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okay so i'm i'm gonna talk about artificial intelligence and what it's for
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i don and i couldn't begin with a very simple definition um one that's
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on the enough in a i haven't i hold it the standard model so um
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of course a eyes about making intelligent machines but what that
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means it has meant the most to history the field is
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um machines whose actions can be expected to achieve their objectives
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and so this is what we do we um we developers optimising machinery uh whether it's um
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turning algorithms or reinforced with any other than zero supervisory no buttons
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and then we um like in the objective and uh said she going to
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solve the problem for us there's lots of different branches of a on my
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um but uh the standard model applies to you all of these and in fact in many other
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disciplines like control theory of statistics operations research
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and economics of them will operate on the same standard model
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of uh optimising some fixed specified objective
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uh and one of the characteristics of their research and
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he is is that the the goal no always explicit
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but certainly implicit in what we do when um is that we would like to g. general purpose they are
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um which would mean roughly a machines are
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capable of quickly learning to produce high quality behaviour
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in any task environment and any here when means certainly any task environment which humans
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and perform well um but terribly many other task environs uh as well
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where are a physical or a comedy limitations prevent us from before well
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uh and the question that i really want to address today's what if we sixty and
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that seems like a reasonable ask given that we are all trying to do this um
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and uh you know one one vision of success would be
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that that we in general but to say i mean it's abuse
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uh use that capability extended with the physical appendages that robotics could use is
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um to do what we already know how to do we already know how to build houses and
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a line water pipes uh and so on so forth and so
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if we can uh simply have a high systems carry out all the complicated process isn't bowl
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uh which apparently very expensive we can use that to lift the living standards of everyone on earth
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to a respectable level um without yeah additional science fiction inventions of
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uh you know eternal life and and possible like travel and so well
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and just doing that just giving everyone a respectable middle class a
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while but you know in the west the cool respectable middle class
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stanton living uh of the foundations a goal of quality of life
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would be a tenfold increase in the g. d. p. the well and if you translate
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that into the net present value uh would be about thirty and a half or trillion dollars
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so that's one sort of ballpark estimate of
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uh the size of the price that a a i is working towards
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and there were also the additional things we might be able to do
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uh i don't currently condom like raise the quality of yeah uh uh
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give everybody enough an extremely high quality individualised personalised to to kind of education
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um and uh improve the rate of scientific research
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progress but oh no if you was to i'm sure
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what if we succeed um this is what he said in nineteen fifty one
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it seems probable once a machine thinking that that had started it would not take long to outstrip of people cowards
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at some stage therefore we should have to expect the machines to take control
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um so he says this with the with
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gnome solution no mitigation uh just a resignation
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um so these are two very different visions and uh obviously the con really coexist
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um you know if we move forward in time from during united
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but you want to present uh we're seeing some of the capabilities that will once dreamed
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of i'm starting to become reality link uh the salt driving car and i will go champion
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ah this is the example for my group be uh the monitoring system
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what a nuclear test ban treaty is now a large
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a probability based a. i. system this is a picture from
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uh north korea showing the you know instantaneous and accurate
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detection of a new to explosion that took place twenty thirty
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um we're of course finding ways to use a i for evil
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not rather than a i for good um were the most worrying developments
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is the use of a i can kill people um and we're already seeing um
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uh events for example libya last year the drone on the left a
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cop who from um from uh s. t. m. which is the turkish
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weapons company uh was used to uh to attack humans autonomously
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so not remotely piloted um but operating which was the um
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and uh in geneva uh in the then the the
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um the member states of the conventions that commission weapons will be once again
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uh to make no progress on the treaty banning these but
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so with all those progress people on our uh uh perhaps taking
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seriously the possibility of success agreed in general but to say hi
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um but as melanie mitchell's tool pointed out uh we're not there yet
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um and in fact i i agree with no these point that we have further away than many people think
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um but when you look back over the longer time scale progress over
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the last seventy years b. c. series a very important breakthroughs a happening
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um and get learning is just the latest in a long series
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of breakthroughs we need more breakthroughs but i think we have to assume
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that those breakthroughs are going to occur and we will have a i systems are what make better decisions
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that humans across the broad range of real world scenarios which humans all
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and ensuring is asking us uh if that happens um then when creating
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machines that are more powerful than human beings 'cause it's our ability to make good decisions intelligent
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decisions in the real world gives us how over the manager we have this disease but it
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um and so we creation machines that are more powerful than us
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how do we retain power over entities remote awfulness rather
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um and sure enough he sees no solution to this problem
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um i actually think there is a solution but it means we can socialising artificial intelligence
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from the ground up so to see an example of how things go wrong we can look at
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um what's happening in social media so when you specify injected light
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maximising click through the probability that the user clicks on the next station that is recommended
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we could also maximising p. h. mm mm of various other kinds of a proxy metrics um
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you might think well the learning algorithm is going to learn what
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is people want to click on and understand things that they're interested in
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um and that seems like a good idea but in fact this is not what happens the
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algorithms don't just no no people want because that's not the best way to maximise quite true
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if your reinforcement learning algorithm um what you learn is
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a sequence of actions a policy that will maximise longterm reward
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and uh the policy does that are changing is take a while ago program
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it changes the go what by adding pieces to it um with the part with the the goal of winning the game in the long run
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if your recommendation over them um then you change the
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board namely the brain of human but you're interacting with
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uh with the goal of winning in the long run so you stand a sequence of content
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that changes the person right into a different person
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who's more predictable and if the person will electable
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then you can get a higher click through rate from them by sending them of the stuff that you know the good click on
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and so this is simply the solution to the optimisation problem on
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that was specified by that's so should media platforms to maximise spectrum
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oh this is probably not the solution they want and so they're not the
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solution we want but this is the solution to the problem that was specified
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um and it's creating a uh i would argue a pretty catastrophic situation uh in the well
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and this is just one example of what goes wrong with miss specified
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objectives that um as they are it gets better the outcome people gets worse
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um because their assistant is um we were able to make a mess the rest of 'em through
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uh through its ability to make decisions in the world optimise the incorrect objective
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that we've given it uh it will also the more able to prevent us from interfering
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with its action so certainly the um the learning algorithms that operate in social me deep levels
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don't have the ability to prevent interference um but the
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corporations that protect them certainly do have the ability to
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prevent interference with the operation of the oh so if it's a if it's the case that
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a a i systems the ceiling incorrectly specified objectives could lead to catastrophic outcomes
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um then that's suggest that there's something fundamentally wrong with the standard model because it
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requires us to specify objectives completely incorrectly and
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coming back to the purpose of the temptation
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uh the quality of life i i'd be willing to bet that the foundation does not think
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that it knows exactly how to define quality of
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life that we could take any future sequence of
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states of the will and write them uh according to which one has a higher quality of life
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oh and do so using it explicitly written down definition of quality of life
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um i don't think the foundation things that uh something uh governments
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think net um to some extent me no but when we see it
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um so if it's not if we experience something that we haven't thought of
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um either as particularly desirable weekly undesirable but we experience it
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be realistically it is um then we can sort of tell
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but uh our duty to write down the bonds completely incorrect specification
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of the quality of life or any other objective in the real world
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uh uh is that that's really an impossible task so the new model that
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um i'm going to try to convince use is the right way to think
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about a guy is based on replacing the definition that i gave you beginning
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that machines are intelligent extent that their actions can be expected to achieve
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their objectives you replace that we have a a just a small change
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um machines uh beneficial to the extent that their actions can be expected to achieve
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our objectives and so this is the key to change from their objectives to our objectives
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um and of course is the more difficult problem because our objectives are
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in us and in particular were unable to explication exactly what they are
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um but this is a much more robust formulation of the problem that we try to
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stall because it doesn't require that we extricate our objectives and plug them into the machine
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which then assumes that those objectives are exactly correct um so i've formulated
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this uh in deference to isaac asimov m. p. three principles unless some overlap
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with icicles but also some uh some very what difference
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um so the first call uh is that a first principles that the robots call
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is to satisfy can move preferences um and trust is
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here uh i'm using it in the same sense that
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uh economists use it which is which
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is really um preferences over lotteries of probability
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distribution over entire futures of the world so everything that you could possibly care about
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um i am not just o'connor pizza do i like that entire features
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um then this one principal and this is the key point is that the robot does not know what
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those preferences are and this uncertainty about the objective uh turns out to be central to
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uh enabling us to retain control of the machines uh the principal
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basically says uh okay so if the rubber doesn't know references are
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working than where is the grounding here the grammy yeah it's o.
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e. in human behaviour that that um our uh our on line preferences
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um generate a behaviour and therefore our behaviour provides evidence of what
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goes on my preferences ah it's not it's a complication in project process
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uh that produces behaviour and therefore there's
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no straightforward mapping from a behaviour fact preferences
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um but nonetheless that is the source of evidence of working preferences ah
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at least to to have better direct understanding right um so you can take these three principles and formulate
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oh oh oh a mathematical definition of the problem that a.
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i. system is trying to solve which we call assistance game
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so it's a game because there's at least two participants at least one robot at least one him
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and uh its insistence came because the robots goal is to satisfy
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used basically to maximise containment payoff in the language of game theory
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i'll laugh robot does not know what that payoff function it's when you write down those games and soul
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um when you can look at this we shall see what's the nash equilibrium
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what is the uh the way that we watch a a dissatisfied three principles
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and and you see they gave us that don't occur in the castle model where the robot lose the payoff
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um for example a robot solving assistance games will be for to humans
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um who are the ones you actually have to pay off
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and no it um the people will ask permission before turning out
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uh an action that would change possible role uh who's down to robot is not sure
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so for example if a robot is to is to ask we're
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i'm fixing that problem dioxide concentrations so taking it back to korea industrial levels to the atmosphere
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um if it comes up with a solution that involves turning the oceans
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into soccer aggressive uh and we haven't folded up references about the oceans
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um then rather than just caring now the plan of what you would
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do if they believed that the objective was to still got lots levels
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uh it would ask permission was that is a good idea for me
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to change the options into software i guess it a while solving the problem
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and we would say no army no let's not do that on something else um and in the string case
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ah if um if the robot uh might be doing something that
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we really don't like um it will allow itself to be switched off
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so this is sort of the extreme case of asking conviction uh it's in a timely
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happy for us could just switch it off because it doesn't want to do whatever it is
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uh that would cause us to switch it off but it because it doesn't know what that is so it always allows us all
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we switched off and that's the offices of the classical machine
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that's 'cause you're in an objective it needs to be completed correct
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uh which would never allow us all to be switched off because that would guarantee failure achieving dejected um so
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oh oh oh at least in civil cases we can even prove that you're
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right so that the raising of the level to the decision of the humans
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to be all this kind of mushy mushy that's all systems things
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we can show that rational for humans to build and deploy she's
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uh that's all systems case but this is a sensible thing for
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us to do um and the nice property you the small ones that
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as you improve it a on a um you're actually getting better rather than worse out
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'cause machine is gonna be better at learning and understanding of preferences i think
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caring enough actions that help us to cheat us you're thinking of all kinds of
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complications and difficulties uh and the problems installing jester
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the last two lots of complications so um they include
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ooh the fact that there are many humans and machines make
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laughter may humans is in the same situation as a as a
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as a government or in some some extent uh as a human being
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um and that means to many of the problems are more velocity and economic
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so um this is not a new problem but perhaps a bus to get
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to know us all the a. i. setting uh will help us to make
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progress on on figuring out how to trade off the p. r. for instance
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but many people um it's only and not suggesting as many people seem
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to think that there's one set of human preferences or one set of values
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there are a billion humans and that means they're really that's a few
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preferences um and of course it or i think ah it's also the case
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that they can be many machines are involved in this assistance again uh they
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want will necessarily be designed by the same company you wear hats in software
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yeah and so you also need to figure out how not to have a strategic
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interactions among the machines equipped for princess di
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them like a failure mode the their interactions
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um i understand preferences of of humans involved
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actually he doing with them and that we're emotional
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a fact that with my okay uh uh and also the very for me the fact that we are
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change your preferences on valuable uh obviously not born with complicated preferences about the
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future oh oh mm and the fury that uh that deal satisfactory if human beings
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preferences can be changed by the actions of the mushy um and then
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if it does approach turns out to be right then that means that because
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every every year they i has a at at its
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foundation the idea that the objective is fixed and known
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uh whether it's in search there's a cost function like goal
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it's in the in for some learning but uh that's what function
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um so we see areas have x. node objectives uh and therefore since that assumption is not correct
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uh we have to be the only into these areas one of what the foundation
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um where both technology the objective is just a screen special case it on and then
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i think to get the rest of the will to fly into this uh this new approach
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uh we need to start of figuring out how to develop real
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uh applications that bodies points so just summarise i
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think a. i has a enormous potential for good
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um enormous economic value and uh and that leads
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to i'm say unstoppable momentum um and some people
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i think that we can um avoid risks simply by stopping a ah
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uh i think that's very unlikely um so for the time being i
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would like to try that by the way i away from the standard model
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um to once a form in which a i would really beneficial
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the humans even though we don't know what that right now there are um
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there are those who are uh uh we want to create a
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and you feel the a. i. f. x. i don't wanna discourage them
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from at um but i would say that the model in which uh
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yeah this is who are sort of wagging their fingers yeah researches
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a thing bad bad bad is less effective than a model in
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which a. i. researchers get up in the morning and and what they
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do uh as a i researches is necessarily beneficial to human beings um
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and we really talk about changing well what chance as good a okay
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as high quality yeah researcher michael t. i. systems development um
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so that it's it it will be good for human beings and um you know there
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are lots of things that uh that doctors
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and medical professionals and pharmaceutical companies could you um
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that would be harmful and some we'd seen some pharmaceutical companies do things that are harmful to human beings in recent years
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um but i think but i i'm not when when are
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medical researcher gets up in the morning and says okay reduces many good medical research
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ah it's going to be good for human beings that that was the successful and uh
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and that should be the case but they are just what counts as good a hour
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is a i that's beneficial e. other other problems that ah i've not
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talked about today and one is misuse and we just all previous two examples
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of misuse if they are right now to generate it falls uh
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information and you got to break a security systems and so on
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um and uh i don't have a solution uh that problem and then over use
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uh meaning that if we have a if we do have a either pays itself and does everything
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um for us uh we have our own um sort of social and cultural problem
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how do we retain the the intellectual bigger if human civilised nation
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uh when it's possible to simply leave the running it's a playstation machines
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um and this is addressed in uh in many works of fiction will me
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this picture here being one of them uh the machine stops which i highly recommend
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story right impostors another one um and the i who we can