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 An outdated vision of technology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