Thomas W. Malone: 2019 National Book Festival

Thomas W. Malone: 2019 National Book Festival


>>Adam Kushner: Hi
I’m Adam Kushner. I run the Sunday Outlook
section at the Washington Post, which is our home for ideas
and essays and arguments and most importantly
today book criticism. I want to thank the
Library of Congress for putting this
festival together. We are extraordinarily
lucky to be joined today by Thomas Malone, the
other of Super Minds, The Surprising Power of People
and Computers Thinking Together. Tom is the founding
director of MIT’s Center for Collective Intelligence. He trained as a mathematician
and social psychologist. His whole career is
devoted to the question of how humans can take best
advantage of technology in an era of self-driving cars and home devices,
like Amazon Alexa. Computers aren’t just
becoming smarter; we’re also making them smarter
and they are making us smarter. Super Minds is about
how computers and people perhaps slowly over
the coming decades are going to advance to amplify
each other, touching almost everything
we think and care about from climate change to
grocery shopping to democracy. Tom is going to take questions
after his speech and he’s going to be signing downstairs
in lane two after the talk, so please join me in
welcoming Thomas Malone. [ Applause ]>>Thomas Malone:
So good afternoon. I’d like to start by giving
you the two main messages of my book. The first message is that I think we spend way
too much time thinking about people or computers. And not nearly enough
time thinking about people and computers. Too much time for
instance thinking about how many jobs
computers are going to take away from people. And not nearly enough time
thinking about what people and computers can do together
that could never be done before; so that’s the first message. The second message
includes that first one, but goes much further
and deeper. The second message is
really about a new way of looking at the world. In a sense this second message
is about how to see ghosts. Now, I don’t mean real ghosts
if there even are such things. But I do mean powerful entities
all around us, all the time that are mostly invisible
unless we know how to look. These ghosts are super minds,
which I define as groups of individuals acting together
in ways that seem intelligent. Now by this definition super
minds are all around us. For instance, every hierarchical
company or other organization that you’ve ever heard of is
an example of a super mind. A group of individuals
acting collectively in ways that at least sometimes
seem intelligent. Also every democracy is a kind
of super mind, whether it’s in the government or a club
or some other organization. Again, not guaranteed to
always be intelligent. But sometimes these democracies
can be very intelligent. Another very important kind
of super mind are the markets that we use for all kinds
of goods and services. And another very important
kind of super mind less visible than the others are communities. Whether those are global
scientific communities like the ones shown here
or local neighborhoods, or many other kinds of groups. Now one of the first things
you realize when you look at the world this way. Is that these super
minds run our world? Almost everything we humans
have ever accomplished from investing writing to making
the turkey sandwiches I have for lunch almost every day. Almost all of those
things were not done by single individuals
acting all alone. They were done by super minds. By groups of people
working together often over time and space. Now like individual people, some of these super
minds are really smart. Some super minds
are pretty stupid. Sometimes we really like
the things super minds do. Some super minds can be evil. But whether we like them or
not, we can’t accomplish much of anything without
somehow working with or influencing these
super minds. Now another thing we see
when we look at the world through this perspective
of super minds is that computers can help
make super minds smarter. To take one of many possible
examples think about Wikipedia where thousands of
people and computers all over the world created a
new kind of super mind. And that super mind created the
largest encyclopedia our planet has ever known. So that’s an example of
computers creating a new and newly intelligent
kind of super mind. Of course it’s not guaranteed that computers will always
make super minds smarter. For instance when fake news
influences voters in a democracy that can make the
democracy less smart. But if we use them wisely, I
think we can use these computers to create human computer
super minds that are smarter than anything we’ve ever known. And that’s what I want to
focus on this afternoon. One way of framing the
core question here is this. How can people and
computers be connected, so that collectively they
act more intelligently than any person or group or
computer has ever done before? Now that’s a big, hard
question but one thing that can help us answer that question is
understanding the difference between two kinds
of intelligence. The first is specialized
intelligence, the ability to achieve specific
goals in a given environment. The second is general
intelligence. The ability to achieve
a wide range of goals in a wide range of environments. Now here’s something a
lot of people don’t know. Even the most advanced computers in the world today have only
specialized intelligence. Take the IBM Watson program
that beat the best human players of Jeopardy a few years ago. I know from the person who led
the development of that software that that program couldn’t
even play tic tac toe, much less chess. It was very specialized to the particular task
of playing jeopardy. At the same time, any normal
human has far more general intelligence than the most
advanced computers in the world. Even a five year old child can
carry on a sensible conversation about a much wider
range of topics than today’s most
advanced AI programs. Now that’s today’s
state of the art, an obvious question is
how soon will that change? When will we have human level
artificial intelligence? If you do a poll asking
people that question today, the average answer you’d
probably get is about 20 years from now we would
have human level AI. Some of you might be very
excited by that possibility. Some might be scared. But here is something else
a lot of people don’t know. People have been
asking that question “When will we have human level
AI” ever since the beginning of the field of artificial
intelligence in the 1950’s. And for that entire time
the average prediction of when we’d have human level AI
has always been about 20 years in the future, for
the last 60 years. So is it theoretically possible that this time the
prediction could be right? Yes, theoretically possible. But I think we should be
very skeptical of anyone who confidently claims that
we’ll have human level AI in the next few decades. In my own view, we’re very
likely to have that someday, but that’s likely to be many,
many decades in the future. And part of what that means is
that in the meantime all uses of computers will have
to involve people. One way people often talk about
that today is to say we need to have humans in the loop. When they say that,
they’re often thinking about one person, one computer. There’s nothing necessarily
wrong with that way of thinking about things, but I think a much
more useful way of thinking is to think about to start
with the human groups that have accomplished almost
everything we humans have ever done. And to add computers
to those groups. Then we can use the specialized
intelligence of the computers to do the things they do
much better than people. Like arithmetic and certain
kinds of pattern recognition. And we can use the general
intelligence of people to do everything else. Perhaps even more importantly
we can use computers to create hyper connectivity,
connecting people to other people at a
scale and in rich new ways that were never possible before. Now one way of summarizing what
I’ve just been saying is to say that we need to move from
thinking about humans in the loop to computers
in the group. That’s my slogan for you today. But how can we do that? How can we create
more intelligent human computer groups? I think useful way of
thinking about this is to think about the cognitive processes that any intelligent entity
needs whether that’s a person or a computer or a group. You can think of those in
terms of five key processes. First to act intelligently
you have to decide what action to take. To do that, you usually need
to create some possibilities for actions you might take. You can usually do both
of those things better if you can sense
the world around you and if you can remember
the past. And if you’re really
smart, you can learn from your own experience
to do all of those things better
and better over time. So one way of using this
framework is to think about how groups can do
these different kinds of cognitive processes. But think about that, let’s
start with the decide process. And think about different ways
groups can make decisions. I think one of the
most important parts of my book is a categorization
of five fundamental types of super minds for
making group decisions. The first and most
obviously is hierarchies, where group decisions are
made by delegating them to specific individuals
in the group. Another possibility
is democracies where group decisions
are made by voting. Another kind of super
mind is markets where the group decisions
are really a combination of all the parawise agreements between individual
buyers and sellers. And in communities the group
decisions are made by a kind of informal consensus based on
shared norms and reputations. Now all four of those kinds
of super minds require at least some cooperation
among the group members. But if you don’t have any
cooperation among the group members you have the
fifth kind of super mind, which I call ecosystems. And an ecosystem the group
decisions are made by the law of the jungle, whoever has the
most power gets what they want. And the survival of the fittest. Now one way of using this
framework of different kinds of super minds is to use it for
analyzing and inventing new ways of doing all kinds of
things we do in the world. For instance to take
just one example, which is in the news
a lot recently. If you think that we
need to change the ways that women are treated
in the work place, you could use this a check list
to think about possibilities. The most obvious way perhaps is to use democratic decision
making to pass laws that are then enforced by
hierarchical governments about what kind of
behavior is legal. But another way, not quite so obvious would be
to use communities. For instance the Me
Too Movement is trying to change our shared
community norms about what kind of behavior is appropriate,
whether that’s legal or illegal it’s a community
norm that can be changed. So this is a way of
thinking about different ways to do things, and I think
there are lots of possibilities for using this framework
to do that. What I want to focus
on for the rest of our time today is
how computers can create or help us create new kinds
of these different varieties of super minds to make the ones
that already exist much smarter. Let’s start with an example of
a decision that’s typically done by a kind of hierarchy
but that could benefit from involving more
community aspects as well. The decision I want to focus on
is medical diagnosis and I want to talk about a project called
the human diagnosis project. This is a system that lets
medical conditions, doctors, nurses and others get
advice from other clinicians about their difficult cases. They can enter information
into the system about the patient’s symptoms,
the lab test results, etc. and then they
can ask for opinions from other people anywhere in the world connected
to this system. For instance a doctor
at Mass General Hospital in Boston might get second
opinions from doctors at Stanford, or more
interestingly a nurse in a remote African village
hundreds of miles away from the nearest doctor could
use this system to get advice from doctors or others
anywhere in the world. Now one of the things they find
when they use this system is that by getting multiple
opinions in this way they get
more accurate diagnoses and medical – better
clinical care. In a sense that’s a
result of using the system to create more hyper
connectivity, connecting medical clinicians
all over the world in a way that they can use
their community to help make diagnostic
decisions. Another thing they find
when they use the system is that they gradually accumulate
a knowledge base of cases. And they’ve applied artificial
intelligence machine learning to the knowledge base
they already have, and found that the
programs learning from that knowledge base can
already for common symptoms like chest pain, they can
already give diagnoses that are almost, not quite, but almost as accurate
as a human physician. What I think is most
interesting about this is to imagine what will
happen when case or systems like this have seen
millions of cases. Far more than any individual
doctor could ever see in a whole lifetime. I think then these
systems will likely be able to do very accurate
diagnoses of many kinds of symptoms and diseases. In fact, it’s likely they’ll
even recognize new diseases that we humans had never
even noticed before. So those are some potential
benefits of using computers to make medical super
minds smarter by involving many more people. Another example I’d like
to talk about is how to create a new kind of democracy that’s not really
feasible without computers. The example I want to talk about is something
called liquid democracies. Now you already know
about direct democracies like in ancient Greece where
all the voters can vote directly on all the questions. You also know about
representative democracies like we use in this and
many other countries to elect representatives,
which then vote on all the important
questions on our behalf. Liquid democracies are
a kind of combination of those two other
kinds of democracies that have the potential to give
us the best of both worlds. In a liquid democracy you can
always vote directly whenever you want to on a given question. But most of us don’t begin to
have time or inclination to do that for all the
things that need to be decided in a democracy. So you can also in a liquid
democracy delegate your proxy for voting to anyone
else you want to. You might give your proxy
for voting on one category of decisions like military
decisions to one person; you might give your
proxy for voting on environmental
issues to somebody else. And those people can in turn
delegate your voting proxy to still other people who
for instance may know more about the details of a
particular decision that needs to be made than the people who originally had
your proxy would know. And if at any time you
feel like the people who have your voting proxies
aren’t doing what you want, you can always take
back your proxy and vote directly yourself, or
give your proxy to someone else. Now his isn’t just a
theoretical possibility. There have been a number
of political parties in different countries around the world who’ve
already been using this, and running on platforms
that say if our representatives
are elected they’ll vote as the liquid democracy in an online system
tells them to vote. And these parties have already
had some success in Europe and Iceland and several other
places around the world. Google has also used a
liquid democracy like this to make some simple
business decisions. And I think the key point here
is that this is a new kind of democracy made
possible by computers. It wouldn’t be feasible
without them. But it has the potential
to be much more responsible to what voters actually
want and to take advantage about specialized knowledge
about particular problems. So that’s a way of making
smarter democratic super minds. Another possibility
is to use markets to not just allocate resources
but to predict the future. I want to talk about something
called prediction markets where they’re kind of like
gambling, but they’re different in some important ways. In a prediction market you
buy and sell predictions about possible future events. For instance, if you think
that your company is going to sell somewhere between 1,500
and 1,600 units of some product in the month of September, you could buy shares
of that prediction. And if at the end of
September the prediction turns out to be right you get
$1.00 for each share. If it turns out to be
wrong you get nothing. Now it turns out that the prices in these prediction markets are
estimates of the probability of the future event in
the collective judgment of all the people
participating in the market. And it also turns out that
when people have experimented with these prediction
markets they turn out to be almost always
at least as accurate and often even more accurate than any other prediction
methods, like opinion polling or focus groups or whatever. And they’ve been
used successfully for predicting things
like product sales and movie box office receipts and election results
and so forth. For instance there’s some public
prediction markets on the web and here is one of them showing
the probability predicted for Brexit occurring in
the UK by November 1. As you can see they’re
predicting the probability is about 57% now, that it
will happen by then. And you can also look at the
price history at the bottom of the screen to see that
that probability has gone significantly up since late July when Boris Johnson
became the Prime Minister. Here’s another of those
prediction markets of who is likely to win the
next US Presidential election. You can see it shows Donald
Trump 42% probability, Elizabeth Warren
23 and so forth. It’s important to realize that these are not how many
people say they’re going to vote for that person. This is people’s best
estimates of the probability that that person will win
whether they like it or not. And it’s very interesting to
be able to see that change over time as new
information becomes available. Now in these prediction markets
we’ve just seen I think it’s very likely that the
– the participants in the market were all people. But what if we could also
have computers participating in these markets? That’s a question that a
student of mine and I began to investigate several
years ago. We wanted to try to make
predictions of things that were analogous to
what would a competitor and business do or what would
an enemy do in war time. But we wanted simpler
example than that. So we tried to predict
whether the next play in a football game would
be a run or a pass. We showed people videos
of a football game. We stopped the video just
before each play began and we let people participate
in a prediction market buying and selling predictions of whether the next play
would be run or pass. We also did that
with computer bots, who had only some
simple information about what yard line the ball
was on and how many yards to the next first down. And we let those bots make
the predictions and then buy and sell those shares
with other computer bots. And then most interestingly
we had some prediction markets where both people and
computers participated in the same prediction markets. So a person wouldn’t ever know
whether the most recent trade was made by another
person or by a computer. What we found was that
the prediction markets that included both people and
computers were more accurate and more robust to
various kinds of errors than either people alone
or computers alone. So I think that’s an example of how markets can
provide an interesting way of combining human and
computer intelligence. Now some of you may be
worried about your jobs. What – are computers
going to take away my job? Should we worry about this? I think we should all
relax a lot about that. I think it’s theoretically
possible that that might happen. But I think and it’s
almost certain that some jobs will go away. But every time this has
happened in the past, more new jobs have been created than were destroyed
by computers. This has been happening
ever since the Luddites in England in the 1800’s. And I think it’s
likely to happen again. Here for instance is what
happened with US employment over the last couple
of hundred years. In 1800 over 90% of people in
the United States were employed in agriculture in some way. Today it’s less than 2%. But the difference was more
than made up for by increases in manufacturing and
increasingly in services. In fact, population has
grown significantly during that period, so way more
new jobs were created in those other industries. And while I think we should
worry about individual people, whose jobs are destroyed
and who can’t find new jobs. I think we should
worry about what to do for those individual people. I think it’s very unlikely that there will be long
term massive unemployment for most people in the economy. It’s never happened in the past
when people have worried about. I think it’s unlikely
to happen again. Here are a couple of examples to illustrate what I think
is very likely to happen and will cause that same
thing that’s happened before to happen again. Let’s start with an example of the technology called
the printing press. Starting in about the 1400’s
the printing press was able to make very rapid,
cheap copies of things that had previously been done by scribes copying laboriously
by hand all day long. So those scribes essentially
had their jobs eliminated. But think of what happened next. Since it was possible to
make very cheap copies of so many things so easily, all kinds of new things
became economically feasible to make and sell. We had not only copies
of a few important books, but we had newspapers
and magazines and novels and comic books. And think of the jobs that were
created to do all those things. Not just printing press
operators, but novelists and newspaper reporters and
editors, and book store owners and newspaper delivers
and on and on and on. So I think the same kind
of thing will happen again. Here is another more recent
example, think about the job of a reference librarian
who helps patrons who come into a library, use
the reference materials that are there in the library. I don’t know the exact numbers for people employed
in that job now. But I think that
a new technology like google search has
certainly decreased the rate at which people with that
job are likely to increase. But think of the new jobs
that have been created for every reference
librarian job that may have been eliminated. We now have not only
software developers and database analysts,
but website developers and search engine
optimization specialists and online advertising sales
people and on and on and on. Now it’s not always easy to predict what the new
jobs created will be. But in my book I give a
lot of example of new kinds of human computer super minds
that are likely in many cases to create lots of new jobs. For instance I talk about how
we could create new super minds for helping deal with global
climate change involving people from all over the world. I talk about how we may be
able to do a much better job of predicting terrorism
by in part, involving lots more
people in pieces of that. I talk about how we may
be able to do a better job of developing corporate
strategic plans that are much – done much more rapidly
and much more innovatively and much more comprehensively. Partly also by involving more
people and also computers. And I talk about how these
super minds can help deal with job loss caused
by automation. Now in the book I also talk about some pretty philosophical
things about super minds. For instance, I talk about whether super
minds can be conscious. There’s a long section about
whether Apple Incorporated, the whole company itself a
kind of super mind is a section about whether that
super mind is conscious. I won’t give you all the
details today but it turns out that Apple is aware of its environment,
it’s aware of itself. Its goal oriented, it integrates
many kinds of information. And if you’re willing to exercise a little empathy
I think it’s quite possible that Apple as a company
experiences feelings of certain types. So I think it’s not
crazy to think that Apple and many other companies
and many other groups, in fact do have a
kind of consciousness. So let me leave you
with one last thought about where I think
we’re headed. I think in the long run
as our world becomes more and more closely
connected by many kinds of electronic communication, it
will become increasingly useful to think of all the people and
computers on our planet as part of a single global super mind. And perhaps our future
as a species will depend on how well we’re able to
use our global super mind to make choices that are not
just smart, but also wise. Thank you very much. [ Applause ]>>Adam Kushner: Thank you, I think we have time
for some questions. I think people are
supposed to come to the microphones,
is that right? Okay if you have a question,
please come to the mic.>>Yes thank you. That was fascinating. The idea that institutions
or organizations are minds, and in some sense
conscious, it also implies – it implies not only that
they make decisions on your – on your neural processes slide,
but also that they have goals. And I guess the question is as
these organizations get more and more efficient – I mean sort of what you’re saying I think is
you’re talking about processes that are not just since
computers, but really go back to the beginning of at least
capitalism and before that.>>Thomas Malone: Absolutely, at
least as far back as humans go and probably even further if
you want to think about bacteria and so forth having
collective intelligence.>>So how – we can get more
efficient given the goals that we have. But, — but how is it that these
– I mean it’s a common place since at least the 19th
century that – that we work – we work for the machines, the
machines don’t work for us. I mean how – how is it that
we get these organizations to have goals that are –
that are actually goals that help humans rather than
helping the organization – the super minds pursuing
their own goals?>>Thomas Malone: I talk about
that question in the book. And even if there are no
machines involved you could say, you could say that a hierarchical organization
there’s a classic sociology paper about unions,
labor unions and how over time the labor unions –
the leaders of the labor unions who have full time jobs doing
that, come to more and more look out for the – the benefit
of the union itself and their own jobs rather than the people they are
supposed to be representing. There’s nothing unique
about labor unions, that same thing happens
in businesses and many other organizations. So I think that’s a risk, whether there are
machines involved or not. I – I’m not sure I would
agree with your premise that we’re always already
working for the machines, but in some cases it
certainly may feel that way. I think that essentially
what we need to do is think about how we can
influence the goals of the super minds
that are important. Sometimes we can do that
in obvious and easy ways, sometimes it’s not at
all obvious or easy but that’s what we
should be thinking about. And I do, in the book talk about
one perhaps cheerful possibility which I call the law of
evolutionary utilitarianism. I won’t go through the detail
of how this all happens, but it turns out
that if you believe that bigger organizations or bigger groups often have
more power than smaller ones. And a way of attracting
people to join a group is to do something that’s good for
them, that they like and want. Then in the long run in general, it’s likely that
super minds will more and more fulfil the desires
of more and more people. Not always, not immediately
but that’s at least one reason for long run optimism
about whether or super minds will actually
fulfil our human desires are not. That make sense? Okay, next question?>>Hi I was wondering
if you could share some of your thoughts
about the future of human computer
interfaces, because I wonder if at some point in the future
the bottleneck is [Inaudible] intelligence in general, but the
ability of humans and computers to understand each other.>>Thomas Malone:
Ability of computers what?>>To understand
humans and vice versa?>>Thomas Malone: Yes, well that’s already
true to some degree. Some computers already guess
what you may be thinking when you get auto complete
and typing a text message. The computer is trying to
guess what you’re thinking and often does a reasonable job,
though certainly not always. So I think increasingly
computers will get better and better at having
accurate models of the humans that they’re dealing with and
humans also will get better at having accurate models of the
computers they’re dealing with. You ask I think about
the long term of that. I don’t talk much about this in the book though I do
mention it a little bit. I think it’s pretty obvious
that where we’re headed in the long term is more
and more neural connections. That is electrical
connections to our brains that connect us to computers. I think it’s quite possible
we’ll have fairly advanced forms of that even before we have
artificial general intelligence. So it’s not going to happen
immediately by any means but I think more and
more that will happen.>>Thank you.>>Thomas Malone: Thank you. Question over there, sorry.>>Hi, I’m a physician and
once we had implementation of electronic health records,
physicians were very hopeful that the gathering
of the data that came from this massive community of doctors would help us make
better diagnosis and so forth. But the reality is at
this point in time, all the data is being gathered
and nothing is coming out of it, perhaps because it
is no collecting – collective effort
gathered the right data. And just gathering
data for the sake of gathering data does not
translate into outcomes. So is there any concerted
effort, in the way that you presented
that case to create that rather than just – because we have
all sorts of data currently with the electronic health
records that we have.>>Thomas Malone: That’s a very
good point, just gathering data by itself doesn’t guarantee
anything good will happen. There are often other
things you need to do to have that data be useful. And I think what you’re
describing is what has happened all too often in the
healthcare system that we gather these
medical records and then we make
electronic medical records but we don’t really
think very hard about how we could use
them, and so the data turns out to not be very useful. One thing that I
think was interesting about the example I described
was that they gathered it in a very particular way
where they wanted to get help from other clinicians and that
led things to be structured in a way that they were
able to learn useful lessons from using the computers
as well as other people. But you’re absolutely
right, it’s not guaranteed and it may take some time
before that actually happens. Let’s see maybe I do
one more over here, because I had several
over there.>>So I’m an elementary school
teacher, so this spoke to me on that level because
you know part of a super mind in
the classroom. And the big buzz word is
community, classroom community and of course now at the
computers it’s global community. So my wondering is
because we talked about the different
decision making. Do you think that it’s
more such logical question, but do you think that humans
have a natural inclination towards that decision making
model but we just use the others because of the fact that it’s
more difficult to get consensus when you have those
large groups of people, and as it becomes
more globalized, we go back to that
community thing or ->>Thomas Malone: Yeah, it’s
a very interesting question. I think it is true that
we humans have a natural inclination to use
communities as super minds, because that’s how we grew up. Hunting and gathering
tribes were communities, so we all have a genetic
understanding of how to be part of a community. But over time we humans
have invented other ways of working together. In fact, you could even say
that hierarchies and democracies and markets are a kind of
artificial intelligence. They’re a kind of artificial
super mind invented by we humans that – and they’ve turned out
to be surprisingly powerful and useful in many situations. They’re not – no
one is always best. In fact, there’s another whole
chapter in my book devoted to analyzing the relative
advantages and disadvantages of these different
types of super minds. So I think that kind of
gets to your question. Okay another question here.>>Like the main character
in 1984, I can see the role of consulting super minds. But I [Inaudible]
hand over individual or collective responsibility
to anything not human. But my specific question
is you mention that Apple has a consciousness and we should have
empathy to understand that. My question is does Apple –
is Apple capable of empathy? Because we all know they’re
unfortunately some human beings even restricted access
to empathy because of [Inaudible] based on
theory of mind and all of that.>>Thomas Malone: I heard two
questions in what you said. The first one was should
we ever delegate decisions to a super mind or should there
always be humans making them? Unfortunately we’ve been
delegating decisions to super minds for
many millennia. Whenever a human tribe or a
human democracy or a market, or a hierarchy, a government,
an army or anywhere. Any time a group of people
makes a group decision like that the individual
humans have in some sense delegated
the decision making power to a super mind that
they are part of, and in many of those cases
may include only other humans, no computers. But we’ve been doing
that for a long time. The question about
Apple and consciousness. When I talked about empathy, what I meant was not
whether Apple has empathy, though I think it may perhaps. What I meant was can we, as
humans have empathy enough to attribute feelings
to a group of humans. In fact, as an individual human
I don’t really know what’s going on inside your mind. I assume if you act in
certain ways that you’re angry or afraid or whatever. But I don’t actually know that, it could just be a robot
pretending all those things. So it requires a certain amount
of empathy on the part of me to understand another
human – human’s emotions and I think the same thing is
necessary if you choose to try to exercise it, to understand
the emotions in a super mind.>>I’ll be interested
to see how this plays out because I understand there’s
something in the structure of the healthy human mind that
has that marrying capacity for empathy and can that
be inserted into computers.>>Thomas Malone: Okay unfortunately I
think we’re out of time. I see the wrap it up sign,
so I’m sorry that the others of you won’t get to ask
your questions publically but I’d be glad to talk to you
afterwards and anyone else. Thank you very much. [ Applause ]

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