♪♪♪(beep)Hello Adam. This is your Central MonitorSorry to bother you on your walkBut I need to let you knowthat your vital signsare outside normal parameters. You are showing signs of ST-elevationtachicardia and high blood pressureI suggest you slow your walk until I cando a full analysis ofyour cardiac helathI will be sending your vital signs to your doctor in just a moment.Please stay on the line...There are significant societal disruptions throughout history, particularly in communication. The invention of writingreally allowed for the creation of what we now know think ofas organised societyin terms of the empires of the past because you could then actuallygovern at a distance you couldcommunicate at a distance.The printing press, the telephone the telegraph,broadcast media, all of that stuff was extremely important. And each on in it’s own way created a revolution in society.I think we are in the midst, on the verge, of one, beginning with communication that we are experiencing in the internet.And now with the ability for machines to become smart and learn,that we are on the cusp of another amazing transformation. This is a perfect futures topicbecause nobody knows what’s going to happen.♪♪♪♪♪♪The brain is what makes us everything that we are,you know, without it, we're really nothing. So, everything we do everything that we think,and everything that we remember, is all based on, it's done by our brain.So, you know, people might think the brain is just your intellect,but your brain is everything,your brain is what allows you to catch a ball,or dig a hole or watch TV,as well as do science or write a novel or,you know, gaze at the stars, all these things happen from our brain. The human brain consists of about 100 billion neurons.And each of those neurons makes about 10,000 connections. And so there's a vast amount of complexity there.And we're really only scratching the surface and understanding how it worksSo a lot of older research focused on the things that we find hard. So reasoning, and logical thinking, and so on. But now we are, I think we understand betterthat a lot of the computational complexity of the brainis actually required just to solve very simple tasks,which sounds simple, but turns out not to be simple.So for instance, just understanding a visual sceneabout third of your cortex is devoted to processing visual information,and yet you have no conscious awareness of what you're doing, it just happens.♪♪♪ One of the things that makes it so complex isthat it's complex on so many levels. If you look at a single neuron,a single neuron is an amazingly complex little, basically nano-machine doing incredible things.And then you have small networks of neurons connected together.And then you have large systems in the brain where billions of neuronsare connected through massive neural pathways,and then you get the brain overall.And then you get the body that the brain interacts with,and then you get the environment and the social world.So all these levels, we have so much to discover about the brain,and we know so little, and they all interact in ways that we can't even yet fathom.♪♪♪We have a small understanding of what the single cells are doing, right?You think maybe we understand really well what the cells dobut in fact, we don't on the single cell level.Of course, there's billions of the cells and they're connected in networks, alright?And there's billions and billions of connections in your brain.Now, we have a better understanding of single connections,how they operate, but we really don't understand together as a network, how all this operates.♪♪♪Nervous systems are both in humans and in and other animals have been the basic structure of the nervous system has been conserved for a very long time,perhaps 500 million years. And so neurons communicate by sending spikes of electrical activity to each other. And these neurons are organized into groups, and also into regions of the brain.And different regions of the brain are specialized for different kinds of tasksbut they all have to work together seamlessly.We've certainly discovered a lot in the last few decades,there's been a lot of new developments in technology,which have helped us understand, particularly at the cellular level,how the brain works. I think one of the really big questions ishow the brain is working at a more systems level.So how networks of neurons interact,and how the activity of those neurons lead to our thoughts and our behavior.And that's really one of the frontiers of neuroscience at the moment.So we're using zebrafishas a model system to understandhow patterns of neural activity develop in early life. And the great thing about the zebrafish is that young zebrafish is transparent. And we can insert a gene, which means that neurons glow when they're active. So we convert the electrical activity of neurons into anoptical signal that we can record under a microscope. So we take young fish, when I say youngI mean, just a few days ago it was a single cell,we embedded in a gel under a microscopeand then we can image the activity of every neuron in the brain,as it sits there thinking it's fishy thoughts.So what we want to do is understand two things, firstly, understand how normalbrain development occurs.So understand how a nervous system becomes wired up during development, how patterns of activity emerge, which appropriately represent and process information. So I think that I mean that important for understanding, obviously, our biology,it's also important for developing new forms of artificial intelligence.So all artificial intelligence at the moment,the hardware is designed by humans and put together by, you know, by humans, but in the long term, one can imagine artificial intelligencethat grows itself more organically, perhaps inspired by the kinds of things we're discovering about how real nervous systems are built. ♪♪♪"...we could also tryto fuse the depth image...."♪♪♪ The brain is an amazing device,it's the most complex thing we know in the universeAnd it had millions of years to perfect what it's doing.And so it's only natural that we look to the brain as an inspirationfor the robots and the artificial intelligence systems that we develop.Copying the brain sounds great, in theory, but doing it in practice is much more difficult than I think we all hoped it would be. There's a few problems. First of all, we need to know what's actually happening in the brain. And cracking open the lid,and looking inside and observing what's happening with all of the neurons and cells in our brain is quite challenging.And we have to use a lot of guesswork to fill in all the gapsabout the things we don't know. Once we've got an idea of what happens in the brain,we then have to actually reconstruct it in software, for example,and that is also challenging. So we have to do things like create artificial neural networks,in software that run on a computer, or in the cloud.And there's a lot of engineering and tinkering,that's needed to get those things to work as well.An artificial neural network is very simple at its core. It's a representation in software of what we think goes on in the brain.It consists of artificial neurons, or units, or cells, depending on what you'd like to call them. And they represent, abstractly at least the neurons that occur in the brain. But it's not enough to just have neurons in this model, they need to be connected together. So the second key component is connecting together these neurons in the artificialneural network.And that's where the real magic of artificial intelligence occurs. The brain is very different from a computer, the way it's structured. A computer basically has the CPU is separate from the memoryand connecting the CPU with the memory,you have this thing called the bus, the memory bus.And the memory bus is working full timecontinuously when a computer is turned on.And it's actually a bottleneck. So the CPU can be very powerful,and the memory can be huge, but you're limited as to how much informationyou can transfer between the two.And that is a very limiting factorin the overall power of the standard computer.The brain, on the other hand,works massively in a massively parallel fashion,every single neuron is doing the best it can all the time. Even the the current best AI that we haveis still very, very different to the brain.It’s… you might say it's brain inspired,but it's not copying the brain.In the brain is massive amounts of feedback connections.So obviously, when we process sensory input,and that comes up intohigher brain regions,and gets further processed and abstractedfrom from the original input that we see. But there's also a massive amounts of feedbackcoming from those higher regionsback to the perceptual areas. And this feedback directs where we lookand it gives us expectations of what we might see.And when those expectations are violatedwhen something unusual happens,you know, we we attend thatwe are forced to attend that we pay attention to it.So a typical neural network will have a stereotypical structure,You'll feed some sort of input into the network:Now, that could be imagery,it could be videos,it could be the sound of your voice. And then that data will go through many layers of neuronswithin the neural network,sometimes hundreds, or eventhousands of these layers,go through all the connections between those layers.Those connections will gradually get changed over timeand that's the training or learning process for the network.And at the end, you'll spit out something like a classification,where the network tells you what it thinks it's hearing,or what it thinks it's looking at.Scientists and engineers have invented a whole myriadof ways to train these neural networks.But the basic premise, it typically revolves around feedback.So you feed some sort of datainto the networkand you look at what the network,for example, classifies it as.Now the network, at the beginning,might be very good at doing it,it might get it wrong.So you then give it some feedbackabout what it got wrong,how badly it got it wrong.And then the network will subtly alter the connections within it,until it does the thing correctly.♪♪♪"Orange.""Mandarine?"...Oh! She's got it!Both the human brainand the brains of other animalsare very good at solving these tasks,like processing sensory information,for instance, visual information.And that has been a great inspiration to artificial intelligencein terms of the kinds of problems it is trying to solveSo in the early days of artificial intelligencethere was a focus on reasoning problems,which feel like hard work to humans.But now people have become more interested in trying to buildthat higher level intelligence from this, what you might call low level intelligence,which is still extremely complicated.And one of the big inspirationsfor recent developmentsin sort of neural network versions of artificial intelligenceis the hierarchical structure of the brain.So for instance, to process visual information,there are several layers of cells in your retina,and they send connections to more centrally in the brain, then there's several stages of processing,which is arranged fairly hierarchically.And so the most popular kinds of neural networks these daysare arranged in this hierarchical form.So each layer of artificial neurons, both artificial and real neurons,each layer extracts more complicated properties from the input.And that turns out to be a very good way to decompose these kinds of computational problems. There's been two major breakthroughs in in the last few years. Those are deep learning and reinforcement learning.And both of those were inspired by biology. So in deep learning, that refers to networkswhich consists of many layers of artificial neurons. So information flows through those many layers,each of those layers extracts more complex information than the layer below.And we had people develop learning algorithms for such networks a few decades ago.But it wasn't really until the past 10 or 15 yearsthat the computing power, and the amount of data we had, were enough to be able to demonstrate the amazing power of these learning algorithms.In reinforcement learning, the idea is how you learn from rewards.So life is one big set of rewards and punishments,and so it's very important the nervous system to be able todecide what actions to take,to maximize the reward, and minimize the punishment.And through through many decades of experiments on animals,classical conditioning experiments,going all the way back to Pavlov’s experiments with his dogs.It's been possible to develop some very powerful mathematical principlesfor how any kind of learning system can learn from these kinds of rewards,including rewards, which are delayed into the future. So it's one problem to learn from rewards presented immediately,and another problem to learn from a reward which you might not achieve for,you know, days or weeks or months, or even even years.Yet, we now understand both the mathematicsof how that can happen.And an amazing result is that about 20 years ago,people discovered that mathematics is essentially implemented in the brain,in the form of signalling of certain molecules like dopamine.So that actually dopamine actually implements a reward signal in the brainin much the same way as a mathematics saysa reward signal should be implemented.I think when it comes to AI theres two sides to it.Theres obviously a negative sideand theres a positive side aswellif you look at the positivesits amazing for humanity inthe long runwe can see the advancement inthe medical field and technologyin terms of...theres a car outside that drivesitself, thats incredibleand right there, just behindwhere you're sittingthere is a little tiny robotthat solves rubics cubeswe're just in the beginnin ofwhats going to be a long journeyin amazing advancements forhumanityand I genuinely believeto always look at the glass halffull when it comes to itit's going to be great(Computer game noises)Going back to 2018there was a league online game, DOTA 2which accepted into it's ranksof playersa new type of team. It's anartificial intelligence team called OpenAI FiveOpenAI Five not only wonThe International WorldChampionshipsit's gone on in April of thisyear, 2019,to expand it's availability forgameplay.Any personwho might want to playDOTA 2can challenge OpenAI Fivesbot team.We have all of the humansagainst five very special botsfrom OpenAI Five.Now why are they sosuccessful?I think that's a good questionto ask.When we consider how fasta human can learnwe're bound by our ability toprocess information over timeAI changes the speed oflearningand when we consider reinforcentlearning techniquesthe DOTA 2 AI bots have spent and accumulated45,000 human years equivalent of training.And that's why they'reso successful.Obviously, gameplay is a very interesting area of development for AIbut it's actually only one small area.And the applications of AI are far more diverse than that in fact .Artificial intelligence now has the abilityto utilize information in a way that wasn't previously possibleData acquisition using, for example, the World Wide Webor alternatively, when we think about smart cities,the amount of data that you can collect through utilities usagemeans that we have the possibility now to see real world applications where massivelearning can take place of a very brief periods of timeto create a scenario where solutions have found to problemsthat humans have struggled with actually for quite a long timeMy idea about artificial intelligence isIt's a promising approach in the futureHowever it requires a jump in the technology.At the momentit has the problem ofthe acuracyand how reliable is the output of this artificial system?Is there any way to have some kind of feedbackabout the output?Whether it's the correctdecision or not?And I think the future of the artificial intelligenceis going that way.To understandto reasoningabout artificial intelligenceoutcomesand the reasons behind the decisions from artificial intelligence.So how do we copy what's happening in the brainand put it in a neural network?Well, one of the easiest areas to start with is choosing a concrete tangible process that we all doand that's navigation. So all animals, all humans, find their way around,and we have maps in our brain that tell us where we are, and where to go.And scientists have found all sorts of beautifully,navigationally relevant neurons in the brain.And we can literally just copy what those neurons dodirectly into our software models in order to create, for example,robots, or autonomous vehicles who can also find their way around.♪♪♪Artificial intelligence plays a key rolein many other large technology arms races.One of the most visible is autonomous cars,self driving cars, or robotic cars, depending on which term you prefer.Now we've seen a lot of progress made over the last 10 years,and there are dozens if not hundredsof major companies and startups, developing autonomous cars around the world.Now we have cars already and have had for 10 or 20 yearsthat can drive pretty well autonomously on the highway.The real trick has been solving that last five or 1%, of driving conditions.So driving in complex urban environments, when it's raining, or snowing,and knowing how to react when that person jumps out in front of the car,looking at my mobile phone, and this is the area where a lot of the state of the art AI development is actually happening.It's how machines like autonomous cars interact with us humanscomplex, unpredictable, and very vulnerable humans.And this is one of the areas where if the AI becomes good enoughto work out what to do under all situations,at least as well as we human drivers do,you will see autonomous cars everywhere.If they can't solve that, then expect it to be much more subdued.♪♪♪The challenge right now, of course,is that autonomous vehicles aren't as good as us in all situations.And for autonomous vehiclesto really be widely commercially viablethey have to be deployed everywhere.A car that you drive yourself,and then goes autonomous on the highway is cool and useful to some people.But it's not this multi trilliondollar market that everyone is imagining.So in order for them to reallyroll out at a wide scale,and potentially save lives,you really still have to solve these remaining problemsof how to drive and complex city situationswith pedestrians and cyclists all over the place.Two years ago, the the general consensus out thereamongst people working in the in the industryand in the in the scientific research, you know,they we're very bullish on having these cars out and workingin the real world within a couple of years.The CEO of Waymo, which is the Google self driving car initiative,which was, you know, one of the biggest,probably still the biggest in the world.And they were incredibly bullish on this technology five years ago,the CEO has actually come out and saidit looks like we’ll neveractually have fully self-drivingcars, ever…. right?There’s always what he said was,"There's always going to be constraints".Now, what that really means in terms of you know, what we've been discussing,is you know AGIis it to do everything that a human driver does, in driving a car,simply driving a car around the streets,you need [it needs] to be as smart as human to do that.♪♪♪The interesting things we are dealing withare perhaps groups of robots working togetheralthough it’s not as disruptive as some things in the past.You can’t predict when fundamental changes in the science will occurand the next big one may be around the corner or it may not be.But substantial progress particularly in the study ofhuman-robot interaction has been going on as well.You get a better understanding of how it is that we relate to each otherthrough robots and how we can do better in respect to that.A social robot is a robot that can communicateand interact with people or other machines.It's completely different to traditional types of robotics,where they've been designed not to have any interaction with people.For example, in car manufacturing plants,social robotics is specifically designed to have a communicationor engagement with people.Artificial intelligence is playing a really big rolein being able to empower the way that social robotscan understand, behave and communicatewith people in their daily life.I see humanoid robots being developed a lot more in future.The world is currently designed for humans.So if you're entering through a door,you're designed to push it using your hand and your amount of force as a human.If you're tidying up a particular workspace,or you're walking around a city,exploring different sights and sounds.So the world is ultimately built for humans.So designing robots that have either a humanoid form,or an understanding of how the human world works,makes it easier for those robots to integrate into society,but also to create some value and benefit without having to restructurebuildings or tasks or the way that the world is designed to accommodate for that human.♪♪♪The question of can wedevelop a disembodied AIan AI in a box or a computeror will we have to havesome sort of awill the AI have to havesome sort of experiencelike we do.or some sort of feedbackfrom the worldand some sort of way ofmodifying the world.The theories at the momentare that simply the AI needsa way ofperceiving a world, whether it'sreal or simulatedperceiving itself in that worldand being able to make adistinctionas we were talking beforeabout self and everything elseand being able to manipulateobjects, and have basicallya causal influence itselfin the world....lazer scanner.So it's using the depth scannerto position itself on the map...It's really important toexplore artificial intelligencein the sense of embodimentand havinga look at robotsas a way to create that wealthof knowledgebecause until your out andmoving around in the worldlike a robot would doyour not seeing the worldfor what it really is.If you're training throughdifferent data sets or information that'sbeen provided to you, it'sa very contained way oflearning knowledgeof how the world operates,how we engage with eachother,and how do we go aboutachieving tasks on a day today basis.But in terms of embodimentif a robot is then goingout and exploringthe way that the world works,learning for itself throughtrial and erroror through observing otherpeoples behaviouror through simply askingthe nearest person whatshould I doin this situationI beleive thats when we'llstart to see a big burst ofcreating a robotthat can understandhuman experienceand then be able tointergrate into society.The thing I focus on is evolutionary learning. And evolutionary learning is quite a bit different. What I do is based on, well, genetics,it's based on Darwinian evolution.And it's based on competition. So for evolution to occur, you need three things,For myself, being in a group that does field robotics,you need selection,need variation,and you need heredity. And this is true for natural features, it's true for computingprograms,it's true for robots.It’s really, really powerful, it's creative,it creates novelty, diversity, all of these things that are really usefulwhen you think about them in the context of learning, right? For myself, being in a groupthat does field roboticswhere we're trying to takerobots and use them tosolve real problemsoutdoors in these nastyenvironments.It's really about taking thatanalogy, that link tonatural evolution.Natural evolution createslife that survives in a huge varietyof really challengingenvironmental conditionsand what we're tryingto do is distil the thingsthat allowthat to work successfullyand apply them todesigning robots.One thing we do here isdeploy robots into, for example, a rainforestto perform biodiversitystudies, and we don'tknowthe conditions in thatrainforest reallyand it would be good tohave robots that can notonlyadapt how they behavebut have the process thatadapts their bodies as well.And when we start todo that we're heading intothe realmsof this thingcalled embodied cognition.Embodied cognition is basicallythe opposite to Descarte's'I think, therefor I am'.So we could considermaybe deep learning tobe that'I think therefor I am'where I am a system and I justlearn things and then,and then I've learnt thatthing.What embodied cognitionis saying is to be intelligentin that sense, in theembodied sense, you needa bodyand you need a brain, andyou need to act in anenvironmentand if you've got all thosethings together it's theinteractions betweenthe body and the brain,and the body and theenvironmentthat generates these reallyuseful, rich behavioursthat can help us solvereally tricky problems.One way of doing that isevolution, so we can useevolutionto design robot bodies,we can use evolution tolearn the controllersthat operate those bodiesthat we see in the senseof informationthat push out commandsto the wheels or to the legsto move it around.And obviously theirsituated in this environmentso the environment playsa key role.If you imagine having alegged robot trying towalk through the jungle,it's going to need to behavevery differentlythan walking across anice rinkand thats where theenvironment really criticallylinks in to how wegenerate these behaviours.Robots will eventually startto intergrate more closelyinto society.They'll learn to operatearound people, and thenit becomesa very seamless integration.It's no longera human, and therefora robot, it becomes asymbiotic relationshipwhere you're providinginformation or objects toa robot to carry for youand the robots providinga service or perhaps supportback to you.(crowd noises)I think that in it's currentstate AI proves to be quitea useful toolfor science and theadvancements in fieldslike roboticsand automation. I thinkthat in the future AI will bea very big part of our livesbut it's not going to havethe same effectthat the media tends tosensationalisethat it's going to take overand become someall-powerful intelligence.Yeah, it's gonna help usin many other ways.The one thing I don't thinkwe've seen quite yetand we may, is the abilityto handlenon routinesometimes called 'wickedproblems'where creativity and humancapacity for ingenuity anddiscoveryis absolutely crucial tobeing succesful.Now one question of courseis are there enough jobs inthe world to supporta workforce of 4 or 5billion people wherecreativty and ingenuityare the core of it.But at least for right nowthat is the path to success.One has to be able tohandle a job, whetherits as an entrepeneur,as a sole proprietor, oreven working for a bigcompanyyou have to be a unique person.Tom Peters called it'brand you'.You have to offer a valuead that nobody else cando.So preparing to be unique,preparing not just to beone of the massbecause the mass is goingaway, the machines are taking overmass jobs.The Chinese took over massjobs to start with, and nowin the long run it's automationand machines that willbe doingmost of the routine workin society.If you look at the history ofwork, up until almostthe present,routine jobs were the breadand butter of the Americanmiddle class.Sales people, office people,factory people, constructionpeople,they went to work and didlargely routine thingsmost of the time.They didn't have to becreative, in fact they werediscouraged frombeing creative.Theres going to come atime when machines aretaking all of those jobsand the only way to earna decent living is to becreative.So the holy grail for manyresearchers is investigatingthe possibilityof developing AGI, orArtificial General Intelligence.There are a lot of definitionsfloating around for exactlywhat that means.The one that I like is youcreate a machine, or anagentthat has the broadintellectual capabilityof a competentadult human.It can do everything we cando. It can learn how to doeverythingthat we learn how to do,and it cancarry out all the tasks wedo on a daily basiswithout thinking.General artificial intelligenceis the idea that you cancreatelearning algorithms thatcan essentially learn inany situation.We've been incrediblysuccessful in the last fewyears at developinglearning algorithms whichcan for instance learn howto play GO.Just recently it learnt toplay Quake, a version ofQuake.that paper was just published a couple of weeks ago.And so these are very impressive learning procedures,but they happen in very confined domains.So general artificial intelligence is the idea that just in any domain,you can apply this, thislearning approach to rapidly figure out what's the appropriate thing to do in different situations.One of the tests that people derived, or designed was the Turing test.And the idea of this test is very simple. Can a artificial intelligence agent talkingtalking to you through a computer screen or perhaps over a phone line fool youinto thinking it's a human? Now, the validity of the test in terms of being a true test of genuineartificial general intelligence is controversial.And it's a case of if you can fake it, does it really meanyou're intelligent.and indeed, some of the initial approaches to going well on that tasktask have been systems that have faked intelligence really, really well. And then you get downto some deeper philosophicalquestionswhich is, if it can fake itso well it seems intelligent,is it actually intelligent?Or is it just a shellpretending to beintelligent?And that is very muchthe realm of philosophersI think.There was a case wheresomeone got a computerto simulate a foreignboy of about 12 or 14years of age or something.But they put these constraintson it. They made him foreign,or made it foreign,so its level of English didn'tneed to be that high.And it was constrained to bea boy, so you wouldn'texpect to have in depthconversations about a lot ofdifficult subjects, likepolitics or science orwhatever. And in that casethere were some peoplewho couldn't detectthat they were talking toa computer in that case.And that was claimed thento be passing the TuringTestbut most people aresaying no it didn't,so we haven't passed theTuring test.When we consider thehistory of artificialintelligenceand the sort of questionswe've asked aboutwhether a machineis intelligent have changedconsiderably over the courseof the last60 or so years.Fundamentally at thebeginning, the questionswere reasonably simple.We have come now tothe conclusion that it'snot enough just tomimic intelligence butto actually attain a state of consciousness.An how we would be ableto subjectively realiseanother personlet alone machinesubjective consciousnessis philosophically a verydifficult question toanswer.When we consider theway that people approach,for examplegameplay. Originally thequestions were couldthe machineplay a game of checkersand win against a humanopponent.And then we consideredwhether it could playchess.And then of course GO.The machines have beenable to not only succeedat playing these gamesbut to beat their humanopponents quitesuccessfully.The AI Effect is this experiencethat we're findingas the technology advancesand we're capable ofansweringso many more questionsabout what is possible.It becomes mundane,and we start to have areductive analysis.So that achievements aresimply relabelled ascomputation.So the AI effect meansthat we say 'that's notintelligence anymore'.We actually change our mindsand have decided that ithas to be somethingmore than that. It has tobe something more thana simple computation.We've changed the goalpostover time.What we were askingquestions about historically,those questionsmay have been answered,but as they have beenansweredwe've reduced them to thisidea of 'it's just acomputation'.And we've expanded ourideas of whats possibleand we start to ask moreof AI.One of the big barriers toachieving this goal isthe idea of common sense.It's a very difficult way toteach a robot or machinewhat exactlyis common sense. So ifyou were to try and scripta variety of different ruleson how a robot shouldbehave ina societal setting, beingable to say what isapproriate behaviourand what isn't approriatebehaviour is argueablybe endless.Theres a variety ofcircumstances, factors,variablesthat would influence the waythat a person would engagewith the world,and therefore trying to translatethose societal norms andbehaviorsand approaches into a robot.So if we can find a way to achievethis idea of common sensewith robotics, it helps to break some of thebiggest barriers we have,which iswhen robots need to be ableto create behavior for the final10% or 5% of scenarios, where something completelyunexpected happens.And we need to have someidea of how the robot couldprovide some actionor a task or informationduring that setting.AI. It's happening whetherwe like it or not. That's thebig thing.The question is, what arewe gonna do with it?So are we gonna push itfor the benefit of everybody,or for the benefit of a few?I'm really hoping foeverybody, butI put as little AI into thesebecause they're dangerous,and I don't like dangerous AI.So, of course, artificial intelligenceis permeating more and moreparts of our lives. And it's becoming something thatwe are starting to rely on moreand more.So, for example, I don't need tohave an encyclopedic knowledgeof the changes in trafficconditions as the day goes, goeson, because I can just look atGoogle Maps.And it will tell mewhat are the best routes of anyparticular time of day.And so I think there'll be anincreasing trend of that.So relying on AI to help adviseus on decisions, obviously, thathas to be very carefully managed,so that the AI does not startmaking decisions, which itthinks is sensible.But we know how not sensiblebecause there's manymany examples where that'shappened in in the past.Right now AI and robotics isalmost completely a privatesector activity. Which is fine,the private sector isingenious, it’s where themoney isit’s where the money to be madeis. But as they become larger And I’ve just heard a newacronym and it’s GAFA Google, Apple, Facebook,and Amazon.So the GAFA companiesnow have such enormouspowerthat they can use that tomake money for themselvesto help their customers,but it can also be abused.And so when do thosemorph over into a publicutility. When does Facebook become a public utility likethe telephone company orthe pipeline company, a common carrier. So where the publichas a chance to have it.So we can have this discussion on radioprograms and on blogs andvideo which we are doing here.But at the end of the day thesedecisions are being madein boardroomsand laboratories that wedon’t know what’s going on.And so in that sense I thinkwe can have the discussiondiscussion but what’s going tohappen is largely outside ofthe civic realm. When we consider the futureof artificial intelligence andneural networksI think at this point in time,it's important to realizethat where we investour money really counts.This is fundamentally aneconomic question. Governmentat the moment is enormouslyin this idea of innovation.And you'll find thatthere's a lot of startupsarising from university culture,for example, and engineeringfirms arechanging and challengingwhat they've done previously,because mechatronicsengineering is becoming amore common and notonly viable, but sustainablefinancially rewardingelement of the businesses.Money's coming from differentdirections. And governmentsare also funding of course, military budgets, bothgovernment and nongovernment military spendingon artificial intelligence, andmechatronic type research in my understanding is now exceeded$8 billion annually acrossthe globe.This is an enormousinvestment in money.from a community perspective,this is changing the way inwhich we seeRobotics moving forward.Artificial Intelligence is oneof these potentiallytransformative technologies.And like all new transformativetechnologies, it acts as sortof a force multiplierif we do the right thing withit will have greatoutcomes for societyBut if we're not careful, andwe do some of thewrong things with it,it could be an overallnegative for society. One of the particular areaswhere people are concernedabout artificial intelligence and related technologies ofautomation and robotics,is future employment.And it's a valid concern,because artificialintelligence is able to do some of thethings that we in our jobsdo every day and sometimes do better than us. It's worthnoting that right now, aicannot do all of almost any one's job, it can onlydo part of it.And it's also worth lookingat historical precedent.If we go all the way back tothings like theIndustrial Revolution,there were new transformativetechnologies that wereincredibly disruptivedisruptive for generation indeed.But over the long term,it can be argued that they resulted in overallincrease in the quality of life. But there was temporarydisruption.Yes robots do pose a threatto employment. As it did in the days of theindustrial revolution as well.How severe is it? Is unclear.Will new jobs be created instead?Absolutely! The fundamental issue is can society provide a safety netas we move through this new industrial revolution?Or this robotics revolution that’s occurring.If society can provide that, then OK. If we just turn a blind eye to the changes that are going to occurthen it is quite worrisome.One could argue thatno job is safe, even that of a professor.In terms of work,let’s start there where everyone wants to talk aboutif the robots become the workers what do we do?And that’s happened already, that’s started in the 1970s and the 1980s with very simple machines.If you used to go to a restaurant people had to take orders and had to cook and all this stuff.You go to MacDonald’s and there’s somebody who might not even havea high school education and they are punching buttonsand the machine is doing all the rest.So it’s the deskilling of society.Now that has been...OK, so if you don’t get an education you have to work at MacDonald’s.Now you have machines that know how to design cars,who know how to build bridges,who know how to do accounting and finance -that’s the deskilling of the professional class.And then what do we all do?How do we prepare for all this?We have to ask companies with this kind of enormous societal influenceto be more transparent about what they dowhat their plans are what their algorithms areand how they are approaching these kinds of things.As we are with the military, as we are with the police -who also have tremendous power at their disposal,but for the most part they try and are supposed to be transparent.Now we have private businesses that do not have to be transparent"Oh it’s all proprietary and there’s nothing we can do about it."That’s a dangerous situation.So we can have all the discussion we wantbut if it’s in the hands of people who don’t have to say what they are doingand can do basically whatever - until some bad happens and then oh Facebook gets a red faceand they have to change and tweak this and tweak that and away they go.But for the most part it’s in their hands.Society is always concerned about how things will play out.One of the barriers with AI technology is that a lot of it is very complex,very sophisticated, and much of it's locked behind proprietary company doors.So making sure that everyone in society,not just the technologists developing it,have a sufficient understanding of what it can actually do,what it will be able to do in the near future,and what is hype,and what is reality is critically important.because we want everyone to be able to voice an informed opinionon how AI should roll out and to be deployed in society.I see robotics as another support toolthat will help what we're currently trying to achieve,which is to advance civilization in different ways.I see robots being another technologythat will help the way that we want to achieve our goalsto help enhance efficiency and productivity.I don't think this is an argumentbetween robots versus humans,I think it's really important to look at what exactly robots are doing to help support human life,what they're doing to help enhance productivity and efficiency,whether it's helping to harvest more food,whether it's helping to move goods in a way that helps support peopleby getting those goods sooner,or being able to provide support in education or healthcare scenarios.So I really see this as a civilization that can work well together.If robots are created in a way that's effective, acceptable,but also making sure that they're deployed in a way ♪♪♪So there's a lot of focus on when artificial general intelligence will ever happen.I don't know.I know that it feels like we aren't anywhere close now.As opposed to other sort of more mundane goals where it might be 5 or 10 years.All I can say is that there is definitely a lot of uncertainty.We've seen with things like the AI beating the go player.that our ability to predict when these events would happen, is very much uncertain.And so we should treat general intelligence with the same I guess, respect in terms of unpredictability.I've always been saying somewhere between 50 and 200 years,looks like a reasonable timeline for that to happen.So and that's for artificial general intelligence,which are I guess you would say is intelligence that was comparable to human intelligence.The other idea is the singularity, right?you might hear something about that idea is when machine intelligence exceeds human intelligence.So it's important to understand that AI is already surpassed human intelligencein many domains.So for instance, computer first beat the best chess playerin the world in 1997, Deep Blue beat Garry Kasparov.And so what happens is that as soon as,so there's this idea of these challengesthat if a computer could do that, it will be really intelligent.But as soon as that happens,then people sort of really sort of reclassify what it means to be really intelligent.So the humans always come out on top.So we had chess and then more recently, we've had Go.And so I see it not so much as conversion towards a singularityis more just a succession of advances in particular domains.And we will, will learn, we will learn to live with these advances .And often, we can embrace those advances and use them to enrich our lives. So for instance, the world of chess is not collapsed,because computers now much better at chess than that humansrather, humans now, help use the computers to help them study their own games.And it enriches humans understanding of the game, the kind of insights that thesecomputers can now now bring.You really need to think about this timeframe,50 to 200 years, we will have machines thatare as smart as people,you know, what will that really mean for society?That's an amazing question.Will people just not need to work?You know, will it be everything can be done by machines,you can work if you want to, but you won't have to.But if you're not working, what are you doing?Just playing games?I mean, you might get bored with thatIf the possibility of uploading minds, and a lot of this stuff sounds like science fiction,but the fact is, if we have artificial general intelligence,and we have super intelligence and we passed that singularity,uploading a mind is probably going to be possible.And, you know, as bizarre as it sounds,as bizarre as it seems,most people say that will happen within 200 years.And these are not crazy people.These are the experts in the field.So given how far we've come in the last 200 years,from the pre-industrial age to now to having smartphonesand talking about AI as areal possibility, the next 200 years, given the pace of change, anything could happen!This is a different way oflooking at technology.Historically, machines have been inanimate objects.They haven't had thisidea of self awarenessawareness of moralreasoning, the abilityfor language or interaction.So what we're looking atis a different way ofutilizing technology andtechnology interacting with us.That doesn't mean thatwe have to lose control. What it means is that wehave a creative impetus,a capacity to express not only our interest, andI think fascination withwhat it means to be conscious,but a very real way thatthis can explain it backto us.It's a collective endeavor,it's not one about handingout authority over to some sort of hypotheticalmachine Overlord, Arnold Schwarzenegger,he played the Terminator.And I think we all enjoyedthose movies that Ipersonally did. But at the end of the day,that's not the futurefuture that we're workingtowards right now.It's far more aboutcollective integration andunderstanding of whatwhat in fact, it is to behuman.What will definitely seeover the next five or 10years is continuingsteady progress in someareas of AI and itsrollout oftenoften invisible to you andI into sort of the thingsthat we do every day from buying acoffee in the morning,getting in a car, to booking a restaurant togo to that night, we'llsee AI playing anincreasing role in oureveryday lives.In terms of significantadvances of AI that'ssmart enough to driveautonomous vehicles oraround us AI that canact as a personal assistant that can help usin our everyday lives.That's still a little moreuncertain that mayhappen soon, it may take us much longer tocreate the technologyto perform well enough to be useful in terms ofthe long term thegeneral intelligence.Well, we really don't know.But there will probably besome flags raisedalong the way in terms ofmilestones that willpreface getting to that artificialgeneral intelligence.I think we've got along way to go,So if you look at selfdriving cars, for instance,they've been just around the corner forquite a long time now.And these are just very,very difficultcomputational problemsAnd so I think we'vestill got a bit of time,There's a lot of hype inthe media about aboutthese, these technologiesBut I think there is stilla way to a way to go.It will require a lot ofinvolvement fromdifferent technologycompanies, as well asregulators and peopleinvolvedin making policy andeconomics, roboticistthemselves emphasissociologists, anthropologist,it's really going to bea huge collective ordeal when we're lookingat placing robots anddifferentkinds of systems andenvironments andsocieties.So I think this is aconversation we allneed to be havingright now.How exactly can we dothis to make sure thateveryone receivesthe most amount ofbenefit from usingrobotic systems,and that they supportand help peoplein a way that benefitseveryone as a civilization.my goal, which is unlikelyto happen duringmy lifetime,is to consider robot as aspecies, speciesthat are selected using aDarwinian mechanismthat can create themselvesin a way using 3dprinting.So not in the sense of,you know, the Terminator, or self reproducingmachines or anythinglike that, we very carefully,don't do that.But what I'm saying is,we could have a smallfactory that has all the components tobuild a load of robots,and we could deploy it out, you know, on an asteroid,in deep sea in deepspace, anywhere where wecan't get humans easily,or we don't want toput humans right.And this factory can learnusing the ability tocreate robots, about the best types ofrobots to us in acertain situation.If we create superintelligent AI that is thenable to then designits successor, you know,even even designeven more capable AI or even smarter AIwill not necessarilyhave the samesafeguards that we built in intothe original AI.Well, you know, Iguess, if we've doneour job correctly,then that super intelligentAI that we've created,will then have the desire just as wedid to build in the samesafeguards into anything that it designs.You know, I guess youcould ask, how manyiterations can thatactually go on forbefore it goesWell, actually, maybethis isn't such agood idea.That's that's reallyspeculative.You know, and, andreally come into thatSo we're talking aboutthe existential threatof AIand how muc of athreat is AI to us orcould it bein the future.And yes there are somepeople who are quiteconcernedabout this. Aboutsuper intelligentAI runningamuck, and forwhatever reasonwiping out thehuman race.Most people who workin the field are not soconcerned about that.I think the generalfeeling is thatby the time we canconstruct an AI suchas thatwhich is obviouslysome way offwe're nowhere nearthat at the moment.We'll also understandways to control it orcontain it.Or at least to renderit harmlesswhether it's controlledor contained or notto make it benignThe more real concernof people in the fielddoing research on thisis not the the maliciousAI, but it's basicallyWell, it's our own incompetence basicallySo the the unintendedside effects of superintelligent AIthat's not going to youknow, just decide towipe us out because it deserves tolive and we don'tThat's not really theconcern. But there isthis potential concernwhere we'll just makean AI that will lose controlIt will be doing somethingthat we want it to doBut it'll be doing it in away that we don't wantit to do it. ♪♪♪when we look what thefuture of AI and thesingularity that people think might be a likelynext step in the waythis technology is moving forward. Well,there's a lot of ideas aboutwhat could reasonablyhappen. Unfortunatelymost of thosehypotheticals just won'tbe. A lot of these ideas actuallycould be very problematicfor us for ourfuture and for our safety.And whilst I do think it'sworth considering them, essentially, it's justscience fiction. And at this point in ourlives, I think it's far moreworthwhileconsidering science fact,where are we right now? What are we investing in?And what do we expectto get from this technology?If and when a singularityarises, it will be a singlelinear trajectory, it won'tbe a hypothetical. And so when we spendtime considering allof these ideas about what's possible,we can lose sight ofwhat's really happening now, which is an incrediblyexciting moment in ourcollective history.♪♪♪ Hello Adam,I have completed a full analysis of your vital signsand have verification from your doctor.There is a 75% chance that you are having a mild heart attack.Please do not be alarmed.An autonomous vehicle has been dispatchedand should intercept your position in approximately 10 minutes.Please walk slowly in your current direction.There is an open landing pad in 300 meters.Please wait there. I will continue to monitor you.Please stay on the line.♪♪♪
