[APPLAUSE]
RICHARD SUSSKIND: Thank you very much,
and thank you, Google, for inviting
us to talk about our book about the future of the professions,
which really focuses on the impact of technology
and how we share expertise in society.
And Daniel and I will be doing a double act today.
I'll be talking after Daniel about trends in technology
and the evolution of professional service
and artificial intelligence.
Before that, though, Daniel will be setting the scene,
discussing what we regard as the two possible futures that
are panning out for the professions,
and he'll also give you a flavor of what's
happening in the vanguard of the professions today.
After I complete my words on AI, Daniel
will then speak about jobs and the future of jobs.
I'll come back and speak about expertise
and a couple of moral issues that arise from our book.
So we will speak to you for about half an hour,
maybe 40 minutes.
And we'll cover these main topics as snappily as we can.
Thank you.
DANIEL SUSSKIND: So we're often asked why we wrote the book.
What can I say about a co-author who, in many ways,
has become like a father to me.
My dad's been working in the field
of artificial intelligence and the law for the past 35 years,
looking at how it all transforms the legal profession.
And what he's found is that in talking, generally,
to audiences of lawyers, at the end, a stray doctor,
a stray accountant, a stray teacher will come up
and say, that's very interesting in the legal profession,
but what you're talking about could apply very well
in our profession as well.
So we were first chatting about this about five years ago.
I was working in a policy unit at Downing Street
at the time, tax policy, health policy, education policy,
and I had a good overview of lots of different professions.
And it was clear that significant change was
in the air, and that these different professions seemed
to be facing a common set of challenges.
So we had the idea of investigating
the professions more generally.
And we set out on a journey together to do exactly that,
and the result was this book.
So our research-- we looked at eight professions-- health,
education, law, journalism, divinity, management,
consulting, tax and audit, and architecture.
But the thinking applies to more than just those professions.
It's about the professions in general.
We have about 100 interviews we draw on,
800, more than 100 sources.
And the picture that we get is a radical change,
and our book is trying to make sense of that.
[INAUDIBLE] my dad said before, we
see two futures for the professions.
And the first one is reassuringly familiar.
Professionals continue to work as they
have done since the middle of the 19th century,
but they use these new technologies
to streamline and optimize the traditional way in which they
worked.
So there's lots of examples of this-- doctors using Skype
to talk to patients, architects using computer-assisted design
technology to build more complicated,
bigger buildings, teachers using online resources to help
in the classroom.
But the second future's a very different proposition.
It involves a transformation in the way
that the expertise of professionals
is made available.
And what we call increasingly capable
systems and machines will not only streamline and optimize
the traditional ways in which professions work,
but it will actively displace work
in traditional professions.
And for now, we see these two futures developing in parallel,
but in the long run, we anticipate
that that second future will dominate.
We'll find new and better ways to share expertise in society,
and that will lead to a gradual dismantling
of the traditional professions.
And that's broadly where the latest evidence
and our thinking leads us.
But our research also led us to ask
a more fundamental question, which is why do we
have the professions at all?
And our answer, and it runs through all the work
that we've done is, that in analogous ways,
the professions are a solution to the same problem, which
is that none of us have sufficient expertise
to cope with all the daily challenges in life.
No one can know everything.
Human beings, the term we use for this,
they have limited understanding.
And so they turn to doctors, lawyers, teachers,
who have the knowledge that they don't to get on.
In what we call a brick-based society,
the professions, as I said, are the way
that we solve these daily challenges.
They have knowledge, experience, skills, and know-how.
And the term we use for this is practical expertise.
They've got the practical expertise
but that those they help do not.
They operate under a grand bargain,
which is an arrangement that gives the professions the right
to provide certain services, often
to the exclusion of others.
And they act as gatekeepers.
They're asked to look offer, to curate
their own respective bodies of knowledge.
The doctors look after medical knowledge.
Lawyers look after legal knowledge, and so on.
So that's our analysis of the professions
in a brick-based society.
But we're no longer in a brick-based society.
We're in what we call a technology-based internet
society, and our professions are creaking.
They're unaffordable.
Most people, most organizations, can't
afford the advice of first-rate professionals,
or indeed, any professionals.
They're antiquated.
They rely upon old techniques for producing and sharing
knowledge and information.
They're opaque.
And sometimes, this is because the work the professionals do
is genuinely too complex for lay people to understand,
but other times, lots of ways there's
intentional obfuscation, and they underperform.
Given the way the professionals are organized,
the work and experience of the very best
can only ever be enjoyed by a privileged and lucky few.
And the expertise of the very best is a very finite resource.
And so we should ask the question,
as we move from this brick-based industrial society
to an internet society, might there
be entirely new ways of organizing professional work,
ways that are more affordable, more accessible
than the traditional approach?
Might we be able to make some of this practical expertise
available online?
Do we still need these old gatekeepers?
And to help us think through these issues,
we decided to find out what was going on at the vanguard.
So we spoke to the thinkers and leading practitioners.
And I want to now just give a flavor of what we found.
So in education, more people signed up in a single year
to Harvard's online courses than had
attended Harvard in its entire 377 year existence
up to that point.
Khan Academy, online collection of instructional videos,
practice problems.
It's great stuff.
I use it to teach some of my students maths.
It has 10 million unique users each month.
That's, in a way, a higher effective attendance
than the entire school population in England.
In medicine, 190 million people, not all of them hypochondriacs,
visit WebMD, which is an online network of help websites.
190 million visits is more than the number of visits
to all the traditional doctors working in the US.
Similarly, the US Food and Drug Agency
said, by 2018, there will be at least 1.5 billion people
in the world with at least one medical app
on their smartphone.
In journalism, on its sixth birthday,
the Huffington Post had more unique visitors
than the New York Times, which was 164 years old.
Bleacher Report, an online blog written by 2,000 people,
now has 22 million unique users each month,
enough to rival CNN Sports.
Associated Press last year started
to use algorithms to computerize the production
of their earnings reports.
And when they did that, they were
going to produce 15 times as many
as they could when they relied upon professional financial
journalists [INAUDIBLE].
In the legal world, every year on eBay
there are 60 million disputes that
arise and are resolved online without lawyers using what's
called an e-mediation platform.
Bear in mind, again, 60 million is 40 times as many
as the number of civil cases that
are launched in the entire English or Welsh justice
system.
Again, the legal world, the best recognized legal brand
in the US isn't a traditional law firm,
it's Legalzoom.com, an online provider of legal advice
and automated document drafting service.
In tax, last year in America, 48 million Americans
used online tax preparation software rather than
a traditional tax accountant.
And here, HMRC, they've got a fraud detection
system called Connect.
It sifts through each time about 1 billion pieces of data,
and it's said that's more data than is
held in the British Library.
No mean feat, given the fact that the British Library
has a copy of every single book that's
ever been published in the UK.
In architecture, a few years ago,
a firm, Gramazio and Kohler, used a swarm
of autonomous flying robots, pictured
there, to build a structure out of 1,500 bricks.
And a Dutch architecture firm, DUS Architects,
has begun to print and assemble a house made entirely
of printed parts, using a printer that
can print objects that are three and half meters tall.
In consulting, Accenture, a consulting firm,
no longer employees just consultants.
It has 750 hospital nurses on staff.
And Deloitte, which was founded 170 years ago as an audit
practice, now has 200,000 professionals,
and its own full scale university set in a 700,000
square foot campus in Texas.
So in Second Life, the online world where
half a million people control their own avatars,
there's a group of Christians.
And on an island, a virtual island called Epiphany,
they've built a slightly gloomy-looking
Anglican cathedral.
It offers daily services, a weekly Bible study class,
and counseling services.
And again in religion, 2011, amidst some controversy,
the Vatican granted the first imprimatur
to this app called Confession.
An imprimatur is the official license
that the Catholic Church gives to religious texts.
So they gave it to this app, which helps
you prepare for confession.
And it's got tools for tracking sin,
and dropdown panels and options for contrition.
And the controversy was that the Vatican said
that this will help you prepare for confession,
but it won't replace confession.
And there was an argument about that with a local church.
So we believe that all these developments
across the professions are related.
They reflect a transformation in the professions,
and that's what we've been studying together
for the past five years.
Thank you.
RICHARD SUSSKIND: Thank you, Dan.
So when we looked across the professions,
looked at the evidence that Daniel has discussed with you,
and much else besides, we identified eight patterns
and 30 trends that we found across the professions.
And I'm not going to delve into each one today.
That's a chapter at the end of the book.
But I wanted to just choose three
to give you a flavor of the trend
level of what's happening.
Above all else, I suspect we're seeing a move away
from what we called bespoke handling of professional work.
So this is professionals acting as craftspeople,
with each case, or problem, or patient, or client
having unique circumstances, and starting
with a blank sheet of paper and fashioning a solution
with a handcrafting method.
We're seeing a move away from that.
We're seeing what we call decomposition, a breaking down
of often very complex professional work
into component parts, and finding the most efficient way
to do each part of work.
This is a new division of labor across professional services.
And what we're also seeing as part of this
is, that many of the components, once you start breaking down
professional work, although the whole looks complicated,
many of the some parts in medicine, in law,
in tax and audit, right across the professional services
market, many of the subparts actually can be routinized.
And here's where we can get great savings,
and here's where we can actually use technology
in new and imaginative ways.
So we've discuss at length each of these trends
and many others.
And we're not saying all professions are
going at the same pace, but we are
saying there's a common set of patterns
visible across the professions.
And technology is clearly underpinning
much of the change.
Now, we're not here to lecture Google on technology.
We just wanted to make a couple of observations.
First of all, in 1996, I wrote a book
called "The Future of Law."
And this was about the impact of technology
on lawyers and the courts.
And in that book-- and this will seem remarkable now-- one
of my themes was that the dominant way
that lawyers and clients would come
to communicate in the future would be by email.
Now, I joke not.
The Law Society of England, they all
said I shouldn't be allowed to speak in public.
They said I was bringing the profession into disrepute
by suggesting that clients and lawyers would use email
with one another, and that I didn't
understand confidentiality, that I didn't understand security.
Please don't take for granted in the professions
that people will embrace technology
very rapidly We have a very resistant group of users.
The way we make sense of technology--
and I'm half a technologist and half a lawyer-- we
organize our thinking about technology under four headings.
We identify, as so many other people
do, an exponential or at least explosive growth
in the underpinning technologies, processing power,
bandwidth, data storage capacity.
We also talk-- and Daniel's used this phrase
before-- a lot of increasingly capable machines.
More of which [INAUDIBLE].
Our assisting machines are becoming increasingly capable.
The point again, as so many others
have done, the fact that our devices are becoming
increasingly pervasive, and that circuitry is also
being embedded in objects around us, and indeed in human beings.
And finally, in making sense of the American world
of technology, we talk about the ways in which human beings are
increasingly connected.
Just to focus on that one question of increasing
capability, to unpack that a little, what interests us
is the progress being made recently on big data,
on data analytics, and the way in which large bodies of data
can yield fascinating and interesting insights, patterns,
correlations.
We're interested in the development of search,
as well as Watson type technologies, where certain
question and answer scenarios, you actually
have systems solving problems.
We're also fascinated by effective computing.
I'll say more on that in a second.
And of course robotics.
Effective computing, this is less known,
even amongst technologists.
This is devoted to the ways in which machines, systems, can
both detect and express human emotions.
The notion that a machine can look at one's face
and tell whether or not you're angry, or surprised,
or disgusted, the idea now that machines, more accurately
than human beings, can tell whether or not a smile
is fake smile or a genuine small.
So this growing body of evidence,
this growing investment in a whole bundle
of technologies, very crudely improving greatly
in user interface, but allowing machines
not just to behave inertly, but actually, as I say,
both express and detect, determine the emotions
of their users.
And the final elements of the big four at the top
was that our machines are increasingly connected.
And of course, one can speak endlessly
about social networking, but just focusing specifically
on the professions, we're finding within professions
there are closed social networks in medicine, in law,
in education, in architecture.
We're also finding-- and a classic example of patients
like me-- the opportunity for what we call communities
of experience, where people who have
been the recipients of professional service
can come along and share their experience in law, in medicine,
in religion, in general problem solving,
in tax, and in architecture as well.
And then, of course, the whole notion of crowd sourcing.
And we're seeing in architecture,
in medicine, in business problems generally,
new ways of reaching out to large communities
of individuals who can contribute to problem solving.
This is radical stuff in the world of professions.
We classically think of lawyers, and doctors, and auditors,
and counselors, one-to-one professional advice,
consultative model, on an hourly billing basis.
This is blowing all of the out the water,
that at our fingertips we might have expertise more readily
usable, accessible, understandable, than ever
before.
And when you think of the capabilities
of human professionals-- and we see a lot about this--
we identify four.
It's the cognitive capabilities, the ability
to think, to solve problems, to reason,
the manual capabilities, to work with one's hands.
And you think of vets.
You think of surgeons or dentists.
You think of the emotional capabilities, to listen to,
to empathize with one's patients and clients, and of course
the moral dimension as well.
So what we track through the book
is to say, well, what elements of these capability,
looking ahead in a sane way, do not think
could be taken on by machines?
And so many of the cognitive tasks, so many
of the manual tasks, and so many of the emotional tasks,
we think it's entirely foreseeable
that these tasks will, in the future,
be undertaken either by machines autonomously,
or by machines with less qualified people
than we use today.
The moral dimension I'm going to turn to at the very end.
But let me put this in a different way.
What we're seeing is an evolution
of professional service, from a model
where the way in which professional service is
delivered is as some form craft, the doctor, the lawyer,
the accountant, to a model where we standardize
in terms of process, checklist, procedure, manuals, practice
guides.
In terms of substance, you've got your standard forms,
templates, precedents.
And then we go further, within our hospitals,
within our firms, within our schools, we systematize.
And so use automatic workflow systems
with automatic document generation systems
and so forth.
And all of this falls pretty much in the category
of the first sort of future, where
we're streamlining and optimizing
the old ways of working.
But what, of course, is fascinating
in our current era is the ability to externalize this,
to make this content, this knowledge,
this practical expertise available on an online basis.
Now, some organizations, particularly
professional firms, see a great commercial opportunity here.
We can make that expertise available
as a chargeable offering.
Others, particularly the government
and charitable sector, will say, actually,
we can make this expertise and knowledge available online
at no charge.
We'll still control the content, but we'll make it available.
And the third option, to which we're very strongly attracted,
is the commons model.
And in the spirit of Wikipedia, rather than
simply encyclopedic information, practical expertise
and guidance available in the open source spirit,
held not by major organizations or governments,
but actually as a commons for the use of everyone.
And this, we see, is the movement that we call,
very crudely, and others use the same term, commoditization,
a move away from the craft of the professions
towards online availability with a charitable,
non-charitable, or available on a commons basis.
Let me turn to say a little bit AI,
and then I'll head to the the '80s,
I wrote my doctorate at Oxford on AI and law.
And looking back now, I'm beginning
to make sense of what's happening in this field.
I was involved in the '80s in what we called our first--
or we now call our first wave of artificial intelligence.
And I did my doctorate from '83 to '86.
Then after for a couple of years, from '86 to '88,
I worked on the development of this.
And I want to reassure you that at the time,
that was quite a cool screen design.
It looked good.
That was the best.
And actually, the colors matched well, as with our book today.
But what happened was this.
I just finished my graduate work .
And the dean of the law school at Oxford, a man
called Professor Capron, a partner
at a major international law firm,
he did a book on a very complex area of law
called the [INAUDIBLE] law, which
is a small corner of the law of limitation.
And he said it's so complicated no one actually understands it.
Why don't you be one of my examiners?
He said, why don't we build a system in this area?
And this was an era when floppy disks genuinely were floppy.
And this is what the system looked like,
5.25 floppy disks, two of them.
We wrote a book, and published it with the two disks
in the back.
But just to give you a flavor of what we were up against,
section 2 of this act shall not apply to an action
to which this section applies.
And that was one of the more readily understandable parts
of the legislation.
So what we were trying to do was-- surely
we can offer a pathway through this complex web
of almost unintelligible, interrelated rules.
And quite frankly, it was a form of decision tree we developed.
But it was a little more complicated
than a decision tree because of the nature of the content
involved.
And typically a question would look like this.
The content doesn't matter, but just saying so far it
looks as if the basis of your claim is negligent,
shall we proceed on this basis?
Yes or no, and navigate through.
Or basically you're offering other people the opportunity
to navigate through this expert model of this area of law.
That is a very small part of the whole system.
It had two million paths through it.
In a sense, we had to chart almost every one
of these ourselves.
So this was the first wave of AI.
What we did was, it was called knowledge elicitation,
knowledge acquisition.
You sat down with a human expert.
You mined the [INAUDIBLE] from their head.
You developed it into some form of flow chart.
You put it into a system so other people could use it.
It wasn't just in law.
At the time, there was a lot of work going on
in rule-based expert systems, as they were known in medicine.
And I at the time, then worked for a few years ago
with [INAUDIBLE] and Young.
They were working in tax, and audit, and consulting as well.
So that was the model of the '80s,
the expertise taken from the human brain
and dropped into a box.
Turns out, though, quite costly to build and maintain systems,
because professional knowledge changes so rapidly.
Little incentive for the commercial organizations
to invest.
Remember, most professionals charge by the hour,
so why would you want to reduce, as we
were reducing a task from 2 hours to 2 minutes
if you're charging by the hour, then
the market's not wanting that of you.
So there's no incentive to introduce
this kind of efficiency.
And of course, the web came along.
And this wasn't doing the same as the AI
that we were aspiring to, but it offered, at least
intuitively, an easy way to make content guidance available
online quickly, cheaply.
And so the AI winter, at least in the professions,
I attribute to the web.
Because people were not distracted,
but people were e-tracted.
to a new, easier way of working with their knowledge.
For us, a turning point came in the '97.
It's well known that's when Kasparov, the world chess
champion, was beaten by Deep Blue.
Now, in the '80s, working in the computer laboratory,
we used to discuss this a lot, and we were of the view
that a computer system could never beat a grandmaster.
Why?
Well, that's because up until the level of a master,
masters could explain how they went about playing chess.
When you got to a certain level, the creative insight,
the bolts out of the blue, the genius, the experts
themselves, the grandmasters themselves
couldn't explain what they were doing.
And this applied right across the professions.
The great lawyers, the great doctors, they would also say,
it's intuition.
It's gut reaction.
It's knee-jerk.
We can't explain it.
And we thought, if you can't explain it, we can't model it.
And therefore, we're limited to a fairly modest, but quite
useful level of performance by these systems.
But what we hadn't banked on, of course,
was exponential increase in processing power.
And Kasparov was beaten not by a system that sometimes was
cleverer or more insightful.
It was by brute force processing,
and, well, the reality is that it's
a system that could feed up to a billion possible moves
a second.
And so it is in so many other professions,
these systems are outperforming human beings,
not by copying what human beings do, but by brute force search,
and also by a very large quantity of data.
Now, Patrick Winston, one of the fathers of AI research,
put it very well.
There are lots of ways of being smart that
aren't smart like us.
In the '80s, we thought the only way of being smart
was being smart like us.
You copied human experts, knowledge, and performance,
and put into a system.
Why would we do that?
Systems can actually outperform us
by working in entirely different ways.
And this leads us to what we feel
is quite an important insight.
We call this the AI fallacy, and it
pervades the academic literature,
the popular literature, and actually
it's just a common misconception.
We define it as this, it's the mistaken assumption
that the only way to develop systems that perform tasks
at the level of experts or higher
is to replicate the thinking process of human specialists.
And so very often, people say, well,
a system can't perform at level, because it can't think,
or because we don't know how human beings operate.
But we think this is fallacious.
Let me give you an example.
Frequently, a lawyer or a doctor will say,
a computer can't have judgement like a human being.
Now, that may well be true, but that's
assuming that that is the right question
to ask, how can we somehow replicate
judgement in a machine?
We prefer to take a step back.
We do this a lot in our book, and ask
the question, to what the problem
is judgement the solution?
Because the purpose of these systems
is not simply to streamline.
The current way of working is find,
using the power of technology, new ways of solving
the problems.
And when you think about it, judgement
solves problems of uncertainty.
And when you think about it also, so much of the technology
that you and others are involved with
is precisely powerful ways of dealing
with handling uncertainty, particularly when large bodies
of data are involved.
And so judgements, the way human beings cope with uncertainty,
very often we're seeing across the professions
that technology, and of course computing,
a very large stores of data, actually
work in entirely different ways.
There's new ways of coping with uncertainty.
People often ask, can machines think?
We love the answer to this in relation to Watson.
The day after Watson beat the contestants on the Jeopardy
quiz program, John Searle, a very well known
American philosopher said, "Watson
doesn't know it won in Jeopardy."
And it didn't take people down to the pub to celebrate.
It didn't reflect on what we had and hadn't won.
It doesn't think the same ways we do.
But the key point is, this non-thinking machine,
it can outperform human beings.
So what we are pointing to is a category
of increasingly capable non-thinking machines.
In a sense, we sidestep a lot of the interesting psychological,
philosophical questions about AI, but point out,
there's a second wave of AI that one
can trace to Kasparov, where we're seeing systems being
developed that outperform human beings,
but they don't work like human beings.
And this is the leap that most people
find hard to make, because they somehow think,
the identify things that human beings do that they can't
imagine computers can do, because they'll say things
like, you can't program a computer to do XYZ,
failing to understand that problems can
be cracked in different ways.
And that's the second wave of AI.
Now, this has profound implications for employment.
Because what we're seeing is more and more of the tasks that
are undertaken by professionals will actually
be undertaken by machines or by less experienced people working
with machines.
The implications of that for the job market
is what Daniel's now going to talk about.
DANIEL SUSSKIND: So I think this leads to two related
questions being asked.
The first is, will there be any jobs left for professionals?
And then the second is, now what can humans do that machines
cannot do?
And I want to look at each of these questions in turn.
So the first question, will there be any jobs left?
And the answer to this largely depends upon time scales.
In the medium term, we think technology
will lead to a reduction in traditional professional roles,
but at the same time, we also think it will give rise
to a whole new set of roles.
So just to pick one, the knowledge engineers.
This is the sort of thing my dad was doing in the '80s,
getting the practical expertise outside of someone's head,
and putting it into a system so that lay people could use it,
paraprofessionals, less expert, less specialist people who
use these new systems and machines to do
the sorts of things that in the past
would have required an expert or a specialist.
But the long run's a very different story,
and that's what I want to focus on now.
And here, broadly, there are two schools of thought.
There's the optimists, and there's the pessimists.
And the pessimists say, no.
There isn't going to be any work for human professionals
to do in the future.
Machines are becoming increasingly capable.
They're able to perform more and more to-do, more and more
types of work.
And so there will be less and less work for humans to do.
That's the pessimists.
Now, the optimists say, that's wrong.
Not only is there work today that only humans can do,
but tomorrow in the future, we'll create new types of work,
perhaps work that we can't conceive of today.
And that will be work that can be done by human beings.
In particular, the technical criticism
that the optimists make of the pessimists is they say,
the pessimists are committing the lump of labor fallacy.
And this is the idea, the mistaken belief,
that there is some lump of work in the economy to be divided up
between people and machines, and machines
doing more of that leaves less for people to do.
The optimists say, no, that's it.
As the economy grows and we become more productive,
we create new types of work.
And people can do that instead.
And it's also here that you see another line of argument, which
is that optimal future involves human beings
and machines working together, each bringing
their own distinctive capability.
And so the question is, who is right here?
One of the difficulties in thinking about this problem
is that when we talk about the future of work,
we tend to talk about the different jobs that people do.
So in the professions, we talk about doctors, and teachers,
and nurses, and lawyers.
But the term "jobs" isn't entirely illuminating,
because a job isn't a monolithic, indivisible lump
of work.
Instead, to think about the future of work,
we have to look at what people actually
do in their jobs, the type of tasks that make up their jobs.
Just to give an example, if you look at nursing, for example,
25 years ago, what a nurse did was very different from what
a nurse does today.
Then it might have involved bedpans and bedside
conversation.
Today, nurses can prescribe certain types of medication
and even perform minor surgery.
The tasks that nurses do has changed,
but we use the same job title, nurses, for both.
So why does this matter about the future of work?
Well, the Economist reviewed our book two weeks ago.
It was a positive review, otherwise,
I wouldn't have mentioned it.
And alongside the piece was this great illustration
of Professor Doctor Robot QC.
And I think there's a sense in a lot of thinking about
the future of work that one day, a doctor, or a nurse,
or a teacher is going to turn up at work
and find Professor Doctor Robot QC or one of his relatives
sitting in their chair.
Their job would have been entirely replaced.
and we just don't think that's how it will play out.
If you imagine a technology that replaces a task
that a professional used to do, so here
a less glamorous-looking remote monitoring system.
This means that people need to go to the doctor
less for a medical examination.
What we want to say is that the job that the professional does
has changed.
The doctor, maybe she'll spend less time
doing medical check-ups, but she might spend more time
reading the latest research.
And this is why the term job is unhelpful.
It's too high level, and it masks
this deeper, underlying churn in the tasks
that people are doing.
Of course, we can now return to the optimists and pessimists,
and rephrase it.
The optimists, they think that there's
going to be enough professional tasks for human beings
to do in the future, whereas the pessimists are saying,
no, there's not going to be enough tasks for human beings
to do in the future.
And the point here is that, of course,
it's true that if enough tasks are lost,
it no longer makes sense to talk of the job under which all
these tasks are bundled.
But we have to start from the level of the tasks.
So what do we think now with this thinking
in terms of tasks?
Well, out both the optimists and pessimists are right and wrong.
The pessimists are right in recognizing that machines
are becoming increasingly capable at existing tasks,
but they're wrong to ignore the fact
that there may be new tasks to be done in the future.
The optimists are right to recognize
there will be new tasks that will done in the future,
but they're wrong to think that machines rather than people
will be best placed to perform these tasks.
So our conclusion is that machines will
become increasingly capable.
They'll take on more and more of today's tasks.
New tasks will no doubt emerge, but it's likely
that machines will take on these tasks as well.
And there is a serious point here,
which is that we're not challenging the presumption
that there'll be new work to be done in the future,
but we are challenging this idea that necessarily
people rather than machines will be best placed to do it.
And so we find it hard to avoid the conclusion that there'll
be a steady decline in the demand for human professional
in the long run.
The best and the brightest will last the longest,
either of those who do tasks that can't be done by machines,
or tasks that ought not to be done by machines.
And that's something that my dad will return to in a moment.
But the key point here is that there just
won't be enough of these professional tasks
to keep armies of traditional professionals in employment.
At this point, talking to professionals,
they say, but surely!
But surely there must be some tasks that machines can never
do, even in the long run.
And this comes to the second question that I set up,
are there things that machines could never do?
When we describe this situation, we tend to say
or we say that professionals have
a Rubik's Cube conception of what machines are capable of.
So here is an expression of a man
who builds a machine out of LEGO and a smartphone
that can solve a Rubik's Cube in 3.4 seconds.
You pop the Rubik's Cube in there, press go, it sinks in,
pops up, it's solved.
And I think most professional see this and say, yeah,
this is clearly a manually dexterous and complicated task,
but of course machines can do it.
It's rule based.
It's clearly defined.
It's logical.
It's a routine task.
And professionals then said, but what we do is non-routine.
We rely on creativity.
We rely on judgement.
We rely on empathy.
All of these are non-routine things,
and surely machines can never do non-routine tasks.
And this is also a distinction, the non-routine routine one,
that runs through lots and lots of academic writing
on the future of work.
And we say not so fast.
Two mistakes are being made here.
And the first is this idea of decomposition
that my dad spoke about.
To start with, when you take professional work
and break it down into its component parts,
it turns out that many of those parts
are routine rather than non-routine.
Actually, not much that a professional
does, in cases not much at all, relies upon empathy, judgement,
creativity in a way that is often said.
But the second point comes back to the AI fantasy,
which is that even those non-routine tasks that exist
can often be done in different ways by machines.
The mistake is to think that because a machine can't
think it can never be created.
Because a machine can't feel, it can't be empathetic.
What we often notice here, and what we find
is that these machines operate often in very inhuman ways,
but they can take on these tasks as well.
So this line of thinking on professional tasks prompts
a more fundamental question, and that's--
RICHARD SUSSKIND: You're done.
DANIEL SUSSKIND: Yeah.
RICHARD SUSSKIND: Thank you.
As Daniel said at the outset, we thought
we were writing a book about the future of the professions,
but it turned out we were really writing
a book about the future of expertise,
how you capture and share expertise in society.
And the question that we therefore ask and then answer
in the book is precisely that in more formal terms,
the production and distribution of practical expertise
in society.
So how do we produce and distribute
practical expertise in society?
The current answer, the traditional model,
is the professions.
And I know it's been that way since the outset.
That's the way that we found best both to generate and make
available expertise.
We put particular occupational groups in charge,
we give them exclusive rights, and they get on with it.
And we've identified problems [INAUDIBLE] with it.
But what we've also identified in our book
were six alternative models.
And there's no time today to delve into each, but in a sense
this is at the heart of the book,
because not only are we saying there's problems
with the current model.
We're actually saying there's some viable alternatives
out there.
And we've touched on a few of them,
for example, the knowledge engineering model.
Rather than having a human expert
dispense the advice on a one-to-one consultative
advisory basis, that human expert's knowledge
and experience is modded into the system
and made available on a one-to-many basis.
That's the knowledge in [INAUDIBLE].
Pretty much, actually, according with the first wave
of AI in the '80s.
Then there's the communities of experience model, for example.
We touched on this.
This is gonna be terribly significant.
Where recipients of professional advice, patients, clients,
actually come together and say, this
is how I sorted out the problem.
This is what I was advised, or this is
how I sorted it out personally.
And I will make that available to others.
And we'll see this community.
We think it may well need to be moderated by specialist
individuals, but we'll see communities
of professional experience build up, which may well
be the first port of call for many individuals
when they need guidance on anything.
The most extreme version of which
are book has been allied with exclusively
is the machine-generated model.
That's the idea that there might be autonomous
machines that are actually both generating and distributing
practical expertise.
We can see decades from now that in many areas
of the professions, this will be the case.
But what we don't want to leave anyone with the impression with
is that it's either the case of autonomous robots dispensing
advice, or the traditional model.
There are a variety of models in the world.
I want to close with the issue of morality.
Because it seemed to us-- and we addressed them
both in the book-- there are two big moral issues that
arise from the subjects we are addressing.
And the first in chapter 7, or actually the conclusion,
we asked the question, what future should we want?
The first question we asked was this, what tasks ought not
be handled by machines?
So even if machines, in one way or another,
machines work with less well qualified people,
can match the cognitive, the manual,
the effective capabilities, of a human professionals,
might there not be some areas of human life
we simply don't want to hand over to machines?
Do we want machines to be making decisions about the turning off
of life support systems?
Do we want machines to be passing life sentences?
Do we really want the moral buck to be stopping at the robot?
And most of us feel-- it may be irrational-- but most of us
feel, I think, an intuitive unease
when we think about this.
And we actually think this is a huge issue, which
probably extends well beyond the professions.
And we liken it to the issue of IVF
as it was discussed in the early '80s, where
a very famous philosopher, Mary Warnock,
was invited to do an inquiry into the moral implications
of this American technology.
So in our book, we called for a similar kind
of public government-led discussion, debate,
about the limits of the use of this kind of technology
in employment.
And of course, the professions were a special example, but so
many pressing moral issues arising within the professions.
Now is the time to start debating whether or not
we think it's appropriate that these tasks, or some
of these tasks, are taken on by machines.
And the second question now weighs even bigger.
We're seeing that this practical expertise, the knowledge,
experience, insight, know-how of human experts
can actually be captured and made available in new ways.
So the question is, if human beings through their labor
and often in the service, and this
is captured and available largely online,
who should own and control tomorrow's practical expertise?
Now, we say in the book, we're actually
coming to a fork in the road.
We're not quite there yet, but there's two very
different paths.
One is where you see big business, government,
withholding the control and therefore dispensing.
It's a new form of bargain.
It's a new grand bargain, a new exclusivity,
dispensing this practical expertise.
And the other, and it might sound fanciful,
but it's more of a commons that insofar as possible,
and if we can provide the incentives to encourage people
to contribute to this, we see a road ahead
where practical expertise is available at no or at low cost
right across the global community,
derived from the best human experts,
but actually delivered in a more effective way.
Now, are money is on that second option,
but that's something that we want to discuss with you.
Thank you for listening.
[APPLAUSE]
MALE SPEAKER: Thank you very much.
For this very interesting talk.
Who has questions?
That is the most hands I've ever seen for questions.
AUDIENCE: So just to start off, when
you saying is the first place.
I was very struck that you didn't mention the competence
aspect, which I imagine most professions would
say was their bar of entry, and that's
what they're guaranteeing in exchange for the exclusivity.
DANIEL SUSSKIND: What you see is in practice [INAUDIBLE]
medicine for example, diagnosis is a bit [INAUDIBLE].
We were on a panel the other day with a doctor
making exactly that defense.
He said the most important thing that I do is diagnose patients.
And that is something that I do very well.
In fact, if you look at the data, up to 20% of the time,
doctors make mistakes with diagnoses.
And what you then see, outside of the professions,
are machines operating in very different ways,
diagnosing in very different ways that
have for certain types of cancer or reviewing
certain types of scans and radiography,
and here is the view that they can outperform human beings.
So it's important not to set a bar
for those outside the professions that
doesn't match the one the professions currently achieve.
Generally, the standard, the instinct
is to say-- there's another example.
There's an autonomous robot in San Francisco
that delivers prescriptions.
And it has delivered 6 million prescriptions,
and it's made one error so far.
They said it's due to human error.
And a lot of people say, that's not acceptable.
It shouldn't be making an error.
At best, human pharmacists make an error 1% of a time.
6 million prescriptions, 1% of the time, that's
60,000 mistakes, not one.
So the short answer is outside of the professions,
there appear to be lots of systems and machines
operating in very different ways that
challenge this idea that professions are necessarily
the only way to provide high quality expertise.
RICHARD SUSSKIND: But you are right that that's the argument
the professionals will use.
It's part of what we call the grand bargain.
The question you ask is, why do we give exclusive rights?
[RINGTONE]
Well, this is tremendously important.
Your health is tremendously important.
Your legal welfare, this is vital stuff.
You don't want any Joe conducting brain surgery,
just as you don't want any joe in the High court standing
up and representing you.
And therefore, we should have the exclusive rights
over certain areas of human activity,
because what we can just about guarantee
is that we'll deliver a higher quality than anyone
else who hasn't got our training or ongoing educations.
As Daniel says, I think our book basically
is challenging the grand bargain, saying
that grand bargain made sense in a brick-based industrial
society from the 19th century onwards,
but actually, once you start decomposing and looking
at much of the work that's done, as Dan says,
there are different ways, systems, processes,
people who can deliver as high a quality of service
without the exclusivity which probably
bring the mystification, the greater cost, the greater
complexity, and the alienation that many of them suffer.
DANIEL SUSSKIND: Just to add one thing to that,
the example we talked about at the start,
with eBay and the 60 millions disputes that
are resolved online, in the absence
of that e-mediation platform, those 60 million disputes
would have probably gone unresolved.
That is 40 times as many of a similar type of claim
that result in the English justice system.
So it may be the case that this e-mediation platform,
on a case by case basis, provides--
unless you get the best judge in the country
to adjudicate each dispute on eBay,
it may be the case that that judge
would reach a better decision.
We're talking about 60 million people
who probably wouldn't have received
a resolution without it.
It would have been a resolution they
weren't happy with otherwise.
There's a quotation from Voltaire,
"you shouldn't let the best be the enemy of the good."
Lots of these systems offer good alternatives not to the best,
but to nothing, which people currently have.
And this is a story that you see across the professions.
What are the professions for?
They're responsible for some of the most important things
that we do in society.
And for the reasons I've said, in lots of different ways,
they're failing.
And these systems, in case, can offer better,
but in some cases, they just offer good,
and good is better than nothing.
And even now that's true. [INAUDIBLE].
AUDIENCE: Yeah.
The next question is, have you considered something
about how you handle outliers?
So you have the case like for the airline pilot, where
most of the time, the autopilot does
a great job of flying the plane, and probably better
than the real pilot.
But then you get into an emergency situation
where he has to choose whether to land
on the river, or the golf course, or the highway.
Doing the routine work makes the airline pilot better
in an emergency.
And so by segmenting the work, you lose that extreme.
So I guess in this situation, if the best
is the enemy of the good, who decides when actually
the best is important enough that we don't go with automated
planes or whatever else.
RICHARD SUSSKIND: There's actually a few questions there.
One is, how do you become an expert?
It's the old training ground.
You used to cut your teeth in more routine work,
so we've got a whole section discussing that.
The other question, actually, funnily enough
is something I can [INAUDIBLE] never
really answered in the '80s.
Because my view, when I was working on expert
systems was that these systems were
very good at solving what I called
clear cases of the expert domain.
By that I meant problems that were easy for experts,
but not too easy for non-experts.
But these systems weren't very good for sorting out
problems for expects.
They'd say, actually, that's quite tricky.
But the difficulty is that if you're not an expert,
you don't know if a problem is easy or hard, even
for an expert.
So there's a border mindset of gray.
Sometimes, it's perfectly obvious
that something's very complicated,
but sometimes also there's a gray area,
where you might default to what looks
like a rather simple solution, but actually miss a nuance.
And our inclination is, actually--
to Dan's point again, the best is the enemy of the good,
in many areas of professional work, which
aren't threats of life of death, we probably just
need to accept that what we're now getting
is 19 times out of 10 rather than 0 out of 0.
Sorry, 19 times out of 20, rather than 0 out of 0.
The great insight you've made there is where there really
are terribly high risks that follow,
we really will need experts involved
doing the triage in the first place,
seeing whether or not this is something
that can actually be disposed of by a machine
or by an individual.
Going back to the earlier question
of how you become an expert, it's
fascinating, because it's a training question.
How do we train people?
And I'll give you example in law.
In law, it could be an indictment, I think,
of major law firms, but the way you want to learn
is during one's traineeship, for example, to be a litigator,
is you're often assigned to a basement
room with a huge body of documents,
either electronically or in folders.
And it's called document review.
You have to review these documents.
And you do that looking for relevant ones.
And I did a little bit of research,
went asking some junior lawyers and said,
if this was automated-- and there's now
technology that could outperform junior lawyers-- would
this mean that you're losing a major learning opportunity?
And the response was, look, we get how to do this
after two or three days.
We don't need to do it for two or three years.
[LAUGHTER]
I rather cynically say, don't confuse training
with exploitation.
The training of many professionals
is a little bit of a deal.
You go in there to a high branded firm.
The market's still happy to pay fairly high rates for people
who are fairly junior.
And the way the market is, the clients just
say, actually, we're just paying for these young professionals
to do our work.
And until 2005, 2006, there wasn't even much wrong
with that, because there was more money swilling around.
But now you're even seeing in accounting, consulting, tax,
law, audit, [INAUDIBLE] clients say we no longer want to pay.
So it's actually a fascinating challenge
to the business model.
Because, actually, what it means is that many professional firms
are actually going to have to invest
in very practical training rather than hoping
it can be picked up on the job.
My answer in the longer run is-- and one would expect this,
and I hope it resonates-- is that-- it's part of our book
as well, [INAUDIBLE] education.
There are far more powerful ways, I think,
of exposing people to a far wider range
of professional problems and situations
using simulated learning environments rather than
natural learning environments.
And we're shown this [INAUDIBLE] university
training young lawyers.
We've created a virtual town called [INAUDIBLE]
in which young, aspiring lawyers practice law for a year.
And actually we have simulated the actual negotiations,
the actual court appearances, the interactions
with the clients in a way the, frankly,
slightly random exposure to the experience
you get in the world of training with a firm doesn't really
provide.
My major point is, most clients say
when they ask you questions, they actually say,
that's your problem.
It's a training problem you've got to sort it out.
But if we can find a way of sorting our problems that's
cheaper, quicker, more effective,
we want you to provide [INAUDIBLE].
But you raise a major challenge.
So these are quite long answers, but they're complex questions.
AUDIENCE: So I want to get your thought about letting
a machine, say, run a country.
RICHARD SUSSKIND: I think that raises really
the first moral question.
Do we think that-- I'll start with the moral question.
Are we comfortable with the notion
that decisions and issues of policy, people's welfare,
and economy, and justice, and health, and so
forth are handed over to machines
from a moral point of view?
And I'm not making an observation there.
I think we need to have a public debate about whether or not
our intuitions about this actually are well founded.
But I think most people would draw the line there.
We'd say we don't really want a robot at the helm.
But I could see a whole bundle of other arguments
that would say, well, actually you
could say a machine might have far better data
at its fingertips, be able to make more rational decisions.
I think, on balance, I would default
to suggesting that what we will do
is have responsible human beings making moral decisions
but increasingly supported by these powerful systems.
We had a fascinating discussion here in the book--
it's not quite your question, but it's
an extreme example-- with a very senior military individual.
Because we're thinking the profession of being of soldier,
we thought we might cover, but we didn't in the end.
And with a lot of reflection in this context of machines
conducting warfare and so forth.
These are tricky issues.
They become trickiest where the machines wildly
outperform human beings in tasks which are very important to us
from a moral decision point of view.
Because the intuition that a human being must involved
may well be trumped by this massive leap in performance
by a machine.
I heard your question as a little bit of a provocation.
It doesn't really need to be one observation though-- we don't
see the professions-- and if you consider politics
to be a profession-- we don't see this
to be an overnight revolution.
Daniel's point about Professor Doctor Robot
you see sitting at the desk one day,
it's not that that appears one day.
It's not a tipping point.
It's not a revolution.
But nor is it a leisurely evolution.
We view it as an incremental transformation.
So what we think we'll see in the professions,
and I suppose in politics too is,
you'll see step changes over the years.
And you'll look back, say, 30 years,
and say, wow, it is unrecognizable.
But it's not like in some areas of life
where there genuinely is a fundamental stopping
of an old era and a starting of a new era.
So I think what we'll find in politics, possibly
a more evolutionary role than a transformational role.
It's steady, rapid change.
Did you want to [INAUDIBLE].
DANIEL SUSSKIND: It's also the case that those big questions
are fascinating, interesting, important,
but there is, just, again, to come back
to the spirit of the conversation we were having
before, in the day-to-day, many of the systems
and machines that exist, they can't run a country,
but they can do a good job at the things that lots of people
don't have access to.
So we shouldn't be blinded or scared by those big questions.
And to thinking about today, and the next months and years,
how can we use these increasingly capable systems
and machines to just do a better job for more people who
currently don't have access to a professional.
RICHARD SUSSKIND: If you think of the division
of the legislature, executive, and judiciary of government,
I'm involved.
I was actually advisor to the [INAUDIBLE] justice.
Since '98, I've been advising the computerization
of the court system.
This is a glacial business.
It moves very, very slowly.
Dan's absolutely right.
Your question's fascinating, but actually, there's
a lot we can do, for example, in our justice system,
how we run our courts with the kind of technologies
that frankly many of you absolutely take for granted.
They would greatly improve access to justice today.
And so investment's probably not so much in the replacement
of the cabinet.
It's more--
DANIEL SUSSKIND: Getting rid of the fax
machines in the court system.
RICHARD SUSSKIND: That's right.
It's the machinery of government.
It could at least be streamlined considerably.
MALE SPEAKER: I think there are many more questions,
but unfortunately, we have run out of time.
MALE SPEAKER: I think you're gonna
stay around for [INAUDIBLE].
RICHARD SUSSKIND: I can stay around, [INAUDIBLE] yes.
FEMALE SPEAKER: I've just been waiting for a long time.
Can you take one more?
[LAUGHTER]
DANIEL SUSSKIND: I'll take one more.
MALE SPEAKER: Can we take one more question?
MALE SPEAKER: With a very quick answer.
[INAUDIBLE]
DANIEL SUSSKIND: Finish with a big one, then.
FEMALE SPEAKER: I was thinking that-- I
don't want to be insulting, but when
you think about the adjectives that you picked, for example,
when you say that we don't need any professionals,
you see that as a pessimistic point of view,
but when you look at-- let's say you're not in the UK anymore,
and let's look from the view of all the countries in the world.
For example, in the Ebola outbreak
in Africa, people really needed doctors,
but they didn't have doctors.
And they really needed nurses, and they didn't really
have nurses.
And some of the failed states, the reason that they are still
failed states is not only because of terrorism,
or military issues, but they don't have enough professions.
For example, I, myself, at my private school,
we were 75 kids for one teacher.
Can you imagine it?
And so it was really hard.
So what I was saying is I think it's an optimistic future when
we don't need professions.
RICHARD SUSSKIND: I hope we didn't come across negatively.
When Daniel was talking about pessimists and optimists,
that was very much from the professionals' point of view.
DANIEL SUSSKIND: Professionals saying that's a pessimistic--
RICHARD SUSSKIND: No, you're absolutely right.
Just a tiny little riff, you know the language.
I'm sure it's been used a lot within Google about disruption.
We, although we're big fans of Clayton Christiansen's book
on "The Innovator's Dilemma" and his work in disruption,
we expressly say we're not using that term.
Because to say that medicine, and law,
and so forth are being disrupted actually
is a very provider-supplier point of view.
From the point of view of the recipient of the service,
this might mean greater, cheaper, better access
to medicine and health.
So we think the word disruption carries with it phrases
which are slightly contentious.
You are absolutely right in your observation.
What you say, though, when you say
the reason these states have failed
is because they don't have professions,
you see, we would put it rather differently.
We'd say the reasons the states have failed
is because they don't have solutions to the which
the professions are thought historically to be the answer.
And what we're saying is, actually, they
need not be the answer.
So what I'm working, for example,
I've done a little bit of work with third world countries
on new justice systems.
I say, don't replicate ours.
You can leapfrog.
You don't need to go through the process we've gone through,
what ends up being rather antiquated and not totally fit
for [INAUDIBLE].
You can jump to, for example, online dispute resolution.
That's a great question to finish on.
Thanks very much, everyone.
DANIEL SUSSKIND: Thank you very much.
[APPLAUSE]