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out the attack in Nice, one of its soldiers. Those are the latest | :00:00. | :00:00. | |
headlines. Yotel in midtown Manhattan has a | :00:00. | :00:00. | |
robot porter. I don't have to carry | :00:00. | :00:09. | |
my bag and their's no What about the person who used | :00:10. | :00:18. | |
to carry out this task? Are we inventing ourselves | :00:19. | :00:22. | |
out of jobs? In this week's Talking Business, | :00:23. | :00:24. | |
we examine the rapid rise of automation and what it means | :00:25. | :00:26. | |
for the country's We are talking revolution here this | :00:27. | :00:28. | |
week, one that is driven by the rise In the 1960s, this is how | :00:29. | :00:58. | |
robots were dreamt about. As you can see from this | :00:59. | :01:02. | |
historic BBC footage. Introducing Mabel, | :01:03. | :01:11. | |
the robot housemaid. An outdated vision of technology | :01:12. | :01:20. | |
and what it could do. By the 80s, automation had become | :01:21. | :01:28. | |
standard in many factories and Now, machines are able to teach | :01:29. | :01:31. | |
themselves and learn What about the workers whose jobs | :01:32. | :01:39. | |
they're going to be doing? Today's transformation | :01:40. | :01:49. | |
has been described by the World Economic Forum | :01:50. | :01:52. | |
as the fourth industrial revolution. According to their research, | :01:53. | :01:54. | |
over 5 million jobs globally will be lost by 2020, due to genetics, | :01:55. | :01:57. | |
artificial intelligence, robotics and other | :01:58. | :01:59. | |
technological change. Two thirds of the projected | :02:00. | :02:00. | |
losses are expected to be As smart machines take | :02:01. | :02:03. | |
over for routine tasks. Another study claims that nearly | :02:04. | :02:11. | |
half of US jobs could be replaced by machines over | :02:12. | :02:14. | |
the next two decades. Today we are asking, | :02:15. | :02:20. | |
"Are machines about to take over?" What is the future of manufacturing | :02:21. | :02:22. | |
and manufacturing employment? More broadly, can machines | :02:23. | :02:25. | |
and humans coexist? To discuss this, I am joined | :02:26. | :02:31. | |
by Eric Verhoogen, associate professor at Columbia University | :02:32. | :02:34. | |
and international growth centre. Julia Kirby, you are co-author | :02:35. | :02:38. | |
of a book, beyond automation. Last but not least, Matthew Putman, | :02:39. | :02:42. | |
chief executive of nano tropics, maker of industrial microscopes used | :02:43. | :02:50. | |
by the world's leading What is the future of | :02:51. | :02:52. | |
manufacturing, certainly as it We are at an incredible time, | :02:53. | :03:01. | |
I think, in manufacturing. If you look back at all the previous | :03:02. | :03:07. | |
industrial revolutions, we have seen a fear of human | :03:08. | :03:10. | |
jobs being replaced. The way this next industrial | :03:11. | :03:16. | |
revolution looks, and the way factories are looking, | :03:17. | :03:18. | |
mundane tasks, not just by factory workers, but even by engineers | :03:19. | :03:24. | |
who have been doing a consider to be That is looked at as a bad thing | :03:25. | :03:32. | |
in some ways but it ends up making better products, | :03:33. | :03:43. | |
it makes the world or abundant place and frees humans | :03:44. | :03:45. | |
to be more creative. When I think about manufacturing | :03:46. | :03:47. | |
traditionally, Julia, Obviously, we have seen | :03:48. | :03:49. | |
changes in the 80s. We saw from the video just there, | :03:50. | :03:52. | |
robotic arms on a car What do you think | :03:53. | :03:55. | |
the factory line looks People think of robotics | :03:56. | :03:58. | |
as, what is new here? We've had robotics in automatic | :03:59. | :04:02. | |
manufacturing, lots of manufacturing But it is the intelligence, | :04:03. | :04:05. | |
the artificial intelligence that is being added to the robotics | :04:06. | :04:14. | |
now that is really changing One thing is the layout | :04:15. | :04:17. | |
of the assembly line. It used to be that the robot | :04:18. | :04:20. | |
was a huge inflexible thing. It was actually dangerous | :04:21. | :04:23. | |
to work around. So you had these massive | :04:24. | :04:27. | |
machines caged off. What you have got now with this | :04:28. | :04:32. | |
machine intelligence built into them is this incredible flexibility and, | :04:33. | :04:35. | |
it is due to advances in machine So, they are now capable of doing | :04:36. | :04:44. | |
different things and they can The line itself looks | :04:45. | :04:52. | |
very different now. The type of work that can be | :04:53. | :05:00. | |
given over to the robots I see you nodding your | :05:01. | :05:03. | |
head there, Eric. It is very impressive the pace at | :05:04. | :05:18. | |
which robotics is advancing and the level of skill that can now be | :05:19. | :05:23. | |
replaced by machine. With a note of scepticism when many tasks cannot be | :05:24. | :05:28. | |
replaced by machines. Anything that requires creativity. The machine has | :05:29. | :05:34. | |
to be given parameters within which to operate. Setting parameters is | :05:35. | :05:37. | |
something we still need humans to do. Much as we will get more and | :05:38. | :05:42. | |
more machines and less mundane work can be done by people, we will still | :05:43. | :05:45. | |
need people to programme the machines and also to approach the | :05:46. | :05:49. | |
problems and figure out what is a smart way to get machines operating. | :05:50. | :05:56. | |
We lived, for the last 50 years, in a time when we were programming | :05:57. | :06:02. | |
everything. We were setting parameters across a factory. The | :06:03. | :06:07. | |
difference now, we are looking how to optimise. We are now no longer | :06:08. | :06:12. | |
having people tuning machines. We're having machines learn themselves in | :06:13. | :06:16. | |
order to optimise make a better product than even a human can do on | :06:17. | :06:22. | |
their own. Humans will study the ones raising new questions. The | :06:23. | :06:25. | |
machines will come up with better and better answers. You're probably | :06:26. | :06:29. | |
toast if you try to come up with a better answer than the machine for a | :06:30. | :06:34. | |
well-defined problem or a codifying process. You are not beat it. The | :06:35. | :06:40. | |
question of, what is next? Customers are still human. It takes one to | :06:41. | :06:47. | |
know one, to think about, what is the next thing our business should | :06:48. | :06:55. | |
be creating to solve human problems? There is an anxiety that people | :06:56. | :07:00. | |
feel, will my job be replaced by a machine? The question I would ask | :07:01. | :07:06. | |
is, how important is innovation of automation, technological change to | :07:07. | :07:10. | |
America's manufacturing competitiveness if you look | :07:11. | :07:16. | |
globally? The productivity numbers notwithstanding, I think it is very | :07:17. | :07:20. | |
clear that these machines are incredible productivity boosters. | :07:21. | :07:24. | |
There are two ways to get productivity. You can either put out | :07:25. | :07:29. | |
the same output, exactly what you are doing yesterday but have fewer | :07:30. | :07:33. | |
people with fewer labour hours, or you can use the same labour hours | :07:34. | :07:39. | |
and up output. Probably by taking it into more creative territory, more | :07:40. | :07:44. | |
customised territory than the manifest that -- the manufacturing | :07:45. | :07:50. | |
space. Productivity is why companies introduce these machines. One would | :07:51. | :07:56. | |
hope they would see their growth oriented option is to do more by | :07:57. | :08:01. | |
bringing machines into augment human strength instead of automate them | :08:02. | :08:09. | |
out of a job. The great thing we have learned over time as people | :08:10. | :08:12. | |
like to make new things. I do not think we will enter a situation | :08:13. | :08:15. | |
where machines will do so much we will have no new ideas. I think this | :08:16. | :08:20. | |
allows us to have new ideas. It allows people to be people. We are | :08:21. | :08:25. | |
seeing that happen. A lot of innovation actually comes from human | :08:26. | :08:33. | |
desire to have that innovation. You need the next best cell phone. We | :08:34. | :08:38. | |
need a new technology that adds to abundance, less expensive. That | :08:39. | :08:45. | |
human ambition will continue to drive things, even as machines come | :08:46. | :08:53. | |
faster. -- smarter. Is the pace of innovation slowing customer when you | :08:54. | :08:57. | |
think back to vast new changes like the advent of electricity and the | :08:58. | :09:01. | |
arrival of the internet? Do you see the pace of innovation slowing? | :09:02. | :09:08. | |
There has been some stagnation or focus on things you do not consider | :09:09. | :09:13. | |
hard Tech in the sense of now we are electrifying Manhattan within three | :09:14. | :09:18. | |
years of the light bulb. I think that is more about human focus and | :09:19. | :09:25. | |
the values we have as a society. It is not actually a stagnation of | :09:26. | :09:28. | |
technology. We have the ability to do these new things that we have the | :09:29. | :09:34. | |
ability to go to Mars. We have the ability to have self driving cars | :09:35. | :09:39. | |
that is not a stagnation of research or ability, it is of human | :09:40. | :09:43. | |
imagination that hopefully something like robotics and artificial | :09:44. | :09:49. | |
intelligence will free us to do. Thank you all for now. Coming up | :09:50. | :09:53. | |
later in the programme, will the factor of the future have any new | :09:54. | :09:56. | |
employees and what does it mean for us? Why life of leisure or comedy? | :09:57. | :10:01. | |
First, our comedy consultant offers his distinct take on the next | :10:02. | :10:07. | |
generation of manufacturing on this week's talking point. I love science | :10:08. | :10:15. | |
fiction. Anything involving a bit of prognostication about an imagined | :10:16. | :10:18. | |
world in the future. It turns out now that the future is becoming the | :10:19. | :10:21. | |
present a lot quicker than it used to in the past, if you know what I | :10:22. | :10:28. | |
mean. For some insight into how tomorrow will look, I have come to | :10:29. | :10:37. | |
the labs in Dublin. It is an after-school coding club which has | :10:38. | :10:43. | |
grown into a global movement. They hold a coolest projects competition. | :10:44. | :10:49. | |
This year, it was won by an 11-year-old girl who designed a | :10:50. | :10:55. | |
robot to solve a Rubik 's cube. I do not need a robot to do that, I have | :10:56. | :10:58. | |
the rest my life in order to work one out. We want to create a | :10:59. | :11:03. | |
generation of children who are active participants in this world. | :11:04. | :11:08. | |
We have had robotics printers which print Braille, a huge amount of | :11:09. | :11:13. | |
projects which are really inspiring. Auto journalist is something that | :11:14. | :11:20. | |
helps journalists and interviewees. Interviewees can use a camera or | :11:21. | :11:23. | |
microphone to record themselves answering a journalist. So you need | :11:24. | :11:28. | |
to get the video saving now and then and then I could put it out for | :11:29. | :11:33. | |
testing. Children are not scared, they are fearless. They approach Rob | :11:34. | :11:37. | |
is without the trepidation and adult might have. You could pay if you | :11:38. | :11:45. | |
scan your face with your finger or something instead of using credit | :11:46. | :11:48. | |
cards. If I could make that, it would be pretty cool. There is | :11:49. | :11:55. | |
nothing like talking to a 13-year-old app developer to show | :11:56. | :11:58. | |
you how much you wasted your teenage years. What is the future of | :11:59. | :12:02. | |
manufacturing in this robotic world? I will talk to a man who is | :12:03. | :12:05. | |
specialising in research on how to make the factory more like a human | :12:06. | :12:10. | |
brain. Like in the human brain, if you become sick or sad, you might be | :12:11. | :12:16. | |
a little less productive. These things can happen as we move towards | :12:17. | :12:19. | |
these wireless factories where things are interacting with one | :12:20. | :12:25. | |
another. That comes to the point of needing to transform our job is to | :12:26. | :12:28. | |
look after the factories, rather than simply use the factories. We | :12:29. | :12:33. | |
might have factory doctors that if you will, that fix up factories | :12:34. | :12:37. | |
after they become sick, by retuning their coordination and interaction | :12:38. | :12:43. | |
amongst individual parts. There is a massive transformation of jobs from | :12:44. | :12:48. | |
more labour oriented tasks to more service oriented tasks, or jobs like | :12:49. | :12:52. | |
mine or yours, where people can do things now with computers they could | :12:53. | :12:58. | |
not do before. It is great to be playing with a Rubik 's cube again | :12:59. | :13:01. | |
after all this time put up the discomfort into no you never learn | :13:02. | :13:06. | |
how to solve on because a robot will do the job for you. We are entering | :13:07. | :13:12. | |
a brave new world of automatic manufacturing. It is the next | :13:13. | :13:15. | |
generation that is already designing our futures. You can see a lot more | :13:16. | :13:27. | |
of those short films on the website. Today we have been talking about how | :13:28. | :13:32. | |
this is not your grandfather or even your father's factory line but I | :13:33. | :13:37. | |
wanted to talk a bit more about this fear that people have that somehow | :13:38. | :13:42. | |
in the future, jobs will be replaced by technology. How realistic a fear | :13:43. | :13:46. | |
is that? Eric, if I could start with you. Some jobs are rendered obsolete | :13:47. | :13:53. | |
by technology. Once the car was introduced, there are fewer travel | :13:54. | :13:56. | |
agents found when I was a kid. Economies typically are able to | :13:57. | :14:01. | |
grow. Those people who are freed up by technology and economies grow. | :14:02. | :14:05. | |
Overall the number of jobs continues to grow. We should not have a sense | :14:06. | :14:10. | |
that their eyes fixed number of jobs. If the machine takes a job, | :14:11. | :14:15. | |
there is one less for a human foot it has not been the case there have | :14:16. | :14:19. | |
been big declines in employment because of automation or | :14:20. | :14:24. | |
technological change. Artificial intelligence, you talked about that | :14:25. | :14:28. | |
short while ago. At what point do they become smarter than us? Smarter | :14:29. | :14:35. | |
is a strange thing to define. There is general artificial intelligence. | :14:36. | :14:41. | |
Something specific for optimising a factory. We want the machine to be | :14:42. | :14:45. | |
smarter than us at doing things humans are not particularly good at. | :14:46. | :14:49. | |
Visualising and looking over huge amounts of data and being able to | :14:50. | :14:53. | |
determine. That will happen very quickly and is already happening. | :14:54. | :14:58. | |
Humans should be allowed to be creative and smart, what humans are | :14:59. | :15:03. | |
best at. As long as we are continuing to invent, we need not | :15:04. | :15:14. | |
worry about AI being smarter than us in that regard. We should embrace | :15:15. | :15:19. | |
that intelligence. About the figures from the World Economic Forum, they | :15:20. | :15:23. | |
talk about a future in which many white-collar jobs can be done by | :15:24. | :15:29. | |
smart machines. It goes back to this idea that people will always find a | :15:30. | :15:34. | |
way to make something new. As long as they know that that possibility | :15:35. | :15:38. | |
exists to make something new and are supported to do that. You know, if | :15:39. | :15:45. | |
you go back to the very first industrial revolution quickly could | :15:46. | :15:48. | |
have had numbers about farm jobs that have been replaced. That number | :15:49. | :15:53. | |
alone does not tell the. . The full story is told by what humans can | :15:54. | :16:00. | |
invent next. The numbers you hear like 47 cents of jobs are at risk by | :16:01. | :16:05. | |
artificial intelligence, they sort of miss the point that jobs really | :16:06. | :16:12. | |
do not get replaced by robots or artificial intelligence. Every job | :16:13. | :16:17. | |
is an amalgam of tasks. For an office worker, a knowledge worker, | :16:18. | :16:23. | |
perhaps 20% of your work in a day is something that follows very regular | :16:24. | :16:28. | |
rules. You probably consider it the most mundane part of your job, you | :16:29. | :16:32. | |
are probably really happy to off-load it. But the net effect of | :16:33. | :16:38. | |
that, of course, is that now, five of you can do, do the maths, eight | :16:39. | :16:48. | |
if you can do the work of ten. We see attrition and those jobs are not | :16:49. | :16:56. | |
replaced. But, what places can do is encourage people to figure out what | :16:57. | :17:01. | |
new tasks to make part of their job because they are providing a higher | :17:02. | :17:05. | |
quality service, or a more customised service and the more | :17:06. | :17:10. | |
empathetic interface to clients and whatever it is they can take on. You | :17:11. | :17:16. | |
need not see that job loss. That is why we have not already seen the | :17:17. | :17:25. | |
much feared job loss. We do have to realise there are winners and losers | :17:26. | :17:29. | |
from technological change. It is important... Not necessarily the | :17:30. | :17:34. | |
task of the firm but the task of society to provide insurance for | :17:35. | :17:38. | |
those who are losers. We want people to invest in skills which may become | :17:39. | :17:41. | |
obsolete but make it easier for them to move to something as if they lose | :17:42. | :17:45. | |
their jobs and be retrained. Otherwise all the cost of this | :17:46. | :17:50. | |
innovation or automation or technological change is falling on | :17:51. | :17:55. | |
workers themselves. You pick up on a good point. Former US Treasury | :17:56. | :17:58. | |
Secretary Larry Summers has raised concerns about this in the UK, Andy | :17:59. | :18:03. | |
how they achieve economist at the Bank of England has said, what | :18:04. | :18:07. | |
happens to those disproportionately affected by technology? Who should | :18:08. | :18:12. | |
come up with policies to try to address the problems or issues which | :18:13. | :18:20. | |
arise out of technological change? Clearly, there are social issues. | :18:21. | :18:24. | |
What people do not appreciate about the industrial revolution, we think | :18:25. | :18:29. | |
of it as this tech catalysed event. This new technology came in and | :18:30. | :18:35. | |
everything changed. But everything worked out well in the end for the | :18:36. | :18:39. | |
workers. We tend to forget that there were a lot of new laws that | :18:40. | :18:44. | |
were made. There were a lot of new Nets put into place. There was a lot | :18:45. | :18:49. | |
of social innovations that had to go along with the technological | :18:50. | :18:52. | |
innovation for this all to work out in the end. Matthew, when you are | :18:53. | :18:57. | |
developing products and thinking about what your clients might be | :18:58. | :19:01. | |
interested in, is this something you think about at all? Talk me through | :19:02. | :19:07. | |
the thought process. I think about that all the time. With user | :19:08. | :19:13. | |
interfaces, I think that we have become accustomed to thinking of | :19:14. | :19:18. | |
either engineering work or more manual tasks in a factory is being | :19:19. | :19:23. | |
miserable. In many cases they are. So, there are things going on and | :19:24. | :19:26. | |
everything from virtual reality to gesture control and gaming back can | :19:27. | :19:32. | |
actually be applied to the creative jobs that will be available in a | :19:33. | :19:38. | |
factory. I think about these all the time. Even when they are not yet | :19:39. | :19:44. | |
deployed quickly try to think about what the factory will look like and | :19:45. | :19:48. | |
that is a place for innovation and creativity. We have been focusing on | :19:49. | :19:54. | |
the fear side of it, what happens when our jobs are done by machines | :19:55. | :19:59. | |
and robots. Some form of technology. What should we be thinking about as | :20:00. | :20:07. | |
the jobs of the future? I think we should be thinking... When you talk | :20:08. | :20:11. | |
about leisure, people will always want to work, regardless. That work | :20:12. | :20:15. | |
need not be drudgery. The jobs of the future may be a lot more like | :20:16. | :20:24. | |
either gaming, training, teaching, and doing those things within a | :20:25. | :20:30. | |
factory or creative environment that we do not currently associate with | :20:31. | :20:35. | |
manufacturing. There will be a lot of jobs attributed to the rise of | :20:36. | :20:40. | |
machines that now the machines are coming into a workplace, there are | :20:41. | :20:45. | |
different ways you can step aside. That seems a little dismissive but | :20:46. | :20:53. | |
you can make room by taking on some tasks around them. Some of them will | :20:54. | :20:57. | |
have to do with figuring out where the machine should be working and | :20:58. | :21:00. | |
where the humans should be working and sorting out what the process | :21:01. | :21:07. | |
should be. Some of it will be simply checking the machines work. At what | :21:08. | :21:10. | |
point does it need to have its logic changed? We are training machines | :21:11. | :21:16. | |
now. We still need people to build and train machines and we will have | :21:17. | :21:20. | |
the next generation of machines to train. There are also a lot of jobs | :21:21. | :21:26. | |
that will be about, we are just here to tend the machines or by the | :21:27. | :21:30. | |
machines that there are still going to be these unique human strength | :21:31. | :21:34. | |
that can we now have the ability, and I think this is your point, to | :21:35. | :21:38. | |
double down on because we not having so much of our time and our | :21:39. | :21:44. | |
cognitive capacity drained by these computational tasks that are really | :21:45. | :21:48. | |
hard humans. We will get to work on the stuff we are actually good at. | :21:49. | :21:54. | |
As the machines get more sophisticated, you need a higher | :21:55. | :21:58. | |
level of education to look after them, manage them and programme | :21:59. | :22:04. | |
them. I do not think that is true. We move away from a world where we | :22:05. | :22:07. | |
are talking about programming and move towards much more of a world | :22:08. | :22:12. | |
that is like artistic innovation. I very much agree with that. You are | :22:13. | :22:17. | |
right that we would be making a mistake in saying we need to channel | :22:18. | :22:21. | |
everyone through stem education as though we're actually going to race | :22:22. | :22:25. | |
the machines and actually some of us will win. That is a race we should | :22:26. | :22:34. | |
not be running. Instead we should be educating people in the humanities, | :22:35. | :22:38. | |
I guess. That is for the humans. There we will have to leave it. | :22:39. | :22:45. | |
We're out of time. Thanks to all my guests. For now, from New York, it | :22:46. | :22:47. | |
is goodbye. It is a summer which has been | :22:48. | :23:08. | |
lacking in 30 Celsius heat or above. Not everyone pulls back up of tea. | :23:09. | :23:10. | |
Tuesday | :23:11. | :23:11. |