0:00:02 > 0:00:03If you have ever wondered what's
0:00:03 > 0:00:05going to happen in the future,
0:00:05 > 0:00:07then this is the programme for you.
0:00:07 > 0:00:09Because we're going to be asking
0:00:09 > 0:00:11the kind of questions that we all have
0:00:11 > 0:00:13about our future.
0:00:13 > 0:00:16In this very special edition of Horizon we'll be
0:00:16 > 0:00:20revealing the ten things you definitely need to know that will,
0:00:20 > 0:00:23for better or worse, change our lives.
0:00:24 > 0:00:26We'll explore how artificial intelligence
0:00:26 > 0:00:28will change the way we work.
0:00:28 > 0:00:30Can I enquire, do you have pain in your mouth?
0:00:30 > 0:00:32- Yes, I do.- I see.
0:00:32 > 0:00:36We'll look at the likely impact of our changing climate.
0:00:36 > 0:00:39It could well top 40 degrees in a few days' time.
0:00:39 > 0:00:43And discover how gene therapy will transform medicine.
0:00:43 > 0:00:46And he said, "There is no evidence of disease.
0:00:46 > 0:00:47"You are cancer-free."
0:00:47 > 0:00:51We'll introduce the people already turning themselves into cyborgs...
0:00:51 > 0:00:53This last year people just shout at me, Pokemon.
0:00:53 > 0:00:55And they try to catch me.
0:00:55 > 0:00:57..ask if renewable energy is here to stay...
0:00:57 > 0:00:59The place to be might not be down here,
0:00:59 > 0:01:02or even up on that hilltop up there.
0:01:02 > 0:01:03It's up there.
0:01:03 > 0:01:05..and reveal how we are mapping our brains...
0:01:05 > 0:01:07It's absolutely astonishing.
0:01:07 > 0:01:10..but jeopardising our very existence.
0:01:10 > 0:01:12What we are doing is removing the ability
0:01:12 > 0:01:14for us to live on this planet.
0:01:14 > 0:01:18We'll also celebrate, finally, the arrival of
0:01:18 > 0:01:21that science fiction cliche, the flying car.
0:01:21 > 0:01:23That's it, that's all it takes.
0:01:23 > 0:01:26So, if you want to know what's in store for you, then keep watching.
0:01:26 > 0:01:28Welcome to the future.
0:01:38 > 0:01:41Now, we are all going to experience the future
0:01:41 > 0:01:44and we all want to know how it will change our lives.
0:01:44 > 0:01:46What's going to be the future of our daily commute?
0:01:46 > 0:01:48What will we spend our money on?
0:01:48 > 0:01:50What's going to happen to our planet?
0:01:50 > 0:01:54Now, over the years, many people have made some pretty bold claims,
0:01:54 > 0:01:58including science fiction legend Arthur C Clarke,
0:01:58 > 0:02:02in an interview that he gave to Horizon in 1964.
0:02:02 > 0:02:05I'm perfectly serious when I suggest a world
0:02:05 > 0:02:08in which we can be in instant contact with each other,
0:02:08 > 0:02:09wherever we may be.
0:02:09 > 0:02:11Where we can contact our friends anywhere on Earth
0:02:11 > 0:02:15even if we don't know their actual physical location.
0:02:15 > 0:02:17It will be possible in that age,
0:02:17 > 0:02:19perhaps only 50 years from now,
0:02:19 > 0:02:24for a man to conduct his business from Tahiti or Bali,
0:02:24 > 0:02:25just as well as he could from London.
0:02:27 > 0:02:31Isn't that incredible? Arthur C Clarke, there, in 1964,
0:02:31 > 0:02:34accurately describing what is effectively the internet age.
0:02:34 > 0:02:38Something, of course, that we all take completely for granted now
0:02:38 > 0:02:41but a world that was totally alien to his audience.
0:02:41 > 0:02:45But, predicting the future is notoriously tricky.
0:02:45 > 0:02:47And while Arthur was bang on the money with that one,
0:02:47 > 0:02:52he didn't stop there. Because here he is with his follow-up prediction.
0:02:52 > 0:02:55The development of intelligent and useful servants
0:02:55 > 0:02:57among the other animals on this planet.
0:02:57 > 0:03:01We could certainly solve the servant problem
0:03:01 > 0:03:04with the help of the monkey kingdom.
0:03:04 > 0:03:06Sorry, come again?
0:03:06 > 0:03:11..solve the servant problem with the help of the monkey kingdom.
0:03:12 > 0:03:15Of course, eventually our super chimpanzees
0:03:15 > 0:03:17would start forming trade unions
0:03:17 > 0:03:20and we would be right back where we started.
0:03:20 > 0:03:22Well, I suppose one out of two ain't bad.
0:03:22 > 0:03:25But it does just go to show that predicting the future
0:03:25 > 0:03:27is usually a mug's game.
0:03:31 > 0:03:35Flying cars, salad-making robots and living in space colonies
0:03:35 > 0:03:40were the embarrassing staples of TV programmes like Tomorrow's World.
0:03:40 > 0:03:42Usually, their earnest predictions were way off.
0:03:44 > 0:03:46But this programme is different.
0:03:46 > 0:03:49And I'm as confident as I can be that these predictions
0:03:49 > 0:03:53aren't going to end up being laughed at by a TV audience in 2050.
0:03:54 > 0:03:56Because we are basing our predictions
0:03:56 > 0:03:59on real data from trusted sources,
0:03:59 > 0:04:01from today.
0:04:02 > 0:04:05And that is why we can confidently tell you that what you're
0:04:05 > 0:04:07about to watch really are the ten things
0:04:07 > 0:04:10that you need to know about the future.
0:04:10 > 0:04:14First up, Dr Kevin Fong on perhaps the biggest question of all.
0:04:14 > 0:04:17How can we cheat death and live longer?
0:04:27 > 0:04:29In the 1980s, if you wanted some constructive advice
0:04:29 > 0:04:31about how to achieve immortality,
0:04:31 > 0:04:34you went to see the film and musical Fame.
0:04:34 > 0:04:37The star of the piece was very clear about her advice,
0:04:37 > 0:04:39"I'm gonna live forever," she told us.
0:04:39 > 0:04:42And she was going to do that by becoming famous
0:04:42 > 0:04:45and living on forever in the memory of her adoring fans.
0:04:48 > 0:04:51And that kind of immortality is all that human beings
0:04:51 > 0:04:53have ever been able to aspire to.
0:04:54 > 0:04:57To be remembered by as many people as possible
0:04:57 > 0:04:59for as long as you possibly can.
0:05:03 > 0:05:06Woody Allen had a very different approach, of course.
0:05:06 > 0:05:09"I don't want to achieve immortality through my work," he said.
0:05:09 > 0:05:12"I want to achieve immortality by not dying."
0:05:14 > 0:05:18And if you want to live for longer, not dying is a great place to start.
0:05:20 > 0:05:22And the good news is that, on average,
0:05:22 > 0:05:24we're living longer today than we ever have in the past.
0:05:28 > 0:05:30But how far can we push that?
0:05:30 > 0:05:33How long can we expect to live in the future?
0:05:37 > 0:05:40Now, births and deaths are one thing that we have quite a lot of data on
0:05:40 > 0:05:44here in the UK. And here is some of that data.
0:05:44 > 0:05:47This shows you how our life expectancy has changed
0:05:47 > 0:05:49over the last 170 years or so.
0:05:49 > 0:05:52Now, Kevin Fong, welcome back to the studio.
0:05:52 > 0:05:55As a doctor, help me make some sense of these numbers.
0:05:55 > 0:05:57Well, I think this is all fascinating data.
0:05:57 > 0:06:01When you look at it, this is average life expectancy which, for almost
0:06:01 > 0:06:02the whole of human history,
0:06:02 > 0:06:05languishes around here, around 40-45 years.
0:06:05 > 0:06:07And then you hit the start of the 20th century.
0:06:07 > 0:06:09You get this massive kick.
0:06:09 > 0:06:11Why? Well, it's all prevention.
0:06:11 > 0:06:14It's all about stopping the things that kill you early on in life
0:06:14 > 0:06:17shortly after you've been born, like measles, mumps, rubella.
0:06:17 > 0:06:20It's all about stopping the diseases of infection.
0:06:20 > 0:06:23And that is achieved through vaccination and better sanitation.
0:06:23 > 0:06:26So that's what explains this big kick and these early gains here.
0:06:26 > 0:06:29So this bit really is all about sort of child mortality?
0:06:29 > 0:06:33Yep. Prevention is always better than cure, and that was vaccination.
0:06:33 > 0:06:36But it continues to go up past then. What about this bit over here?
0:06:36 > 0:06:39So this kick up the top here is us getting better at
0:06:39 > 0:06:41advanced modern medicine.
0:06:41 > 0:06:43So we have better lifestyles,
0:06:43 > 0:06:47but also we get better at treating diseases like heart disease and also
0:06:47 > 0:06:49cancer, so that gives us a bit of an extra gain here.
0:06:49 > 0:06:51So we still see increases all the way through.
0:06:51 > 0:06:53And then what about going forward to the future, then?
0:06:53 > 0:06:55Will this continue to increase?
0:06:55 > 0:06:57Is there a limit to how long we can live for?
0:06:57 > 0:06:58It would be lovely, wouldn't it,
0:06:58 > 0:07:01if this line kept going up and up and up to infinity?
0:07:01 > 0:07:02It doesn't look like it.
0:07:02 > 0:07:05It looks like this data plateaus.
0:07:06 > 0:07:09This graph shows that being born in 2011
0:07:09 > 0:07:12gives you a very high average life expectancy.
0:07:14 > 0:07:16But in terms of maximum possible age,
0:07:16 > 0:07:20you're no better off than your early Victorian ancestors.
0:07:21 > 0:07:23There's a fundamental limit, it would appear,
0:07:23 > 0:07:27at least at the moment, around 110, 120 years,
0:07:27 > 0:07:29to which we seem to be limited.
0:07:31 > 0:07:33If we want to improve things, and who doesn't,
0:07:33 > 0:07:38we could do a lot worse than to investigate the naked mole rat.
0:07:38 > 0:07:42It lives at least ten times longer than other rats.
0:07:42 > 0:07:47In human terms, that's a potential lifespan of upwards of 700 years.
0:07:48 > 0:07:52As it happens, we actually have a naked mole rat expert
0:07:52 > 0:07:55here in the studio with your mole rats.
0:07:55 > 0:07:57Welcome to the studio, Chris Faulkes.
0:07:57 > 0:07:59Tell me about these amazing creatures.
0:07:59 > 0:08:01Well, naked mole rats,
0:08:01 > 0:08:03just about everything about their biology
0:08:03 > 0:08:06is weird and wonderful and exceptional.
0:08:06 > 0:08:09And recently they've been generating a lot of interest
0:08:09 > 0:08:11because of their extreme longevity.
0:08:11 > 0:08:15Which can be... We don't know the upper limit, but more than 30 years.
0:08:15 > 0:08:18Gosh. Cos I guess a rat only lives, what, two or three years,
0:08:18 > 0:08:20- maybe at the most.- Yeah, exactly. - And these live to 30, did you say?
0:08:20 > 0:08:22Yeah. And counting.
0:08:22 > 0:08:24Wow. And do they get old in that time?
0:08:24 > 0:08:27Well, this is really the remarkable thing.
0:08:27 > 0:08:29Because not only do they have a long lifespan,
0:08:29 > 0:08:31but their health span is very long,
0:08:31 > 0:08:35which means that they go through a huge proportion of their life
0:08:35 > 0:08:38without showing any signs of ageing whatsoever,
0:08:38 > 0:08:41like an 80-year-old having the body of a 30-year-old.
0:08:41 > 0:08:44- Well, that would be nice, wouldn't it?- It sure would.- So, why is that?
0:08:44 > 0:08:46What is it about them that means that they have that feature?
0:08:46 > 0:08:49Well, it seems that it's not a single thing,
0:08:49 > 0:08:52but there's a whole mosaic of adaptations
0:08:52 > 0:08:54that have given them very low metabolic rate,
0:08:54 > 0:08:58a low body temperature, there's resistance to cancer,
0:08:58 > 0:08:59and a whole bunch of other things
0:08:59 > 0:09:01which all collectively give them
0:09:01 > 0:09:03this really long lifespan and health span.
0:09:03 > 0:09:04So, do they not get cancer?
0:09:04 > 0:09:07There's been virtually no recorded cases.
0:09:07 > 0:09:12As opposed to lab mice, for example, where 70% will die of cancer.
0:09:12 > 0:09:14Wow. Gosh, that's extraordinary.
0:09:14 > 0:09:16So what is it about them that means that they don't get cancer?
0:09:16 > 0:09:20There's a substance in the skin called hyaluronan that we all have.
0:09:20 > 0:09:24These guys have a very special version of that substance,
0:09:24 > 0:09:27which we think is what gives them their really elasticky skin,
0:09:27 > 0:09:30which is useful when you're living in tight tunnels.
0:09:30 > 0:09:36And this hyaluronan is implicated in one part of their cancer resistance.
0:09:36 > 0:09:38How stretchy is their skin?
0:09:38 > 0:09:39It's pretty stretchy.
0:09:39 > 0:09:41Do you want me to see if I can demonstrate?
0:09:41 > 0:09:43Yeah, go on, why not, let's have a look.
0:09:43 > 0:09:45They'll probably all go running off.
0:09:45 > 0:09:49And this sort of stretchy substance is one of
0:09:49 > 0:09:51the things that protects them against... Oh, my goodness.
0:09:51 > 0:09:53Yeah, so, there we go, they don't mind
0:09:53 > 0:09:55being picked up like this at all as you can see.
0:09:55 > 0:10:00The animal's quite relaxed. But see how really elasticky the skin is.
0:10:00 > 0:10:01Incredible. My goodness me.
0:10:01 > 0:10:03Let me just pop him back there. There we go.
0:10:03 > 0:10:05- And he's quite happy. - Yeah, no problem.
0:10:05 > 0:10:06He has made a run for it, though.
0:10:06 > 0:10:10- Yes.- Well, I guess the question is, really,
0:10:10 > 0:10:13how do we get that substance in ourselves?
0:10:13 > 0:10:16As labs around the world have been studying
0:10:16 > 0:10:18the genomes of naked mole rats,
0:10:18 > 0:10:20we're finding a whole bunch of
0:10:20 > 0:10:23candidate genes that are responsible,
0:10:23 > 0:10:26perhaps, for their longevity.
0:10:26 > 0:10:28And, you know, their health span, as well.
0:10:28 > 0:10:31And potentially apply them to ourselves.
0:10:31 > 0:10:33That could well be the case, I think.
0:10:33 > 0:10:37- It's not beyond the realms of possibility.- Wow.
0:10:37 > 0:10:41So, what you need to know about the future of lifespan is this.
0:10:41 > 0:10:45The good news is that we are likely to live longer on average.
0:10:45 > 0:10:48But, until we crack the secrets of the mole rats' longevity,
0:10:48 > 0:10:50it does seem that our luck, on average,
0:10:50 > 0:10:53runs out just after the telegram from the Queen arrives.
0:10:58 > 0:11:01Right now, one in four of us is likely
0:11:01 > 0:11:05to be affected by mental health issues at some point in our lives.
0:11:05 > 0:11:07But when it comes to effective treatment,
0:11:07 > 0:11:10mental health really does lag behind other diseases.
0:11:10 > 0:11:14So, what does the future hold for our state of mind?
0:11:14 > 0:11:17Now, there is a very big incentive to answer this question.
0:11:17 > 0:11:20And it's because mental health issues
0:11:20 > 0:11:23are the number one cause of people being unable to work.
0:11:23 > 0:11:26So this graph here from 2010 shows
0:11:26 > 0:11:29the amount of money lost by people not being able to work
0:11:29 > 0:11:31with different diseases.
0:11:31 > 0:11:34And the results here are shown in trillions of dollars.
0:11:34 > 0:11:36But the exact numbers here aren't really the story.
0:11:36 > 0:11:39The story is that mental health illness
0:11:39 > 0:11:41really does top the pile here.
0:11:41 > 0:11:43Beating both cardiovascular disease
0:11:43 > 0:11:45and also cancer, just there.
0:11:45 > 0:11:48And if current trends continue,
0:11:48 > 0:11:51then the World Health Organization has made some predictions
0:11:51 > 0:11:53for what we can expect by 2030.
0:11:53 > 0:11:56And you can see things really are set to get a lot worse.
0:11:56 > 0:11:59And what's particularly bad about mental health illness
0:11:59 > 0:12:01is that it really is the disease
0:12:01 > 0:12:04that people end up living with for the longest.
0:12:04 > 0:12:06So this graph here shows you how long
0:12:06 > 0:12:08people live with different kinds of illness,
0:12:08 > 0:12:11so you can see unintentional injuries just there.
0:12:11 > 0:12:13There's cardiovascular diseases down there.
0:12:13 > 0:12:18And once again, mental health disorders and neurological disorders
0:12:18 > 0:12:20really are very much out in front.
0:12:20 > 0:12:23So what is being done to help understand the brain and to
0:12:23 > 0:12:24improve these things?
0:12:24 > 0:12:26Well, Michael Mosley has been to visit
0:12:26 > 0:12:29a research project in London to find out.
0:12:38 > 0:12:42The human brain is the most complex object in the known universe.
0:12:42 > 0:12:46And for a long time its workings were a complete mystery.
0:12:46 > 0:12:47Then, in the 19th century,
0:12:47 > 0:12:50scientists identified that some of our abilities,
0:12:50 > 0:12:51like being able to speak
0:12:51 > 0:12:53are linked to particular regions of the brain.
0:12:53 > 0:12:57But an awful lot of what goes on up here is not to do with regions.
0:12:57 > 0:13:00It's absolutely to do with connections.
0:13:02 > 0:13:04If your brain isn't properly wired up,
0:13:04 > 0:13:08then this puts you at greater risk of things like dementia,
0:13:08 > 0:13:11schizophrenia and, early in life, autism.
0:13:12 > 0:13:14Autism is the result of changes in the way
0:13:14 > 0:13:16that the brain processes information.
0:13:16 > 0:13:20By studying these physical changes,
0:13:20 > 0:13:23it's hoped new light will be shed on the workings of the whole brain.
0:13:25 > 0:13:28Here, at Evelina London Children's Hospital,
0:13:28 > 0:13:31a team from King's College London are looking at
0:13:31 > 0:13:36brain development in babies, using MRI scanners.
0:13:36 > 0:13:39Hello, my name is Anna, I'm one of the radiographers, OK?
0:13:40 > 0:13:42Even before they're born.
0:13:44 > 0:13:45I've never seen an MRI of a foetus.
0:13:46 > 0:13:49- That is amazing.- It is amazing.
0:13:49 > 0:13:51It is absolutely astonishing.
0:13:51 > 0:13:53I mean, what's really amazing about it,
0:13:53 > 0:13:57it's relatively easy to get one quick flash through a scan
0:13:57 > 0:14:00but collecting all of the slices
0:14:00 > 0:14:03to make a three-dimensional reconstruction is the hard bit.
0:14:04 > 0:14:08This is a 3-D model made from one of the images of a baby who is about
0:14:08 > 0:14:12three months early. And as you can see, it's very small.
0:14:12 > 0:14:14It's very smooth, it's very underdeveloped.
0:14:14 > 0:14:16It has a lot more growing to do.
0:14:16 > 0:14:18And this is the baby who's about term,
0:14:18 > 0:14:20and the growth that has to happen
0:14:20 > 0:14:23between there and there is phenomenal.
0:14:23 > 0:14:27As the brain grows, more and more connections are made inside.
0:14:29 > 0:14:31And the team is able to identify that wiring
0:14:31 > 0:14:33using two kinds of MRI scanning.
0:14:35 > 0:14:37I have to say, I'm still blown away by those images.
0:14:39 > 0:14:40I don't know what I was expecting,
0:14:40 > 0:14:43but I wasn't expecting something as clear as that.
0:14:43 > 0:14:45I think, to be honest, we're blown away by the images.
0:14:45 > 0:14:47Once the MRI data is processed,
0:14:47 > 0:14:50it's translated into a basic map of connections,
0:14:50 > 0:14:54showing which parts of a baby's brain can talk to each other.
0:14:56 > 0:14:59These are much better than the images we used to get in the past.
0:14:59 > 0:15:01And the information content has gone up massively.
0:15:03 > 0:15:08At birth, the brain already has about 100 billion neurons.
0:15:08 > 0:15:11And each one is connected to thousands of others.
0:15:11 > 0:15:14So the brain's wiring diagram is enormously complex.
0:15:17 > 0:15:20Nonetheless, researchers have started to decipher it.
0:15:20 > 0:15:23The resulting map is called the connectome.
0:15:24 > 0:15:27And while everyone's individual connectome is unique,
0:15:27 > 0:15:31the hope is the average data will give us useful information
0:15:31 > 0:15:33about how all our brains work.
0:15:35 > 0:15:38So, one of the major uses for this is to define the connectome.
0:15:38 > 0:15:41Now, we obviously can't have all the connections in the brain because
0:15:41 > 0:15:43we're not looking at single nerve fibres.
0:15:43 > 0:15:46But we can get a pretty good idea of what that connection map would be.
0:15:46 > 0:15:48I think of it as being a bit like the Tube map of London.
0:15:48 > 0:15:51You can't find an individual road on the Tube map but you can find your
0:15:51 > 0:15:52way round London on it.
0:15:54 > 0:15:57Connectome studies around the world are gathering data
0:15:57 > 0:16:01in a bid to understand a whole range of mental health conditions
0:16:01 > 0:16:03and brain disorders.
0:16:03 > 0:16:07There's autism and ADHD at the beginning of life.
0:16:07 > 0:16:10Then psychoses like schizophrenia.
0:16:10 > 0:16:13Finally, as the level of connectivity deteriorates
0:16:13 > 0:16:16in the ageing brain, dementia.
0:16:16 > 0:16:18I do think that mapping the connectome
0:16:18 > 0:16:21could be the next big thing in our understanding of the brain.
0:16:21 > 0:16:24And that's important, not just because
0:16:24 > 0:16:26it will increase our knowledge of a normal brain,
0:16:26 > 0:16:30but it could also lead to better treatments when things go wrong.
0:16:36 > 0:16:39And now to our national obsession - the weather.
0:16:39 > 0:16:42We live, so we're told, in dangerous times.
0:16:42 > 0:16:46Planet Earth is getting hotter and that is going to change our climate,
0:16:46 > 0:16:48and there is a lot of data around this point.
0:16:48 > 0:16:50So here, for instance,
0:16:50 > 0:16:54is a map of what's happened around the world in the last 130 years,
0:16:54 > 0:16:57compared to an average in the middle of the 20th century.
0:16:57 > 0:16:59And as you're in the sort of '20s and '30s you can see a lot of blue
0:16:59 > 0:17:01and white on this map, the odd splodge of orange
0:17:01 > 0:17:03comes in every now and then.
0:17:03 > 0:17:06But, as time rolls on and we get closer to now,
0:17:06 > 0:17:08these orange splodges end up connecting to each other
0:17:08 > 0:17:11and we see some red coming in, in the north up there.
0:17:11 > 0:17:14Now, this effect actually is very extreme.
0:17:14 > 0:17:162016, for instance,
0:17:16 > 0:17:20was the hottest year on record that the Earth has ever had.
0:17:20 > 0:17:22But what about if we go a bit further back in time?
0:17:22 > 0:17:26So this graph here shows you the global temperature
0:17:26 > 0:17:29for the last 11,000 years.
0:17:29 > 0:17:31Now it's certainly true that we have had
0:17:31 > 0:17:32changes in temperature before.
0:17:32 > 0:17:36There's an ice age just here, a little hot patch just there,
0:17:36 > 0:17:38about the same as we have now.
0:17:38 > 0:17:40Another little mini ice age just there.
0:17:40 > 0:17:43But what is important about this graph
0:17:43 > 0:17:45is this section that we have just over here.
0:17:45 > 0:17:46Because this here,
0:17:46 > 0:17:51isn't a little flag pointing downwards to the data,
0:17:51 > 0:17:53this line IS the data.
0:17:53 > 0:17:57The changes that we've seen in global temperature recently
0:17:57 > 0:17:58are so quick and so dramatic
0:17:58 > 0:18:00that on this graph of 11,000 years,
0:18:00 > 0:18:03it looks like a straight line going upwards.
0:18:03 > 0:18:06And in fact, if this trend continues,
0:18:06 > 0:18:09we expect to see the global temperature ending up looking
0:18:09 > 0:18:11something like this as we move forward in time.
0:18:11 > 0:18:15But the trouble is that we have heard a lot of this before.
0:18:15 > 0:18:17And nothing really seems to change.
0:18:17 > 0:18:19It doesn't feel like it's getting any warmer.
0:18:19 > 0:18:23But there are some people out there who have already noticed the changes
0:18:23 > 0:18:27that global warming is having, literally in their own backyard.
0:18:27 > 0:18:30And weatherman Peter Gibbs is one of them.
0:18:41 > 0:18:46Gardeners are ruled by the seasons, that annual cycle of sowing,
0:18:46 > 0:18:47growing and harvesting.
0:18:49 > 0:18:52But global warming has put a spanner in the works,
0:18:52 > 0:18:54because spring now arrives
0:18:54 > 0:18:57a few days earlier with every passing decade.
0:19:01 > 0:19:03For a start, that means I need to start
0:19:03 > 0:19:05mowing the lawn earlier in the year.
0:19:05 > 0:19:09And while I used to retire the mower for the winter in, say,
0:19:09 > 0:19:12late September, it's now not unusual to
0:19:12 > 0:19:14cut the grass as late as November.
0:19:17 > 0:19:20The growing season is the best part of six weeks longer
0:19:20 > 0:19:22than it was before climate change kicked in.
0:19:22 > 0:19:24Which is great if you want to grow grapes.
0:19:24 > 0:19:26I'm getting a decent crop most years now.
0:19:26 > 0:19:30Problem is, because it's all developing so much earlier
0:19:30 > 0:19:32in the spring, that extends, surprisingly,
0:19:32 > 0:19:36the season over which these little baby grapes
0:19:36 > 0:19:39can actually be hit by a rogue frost.
0:19:42 > 0:19:45The effects of climate change are frustratingly complicated.
0:19:45 > 0:19:49It varies from one kind of plant to the next.
0:19:49 > 0:19:52In winter, apple trees and blackcurrant bushes are dormant,
0:19:52 > 0:19:55storing up energy reserves for the spring.
0:19:57 > 0:19:59But as British winters get steadily warmer,
0:19:59 > 0:20:02that chilling time will be cut short,
0:20:02 > 0:20:06resulting in poor flowering and a lack of fruit.
0:20:09 > 0:20:12The effects of climate change are already pretty apparent,
0:20:12 > 0:20:15and that's with just a one-degree rise in temperature.
0:20:15 > 0:20:19Trouble is, temperatures are expected to keep on rising.
0:20:19 > 0:20:24If and when that happens, the impact will be even more dramatic.
0:20:24 > 0:20:27Higher temperatures mean melting ice.
0:20:27 > 0:20:31And here is what has been happening to Arctic sea ice since 1980.
0:20:31 > 0:20:35It's been reducing at the rate of 13% per decade.
0:20:35 > 0:20:37It's not just the sea ice, though.
0:20:37 > 0:20:41Melting ice caps means rising sea levels, too.
0:20:41 > 0:20:44And climate scientists have run some mathematical models to try and
0:20:44 > 0:20:47predict what that will mean for us on land.
0:20:47 > 0:20:51So, if all of Greenland melts, which admittedly would be quite dramatic,
0:20:51 > 0:20:55but that would have a six metre change in the level of the sea.
0:20:55 > 0:20:58And this is what Europe would look like as a result.
0:20:58 > 0:21:00Holland - not looking great for Holland.
0:21:00 > 0:21:02Norfolk also very badly hit.
0:21:02 > 0:21:03And London too.
0:21:03 > 0:21:05And over the other side of the Atlantic,
0:21:05 > 0:21:08this is what would happen to Florida.
0:21:08 > 0:21:12Quite a lot of it would end up being completely underwater,
0:21:12 > 0:21:14including Cape Canaveral, just there.
0:21:14 > 0:21:17You're going to need a new place to try and launch the rockets from.
0:21:17 > 0:21:19But the big question is,
0:21:19 > 0:21:21is our weather here in the UK going to be affected
0:21:21 > 0:21:23by all of this in the future?
0:21:23 > 0:21:27Now, I'm not a meteorologist, but happily Peter Gibbs, who is,
0:21:27 > 0:21:30has rushed back from his garden to the BBC Weather Studio,
0:21:30 > 0:21:34where he has prepared a weather forecast for 2050.
0:21:34 > 0:21:37Hello, and welcome to Weather 2050.
0:21:37 > 0:21:40Well, the usual floods and saturated ground that we saw during the winter
0:21:40 > 0:21:43months are just a distant memory for most of us now,
0:21:43 > 0:21:45as we see the familiar signs of
0:21:45 > 0:21:49developing drought now that the summer heat has really kicked in.
0:21:49 > 0:21:52And those temperatures are expected to build over the next few days,
0:21:52 > 0:21:56so we'll all be cranking up the air-con, avoiding the daytime heat,
0:21:56 > 0:21:58and struggling to sleep at night.
0:21:58 > 0:22:02And that's certainly true in the south, where we'll hit 35 Celsius,
0:22:02 > 0:22:03not unusual these days, of course.
0:22:03 > 0:22:07We could well top 40 degrees in a few days' time.
0:22:07 > 0:22:09A little bit more comfortable in the north,
0:22:09 > 0:22:12thanks to patchy cloud and a breeze coming in from the sea.
0:22:12 > 0:22:14But still, pretty humid.
0:22:14 > 0:22:17Then eventually that heat will break down into scattered thunderstorms,
0:22:17 > 0:22:20the warmer atmosphere of course able to hold more moisture these days,
0:22:20 > 0:22:23so there will be some really intense downpours.
0:22:23 > 0:22:25Probably won't too much for the droughts, though,
0:22:25 > 0:22:28with the rain just running off the parched ground
0:22:28 > 0:22:30and causing flash flooding.
0:22:30 > 0:22:33So some disruption looks likely.
0:22:33 > 0:22:35What you need to know about the weather of the future
0:22:35 > 0:22:37is that we expect, on average,
0:22:37 > 0:22:39the north to become warmer and wetter,
0:22:39 > 0:22:42and in the south, hotter and drier.
0:22:42 > 0:22:43But across the whole country,
0:22:43 > 0:22:47weather patterns will become a lot more variable, with bigger extremes.
0:22:47 > 0:22:49So whoever is doing the forecast then
0:22:49 > 0:22:53will have a much more difficult job than I've had.
0:22:58 > 0:23:00When it comes to the future of health care,
0:23:00 > 0:23:04past triumphs give us good reason to be optimistic.
0:23:04 > 0:23:06But what about the big C?
0:23:06 > 0:23:09Now, a cure for cancer is something we'd all like to happen,
0:23:09 > 0:23:12but how realistic a hope is that?
0:23:12 > 0:23:13Well, in the year 2000,
0:23:13 > 0:23:18Bill Clinton and Tony Blair announced a very big breakthrough.
0:23:19 > 0:23:22We're here to celebrate the completion
0:23:22 > 0:23:25of the first survey of the entire human genome.
0:23:25 > 0:23:27Without a doubt, this is the most important,
0:23:27 > 0:23:32most wondrous map ever produced by humankind.
0:23:32 > 0:23:37Well, in just 50 years after the discovery of the structure of DNA,
0:23:37 > 0:23:39teams from the US and the UK between them
0:23:39 > 0:23:43had mapped all of the genes contained in us.
0:23:43 > 0:23:46Now, the Human Genome Project, said Bill and Tony,
0:23:46 > 0:23:48would cure all human suffering.
0:23:48 > 0:23:50The blind would see, the lame would walk,
0:23:50 > 0:23:53and cancers would be a thing of the past.
0:23:53 > 0:23:56Well, Bill and Tony are now distant memories.
0:23:56 > 0:24:01But what happened to the genome and its bid to end all suffering?
0:24:01 > 0:24:04Geneticist Giles Yeo went to New York to find out.
0:24:13 > 0:24:18Half of us will be diagnosed with cancer at some point in our lives.
0:24:18 > 0:24:20The problem with cancer is that our body's defences
0:24:20 > 0:24:23does not recognise it as a threat.
0:24:23 > 0:24:26Our immune systems have evolved to distinguish between our
0:24:26 > 0:24:29own bodies and those of foreign invaders,
0:24:29 > 0:24:31such as bacteria and viruses.
0:24:31 > 0:24:34But cancer is simply a mutated version of our own cells,
0:24:34 > 0:24:37so it doesn't carry the typical characteristics
0:24:37 > 0:24:39of an invading organism.
0:24:39 > 0:24:43So our immune system doesn't recognise it, attack it and kill it.
0:24:46 > 0:24:49In 2011, Karen was diagnosed with
0:24:49 > 0:24:51a form of blood cancer called leukaemia.
0:24:53 > 0:24:55It's caused by the uncontrolled production
0:24:55 > 0:24:57of abnormal white blood cells.
0:24:59 > 0:25:02Karen lived with the disease for three years.
0:25:03 > 0:25:08But in 2014, she took a turn for the worse,
0:25:08 > 0:25:10and was given two years to live.
0:25:11 > 0:25:15That was brutal. Your brain is just going, "Oh, my God,
0:25:15 > 0:25:18"now I have this nuclear bomb in my body.
0:25:18 > 0:25:21"And it's going to kill me."
0:25:23 > 0:25:24But Karen was offered a lifeline.
0:25:26 > 0:25:28'In an experimental new treatment,
0:25:28 > 0:25:32'Dr Michel Sadelain is able to hack the immune system
0:25:32 > 0:25:34'and equip it with the ability to fight back.'
0:25:36 > 0:25:39So in many cancers, as in Karen's cancer,
0:25:39 > 0:25:44the immune system on its own is not capable of taking over the tumour.
0:25:44 > 0:25:46It needs a little help.
0:25:47 > 0:25:50Michel's target is a type of white blood cell called a T-cell.
0:25:51 > 0:25:54These are the foot soldiers of the immune system,
0:25:54 > 0:25:57whose purpose is to attack viruses and bacteria.
0:25:58 > 0:26:01The first step was to collect some of Karen's T-cells.
0:26:02 > 0:26:05And there a team of scientists and technicians
0:26:05 > 0:26:11insert into those T-cells into a gene, a synthetic gene,
0:26:11 > 0:26:14that provides this instruction to the T-cell which says,
0:26:14 > 0:26:18"Recognise the cancer, seek it out and destroy it."
0:26:18 > 0:26:22Michel used a virus to get the new gene into Karen's T-cells.
0:26:22 > 0:26:26The T-cells were then infused back into Karen's bloodstream.
0:26:26 > 0:26:29He's had some remarkable success with this technique.
0:26:29 > 0:26:33The first clinical results showed a dramatic effect
0:26:33 > 0:26:36in a majority of patients -
0:26:36 > 0:26:39about 85% of these patients went into
0:26:39 > 0:26:43what we call a complete remission.
0:26:44 > 0:26:47Michel has treated about 100 patients using this technique.
0:26:48 > 0:26:50Karen was one of the first.
0:26:51 > 0:26:53Never did we expect what we were going to hear.
0:26:53 > 0:26:59And he said, "There is no evidence of disease, you are cancer-free."
0:26:59 > 0:27:02So what was it like to hear the doctor say, "You are cancer-free?"
0:27:04 > 0:27:05It was unbelievable.
0:27:07 > 0:27:10I still get... I obviously get emotional about it.
0:27:11 > 0:27:13Because we didn't expect it.
0:27:13 > 0:27:16For Karen, the results of the trial have been incredible.
0:27:16 > 0:27:18I mean, they have saved her life.
0:27:18 > 0:27:20But that's only half the story.
0:27:20 > 0:27:23You know, because the technique doesn't always work,
0:27:23 > 0:27:25and bespoke gene editing for every single patient,
0:27:25 > 0:27:27it's just unrealistic.
0:27:27 > 0:27:30It is expensive, it is complex, it is lengthy.
0:27:30 > 0:27:32And patients have been known to die waiting
0:27:32 > 0:27:36during the intervening period for their cells to be re-engineered.
0:27:37 > 0:27:40The answer is a new technique called Crispr.
0:27:40 > 0:27:46Crispr stands for Clustered Regularly Interspaced Short Palindromic Repeats,
0:27:46 > 0:27:49which describes a property of bacterial DNA
0:27:49 > 0:27:52that scientists have been able to exploit -
0:27:52 > 0:27:54giving them, for the first time,
0:27:54 > 0:27:56ultimate accuracy in gene editing.
0:27:56 > 0:28:00What Crispr gives you is a pair of tweezers for you to be
0:28:00 > 0:28:04able to place the DNA anywhere you want in any cell.
0:28:04 > 0:28:10This new-found precision allows Michel to fix a fundamental problem.
0:28:10 > 0:28:14The problem in taking someone else's T-cells is that those T-cells will
0:28:14 > 0:28:18attack you. Because they will sense that, "It's not my body,
0:28:18 > 0:28:22"it's not my molecules, I have to go on the attack."
0:28:23 > 0:28:28So what Crispr Cas9 makes possible is the removal of those molecules
0:28:28 > 0:28:30that initiate that attack.
0:28:32 > 0:28:35It means that T-cells could be provided by a donor,
0:28:35 > 0:28:38and can be genetically engineered to only attack the cancer.
0:28:39 > 0:28:41From one donor,
0:28:41 > 0:28:45you could make cells that could be administered to multiple recipients.
0:28:46 > 0:28:51These cells would then be ready, and in pharmacies.
0:28:51 > 0:28:54And now, we preserve them.
0:28:56 > 0:29:00We may someday have pharmacies of T-cells, frozen vials of T-cells,
0:29:00 > 0:29:02ready-made, ready for injection.
0:29:02 > 0:29:05So there's the living drug.
0:29:05 > 0:29:08Fast asleep in liquid nitrogen.
0:29:08 > 0:29:09That's fantastic.
0:29:09 > 0:29:11And this would make this form of therapy
0:29:11 > 0:29:14accessible to many more individuals.
0:29:16 > 0:29:20Donor T-cells have yet to be trialled in humans.
0:29:20 > 0:29:23But the idea offers hope that in the future,
0:29:23 > 0:29:24many more people could be treated.
0:29:26 > 0:29:30We are now entering an unprecedented era of progress for gene therapy.
0:29:30 > 0:29:31And we're not just talking about cancer,
0:29:31 > 0:29:34because Crispr is now being used to treat muscular dystrophy,
0:29:34 > 0:29:38and it's being talked about as an alternative to antibiotics.
0:29:38 > 0:29:40What you need to know about the future is
0:29:40 > 0:29:42that the Human Genome Project
0:29:42 > 0:29:44may finally be delivering on its promise
0:29:44 > 0:29:47to revolutionise the way we treat disease.
0:29:53 > 0:29:54And now, work.
0:29:54 > 0:29:57What will you be doing as a job in the future?
0:29:57 > 0:30:00Well, 50 years ago, the world of work was pretty easy to understand.
0:30:00 > 0:30:02You either did manual work,
0:30:02 > 0:30:05which basically meant supervising machines.
0:30:05 > 0:30:07Or you worked in an office,
0:30:07 > 0:30:09which basically meant doing a lot of typing,
0:30:09 > 0:30:11or getting somebody else to do a lot of typing for you.
0:30:11 > 0:30:15Bottom line, people were integral to the workforce.
0:30:15 > 0:30:17But no more.
0:30:17 > 0:30:20Because, in the 1970s, machines got clever.
0:30:20 > 0:30:23Car plants were filled with robots,
0:30:23 > 0:30:25helplines were answered by computers,
0:30:25 > 0:30:28and almost all bank clerks became extinct.
0:30:28 > 0:30:30But I suspect that most of you are saying,
0:30:30 > 0:30:33"Well, a robot couldn't possibly take my job."
0:30:33 > 0:30:35But are you sure?
0:30:35 > 0:30:36Have a look at this.
0:30:38 > 0:30:40We sent Dr Zoe Williams to check out
0:30:40 > 0:30:42a piece of software which is rumoured
0:30:42 > 0:30:46to diagnose illnesses faster and more accurately
0:30:46 > 0:30:49than human medical professionals.
0:30:49 > 0:30:52So, should I be worried, as a GP?
0:30:52 > 0:30:55The thought that a robot or artificial intelligence
0:30:55 > 0:30:58could take my job just seems crazy.
0:30:58 > 0:31:01I mean, I've spent six years at medical school,
0:31:01 > 0:31:03ten years practising as a doctor.
0:31:03 > 0:31:06Now, surely all of that can't be boiled down to a few lines of code?
0:31:10 > 0:31:13Babylon Health are a medical tech company.
0:31:14 > 0:31:17They've just received 60 million of funding
0:31:17 > 0:31:19to develop an AI doctor.
0:31:21 > 0:31:24The system works by asking questions.
0:31:24 > 0:31:26But anyone can ask questions.
0:31:26 > 0:31:28If it's going to replace me,
0:31:28 > 0:31:31I really want to put it through its paces.
0:31:31 > 0:31:35I'm going to pose as a patient and give myself an imaginary condition.
0:31:35 > 0:31:39But I'm not going to tell anybody, I'm just going to write it down.
0:31:41 > 0:31:44And then we can see just how accurate the machine really is.
0:31:46 > 0:31:49May I ask, please, what's troubling you today?
0:31:49 > 0:31:53I'm feeling tired all the time.
0:31:53 > 0:31:58So, as well as feeling tired, I've been feeling kind of weak.
0:31:58 > 0:32:00Let's tell the computer that.
0:32:01 > 0:32:03And I've also been feeling...
0:32:04 > 0:32:06..a bit of dizziness.
0:32:08 > 0:32:12Is it OK to ask, "Do you have painful periods?"
0:32:12 > 0:32:13There we go, that's better.
0:32:13 > 0:32:15Painful periods as well.
0:32:16 > 0:32:18Do you get breathless on exertion?
0:32:18 > 0:32:20Yes, I do!
0:32:20 > 0:32:22Thanks, I've noted this.
0:32:23 > 0:32:26So, I've given the computer all of my symptoms now.
0:32:26 > 0:32:29And it's come up with a diagnosis.
0:32:29 > 0:32:31So, let's see if it's correct.
0:32:31 > 0:32:33Here's my bit of paper from earlier.
0:32:33 > 0:32:39And you can see that I have put down fibroids.
0:32:39 > 0:32:43And the computer has said uterine leiomyoma,
0:32:43 > 0:32:44which is actually the same thing.
0:32:45 > 0:32:47That's impressive.
0:32:47 > 0:32:49But how is it done?
0:32:49 > 0:32:51Time to face down the evil genius
0:32:51 > 0:32:55hell-bent on replacing me with my laptop.
0:32:55 > 0:32:58So, you start off with a knowledge base.
0:32:58 > 0:33:01And this is essentially a medical database which contains hundreds of
0:33:01 > 0:33:03millions of medical concepts.
0:33:03 > 0:33:06That's kind of like being at medical school and all the knowledge that is
0:33:06 > 0:33:07inputted into the brain.
0:33:07 > 0:33:10Exactly, so this might be all of the textbooks which you've read at
0:33:10 > 0:33:14medical school, all of the papers which you've read at medical school,
0:33:14 > 0:33:16and then applied to all of that information
0:33:16 > 0:33:19we'll apply a set of methods known as machine-learning methods.
0:33:21 > 0:33:24Machine learning is the ability of computers
0:33:24 > 0:33:28to take vast amounts of data and make sense of it themselves.
0:33:30 > 0:33:32Like this network of medical information,
0:33:32 > 0:33:35which the computer uses to make a diagnosis.
0:33:36 > 0:33:39What these circles represent are diseases,
0:33:39 > 0:33:40- symptoms and risk factors.- OK.
0:33:40 > 0:33:43And what those lines represent are the relationships between those.
0:33:43 > 0:33:45So, based on that,
0:33:45 > 0:33:49the computer has taught itself actually how strongly related those
0:33:49 > 0:33:51diseases, symptoms and risk factors are.
0:33:51 > 0:33:52OK, so that's how it determines
0:33:52 > 0:33:56the probability is from looking at past real-life cases?
0:33:56 > 0:33:59Absolutely, and that's why this is machine learning.
0:34:02 > 0:34:06As the network learns about more and more symptoms and diseases,
0:34:06 > 0:34:10it's tested and refined by a team of doctors and programmers.
0:34:12 > 0:34:13It's early days,
0:34:13 > 0:34:16but the company sees a big future for their virtual medic.
0:34:17 > 0:34:20We want to do with health care what, say, Google did with information.
0:34:20 > 0:34:25It'll be in your phone, it'll be in the devices you carry with you.
0:34:25 > 0:34:30Do you think that a machine could ever replace my role as a GP?
0:34:30 > 0:34:34I don't think this is a competition between machines and humans.
0:34:34 > 0:34:38This is machines being an aid to humans.
0:34:38 > 0:34:41Half of the world's population has no access or very,
0:34:41 > 0:34:43very little access to doctors.
0:34:43 > 0:34:47Right? Imagine if you could see so many more because the machines do
0:34:47 > 0:34:50the easier part, they save your time.
0:34:50 > 0:34:54But can a machine put its hand on your shoulder and say, "Trust me,
0:34:54 > 0:34:56"I'll look after you?" That's a different story.
0:34:58 > 0:35:02It's not just in medicine that software's on the march.
0:35:02 > 0:35:07In banking, AI is approving or not approving loan applications.
0:35:07 > 0:35:09And even making investment decisions.
0:35:10 > 0:35:13And, with autonomous vehicles on the horizon,
0:35:13 > 0:35:17many who drive for a living will soon be superseded.
0:35:19 > 0:35:21What you need to know about the future
0:35:21 > 0:35:23is that no job is immune from the influence
0:35:23 > 0:35:24of artificial intelligence.
0:35:24 > 0:35:26If it doesn't take your job,
0:35:26 > 0:35:29then it's likely to change the way in which you do it.
0:35:35 > 0:35:37Now, whenever we think about the future,
0:35:37 > 0:35:40however outlandish we imagine our housing,
0:35:40 > 0:35:42our transport or our gadgets to be,
0:35:42 > 0:35:44one thing that we never seem to question is that
0:35:44 > 0:35:48there will always be a constant supply of electricity.
0:35:48 > 0:35:51But if you look at the data, that's not necessarily a safe assumption.
0:35:51 > 0:35:54So here is what's been happening in the UK
0:35:54 > 0:35:55over the last 100 years or so.
0:35:55 > 0:35:57And in the 20th century,
0:35:57 > 0:36:00we've become very dependent on fossil fuels.
0:36:00 > 0:36:01So you can see coal here in purple,
0:36:01 > 0:36:03gas in blue coming in slightly later,
0:36:03 > 0:36:05and then a bit of oil there throughout.
0:36:05 > 0:36:08Now, there's a bit of nuclear there in red,
0:36:08 > 0:36:10which has stayed pretty constant over time.
0:36:10 > 0:36:13And some renewables coming in much later in green.
0:36:13 > 0:36:15But the main story from this graph
0:36:15 > 0:36:20is that we are very dependent on fossil fuels to heat our homes,
0:36:20 > 0:36:23provide our transport and to generate our electricity.
0:36:23 > 0:36:26Now, if you look at the picture globally,
0:36:26 > 0:36:29the demand has been steadily increasing over time.
0:36:29 > 0:36:33So you can see a little blip here for the 2008 financial crisis.
0:36:33 > 0:36:35But generally speaking, the trend has been upwards.
0:36:35 > 0:36:39And if this trend continues, then over the next 50 years,
0:36:39 > 0:36:44we can expect the demand for energy consumption to increase by 48%.
0:36:44 > 0:36:48But the trouble is, burning fossil fuels is pretty bad for the planet.
0:36:48 > 0:36:52And in any case, we're going to run out of oil at some point anyway.
0:36:52 > 0:36:55So the question is, how do we keep the lights on
0:36:55 > 0:36:57while helping to save the planet?
0:36:57 > 0:36:59So, we sent physicist Helen Czerski
0:36:59 > 0:37:01to a place where they've already done it.
0:37:15 > 0:37:17I'm in Norway, and this stunning country
0:37:17 > 0:37:21is one of the world's biggest producers of renewable energy.
0:37:21 > 0:37:24Almost all of their electricity comes from hydropower.
0:37:24 > 0:37:27And government incentives mean that electric vehicles like this one are
0:37:27 > 0:37:30becoming more and more common.
0:37:30 > 0:37:32Relying on hydropower is fine if
0:37:32 > 0:37:35you've got plenty of mountains and lakes.
0:37:35 > 0:37:37But what about the rest of the world?
0:37:37 > 0:37:40In the UK, just under half of our renewable energy
0:37:40 > 0:37:43comes from wind turbines. But in spite of that,
0:37:43 > 0:37:48wind energy only contributes 11% of our total electricity generation.
0:37:53 > 0:37:57Part of the problem is finding enough places with strong winds.
0:37:58 > 0:38:01But perhaps there are some other opportunities.
0:38:01 > 0:38:03This is data from the University of Reading
0:38:03 > 0:38:05showing typical wind speeds in this area.
0:38:05 > 0:38:07And you can see that down here near the ground,
0:38:07 > 0:38:10the wind speeds are almost always really low.
0:38:10 > 0:38:13But as you go up, the wind speeds go right up.
0:38:13 > 0:38:17And what that suggests is that if you are serious about wind energy,
0:38:17 > 0:38:19the place to be might not be down here,
0:38:19 > 0:38:21or even up on that hilltop up there.
0:38:21 > 0:38:23It's up there.
0:38:25 > 0:38:30That's exactly what engineer Dr Lode Carnel is doing
0:38:30 > 0:38:33with this tiny plane he calls kitemill.
0:38:33 > 0:38:37Kitemill is designed to fly in the high-altitude winds.
0:38:39 > 0:38:43The force of the wind will cause it to pull on the tether,
0:38:43 > 0:38:44and that generates electricity.
0:38:48 > 0:38:51So, the tether's out, the kite's been assembled,
0:38:51 > 0:38:52and it's ready to launch.
0:38:52 > 0:38:54So the next step is to get it up into the air.
0:38:54 > 0:38:55Here we go!
0:39:07 > 0:39:09It's tiny in the sky.
0:39:09 > 0:39:10It looks so, so small.
0:39:10 > 0:39:14The idea that that could generate any energy at all
0:39:14 > 0:39:15is really quite weird.
0:39:16 > 0:39:19And it's fast, wow! Look at that!
0:39:19 > 0:39:22So, the kite's up in the sky and it's doing two things.
0:39:22 > 0:39:26It's either sitting flat, or it's going round and round in circles.
0:39:26 > 0:39:28What are the circles about?
0:39:28 > 0:39:30Yes, so when the system is located on the ground,
0:39:30 > 0:39:33we need to take it up to a certain altitude,
0:39:33 > 0:39:36and then it will fly in a circle, a pattern that we now see,
0:39:36 > 0:39:38during which it can produce electricity.
0:39:40 > 0:39:42Once the plane is high enough,
0:39:42 > 0:39:45it glides upwards into a corkscrew pattern,
0:39:45 > 0:39:47pulling on the tether.
0:39:48 > 0:39:51So, here we have the ground station where we generate the energy.
0:39:51 > 0:39:55You have the drum, where the tether is wound around.
0:39:55 > 0:39:57But it is connected directly to a
0:39:57 > 0:39:59motor or a generator, which is on the back.
0:39:59 > 0:40:02So, when we wind off, the motor turns in one direction
0:40:02 > 0:40:03and produces energy.
0:40:05 > 0:40:08And so how much energy is this generating at the moment?
0:40:09 > 0:40:11Two kilowatts roughly now,
0:40:11 > 0:40:14which is roughly the consumption of one family in the UK.
0:40:14 > 0:40:17Our next model has a capacity of 30 kilowatts.
0:40:17 > 0:40:20That can power automatically 20 families in the UK.
0:40:20 > 0:40:22However, this is not the end.
0:40:22 > 0:40:23We need to scale up.
0:40:23 > 0:40:25We want to produce energy with the lowest possible cost.
0:40:25 > 0:40:29So then we are talking 500 kilowatts,
0:40:29 > 0:40:33and that will be sufficient to power 300-400 families in the UK.
0:40:35 > 0:40:38Once it reaches the end of its tether,
0:40:38 > 0:40:435% of the energy it's generated is used to reel it back in,
0:40:43 > 0:40:44and the process starts again.
0:40:46 > 0:40:51The plan is for the plane to stay up indefinitely, but this test is over,
0:40:51 > 0:40:53so the plane is brought back in to land.
0:40:55 > 0:40:58It's hard to imagine it working in the airspace
0:40:58 > 0:41:01above our already crowded cities.
0:41:01 > 0:41:04But it could have advantages in remote locations
0:41:04 > 0:41:06and in developing countries.
0:41:07 > 0:41:10And you can put it in places where you can't put a wind turbine.
0:41:10 > 0:41:12That's important, isn't it?
0:41:12 > 0:41:13This can extract energy from places
0:41:13 > 0:41:15that are not accessible at the moment.
0:41:15 > 0:41:19Correct, places where there is for example low wind speeds close to the
0:41:19 > 0:41:22ground, but higher wind speeds at higher altitudes could use this
0:41:22 > 0:41:24technology. Also, it's quite movable.
0:41:24 > 0:41:27It's a flexible technology, so one truck can come,
0:41:27 > 0:41:28and you can install everything.
0:41:28 > 0:41:29Contrary to windmills,
0:41:29 > 0:41:33where you need a lot of infrastructure and so on.
0:41:36 > 0:41:39Kitemill alone isn't the answer to our energy crisis.
0:41:43 > 0:41:46Power will have to come from a range of renewable sources.
0:41:48 > 0:41:50I'm optimistic about the future
0:41:50 > 0:41:51because I see lots of new technologies
0:41:51 > 0:41:53like kitemill coming along,
0:41:53 > 0:41:57each appropriate at a specific place or in a specific time.
0:41:57 > 0:41:59And together, these are the building blocks
0:41:59 > 0:42:02that will let us design a much more sophisticated energy future.
0:42:08 > 0:42:11A lot of people look to science fiction
0:42:11 > 0:42:14for a steer on what's going to happen in the future.
0:42:14 > 0:42:16And if sci-fi tells us one thing,
0:42:16 > 0:42:19it's that the future is littered with cyborgs.
0:42:19 > 0:42:23Now, the idea of some kind of human-machine hybrid is certainly an
0:42:23 > 0:42:27interesting one, and it's been explored in a number of different TV
0:42:27 > 0:42:29programmes, from Six Million Dollar Man
0:42:29 > 0:42:31all the way through to Star Trek.
0:42:31 > 0:42:34But so far, the reality hasn't quite managed
0:42:34 > 0:42:36to measure up for most of us.
0:42:36 > 0:42:39Now, is that a relief, or an opportunity missed?
0:42:51 > 0:42:53My name is James Young.
0:42:55 > 0:42:57And I am a cyborg.
0:42:59 > 0:43:02OK, and relax.
0:43:05 > 0:43:07Terrible.
0:43:08 > 0:43:10Taking a load off here.
0:43:10 > 0:43:14Just over five years ago, I lost an arm and a leg in a train accident.
0:43:16 > 0:43:19While I was coming to terms with the loss of my arm,
0:43:19 > 0:43:21I won a competition to have something different made.
0:43:24 > 0:43:25Yeah.
0:43:27 > 0:43:28It's not great!
0:43:33 > 0:43:36It was created to be more of an art piece,
0:43:36 > 0:43:38so it was like a prototype from the day it was made.
0:43:40 > 0:43:43It's got the lights that work and the hand...
0:43:43 > 0:43:45four of the digits work on the hand, so it's kind of like...
0:43:47 > 0:43:49..it's trying, it's trying its best,
0:43:49 > 0:43:52but it's not in tiptop condition, basically.
0:43:56 > 0:43:59My brief taste of being a cyborg has left me wanting more.
0:44:00 > 0:44:03I'm now looking at prostheses that attach
0:44:03 > 0:44:05directly to my skeleton and nervous system.
0:44:08 > 0:44:10If I can replace my old abilities,
0:44:10 > 0:44:13then can I go a step further and gain new ones?
0:44:14 > 0:44:18The idea of expanding my abilities beyond
0:44:18 > 0:44:22the human baseline is something that really, really intrigues me.
0:44:22 > 0:44:25And because I almost studied cybernetics when I was considering
0:44:25 > 0:44:26university, and it's a field that
0:44:26 > 0:44:28everybody's kind of thinking about now,
0:44:28 > 0:44:32because you get Elon Musk starting up initiatives to find, like,
0:44:32 > 0:44:34a neural lace that would enhance human abilities
0:44:34 > 0:44:36to kind of compete with AI and computing.
0:44:38 > 0:44:41If you're really serious about becoming a cyborg,
0:44:41 > 0:44:43tapping into the brain is the way you have to go.
0:44:45 > 0:44:47There's been a lot of research into this.
0:44:47 > 0:44:51Some brains have already been linked to a variety of devices in a bid to
0:44:51 > 0:44:52help people with disabilities.
0:44:54 > 0:44:55But I'm going to meet someone
0:44:55 > 0:44:58who's hacked their brain in a different way.
0:45:00 > 0:45:03At age 11, Neil Harbisson was diagnosed with
0:45:03 > 0:45:05a condition called achromatopsia.
0:45:05 > 0:45:08A form of total colour blindness,
0:45:08 > 0:45:10meaning Neil has only ever seen in grayscale.
0:45:13 > 0:45:16But in 2003, as part of an art project,
0:45:16 > 0:45:18Neil found a new way to perceive colour.
0:45:19 > 0:45:21By integrating technology into his skull.
0:45:23 > 0:45:25He can now hear colour.
0:45:27 > 0:45:30Could you explain how your antenna works front to back?
0:45:30 > 0:45:33- What's it...- Well, I thought that I should have a new body part,
0:45:33 > 0:45:37a new sensory organ, specifically for colour perception.
0:45:37 > 0:45:40So the light frequency goes inside the antenna,
0:45:40 > 0:45:43and then it touches a chip inside my bone that vibrates,
0:45:43 > 0:45:47so these vibrations in my head create inner sounds,
0:45:47 > 0:45:49so I can hear different notes for different colours.
0:45:49 > 0:45:52We created an app that tries to mimic
0:45:52 > 0:45:56the sounds that I hear for each colour. So you'll notice...
0:45:56 > 0:46:00HUMMING AND WHIRRING
0:46:00 > 0:46:02This is the sound of yellow.
0:46:02 > 0:46:04Cool.
0:46:05 > 0:46:09HIGH-PITCH PULSE
0:46:09 > 0:46:11- So it's kind of like...- Pink is a higher frequency.
0:46:11 > 0:46:12..a frequency shift.
0:46:12 > 0:46:16FAST-PACED BEEPS
0:46:16 > 0:46:18This is the sound of my jacket.
0:46:18 > 0:46:19So I'm wearing electronic music!
0:46:21 > 0:46:24So, with your antenna, is it kind of like when you have a new watch and
0:46:24 > 0:46:25you have to become used to
0:46:25 > 0:46:28the weight and feel of it when you're moving around?
0:46:28 > 0:46:33I'm not using or wearing technology - I am technology.
0:46:33 > 0:46:34So that's the difference.
0:46:34 > 0:46:38I guess it's... I can't compare it with anything else,
0:46:38 > 0:46:41because it's part of my skeleton.
0:46:43 > 0:46:46Meeting Neil has given me an insight into what it might be like to have
0:46:46 > 0:46:47genuine cyborg abilities.
0:46:50 > 0:46:54What has it been like, you being in the public with your extra sense?
0:46:54 > 0:46:58Some children ask me if it was some kind of extendable selfie stick!
0:46:58 > 0:47:01And since last year, people just shout at me, Pokemon,
0:47:01 > 0:47:02and they try to catch me!
0:47:02 > 0:47:04So it changes, what people think it is.
0:47:05 > 0:47:10Cyborgs are already amongst us, but it's not for everybody just yet.
0:47:10 > 0:47:14So, for now, maybe it's kind of up to people like Neil and I,
0:47:14 > 0:47:18who want to augment our bodies, to push the envelope.
0:47:24 > 0:47:26Next up, nature.
0:47:26 > 0:47:28Now, we all love nature,
0:47:28 > 0:47:30and we know this because of the vast audiences that
0:47:30 > 0:47:33BBC's Natural History Department gets, and also the fact that
0:47:33 > 0:47:36David Attenborough is now Sir David Attenborough.
0:47:36 > 0:47:41But, as much as we all claim to love the natural world,
0:47:41 > 0:47:44apparently it is vanishing before our eyes.
0:47:44 > 0:47:46If you have a little look at this graph here,
0:47:46 > 0:47:50this is what has been happening to vertebrate populations from 1970 all
0:47:50 > 0:47:52the way up to now. Now, this group,
0:47:52 > 0:47:56they track the populations of almost 4,000 different species,
0:47:56 > 0:47:59and come up with a score to say how well they are doing.
0:47:59 > 0:48:01It turns out, not great.
0:48:01 > 0:48:03Both the number of species
0:48:03 > 0:48:06and the number of animals is in steady decline.
0:48:06 > 0:48:10On land, there has been a 38% decrease in animal numbers.
0:48:10 > 0:48:13In the sea, there has been a 36% decrease.
0:48:13 > 0:48:15And worst of all, in freshwater,
0:48:15 > 0:48:20there has been a huge 81% drop in population numbers.
0:48:20 > 0:48:24And we can see from this graph that if this trend continues,
0:48:24 > 0:48:32by 2020 we can expect to see a 67% drop based on what we had in 1970.
0:48:32 > 0:48:34So, to find out if this really is as bad as it sounds,
0:48:34 > 0:48:37evolutionary geneticist, writer and Renaissance man,
0:48:37 > 0:48:39my very good friend Dr Adam Rutherford
0:48:39 > 0:48:41is here to help us with the science.
0:48:41 > 0:48:43Adam, we've been here before.
0:48:43 > 0:48:44There have been extinctions before, right?
0:48:44 > 0:48:47Yes, there have. In fact, over the last billion years or so,
0:48:47 > 0:48:49the evolutionary trajectory of life on Earth,
0:48:49 > 0:48:52extinction is completely the normal state of affairs.
0:48:52 > 0:48:53I've got my own graph here.
0:48:53 > 0:48:57If you look at the last 542 million years,
0:48:57 > 0:49:00what this shows is extinction rates over that time period.
0:49:00 > 0:49:02And there are five big peaks.
0:49:02 > 0:49:05So there have been five great extinction events.
0:49:05 > 0:49:08Everyone knows about the one that happened 66 million years ago,
0:49:08 > 0:49:09it is called the K-Pg boundary,
0:49:09 > 0:49:11and that was when a meteor dropped out of the sky.
0:49:11 > 0:49:13- The dinosaurs.- It did for the dinosaurs.
0:49:13 > 0:49:17But also, 75% of all species on land and in the sea.
0:49:17 > 0:49:19And that's not even the big one.
0:49:19 > 0:49:22The big one is called the Great Dying, or the P-T boundary,
0:49:22 > 0:49:25and that happens 252 million years ago.
0:49:25 > 0:49:2895% of all species go extinct.
0:49:28 > 0:49:30So, what's so different about this one?
0:49:30 > 0:49:32The timescale is what's different.
0:49:32 > 0:49:34So, the full extent of the Great Dying
0:49:34 > 0:49:37really pans out over a million or two million years.
0:49:37 > 0:49:39The dinosaur one, 66 million years.
0:49:39 > 0:49:43We find dinosaurs 10,000 years after that happened.
0:49:43 > 0:49:47What you just said was, 67% of species
0:49:47 > 0:49:49will be lost since the 1970s.
0:49:51 > 0:49:54If this is the start of another mass extinction,
0:49:54 > 0:49:58its speed means that ecosystems and food chains will break down
0:49:58 > 0:49:59catastrophically.
0:50:01 > 0:50:04For some species, it is already too late.
0:50:04 > 0:50:06Human activity means that coming generations
0:50:06 > 0:50:10will never see a live white rhino or a Sumatran orangutan.
0:50:11 > 0:50:14But what's worse is the potential impact
0:50:14 > 0:50:17of losing less charismatic wildlife.
0:50:17 > 0:50:21In the sea, rising temperatures have already disrupted
0:50:21 > 0:50:23the food chain by killing coral.
0:50:23 > 0:50:25And if the predicted further rises occur,
0:50:25 > 0:50:29Asian seagrass could go extinct within 50 years,
0:50:29 > 0:50:31causing the collapse of the entire
0:50:31 > 0:50:34marine ecosystem in that part of the world.
0:50:35 > 0:50:38In the future, our children will
0:50:38 > 0:50:41almost certainly see less diversity of animals.
0:50:41 > 0:50:44But unchecked, these changes also mean that
0:50:44 > 0:50:47they themselves could be facing much more serious problems.
0:50:49 > 0:50:52And as humans, are we immune to this?
0:50:52 > 0:50:54No, absolutely not.
0:50:54 > 0:50:57We are special, but we are living on this planet.
0:50:57 > 0:51:00And what we are doing is removing the ability for us to live on this
0:51:00 > 0:51:04planet whilst letting many, many species go extinct.
0:51:04 > 0:51:07And I think the key thing is to recognise that we have created this
0:51:07 > 0:51:10situation, and we also have the power to stop it.
0:51:15 > 0:51:17Flying cars - there you go, I've said it.
0:51:17 > 0:51:19Well, it wouldn't be a programme
0:51:19 > 0:51:21about the future if someone didn't mention them.
0:51:21 > 0:51:24But, let's be honest, they certainly would be pretty handy,
0:51:24 > 0:51:27because our love of earthbound cars
0:51:27 > 0:51:29has seen a steady increase in how many cars
0:51:29 > 0:51:31there now are on the roads.
0:51:31 > 0:51:36This graph here shows you how far we travel in motor vehicles since 1949.
0:51:36 > 0:51:39And you can see here that car journeys, in red,
0:51:39 > 0:51:41really have become all-conquering.
0:51:41 > 0:51:44We're travelling a lot further, and we're doing it in cars.
0:51:44 > 0:51:48And that makes the experience of actually driving them
0:51:48 > 0:51:49a lot less appealing.
0:51:49 > 0:51:54In fact, the average speed of traffic in central London
0:51:54 > 0:51:55is now 7.3mph.
0:51:55 > 0:51:57Basically, you'd be better off on a horse.
0:51:57 > 0:52:00Now, we need to travel faster.
0:52:00 > 0:52:02And dynamicist Teena Gade, who works
0:52:02 > 0:52:04for Formula 1 team Sahara Force India,
0:52:04 > 0:52:06she knows all about travelling quickly.
0:52:06 > 0:52:08And she has been looking at, well,
0:52:08 > 0:52:11there's no other way to put this, really - flying cars.
0:52:20 > 0:52:23The worst thing about city driving is the traffic.
0:52:23 > 0:52:27I think we're probably doing five, maybe 10mph, if that.
0:52:27 > 0:52:29Right now, if my car could fly,
0:52:29 > 0:52:32I'd take off and I'd jump this long queue of people in front of me
0:52:32 > 0:52:33and be at my destination in no time.
0:52:36 > 0:52:40History is littered with attempts at the fabled flying car.
0:52:43 > 0:52:46Most of them hard to take seriously.
0:52:46 > 0:52:48And moving it around is a job for a secretary
0:52:48 > 0:52:52rather than a highly skilled and highly expensive helicopter pilot.
0:52:59 > 0:53:02But prototypes for personal flying machines
0:53:02 > 0:53:04have started to appear once again.
0:53:05 > 0:53:08This time with serious financial backing.
0:53:16 > 0:53:19I would absolutely love a personal flying car.
0:53:19 > 0:53:21I think that would be absolutely fantastic.
0:53:21 > 0:53:22Imagine going to work every day
0:53:22 > 0:53:27and not having to sit in the traffic queues.
0:53:27 > 0:53:28It might not be a good idea, though,
0:53:28 > 0:53:31for everyone to have their own personal flying machine.
0:53:31 > 0:53:34As I'm showing here, occasionally I can keep control of it,
0:53:34 > 0:53:36but some of the time I'm not doing a very good job.
0:53:37 > 0:53:40Right, that's it down. I'm just going to go and get it.
0:53:43 > 0:53:46So, if we're all going to buzz around our cities in personal flying
0:53:46 > 0:53:48machines, how do we keep from crashing into each other?
0:53:53 > 0:53:57To find out, I've come to Zurich to meet robotics expert
0:53:57 > 0:53:59Professor Raffaello D'Andrea.
0:54:01 > 0:54:03He developed the Kiva robot system.
0:54:05 > 0:54:08A network of thousands of bots
0:54:08 > 0:54:11that work together to fulfil online orders.
0:54:12 > 0:54:14This is basically a very large warehouse
0:54:14 > 0:54:16where orders come in and they need to be fulfilled.
0:54:16 > 0:54:22So, the system figures out which robots need to go to which pods,
0:54:22 > 0:54:25pick it up and bring it to the perimeter of the warehouse,
0:54:25 > 0:54:27where then people take things off of the pods
0:54:27 > 0:54:30and put them into the orders which eventually go out.
0:54:30 > 0:54:32There's a lot of robots in action here.
0:54:32 > 0:54:33How come they don't collide?
0:54:33 > 0:54:37The robots have to generate trajectories and plans.
0:54:37 > 0:54:40And those plans are then shared to a coordinator,
0:54:40 > 0:54:43which then figures out how they should go and execute their plan
0:54:43 > 0:54:45so that they don't hit each other.
0:54:46 > 0:54:50And with over 80,000 in operation,
0:54:50 > 0:54:52the Kiva bots are the largest network of
0:54:52 > 0:54:54autonomous vehicles in the world.
0:54:54 > 0:54:56And so far, there haven't been any accidents.
0:55:01 > 0:55:04If only something similar could be done with flying machines.
0:55:07 > 0:55:10Oh, wow! That's incredible.
0:55:10 > 0:55:12So what do we have here?
0:55:12 > 0:55:15A swarm of 32 of these flying machines.
0:55:15 > 0:55:19And they're going to do a little choreographed performance.
0:55:19 > 0:55:21'This is more like it.'
0:55:21 > 0:55:23What they are doing right now is a choreography
0:55:23 > 0:55:25that is pre-programmed,
0:55:25 > 0:55:28and ensures that they do not collide with each other.
0:55:31 > 0:55:33So, what's in one of these?
0:55:33 > 0:55:35- How are these made?- They have four motors for propellers.
0:55:35 > 0:55:37They have a cage to keep the propellers
0:55:37 > 0:55:39away from other vehicles
0:55:39 > 0:55:42and from people, and they have some custom electronics
0:55:42 > 0:55:44that creates all the magic and intelligence.
0:55:45 > 0:55:48And now, we just move out of the way.
0:55:48 > 0:55:50Oh, they're coming in to land. If I put my hand out...
0:55:50 > 0:55:52- I can catch one!- Exactly.
0:55:56 > 0:56:00So, if you could have this level of automation and planning in personal
0:56:00 > 0:56:04flying machines, then perhaps they could become a reality.
0:56:04 > 0:56:07How would that lead us to, for example, a transport system?
0:56:07 > 0:56:10Well, so, what they would share with a transport system is the use of a
0:56:10 > 0:56:11global positioning system.
0:56:11 > 0:56:14We've developed an indoor global positioning system,
0:56:14 > 0:56:16just like the one that exists outdoors.
0:56:16 > 0:56:19And this is important, because then the vehicles
0:56:19 > 0:56:21know where they are in space.
0:56:21 > 0:56:23How it would differ is that the choreographies,
0:56:23 > 0:56:25the trajectories wouldn't be preplanned.
0:56:25 > 0:56:27The system is going to have to be much more reactive
0:56:27 > 0:56:30when people want to fly from point A to point B.
0:56:34 > 0:56:38Is this now setting us up for having our own personal flying machines?
0:56:38 > 0:56:40I think we've certainly come a long way.
0:56:40 > 0:56:43We can make flying machines that can do vertical take-off and landing,
0:56:43 > 0:56:46fully electric, which has its own benefits.
0:56:46 > 0:56:48I think that we will, in the near future.
0:56:49 > 0:56:53Could this be the aerial highway of the future in miniature?
0:56:56 > 0:56:59These drones were designed to perform at live events.
0:56:59 > 0:57:01But they offer a glimpse into
0:57:01 > 0:57:03what the cities of the future might look like.
0:57:03 > 0:57:05Many of the big questions around safety
0:57:05 > 0:57:07can be answered with current tech.
0:57:07 > 0:57:09So what you need to know about the future of transport is
0:57:09 > 0:57:11we may finally get our flying cars.
0:57:19 > 0:57:23Making predictions about the future is a pretty risky business.
0:57:23 > 0:57:26And many people have come unstuck in the past.
0:57:26 > 0:57:28But I'm pretty confident that most
0:57:28 > 0:57:32of what we've covered in this programme will actually happen.
0:57:32 > 0:57:34But I'm also confident of something else.
0:57:34 > 0:57:38The tenth thing that you need to know about the future
0:57:38 > 0:57:41is that there will be many other developments
0:57:41 > 0:57:43that none of us have even thought of.
0:57:43 > 0:57:46Because, although Arthur C Clarke may have predicted
0:57:46 > 0:57:47universal mobile communication,
0:57:47 > 0:57:52even he would have been surprised that we now carry around
0:57:52 > 0:57:57with us a little hand-held device that contains a typewriter,
0:57:57 > 0:58:02a camera, a postbox, a television, a calculator, a calendar, a light,
0:58:02 > 0:58:08a record player, a tape recorder, and not forgetting a telephone.
0:58:08 > 0:58:10And that, for me at least,
0:58:10 > 0:58:14is a lot cleverer and certainly a lot cleaner than a genetically
0:58:14 > 0:58:16engineered monkey servant.
0:58:17 > 0:58:20To find out more about the innovations that are changing
0:58:20 > 0:58:22our health, our leisure and our work,
0:58:22 > 0:58:24and will continue to shape our future,
0:58:24 > 0:58:29go to bbc.co.uk/horizon and follow the Open University link.