my mum,
0:13:06 > 0:13:14saying that my dad was having trouble finishing his sentences.
0:13:14 > 0:13:17They did an MRI and they found three unidentified masses on his brain,
0:13:17 > 0:13:24which turned out to be glioblastoma multiforma,
0:13:24 > 0:13:28which is the most common and aggressive brain tumour in adults.
0:13:30 > 0:13:33Matt Da Silva's story is in many ways very similar to anyone who
0:13:33 > 0:13:36has lost someone to cancer but it becomes extraordinary when you hear
0:13:36 > 0:13:45about his ambition to revolutionise the way that we treat the disease.
0:13:45 > 0:13:48In his laboratory in San Francisco, he is looking to develop
0:13:48 > 0:13:52a treatment method that could be custom made for each patient.
0:13:52 > 0:13:55The idea is to combine off the shelf already approved
0:13:55 > 0:13:59medicines to create a drug therapy regime that results
0:13:59 > 0:14:05in shrinking tumours and hopefully complete recovery.
0:14:05 > 0:14:08The problem is that there are far too many approved drugs
0:14:08 > 0:14:12on the market, containing many different chemical compounds.
0:14:12 > 0:14:15To test all of the possible combinations in a lab is impossible.
0:14:19 > 0:14:22To test all of the possible combinations in a lab is impossible.
0:14:22 > 0:14:24This is where artificial intelligence comes to the rescue.
0:14:24 > 0:14:27Notable Labs has partnered up with Atomwise, a company that has
0:14:27 > 0:14:29developed an intelligent algorithm that can simulate how an illness
0:14:29 > 0:14:32attacks the human body, and more crucially, test chemical compounds
0:14:32 > 0:14:34artificially to see which treatments would be most
0:14:34 > 0:14:41effective in blocking its progress.
0:14:41 > 0:14:44If you tried to, as a human, consider all of the possible
0:14:44 > 0:14:46factors that relate to each of these interactions,
0:14:46 > 0:14:47it could take a lifetime.
0:14:47 > 0:14:49Hundreds of thousands of concurrent factors that interact
0:14:49 > 0:14:56in highly non-lineal ways.
0:14:56 > 0:14:57The algorith narrows down the possible combinations
0:14:57 > 0:14:59from millions to just a few hundred.
0:14:59 > 0:15:02Back at the lab, these combinations are tested on real cancer cells that
0:15:02 > 0:15:09have been taken from patients.
0:15:09 > 0:15:12This is a patient that had surgery in San Francisco three weeks ago.
0:15:12 > 0:15:15We're waiting for their cells to grow and form spheres.
0:15:15 > 0:15:18The reason we want those cells to form spheres is because we want
0:15:18 > 0:15:30them to be like miniature tumours.
0:15:30 > 0:15:33When we test it with drugs, we want to make sure that what
0:15:33 > 0:15:35happens here will translate back to the patient themselves.
0:15:35 > 0:15:38Matt is hoping to certify his method within a year
0:15:38 > 0:15:40so he can treat large numbers of people.
0:15:40 > 0:15:43And if it really does work, we could start treating some cancers with
0:15:43 > 0:15:45medications that are already sitting on a shelf
0:15:45 > 0:15:55and also massively cut the costs of those treatments.
0:15:55 > 0:15:58It is one of the leading cancer research hospitals in the world,
0:15:58 > 0:16:01with a reputation and a name to live up to.
0:16:01 > 0:16:03Three years ago, to mark its centenary, the doctors invited
0:16:03 > 0:16:12patients and their families to write messages and tie them to the trees.
0:16:12 > 0:16:23They have stayed there ever since.
0:16:23 > 0:16:26But inside they are not pinning the future of beating brain cancer
0:16:26 > 0:16:32on hope alone.
0:16:32 > 0:16:36This is one of the first places in the world to get some new kit
0:16:36 > 0:16:39that uses robotics.
0:16:39 > 0:16:41In most cases, neurosurgeons also try to remove
0:16:41 > 0:16:45as much of the brain tumour as possible if it is safe to do so.
0:16:45 > 0:16:53And crucially, that means avoiding damaging
0:16:53 > 0:16:56Through a tiny hole made in the skull, a tube, which houses
0:16:56 > 0:17:00a laser, can be fed to the exact spot, using an MRI scanner.
0:17:00 > 0:17:03The laser is twisted towards the direction of the cancerous
0:17:03 > 0:17:06tissue, while the healthy tissue on the other side is left untouched.
0:17:06 > 0:17:08This is one of the first patients to use the system.
0:17:08 > 0:17:10The initial results appear positive.
0:17:10 > 0:17:12But the man in charge of brain cancer research here
0:17:12 > 0:17:24doesn't want to stop there.
0:17:24 > 0:17:27He is going beyond stem and T-cell treatments to help develop
0:17:27 > 0:17:29international nano particles that attack cancer growth.
0:17:29 > 0:17:31He's adapted new equipment used to help deliver them,
0:17:31 > 0:17:43straight to the front line.
0:17:43 > 0:17:45By removing the remaining burnt tumour after the treatment, space is
0:17:45 > 0:17:48left inside the brain for the nano particles to then be delivered.
0:17:48 > 0:17:51Either drugs or these designer cells then go to work fighting
0:17:51 > 0:18:07any remaining cancer threat.
0:18:07 > 0:18:08But tumours re-emerge often after treatment.
0:18:08 > 0:18:10So the doctor's team wants to direction
0:18:10 > 0:18:13they should special fighter cells once they are inside the brain.
0:18:13 > 0:18:16By attaching microscopic magnet to the particles he hopes to move the
0:18:16 > 0:18:19treatment to any area of the brain.
0:18:19 > 0:18:21Simply by using a magnet.
0:18:21 > 0:18:22Magnet-guided treatments are attracting serious attention.
0:18:22 > 0:18:25Three months ago Google X, the scientific research arm of Google,
0:18:25 > 0:18:30got to work on a similar idea.
0:18:30 > 0:18:33Both teams expect new treatments in five years' time.
0:18:33 > 0:18:36Now, you have heard of tug boats, well let me introduce you to
0:18:36 > 0:18:52the tug gots.
0:18:52 > 0:18:53Press go to continue.
0:18:53 > 0:18:5625 of them roaming up and down the hospital halls, ferrying, meals,
0:18:56 > 0:18:58trash and pharmaceutical supply, the latter being securely locked
0:18:58 > 0:19:02in so when they arrive at their destination only people with the PIN
0:19:02 > 0:19:04code or fingerprint authentication can open them up.
0:19:04 > 0:19:06We have learnt of 14 football feeds to navigate,
0:19:06 > 0:19:14they rely on built-in maps together with Wi-Fi to get their bearings.
0:19:14 > 0:19:16This robot has summoned the elevator and now
0:19:16 > 0:19:19after making sure there is no-one in it, he-she-it is going to take the
0:19:19 > 0:19:21supplies up where they need to go.
0:19:21 > 0:19:22Hold that lift!
0:19:22 > 0:19:25This is Hugo, and it is about to embark on the
0:19:25 > 0:19:47toughest test known to robot-kind.
0:19:47 > 0:19:50Next weekend it's the DARPA rot ticks challenge where teams
0:19:50 > 0:19:58from around the world will show up in California with their bots.
0:19:58 > 0:20:01The mission - to complete a series of human tasks with minimal human
0:20:01 > 0:20:01help.
0:20:01 > 0:20:04Wow!
0:20:04 > 0:20:07Oh, my gosh!
0:20:07 > 0:20:10Tomorrow the team pack-up and fly out, which means today is
0:20:10 > 0:20:12the last day of practice around their practice course.
0:20:12 > 0:20:16Which is unbelievably tough!
0:20:16 > 0:20:20He has to find and close a gas valve, use a freaking drill to got a
0:20:20 > 0:20:36hole, pull a handle, push a button, and fight through rough terrain.
0:20:36 > 0:20:40The aim is to complete the course in the fastest time, and anything under
0:20:40 > 0:20:4335 minutes puts them in the running to win the $2 million prize.
0:20:43 > 0:20:44So that's how it drives.
0:20:44 > 0:20:45One hand on the robot...
0:20:45 > 0:20:49Yes.
0:20:49 > 0:20:50One hand on the steering wheel.
0:20:50 > 0:20:53Right.
0:20:53 > 0:20:57This is a robot driving a car using controls that were made for humans.
0:20:57 > 0:20:57OK.
0:20:57 > 0:21:00This is going to be the coolest exit from a car since
0:21:00 > 0:21:02the Dukes of Hazard got in one.
0:21:02 > 0:21:04Once out, he reveals he has wheels of his own.
0:21:04 > 0:21:11In roll mode he can travel further faster.
0:21:11 > 0:21:12He has the handle!
0:21:12 > 0:21:14Handling the drill is an even bigger test.
0:21:14 > 0:21:17Once it's been identified by the team, it's up to
0:21:17 > 0:21:20the sensors in his hand to feel it, find the button and apply
0:21:20 > 0:21:24the correct pressure to cut a hole.
0:21:24 > 0:21:28Robot DIY!
0:21:28 > 0:21:32The DARPA challenge will contain one task which the teams won't know
0:21:32 > 0:21:33in advance.
0:21:33 > 0:21:36The robot will need to analyse the scene, relay the 3D information
0:21:36 > 0:21:41back to the humans and they will need to workshop a solution.
0:21:41 > 0:21:44Once they have done that in virtual space, they will upload
0:21:44 > 0:21:47the instructions back to the bot.
0:21:47 > 0:21:51In this case, it's pushing a button, which I have to say is no match
0:21:51 > 0:21:56for this brilliant butch, block of silicon!
0:21:56 > 0:21:59Is it wrong to say I am ever so slightly in love?
0:21:59 > 0:22:00What do you mean too exited?
0:22:00 > 0:22:02It was amazing!
0:22:02 > 0:22:05And Hubo ended up winning the DARPA challenge too.
0:22:05 > 0:22:07So there!
0:22:07 > 0:22:10I have to say, though, during the our travels this year, we
0:22:10 > 0:22:15have seen some robots which didn't match up with our expectations.
0:22:16 > 0:22:25Hello.
0:22:25 > 0:22:28Do you speak English?
0:22:28 > 0:22:33REPLAYS.
0:22:33 > 0:22:36Mmm, no.
0:22:36 > 0:22:37OK.
0:22:37 > 0:22:40Well, let's try the next receptionist.
0:22:40 > 0:22:43Who turns out to be...
0:22:43 > 0:22:46A...
0:22:46 > 0:22:52Dinosaur!
0:22:52 > 0:22:54And he does speak my language.
0:22:54 > 0:23:02Welcome.
0:23:02 > 0:23:04Welcome to the hotel.
0:23:04 > 0:23:06LAUGHTER Thank you for your visitors.
0:23:06 > 0:23:10Your name on the room card on top of the fill in the phone number, please
0:23:10 > 0:23:12put us to the bottom of the post.
0:23:12 > 0:23:13Please press to proceed with...
0:23:13 > 0:23:19Did you get that?
0:23:19 > 0:23:21LAUGHTER I think I did!
0:23:21 > 0:23:24Please move to the right touch panel and check in.
0:23:24 > 0:23:27Do you wish to use facial recognition for entrance?
0:23:27 > 0:23:28Thank you so much.
0:23:28 > 0:23:33OK.
0:23:33 > 0:23:36It was more of not a real robot experience but staying at the hot
0:23:36 > 0:23:38natural Japan was a real blast.
0:23:38 > 0:23:44That's the end of the first of looking back at 2015.
0:23:44 > 0:23:45There is another one next week.
0:23:45 > 0:23:46Thanks for watching.
0:23:46 > 0:24:14We will see you then.
0:24:14 > 0:24:18The weather is going to turn a bit calmer for the last day of 2015.