True Artificial Intelligence Will Change Everything - Prof. Jürgen Schmidhuber

True Artificial Intelligence Will Change Everything - Prof. Jürgen Schmidhuber

Recorded on July 7th 2016

Video Transcript

My speech will be about the most important about the grand theme of the 1st century which is the rise of artificial intelligence which is going o transform every aspect of our civilization and before we will look at the content rillettes have a brief look at the previous century what was the most important thing in the previous century the journal nature in 1999 made a list of the most influential inventions after twenty century and number one of class once the invention from 1908 which made the 20th century stand out among our centuries ever in the history of mankind because it was the one that drove the population explosion from 1.6 billion people in the year nineteen hundred too soon 10 billion it's a chemical thing and a high pressure and high temperature nitrogen is extracted from thin air to make still 500 million tons of artificial fertilizer for a year now without that stuff half of humankind would not even exist this planet could sustain at most four billion people without that one invention billions and billions and billions would never have lived without it and soon two out of three people on this planet will depend on this one single mention nothing else was remotely as influential as an however the way I explosion of the present century is going to be much more impactful and grander than that because that we are not talking about smaller numbers such as for or 10 but we are talking about trillions of trillions and this has a lot to do with the fact that computers are getting faster by a factor of 10 per euro per five years and this trend has held at least since nineteen forty one man cannot souza built the first working program controlled computer and nineteen forty one seventy five years gone every five years since then computers became roughly 10 times cheaper which means that now we have a factor of a million billions and this trend has been running for a long time but only recently we have approached the computational power of a small animal brain and in the near future for the first time for a thousand euros.

We will have small computers which can compute as much as a human brain and
4:01then if the trend doesn't break and there's no reason why it should break it
4:05will take another 25 years and we will have it would take another 50 years and
4:13we will have a small device for the same price which can compute as much as our
4:1810 billion brains combined as all brains of humankind together and that will not
4:23be only one of these devices but many many many we want programs these
4:30computers like we do with the con computers we are going to train them we
4:36are going to educate them like we educate babies and kids and one of the
4:41techniques that recently has become rebranded under the name deep learning
4:46which is just a rebranding of an attitude of an old hat is this technique
4:53card artificial neuron networks
4:56has anybody ever heard about artificial neural networks
4:59mhm
5:00has anybody not ever heard of artificial neural networks and came
5:07we have a third group in this room
5:11probably already asleep
5:15d planning goes back at least 50 years because and and much of that and there's
5:20a good place this is in the middle of Europe and it's a good place to point
5:24out that most of this research many of the basic insights come from Europe
5:29started 50 years ago in the Ukraine where a mathematician called even ankle
5:34but the first really deep neuron the tracks and efficient and networks that
5:39could learn from experience and we have made a lot of progress since then this
5:44Renaissance that you'd see down there is not a typo
5:51it's really an RN s office because that stands for recon your networks which are
5:57the deepest of them are the recurrent neural networks are the deepest and most
6:02powerful artificial networks and they inspired a little bit by the human brain
6:07the human brain your brain has in your cortex about 10 billion little
6:12processors which are called neurons and each of them is connected to on average
6:1710,000 other neurons which means you have got a hundred thousand billion
6:21connections in your brain
6:24in the beginning these most of these connections are apparently randomly
6:29pre-wired but through learning through experience through trial and error
6:35they change and each connection strength indicates how much does this neuron
6:40influence this neuron over there and here we see our simple we can Network an
6:45artificial recurrent Network which also has input units like for example your
6:49retina or a camera where pixels are streaming into the system or acoustic
6:54sounds or whatever or pain signals or pleasure signals are streaming to those
7:00little artificial brain and their output nodes which produce actions action
7:04sequences control robot muscles or control your muscles in your case and in
7:09between thinking takes place and these hidden neurons and the art of my
7:15profession is to come up with a learning algorithm that changes these connections
7:20such that he initially dump that right
7:23overtime learns to solve problems that it couldn't solve before
7:28just like your kids too and like you did and and one of the best methods that is
7:34now widely used is called long short term memory loss of short-term memory is
7:40a method that we have developed in our labs and munich and in Switzerland
7:45so European tax money since since the beginning of the nineties and they are
7:55much better than previous networks able to learn to deal with t problems where
8:00you have to memorize things for a long time and and and launch short-term
8:06memory back then was just a curiosity but today it's widely used by the
8:11world's most valuable companies such as Google and Apple and so on which makes
8:15us happy and some people ask me do you have a demo and I just have to ask do
8:20you have a smartphone because you have a little piece of us in your pocket
8:24whenever you take out your smartphone and you don't want to type into it but
8:30instead you press on the little microphone there in the google voice
8:33icon then you can speak to it and and and the google white speech recognition
8:39is based now on our long short term memory which was developed since the
8:45yeah that's the early nineties with the first author back then set pork right on
8:51my first you never been feeding skiers
8:53Alex graves and a couple of other great students in my lab and it reached the
8:58current form or less in the year two thousand six that's what is now on your
9:02smartphone so I'm basically basically its 10-year old technology and and this
9:07thing has learned from experience to recognize speech
9:11so how does that did that work lots of people spoke and then in these training
9:18examples there was a teacher who knew what they meant
9:22so in comes speech signals which is every 10 milliseconds another number
9:28vector coming from the microphone so you get a hundred inputs / per second and
9:32then these go in through these input units and they suck around along these
9:36week
9:36connections in the network and later the network is supposed to generate a
9:41sequence of letters which correspond to the recognize speech and until 2015 this
9:49didn't work well and we had another system for doing the speech recognition
9:53and it was really annoying but then they replace it by a lunch order memory and
9:58since then
9:59speech recognition is not only five percent better a ten percent which would
10:03have been great but almost fifty percent and now even a noisy restaurant you can
10:07talk to it and it can recognize what you're saying
10:10now this lcms all Universal powerful you can also use it to translate from one
10:14language into the other
10:15for example you give it lots of here we on in Brussels so you give it lots of
10:20examples of texts from from the European Parliament which is written in English
10:24and then
10:26translation & French in the beginning the network is totally stupid and knows
10:29nothing about English it knows nothing about French then it sees lots of
10:33examples of in a sentence as being fed in and be force it to output the
10:39corresponding translations in French in between the network is randomly pretty
10:44wide it has no idea what to do
10:45however through allowing out with him all these connections change their
10:49strength such that after a long period of training the thing has discovered the
10:55rules of English and syntactic rules are English the structure friends and how to
10:59go from the meaning and English to the meaning in French and the best system
11:04currently for translating automatically from one language to the other is based
11:07on this LCM and we had to have greatly profit of class from the fact that every
11:1210 years
11:13we are gaining a fact of one under it when you started that computers were a
11:18million times lower than today for the same price
11:21now there are a million times faster and that makes a big difference
11:24I don't network itself hours and feed forward networks that you can use for
11:30computer vision and 2011 for the first time we're able to achieve even super
11:35human performance when it came to a pattern recognition
11:38pattern recognition is something that kids are doing very well and it's much
11:43harder than playing chance
11:45how do I know that because in 20 years ago and the year nineteen
11:4997 the best chess player on this planet was not human anymore
11:54it was a machine however back then
11:57no computer was able to recognize patterns just glasses or traffic signs
12:02or microphones or faces as well as humans to him but then in 2011 in a
12:08traffic sign recognition competition for the first time we had a super human
12:11performance there and three times better than the nearest competitor
12:15so the shot was that we are slowly creeping in there and into that level of
12:22human performance in more and more domains and remember every 10 years we
12:26are getting effect of 100 at the moment it's scaling linearly and soon we will
12:31have rather big networks which can not only achieve one super human performance
12:36on a particular task task but on many many different to us we can apply the
12:42same techniques to medical imaging the brain image let me skip that for example
12:47when you see here is a slice through a female breasts and you see all these
12:55little cells there and this microscopic image and some of them are good cells
13:00and some of them are dangerous they are in the pre cancer stage it ourselves as
13:08their card
13:09normally you need a trained doctor a histology just who looks at these images
13:14and then says for each of these little details that that's a good one that's a
13:19bad one that's a bad one
13:21i'm not a doctor but we can train our artificial neural networks on lots of
13:27theta to achieve a performance of recognition performance which is
13:31comparable to the one of human doctors
13:33that's how it was and 12 for the first time we were able to win pattern
13:36recognition competitions and that really really important domain so this type of
13:41AI a little a small type of AI is already good enough to replace certain
13:48things that doctors are good and doesn't mean that doctors are going to be is
13:52abolished not at all just means that the same guy
13:55the same doctor will be able to treat ten times as many patrons in the same
14:00time
14:00with high quality and we'll have more time for the thing which is often
14:04neglected today which is into it which is doctor-patient discussions and stuff
14:08like that this is super important because the wall GP is about 70 trillion
14:13is it already 80 trillion armature and ten percent of that is for healthcare
14:18which is about seven trillion and at least ten percent of that per year is
14:22just for a medical diagnosis like that which is seven hundred billion per year
14:26but apart from the numbers and finance an economy that's much more important is
14:31that lots of people who at the moment
14:34don't have any significant access to health care at all through artificial
14:38doctors like that are going to have decent health care we were able to win
14:44additional competitions along these lines here is a thing that google is
14:48doing with our LCM combined with the technique on commercial networks there
14:53you in the lower left corner you see an image where where you see a text below
14:58the image the text says a herd of elephants walking across a dry grass
15:04field and you look at the image yes room and the interesting thing is this was
15:09automatically generated so there was a and an LLC and network combined with
15:14another few for network which has learned from many many training examples
15:18of images and captions to recognize what is in the image and then give a short
15:23summary in an English paragraph
15:27what you can see there no teacher just from lots of training examples like that
15:34sometimes it goes bad for example the second the second image in the top roll
15:39says two dogs play in the grass and you look at it and it's actually three dogs
15:46but it's not complete completely on
15:51before I came here I thought this is going to be just a little tech talk and
15:55there won't be much of an audience but you are actually a large audience by my
15:58standards
16:00the other day i gave a talk and there was just a single person in the audience
16:06it was a young lady and I said young lady it's very embarrassing but
16:13apparently today i'm going to give this talk just to you and she said okay but
16:19please hurry
16:20I'm the next speaker recently recently it google deepmind made a program that
16:35became the best goal player in the world and it wasn't pre-programmed it learned
16:40that from lots of games playing against itself
16:44it's not a totally new thing in 1994 already a backgammon program
16:50well I'm to become the best back and play and the wild by playing against
16:55itself using very similar principles
16:57however go is more complex and backgammon and a couple of additional
17:01tracks were employed there and it received a lot of attention
17:04these are neural networks which one over time to become better and go players and
17:10I'm proud of that because deep mind is a company which doesn't even exist five
17:14years ago and then in 2014 was bought by google and they were heavily influenced
17:20by my students actually the first two guys a deep mind who are doing one of
17:26mine is doing which is artificial intelligence and and machine learning
17:30they were both students in my lab where they met and they were the first at the
17:34mind who had really phd's in that field then later they hired a couple of
17:38additional guys from my lab
17:41so this shows that there is now a lot of commercial interest in in this stuff and
17:47Google and many communication companies are massively using artificial
17:51intelligence or at least these artificial neural networks all the time
17:55to place better and whenever you are searching for something
18:02so these are marketing companies communication companies and they have
18:07taken i think half the advertising business after the world
18:11google and facebook ugh which is using similar techniques by just being better
18:17able to tailor ads by looking at what kind of data can i get from these users
18:23search that i can increase the probability that they will click at
18:27these ads so the techniques that I've mentioned so far can be used for things
18:32like that and are part of the money making machine behind these search
18:39engines what we will see in the near future I extensions of stuff that we did
18:44maybe you already also 10 years ago where you also can control robots them
18:48to that sex we had lstm networks that learn to control the surgery robots like
18:53that to tie knots into in environment fine settings and artificial pics in
18:59this example
19:01no real pics harmed some people think that creativity and curiosity are
19:06something that will always remain a domain of humans but this is natural and
19:11we have a formal theory we are fun and curiosity and and and creativity which
19:17allows us to already build simple artificial scientists and artists and
19:23here we have a little robot a baby robot which in the beginning you nothing but
19:29then over time going through experiments to to interact with the world
19:33usually when you have in systems like that as two systems one is the neural
19:37network which is interacting with the world
19:40and then there's another one it's friend you you might say which lines to predict
19:45what happens if I do is add on that
19:46what happens if I do is add on that
19:48and then this production machine and in the beginning knows nothing but over
19:53time like a baby learns how work that gravity works
19:57how do the apples fall to the ground if i push them from the table and so on and
20:01so on so it lasts to become a better and better predictor of what's going to
20:04happen if i do that and that and then we can measure the insights office second
20:12module office world model if you will
20:15we can measure the depth of these in science as it learns something that I
20:19didn't know and that's a number and we get that to the first guy who is
20:23creating the experiments that lead to the data that has the property that the
20:28one model can become better and now the first guy is motivated to maximize its
20:33all these rewards all these curiosity rewards these intrinsic joy signals
20:38which motivated to come up with additional experiments that tell it even
20:44more about how the world works
20:46so I'd official scientists in a certain sense that we already have had running
20:51for her a couple of times for a couple of years
20:55how much more do I have not so many additional minutes left however let me
21:01let me again point out that we are currently greatly profiting from the
21:07fact that every five years we are getting a factor of 10
21:11now we have 75 years after tues er which means we have now
21:16even we have now lstm networks long short term memory networks after type
21:21that we development switzerland and yannick with about a billion connections
21:25your brains have about 100,000 billion connections one hand thousand means 25
21:32years because it's five to the 10 which means we have to meet way for 25 more
21:39years and for the same prize
21:41we will for the first time have LSD and networks that have the size of a human
21:44brain and they would be much faster than human brains because they have
21:48electronic connections not the slow connections that we have
21:52things are going to change the price on many people don't realize how quickly
21:55this is now moving forward
21:58what was the be the next thing I think in the not-so-distant future we will
22:02have something we don't have that yet which is like a little animal like
22:06intelligence like a little monkey little monkeys at the moment cans do many many
22:10things that our best robot cannot do it on can learn lots of things that
22:15machines cannot be at learned however we think we understand how to get there
22:20and within not so many years we will have little and artificial intelligences
22:26on the level of arm of a simple of a small animal like a crow or monkey
22:30capuchin monkey
22:31and once we have that at the step towards human level intelligence won't
22:39be that huge because look at evolution
22:42it took billions of years to come up with a little monkey
22:47but then only a few millions or tens of millions of years to add human level
22:52intelligence on top of an because technological evolution is a million
22:58times faster than biological evolution because the dead ends are weeded out
23:01much faster
23:02so it to me it would be super surprising if within a decade in within a few
23:08decades
23:09we won't have a human level intelligence of the artificial kind
23:14I don't want to deny that we have a company not only the academic lab but
23:20also a company which is called nations which is trying to make that a reality
23:25in a sentence is pronounced like birth
23:28Mason's but it's spelled in a different way
23:32nn4 neural networks hey I for artificial intelligence
23:35what will be the far future of course once a eyes are going to be smarter than
23:44humans and it there is no doubt in my mind that this will come within the
23:48century
23:49what will they do they will not stick to this thin film of biosphere around the
23:56third planet because almost all resources in the solar system out there
24:01in space less than 1 billions of the solar energy is hitting our planet and
24:07the rest at the moment is wasted
24:09it's not going to stay like that and they will move out there and they will
24:13build billions and billions of self-replicating robot factories and the
24:20asteroid belt and spread from there in a way that is completely impossible
24:24physically impossible for humans space is hostile to you mine
24:30humans but it's really friendly to appropriately designed robots and they
24:34are going to spread slowly out through the Milky Way and within a couple of
24:40millions of years completely within the limits of physics and light speed and so
24:44on
24:45they were established a network of senders and receivers all over the
24:50galaxy and of course from then on they were a eyes will travel the way is
24:55always have trouble namely by litespeed by radio from send us to receive us in a
25:00way completely infeasible for humans
25:02we are currently witnessing the beginning of something that is HUGE
25:06this is not just another industrial revolution
25:09this is more than all of civilization this is a step and you step on the path
25:15of the universe towards higher and higher complexity and the last time we
25:19had a step of that significant I think was about 3.5 billion years ago with the
25:23invention of life
25:25so this goes beyond human kind this transcends human kind and and it's a
25:30privilege to be part of that and fitness the beginnings and
25:37with that final font i would like to point out that we shouldn't think of us
25:50versus them as humans versus those super uber robots of the future but view all
25:59of us including human kind of civilization and these future beings as
26:04part of one grand scheme that allows the universe to go from small complete from
26:12from simple States towards more complex States and its it's great to be a part
26:20of an thank you very much for your attention
26:30thank you very much you're doing some exciting predictions for true artificial
26:35intelligence coming at us within our own lifetimes basically what you're telling
26:39us we're going to say I'm true just run it by us again what classifies true in
26:44that definition as opposed to what we can already see how you doing around us
26:47today
26:48what can be always easy when you're talking to your smartphone and it's
26:51mostly pure pattern recognition your smart phone doesn't have arms it doesn't
26:55shape the world it can influence you by giving you advice
26:58I said for example it says now you are on this fine City and you can and i know
27:04there is a secondhand shop not far from here which is after type you like and
27:08they have a special thing on offer and you go there because that's a good deal
27:12for your but they don't have a robot arms and the moment what we see is that
27:18robotics and mechanics are lagging behind what we can do in pattern
27:22recognition
27:23it's not going to stay like that so i think within the next year's and and few
27:28decades more number of decades
27:30we will see very sophisticated robots that will be able to solve all kinds of
27:34problems that humans at the moment have to solve by themselves including
27:39strawberry plucking which is much harder than most people think simply because
27:45there is no really good strawberry plucking robot
27:48it's not going to stay like that and then of pants
27:51hey I and general is really not just pattern recognition but interaction with
27:55the wilds are you
27:56you act you perceive you act you perceive you get a stream of input state
28:01on that you're shaking yourself on your way to solving goals because all of AI
28:06is about problem solving and this is currently becoming a reality
28:11although most commercial stuff is just pattern recognition just better speech
28:14recognition better gesture recognition better prediction of the stock market
28:19another thing that our company is pretty good at and and
28:24better prediction of what you want to do next given the data that you have on
28:29your smartphone
28:30I'm just going to pick up on that point of strawberry plucking or picking
28:34I'm simply because that leads to the question obviously there are lots of
28:39concerns about the implications of artificial intelligence for employment
28:44for instance we've already seen robot journalism happening just this week or
28:49last week it was perhaps how worried should we be that those of us who are
28:52involved in in a worthy kinds of kinds of professions are are simply going to
28:59be replaced i mean many professions obviously strawberry plucking being
29:02amongst them
29:03yeah so in the eighties there already said always armed
29:08it is very easy to predict which jobs are going to go like taxi driver and
29:13stuff like that
29:14it's very hard to predict all the new jobs which are being created all the
29:18time and that's this playing mounting homo Luden's homo Luden's the playing
29:22man is inventing new professions all the time and most of these professions are
29:26really luxury and professions for example although the best chess players
29:31in the world are not humans anymore
29:34you have so humans making money by its playing chess against each other
29:38well he was involved is much slower than the fastest machines but he's still
29:42making hundreds of millions just by running against other humans and all
29:47these new types of interactions with other people that you see on social
29:51networks bloggers youtubers and so on
29:54who could have predicted that 20 years ago so if you look at the unemployment
29:59rates today they are pretty much the same that we had back then so adaptation
30:04is obviously the secret there you know and like Matt peacocks and earlier in
30:08the earlier session of your kids are wondering what they should do they
30:11should become data analysts are amongst other things
30:14questions from the floor for your continued to bomb we have over here get
30:19you a microphone
30:20thank you i was interested by the comparison you made about parenting and
30:27teaching machines to as you teach a child
30:31and I recall when I he first became apparent never once said well you know
30:35there's no manual for you need to figure it out
30:38and I realized very quickly that it's not that easy
30:41and some people do it better than others so using that comparison
30:46how do you sort of respond to the future that you've sent out
30:49yeah so you are worried that some pounds are going to teach them the wrong things
30:57for example in military applications where some of those guys will be taught
31:03to do military jobs where for example self-driving cars are going to be used
31:09as a self-driving landmines seekers
31:11which of course every general will want because he wants to protect the soldiers
31:16and so on and so you're worried about educating them to do things that are
31:21detrimental at least to the lives of certain people
31:25however on the other hand it is clear that almost on and off the commercial
31:30research in this field is driven towards making a is artificial neural networks
31:38that alarm to help humans to make us happier
31:42such as a smarter friend in your pocket your smartphone which is even better at
31:47understanding you weren't talking back to you and and giving you advice and so
31:51on or in health care where people will just live longer because of this
31:58partially automated health care that we are going to see
32:02so am i correct that you are worried about the relation between on the one
32:08hand these military applications as opposed to these much larger and much
32:15more valuable commercial and health care or Internet applications
32:21well not quite actually what I'm worried about is the fact that as a parent again
32:26I do the best I can raising my children but there are things that I might do at
32:31the age of five that might have an implication for 10 1015 for the choices
32:36they make so
32:37sure that a lot of people who are who are the lots of effort to do the right
32:42things but actually you know take the comparison of a smartphone certain
32:47people now you we talked about having digital detox weekends or whatever it
32:50might be because we realize that there are consequences that only become
32:54apparent later on and its technology is a fantastic thing but the constant these
33:01consequences and that's really what i'm getting at because you're taking sort of
33:06science and you're going into room where it like you say it's an unpredictable
33:10thing like parenting
33:12yes but I'm not the first on this path
33:16it reminds me a little bit of the discussion that we have six hundred
33:20thousand years ago when fire was invented and
33:25and back then and ethics committee was established which which which weigh the
33:32pros and cons and some people said yeah it's going to keep us warm at night
33:36but the other set but you can also use it to blind people and then at some
33:41point the Commission came to a conclusion and they said we are not
33:45going to stop that development because we can't even stop it and let's move
33:50forward and I think weezy the same thing happening now
33:55there with the thank you very much for that for those questions certainly
34:01inviting analog
34:06hello thank you for your talk we hear a lot about trust in society and we had
34:13about society splitting in two
34:15and basically apart from the discussion on artificial intelligence intelligence
34:22per se is not equally distributed in society
34:26do you think that the rich well educated people are replacing the other half with
34:31robots and what the robots have voted for breaks it so in my profession it's
34:43not unusual that people like the idea of unconditional base salary which recently
34:50we had as a discussion in Switzerland so I'm not twist but i'm living down and so
34:55on
34:56I see what's going on so I think almost was it a third of someone a third of the
35:02population would support support that idea and I think in a couple of tens of
35:06years we will have many more are so many people in in this profession of building
35:11machines that become smarter over time I think it's a good idea to have robots
35:18pay taxes and to have robot owners pay taxes and of class society will have to
35:23come up with systems like that with a social response to the technological
35:28advances and it will happen as it always happens otherwise we will get a
35:33revolution
35:35thanks for that question we have another question over here in the second row
35:38yes professor you made it clear calculation and beginning about how much
35:45time it will take until this or that happened and now my question is if you
35:50extend that calculation
35:53how long will it take until the robots will definitely take over the earth
36:01yeah well they really take over the earth that is a very debatable seen
36:06inspired by arnold schwarzenegger movies and and it has not so much to do with
36:12reality
36:13we can see you are being taken over or you are being enslaved only by others
36:19who are like yourself who have similar goals and share the same goals
36:23so that's why humans usually quarrelled with other humans but not so much with
36:28some with kangaroos and and it is the case that almost all people are
36:38interested in other people who are similar to themselves because either
36:41they can collaborate with them or compete with them or sometimes both and
36:47knit one nation for one company competes against another company
36:52each of them being the collection of humans and and and the fundamental
36:59condition for that is that you shared goals because you are similar
37:02now the super smarty is the future will not be so interested in humans just like
37:14humans are not so interested in the ants the super smart eyes of the future will
37:18mostly be interested in other super smart the eyes of the future
37:21simply because those will be much more interesting and share similar goals in
37:27an environment which may be quite disconnected from what we have here in
37:31this little white sphere we ask much smarter than the arm and Santi
37:37but only when they invite our houses we take measures against them but most of
37:43the arms in the world they are happily living in the forest and be i'm glad
37:48they are doing that and the weights of all time
37:52it's still comparable to the weight of all humans simply because we don't have
37:57too many old conflicts with each other and that's going to be the same thing
38:00with the super smart Romans we have time I'm going to I'm going to make
38:07allowances for one more question here in the front because they were going to
38:13have to a very short follow-up you were mentioning a movie i mentioned another 1
38:172001 a Space Odyssey the Durham always not again the humor he just has a logic
38:24has a mission to follow and man is on his way so he's had different goals
38:29these things differently than a human but it soon becomes a danger and if
38:33potentially and interactive intelligence says for instance there are about to
38:38begin to many people
38:40so logical step will be to kill for instant to be other people to make sure
38:44that the seven other ppl billion can survive that work for instance a
38:48different kind of logic than a human logic so i don't know if it's that clear
38:54that the machine will not be a problem
38:56so what you are saying sounds more like human logic to me rather than machine
39:00lodging and we do have people are in the history of mankind there have been
39:04people who had ideas like that
39:07let's kill all the others that such as just we remain and then beyond the
39:11achieves but again it's always about similar guys against other similar guys
39:16because they share the same goals if you don't share the same goals then there's
39:20no interest in fighting for example what we do the you
39:23fights off the past come from because this country has something that this
39:27country also ones are like oil or
39:32land or whatever and and and then there are these fights but generally speaking
39:40as soon as you have disconnected life and goals of a different very different
39:45type which you can expect from future suppose my eyes then you don't have to
39:51worry too much about the things that you see in scientific in science fiction
39:55novels for example and in some some of you may have seen the film matrix matrix
40:00huh has a silly plot it has great computer graphics and the coach are
40:04great the blackboard a great but but the plotters the silliest plot ever so they
40:09have the way eyes of the future
40:11they live RC energy after human brains
40:14so each of each brain produces maybe 30 watts of energy and the coal power plant
40:20that you need to keep thinking man alive produces much more energy than that
40:25so all of these plants are still legal conflicts invented by film producers who
40:29just wanted to have a clash between robots and and and machine and horses
40:35and joins and it's are very unrealistic as seems clear that this is not the
40:41future
40:42thank you very much you're going to meet her but I'm going to have to stop it
40:45there the bad news is we're stopping it now but the good news is that your guns
40:48- <operand> twenty </operand> has decided to stay with us and I believe
40:51also attend the evening festivities
40:54so he also be around here in the ensuing moments after this session for some
40:57one-on-one questioning please give my hand thank you very much
41:01yeah

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Train Your Self-Driving Car AI in Grand Theft Auto V

Hello World - Machine Learning Recipes #1

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