1 00:00:02,000 --> 00:00:03,960 If you have ever wondered what's 2 00:00:03,960 --> 00:00:05,400 going to happen in the future, 3 00:00:05,400 --> 00:00:07,800 then this is the programme for you. 4 00:00:07,800 --> 00:00:09,680 Because we're going to be asking 5 00:00:09,680 --> 00:00:11,720 the kind of questions that we all have 6 00:00:11,720 --> 00:00:13,400 about our future. 7 00:00:13,400 --> 00:00:16,360 In this very special edition of Horizon we'll be 8 00:00:16,360 --> 00:00:20,560 revealing the ten things you definitely need to know that will, 9 00:00:20,560 --> 00:00:23,480 for better or worse, change our lives. 10 00:00:24,720 --> 00:00:27,120 We'll explore how artificial intelligence 11 00:00:27,120 --> 00:00:28,760 will change the way we work. 12 00:00:28,760 --> 00:00:31,280 Can I enquire, do you have pain in your mouth? 13 00:00:31,280 --> 00:00:33,280 Yes, I do. I see. 14 00:00:33,280 --> 00:00:36,680 We'll look at the likely impact of our changing climate. 15 00:00:36,680 --> 00:00:40,080 It could well top 40 degrees in a few days' time. 16 00:00:40,080 --> 00:00:43,440 And discover how gene therapy will transform medicine. 17 00:00:43,440 --> 00:00:46,320 And he said, "There is no evidence of disease. 18 00:00:46,320 --> 00:00:47,760 "You are cancer-free." 19 00:00:47,760 --> 00:00:51,880 We'll introduce the people already turning themselves into cyborgs... 20 00:00:51,880 --> 00:00:54,120 This last year people just shout at me, Pokemon. 21 00:00:54,120 --> 00:00:55,360 And they try to catch me. 22 00:00:55,360 --> 00:00:58,000 ..ask if renewable energy is here to stay... 23 00:00:58,000 --> 00:01:00,000 The place to be might not be down here, 24 00:01:00,000 --> 00:01:02,880 or even up on that hilltop up there. 25 00:01:02,880 --> 00:01:04,120 It's up there. 26 00:01:04,120 --> 00:01:06,200 ..and reveal how we are mapping our brains... 27 00:01:06,200 --> 00:01:07,760 It's absolutely astonishing. 28 00:01:07,760 --> 00:01:10,400 ..but jeopardising our very existence. 29 00:01:10,400 --> 00:01:13,200 What we are doing is removing the ability 30 00:01:13,200 --> 00:01:15,000 for us to live on this planet. 31 00:01:15,000 --> 00:01:18,520 We'll also celebrate, finally, the arrival of 32 00:01:18,520 --> 00:01:21,960 that science fiction cliche, the flying car. 33 00:01:21,960 --> 00:01:23,800 That's it, that's all it takes. 34 00:01:23,800 --> 00:01:26,840 So, if you want to know what's in store for you, then keep watching. 35 00:01:26,840 --> 00:01:28,480 Welcome to the future. 36 00:01:38,680 --> 00:01:41,560 Now, we are all going to experience the future 37 00:01:41,560 --> 00:01:44,760 and we all want to know how it will change our lives. 38 00:01:44,760 --> 00:01:47,120 What's going to be the future of our daily commute? 39 00:01:47,120 --> 00:01:48,640 What will we spend our money on? 40 00:01:48,640 --> 00:01:50,760 What's going to happen to our planet? 41 00:01:50,760 --> 00:01:54,840 Now, over the years, many people have made some pretty bold claims, 42 00:01:54,840 --> 00:01:58,960 including science fiction legend Arthur C Clarke, 43 00:01:58,960 --> 00:02:03,240 in an interview that he gave to Horizon in 1964. 44 00:02:03,240 --> 00:02:05,480 I'm perfectly serious when I suggest a world 45 00:02:05,480 --> 00:02:08,360 in which we can be in instant contact with each other, 46 00:02:08,360 --> 00:02:09,520 wherever we may be. 47 00:02:09,520 --> 00:02:12,040 Where we can contact our friends anywhere on Earth 48 00:02:12,040 --> 00:02:15,560 even if we don't know their actual physical location. 49 00:02:15,560 --> 00:02:17,600 It will be possible in that age, 50 00:02:17,600 --> 00:02:19,840 perhaps only 50 years from now, 51 00:02:19,840 --> 00:02:24,480 for a man to conduct his business from Tahiti or Bali, 52 00:02:24,480 --> 00:02:26,160 just as well as he could from London. 53 00:02:27,800 --> 00:02:32,200 Isn't that incredible? Arthur C Clarke, there, in 1964, 54 00:02:32,200 --> 00:02:34,840 accurately describing what is effectively the internet age. 55 00:02:34,840 --> 00:02:38,560 Something, of course, that we all take completely for granted now 56 00:02:38,560 --> 00:02:42,040 but a world that was totally alien to his audience. 57 00:02:42,040 --> 00:02:46,000 But, predicting the future is notoriously tricky. 58 00:02:46,000 --> 00:02:48,320 And while Arthur was bang on the money with that one, 59 00:02:48,320 --> 00:02:52,360 he didn't stop there. Because here he is with his follow-up prediction. 60 00:02:52,360 --> 00:02:55,480 The development of intelligent and useful servants 61 00:02:55,480 --> 00:02:58,320 among the other animals on this planet. 62 00:02:58,320 --> 00:03:02,080 We could certainly solve the servant problem 63 00:03:02,080 --> 00:03:05,120 with the help of the monkey kingdom. 64 00:03:05,120 --> 00:03:07,040 Sorry, come again? 65 00:03:07,040 --> 00:03:11,640 ..solve the servant problem with the help of the monkey kingdom. 66 00:03:12,920 --> 00:03:15,400 Of course, eventually our super chimpanzees 67 00:03:15,400 --> 00:03:17,800 would start forming trade unions 68 00:03:17,800 --> 00:03:20,600 and we would be right back where we started. 69 00:03:20,600 --> 00:03:22,880 Well, I suppose one out of two ain't bad. 70 00:03:22,880 --> 00:03:26,240 But it does just go to show that predicting the future 71 00:03:26,240 --> 00:03:28,280 is usually a mug's game. 72 00:03:31,720 --> 00:03:36,080 Flying cars, salad-making robots and living in space colonies 73 00:03:36,080 --> 00:03:40,520 were the embarrassing staples of TV programmes like Tomorrow's World. 74 00:03:40,520 --> 00:03:43,240 Usually, their earnest predictions were way off. 75 00:03:44,360 --> 00:03:46,720 But this programme is different. 76 00:03:46,720 --> 00:03:49,880 And I'm as confident as I can be that these predictions 77 00:03:49,880 --> 00:03:53,480 aren't going to end up being laughed at by a TV audience in 2050. 78 00:03:54,720 --> 00:03:56,960 Because we are basing our predictions 79 00:03:56,960 --> 00:04:00,000 on real data from trusted sources, 80 00:04:00,000 --> 00:04:01,640 from today. 81 00:04:02,880 --> 00:04:05,840 And that is why we can confidently tell you that what you're 82 00:04:05,840 --> 00:04:07,880 about to watch really are the ten things 83 00:04:07,880 --> 00:04:10,360 that you need to know about the future. 84 00:04:10,360 --> 00:04:14,960 First up, Dr Kevin Fong on perhaps the biggest question of all. 85 00:04:14,960 --> 00:04:17,640 How can we cheat death and live longer? 86 00:04:27,440 --> 00:04:30,240 In the 1980s, if you wanted some constructive advice 87 00:04:30,240 --> 00:04:32,120 about how to achieve immortality, 88 00:04:32,120 --> 00:04:35,160 you went to see the film and musical Fame. 89 00:04:35,160 --> 00:04:38,120 The star of the piece was very clear about her advice, 90 00:04:38,120 --> 00:04:39,960 "I'm gonna live forever," she told us. 91 00:04:39,960 --> 00:04:42,480 And she was going to do that by becoming famous 92 00:04:42,480 --> 00:04:45,600 and living on forever in the memory of her adoring fans. 93 00:04:48,880 --> 00:04:51,960 And that kind of immortality is all that human beings 94 00:04:51,960 --> 00:04:53,840 have ever been able to aspire to. 95 00:04:55,120 --> 00:04:58,120 To be remembered by as many people as possible 96 00:04:58,120 --> 00:05:00,120 for as long as you possibly can. 97 00:05:04,200 --> 00:05:06,840 Woody Allen had a very different approach, of course. 98 00:05:06,840 --> 00:05:10,120 "I don't want to achieve immortality through my work," he said. 99 00:05:10,120 --> 00:05:12,520 "I want to achieve immortality by not dying." 100 00:05:15,200 --> 00:05:19,200 And if you want to live for longer, not dying is a great place to start. 101 00:05:20,680 --> 00:05:22,520 And the good news is that, on average, 102 00:05:22,520 --> 00:05:25,280 we're living longer today than we ever have in the past. 103 00:05:28,520 --> 00:05:30,600 But how far can we push that? 104 00:05:30,600 --> 00:05:33,560 How long can we expect to live in the future? 105 00:05:37,800 --> 00:05:41,400 Now, births and deaths are one thing that we have quite a lot of data on 106 00:05:41,400 --> 00:05:44,760 here in the UK. And here is some of that data. 107 00:05:44,760 --> 00:05:48,080 This shows you how our life expectancy has changed 108 00:05:48,080 --> 00:05:50,240 over the last 170 years or so. 109 00:05:50,240 --> 00:05:52,600 Now, Kevin Fong, welcome back to the studio. 110 00:05:52,600 --> 00:05:55,960 As a doctor, help me make some sense of these numbers. 111 00:05:55,960 --> 00:05:58,160 Well, I think this is all fascinating data. 112 00:05:58,160 --> 00:06:01,440 When you look at it, this is average life expectancy which, for almost 113 00:06:01,440 --> 00:06:03,040 the whole of human history, 114 00:06:03,040 --> 00:06:05,680 languishes around here, around 40-45 years. 115 00:06:05,680 --> 00:06:07,960 And then you hit the start of the 20th century. 116 00:06:07,960 --> 00:06:09,600 You get this massive kick. 117 00:06:09,600 --> 00:06:11,680 Why? Well, it's all prevention. 118 00:06:11,680 --> 00:06:14,920 It's all about stopping the things that kill you early on in life 119 00:06:14,920 --> 00:06:18,200 shortly after you've been born, like measles, mumps, rubella. 120 00:06:18,200 --> 00:06:20,920 It's all about stopping the diseases of infection. 121 00:06:20,920 --> 00:06:24,240 And that is achieved through vaccination and better sanitation. 122 00:06:24,240 --> 00:06:27,280 So that's what explains this big kick and these early gains here. 123 00:06:27,280 --> 00:06:29,960 So this bit really is all about sort of child mortality? 124 00:06:29,960 --> 00:06:34,040 Yep. Prevention is always better than cure, and that was vaccination. 125 00:06:34,040 --> 00:06:36,760 But it continues to go up past then. What about this bit over here? 126 00:06:36,760 --> 00:06:39,680 So this kick up the top here is us getting better at 127 00:06:39,680 --> 00:06:41,680 advanced modern medicine. 128 00:06:41,680 --> 00:06:43,440 So we have better lifestyles, 129 00:06:43,440 --> 00:06:47,920 but also we get better at treating diseases like heart disease and also 130 00:06:47,920 --> 00:06:50,040 cancer, so that gives us a bit of an extra gain here. 131 00:06:50,040 --> 00:06:52,080 So we still see increases all the way through. 132 00:06:52,080 --> 00:06:54,080 And then what about going forward to the future, then? 133 00:06:54,080 --> 00:06:55,520 Will this continue to increase? 134 00:06:55,520 --> 00:06:57,480 Is there a limit to how long we can live for? 135 00:06:57,480 --> 00:06:58,880 It would be lovely, wouldn't it, 136 00:06:58,880 --> 00:07:01,800 if this line kept going up and up and up to infinity? 137 00:07:01,800 --> 00:07:03,120 It doesn't look like it. 138 00:07:03,120 --> 00:07:05,440 It looks like this data plateaus. 139 00:07:06,920 --> 00:07:09,960 This graph shows that being born in 2011 140 00:07:09,960 --> 00:07:12,360 gives you a very high average life expectancy. 141 00:07:14,600 --> 00:07:17,040 But in terms of maximum possible age, 142 00:07:17,040 --> 00:07:20,680 you're no better off than your early Victorian ancestors. 143 00:07:21,880 --> 00:07:23,960 There's a fundamental limit, it would appear, 144 00:07:23,960 --> 00:07:28,000 at least at the moment, around 110, 120 years, 145 00:07:28,000 --> 00:07:30,240 to which we seem to be limited. 146 00:07:31,600 --> 00:07:34,320 If we want to improve things, and who doesn't, 147 00:07:34,320 --> 00:07:38,600 we could do a lot worse than to investigate the naked mole rat. 148 00:07:38,600 --> 00:07:42,760 It lives at least ten times longer than other rats. 149 00:07:42,760 --> 00:07:47,680 In human terms, that's a potential lifespan of upwards of 700 years. 150 00:07:49,360 --> 00:07:52,960 As it happens, we actually have a naked mole rat expert 151 00:07:52,960 --> 00:07:55,640 here in the studio with your mole rats. 152 00:07:55,640 --> 00:07:57,800 Welcome to the studio, Chris Faulkes. 153 00:07:57,800 --> 00:08:00,280 Tell me about these amazing creatures. 154 00:08:00,280 --> 00:08:02,360 Well, naked mole rats, 155 00:08:02,360 --> 00:08:04,200 just about everything about their biology 156 00:08:04,200 --> 00:08:06,880 is weird and wonderful and exceptional. 157 00:08:06,880 --> 00:08:09,840 And recently they've been generating a lot of interest 158 00:08:09,840 --> 00:08:12,120 because of their extreme longevity. 159 00:08:12,120 --> 00:08:15,760 Which can be... We don't know the upper limit, but more than 30 years. 160 00:08:15,760 --> 00:08:18,400 Gosh. Cos I guess a rat only lives, what, two or three years, 161 00:08:18,400 --> 00:08:21,400 maybe at the most. Yeah, exactly. And these live to 30, did you say? 162 00:08:21,400 --> 00:08:23,000 Yeah. And counting. 163 00:08:23,000 --> 00:08:25,520 Wow. And do they get old in that time? 164 00:08:25,520 --> 00:08:27,720 Well, this is really the remarkable thing. 165 00:08:27,720 --> 00:08:29,960 Because not only do they have a long lifespan, 166 00:08:29,960 --> 00:08:32,000 but their health span is very long, 167 00:08:32,000 --> 00:08:35,880 which means that they go through a huge proportion of their life 168 00:08:35,880 --> 00:08:38,560 without showing any signs of ageing whatsoever, 169 00:08:38,560 --> 00:08:41,760 like an 80-year-old having the body of a 30-year-old. 170 00:08:41,760 --> 00:08:44,600 Well, that would be nice, wouldn't it? It sure would. So, why is that? 171 00:08:44,600 --> 00:08:47,040 What is it about them that means that they have that feature? 172 00:08:47,040 --> 00:08:49,880 Well, it seems that it's not a single thing, 173 00:08:49,880 --> 00:08:52,520 but there's a whole mosaic of adaptations 174 00:08:52,520 --> 00:08:55,320 that have given them very low metabolic rate, 175 00:08:55,320 --> 00:08:58,680 a low body temperature, there's resistance to cancer, 176 00:08:58,680 --> 00:09:00,040 and a whole bunch of other things 177 00:09:00,040 --> 00:09:01,680 which all collectively give them 178 00:09:01,680 --> 00:09:03,640 this really long lifespan and health span. 179 00:09:03,640 --> 00:09:05,320 So, do they not get cancer? 180 00:09:05,320 --> 00:09:08,000 There's been virtually no recorded cases. 181 00:09:08,000 --> 00:09:12,400 As opposed to lab mice, for example, where 70% will die of cancer. 182 00:09:12,400 --> 00:09:14,480 Wow. Gosh, that's extraordinary. 183 00:09:14,480 --> 00:09:17,160 So what is it about them that means that they don't get cancer? 184 00:09:17,160 --> 00:09:21,360 There's a substance in the skin called hyaluronan that we all have. 185 00:09:21,360 --> 00:09:25,280 These guys have a very special version of that substance, 186 00:09:25,280 --> 00:09:28,280 which we think is what gives them their really elasticky skin, 187 00:09:28,280 --> 00:09:31,320 which is useful when you're living in tight tunnels. 188 00:09:31,320 --> 00:09:36,960 And this hyaluronan is implicated in one part of their cancer resistance. 189 00:09:36,960 --> 00:09:38,640 How stretchy is their skin? 190 00:09:38,640 --> 00:09:40,320 It's pretty stretchy. 191 00:09:40,320 --> 00:09:42,360 Do you want me to see if I can demonstrate? 192 00:09:42,360 --> 00:09:44,120 Yeah, go on, why not, let's have a look. 193 00:09:44,120 --> 00:09:46,000 They'll probably all go running off. 194 00:09:46,000 --> 00:09:49,600 And this sort of stretchy substance is one of 195 00:09:49,600 --> 00:09:52,120 the things that protects them against... Oh, my goodness. 196 00:09:52,120 --> 00:09:53,720 Yeah, so, there we go, they don't mind 197 00:09:53,720 --> 00:09:56,120 being picked up like this at all as you can see. 198 00:09:56,120 --> 00:10:00,560 The animal's quite relaxed. But see how really elasticky the skin is. 199 00:10:00,560 --> 00:10:01,840 Incredible. My goodness me. 200 00:10:01,840 --> 00:10:03,840 Let me just pop him back there. There we go. 201 00:10:03,840 --> 00:10:05,440 And he's quite happy. Yeah, no problem. 202 00:10:05,440 --> 00:10:06,880 He has made a run for it, though. 203 00:10:06,880 --> 00:10:10,880 Yes. Well, I guess the question is, really, 204 00:10:10,880 --> 00:10:13,640 how do we get that substance in ourselves? 205 00:10:13,640 --> 00:10:16,680 As labs around the world have been studying 206 00:10:16,680 --> 00:10:19,040 the genomes of naked mole rats, 207 00:10:19,040 --> 00:10:21,280 we're finding a whole bunch of 208 00:10:21,280 --> 00:10:24,120 candidate genes that are responsible, 209 00:10:24,120 --> 00:10:26,640 perhaps, for their longevity. 210 00:10:26,640 --> 00:10:29,280 And, you know, their health span, as well. 211 00:10:29,280 --> 00:10:31,520 And potentially apply them to ourselves. 212 00:10:31,520 --> 00:10:33,480 That could well be the case, I think. 213 00:10:33,480 --> 00:10:38,120 It's not beyond the realms of possibility. Wow. 214 00:10:38,120 --> 00:10:41,800 So, what you need to know about the future of lifespan is this. 215 00:10:41,800 --> 00:10:45,480 The good news is that we are likely to live longer on average. 216 00:10:45,480 --> 00:10:49,120 But, until we crack the secrets of the mole rats' longevity, 217 00:10:49,120 --> 00:10:51,160 it does seem that our luck, on average, 218 00:10:51,160 --> 00:10:54,200 runs out just after the telegram from the Queen arrives. 219 00:10:59,160 --> 00:11:01,640 Right now, one in four of us is likely 220 00:11:01,640 --> 00:11:05,560 to be affected by mental health issues at some point in our lives. 221 00:11:05,560 --> 00:11:07,760 But when it comes to effective treatment, 222 00:11:07,760 --> 00:11:11,280 mental health really does lag behind other diseases. 223 00:11:11,280 --> 00:11:14,920 So, what does the future hold for our state of mind? 224 00:11:14,920 --> 00:11:18,080 Now, there is a very big incentive to answer this question. 225 00:11:18,080 --> 00:11:20,800 And it's because mental health issues 226 00:11:20,800 --> 00:11:24,240 are the number one cause of people being unable to work. 227 00:11:24,240 --> 00:11:26,480 So this graph here from 2010 shows 228 00:11:26,480 --> 00:11:30,120 the amount of money lost by people not being able to work 229 00:11:30,120 --> 00:11:32,000 with different diseases. 230 00:11:32,000 --> 00:11:35,040 And the results here are shown in trillions of dollars. 231 00:11:35,040 --> 00:11:37,280 But the exact numbers here aren't really the story. 232 00:11:37,280 --> 00:11:39,680 The story is that mental health illness 233 00:11:39,680 --> 00:11:41,680 really does top the pile here. 234 00:11:41,680 --> 00:11:44,000 Beating both cardiovascular disease 235 00:11:44,000 --> 00:11:45,960 and also cancer, just there. 236 00:11:45,960 --> 00:11:48,360 And if current trends continue, 237 00:11:48,360 --> 00:11:51,680 then the World Health Organization has made some predictions 238 00:11:51,680 --> 00:11:53,440 for what we can expect by 2030. 239 00:11:53,440 --> 00:11:56,480 And you can see things really are set to get a lot worse. 240 00:11:56,480 --> 00:11:59,600 And what's particularly bad about mental health illness 241 00:11:59,600 --> 00:12:01,640 is that it really is the disease 242 00:12:01,640 --> 00:12:04,480 that people end up living with for the longest. 243 00:12:04,480 --> 00:12:06,520 So this graph here shows you how long 244 00:12:06,520 --> 00:12:09,120 people live with different kinds of illness, 245 00:12:09,120 --> 00:12:12,120 so you can see unintentional injuries just there. 246 00:12:12,120 --> 00:12:14,360 There's cardiovascular diseases down there. 247 00:12:14,360 --> 00:12:18,520 And once again, mental health disorders and neurological disorders 248 00:12:18,520 --> 00:12:20,520 really are very much out in front. 249 00:12:20,520 --> 00:12:23,720 So what is being done to help understand the brain and to 250 00:12:23,720 --> 00:12:25,000 improve these things? 251 00:12:25,000 --> 00:12:27,360 Well, Michael Mosley has been to visit 252 00:12:27,360 --> 00:12:29,640 a research project in London to find out. 253 00:12:39,160 --> 00:12:42,880 The human brain is the most complex object in the known universe. 254 00:12:42,880 --> 00:12:46,600 And for a long time its workings were a complete mystery. 255 00:12:46,600 --> 00:12:48,160 Then, in the 19th century, 256 00:12:48,160 --> 00:12:50,640 scientists identified that some of our abilities, 257 00:12:50,640 --> 00:12:51,920 like being able to speak 258 00:12:51,920 --> 00:12:54,240 are linked to particular regions of the brain. 259 00:12:54,240 --> 00:12:57,920 But an awful lot of what goes on up here is not to do with regions. 260 00:12:57,920 --> 00:13:00,400 It's absolutely to do with connections. 261 00:13:02,800 --> 00:13:05,240 If your brain isn't properly wired up, 262 00:13:05,240 --> 00:13:08,880 then this puts you at greater risk of things like dementia, 263 00:13:08,880 --> 00:13:11,600 schizophrenia and, early in life, autism. 264 00:13:12,840 --> 00:13:14,960 Autism is the result of changes in the way 265 00:13:14,960 --> 00:13:17,000 that the brain processes information. 266 00:13:17,000 --> 00:13:20,720 By studying these physical changes, 267 00:13:20,720 --> 00:13:24,160 it's hoped new light will be shed on the workings of the whole brain. 268 00:13:26,160 --> 00:13:28,840 Here, at Evelina London Children's Hospital, 269 00:13:28,840 --> 00:13:31,840 a team from King's College London are looking at 270 00:13:31,840 --> 00:13:36,720 brain development in babies, using MRI scanners. 271 00:13:36,720 --> 00:13:39,520 Hello, my name is Anna, I'm one of the radiographers, OK? 272 00:13:41,160 --> 00:13:43,160 Even before they're born. 273 00:13:44,400 --> 00:13:46,200 I've never seen an MRI of a foetus. 274 00:13:47,240 --> 00:13:50,280 That is amazing. It is amazing. 275 00:13:50,280 --> 00:13:51,800 It is absolutely astonishing. 276 00:13:51,800 --> 00:13:53,360 I mean, what's really amazing about it, 277 00:13:53,360 --> 00:13:57,600 it's relatively easy to get one quick flash through a scan 278 00:13:57,600 --> 00:14:00,600 but collecting all of the slices 279 00:14:00,600 --> 00:14:03,800 to make a three-dimensional reconstruction is the hard bit. 280 00:14:04,960 --> 00:14:09,000 This is a 3-D model made from one of the images of a baby who is about 281 00:14:09,000 --> 00:14:12,720 three months early. And as you can see, it's very small. 282 00:14:12,720 --> 00:14:14,680 It's very smooth, it's very underdeveloped. 283 00:14:14,680 --> 00:14:16,840 It has a lot more growing to do. 284 00:14:16,840 --> 00:14:18,920 And this is the baby who's about term, 285 00:14:18,920 --> 00:14:21,200 and the growth that has to happen 286 00:14:21,200 --> 00:14:23,840 between there and there is phenomenal. 287 00:14:23,840 --> 00:14:27,480 As the brain grows, more and more connections are made inside. 288 00:14:29,360 --> 00:14:32,040 And the team is able to identify that wiring 289 00:14:32,040 --> 00:14:34,040 using two kinds of MRI scanning. 290 00:14:35,600 --> 00:14:38,000 I have to say, I'm still blown away by those images. 291 00:14:39,640 --> 00:14:41,280 I don't know what I was expecting, 292 00:14:41,280 --> 00:14:43,320 but I wasn't expecting something as clear as that. 293 00:14:43,320 --> 00:14:45,760 I think, to be honest, we're blown away by the images. 294 00:14:45,760 --> 00:14:47,840 Once the MRI data is processed, 295 00:14:47,840 --> 00:14:51,240 it's translated into a basic map of connections, 296 00:14:51,240 --> 00:14:54,680 showing which parts of a baby's brain can talk to each other. 297 00:14:57,000 --> 00:14:59,760 These are much better than the images we used to get in the past. 298 00:14:59,760 --> 00:15:01,880 And the information content has gone up massively. 299 00:15:03,560 --> 00:15:08,360 At birth, the brain already has about 100 billion neurons. 300 00:15:08,360 --> 00:15:11,720 And each one is connected to thousands of others. 301 00:15:11,720 --> 00:15:14,960 So the brain's wiring diagram is enormously complex. 302 00:15:17,560 --> 00:15:20,920 Nonetheless, researchers have started to decipher it. 303 00:15:20,920 --> 00:15:23,680 The resulting map is called the connectome. 304 00:15:25,320 --> 00:15:28,200 And while everyone's individual connectome is unique, 305 00:15:28,200 --> 00:15:32,160 the hope is the average data will give us useful information 306 00:15:32,160 --> 00:15:34,080 about how all our brains work. 307 00:15:35,320 --> 00:15:38,480 So, one of the major uses for this is to define the connectome. 308 00:15:38,480 --> 00:15:41,840 Now, we obviously can't have all the connections in the brain because 309 00:15:41,840 --> 00:15:43,440 we're not looking at single nerve fibres. 310 00:15:43,440 --> 00:15:46,640 But we can get a pretty good idea of what that connection map would be. 311 00:15:46,640 --> 00:15:49,200 I think of it as being a bit like the Tube map of London. 312 00:15:49,200 --> 00:15:52,320 You can't find an individual road on the Tube map but you can find your 313 00:15:52,320 --> 00:15:53,160 way round London on it. 314 00:15:54,440 --> 00:15:58,080 Connectome studies around the world are gathering data 315 00:15:58,080 --> 00:16:01,920 in a bid to understand a whole range of mental health conditions 316 00:16:01,920 --> 00:16:03,960 and brain disorders. 317 00:16:03,960 --> 00:16:07,600 There's autism and ADHD at the beginning of life. 318 00:16:07,600 --> 00:16:11,080 Then psychoses like schizophrenia. 319 00:16:11,080 --> 00:16:13,720 Finally, as the level of connectivity deteriorates 320 00:16:13,720 --> 00:16:17,160 in the ageing brain, dementia. 321 00:16:17,160 --> 00:16:19,000 I do think that mapping the connectome 322 00:16:19,000 --> 00:16:22,240 could be the next big thing in our understanding of the brain. 323 00:16:22,240 --> 00:16:24,360 And that's important, not just because 324 00:16:24,360 --> 00:16:27,120 it will increase our knowledge of a normal brain, 325 00:16:27,120 --> 00:16:30,560 but it could also lead to better treatments when things go wrong. 326 00:16:36,520 --> 00:16:40,120 And now to our national obsession - the weather. 327 00:16:40,120 --> 00:16:42,640 We live, so we're told, in dangerous times. 328 00:16:42,640 --> 00:16:46,640 Planet Earth is getting hotter and that is going to change our climate, 329 00:16:46,640 --> 00:16:49,200 and there is a lot of data around this point. 330 00:16:49,200 --> 00:16:50,840 So here, for instance, 331 00:16:50,840 --> 00:16:54,760 is a map of what's happened around the world in the last 130 years, 332 00:16:54,760 --> 00:16:57,600 compared to an average in the middle of the 20th century. 333 00:16:57,600 --> 00:17:00,240 And as you're in the sort of '20s and '30s you can see a lot of blue 334 00:17:00,240 --> 00:17:02,240 and white on this map, the odd splodge of orange 335 00:17:02,240 --> 00:17:03,880 comes in every now and then. 336 00:17:03,880 --> 00:17:06,760 But, as time rolls on and we get closer to now, 337 00:17:06,760 --> 00:17:09,360 these orange splodges end up connecting to each other 338 00:17:09,360 --> 00:17:12,040 and we see some red coming in, in the north up there. 339 00:17:12,040 --> 00:17:15,400 Now, this effect actually is very extreme. 340 00:17:15,400 --> 00:17:17,080 2016, for instance, 341 00:17:17,080 --> 00:17:21,160 was the hottest year on record that the Earth has ever had. 342 00:17:21,160 --> 00:17:23,320 But what about if we go a bit further back in time? 343 00:17:23,320 --> 00:17:27,040 So this graph here shows you the global temperature 344 00:17:27,040 --> 00:17:29,480 for the last 11,000 years. 345 00:17:29,480 --> 00:17:31,480 Now it's certainly true that we have had 346 00:17:31,480 --> 00:17:33,120 changes in temperature before. 347 00:17:33,120 --> 00:17:37,160 There's an ice age just here, a little hot patch just there, 348 00:17:37,160 --> 00:17:39,040 about the same as we have now. 349 00:17:39,040 --> 00:17:41,280 Another little mini ice age just there. 350 00:17:41,280 --> 00:17:43,480 But what is important about this graph 351 00:17:43,480 --> 00:17:45,960 is this section that we have just over here. 352 00:17:45,960 --> 00:17:47,160 Because this here, 353 00:17:47,160 --> 00:17:51,720 isn't a little flag pointing downwards to the data, 354 00:17:51,720 --> 00:17:53,880 this line IS the data. 355 00:17:53,880 --> 00:17:57,480 The changes that we've seen in global temperature recently 356 00:17:57,480 --> 00:17:59,200 are so quick and so dramatic 357 00:17:59,200 --> 00:18:01,400 that on this graph of 11,000 years, 358 00:18:01,400 --> 00:18:04,000 it looks like a straight line going upwards. 359 00:18:04,000 --> 00:18:06,400 And in fact, if this trend continues, 360 00:18:06,400 --> 00:18:09,520 we expect to see the global temperature ending up looking 361 00:18:09,520 --> 00:18:12,200 something like this as we move forward in time. 362 00:18:12,200 --> 00:18:15,560 But the trouble is that we have heard a lot of this before. 363 00:18:15,560 --> 00:18:17,480 And nothing really seems to change. 364 00:18:17,480 --> 00:18:19,920 It doesn't feel like it's getting any warmer. 365 00:18:19,920 --> 00:18:24,120 But there are some people out there who have already noticed the changes 366 00:18:24,120 --> 00:18:28,320 that global warming is having, literally in their own backyard. 367 00:18:28,320 --> 00:18:30,400 And weatherman Peter Gibbs is one of them. 368 00:18:41,600 --> 00:18:46,840 Gardeners are ruled by the seasons, that annual cycle of sowing, 369 00:18:46,840 --> 00:18:48,080 growing and harvesting. 370 00:18:49,680 --> 00:18:52,560 But global warming has put a spanner in the works, 371 00:18:52,560 --> 00:18:54,560 because spring now arrives 372 00:18:54,560 --> 00:18:57,640 a few days earlier with every passing decade. 373 00:19:02,120 --> 00:19:04,080 For a start, that means I need to start 374 00:19:04,080 --> 00:19:06,000 mowing the lawn earlier in the year. 375 00:19:06,000 --> 00:19:09,840 And while I used to retire the mower for the winter in, say, 376 00:19:09,840 --> 00:19:12,440 late September, it's now not unusual to 377 00:19:12,440 --> 00:19:14,880 cut the grass as late as November. 378 00:19:17,760 --> 00:19:20,520 The growing season is the best part of six weeks longer 379 00:19:20,520 --> 00:19:22,840 than it was before climate change kicked in. 380 00:19:22,840 --> 00:19:25,000 Which is great if you want to grow grapes. 381 00:19:25,000 --> 00:19:27,160 I'm getting a decent crop most years now. 382 00:19:27,160 --> 00:19:30,560 Problem is, because it's all developing so much earlier 383 00:19:30,560 --> 00:19:33,160 in the spring, that extends, surprisingly, 384 00:19:33,160 --> 00:19:36,840 the season over which these little baby grapes 385 00:19:36,840 --> 00:19:39,600 can actually be hit by a rogue frost. 386 00:19:42,520 --> 00:19:46,200 The effects of climate change are frustratingly complicated. 387 00:19:46,200 --> 00:19:49,440 It varies from one kind of plant to the next. 388 00:19:49,440 --> 00:19:52,800 In winter, apple trees and blackcurrant bushes are dormant, 389 00:19:52,800 --> 00:19:55,440 storing up energy reserves for the spring. 390 00:19:57,560 --> 00:20:00,200 But as British winters get steadily warmer, 391 00:20:00,200 --> 00:20:02,840 that chilling time will be cut short, 392 00:20:02,840 --> 00:20:06,280 resulting in poor flowering and a lack of fruit. 393 00:20:09,960 --> 00:20:12,880 The effects of climate change are already pretty apparent, 394 00:20:12,880 --> 00:20:16,240 and that's with just a one-degree rise in temperature. 395 00:20:16,240 --> 00:20:19,960 Trouble is, temperatures are expected to keep on rising. 396 00:20:19,960 --> 00:20:25,200 If and when that happens, the impact will be even more dramatic. 397 00:20:25,200 --> 00:20:28,240 Higher temperatures mean melting ice. 398 00:20:28,240 --> 00:20:32,080 And here is what has been happening to Arctic sea ice since 1980. 399 00:20:32,080 --> 00:20:36,120 It's been reducing at the rate of 13% per decade. 400 00:20:36,120 --> 00:20:37,840 It's not just the sea ice, though. 401 00:20:37,840 --> 00:20:41,520 Melting ice caps means rising sea levels, too. 402 00:20:41,520 --> 00:20:44,480 And climate scientists have run some mathematical models to try and 403 00:20:44,480 --> 00:20:47,800 predict what that will mean for us on land. 404 00:20:47,800 --> 00:20:52,000 So, if all of Greenland melts, which admittedly would be quite dramatic, 405 00:20:52,000 --> 00:20:56,280 but that would have a six metre change in the level of the sea. 406 00:20:56,280 --> 00:20:58,960 And this is what Europe would look like as a result. 407 00:20:58,960 --> 00:21:01,120 Holland - not looking great for Holland. 408 00:21:01,120 --> 00:21:02,560 Norfolk also very badly hit. 409 00:21:02,560 --> 00:21:04,200 And London too. 410 00:21:04,200 --> 00:21:06,200 And over the other side of the Atlantic, 411 00:21:06,200 --> 00:21:09,000 this is what would happen to Florida. 412 00:21:09,000 --> 00:21:12,880 Quite a lot of it would end up being completely underwater, 413 00:21:12,880 --> 00:21:14,880 including Cape Canaveral, just there. 414 00:21:14,880 --> 00:21:18,160 You're going to need a new place to try and launch the rockets from. 415 00:21:18,160 --> 00:21:19,640 But the big question is, 416 00:21:19,640 --> 00:21:21,840 is our weather here in the UK going to be affected 417 00:21:21,840 --> 00:21:24,160 by all of this in the future? 418 00:21:24,160 --> 00:21:27,800 Now, I'm not a meteorologist, but happily Peter Gibbs, who is, 419 00:21:27,800 --> 00:21:31,000 has rushed back from his garden to the BBC Weather Studio, 420 00:21:31,000 --> 00:21:34,920 where he has prepared a weather forecast for 2050. 421 00:21:34,920 --> 00:21:37,720 Hello, and welcome to Weather 2050. 422 00:21:37,720 --> 00:21:40,920 Well, the usual floods and saturated ground that we saw during the winter 423 00:21:40,920 --> 00:21:43,560 months are just a distant memory for most of us now, 424 00:21:43,560 --> 00:21:45,680 as we see the familiar signs of 425 00:21:45,680 --> 00:21:49,920 developing drought now that the summer heat has really kicked in. 426 00:21:49,920 --> 00:21:53,280 And those temperatures are expected to build over the next few days, 427 00:21:53,280 --> 00:21:56,840 so we'll all be cranking up the air-con, avoiding the daytime heat, 428 00:21:56,840 --> 00:21:58,840 and struggling to sleep at night. 429 00:21:58,840 --> 00:22:02,440 And that's certainly true in the south, where we'll hit 35 Celsius, 430 00:22:02,440 --> 00:22:04,360 not unusual these days, of course. 431 00:22:04,360 --> 00:22:07,360 We could well top 40 degrees in a few days' time. 432 00:22:07,360 --> 00:22:09,840 A little bit more comfortable in the north, 433 00:22:09,840 --> 00:22:12,600 thanks to patchy cloud and a breeze coming in from the sea. 434 00:22:12,600 --> 00:22:14,680 But still, pretty humid. 435 00:22:14,680 --> 00:22:17,720 Then eventually that heat will break down into scattered thunderstorms, 436 00:22:17,720 --> 00:22:20,960 the warmer atmosphere of course able to hold more moisture these days, 437 00:22:20,960 --> 00:22:24,000 so there will be some really intense downpours. 438 00:22:24,000 --> 00:22:26,360 Probably won't too much for the droughts, though, 439 00:22:26,360 --> 00:22:28,640 with the rain just running off the parched ground 440 00:22:28,640 --> 00:22:30,320 and causing flash flooding. 441 00:22:30,320 --> 00:22:33,440 So some disruption looks likely. 442 00:22:33,440 --> 00:22:35,960 What you need to know about the weather of the future 443 00:22:35,960 --> 00:22:37,840 is that we expect, on average, 444 00:22:37,840 --> 00:22:40,240 the north to become warmer and wetter, 445 00:22:40,240 --> 00:22:42,440 and in the south, hotter and drier. 446 00:22:42,440 --> 00:22:44,080 But across the whole country, 447 00:22:44,080 --> 00:22:48,000 weather patterns will become a lot more variable, with bigger extremes. 448 00:22:48,000 --> 00:22:50,160 So whoever is doing the forecast then 449 00:22:50,160 --> 00:22:53,240 will have a much more difficult job than I've had. 450 00:22:58,600 --> 00:23:01,200 When it comes to the future of health care, 451 00:23:01,200 --> 00:23:04,720 past triumphs give us good reason to be optimistic. 452 00:23:04,720 --> 00:23:07,240 But what about the big C? 453 00:23:07,240 --> 00:23:09,920 Now, a cure for cancer is something we'd all like to happen, 454 00:23:09,920 --> 00:23:12,520 but how realistic a hope is that? 455 00:23:12,520 --> 00:23:14,240 Well, in the year 2000, 456 00:23:14,240 --> 00:23:18,640 Bill Clinton and Tony Blair announced a very big breakthrough. 457 00:23:19,720 --> 00:23:22,720 We're here to celebrate the completion 458 00:23:22,720 --> 00:23:25,760 of the first survey of the entire human genome. 459 00:23:25,760 --> 00:23:28,080 Without a doubt, this is the most important, 460 00:23:28,080 --> 00:23:32,920 most wondrous map ever produced by humankind. 461 00:23:32,920 --> 00:23:37,400 Well, in just 50 years after the discovery of the structure of DNA, 462 00:23:37,400 --> 00:23:40,320 teams from the US and the UK between them 463 00:23:40,320 --> 00:23:43,440 had mapped all of the genes contained in us. 464 00:23:43,440 --> 00:23:46,440 Now, the Human Genome Project, said Bill and Tony, 465 00:23:46,440 --> 00:23:48,720 would cure all human suffering. 466 00:23:48,720 --> 00:23:51,160 The blind would see, the lame would walk, 467 00:23:51,160 --> 00:23:54,000 and cancers would be a thing of the past. 468 00:23:54,000 --> 00:23:56,840 Well, Bill and Tony are now distant memories. 469 00:23:56,840 --> 00:24:01,720 But what happened to the genome and its bid to end all suffering? 470 00:24:01,720 --> 00:24:04,520 Geneticist Giles Yeo went to New York to find out. 471 00:24:13,920 --> 00:24:19,040 Half of us will be diagnosed with cancer at some point in our lives. 472 00:24:19,040 --> 00:24:21,320 The problem with cancer is that our body's defences 473 00:24:21,320 --> 00:24:23,640 does not recognise it as a threat. 474 00:24:23,640 --> 00:24:26,720 Our immune systems have evolved to distinguish between our 475 00:24:26,720 --> 00:24:29,520 own bodies and those of foreign invaders, 476 00:24:29,520 --> 00:24:32,000 such as bacteria and viruses. 477 00:24:32,000 --> 00:24:35,280 But cancer is simply a mutated version of our own cells, 478 00:24:35,280 --> 00:24:38,080 so it doesn't carry the typical characteristics 479 00:24:38,080 --> 00:24:39,520 of an invading organism. 480 00:24:39,520 --> 00:24:43,840 So our immune system doesn't recognise it, attack it and kill it. 481 00:24:46,560 --> 00:24:50,040 In 2011, Karen was diagnosed with 482 00:24:50,040 --> 00:24:52,080 a form of blood cancer called leukaemia. 483 00:24:53,440 --> 00:24:55,720 It's caused by the uncontrolled production 484 00:24:55,720 --> 00:24:57,360 of abnormal white blood cells. 485 00:25:00,280 --> 00:25:02,920 Karen lived with the disease for three years. 486 00:25:03,920 --> 00:25:08,560 But in 2014, she took a turn for the worse, 487 00:25:08,560 --> 00:25:10,760 and was given two years to live. 488 00:25:12,200 --> 00:25:15,680 That was brutal. Your brain is just going, "Oh, my God, 489 00:25:15,680 --> 00:25:18,680 "now I have this nuclear bomb in my body. 490 00:25:18,680 --> 00:25:21,440 "And it's going to kill me." 491 00:25:23,360 --> 00:25:25,160 But Karen was offered a lifeline. 492 00:25:26,400 --> 00:25:28,800 'In an experimental new treatment, 493 00:25:28,800 --> 00:25:32,520 'Dr Michel Sadelain is able to hack the immune system 494 00:25:32,520 --> 00:25:34,840 'and equip it with the ability to fight back.' 495 00:25:36,400 --> 00:25:39,560 So in many cancers, as in Karen's cancer, 496 00:25:39,560 --> 00:25:45,120 the immune system on its own is not capable of taking over the tumour. 497 00:25:45,120 --> 00:25:46,520 It needs a little help. 498 00:25:47,720 --> 00:25:50,920 Michel's target is a type of white blood cell called a T-cell. 499 00:25:52,240 --> 00:25:55,080 These are the foot soldiers of the immune system, 500 00:25:55,080 --> 00:25:58,080 whose purpose is to attack viruses and bacteria. 501 00:25:59,280 --> 00:26:01,960 The first step was to collect some of Karen's T-cells. 502 00:26:03,400 --> 00:26:06,080 And there a team of scientists and technicians 503 00:26:06,080 --> 00:26:11,320 insert into those T-cells into a gene, a synthetic gene, 504 00:26:11,320 --> 00:26:14,760 that provides this instruction to the T-cell which says, 505 00:26:14,760 --> 00:26:18,640 "Recognise the cancer, seek it out and destroy it." 506 00:26:18,640 --> 00:26:23,320 Michel used a virus to get the new gene into Karen's T-cells. 507 00:26:23,320 --> 00:26:26,960 The T-cells were then infused back into Karen's bloodstream. 508 00:26:26,960 --> 00:26:30,400 He's had some remarkable success with this technique. 509 00:26:30,400 --> 00:26:33,880 The first clinical results showed a dramatic effect 510 00:26:33,880 --> 00:26:36,640 in a majority of patients - 511 00:26:36,640 --> 00:26:40,120 about 85% of these patients went into 512 00:26:40,120 --> 00:26:43,360 what we call a complete remission. 513 00:26:44,800 --> 00:26:48,080 Michel has treated about 100 patients using this technique. 514 00:26:49,280 --> 00:26:50,560 Karen was one of the first. 515 00:26:51,880 --> 00:26:54,240 Never did we expect what we were going to hear. 516 00:26:54,240 --> 00:26:59,480 And he said, "There is no evidence of disease, you are cancer-free." 517 00:26:59,480 --> 00:27:02,880 So what was it like to hear the doctor say, "You are cancer-free?" 518 00:27:04,720 --> 00:27:06,120 It was unbelievable. 519 00:27:07,800 --> 00:27:10,280 I still get... I obviously get emotional about it. 520 00:27:11,320 --> 00:27:13,680 Because we didn't expect it. 521 00:27:13,680 --> 00:27:16,800 For Karen, the results of the trial have been incredible. 522 00:27:16,800 --> 00:27:18,720 I mean, they have saved her life. 523 00:27:18,720 --> 00:27:21,000 But that's only half the story. 524 00:27:21,000 --> 00:27:23,440 You know, because the technique doesn't always work, 525 00:27:23,440 --> 00:27:26,360 and bespoke gene editing for every single patient, 526 00:27:26,360 --> 00:27:27,680 it's just unrealistic. 527 00:27:27,680 --> 00:27:30,600 It is expensive, it is complex, it is lengthy. 528 00:27:30,600 --> 00:27:33,160 And patients have been known to die waiting 529 00:27:33,160 --> 00:27:36,400 during the intervening period for their cells to be re-engineered. 530 00:27:37,840 --> 00:27:41,040 The answer is a new technique called Crispr. 531 00:27:41,040 --> 00:27:47,240 Crispr stands for Clustered Regularly Interspaced Short Palindromic Repeats, 532 00:27:47,240 --> 00:27:49,840 which describes a property of bacterial DNA 533 00:27:49,840 --> 00:27:52,360 that scientists have been able to exploit - 534 00:27:52,360 --> 00:27:54,280 giving them, for the first time, 535 00:27:54,280 --> 00:27:57,360 ultimate accuracy in gene editing. 536 00:27:57,360 --> 00:28:00,800 What Crispr gives you is a pair of tweezers for you to be 537 00:28:00,800 --> 00:28:05,000 able to place the DNA anywhere you want in any cell. 538 00:28:05,000 --> 00:28:10,400 This new-found precision allows Michel to fix a fundamental problem. 539 00:28:10,400 --> 00:28:15,240 The problem in taking someone else's T-cells is that those T-cells will 540 00:28:15,240 --> 00:28:19,320 attack you. Because they will sense that, "It's not my body, 541 00:28:19,320 --> 00:28:22,680 "it's not my molecules, I have to go on the attack." 542 00:28:23,960 --> 00:28:28,840 So what Crispr Cas9 makes possible is the removal of those molecules 543 00:28:28,840 --> 00:28:30,600 that initiate that attack. 544 00:28:32,280 --> 00:28:35,760 It means that T-cells could be provided by a donor, 545 00:28:35,760 --> 00:28:39,000 and can be genetically engineered to only attack the cancer. 546 00:28:40,160 --> 00:28:42,200 From one donor, 547 00:28:42,200 --> 00:28:45,760 you could make cells that could be administered to multiple recipients. 548 00:28:47,040 --> 00:28:52,160 These cells would then be ready, and in pharmacies. 549 00:28:52,160 --> 00:28:55,120 And now, we preserve them. 550 00:28:56,480 --> 00:29:01,200 We may someday have pharmacies of T-cells, frozen vials of T-cells, 551 00:29:01,200 --> 00:29:03,160 ready-made, ready for injection. 552 00:29:03,160 --> 00:29:05,560 So there's the living drug. 553 00:29:05,560 --> 00:29:08,400 Fast asleep in liquid nitrogen. 554 00:29:08,400 --> 00:29:09,680 That's fantastic. 555 00:29:09,680 --> 00:29:12,360 And this would make this form of therapy 556 00:29:12,360 --> 00:29:14,720 accessible to many more individuals. 557 00:29:17,240 --> 00:29:20,760 Donor T-cells have yet to be trialled in humans. 558 00:29:20,760 --> 00:29:23,440 But the idea offers hope that in the future, 559 00:29:23,440 --> 00:29:25,280 many more people could be treated. 560 00:29:26,400 --> 00:29:30,400 We are now entering an unprecedented era of progress for gene therapy. 561 00:29:30,400 --> 00:29:32,000 And we're not just talking about cancer, 562 00:29:32,000 --> 00:29:35,320 because Crispr is now being used to treat muscular dystrophy, 563 00:29:35,320 --> 00:29:38,920 and it's being talked about as an alternative to antibiotics. 564 00:29:38,920 --> 00:29:40,840 What you need to know about the future is 565 00:29:40,840 --> 00:29:42,360 that the Human Genome Project 566 00:29:42,360 --> 00:29:45,040 may finally be delivering on its promise 567 00:29:45,040 --> 00:29:47,720 to revolutionise the way we treat disease. 568 00:29:53,320 --> 00:29:54,840 And now, work. 569 00:29:54,840 --> 00:29:57,400 What will you be doing as a job in the future? 570 00:29:57,400 --> 00:30:01,040 Well, 50 years ago, the world of work was pretty easy to understand. 571 00:30:01,040 --> 00:30:02,800 You either did manual work, 572 00:30:02,800 --> 00:30:05,680 which basically meant supervising machines. 573 00:30:05,680 --> 00:30:07,560 Or you worked in an office, 574 00:30:07,560 --> 00:30:09,400 which basically meant doing a lot of typing, 575 00:30:09,400 --> 00:30:11,840 or getting somebody else to do a lot of typing for you. 576 00:30:11,840 --> 00:30:16,080 Bottom line, people were integral to the workforce. 577 00:30:16,080 --> 00:30:17,720 But no more. 578 00:30:17,720 --> 00:30:21,160 Because, in the 1970s, machines got clever. 579 00:30:21,160 --> 00:30:23,360 Car plants were filled with robots, 580 00:30:23,360 --> 00:30:25,840 helplines were answered by computers, 581 00:30:25,840 --> 00:30:29,160 and almost all bank clerks became extinct. 582 00:30:29,160 --> 00:30:31,320 But I suspect that most of you are saying, 583 00:30:31,320 --> 00:30:33,520 "Well, a robot couldn't possibly take my job." 584 00:30:33,520 --> 00:30:35,560 But are you sure? 585 00:30:35,560 --> 00:30:36,480 Have a look at this. 586 00:30:38,800 --> 00:30:40,720 We sent Dr Zoe Williams to check out 587 00:30:40,720 --> 00:30:42,880 a piece of software which is rumoured 588 00:30:42,880 --> 00:30:46,720 to diagnose illnesses faster and more accurately 589 00:30:46,720 --> 00:30:49,600 than human medical professionals. 590 00:30:49,600 --> 00:30:52,480 So, should I be worried, as a GP? 591 00:30:52,480 --> 00:30:55,880 The thought that a robot or artificial intelligence 592 00:30:55,880 --> 00:30:58,800 could take my job just seems crazy. 593 00:30:58,800 --> 00:31:01,360 I mean, I've spent six years at medical school, 594 00:31:01,360 --> 00:31:03,440 ten years practising as a doctor. 595 00:31:03,440 --> 00:31:07,040 Now, surely all of that can't be boiled down to a few lines of code? 596 00:31:11,120 --> 00:31:13,560 Babylon Health are a medical tech company. 597 00:31:15,080 --> 00:31:18,200 They've just received $60 million of funding 598 00:31:18,200 --> 00:31:19,720 to develop an AI doctor. 599 00:31:22,040 --> 00:31:25,120 The system works by asking questions. 600 00:31:25,120 --> 00:31:26,960 But anyone can ask questions. 601 00:31:26,960 --> 00:31:28,960 If it's going to replace me, 602 00:31:28,960 --> 00:31:31,760 I really want to put it through its paces. 603 00:31:31,760 --> 00:31:35,880 I'm going to pose as a patient and give myself an imaginary condition. 604 00:31:35,880 --> 00:31:39,560 But I'm not going to tell anybody, I'm just going to write it down. 605 00:31:42,160 --> 00:31:45,240 And then we can see just how accurate the machine really is. 606 00:31:46,480 --> 00:31:49,920 May I ask, please, what's troubling you today? 607 00:31:49,920 --> 00:31:53,400 I'm feeling tired all the time. 608 00:31:53,400 --> 00:31:59,040 So, as well as feeling tired, I've been feeling kind of weak. 609 00:31:59,040 --> 00:32:00,720 Let's tell the computer that. 610 00:32:01,720 --> 00:32:03,320 And I've also been feeling... 611 00:32:05,320 --> 00:32:06,960 ..a bit of dizziness. 612 00:32:09,120 --> 00:32:12,560 Is it OK to ask, "Do you have painful periods?" 613 00:32:12,560 --> 00:32:14,160 There we go, that's better. 614 00:32:14,160 --> 00:32:15,520 Painful periods as well. 615 00:32:16,920 --> 00:32:19,440 Do you get breathless on exertion? 616 00:32:19,440 --> 00:32:20,280 Yes, I do! 617 00:32:21,360 --> 00:32:22,520 Thanks, I've noted this. 618 00:32:24,120 --> 00:32:26,880 So, I've given the computer all of my symptoms now. 619 00:32:26,880 --> 00:32:29,800 And it's come up with a diagnosis. 620 00:32:29,800 --> 00:32:31,560 So, let's see if it's correct. 621 00:32:31,560 --> 00:32:34,240 Here's my bit of paper from earlier. 622 00:32:34,240 --> 00:32:39,640 And you can see that I have put down fibroids. 623 00:32:39,640 --> 00:32:43,520 And the computer has said uterine leiomyoma, 624 00:32:43,520 --> 00:32:45,120 which is actually the same thing. 625 00:32:46,160 --> 00:32:47,800 That's impressive. 626 00:32:47,800 --> 00:32:49,600 But how is it done? 627 00:32:49,600 --> 00:32:52,280 Time to face down the evil genius 628 00:32:52,280 --> 00:32:55,760 hell-bent on replacing me with my laptop. 629 00:32:55,760 --> 00:32:59,200 So, you start off with a knowledge base. 630 00:32:59,200 --> 00:33:02,040 And this is essentially a medical database which contains hundreds of 631 00:33:02,040 --> 00:33:03,520 millions of medical concepts. 632 00:33:03,520 --> 00:33:07,280 That's kind of like being at medical school and all the knowledge that is 633 00:33:07,280 --> 00:33:08,280 inputted into the brain. 634 00:33:08,280 --> 00:33:11,080 Exactly, so this might be all of the textbooks which you've read at 635 00:33:11,080 --> 00:33:14,400 medical school, all of the papers which you've read at medical school, 636 00:33:14,400 --> 00:33:16,640 and then applied to all of that information 637 00:33:16,640 --> 00:33:19,880 we'll apply a set of methods known as machine-learning methods. 638 00:33:21,920 --> 00:33:24,800 Machine learning is the ability of computers 639 00:33:24,800 --> 00:33:28,400 to take vast amounts of data and make sense of it themselves. 640 00:33:30,480 --> 00:33:32,920 Like this network of medical information, 641 00:33:32,920 --> 00:33:35,440 which the computer uses to make a diagnosis. 642 00:33:37,000 --> 00:33:39,520 What these circles represent are diseases, 643 00:33:39,520 --> 00:33:41,360 symptoms and risk factors. OK. 644 00:33:41,360 --> 00:33:44,240 And what those lines represent are the relationships between those. 645 00:33:44,240 --> 00:33:45,520 So, based on that, 646 00:33:45,520 --> 00:33:50,120 the computer has taught itself actually how strongly related those 647 00:33:50,120 --> 00:33:51,800 diseases, symptoms and risk factors are. 648 00:33:51,800 --> 00:33:53,360 OK, so that's how it determines 649 00:33:53,360 --> 00:33:57,160 the probability is from looking at past real-life cases? 650 00:33:57,160 --> 00:33:59,680 Absolutely, and that's why this is machine learning. 651 00:34:03,400 --> 00:34:07,040 As the network learns about more and more symptoms and diseases, 652 00:34:07,040 --> 00:34:10,480 it's tested and refined by a team of doctors and programmers. 653 00:34:12,400 --> 00:34:13,480 It's early days, 654 00:34:13,480 --> 00:34:16,360 but the company sees a big future for their virtual medic. 655 00:34:17,400 --> 00:34:20,880 We want to do with health care what, say, Google did with information. 656 00:34:20,880 --> 00:34:25,760 It'll be in your phone, it'll be in the devices you carry with you. 657 00:34:25,760 --> 00:34:30,800 Do you think that a machine could ever replace my role as a GP? 658 00:34:30,800 --> 00:34:34,800 I don't think this is a competition between machines and humans. 659 00:34:34,800 --> 00:34:38,640 This is machines being an aid to humans. 660 00:34:38,640 --> 00:34:41,760 Half of the world's population has no access or very, 661 00:34:41,760 --> 00:34:44,120 very little access to doctors. 662 00:34:44,120 --> 00:34:48,320 Right? Imagine if you could see so many more because the machines do 663 00:34:48,320 --> 00:34:50,720 the easier part, they save your time. 664 00:34:50,720 --> 00:34:54,720 But can a machine put its hand on your shoulder and say, "Trust me, 665 00:34:54,720 --> 00:34:57,280 "I'll look after you?" That's a different story. 666 00:34:59,440 --> 00:35:02,800 It's not just in medicine that software's on the march. 667 00:35:02,800 --> 00:35:07,840 In banking, AI is approving or not approving loan applications. 668 00:35:07,840 --> 00:35:10,080 And even making investment decisions. 669 00:35:11,080 --> 00:35:14,080 And, with autonomous vehicles on the horizon, 670 00:35:14,080 --> 00:35:17,280 many who drive for a living will soon be superseded. 671 00:35:19,720 --> 00:35:21,600 What you need to know about the future 672 00:35:21,600 --> 00:35:23,760 is that no job is immune from the influence 673 00:35:23,760 --> 00:35:25,240 of artificial intelligence. 674 00:35:25,240 --> 00:35:27,080 If it doesn't take your job, 675 00:35:27,080 --> 00:35:29,720 then it's likely to change the way in which you do it. 676 00:35:35,400 --> 00:35:37,400 Now, whenever we think about the future, 677 00:35:37,400 --> 00:35:40,440 however outlandish we imagine our housing, 678 00:35:40,440 --> 00:35:42,640 our transport or our gadgets to be, 679 00:35:42,640 --> 00:35:45,360 one thing that we never seem to question is that 680 00:35:45,360 --> 00:35:48,400 there will always be a constant supply of electricity. 681 00:35:48,400 --> 00:35:52,280 But if you look at the data, that's not necessarily a safe assumption. 682 00:35:52,280 --> 00:35:54,480 So here is what's been happening in the UK 683 00:35:54,480 --> 00:35:56,080 over the last 100 years or so. 684 00:35:56,080 --> 00:35:57,760 And in the 20th century, 685 00:35:57,760 --> 00:36:00,360 we've become very dependent on fossil fuels. 686 00:36:00,360 --> 00:36:02,200 So you can see coal here in purple, 687 00:36:02,200 --> 00:36:04,360 gas in blue coming in slightly later, 688 00:36:04,360 --> 00:36:06,280 and then a bit of oil there throughout. 689 00:36:06,280 --> 00:36:08,440 Now, there's a bit of nuclear there in red, 690 00:36:08,440 --> 00:36:10,560 which has stayed pretty constant over time. 691 00:36:10,560 --> 00:36:13,360 And some renewables coming in much later in green. 692 00:36:13,360 --> 00:36:15,840 But the main story from this graph 693 00:36:15,840 --> 00:36:20,520 is that we are very dependent on fossil fuels to heat our homes, 694 00:36:20,520 --> 00:36:23,760 provide our transport and to generate our electricity. 695 00:36:23,760 --> 00:36:26,800 Now, if you look at the picture globally, 696 00:36:26,800 --> 00:36:30,240 the demand has been steadily increasing over time. 697 00:36:30,240 --> 00:36:33,840 So you can see a little blip here for the 2008 financial crisis. 698 00:36:33,840 --> 00:36:36,360 But generally speaking, the trend has been upwards. 699 00:36:36,360 --> 00:36:40,120 And if this trend continues, then over the next 50 years, 700 00:36:40,120 --> 00:36:44,880 we can expect the demand for energy consumption to increase by 48%. 701 00:36:44,880 --> 00:36:48,440 But the trouble is, burning fossil fuels is pretty bad for the planet. 702 00:36:48,440 --> 00:36:52,600 And in any case, we're going to run out of oil at some point anyway. 703 00:36:52,600 --> 00:36:55,440 So the question is, how do we keep the lights on 704 00:36:55,440 --> 00:36:58,280 while helping to save the planet? 705 00:36:58,280 --> 00:37:00,120 So, we sent physicist Helen Czerski 706 00:37:00,120 --> 00:37:02,200 to a place where they've already done it. 707 00:37:15,560 --> 00:37:17,560 I'm in Norway, and this stunning country 708 00:37:17,560 --> 00:37:21,440 is one of the world's biggest producers of renewable energy. 709 00:37:21,440 --> 00:37:24,960 Almost all of their electricity comes from hydropower. 710 00:37:24,960 --> 00:37:28,120 And government incentives mean that electric vehicles like this one are 711 00:37:28,120 --> 00:37:30,640 becoming more and more common. 712 00:37:30,640 --> 00:37:32,800 Relying on hydropower is fine if 713 00:37:32,800 --> 00:37:35,640 you've got plenty of mountains and lakes. 714 00:37:35,640 --> 00:37:38,200 But what about the rest of the world? 715 00:37:38,200 --> 00:37:41,400 In the UK, just under half of our renewable energy 716 00:37:41,400 --> 00:37:44,320 comes from wind turbines. But in spite of that, 717 00:37:44,320 --> 00:37:49,040 wind energy only contributes 11% of our total electricity generation. 718 00:37:53,960 --> 00:37:57,360 Part of the problem is finding enough places with strong winds. 719 00:37:58,600 --> 00:38:01,440 But perhaps there are some other opportunities. 720 00:38:01,440 --> 00:38:03,880 This is data from the University of Reading 721 00:38:03,880 --> 00:38:06,200 showing typical wind speeds in this area. 722 00:38:06,200 --> 00:38:08,320 And you can see that down here near the ground, 723 00:38:08,320 --> 00:38:11,200 the wind speeds are almost always really low. 724 00:38:11,200 --> 00:38:13,800 But as you go up, the wind speeds go right up. 725 00:38:13,800 --> 00:38:17,480 And what that suggests is that if you are serious about wind energy, 726 00:38:17,480 --> 00:38:19,520 the place to be might not be down here, 727 00:38:19,520 --> 00:38:22,120 or even up on that hilltop up there. 728 00:38:22,120 --> 00:38:23,560 It's up there. 729 00:38:25,960 --> 00:38:30,400 That's exactly what engineer Dr Lode Carnel is doing 730 00:38:30,400 --> 00:38:33,760 with this tiny plane he calls kitemill. 731 00:38:33,760 --> 00:38:38,200 Kitemill is designed to fly in the high-altitude winds. 732 00:38:40,200 --> 00:38:43,560 The force of the wind will cause it to pull on the tether, 733 00:38:43,560 --> 00:38:45,280 and that generates electricity. 734 00:38:48,760 --> 00:38:51,600 So, the tether's out, the kite's been assembled, 735 00:38:51,600 --> 00:38:53,080 and it's ready to launch. 736 00:38:53,080 --> 00:38:55,080 So the next step is to get it up into the air. 737 00:38:55,080 --> 00:38:56,080 Here we go! 738 00:39:08,080 --> 00:39:09,800 It's tiny in the sky. 739 00:39:09,800 --> 00:39:11,320 It looks so, so small. 740 00:39:11,320 --> 00:39:14,560 The idea that that could generate any energy at all 741 00:39:14,560 --> 00:39:15,960 is really quite weird. 742 00:39:17,200 --> 00:39:20,120 And it's fast, wow! Look at that! 743 00:39:20,120 --> 00:39:22,960 So, the kite's up in the sky and it's doing two things. 744 00:39:22,960 --> 00:39:26,920 It's either sitting flat, or it's going round and round in circles. 745 00:39:26,920 --> 00:39:28,600 What are the circles about? 746 00:39:28,600 --> 00:39:31,080 Yes, so when the system is located on the ground, 747 00:39:31,080 --> 00:39:33,800 we need to take it up to a certain altitude, 748 00:39:33,800 --> 00:39:36,720 and then it will fly in a circle, a pattern that we now see, 749 00:39:36,720 --> 00:39:38,720 during which it can produce electricity. 750 00:39:40,960 --> 00:39:43,120 Once the plane is high enough, 751 00:39:43,120 --> 00:39:46,240 it glides upwards into a corkscrew pattern, 752 00:39:46,240 --> 00:39:47,360 pulling on the tether. 753 00:39:48,920 --> 00:39:51,880 So, here we have the ground station where we generate the energy. 754 00:39:51,880 --> 00:39:55,560 You have the drum, where the tether is wound around. 755 00:39:55,560 --> 00:39:57,640 But it is connected directly to a 756 00:39:57,640 --> 00:40:00,120 motor or a generator, which is on the back. 757 00:40:00,120 --> 00:40:02,800 So, when we wind off, the motor turns in one direction 758 00:40:02,800 --> 00:40:03,680 and produces energy. 759 00:40:06,160 --> 00:40:08,280 And so how much energy is this generating at the moment? 760 00:40:09,560 --> 00:40:11,520 Two kilowatts roughly now, 761 00:40:11,520 --> 00:40:14,680 which is roughly the consumption of one family in the UK. 762 00:40:14,680 --> 00:40:17,600 Our next model has a capacity of 30 kilowatts. 763 00:40:17,600 --> 00:40:21,160 That can power automatically 20 families in the UK. 764 00:40:21,160 --> 00:40:22,560 However, this is not the end. 765 00:40:22,560 --> 00:40:23,720 We need to scale up. 766 00:40:23,720 --> 00:40:26,280 We want to produce energy with the lowest possible cost. 767 00:40:26,280 --> 00:40:29,520 So then we are talking 500 kilowatts, 768 00:40:29,520 --> 00:40:34,000 and that will be sufficient to power 300-400 families in the UK. 769 00:40:36,240 --> 00:40:38,840 Once it reaches the end of its tether, 770 00:40:38,840 --> 00:40:43,320 5% of the energy it's generated is used to reel it back in, 771 00:40:43,320 --> 00:40:44,760 and the process starts again. 772 00:40:47,120 --> 00:40:51,960 The plan is for the plane to stay up indefinitely, but this test is over, 773 00:40:51,960 --> 00:40:54,280 so the plane is brought back in to land. 774 00:40:55,760 --> 00:40:59,160 It's hard to imagine it working in the airspace 775 00:40:59,160 --> 00:41:01,760 above our already crowded cities. 776 00:41:01,760 --> 00:41:04,640 But it could have advantages in remote locations 777 00:41:04,640 --> 00:41:06,480 and in developing countries. 778 00:41:08,200 --> 00:41:10,920 And you can put it in places where you can't put a wind turbine. 779 00:41:10,920 --> 00:41:12,560 That's important, isn't it? 780 00:41:12,560 --> 00:41:14,280 This can extract energy from places 781 00:41:14,280 --> 00:41:16,120 that are not accessible at the moment. 782 00:41:16,120 --> 00:41:19,720 Correct, places where there is for example low wind speeds close to the 783 00:41:19,720 --> 00:41:22,400 ground, but higher wind speeds at higher altitudes could use this 784 00:41:22,400 --> 00:41:25,200 technology. Also, it's quite movable. 785 00:41:25,200 --> 00:41:27,600 It's a flexible technology, so one truck can come, 786 00:41:27,600 --> 00:41:29,240 and you can install everything. 787 00:41:29,240 --> 00:41:30,320 Contrary to windmills, 788 00:41:30,320 --> 00:41:33,520 where you need a lot of infrastructure and so on. 789 00:41:36,560 --> 00:41:39,680 Kitemill alone isn't the answer to our energy crisis. 790 00:41:43,720 --> 00:41:47,200 Power will have to come from a range of renewable sources. 791 00:41:49,080 --> 00:41:50,520 I'm optimistic about the future 792 00:41:50,520 --> 00:41:52,200 because I see lots of new technologies 793 00:41:52,200 --> 00:41:53,640 like kitemill coming along, 794 00:41:53,640 --> 00:41:57,640 each appropriate at a specific place or in a specific time. 795 00:41:57,640 --> 00:41:59,720 And together, these are the building blocks 796 00:41:59,720 --> 00:42:03,320 that will let us design a much more sophisticated energy future. 797 00:42:09,080 --> 00:42:11,840 A lot of people look to science fiction 798 00:42:11,840 --> 00:42:14,520 for a steer on what's going to happen in the future. 799 00:42:14,520 --> 00:42:16,800 And if sci-fi tells us one thing, 800 00:42:16,800 --> 00:42:20,080 it's that the future is littered with cyborgs. 801 00:42:20,080 --> 00:42:24,200 Now, the idea of some kind of human-machine hybrid is certainly an 802 00:42:24,200 --> 00:42:27,560 interesting one, and it's been explored in a number of different TV 803 00:42:27,560 --> 00:42:29,720 programmes, from Six Million Dollar Man 804 00:42:29,720 --> 00:42:31,600 all the way through to Star Trek. 805 00:42:31,600 --> 00:42:34,840 But so far, the reality hasn't quite managed 806 00:42:34,840 --> 00:42:36,960 to measure up for most of us. 807 00:42:36,960 --> 00:42:40,280 Now, is that a relief, or an opportunity missed? 808 00:42:52,120 --> 00:42:53,720 My name is James Young. 809 00:42:55,680 --> 00:42:57,800 And I am a cyborg. 810 00:43:00,280 --> 00:43:02,240 OK, and relax. 811 00:43:05,840 --> 00:43:07,440 Terrible. 812 00:43:09,160 --> 00:43:11,160 Taking a load off here. 813 00:43:11,160 --> 00:43:14,760 Just over five years ago, I lost an arm and a leg in a train accident. 814 00:43:16,560 --> 00:43:19,400 While I was coming to terms with the loss of my arm, 815 00:43:19,400 --> 00:43:22,000 I won a competition to have something different made. 816 00:43:24,440 --> 00:43:25,400 Yeah. 817 00:43:27,640 --> 00:43:29,040 It's not great! 818 00:43:33,960 --> 00:43:36,800 It was created to be more of an art piece, 819 00:43:36,800 --> 00:43:39,000 so it was like a prototype from the day it was made. 820 00:43:40,480 --> 00:43:43,600 It's got the lights that work and the hand... 821 00:43:43,600 --> 00:43:46,240 four of the digits work on the hand, so it's kind of like... 822 00:43:48,160 --> 00:43:50,240 ..it's trying, it's trying its best, 823 00:43:50,240 --> 00:43:52,640 but it's not in tiptop condition, basically. 824 00:43:56,720 --> 00:43:59,920 My brief taste of being a cyborg has left me wanting more. 825 00:44:01,400 --> 00:44:03,560 I'm now looking at prostheses that attach 826 00:44:03,560 --> 00:44:06,000 directly to my skeleton and nervous system. 827 00:44:08,480 --> 00:44:11,280 If I can replace my old abilities, 828 00:44:11,280 --> 00:44:13,760 then can I go a step further and gain new ones? 829 00:44:15,040 --> 00:44:18,680 The idea of expanding my abilities beyond 830 00:44:18,680 --> 00:44:22,640 the human baseline is something that really, really intrigues me. 831 00:44:22,640 --> 00:44:25,720 And because I almost studied cybernetics when I was considering 832 00:44:25,720 --> 00:44:27,240 university, and it's a field that 833 00:44:27,240 --> 00:44:29,240 everybody's kind of thinking about now, 834 00:44:29,240 --> 00:44:32,600 because you get Elon Musk starting up initiatives to find, like, 835 00:44:32,600 --> 00:44:34,720 a neural lace that would enhance human abilities 836 00:44:34,720 --> 00:44:36,400 to kind of compete with AI and computing. 837 00:44:39,000 --> 00:44:41,800 If you're really serious about becoming a cyborg, 838 00:44:41,800 --> 00:44:43,800 tapping into the brain is the way you have to go. 839 00:44:45,600 --> 00:44:48,000 There's been a lot of research into this. 840 00:44:48,000 --> 00:44:51,760 Some brains have already been linked to a variety of devices in a bid to 841 00:44:51,760 --> 00:44:53,160 help people with disabilities. 842 00:44:54,800 --> 00:44:56,360 But I'm going to meet someone 843 00:44:56,360 --> 00:44:58,480 who's hacked their brain in a different way. 844 00:45:00,920 --> 00:45:03,760 At age 11, Neil Harbisson was diagnosed with 845 00:45:03,760 --> 00:45:06,280 a condition called achromatopsia. 846 00:45:06,280 --> 00:45:08,440 A form of total colour blindness, 847 00:45:08,440 --> 00:45:10,640 meaning Neil has only ever seen in grayscale. 848 00:45:13,640 --> 00:45:16,680 But in 2003, as part of an art project, 849 00:45:16,680 --> 00:45:18,440 Neil found a new way to perceive colour. 850 00:45:19,840 --> 00:45:22,080 By integrating technology into his skull. 851 00:45:24,080 --> 00:45:25,960 He can now hear colour. 852 00:45:27,480 --> 00:45:30,560 Could you explain how your antenna works front to back? 853 00:45:30,560 --> 00:45:34,240 What's it... Well, I thought that I should have a new body part, 854 00:45:34,240 --> 00:45:37,480 a new sensory organ, specifically for colour perception. 855 00:45:37,480 --> 00:45:40,720 So the light frequency goes inside the antenna, 856 00:45:40,720 --> 00:45:44,200 and then it touches a chip inside my bone that vibrates, 857 00:45:44,200 --> 00:45:47,840 so these vibrations in my head create inner sounds, 858 00:45:47,840 --> 00:45:50,200 so I can hear different notes for different colours. 859 00:45:50,200 --> 00:45:52,720 We created an app that tries to mimic 860 00:45:52,720 --> 00:45:56,680 the sounds that I hear for each colour. So you'll notice... 861 00:45:56,680 --> 00:46:01,320 HUMMING AND WHIRRING 862 00:46:01,320 --> 00:46:03,240 This is the sound of yellow. 863 00:46:03,240 --> 00:46:04,400 Cool. 864 00:46:06,280 --> 00:46:09,560 HIGH-PITCH PULSE 865 00:46:09,560 --> 00:46:11,680 So it's kind of like... Pink is a higher frequency. 866 00:46:11,680 --> 00:46:12,960 ..a frequency shift. 867 00:46:12,960 --> 00:46:16,840 FAST-PACED BEEPS 868 00:46:16,840 --> 00:46:18,280 This is the sound of my jacket. 869 00:46:18,280 --> 00:46:20,200 So I'm wearing electronic music! 870 00:46:21,760 --> 00:46:24,560 So, with your antenna, is it kind of like when you have a new watch and 871 00:46:24,560 --> 00:46:26,160 you have to become used to 872 00:46:26,160 --> 00:46:28,720 the weight and feel of it when you're moving around? 873 00:46:28,720 --> 00:46:33,920 I'm not using or wearing technology - I am technology. 874 00:46:33,920 --> 00:46:35,360 So that's the difference. 875 00:46:35,360 --> 00:46:38,360 I guess it's... I can't compare it with anything else, 876 00:46:38,360 --> 00:46:41,440 because it's part of my skeleton. 877 00:46:44,040 --> 00:46:47,000 Meeting Neil has given me an insight into what it might be like to have 878 00:46:47,000 --> 00:46:48,240 genuine cyborg abilities. 879 00:46:50,600 --> 00:46:54,880 What has it been like, you being in the public with your extra sense? 880 00:46:54,880 --> 00:46:59,120 Some children ask me if it was some kind of extendable selfie stick! 881 00:46:59,120 --> 00:47:01,680 And since last year, people just shout at me, Pokemon, 882 00:47:01,680 --> 00:47:03,200 and they try to catch me! 883 00:47:03,200 --> 00:47:05,000 So it changes, what people think it is. 884 00:47:06,200 --> 00:47:11,240 Cyborgs are already amongst us, but it's not for everybody just yet. 885 00:47:11,240 --> 00:47:15,200 So, for now, maybe it's kind of up to people like Neil and I, 886 00:47:15,200 --> 00:47:18,400 who want to augment our bodies, to push the envelope. 887 00:47:25,200 --> 00:47:26,720 Next up, nature. 888 00:47:26,720 --> 00:47:28,400 Now, we all love nature, 889 00:47:28,400 --> 00:47:31,360 and we know this because of the vast audiences that 890 00:47:31,360 --> 00:47:34,000 BBC's Natural History Department gets, and also the fact that 891 00:47:34,000 --> 00:47:36,960 David Attenborough is now Sir David Attenborough. 892 00:47:36,960 --> 00:47:41,800 But, as much as we all claim to love the natural world, 893 00:47:41,800 --> 00:47:44,840 apparently it is vanishing before our eyes. 894 00:47:44,840 --> 00:47:47,080 If you have a little look at this graph here, 895 00:47:47,080 --> 00:47:50,680 this is what has been happening to vertebrate populations from 1970 all 896 00:47:50,680 --> 00:47:52,760 the way up to now. Now, this group, 897 00:47:52,760 --> 00:47:56,840 they track the populations of almost 4,000 different species, 898 00:47:56,840 --> 00:47:59,760 and come up with a score to say how well they are doing. 899 00:47:59,760 --> 00:48:02,280 It turns out, not great. 900 00:48:02,280 --> 00:48:03,920 Both the number of species 901 00:48:03,920 --> 00:48:07,080 and the number of animals is in steady decline. 902 00:48:07,080 --> 00:48:10,960 On land, there has been a 38% decrease in animal numbers. 903 00:48:10,960 --> 00:48:13,640 In the sea, there has been a 36% decrease. 904 00:48:13,640 --> 00:48:16,320 And worst of all, in freshwater, 905 00:48:16,320 --> 00:48:21,240 there has been a huge 81% drop in population numbers. 906 00:48:21,240 --> 00:48:24,440 And we can see from this graph that if this trend continues, 907 00:48:24,440 --> 00:48:32,360 by 2020 we can expect to see a 67% drop based on what we had in 1970. 908 00:48:32,360 --> 00:48:35,200 So, to find out if this really is as bad as it sounds, 909 00:48:35,200 --> 00:48:37,880 evolutionary geneticist, writer and Renaissance man, 910 00:48:37,880 --> 00:48:39,880 my very good friend Dr Adam Rutherford 911 00:48:39,880 --> 00:48:41,680 is here to help us with the science. 912 00:48:41,680 --> 00:48:43,360 Adam, we've been here before. 913 00:48:43,360 --> 00:48:45,240 There have been extinctions before, right? 914 00:48:45,240 --> 00:48:47,800 Yes, there have. In fact, over the last billion years or so, 915 00:48:47,800 --> 00:48:50,240 the evolutionary trajectory of life on Earth, 916 00:48:50,240 --> 00:48:52,920 extinction is completely the normal state of affairs. 917 00:48:52,920 --> 00:48:54,320 I've got my own graph here. 918 00:48:54,320 --> 00:48:57,440 If you look at the last 542 million years, 919 00:48:57,440 --> 00:49:01,000 what this shows is extinction rates over that time period. 920 00:49:01,000 --> 00:49:02,840 And there are five big peaks. 921 00:49:02,840 --> 00:49:05,640 So there have been five great extinction events. 922 00:49:05,640 --> 00:49:08,680 Everyone knows about the one that happened 66 million years ago, 923 00:49:08,680 --> 00:49:10,080 it is called the K-Pg boundary, 924 00:49:10,080 --> 00:49:12,200 and that was when a meteor dropped out of the sky. 925 00:49:12,200 --> 00:49:14,080 The dinosaurs. It did for the dinosaurs. 926 00:49:14,080 --> 00:49:18,240 But also, 75% of all species on land and in the sea. 927 00:49:18,240 --> 00:49:19,920 And that's not even the big one. 928 00:49:19,920 --> 00:49:22,480 The big one is called the Great Dying, or the P-T boundary, 929 00:49:22,480 --> 00:49:25,960 and that happens 252 million years ago. 930 00:49:25,960 --> 00:49:28,760 95% of all species go extinct. 931 00:49:28,760 --> 00:49:30,640 So, what's so different about this one? 932 00:49:30,640 --> 00:49:32,520 The timescale is what's different. 933 00:49:32,520 --> 00:49:34,800 So, the full extent of the Great Dying 934 00:49:34,800 --> 00:49:37,880 really pans out over a million or two million years. 935 00:49:37,880 --> 00:49:40,000 The dinosaur one, 66 million years. 936 00:49:40,000 --> 00:49:44,120 We find dinosaurs 10,000 years after that happened. 937 00:49:44,120 --> 00:49:47,320 What you just said was, 67% of species 938 00:49:47,320 --> 00:49:49,560 will be lost since the 1970s. 939 00:49:52,000 --> 00:49:55,040 If this is the start of another mass extinction, 940 00:49:55,040 --> 00:49:58,680 its speed means that ecosystems and food chains will break down 941 00:49:58,680 --> 00:49:59,880 catastrophically. 942 00:50:01,800 --> 00:50:04,840 For some species, it is already too late. 943 00:50:04,840 --> 00:50:07,240 Human activity means that coming generations 944 00:50:07,240 --> 00:50:10,920 will never see a live white rhino or a Sumatran orangutan. 945 00:50:12,320 --> 00:50:15,120 But what's worse is the potential impact 946 00:50:15,120 --> 00:50:18,040 of losing less charismatic wildlife. 947 00:50:18,040 --> 00:50:21,640 In the sea, rising temperatures have already disrupted 948 00:50:21,640 --> 00:50:23,880 the food chain by killing coral. 949 00:50:23,880 --> 00:50:26,320 And if the predicted further rises occur, 950 00:50:26,320 --> 00:50:29,720 Asian seagrass could go extinct within 50 years, 951 00:50:29,720 --> 00:50:31,920 causing the collapse of the entire 952 00:50:31,920 --> 00:50:34,600 marine ecosystem in that part of the world. 953 00:50:36,080 --> 00:50:38,840 In the future, our children will 954 00:50:38,840 --> 00:50:42,040 almost certainly see less diversity of animals. 955 00:50:42,040 --> 00:50:44,560 But unchecked, these changes also mean that 956 00:50:44,560 --> 00:50:48,160 they themselves could be facing much more serious problems. 957 00:50:50,160 --> 00:50:53,000 And as humans, are we immune to this? 958 00:50:53,000 --> 00:50:54,560 No, absolutely not. 959 00:50:54,560 --> 00:50:57,560 We are special, but we are living on this planet. 960 00:50:57,560 --> 00:51:00,800 And what we are doing is removing the ability for us to live on this 961 00:51:00,800 --> 00:51:04,640 planet whilst letting many, many species go extinct. 962 00:51:04,640 --> 00:51:08,000 And I think the key thing is to recognise that we have created this 963 00:51:08,000 --> 00:51:11,000 situation, and we also have the power to stop it. 964 00:51:15,880 --> 00:51:18,280 Flying cars - there you go, I've said it. 965 00:51:18,280 --> 00:51:19,600 Well, it wouldn't be a programme 966 00:51:19,600 --> 00:51:21,760 about the future if someone didn't mention them. 967 00:51:21,760 --> 00:51:24,800 But, let's be honest, they certainly would be pretty handy, 968 00:51:24,800 --> 00:51:27,400 because our love of earthbound cars 969 00:51:27,400 --> 00:51:30,040 has seen a steady increase in how many cars 970 00:51:30,040 --> 00:51:31,720 there now are on the roads. 971 00:51:31,720 --> 00:51:37,240 This graph here shows you how far we travel in motor vehicles since 1949. 972 00:51:37,240 --> 00:51:39,880 And you can see here that car journeys, in red, 973 00:51:39,880 --> 00:51:42,000 really have become all-conquering. 974 00:51:42,000 --> 00:51:45,360 We're travelling a lot further, and we're doing it in cars. 975 00:51:45,360 --> 00:51:48,680 And that makes the experience of actually driving them 976 00:51:48,680 --> 00:51:50,080 a lot less appealing. 977 00:51:50,080 --> 00:51:54,440 In fact, the average speed of traffic in central London 978 00:51:54,440 --> 00:51:55,680 is now 7.3mph. 979 00:51:55,680 --> 00:51:58,280 Basically, you'd be better off on a horse. 980 00:51:58,280 --> 00:52:00,560 Now, we need to travel faster. 981 00:52:00,560 --> 00:52:02,560 And dynamicist Teena Gade, who works 982 00:52:02,560 --> 00:52:05,000 for Formula 1 team Sahara Force India, 983 00:52:05,000 --> 00:52:07,040 she knows all about travelling quickly. 984 00:52:07,040 --> 00:52:08,880 And she has been looking at, well, 985 00:52:08,880 --> 00:52:11,720 there's no other way to put this, really - flying cars. 986 00:52:21,080 --> 00:52:24,160 The worst thing about city driving is the traffic. 987 00:52:24,160 --> 00:52:28,000 I think we're probably doing five, maybe 10mph, if that. 988 00:52:28,000 --> 00:52:29,840 Right now, if my car could fly, 989 00:52:29,840 --> 00:52:32,440 I'd take off and I'd jump this long queue of people in front of me 990 00:52:32,440 --> 00:52:34,080 and be at my destination in no time. 991 00:52:37,320 --> 00:52:40,840 History is littered with attempts at the fabled flying car. 992 00:52:43,920 --> 00:52:46,760 Most of them hard to take seriously. 993 00:52:46,760 --> 00:52:48,960 And moving it around is a job for a secretary 994 00:52:48,960 --> 00:52:52,480 rather than a highly skilled and highly expensive helicopter pilot. 995 00:52:59,440 --> 00:53:02,480 But prototypes for personal flying machines 996 00:53:02,480 --> 00:53:04,920 have started to appear once again. 997 00:53:06,240 --> 00:53:08,400 This time with serious financial backing. 998 00:53:16,760 --> 00:53:19,600 I would absolutely love a personal flying car. 999 00:53:19,600 --> 00:53:21,880 I think that would be absolutely fantastic. 1000 00:53:21,880 --> 00:53:23,240 Imagine going to work every day 1001 00:53:23,240 --> 00:53:27,320 and not having to sit in the traffic queues. 1002 00:53:27,320 --> 00:53:28,960 It might not be a good idea, though, 1003 00:53:28,960 --> 00:53:31,440 for everyone to have their own personal flying machine. 1004 00:53:31,440 --> 00:53:34,440 As I'm showing here, occasionally I can keep control of it, 1005 00:53:34,440 --> 00:53:37,000 but some of the time I'm not doing a very good job. 1006 00:53:38,000 --> 00:53:40,600 Right, that's it down. I'm just going to go and get it. 1007 00:53:43,360 --> 00:53:46,400 So, if we're all going to buzz around our cities in personal flying 1008 00:53:46,400 --> 00:53:49,240 machines, how do we keep from crashing into each other? 1009 00:53:53,640 --> 00:53:57,560 To find out, I've come to Zurich to meet robotics expert 1010 00:53:57,560 --> 00:54:00,000 Professor Raffaello D'Andrea. 1011 00:54:01,800 --> 00:54:04,440 He developed the Kiva robot system. 1012 00:54:06,160 --> 00:54:08,600 A network of thousands of bots 1013 00:54:08,600 --> 00:54:11,560 that work together to fulfil online orders. 1014 00:54:12,800 --> 00:54:14,840 This is basically a very large warehouse 1015 00:54:14,840 --> 00:54:17,280 where orders come in and they need to be fulfilled. 1016 00:54:17,280 --> 00:54:22,360 So, the system figures out which robots need to go to which pods, 1017 00:54:22,360 --> 00:54:25,400 pick it up and bring it to the perimeter of the warehouse, 1018 00:54:25,400 --> 00:54:27,840 where then people take things off of the pods 1019 00:54:27,840 --> 00:54:31,080 and put them into the orders which eventually go out. 1020 00:54:31,080 --> 00:54:32,800 There's a lot of robots in action here. 1021 00:54:32,800 --> 00:54:34,120 How come they don't collide? 1022 00:54:34,120 --> 00:54:37,440 The robots have to generate trajectories and plans. 1023 00:54:37,440 --> 00:54:40,680 And those plans are then shared to a coordinator, 1024 00:54:40,680 --> 00:54:43,920 which then figures out how they should go and execute their plan 1025 00:54:43,920 --> 00:54:45,480 so that they don't hit each other. 1026 00:54:47,320 --> 00:54:50,600 And with over 80,000 in operation, 1027 00:54:50,600 --> 00:54:52,760 the Kiva bots are the largest network of 1028 00:54:52,760 --> 00:54:54,760 autonomous vehicles in the world. 1029 00:54:54,760 --> 00:54:56,840 And so far, there haven't been any accidents. 1030 00:55:01,960 --> 00:55:05,000 If only something similar could be done with flying machines. 1031 00:55:08,240 --> 00:55:10,680 Oh, wow! That's incredible. 1032 00:55:10,680 --> 00:55:12,360 So what do we have here? 1033 00:55:12,360 --> 00:55:16,400 A swarm of 32 of these flying machines. 1034 00:55:16,400 --> 00:55:19,600 And they're going to do a little choreographed performance. 1035 00:55:19,600 --> 00:55:21,560 'This is more like it.' 1036 00:55:21,560 --> 00:55:24,200 What they are doing right now is a choreography 1037 00:55:24,200 --> 00:55:25,600 that is pre-programmed, 1038 00:55:25,600 --> 00:55:28,560 and ensures that they do not collide with each other. 1039 00:55:32,200 --> 00:55:33,480 So, what's in one of these? 1040 00:55:33,480 --> 00:55:36,160 How are these made? They have four motors for propellers. 1041 00:55:36,160 --> 00:55:38,280 They have a cage to keep the propellers 1042 00:55:38,280 --> 00:55:39,680 away from other vehicles 1043 00:55:39,680 --> 00:55:42,600 and from people, and they have some custom electronics 1044 00:55:42,600 --> 00:55:44,760 that creates all the magic and intelligence. 1045 00:55:46,440 --> 00:55:48,480 And now, we just move out of the way. 1046 00:55:48,480 --> 00:55:51,000 Oh, they're coming in to land. If I put my hand out... 1047 00:55:51,000 --> 00:55:52,600 I can catch one! Exactly. 1048 00:55:56,400 --> 00:56:00,600 So, if you could have this level of automation and planning in personal 1049 00:56:00,600 --> 00:56:04,520 flying machines, then perhaps they could become a reality. 1050 00:56:04,520 --> 00:56:07,680 How would that lead us to, for example, a transport system? 1051 00:56:07,680 --> 00:56:11,000 Well, so, what they would share with a transport system is the use of a 1052 00:56:11,000 --> 00:56:12,200 global positioning system. 1053 00:56:12,200 --> 00:56:14,560 We've developed an indoor global positioning system, 1054 00:56:14,560 --> 00:56:17,480 just like the one that exists outdoors. 1055 00:56:17,480 --> 00:56:19,840 And this is important, because then the vehicles 1056 00:56:19,840 --> 00:56:21,400 know where they are in space. 1057 00:56:21,400 --> 00:56:23,680 How it would differ is that the choreographies, 1058 00:56:23,680 --> 00:56:25,440 the trajectories wouldn't be preplanned. 1059 00:56:25,440 --> 00:56:28,320 The system is going to have to be much more reactive 1060 00:56:28,320 --> 00:56:30,800 when people want to fly from point A to point B. 1061 00:56:35,200 --> 00:56:38,560 Is this now setting us up for having our own personal flying machines? 1062 00:56:38,560 --> 00:56:40,480 I think we've certainly come a long way. 1063 00:56:40,480 --> 00:56:44,080 We can make flying machines that can do vertical take-off and landing, 1064 00:56:44,080 --> 00:56:46,600 fully electric, which has its own benefits. 1065 00:56:46,600 --> 00:56:48,680 I think that we will, in the near future. 1066 00:56:50,360 --> 00:56:54,080 Could this be the aerial highway of the future in miniature? 1067 00:56:57,000 --> 00:57:00,040 These drones were designed to perform at live events. 1068 00:57:00,040 --> 00:57:01,600 But they offer a glimpse into 1069 00:57:01,600 --> 00:57:03,640 what the cities of the future might look like. 1070 00:57:03,640 --> 00:57:05,640 Many of the big questions around safety 1071 00:57:05,640 --> 00:57:07,520 can be answered with current tech. 1072 00:57:07,520 --> 00:57:09,680 So what you need to know about the future of transport is 1073 00:57:09,680 --> 00:57:11,520 we may finally get our flying cars. 1074 00:57:20,080 --> 00:57:24,000 Making predictions about the future is a pretty risky business. 1075 00:57:24,000 --> 00:57:26,640 And many people have come unstuck in the past. 1076 00:57:26,640 --> 00:57:28,840 But I'm pretty confident that most 1077 00:57:28,840 --> 00:57:32,680 of what we've covered in this programme will actually happen. 1078 00:57:32,680 --> 00:57:35,120 But I'm also confident of something else. 1079 00:57:35,120 --> 00:57:38,560 The tenth thing that you need to know about the future 1080 00:57:38,560 --> 00:57:41,480 is that there will be many other developments 1081 00:57:41,480 --> 00:57:43,880 that none of us have even thought of. 1082 00:57:43,880 --> 00:57:46,480 Because, although Arthur C Clarke may have predicted 1083 00:57:46,480 --> 00:57:48,200 universal mobile communication, 1084 00:57:48,200 --> 00:57:53,200 even he would have been surprised that we now carry around 1085 00:57:53,200 --> 00:57:57,920 with us a little hand-held device that contains a typewriter, 1086 00:57:57,920 --> 00:58:03,400 a camera, a postbox, a television, a calculator, a calendar, a light, 1087 00:58:03,400 --> 00:58:08,960 a record player, a tape recorder, and not forgetting a telephone. 1088 00:58:08,960 --> 00:58:10,880 And that, for me at least, 1089 00:58:10,880 --> 00:58:15,080 is a lot cleverer and certainly a lot cleaner than a genetically 1090 00:58:15,080 --> 00:58:16,840 engineered monkey servant. 1091 00:58:18,120 --> 00:58:20,880 To find out more about the innovations that are changing 1092 00:58:20,880 --> 00:58:22,880 our health, our leisure and our work, 1093 00:58:22,880 --> 00:58:24,880 and will continue to shape our future, 1094 00:58:24,880 --> 00:58:29,520 go to bbc.co.uk/horizon and follow the Open University link.