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If you have ever wondered what's | 0:00:02 | 0:00:03 | |
going to happen in the future, | 0:00:03 | 0:00:05 | |
then this is the programme for you. | 0:00:05 | 0:00:07 | |
Because we're going to be asking | 0:00:07 | 0:00:09 | |
the kind of questions that we all have | 0:00:09 | 0:00:11 | |
about our future. | 0:00:11 | 0:00:13 | |
In this very special edition of Horizon we'll be | 0:00:13 | 0:00:16 | |
revealing the ten things you definitely need to know that will, | 0:00:16 | 0:00:20 | |
for better or worse, change our lives. | 0:00:20 | 0:00:23 | |
We'll explore how artificial intelligence | 0:00:24 | 0:00:26 | |
will change the way we work. | 0:00:26 | 0:00:28 | |
Can I enquire, do you have pain in your mouth? | 0:00:28 | 0:00:30 | |
-Yes, I do. -I see. | 0:00:30 | 0:00:32 | |
We'll look at the likely impact of our changing climate. | 0:00:32 | 0:00:36 | |
It could well top 40 degrees in a few days' time. | 0:00:36 | 0:00:39 | |
And discover how gene therapy will transform medicine. | 0:00:39 | 0:00:43 | |
And he said, "There is no evidence of disease. | 0:00:43 | 0:00:46 | |
"You are cancer-free." | 0:00:46 | 0:00:47 | |
We'll introduce the people already turning themselves into cyborgs... | 0:00:47 | 0:00:51 | |
This last year people just shout at me, Pokemon. | 0:00:51 | 0:00:53 | |
And they try to catch me. | 0:00:53 | 0:00:55 | |
..ask if renewable energy is here to stay... | 0:00:55 | 0:00:57 | |
The place to be might not be down here, | 0:00:57 | 0:00:59 | |
or even up on that hilltop up there. | 0:00:59 | 0:01:02 | |
It's up there. | 0:01:02 | 0:01:03 | |
..and reveal how we are mapping our brains... | 0:01:03 | 0:01:05 | |
It's absolutely astonishing. | 0:01:05 | 0:01:07 | |
..but jeopardising our very existence. | 0:01:07 | 0:01:10 | |
What we are doing is removing the ability | 0:01:10 | 0:01:12 | |
for us to live on this planet. | 0:01:12 | 0:01:14 | |
We'll also celebrate, finally, the arrival of | 0:01:14 | 0:01:18 | |
that science fiction cliche, the flying car. | 0:01:18 | 0:01:21 | |
That's it, that's all it takes. | 0:01:21 | 0:01:23 | |
So, if you want to know what's in store for you, then keep watching. | 0:01:23 | 0:01:26 | |
Welcome to the future. | 0:01:26 | 0:01:28 | |
Now, we are all going to experience the future | 0:01:38 | 0:01:41 | |
and we all want to know how it will change our lives. | 0:01:41 | 0:01:44 | |
What's going to be the future of our daily commute? | 0:01:44 | 0:01:46 | |
What will we spend our money on? | 0:01:46 | 0:01:48 | |
What's going to happen to our planet? | 0:01:48 | 0:01:50 | |
Now, over the years, many people have made some pretty bold claims, | 0:01:50 | 0:01:54 | |
including science fiction legend Arthur C Clarke, | 0:01:54 | 0:01:58 | |
in an interview that he gave to Horizon in 1964. | 0:01:58 | 0:02:02 | |
I'm perfectly serious when I suggest a world | 0:02:02 | 0:02:05 | |
in which we can be in instant contact with each other, | 0:02:05 | 0:02:08 | |
wherever we may be. | 0:02:08 | 0:02:09 | |
Where we can contact our friends anywhere on Earth | 0:02:09 | 0:02:11 | |
even if we don't know their actual physical location. | 0:02:11 | 0:02:15 | |
It will be possible in that age, | 0:02:15 | 0:02:17 | |
perhaps only 50 years from now, | 0:02:17 | 0:02:19 | |
for a man to conduct his business from Tahiti or Bali, | 0:02:19 | 0:02:24 | |
just as well as he could from London. | 0:02:24 | 0:02:25 | |
Isn't that incredible? Arthur C Clarke, there, in 1964, | 0:02:27 | 0:02:31 | |
accurately describing what is effectively the internet age. | 0:02:31 | 0:02:34 | |
Something, of course, that we all take completely for granted now | 0:02:34 | 0:02:38 | |
but a world that was totally alien to his audience. | 0:02:38 | 0:02:41 | |
But, predicting the future is notoriously tricky. | 0:02:41 | 0:02:45 | |
And while Arthur was bang on the money with that one, | 0:02:45 | 0:02:47 | |
he didn't stop there. Because here he is with his follow-up prediction. | 0:02:47 | 0:02:52 | |
The development of intelligent and useful servants | 0:02:52 | 0:02:55 | |
among the other animals on this planet. | 0:02:55 | 0:02:57 | |
We could certainly solve the servant problem | 0:02:57 | 0:03:01 | |
with the help of the monkey kingdom. | 0:03:01 | 0:03:04 | |
Sorry, come again? | 0:03:04 | 0:03:06 | |
..solve the servant problem with the help of the monkey kingdom. | 0:03:06 | 0:03:11 | |
Of course, eventually our super chimpanzees | 0:03:12 | 0:03:15 | |
would start forming trade unions | 0:03:15 | 0:03:17 | |
and we would be right back where we started. | 0:03:17 | 0:03:20 | |
Well, I suppose one out of two ain't bad. | 0:03:20 | 0:03:22 | |
But it does just go to show that predicting the future | 0:03:22 | 0:03:25 | |
is usually a mug's game. | 0:03:25 | 0:03:27 | |
Flying cars, salad-making robots and living in space colonies | 0:03:31 | 0:03:35 | |
were the embarrassing staples of TV programmes like Tomorrow's World. | 0:03:35 | 0:03:40 | |
Usually, their earnest predictions were way off. | 0:03:40 | 0:03:42 | |
But this programme is different. | 0:03:44 | 0:03:46 | |
And I'm as confident as I can be that these predictions | 0:03:46 | 0:03:49 | |
aren't going to end up being laughed at by a TV audience in 2050. | 0:03:49 | 0:03:53 | |
Because we are basing our predictions | 0:03:54 | 0:03:56 | |
on real data from trusted sources, | 0:03:56 | 0:03:59 | |
from today. | 0:03:59 | 0:04:01 | |
And that is why we can confidently tell you that what you're | 0:04:02 | 0:04:05 | |
about to watch really are the ten things | 0:04:05 | 0:04:07 | |
that you need to know about the future. | 0:04:07 | 0:04:10 | |
First up, Dr Kevin Fong on perhaps the biggest question of all. | 0:04:10 | 0:04:14 | |
How can we cheat death and live longer? | 0:04:14 | 0:04:17 | |
In the 1980s, if you wanted some constructive advice | 0:04:27 | 0:04:29 | |
about how to achieve immortality, | 0:04:29 | 0:04:31 | |
you went to see the film and musical Fame. | 0:04:31 | 0:04:34 | |
The star of the piece was very clear about her advice, | 0:04:34 | 0:04:37 | |
"I'm gonna live forever," she told us. | 0:04:37 | 0:04:39 | |
And she was going to do that by becoming famous | 0:04:39 | 0:04:42 | |
and living on forever in the memory of her adoring fans. | 0:04:42 | 0:04:45 | |
And that kind of immortality is all that human beings | 0:04:48 | 0:04:51 | |
have ever been able to aspire to. | 0:04:51 | 0:04:53 | |
To be remembered by as many people as possible | 0:04:54 | 0:04:57 | |
for as long as you possibly can. | 0:04:57 | 0:04:59 | |
Woody Allen had a very different approach, of course. | 0:05:03 | 0:05:06 | |
"I don't want to achieve immortality through my work," he said. | 0:05:06 | 0:05:09 | |
"I want to achieve immortality by not dying." | 0:05:09 | 0:05:12 | |
And if you want to live for longer, not dying is a great place to start. | 0:05:14 | 0:05:18 | |
And the good news is that, on average, | 0:05:20 | 0:05:22 | |
we're living longer today than we ever have in the past. | 0:05:22 | 0:05:24 | |
But how far can we push that? | 0:05:28 | 0:05:30 | |
How long can we expect to live in the future? | 0:05:30 | 0:05:33 | |
Now, births and deaths are one thing that we have quite a lot of data on | 0:05:37 | 0:05:40 | |
here in the UK. And here is some of that data. | 0:05:40 | 0:05:44 | |
This shows you how our life expectancy has changed | 0:05:44 | 0:05:47 | |
over the last 170 years or so. | 0:05:47 | 0:05:49 | |
Now, Kevin Fong, welcome back to the studio. | 0:05:49 | 0:05:52 | |
As a doctor, help me make some sense of these numbers. | 0:05:52 | 0:05:55 | |
Well, I think this is all fascinating data. | 0:05:55 | 0:05:57 | |
When you look at it, this is average life expectancy which, for almost | 0:05:57 | 0:06:01 | |
the whole of human history, | 0:06:01 | 0:06:02 | |
languishes around here, around 40-45 years. | 0:06:02 | 0:06:05 | |
And then you hit the start of the 20th century. | 0:06:05 | 0:06:07 | |
You get this massive kick. | 0:06:07 | 0:06:09 | |
Why? Well, it's all prevention. | 0:06:09 | 0:06:11 | |
It's all about stopping the things that kill you early on in life | 0:06:11 | 0:06:14 | |
shortly after you've been born, like measles, mumps, rubella. | 0:06:14 | 0:06:17 | |
It's all about stopping the diseases of infection. | 0:06:17 | 0:06:20 | |
And that is achieved through vaccination and better sanitation. | 0:06:20 | 0:06:23 | |
So that's what explains this big kick and these early gains here. | 0:06:23 | 0:06:26 | |
So this bit really is all about sort of child mortality? | 0:06:26 | 0:06:29 | |
Yep. Prevention is always better than cure, and that was vaccination. | 0:06:29 | 0:06:33 | |
But it continues to go up past then. What about this bit over here? | 0:06:33 | 0:06:36 | |
So this kick up the top here is us getting better at | 0:06:36 | 0:06:39 | |
advanced modern medicine. | 0:06:39 | 0:06:41 | |
So we have better lifestyles, | 0:06:41 | 0:06:43 | |
but also we get better at treating diseases like heart disease and also | 0:06:43 | 0:06:47 | |
cancer, so that gives us a bit of an extra gain here. | 0:06:47 | 0:06:49 | |
So we still see increases all the way through. | 0:06:49 | 0:06:51 | |
And then what about going forward to the future, then? | 0:06:51 | 0:06:53 | |
Will this continue to increase? | 0:06:53 | 0:06:55 | |
Is there a limit to how long we can live for? | 0:06:55 | 0:06:57 | |
It would be lovely, wouldn't it, | 0:06:57 | 0:06:58 | |
if this line kept going up and up and up to infinity? | 0:06:58 | 0:07:01 | |
It doesn't look like it. | 0:07:01 | 0:07:02 | |
It looks like this data plateaus. | 0:07:02 | 0:07:05 | |
This graph shows that being born in 2011 | 0:07:06 | 0:07:09 | |
gives you a very high average life expectancy. | 0:07:09 | 0:07:12 | |
But in terms of maximum possible age, | 0:07:14 | 0:07:16 | |
you're no better off than your early Victorian ancestors. | 0:07:16 | 0:07:20 | |
There's a fundamental limit, it would appear, | 0:07:21 | 0:07:23 | |
at least at the moment, around 110, 120 years, | 0:07:23 | 0:07:27 | |
to which we seem to be limited. | 0:07:27 | 0:07:29 | |
If we want to improve things, and who doesn't, | 0:07:31 | 0:07:33 | |
we could do a lot worse than to investigate the naked mole rat. | 0:07:33 | 0:07:38 | |
It lives at least ten times longer than other rats. | 0:07:38 | 0:07:42 | |
In human terms, that's a potential lifespan of upwards of 700 years. | 0:07:42 | 0:07:47 | |
As it happens, we actually have a naked mole rat expert | 0:07:48 | 0:07:52 | |
here in the studio with your mole rats. | 0:07:52 | 0:07:55 | |
Welcome to the studio, Chris Faulkes. | 0:07:55 | 0:07:57 | |
Tell me about these amazing creatures. | 0:07:57 | 0:07:59 | |
Well, naked mole rats, | 0:07:59 | 0:08:01 | |
just about everything about their biology | 0:08:01 | 0:08:03 | |
is weird and wonderful and exceptional. | 0:08:03 | 0:08:06 | |
And recently they've been generating a lot of interest | 0:08:06 | 0:08:09 | |
because of their extreme longevity. | 0:08:09 | 0:08:11 | |
Which can be... We don't know the upper limit, but more than 30 years. | 0:08:11 | 0:08:15 | |
Gosh. Cos I guess a rat only lives, what, two or three years, | 0:08:15 | 0:08:18 | |
-maybe at the most. -Yeah, exactly. -And these live to 30, did you say? | 0:08:18 | 0:08:20 | |
Yeah. And counting. | 0:08:20 | 0:08:22 | |
Wow. And do they get old in that time? | 0:08:22 | 0:08:24 | |
Well, this is really the remarkable thing. | 0:08:24 | 0:08:27 | |
Because not only do they have a long lifespan, | 0:08:27 | 0:08:29 | |
but their health span is very long, | 0:08:29 | 0:08:31 | |
which means that they go through a huge proportion of their life | 0:08:31 | 0:08:35 | |
without showing any signs of ageing whatsoever, | 0:08:35 | 0:08:38 | |
like an 80-year-old having the body of a 30-year-old. | 0:08:38 | 0:08:41 | |
-Well, that would be nice, wouldn't it? -It sure would. -So, why is that? | 0:08:41 | 0:08:44 | |
What is it about them that means that they have that feature? | 0:08:44 | 0:08:46 | |
Well, it seems that it's not a single thing, | 0:08:46 | 0:08:49 | |
but there's a whole mosaic of adaptations | 0:08:49 | 0:08:52 | |
that have given them very low metabolic rate, | 0:08:52 | 0:08:54 | |
a low body temperature, there's resistance to cancer, | 0:08:54 | 0:08:58 | |
and a whole bunch of other things | 0:08:58 | 0:08:59 | |
which all collectively give them | 0:08:59 | 0:09:01 | |
this really long lifespan and health span. | 0:09:01 | 0:09:03 | |
So, do they not get cancer? | 0:09:03 | 0:09:04 | |
There's been virtually no recorded cases. | 0:09:04 | 0:09:07 | |
As opposed to lab mice, for example, where 70% will die of cancer. | 0:09:07 | 0:09:12 | |
Wow. Gosh, that's extraordinary. | 0:09:12 | 0:09:14 | |
So what is it about them that means that they don't get cancer? | 0:09:14 | 0:09:16 | |
There's a substance in the skin called hyaluronan that we all have. | 0:09:16 | 0:09:20 | |
These guys have a very special version of that substance, | 0:09:20 | 0:09:24 | |
which we think is what gives them their really elasticky skin, | 0:09:24 | 0:09:27 | |
which is useful when you're living in tight tunnels. | 0:09:27 | 0:09:30 | |
And this hyaluronan is implicated in one part of their cancer resistance. | 0:09:30 | 0:09:36 | |
How stretchy is their skin? | 0:09:36 | 0:09:38 | |
It's pretty stretchy. | 0:09:38 | 0:09:39 | |
Do you want me to see if I can demonstrate? | 0:09:39 | 0:09:41 | |
Yeah, go on, why not, let's have a look. | 0:09:41 | 0:09:43 | |
They'll probably all go running off. | 0:09:43 | 0:09:45 | |
And this sort of stretchy substance is one of | 0:09:45 | 0:09:49 | |
the things that protects them against... Oh, my goodness. | 0:09:49 | 0:09:51 | |
Yeah, so, there we go, they don't mind | 0:09:51 | 0:09:53 | |
being picked up like this at all as you can see. | 0:09:53 | 0:09:55 | |
The animal's quite relaxed. But see how really elasticky the skin is. | 0:09:55 | 0:10:00 | |
Incredible. My goodness me. | 0:10:00 | 0:10:01 | |
Let me just pop him back there. There we go. | 0:10:01 | 0:10:03 | |
-And he's quite happy. -Yeah, no problem. | 0:10:03 | 0:10:05 | |
He has made a run for it, though. | 0:10:05 | 0:10:06 | |
-Yes. -Well, I guess the question is, really, | 0:10:06 | 0:10:10 | |
how do we get that substance in ourselves? | 0:10:10 | 0:10:13 | |
As labs around the world have been studying | 0:10:13 | 0:10:16 | |
the genomes of naked mole rats, | 0:10:16 | 0:10:18 | |
we're finding a whole bunch of | 0:10:18 | 0:10:20 | |
candidate genes that are responsible, | 0:10:20 | 0:10:23 | |
perhaps, for their longevity. | 0:10:23 | 0:10:26 | |
And, you know, their health span, as well. | 0:10:26 | 0:10:28 | |
And potentially apply them to ourselves. | 0:10:28 | 0:10:31 | |
That could well be the case, I think. | 0:10:31 | 0:10:33 | |
-It's not beyond the realms of possibility. -Wow. | 0:10:33 | 0:10:37 | |
So, what you need to know about the future of lifespan is this. | 0:10:37 | 0:10:41 | |
The good news is that we are likely to live longer on average. | 0:10:41 | 0:10:45 | |
But, until we crack the secrets of the mole rats' longevity, | 0:10:45 | 0:10:48 | |
it does seem that our luck, on average, | 0:10:48 | 0:10:50 | |
runs out just after the telegram from the Queen arrives. | 0:10:50 | 0:10:53 | |
Right now, one in four of us is likely | 0:10:58 | 0:11:01 | |
to be affected by mental health issues at some point in our lives. | 0:11:01 | 0:11:05 | |
But when it comes to effective treatment, | 0:11:05 | 0:11:07 | |
mental health really does lag behind other diseases. | 0:11:07 | 0:11:10 | |
So, what does the future hold for our state of mind? | 0:11:10 | 0:11:14 | |
Now, there is a very big incentive to answer this question. | 0:11:14 | 0:11:17 | |
And it's because mental health issues | 0:11:17 | 0:11:20 | |
are the number one cause of people being unable to work. | 0:11:20 | 0:11:23 | |
So this graph here from 2010 shows | 0:11:23 | 0:11:26 | |
the amount of money lost by people not being able to work | 0:11:26 | 0:11:29 | |
with different diseases. | 0:11:29 | 0:11:31 | |
And the results here are shown in trillions of dollars. | 0:11:31 | 0:11:34 | |
But the exact numbers here aren't really the story. | 0:11:34 | 0:11:36 | |
The story is that mental health illness | 0:11:36 | 0:11:39 | |
really does top the pile here. | 0:11:39 | 0:11:41 | |
Beating both cardiovascular disease | 0:11:41 | 0:11:43 | |
and also cancer, just there. | 0:11:43 | 0:11:45 | |
And if current trends continue, | 0:11:45 | 0:11:48 | |
then the World Health Organization has made some predictions | 0:11:48 | 0:11:51 | |
for what we can expect by 2030. | 0:11:51 | 0:11:53 | |
And you can see things really are set to get a lot worse. | 0:11:53 | 0:11:56 | |
And what's particularly bad about mental health illness | 0:11:56 | 0:11:59 | |
is that it really is the disease | 0:11:59 | 0:12:01 | |
that people end up living with for the longest. | 0:12:01 | 0:12:04 | |
So this graph here shows you how long | 0:12:04 | 0:12:06 | |
people live with different kinds of illness, | 0:12:06 | 0:12:08 | |
so you can see unintentional injuries just there. | 0:12:08 | 0:12:11 | |
There's cardiovascular diseases down there. | 0:12:11 | 0:12:13 | |
And once again, mental health disorders and neurological disorders | 0:12:13 | 0:12:18 | |
really are very much out in front. | 0:12:18 | 0:12:20 | |
So what is being done to help understand the brain and to | 0:12:20 | 0:12:23 | |
improve these things? | 0:12:23 | 0:12:24 | |
Well, Michael Mosley has been to visit | 0:12:24 | 0:12:26 | |
a research project in London to find out. | 0:12:26 | 0:12:29 | |
The human brain is the most complex object in the known universe. | 0:12:38 | 0:12:42 | |
And for a long time its workings were a complete mystery. | 0:12:42 | 0:12:46 | |
Then, in the 19th century, | 0:12:46 | 0:12:47 | |
scientists identified that some of our abilities, | 0:12:47 | 0:12:50 | |
like being able to speak | 0:12:50 | 0:12:51 | |
are linked to particular regions of the brain. | 0:12:51 | 0:12:53 | |
But an awful lot of what goes on up here is not to do with regions. | 0:12:53 | 0:12:57 | |
It's absolutely to do with connections. | 0:12:57 | 0:13:00 | |
If your brain isn't properly wired up, | 0:13:02 | 0:13:04 | |
then this puts you at greater risk of things like dementia, | 0:13:04 | 0:13:08 | |
schizophrenia and, early in life, autism. | 0:13:08 | 0:13:11 | |
Autism is the result of changes in the way | 0:13:12 | 0:13:14 | |
that the brain processes information. | 0:13:14 | 0:13:16 | |
By studying these physical changes, | 0:13:16 | 0:13:20 | |
it's hoped new light will be shed on the workings of the whole brain. | 0:13:20 | 0:13:23 | |
Here, at Evelina London Children's Hospital, | 0:13:25 | 0:13:28 | |
a team from King's College London are looking at | 0:13:28 | 0:13:31 | |
brain development in babies, using MRI scanners. | 0:13:31 | 0:13:36 | |
Hello, my name is Anna, I'm one of the radiographers, OK? | 0:13:36 | 0:13:39 | |
Even before they're born. | 0:13:40 | 0:13:42 | |
I've never seen an MRI of a foetus. | 0:13:44 | 0:13:45 | |
-That is amazing. -It is amazing. | 0:13:46 | 0:13:49 | |
It is absolutely astonishing. | 0:13:49 | 0:13:51 | |
I mean, what's really amazing about it, | 0:13:51 | 0:13:53 | |
it's relatively easy to get one quick flash through a scan | 0:13:53 | 0:13:57 | |
but collecting all of the slices | 0:13:57 | 0:14:00 | |
to make a three-dimensional reconstruction is the hard bit. | 0:14:00 | 0:14:03 | |
This is a 3-D model made from one of the images of a baby who is about | 0:14:04 | 0:14:08 | |
three months early. And as you can see, it's very small. | 0:14:08 | 0:14:12 | |
It's very smooth, it's very underdeveloped. | 0:14:12 | 0:14:14 | |
It has a lot more growing to do. | 0:14:14 | 0:14:16 | |
And this is the baby who's about term, | 0:14:16 | 0:14:18 | |
and the growth that has to happen | 0:14:18 | 0:14:20 | |
between there and there is phenomenal. | 0:14:20 | 0:14:23 | |
As the brain grows, more and more connections are made inside. | 0:14:23 | 0:14:27 | |
And the team is able to identify that wiring | 0:14:29 | 0:14:31 | |
using two kinds of MRI scanning. | 0:14:31 | 0:14:33 | |
I have to say, I'm still blown away by those images. | 0:14:35 | 0:14:37 | |
I don't know what I was expecting, | 0:14:39 | 0:14:40 | |
but I wasn't expecting something as clear as that. | 0:14:40 | 0:14:43 | |
I think, to be honest, we're blown away by the images. | 0:14:43 | 0:14:45 | |
Once the MRI data is processed, | 0:14:45 | 0:14:47 | |
it's translated into a basic map of connections, | 0:14:47 | 0:14:50 | |
showing which parts of a baby's brain can talk to each other. | 0:14:50 | 0:14:54 | |
These are much better than the images we used to get in the past. | 0:14:56 | 0:14:59 | |
And the information content has gone up massively. | 0:14:59 | 0:15:01 | |
At birth, the brain already has about 100 billion neurons. | 0:15:03 | 0:15:08 | |
And each one is connected to thousands of others. | 0:15:08 | 0:15:11 | |
So the brain's wiring diagram is enormously complex. | 0:15:11 | 0:15:14 | |
Nonetheless, researchers have started to decipher it. | 0:15:17 | 0:15:20 | |
The resulting map is called the connectome. | 0:15:20 | 0:15:23 | |
And while everyone's individual connectome is unique, | 0:15:24 | 0:15:27 | |
the hope is the average data will give us useful information | 0:15:27 | 0:15:31 | |
about how all our brains work. | 0:15:31 | 0:15:33 | |
So, one of the major uses for this is to define the connectome. | 0:15:35 | 0:15:38 | |
Now, we obviously can't have all the connections in the brain because | 0:15:38 | 0:15:41 | |
we're not looking at single nerve fibres. | 0:15:41 | 0:15:43 | |
But we can get a pretty good idea of what that connection map would be. | 0:15:43 | 0:15:46 | |
I think of it as being a bit like the Tube map of London. | 0:15:46 | 0:15:48 | |
You can't find an individual road on the Tube map but you can find your | 0:15:48 | 0:15:51 | |
way round London on it. | 0:15:51 | 0:15:52 | |
Connectome studies around the world are gathering data | 0:15:54 | 0:15:57 | |
in a bid to understand a whole range of mental health conditions | 0:15:57 | 0:16:01 | |
and brain disorders. | 0:16:01 | 0:16:03 | |
There's autism and ADHD at the beginning of life. | 0:16:03 | 0:16:07 | |
Then psychoses like schizophrenia. | 0:16:07 | 0:16:10 | |
Finally, as the level of connectivity deteriorates | 0:16:10 | 0:16:13 | |
in the ageing brain, dementia. | 0:16:13 | 0:16:16 | |
I do think that mapping the connectome | 0:16:16 | 0:16:18 | |
could be the next big thing in our understanding of the brain. | 0:16:18 | 0:16:21 | |
And that's important, not just because | 0:16:21 | 0:16:24 | |
it will increase our knowledge of a normal brain, | 0:16:24 | 0:16:26 | |
but it could also lead to better treatments when things go wrong. | 0:16:26 | 0:16:30 | |
And now to our national obsession - the weather. | 0:16:36 | 0:16:39 | |
We live, so we're told, in dangerous times. | 0:16:39 | 0:16:42 | |
Planet Earth is getting hotter and that is going to change our climate, | 0:16:42 | 0:16:46 | |
and there is a lot of data around this point. | 0:16:46 | 0:16:48 | |
So here, for instance, | 0:16:48 | 0:16:50 | |
is a map of what's happened around the world in the last 130 years, | 0:16:50 | 0:16:54 | |
compared to an average in the middle of the 20th century. | 0:16:54 | 0:16:57 | |
And as you're in the sort of '20s and '30s you can see a lot of blue | 0:16:57 | 0:16:59 | |
and white on this map, the odd splodge of orange | 0:16:59 | 0:17:01 | |
comes in every now and then. | 0:17:01 | 0:17:03 | |
But, as time rolls on and we get closer to now, | 0:17:03 | 0:17:06 | |
these orange splodges end up connecting to each other | 0:17:06 | 0:17:08 | |
and we see some red coming in, in the north up there. | 0:17:08 | 0:17:11 | |
Now, this effect actually is very extreme. | 0:17:11 | 0:17:14 | |
2016, for instance, | 0:17:14 | 0:17:16 | |
was the hottest year on record that the Earth has ever had. | 0:17:16 | 0:17:20 | |
But what about if we go a bit further back in time? | 0:17:20 | 0:17:22 | |
So this graph here shows you the global temperature | 0:17:22 | 0:17:26 | |
for the last 11,000 years. | 0:17:26 | 0:17:29 | |
Now it's certainly true that we have had | 0:17:29 | 0:17:31 | |
changes in temperature before. | 0:17:31 | 0:17:32 | |
There's an ice age just here, a little hot patch just there, | 0:17:32 | 0:17:36 | |
about the same as we have now. | 0:17:36 | 0:17:38 | |
Another little mini ice age just there. | 0:17:38 | 0:17:40 | |
But what is important about this graph | 0:17:40 | 0:17:43 | |
is this section that we have just over here. | 0:17:43 | 0:17:45 | |
Because this here, | 0:17:45 | 0:17:46 | |
isn't a little flag pointing downwards to the data, | 0:17:46 | 0:17:51 | |
this line IS the data. | 0:17:51 | 0:17:53 | |
The changes that we've seen in global temperature recently | 0:17:53 | 0:17:57 | |
are so quick and so dramatic | 0:17:57 | 0:17:58 | |
that on this graph of 11,000 years, | 0:17:58 | 0:18:00 | |
it looks like a straight line going upwards. | 0:18:00 | 0:18:03 | |
And in fact, if this trend continues, | 0:18:03 | 0:18:06 | |
we expect to see the global temperature ending up looking | 0:18:06 | 0:18:09 | |
something like this as we move forward in time. | 0:18:09 | 0:18:11 | |
But the trouble is that we have heard a lot of this before. | 0:18:11 | 0:18:15 | |
And nothing really seems to change. | 0:18:15 | 0:18:17 | |
It doesn't feel like it's getting any warmer. | 0:18:17 | 0:18:19 | |
But there are some people out there who have already noticed the changes | 0:18:19 | 0:18:23 | |
that global warming is having, literally in their own backyard. | 0:18:23 | 0:18:27 | |
And weatherman Peter Gibbs is one of them. | 0:18:27 | 0:18:30 | |
Gardeners are ruled by the seasons, that annual cycle of sowing, | 0:18:41 | 0:18:46 | |
growing and harvesting. | 0:18:46 | 0:18:47 | |
But global warming has put a spanner in the works, | 0:18:49 | 0:18:52 | |
because spring now arrives | 0:18:52 | 0:18:54 | |
a few days earlier with every passing decade. | 0:18:54 | 0:18:57 | |
For a start, that means I need to start | 0:19:01 | 0:19:03 | |
mowing the lawn earlier in the year. | 0:19:03 | 0:19:05 | |
And while I used to retire the mower for the winter in, say, | 0:19:05 | 0:19:09 | |
late September, it's now not unusual to | 0:19:09 | 0:19:12 | |
cut the grass as late as November. | 0:19:12 | 0:19:14 | |
The growing season is the best part of six weeks longer | 0:19:17 | 0:19:20 | |
than it was before climate change kicked in. | 0:19:20 | 0:19:22 | |
Which is great if you want to grow grapes. | 0:19:22 | 0:19:24 | |
I'm getting a decent crop most years now. | 0:19:24 | 0:19:26 | |
Problem is, because it's all developing so much earlier | 0:19:26 | 0:19:30 | |
in the spring, that extends, surprisingly, | 0:19:30 | 0:19:32 | |
the season over which these little baby grapes | 0:19:32 | 0:19:36 | |
can actually be hit by a rogue frost. | 0:19:36 | 0:19:39 | |
The effects of climate change are frustratingly complicated. | 0:19:42 | 0:19:45 | |
It varies from one kind of plant to the next. | 0:19:45 | 0:19:49 | |
In winter, apple trees and blackcurrant bushes are dormant, | 0:19:49 | 0:19:52 | |
storing up energy reserves for the spring. | 0:19:52 | 0:19:55 | |
But as British winters get steadily warmer, | 0:19:57 | 0:19:59 | |
that chilling time will be cut short, | 0:19:59 | 0:20:02 | |
resulting in poor flowering and a lack of fruit. | 0:20:02 | 0:20:06 | |
The effects of climate change are already pretty apparent, | 0:20:09 | 0:20:12 | |
and that's with just a one-degree rise in temperature. | 0:20:12 | 0:20:15 | |
Trouble is, temperatures are expected to keep on rising. | 0:20:15 | 0:20:19 | |
If and when that happens, the impact will be even more dramatic. | 0:20:19 | 0:20:24 | |
Higher temperatures mean melting ice. | 0:20:24 | 0:20:27 | |
And here is what has been happening to Arctic sea ice since 1980. | 0:20:27 | 0:20:31 | |
It's been reducing at the rate of 13% per decade. | 0:20:31 | 0:20:35 | |
It's not just the sea ice, though. | 0:20:35 | 0:20:37 | |
Melting ice caps means rising sea levels, too. | 0:20:37 | 0:20:41 | |
And climate scientists have run some mathematical models to try and | 0:20:41 | 0:20:44 | |
predict what that will mean for us on land. | 0:20:44 | 0:20:47 | |
So, if all of Greenland melts, which admittedly would be quite dramatic, | 0:20:47 | 0:20:51 | |
but that would have a six metre change in the level of the sea. | 0:20:51 | 0:20:55 | |
And this is what Europe would look like as a result. | 0:20:55 | 0:20:58 | |
Holland - not looking great for Holland. | 0:20:58 | 0:21:00 | |
Norfolk also very badly hit. | 0:21:00 | 0:21:02 | |
And London too. | 0:21:02 | 0:21:03 | |
And over the other side of the Atlantic, | 0:21:03 | 0:21:05 | |
this is what would happen to Florida. | 0:21:05 | 0:21:08 | |
Quite a lot of it would end up being completely underwater, | 0:21:08 | 0:21:12 | |
including Cape Canaveral, just there. | 0:21:12 | 0:21:14 | |
You're going to need a new place to try and launch the rockets from. | 0:21:14 | 0:21:17 | |
But the big question is, | 0:21:17 | 0:21:19 | |
is our weather here in the UK going to be affected | 0:21:19 | 0:21:21 | |
by all of this in the future? | 0:21:21 | 0:21:23 | |
Now, I'm not a meteorologist, but happily Peter Gibbs, who is, | 0:21:23 | 0:21:27 | |
has rushed back from his garden to the BBC Weather Studio, | 0:21:27 | 0:21:30 | |
where he has prepared a weather forecast for 2050. | 0:21:30 | 0:21:34 | |
Hello, and welcome to Weather 2050. | 0:21:34 | 0:21:37 | |
Well, the usual floods and saturated ground that we saw during the winter | 0:21:37 | 0:21:40 | |
months are just a distant memory for most of us now, | 0:21:40 | 0:21:43 | |
as we see the familiar signs of | 0:21:43 | 0:21:45 | |
developing drought now that the summer heat has really kicked in. | 0:21:45 | 0:21:49 | |
And those temperatures are expected to build over the next few days, | 0:21:49 | 0:21:52 | |
so we'll all be cranking up the air-con, avoiding the daytime heat, | 0:21:52 | 0:21:56 | |
and struggling to sleep at night. | 0:21:56 | 0:21:58 | |
And that's certainly true in the south, where we'll hit 35 Celsius, | 0:21:58 | 0:22:02 | |
not unusual these days, of course. | 0:22:02 | 0:22:03 | |
We could well top 40 degrees in a few days' time. | 0:22:03 | 0:22:07 | |
A little bit more comfortable in the north, | 0:22:07 | 0:22:09 | |
thanks to patchy cloud and a breeze coming in from the sea. | 0:22:09 | 0:22:12 | |
But still, pretty humid. | 0:22:12 | 0:22:14 | |
Then eventually that heat will break down into scattered thunderstorms, | 0:22:14 | 0:22:17 | |
the warmer atmosphere of course able to hold more moisture these days, | 0:22:17 | 0:22:20 | |
so there will be some really intense downpours. | 0:22:20 | 0:22:23 | |
Probably won't too much for the droughts, though, | 0:22:23 | 0:22:25 | |
with the rain just running off the parched ground | 0:22:25 | 0:22:28 | |
and causing flash flooding. | 0:22:28 | 0:22:30 | |
So some disruption looks likely. | 0:22:30 | 0:22:33 | |
What you need to know about the weather of the future | 0:22:33 | 0:22:35 | |
is that we expect, on average, | 0:22:35 | 0:22:37 | |
the north to become warmer and wetter, | 0:22:37 | 0:22:39 | |
and in the south, hotter and drier. | 0:22:39 | 0:22:42 | |
But across the whole country, | 0:22:42 | 0:22:43 | |
weather patterns will become a lot more variable, with bigger extremes. | 0:22:43 | 0:22:47 | |
So whoever is doing the forecast then | 0:22:47 | 0:22:49 | |
will have a much more difficult job than I've had. | 0:22:49 | 0:22:53 | |
When it comes to the future of health care, | 0:22:58 | 0:23:00 | |
past triumphs give us good reason to be optimistic. | 0:23:00 | 0:23:04 | |
But what about the big C? | 0:23:04 | 0:23:06 | |
Now, a cure for cancer is something we'd all like to happen, | 0:23:06 | 0:23:09 | |
but how realistic a hope is that? | 0:23:09 | 0:23:12 | |
Well, in the year 2000, | 0:23:12 | 0:23:13 | |
Bill Clinton and Tony Blair announced a very big breakthrough. | 0:23:13 | 0:23:18 | |
We're here to celebrate the completion | 0:23:19 | 0:23:22 | |
of the first survey of the entire human genome. | 0:23:22 | 0:23:25 | |
Without a doubt, this is the most important, | 0:23:25 | 0:23:27 | |
most wondrous map ever produced by humankind. | 0:23:27 | 0:23:32 | |
Well, in just 50 years after the discovery of the structure of DNA, | 0:23:32 | 0:23:37 | |
teams from the US and the UK between them | 0:23:37 | 0:23:39 | |
had mapped all of the genes contained in us. | 0:23:39 | 0:23:43 | |
Now, the Human Genome Project, said Bill and Tony, | 0:23:43 | 0:23:46 | |
would cure all human suffering. | 0:23:46 | 0:23:48 | |
The blind would see, the lame would walk, | 0:23:48 | 0:23:50 | |
and cancers would be a thing of the past. | 0:23:50 | 0:23:53 | |
Well, Bill and Tony are now distant memories. | 0:23:53 | 0:23:56 | |
But what happened to the genome and its bid to end all suffering? | 0:23:56 | 0:24:01 | |
Geneticist Giles Yeo went to New York to find out. | 0:24:01 | 0:24:04 | |
Half of us will be diagnosed with cancer at some point in our lives. | 0:24:13 | 0:24:18 | |
The problem with cancer is that our body's defences | 0:24:18 | 0:24:20 | |
does not recognise it as a threat. | 0:24:20 | 0:24:23 | |
Our immune systems have evolved to distinguish between our | 0:24:23 | 0:24:26 | |
own bodies and those of foreign invaders, | 0:24:26 | 0:24:29 | |
such as bacteria and viruses. | 0:24:29 | 0:24:31 | |
But cancer is simply a mutated version of our own cells, | 0:24:31 | 0:24:34 | |
so it doesn't carry the typical characteristics | 0:24:34 | 0:24:37 | |
of an invading organism. | 0:24:37 | 0:24:39 | |
So our immune system doesn't recognise it, attack it and kill it. | 0:24:39 | 0:24:43 | |
In 2011, Karen was diagnosed with | 0:24:46 | 0:24:49 | |
a form of blood cancer called leukaemia. | 0:24:49 | 0:24:51 | |
It's caused by the uncontrolled production | 0:24:53 | 0:24:55 | |
of abnormal white blood cells. | 0:24:55 | 0:24:57 | |
Karen lived with the disease for three years. | 0:24:59 | 0:25:02 | |
But in 2014, she took a turn for the worse, | 0:25:03 | 0:25:08 | |
and was given two years to live. | 0:25:08 | 0:25:10 | |
That was brutal. Your brain is just going, "Oh, my God, | 0:25:11 | 0:25:15 | |
"now I have this nuclear bomb in my body. | 0:25:15 | 0:25:18 | |
"And it's going to kill me." | 0:25:18 | 0:25:21 | |
But Karen was offered a lifeline. | 0:25:23 | 0:25:24 | |
'In an experimental new treatment, | 0:25:26 | 0:25:28 | |
'Dr Michel Sadelain is able to hack the immune system | 0:25:28 | 0:25:32 | |
'and equip it with the ability to fight back.' | 0:25:32 | 0:25:34 | |
So in many cancers, as in Karen's cancer, | 0:25:36 | 0:25:39 | |
the immune system on its own is not capable of taking over the tumour. | 0:25:39 | 0:25:44 | |
It needs a little help. | 0:25:44 | 0:25:46 | |
Michel's target is a type of white blood cell called a T-cell. | 0:25:47 | 0:25:50 | |
These are the foot soldiers of the immune system, | 0:25:51 | 0:25:54 | |
whose purpose is to attack viruses and bacteria. | 0:25:54 | 0:25:57 | |
The first step was to collect some of Karen's T-cells. | 0:25:58 | 0:26:01 | |
And there a team of scientists and technicians | 0:26:02 | 0:26:05 | |
insert into those T-cells into a gene, a synthetic gene, | 0:26:05 | 0:26:11 | |
that provides this instruction to the T-cell which says, | 0:26:11 | 0:26:14 | |
"Recognise the cancer, seek it out and destroy it." | 0:26:14 | 0:26:18 | |
Michel used a virus to get the new gene into Karen's T-cells. | 0:26:18 | 0:26:22 | |
The T-cells were then infused back into Karen's bloodstream. | 0:26:22 | 0:26:26 | |
He's had some remarkable success with this technique. | 0:26:26 | 0:26:29 | |
The first clinical results showed a dramatic effect | 0:26:29 | 0:26:33 | |
in a majority of patients - | 0:26:33 | 0:26:36 | |
about 85% of these patients went into | 0:26:36 | 0:26:39 | |
what we call a complete remission. | 0:26:39 | 0:26:43 | |
Michel has treated about 100 patients using this technique. | 0:26:44 | 0:26:47 | |
Karen was one of the first. | 0:26:48 | 0:26:50 | |
Never did we expect what we were going to hear. | 0:26:51 | 0:26:53 | |
And he said, "There is no evidence of disease, you are cancer-free." | 0:26:53 | 0:26:59 | |
So what was it like to hear the doctor say, "You are cancer-free?" | 0:26:59 | 0:27:02 | |
It was unbelievable. | 0:27:04 | 0:27:05 | |
I still get... I obviously get emotional about it. | 0:27:07 | 0:27:10 | |
Because we didn't expect it. | 0:27:11 | 0:27:13 | |
For Karen, the results of the trial have been incredible. | 0:27:13 | 0:27:16 | |
I mean, they have saved her life. | 0:27:16 | 0:27:18 | |
But that's only half the story. | 0:27:18 | 0:27:20 | |
You know, because the technique doesn't always work, | 0:27:20 | 0:27:23 | |
and bespoke gene editing for every single patient, | 0:27:23 | 0:27:25 | |
it's just unrealistic. | 0:27:25 | 0:27:27 | |
It is expensive, it is complex, it is lengthy. | 0:27:27 | 0:27:30 | |
And patients have been known to die waiting | 0:27:30 | 0:27:32 | |
during the intervening period for their cells to be re-engineered. | 0:27:32 | 0:27:36 | |
The answer is a new technique called Crispr. | 0:27:37 | 0:27:40 | |
Crispr stands for Clustered Regularly Interspaced Short Palindromic Repeats, | 0:27:40 | 0:27:46 | |
which describes a property of bacterial DNA | 0:27:46 | 0:27:49 | |
that scientists have been able to exploit - | 0:27:49 | 0:27:52 | |
giving them, for the first time, | 0:27:52 | 0:27:54 | |
ultimate accuracy in gene editing. | 0:27:54 | 0:27:56 | |
What Crispr gives you is a pair of tweezers for you to be | 0:27:56 | 0:28:00 | |
able to place the DNA anywhere you want in any cell. | 0:28:00 | 0:28:04 | |
This new-found precision allows Michel to fix a fundamental problem. | 0:28:04 | 0:28:10 | |
The problem in taking someone else's T-cells is that those T-cells will | 0:28:10 | 0:28:14 | |
attack you. Because they will sense that, "It's not my body, | 0:28:14 | 0:28:18 | |
"it's not my molecules, I have to go on the attack." | 0:28:18 | 0:28:22 | |
So what Crispr Cas9 makes possible is the removal of those molecules | 0:28:23 | 0:28:28 | |
that initiate that attack. | 0:28:28 | 0:28:30 | |
It means that T-cells could be provided by a donor, | 0:28:32 | 0:28:35 | |
and can be genetically engineered to only attack the cancer. | 0:28:35 | 0:28:38 | |
From one donor, | 0:28:39 | 0:28:41 | |
you could make cells that could be administered to multiple recipients. | 0:28:41 | 0:28:45 | |
These cells would then be ready, and in pharmacies. | 0:28:46 | 0:28:51 | |
And now, we preserve them. | 0:28:51 | 0:28:54 | |
We may someday have pharmacies of T-cells, frozen vials of T-cells, | 0:28:56 | 0:29:00 | |
ready-made, ready for injection. | 0:29:00 | 0:29:02 | |
So there's the living drug. | 0:29:02 | 0:29:05 | |
Fast asleep in liquid nitrogen. | 0:29:05 | 0:29:08 | |
That's fantastic. | 0:29:08 | 0:29:09 | |
And this would make this form of therapy | 0:29:09 | 0:29:11 | |
accessible to many more individuals. | 0:29:11 | 0:29:14 | |
Donor T-cells have yet to be trialled in humans. | 0:29:16 | 0:29:20 | |
But the idea offers hope that in the future, | 0:29:20 | 0:29:23 | |
many more people could be treated. | 0:29:23 | 0:29:24 | |
We are now entering an unprecedented era of progress for gene therapy. | 0:29:26 | 0:29:30 | |
And we're not just talking about cancer, | 0:29:30 | 0:29:31 | |
because Crispr is now being used to treat muscular dystrophy, | 0:29:31 | 0:29:34 | |
and it's being talked about as an alternative to antibiotics. | 0:29:34 | 0:29:38 | |
What you need to know about the future is | 0:29:38 | 0:29:40 | |
that the Human Genome Project | 0:29:40 | 0:29:42 | |
may finally be delivering on its promise | 0:29:42 | 0:29:44 | |
to revolutionise the way we treat disease. | 0:29:44 | 0:29:47 | |
And now, work. | 0:29:53 | 0:29:54 | |
What will you be doing as a job in the future? | 0:29:54 | 0:29:57 | |
Well, 50 years ago, the world of work was pretty easy to understand. | 0:29:57 | 0:30:00 | |
You either did manual work, | 0:30:00 | 0:30:02 | |
which basically meant supervising machines. | 0:30:02 | 0:30:05 | |
Or you worked in an office, | 0:30:05 | 0:30:07 | |
which basically meant doing a lot of typing, | 0:30:07 | 0:30:09 | |
or getting somebody else to do a lot of typing for you. | 0:30:09 | 0:30:11 | |
Bottom line, people were integral to the workforce. | 0:30:11 | 0:30:15 | |
But no more. | 0:30:15 | 0:30:17 | |
Because, in the 1970s, machines got clever. | 0:30:17 | 0:30:20 | |
Car plants were filled with robots, | 0:30:20 | 0:30:23 | |
helplines were answered by computers, | 0:30:23 | 0:30:25 | |
and almost all bank clerks became extinct. | 0:30:25 | 0:30:28 | |
But I suspect that most of you are saying, | 0:30:28 | 0:30:30 | |
"Well, a robot couldn't possibly take my job." | 0:30:30 | 0:30:33 | |
But are you sure? | 0:30:33 | 0:30:35 | |
Have a look at this. | 0:30:35 | 0:30:36 | |
We sent Dr Zoe Williams to check out | 0:30:38 | 0:30:40 | |
a piece of software which is rumoured | 0:30:40 | 0:30:42 | |
to diagnose illnesses faster and more accurately | 0:30:42 | 0:30:46 | |
than human medical professionals. | 0:30:46 | 0:30:49 | |
So, should I be worried, as a GP? | 0:30:49 | 0:30:52 | |
The thought that a robot or artificial intelligence | 0:30:52 | 0:30:55 | |
could take my job just seems crazy. | 0:30:55 | 0:30:58 | |
I mean, I've spent six years at medical school, | 0:30:58 | 0:31:01 | |
ten years practising as a doctor. | 0:31:01 | 0:31:03 | |
Now, surely all of that can't be boiled down to a few lines of code? | 0:31:03 | 0:31:06 | |
Babylon Health are a medical tech company. | 0:31:10 | 0:31:13 | |
They've just received 60 million of funding | 0:31:14 | 0:31:17 | |
to develop an AI doctor. | 0:31:17 | 0:31:19 | |
The system works by asking questions. | 0:31:21 | 0:31:24 | |
But anyone can ask questions. | 0:31:24 | 0:31:26 | |
If it's going to replace me, | 0:31:26 | 0:31:28 | |
I really want to put it through its paces. | 0:31:28 | 0:31:31 | |
I'm going to pose as a patient and give myself an imaginary condition. | 0:31:31 | 0:31:35 | |
But I'm not going to tell anybody, I'm just going to write it down. | 0:31:35 | 0:31:39 | |
And then we can see just how accurate the machine really is. | 0:31:41 | 0:31:44 | |
May I ask, please, what's troubling you today? | 0:31:46 | 0:31:49 | |
I'm feeling tired all the time. | 0:31:49 | 0:31:53 | |
So, as well as feeling tired, I've been feeling kind of weak. | 0:31:53 | 0:31:58 | |
Let's tell the computer that. | 0:31:58 | 0:32:00 | |
And I've also been feeling... | 0:32:01 | 0:32:03 | |
..a bit of dizziness. | 0:32:04 | 0:32:06 | |
Is it OK to ask, "Do you have painful periods?" | 0:32:08 | 0:32:12 | |
There we go, that's better. | 0:32:12 | 0:32:13 | |
Painful periods as well. | 0:32:13 | 0:32:15 | |
Do you get breathless on exertion? | 0:32:16 | 0:32:18 | |
Yes, I do! | 0:32:18 | 0:32:20 | |
Thanks, I've noted this. | 0:32:20 | 0:32:22 | |
So, I've given the computer all of my symptoms now. | 0:32:23 | 0:32:26 | |
And it's come up with a diagnosis. | 0:32:26 | 0:32:29 | |
So, let's see if it's correct. | 0:32:29 | 0:32:31 | |
Here's my bit of paper from earlier. | 0:32:31 | 0:32:33 | |
And you can see that I have put down fibroids. | 0:32:33 | 0:32:39 | |
And the computer has said uterine leiomyoma, | 0:32:39 | 0:32:43 | |
which is actually the same thing. | 0:32:43 | 0:32:44 | |
That's impressive. | 0:32:45 | 0:32:47 | |
But how is it done? | 0:32:47 | 0:32:49 | |
Time to face down the evil genius | 0:32:49 | 0:32:51 | |
hell-bent on replacing me with my laptop. | 0:32:51 | 0:32:55 | |
So, you start off with a knowledge base. | 0:32:55 | 0:32:58 | |
And this is essentially a medical database which contains hundreds of | 0:32:58 | 0:33:01 | |
millions of medical concepts. | 0:33:01 | 0:33:03 | |
That's kind of like being at medical school and all the knowledge that is | 0:33:03 | 0:33:06 | |
inputted into the brain. | 0:33:06 | 0:33:07 | |
Exactly, so this might be all of the textbooks which you've read at | 0:33:07 | 0:33:10 | |
medical school, all of the papers which you've read at medical school, | 0:33:10 | 0:33:14 | |
and then applied to all of that information | 0:33:14 | 0:33:16 | |
we'll apply a set of methods known as machine-learning methods. | 0:33:16 | 0:33:19 | |
Machine learning is the ability of computers | 0:33:21 | 0:33:24 | |
to take vast amounts of data and make sense of it themselves. | 0:33:24 | 0:33:28 | |
Like this network of medical information, | 0:33:30 | 0:33:32 | |
which the computer uses to make a diagnosis. | 0:33:32 | 0:33:35 | |
What these circles represent are diseases, | 0:33:36 | 0:33:39 | |
-symptoms and risk factors. -OK. | 0:33:39 | 0:33:40 | |
And what those lines represent are the relationships between those. | 0:33:40 | 0:33:43 | |
So, based on that, | 0:33:43 | 0:33:45 | |
the computer has taught itself actually how strongly related those | 0:33:45 | 0:33:49 | |
diseases, symptoms and risk factors are. | 0:33:49 | 0:33:51 | |
OK, so that's how it determines | 0:33:51 | 0:33:52 | |
the probability is from looking at past real-life cases? | 0:33:52 | 0:33:56 | |
Absolutely, and that's why this is machine learning. | 0:33:56 | 0:33:59 | |
As the network learns about more and more symptoms and diseases, | 0:34:02 | 0:34:06 | |
it's tested and refined by a team of doctors and programmers. | 0:34:06 | 0:34:10 | |
It's early days, | 0:34:12 | 0:34:13 | |
but the company sees a big future for their virtual medic. | 0:34:13 | 0:34:16 | |
We want to do with health care what, say, Google did with information. | 0:34:17 | 0:34:20 | |
It'll be in your phone, it'll be in the devices you carry with you. | 0:34:20 | 0:34:25 | |
Do you think that a machine could ever replace my role as a GP? | 0:34:25 | 0:34:30 | |
I don't think this is a competition between machines and humans. | 0:34:30 | 0:34:34 | |
This is machines being an aid to humans. | 0:34:34 | 0:34:38 | |
Half of the world's population has no access or very, | 0:34:38 | 0:34:41 | |
very little access to doctors. | 0:34:41 | 0:34:43 | |
Right? Imagine if you could see so many more because the machines do | 0:34:43 | 0:34:47 | |
the easier part, they save your time. | 0:34:47 | 0:34:50 | |
But can a machine put its hand on your shoulder and say, "Trust me, | 0:34:50 | 0:34:54 | |
"I'll look after you?" That's a different story. | 0:34:54 | 0:34:56 | |
It's not just in medicine that software's on the march. | 0:34:58 | 0:35:02 | |
In banking, AI is approving or not approving loan applications. | 0:35:02 | 0:35:07 | |
And even making investment decisions. | 0:35:07 | 0:35:09 | |
And, with autonomous vehicles on the horizon, | 0:35:10 | 0:35:13 | |
many who drive for a living will soon be superseded. | 0:35:13 | 0:35:17 | |
What you need to know about the future | 0:35:19 | 0:35:21 | |
is that no job is immune from the influence | 0:35:21 | 0:35:23 | |
of artificial intelligence. | 0:35:23 | 0:35:24 | |
If it doesn't take your job, | 0:35:24 | 0:35:26 | |
then it's likely to change the way in which you do it. | 0:35:26 | 0:35:29 | |
Now, whenever we think about the future, | 0:35:35 | 0:35:37 | |
however outlandish we imagine our housing, | 0:35:37 | 0:35:40 | |
our transport or our gadgets to be, | 0:35:40 | 0:35:42 | |
one thing that we never seem to question is that | 0:35:42 | 0:35:44 | |
there will always be a constant supply of electricity. | 0:35:44 | 0:35:48 | |
But if you look at the data, that's not necessarily a safe assumption. | 0:35:48 | 0:35:51 | |
So here is what's been happening in the UK | 0:35:51 | 0:35:54 | |
over the last 100 years or so. | 0:35:54 | 0:35:55 | |
And in the 20th century, | 0:35:55 | 0:35:57 | |
we've become very dependent on fossil fuels. | 0:35:57 | 0:36:00 | |
So you can see coal here in purple, | 0:36:00 | 0:36:01 | |
gas in blue coming in slightly later, | 0:36:01 | 0:36:03 | |
and then a bit of oil there throughout. | 0:36:03 | 0:36:05 | |
Now, there's a bit of nuclear there in red, | 0:36:05 | 0:36:08 | |
which has stayed pretty constant over time. | 0:36:08 | 0:36:10 | |
And some renewables coming in much later in green. | 0:36:10 | 0:36:13 | |
But the main story from this graph | 0:36:13 | 0:36:15 | |
is that we are very dependent on fossil fuels to heat our homes, | 0:36:15 | 0:36:20 | |
provide our transport and to generate our electricity. | 0:36:20 | 0:36:23 | |
Now, if you look at the picture globally, | 0:36:23 | 0:36:26 | |
the demand has been steadily increasing over time. | 0:36:26 | 0:36:29 | |
So you can see a little blip here for the 2008 financial crisis. | 0:36:29 | 0:36:33 | |
But generally speaking, the trend has been upwards. | 0:36:33 | 0:36:35 | |
And if this trend continues, then over the next 50 years, | 0:36:35 | 0:36:39 | |
we can expect the demand for energy consumption to increase by 48%. | 0:36:39 | 0:36:44 | |
But the trouble is, burning fossil fuels is pretty bad for the planet. | 0:36:44 | 0:36:48 | |
And in any case, we're going to run out of oil at some point anyway. | 0:36:48 | 0:36:52 | |
So the question is, how do we keep the lights on | 0:36:52 | 0:36:55 | |
while helping to save the planet? | 0:36:55 | 0:36:57 | |
So, we sent physicist Helen Czerski | 0:36:57 | 0:36:59 | |
to a place where they've already done it. | 0:36:59 | 0:37:01 | |
I'm in Norway, and this stunning country | 0:37:15 | 0:37:17 | |
is one of the world's biggest producers of renewable energy. | 0:37:17 | 0:37:21 | |
Almost all of their electricity comes from hydropower. | 0:37:21 | 0:37:24 | |
And government incentives mean that electric vehicles like this one are | 0:37:24 | 0:37:27 | |
becoming more and more common. | 0:37:27 | 0:37:30 | |
Relying on hydropower is fine if | 0:37:30 | 0:37:32 | |
you've got plenty of mountains and lakes. | 0:37:32 | 0:37:35 | |
But what about the rest of the world? | 0:37:35 | 0:37:37 | |
In the UK, just under half of our renewable energy | 0:37:37 | 0:37:40 | |
comes from wind turbines. But in spite of that, | 0:37:40 | 0:37:43 | |
wind energy only contributes 11% of our total electricity generation. | 0:37:43 | 0:37:48 | |
Part of the problem is finding enough places with strong winds. | 0:37:53 | 0:37:57 | |
But perhaps there are some other opportunities. | 0:37:58 | 0:38:01 | |
This is data from the University of Reading | 0:38:01 | 0:38:03 | |
showing typical wind speeds in this area. | 0:38:03 | 0:38:05 | |
And you can see that down here near the ground, | 0:38:05 | 0:38:07 | |
the wind speeds are almost always really low. | 0:38:07 | 0:38:10 | |
But as you go up, the wind speeds go right up. | 0:38:10 | 0:38:13 | |
And what that suggests is that if you are serious about wind energy, | 0:38:13 | 0:38:17 | |
the place to be might not be down here, | 0:38:17 | 0:38:19 | |
or even up on that hilltop up there. | 0:38:19 | 0:38:21 | |
It's up there. | 0:38:21 | 0:38:23 | |
That's exactly what engineer Dr Lode Carnel is doing | 0:38:25 | 0:38:30 | |
with this tiny plane he calls kitemill. | 0:38:30 | 0:38:33 | |
Kitemill is designed to fly in the high-altitude winds. | 0:38:33 | 0:38:37 | |
The force of the wind will cause it to pull on the tether, | 0:38:39 | 0:38:43 | |
and that generates electricity. | 0:38:43 | 0:38:44 | |
So, the tether's out, the kite's been assembled, | 0:38:48 | 0:38:51 | |
and it's ready to launch. | 0:38:51 | 0:38:52 | |
So the next step is to get it up into the air. | 0:38:52 | 0:38:54 | |
Here we go! | 0:38:54 | 0:38:55 | |
It's tiny in the sky. | 0:39:07 | 0:39:09 | |
It looks so, so small. | 0:39:09 | 0:39:10 | |
The idea that that could generate any energy at all | 0:39:10 | 0:39:14 | |
is really quite weird. | 0:39:14 | 0:39:15 | |
And it's fast, wow! Look at that! | 0:39:16 | 0:39:19 | |
So, the kite's up in the sky and it's doing two things. | 0:39:19 | 0:39:22 | |
It's either sitting flat, or it's going round and round in circles. | 0:39:22 | 0:39:26 | |
What are the circles about? | 0:39:26 | 0:39:28 | |
Yes, so when the system is located on the ground, | 0:39:28 | 0:39:30 | |
we need to take it up to a certain altitude, | 0:39:30 | 0:39:33 | |
and then it will fly in a circle, a pattern that we now see, | 0:39:33 | 0:39:36 | |
during which it can produce electricity. | 0:39:36 | 0:39:38 | |
Once the plane is high enough, | 0:39:40 | 0:39:42 | |
it glides upwards into a corkscrew pattern, | 0:39:42 | 0:39:45 | |
pulling on the tether. | 0:39:45 | 0:39:47 | |
So, here we have the ground station where we generate the energy. | 0:39:48 | 0:39:51 | |
You have the drum, where the tether is wound around. | 0:39:51 | 0:39:55 | |
But it is connected directly to a | 0:39:55 | 0:39:57 | |
motor or a generator, which is on the back. | 0:39:57 | 0:39:59 | |
So, when we wind off, the motor turns in one direction | 0:39:59 | 0:40:02 | |
and produces energy. | 0:40:02 | 0:40:03 | |
And so how much energy is this generating at the moment? | 0:40:05 | 0:40:08 | |
Two kilowatts roughly now, | 0:40:09 | 0:40:11 | |
which is roughly the consumption of one family in the UK. | 0:40:11 | 0:40:14 | |
Our next model has a capacity of 30 kilowatts. | 0:40:14 | 0:40:17 | |
That can power automatically 20 families in the UK. | 0:40:17 | 0:40:20 | |
However, this is not the end. | 0:40:20 | 0:40:22 | |
We need to scale up. | 0:40:22 | 0:40:23 | |
We want to produce energy with the lowest possible cost. | 0:40:23 | 0:40:25 | |
So then we are talking 500 kilowatts, | 0:40:25 | 0:40:29 | |
and that will be sufficient to power 300-400 families in the UK. | 0:40:29 | 0:40:33 | |
Once it reaches the end of its tether, | 0:40:35 | 0:40:38 | |
5% of the energy it's generated is used to reel it back in, | 0:40:38 | 0:40:43 | |
and the process starts again. | 0:40:43 | 0:40:44 | |
The plan is for the plane to stay up indefinitely, but this test is over, | 0:40:46 | 0:40:51 | |
so the plane is brought back in to land. | 0:40:51 | 0:40:53 | |
It's hard to imagine it working in the airspace | 0:40:55 | 0:40:58 | |
above our already crowded cities. | 0:40:58 | 0:41:01 | |
But it could have advantages in remote locations | 0:41:01 | 0:41:04 | |
and in developing countries. | 0:41:04 | 0:41:06 | |
And you can put it in places where you can't put a wind turbine. | 0:41:07 | 0:41:10 | |
That's important, isn't it? | 0:41:10 | 0:41:12 | |
This can extract energy from places | 0:41:12 | 0:41:13 | |
that are not accessible at the moment. | 0:41:13 | 0:41:15 | |
Correct, places where there is for example low wind speeds close to the | 0:41:15 | 0:41:19 | |
ground, but higher wind speeds at higher altitudes could use this | 0:41:19 | 0:41:22 | |
technology. Also, it's quite movable. | 0:41:22 | 0:41:24 | |
It's a flexible technology, so one truck can come, | 0:41:24 | 0:41:27 | |
and you can install everything. | 0:41:27 | 0:41:28 | |
Contrary to windmills, | 0:41:28 | 0:41:29 | |
where you need a lot of infrastructure and so on. | 0:41:29 | 0:41:33 | |
Kitemill alone isn't the answer to our energy crisis. | 0:41:36 | 0:41:39 | |
Power will have to come from a range of renewable sources. | 0:41:43 | 0:41:46 | |
I'm optimistic about the future | 0:41:48 | 0:41:50 | |
because I see lots of new technologies | 0:41:50 | 0:41:51 | |
like kitemill coming along, | 0:41:51 | 0:41:53 | |
each appropriate at a specific place or in a specific time. | 0:41:53 | 0:41:57 | |
And together, these are the building blocks | 0:41:57 | 0:41:59 | |
that will let us design a much more sophisticated energy future. | 0:41:59 | 0:42:02 | |
A lot of people look to science fiction | 0:42:08 | 0:42:11 | |
for a steer on what's going to happen in the future. | 0:42:11 | 0:42:14 | |
And if sci-fi tells us one thing, | 0:42:14 | 0:42:16 | |
it's that the future is littered with cyborgs. | 0:42:16 | 0:42:19 | |
Now, the idea of some kind of human-machine hybrid is certainly an | 0:42:19 | 0:42:23 | |
interesting one, and it's been explored in a number of different TV | 0:42:23 | 0:42:27 | |
programmes, from Six Million Dollar Man | 0:42:27 | 0:42:29 | |
all the way through to Star Trek. | 0:42:29 | 0:42:31 | |
But so far, the reality hasn't quite managed | 0:42:31 | 0:42:34 | |
to measure up for most of us. | 0:42:34 | 0:42:36 | |
Now, is that a relief, or an opportunity missed? | 0:42:36 | 0:42:39 | |
My name is James Young. | 0:42:51 | 0:42:53 | |
And I am a cyborg. | 0:42:55 | 0:42:57 | |
OK, and relax. | 0:42:59 | 0:43:02 | |
Terrible. | 0:43:05 | 0:43:07 | |
Taking a load off here. | 0:43:08 | 0:43:10 | |
Just over five years ago, I lost an arm and a leg in a train accident. | 0:43:10 | 0:43:14 | |
While I was coming to terms with the loss of my arm, | 0:43:16 | 0:43:19 | |
I won a competition to have something different made. | 0:43:19 | 0:43:21 | |
Yeah. | 0:43:24 | 0:43:25 | |
It's not great! | 0:43:27 | 0:43:28 | |
It was created to be more of an art piece, | 0:43:33 | 0:43:36 | |
so it was like a prototype from the day it was made. | 0:43:36 | 0:43:38 | |
It's got the lights that work and the hand... | 0:43:40 | 0:43:43 | |
four of the digits work on the hand, so it's kind of like... | 0:43:43 | 0:43:45 | |
..it's trying, it's trying its best, | 0:43:47 | 0:43:49 | |
but it's not in tiptop condition, basically. | 0:43:49 | 0:43:52 | |
My brief taste of being a cyborg has left me wanting more. | 0:43:56 | 0:43:59 | |
I'm now looking at prostheses that attach | 0:44:00 | 0:44:03 | |
directly to my skeleton and nervous system. | 0:44:03 | 0:44:05 | |
If I can replace my old abilities, | 0:44:08 | 0:44:10 | |
then can I go a step further and gain new ones? | 0:44:10 | 0:44:13 | |
The idea of expanding my abilities beyond | 0:44:14 | 0:44:18 | |
the human baseline is something that really, really intrigues me. | 0:44:18 | 0:44:22 | |
And because I almost studied cybernetics when I was considering | 0:44:22 | 0:44:25 | |
university, and it's a field that | 0:44:25 | 0:44:26 | |
everybody's kind of thinking about now, | 0:44:26 | 0:44:28 | |
because you get Elon Musk starting up initiatives to find, like, | 0:44:28 | 0:44:32 | |
a neural lace that would enhance human abilities | 0:44:32 | 0:44:34 | |
to kind of compete with AI and computing. | 0:44:34 | 0:44:36 | |
If you're really serious about becoming a cyborg, | 0:44:38 | 0:44:41 | |
tapping into the brain is the way you have to go. | 0:44:41 | 0:44:43 | |
There's been a lot of research into this. | 0:44:45 | 0:44:47 | |
Some brains have already been linked to a variety of devices in a bid to | 0:44:47 | 0:44:51 | |
help people with disabilities. | 0:44:51 | 0:44:52 | |
But I'm going to meet someone | 0:44:54 | 0:44:55 | |
who's hacked their brain in a different way. | 0:44:55 | 0:44:58 | |
At age 11, Neil Harbisson was diagnosed with | 0:45:00 | 0:45:03 | |
a condition called achromatopsia. | 0:45:03 | 0:45:05 | |
A form of total colour blindness, | 0:45:05 | 0:45:08 | |
meaning Neil has only ever seen in grayscale. | 0:45:08 | 0:45:10 | |
But in 2003, as part of an art project, | 0:45:13 | 0:45:16 | |
Neil found a new way to perceive colour. | 0:45:16 | 0:45:18 | |
By integrating technology into his skull. | 0:45:19 | 0:45:21 | |
He can now hear colour. | 0:45:23 | 0:45:25 | |
Could you explain how your antenna works front to back? | 0:45:27 | 0:45:30 | |
-What's it... -Well, I thought that I should have a new body part, | 0:45:30 | 0:45:33 | |
a new sensory organ, specifically for colour perception. | 0:45:33 | 0:45:37 | |
So the light frequency goes inside the antenna, | 0:45:37 | 0:45:40 | |
and then it touches a chip inside my bone that vibrates, | 0:45:40 | 0:45:43 | |
so these vibrations in my head create inner sounds, | 0:45:43 | 0:45:47 | |
so I can hear different notes for different colours. | 0:45:47 | 0:45:49 | |
We created an app that tries to mimic | 0:45:49 | 0:45:52 | |
the sounds that I hear for each colour. So you'll notice... | 0:45:52 | 0:45:56 | |
HUMMING AND WHIRRING | 0:45:56 | 0:46:00 | |
This is the sound of yellow. | 0:46:00 | 0:46:02 | |
Cool. | 0:46:02 | 0:46:04 | |
HIGH-PITCH PULSE | 0:46:05 | 0:46:09 | |
-So it's kind of like... -Pink is a higher frequency. | 0:46:09 | 0:46:11 | |
..a frequency shift. | 0:46:11 | 0:46:12 | |
FAST-PACED BEEPS | 0:46:12 | 0:46:16 | |
This is the sound of my jacket. | 0:46:16 | 0:46:18 | |
So I'm wearing electronic music! | 0:46:18 | 0:46:19 | |
So, with your antenna, is it kind of like when you have a new watch and | 0:46:21 | 0:46:24 | |
you have to become used to | 0:46:24 | 0:46:25 | |
the weight and feel of it when you're moving around? | 0:46:25 | 0:46:28 | |
I'm not using or wearing technology - I am technology. | 0:46:28 | 0:46:33 | |
So that's the difference. | 0:46:33 | 0:46:34 | |
I guess it's... I can't compare it with anything else, | 0:46:34 | 0:46:38 | |
because it's part of my skeleton. | 0:46:38 | 0:46:41 | |
Meeting Neil has given me an insight into what it might be like to have | 0:46:43 | 0:46:46 | |
genuine cyborg abilities. | 0:46:46 | 0:46:47 | |
What has it been like, you being in the public with your extra sense? | 0:46:50 | 0:46:54 | |
Some children ask me if it was some kind of extendable selfie stick! | 0:46:54 | 0:46:58 | |
And since last year, people just shout at me, Pokemon, | 0:46:58 | 0:47:01 | |
and they try to catch me! | 0:47:01 | 0:47:02 | |
So it changes, what people think it is. | 0:47:02 | 0:47:04 | |
Cyborgs are already amongst us, but it's not for everybody just yet. | 0:47:05 | 0:47:10 | |
So, for now, maybe it's kind of up to people like Neil and I, | 0:47:10 | 0:47:14 | |
who want to augment our bodies, to push the envelope. | 0:47:14 | 0:47:18 | |
Next up, nature. | 0:47:24 | 0:47:26 | |
Now, we all love nature, | 0:47:26 | 0:47:28 | |
and we know this because of the vast audiences that | 0:47:28 | 0:47:30 | |
BBC's Natural History Department gets, and also the fact that | 0:47:30 | 0:47:33 | |
David Attenborough is now Sir David Attenborough. | 0:47:33 | 0:47:36 | |
But, as much as we all claim to love the natural world, | 0:47:36 | 0:47:41 | |
apparently it is vanishing before our eyes. | 0:47:41 | 0:47:44 | |
If you have a little look at this graph here, | 0:47:44 | 0:47:46 | |
this is what has been happening to vertebrate populations from 1970 all | 0:47:46 | 0:47:50 | |
the way up to now. Now, this group, | 0:47:50 | 0:47:52 | |
they track the populations of almost 4,000 different species, | 0:47:52 | 0:47:56 | |
and come up with a score to say how well they are doing. | 0:47:56 | 0:47:59 | |
It turns out, not great. | 0:47:59 | 0:48:01 | |
Both the number of species | 0:48:01 | 0:48:03 | |
and the number of animals is in steady decline. | 0:48:03 | 0:48:06 | |
On land, there has been a 38% decrease in animal numbers. | 0:48:06 | 0:48:10 | |
In the sea, there has been a 36% decrease. | 0:48:10 | 0:48:13 | |
And worst of all, in freshwater, | 0:48:13 | 0:48:15 | |
there has been a huge 81% drop in population numbers. | 0:48:15 | 0:48:20 | |
And we can see from this graph that if this trend continues, | 0:48:20 | 0:48:24 | |
by 2020 we can expect to see a 67% drop based on what we had in 1970. | 0:48:24 | 0:48:32 | |
So, to find out if this really is as bad as it sounds, | 0:48:32 | 0:48:34 | |
evolutionary geneticist, writer and Renaissance man, | 0:48:34 | 0:48:37 | |
my very good friend Dr Adam Rutherford | 0:48:37 | 0:48:39 | |
is here to help us with the science. | 0:48:39 | 0:48:41 | |
Adam, we've been here before. | 0:48:41 | 0:48:43 | |
There have been extinctions before, right? | 0:48:43 | 0:48:44 | |
Yes, there have. In fact, over the last billion years or so, | 0:48:44 | 0:48:47 | |
the evolutionary trajectory of life on Earth, | 0:48:47 | 0:48:49 | |
extinction is completely the normal state of affairs. | 0:48:49 | 0:48:52 | |
I've got my own graph here. | 0:48:52 | 0:48:53 | |
If you look at the last 542 million years, | 0:48:53 | 0:48:57 | |
what this shows is extinction rates over that time period. | 0:48:57 | 0:49:00 | |
And there are five big peaks. | 0:49:00 | 0:49:02 | |
So there have been five great extinction events. | 0:49:02 | 0:49:05 | |
Everyone knows about the one that happened 66 million years ago, | 0:49:05 | 0:49:08 | |
it is called the K-Pg boundary, | 0:49:08 | 0:49:09 | |
and that was when a meteor dropped out of the sky. | 0:49:09 | 0:49:11 | |
-The dinosaurs. -It did for the dinosaurs. | 0:49:11 | 0:49:13 | |
But also, 75% of all species on land and in the sea. | 0:49:13 | 0:49:17 | |
And that's not even the big one. | 0:49:17 | 0:49:19 | |
The big one is called the Great Dying, or the P-T boundary, | 0:49:19 | 0:49:22 | |
and that happens 252 million years ago. | 0:49:22 | 0:49:25 | |
95% of all species go extinct. | 0:49:25 | 0:49:28 | |
So, what's so different about this one? | 0:49:28 | 0:49:30 | |
The timescale is what's different. | 0:49:30 | 0:49:32 | |
So, the full extent of the Great Dying | 0:49:32 | 0:49:34 | |
really pans out over a million or two million years. | 0:49:34 | 0:49:37 | |
The dinosaur one, 66 million years. | 0:49:37 | 0:49:39 | |
We find dinosaurs 10,000 years after that happened. | 0:49:39 | 0:49:43 | |
What you just said was, 67% of species | 0:49:43 | 0:49:47 | |
will be lost since the 1970s. | 0:49:47 | 0:49:49 | |
If this is the start of another mass extinction, | 0:49:51 | 0:49:54 | |
its speed means that ecosystems and food chains will break down | 0:49:54 | 0:49:58 | |
catastrophically. | 0:49:58 | 0:49:59 | |
For some species, it is already too late. | 0:50:01 | 0:50:04 | |
Human activity means that coming generations | 0:50:04 | 0:50:06 | |
will never see a live white rhino or a Sumatran orangutan. | 0:50:06 | 0:50:10 | |
But what's worse is the potential impact | 0:50:11 | 0:50:14 | |
of losing less charismatic wildlife. | 0:50:14 | 0:50:17 | |
In the sea, rising temperatures have already disrupted | 0:50:17 | 0:50:21 | |
the food chain by killing coral. | 0:50:21 | 0:50:23 | |
And if the predicted further rises occur, | 0:50:23 | 0:50:25 | |
Asian seagrass could go extinct within 50 years, | 0:50:25 | 0:50:29 | |
causing the collapse of the entire | 0:50:29 | 0:50:31 | |
marine ecosystem in that part of the world. | 0:50:31 | 0:50:34 | |
In the future, our children will | 0:50:35 | 0:50:38 | |
almost certainly see less diversity of animals. | 0:50:38 | 0:50:41 | |
But unchecked, these changes also mean that | 0:50:41 | 0:50:44 | |
they themselves could be facing much more serious problems. | 0:50:44 | 0:50:47 | |
And as humans, are we immune to this? | 0:50:49 | 0:50:52 | |
No, absolutely not. | 0:50:52 | 0:50:54 | |
We are special, but we are living on this planet. | 0:50:54 | 0:50:57 | |
And what we are doing is removing the ability for us to live on this | 0:50:57 | 0:51:00 | |
planet whilst letting many, many species go extinct. | 0:51:00 | 0:51:04 | |
And I think the key thing is to recognise that we have created this | 0:51:04 | 0:51:07 | |
situation, and we also have the power to stop it. | 0:51:07 | 0:51:10 | |
Flying cars - there you go, I've said it. | 0:51:15 | 0:51:17 | |
Well, it wouldn't be a programme | 0:51:17 | 0:51:19 | |
about the future if someone didn't mention them. | 0:51:19 | 0:51:21 | |
But, let's be honest, they certainly would be pretty handy, | 0:51:21 | 0:51:24 | |
because our love of earthbound cars | 0:51:24 | 0:51:27 | |
has seen a steady increase in how many cars | 0:51:27 | 0:51:29 | |
there now are on the roads. | 0:51:29 | 0:51:31 | |
This graph here shows you how far we travel in motor vehicles since 1949. | 0:51:31 | 0:51:36 | |
And you can see here that car journeys, in red, | 0:51:36 | 0:51:39 | |
really have become all-conquering. | 0:51:39 | 0:51:41 | |
We're travelling a lot further, and we're doing it in cars. | 0:51:41 | 0:51:44 | |
And that makes the experience of actually driving them | 0:51:44 | 0:51:48 | |
a lot less appealing. | 0:51:48 | 0:51:49 | |
In fact, the average speed of traffic in central London | 0:51:49 | 0:51:54 | |
is now 7.3mph. | 0:51:54 | 0:51:55 | |
Basically, you'd be better off on a horse. | 0:51:55 | 0:51:57 | |
Now, we need to travel faster. | 0:51:57 | 0:52:00 | |
And dynamicist Teena Gade, who works | 0:52:00 | 0:52:02 | |
for Formula 1 team Sahara Force India, | 0:52:02 | 0:52:04 | |
she knows all about travelling quickly. | 0:52:04 | 0:52:06 | |
And she has been looking at, well, | 0:52:06 | 0:52:08 | |
there's no other way to put this, really - flying cars. | 0:52:08 | 0:52:11 | |
The worst thing about city driving is the traffic. | 0:52:20 | 0:52:23 | |
I think we're probably doing five, maybe 10mph, if that. | 0:52:23 | 0:52:27 | |
Right now, if my car could fly, | 0:52:27 | 0:52:29 | |
I'd take off and I'd jump this long queue of people in front of me | 0:52:29 | 0:52:32 | |
and be at my destination in no time. | 0:52:32 | 0:52:33 | |
History is littered with attempts at the fabled flying car. | 0:52:36 | 0:52:40 | |
Most of them hard to take seriously. | 0:52:43 | 0:52:46 | |
And moving it around is a job for a secretary | 0:52:46 | 0:52:48 | |
rather than a highly skilled and highly expensive helicopter pilot. | 0:52:48 | 0:52:52 | |
But prototypes for personal flying machines | 0:52:59 | 0:53:02 | |
have started to appear once again. | 0:53:02 | 0:53:04 | |
This time with serious financial backing. | 0:53:05 | 0:53:08 | |
I would absolutely love a personal flying car. | 0:53:16 | 0:53:19 | |
I think that would be absolutely fantastic. | 0:53:19 | 0:53:21 | |
Imagine going to work every day | 0:53:21 | 0:53:22 | |
and not having to sit in the traffic queues. | 0:53:22 | 0:53:27 | |
It might not be a good idea, though, | 0:53:27 | 0:53:28 | |
for everyone to have their own personal flying machine. | 0:53:28 | 0:53:31 | |
As I'm showing here, occasionally I can keep control of it, | 0:53:31 | 0:53:34 | |
but some of the time I'm not doing a very good job. | 0:53:34 | 0:53:36 | |
Right, that's it down. I'm just going to go and get it. | 0:53:37 | 0:53:40 | |
So, if we're all going to buzz around our cities in personal flying | 0:53:43 | 0:53:46 | |
machines, how do we keep from crashing into each other? | 0:53:46 | 0:53:48 | |
To find out, I've come to Zurich to meet robotics expert | 0:53:53 | 0:53:57 | |
Professor Raffaello D'Andrea. | 0:53:57 | 0:53:59 | |
He developed the Kiva robot system. | 0:54:01 | 0:54:03 | |
A network of thousands of bots | 0:54:05 | 0:54:08 | |
that work together to fulfil online orders. | 0:54:08 | 0:54:11 | |
This is basically a very large warehouse | 0:54:12 | 0:54:14 | |
where orders come in and they need to be fulfilled. | 0:54:14 | 0:54:16 | |
So, the system figures out which robots need to go to which pods, | 0:54:16 | 0:54:22 | |
pick it up and bring it to the perimeter of the warehouse, | 0:54:22 | 0:54:25 | |
where then people take things off of the pods | 0:54:25 | 0:54:27 | |
and put them into the orders which eventually go out. | 0:54:27 | 0:54:30 | |
There's a lot of robots in action here. | 0:54:30 | 0:54:32 | |
How come they don't collide? | 0:54:32 | 0:54:33 | |
The robots have to generate trajectories and plans. | 0:54:33 | 0:54:37 | |
And those plans are then shared to a coordinator, | 0:54:37 | 0:54:40 | |
which then figures out how they should go and execute their plan | 0:54:40 | 0:54:43 | |
so that they don't hit each other. | 0:54:43 | 0:54:45 | |
And with over 80,000 in operation, | 0:54:46 | 0:54:50 | |
the Kiva bots are the largest network of | 0:54:50 | 0:54:52 | |
autonomous vehicles in the world. | 0:54:52 | 0:54:54 | |
And so far, there haven't been any accidents. | 0:54:54 | 0:54:56 | |
If only something similar could be done with flying machines. | 0:55:01 | 0:55:04 | |
Oh, wow! That's incredible. | 0:55:07 | 0:55:10 | |
So what do we have here? | 0:55:10 | 0:55:12 | |
A swarm of 32 of these flying machines. | 0:55:12 | 0:55:15 | |
And they're going to do a little choreographed performance. | 0:55:15 | 0:55:19 | |
'This is more like it.' | 0:55:19 | 0:55:21 | |
What they are doing right now is a choreography | 0:55:21 | 0:55:23 | |
that is pre-programmed, | 0:55:23 | 0:55:25 | |
and ensures that they do not collide with each other. | 0:55:25 | 0:55:28 | |
So, what's in one of these? | 0:55:31 | 0:55:33 | |
-How are these made? -They have four motors for propellers. | 0:55:33 | 0:55:35 | |
They have a cage to keep the propellers | 0:55:35 | 0:55:37 | |
away from other vehicles | 0:55:37 | 0:55:39 | |
and from people, and they have some custom electronics | 0:55:39 | 0:55:42 | |
that creates all the magic and intelligence. | 0:55:42 | 0:55:44 | |
And now, we just move out of the way. | 0:55:45 | 0:55:48 | |
Oh, they're coming in to land. If I put my hand out... | 0:55:48 | 0:55:50 | |
-I can catch one! -Exactly. | 0:55:50 | 0:55:52 | |
So, if you could have this level of automation and planning in personal | 0:55:56 | 0:56:00 | |
flying machines, then perhaps they could become a reality. | 0:56:00 | 0:56:04 | |
How would that lead us to, for example, a transport system? | 0:56:04 | 0:56:07 | |
Well, so, what they would share with a transport system is the use of a | 0:56:07 | 0:56:10 | |
global positioning system. | 0:56:10 | 0:56:11 | |
We've developed an indoor global positioning system, | 0:56:11 | 0:56:14 | |
just like the one that exists outdoors. | 0:56:14 | 0:56:16 | |
And this is important, because then the vehicles | 0:56:16 | 0:56:19 | |
know where they are in space. | 0:56:19 | 0:56:21 | |
How it would differ is that the choreographies, | 0:56:21 | 0:56:23 | |
the trajectories wouldn't be preplanned. | 0:56:23 | 0:56:25 | |
The system is going to have to be much more reactive | 0:56:25 | 0:56:27 | |
when people want to fly from point A to point B. | 0:56:27 | 0:56:30 | |
Is this now setting us up for having our own personal flying machines? | 0:56:34 | 0:56:38 | |
I think we've certainly come a long way. | 0:56:38 | 0:56:40 | |
We can make flying machines that can do vertical take-off and landing, | 0:56:40 | 0:56:43 | |
fully electric, which has its own benefits. | 0:56:43 | 0:56:46 | |
I think that we will, in the near future. | 0:56:46 | 0:56:48 | |
Could this be the aerial highway of the future in miniature? | 0:56:49 | 0:56:53 | |
These drones were designed to perform at live events. | 0:56:56 | 0:56:59 | |
But they offer a glimpse into | 0:56:59 | 0:57:01 | |
what the cities of the future might look like. | 0:57:01 | 0:57:03 | |
Many of the big questions around safety | 0:57:03 | 0:57:05 | |
can be answered with current tech. | 0:57:05 | 0:57:07 | |
So what you need to know about the future of transport is | 0:57:07 | 0:57:09 | |
we may finally get our flying cars. | 0:57:09 | 0:57:11 | |
Making predictions about the future is a pretty risky business. | 0:57:19 | 0:57:23 | |
And many people have come unstuck in the past. | 0:57:23 | 0:57:26 | |
But I'm pretty confident that most | 0:57:26 | 0:57:28 | |
of what we've covered in this programme will actually happen. | 0:57:28 | 0:57:32 | |
But I'm also confident of something else. | 0:57:32 | 0:57:34 | |
The tenth thing that you need to know about the future | 0:57:34 | 0:57:38 | |
is that there will be many other developments | 0:57:38 | 0:57:41 | |
that none of us have even thought of. | 0:57:41 | 0:57:43 | |
Because, although Arthur C Clarke may have predicted | 0:57:43 | 0:57:46 | |
universal mobile communication, | 0:57:46 | 0:57:47 | |
even he would have been surprised that we now carry around | 0:57:47 | 0:57:52 | |
with us a little hand-held device that contains a typewriter, | 0:57:52 | 0:57:57 | |
a camera, a postbox, a television, a calculator, a calendar, a light, | 0:57:57 | 0:58:02 | |
a record player, a tape recorder, and not forgetting a telephone. | 0:58:02 | 0:58:08 | |
And that, for me at least, | 0:58:08 | 0:58:10 | |
is a lot cleverer and certainly a lot cleaner than a genetically | 0:58:10 | 0:58:14 | |
engineered monkey servant. | 0:58:14 | 0:58:16 | |
To find out more about the innovations that are changing | 0:58:17 | 0:58:20 | |
our health, our leisure and our work, | 0:58:20 | 0:58:22 | |
and will continue to shape our future, | 0:58:22 | 0:58:24 | |
go to bbc.co.uk/horizon and follow the Open University link. | 0:58:24 | 0:58:29 |