Datasets:

pinecone

/

yt-transcriptions

like 1

3FIo6evmweo

outside of the hundred problems the very solved in a genuine creative way no because that's the nature of power play that it's always trying to break its current generalization abilities by coming up with a new problem which is beyond the current horizon just shifting the horizon of knowledge a little bit out there breaking the existing rules search says the new thing becomes

https://www.youtube.com/watch?v=3FIo6evmweo&t=2081s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

solvable but wasn't solvable by the old thing so like adding a new axiom like what Google did when he came up with these new sentences new theorems that didn't have a proof in the phone system which means you can add them to the repertoire hoping that that they are not going to damage the consistency of the whole thing so in the paper with the amazing title formal theory of creativity fun in

https://www.youtube.com/watch?v=3FIo6evmweo&t=2110s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

intrinsic motivation you talk about discovery as intrinsic reward so if you view humans as intelligent agents what do you think is the purpose and meaning of life far as humans is you've talked about this discovery do you see humans as an instance of power play agents yeah so humans are curious and that means they behave like scientists not only the official scientists but

https://www.youtube.com/watch?v=3FIo6evmweo&t=2142s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

even the babies behave like scientists and they play around with toys to figure out how the world works and how it is responding to their actions and that's how they learn about gravity and everything and yeah in 1990 we had the first systems like the hand would just try to to play around with the environment and come up with situations that go beyond what they knew at that

https://www.youtube.com/watch?v=3FIo6evmweo&t=2173s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

time and then get a reward for creating these situations and then becoming more general problem solvers and being able to understand more of the world so yeah I think in principle that that that curiosity strategy or sophisticated versions of whether chess is quiet they are what we have built-in as well because evolution discovered that's a good way of exploring the unknown world

https://www.youtube.com/watch?v=3FIo6evmweo&t=2199s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

and a guy who explores the unknown world has a higher chance of solving problems that he needs to survive in this world on the other hand those guys who were too curious they were weeded out as well so you have to find this trade-off evolution found a certain trade-off apparently in our society there are as a certain percentage of extremely exploitive guy and it doesn't matter if they die

https://www.youtube.com/watch?v=3FIo6evmweo&t=2230s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

because many of the others are more conservative and and and so yeah it would be surprising to me if if that principle of artificial curiosity wouldn't be present and almost exactly the same form here in our brains so you're a bit of a musician and an artist so continuing on this topic of creativity what do you think is the role of creativity and intelligence so you've

https://www.youtube.com/watch?v=3FIo6evmweo&t=2257s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

kind of implied that it's essential for intelligence if you think of intelligence as a problem-solving system as ability to solve problems but do you think it's essential this idea of creativity we never have a program a sub program that is called creativity or something it's just a side effect of when our problem solvers do they are searching a space of problems or a space

https://www.youtube.com/watch?v=3FIo6evmweo&t=2290s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

of candidates of solution candidates until they hopefully find a solution to have given from them but then there are these two types of creativity and both of them are now present in our machines the first one has been around for a long time which is human gives problem to machine machine tries to find a solution to that and this has been happening for many decades and for many decades

https://www.youtube.com/watch?v=3FIo6evmweo&t=2320s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

machines have found creative solutions to interesting problems where humans were not aware of these particularly in creative solutions but then appreciated that the machine found that the second is the pure creativity that I would call what I just mentioned I would call the applied creativity like applied art where somebody tells you now make a nice picture off of this Pope and you will

https://www.youtube.com/watch?v=3FIo6evmweo&t=2346s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

get money for that okay so here is the artist and he makes a convincing picture of the Pope and the Pope likes it and gives him the money and then there is the pure creative creativity which is more like the power play and the artificial curiosity thing where you have the freedom to select your own problem like a scientist who defines his own question to study and so

https://www.youtube.com/watch?v=3FIo6evmweo&t=2375s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

that is the pure creativity of UL and opposed to the applied creativity which serves another and in that distinction there's almost echoes of narrow AI versus general AI so this kind of constrained painting of a pope seems like the the approaches of what people are calling narrow AI and pure creativity seems to be maybe I'm just biased as a human but it seems to be an

https://www.youtube.com/watch?v=3FIo6evmweo&t=2403s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

essential element of human level intelligence is that what you're implying to a degree if you zoom back a little bit and you just look at a general problem-solving machine which is trying to solve arbitrary problems then this machine will figure out in the course of solving problems that it's good to be curious so all of what I said just now about this prewired curiosity and this

https://www.youtube.com/watch?v=3FIo6evmweo&t=2436s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

will to invent new problems that the system doesn't know how to solve yet should be just a byproduct of the general search however apparently evolution has built it into us because it turned out to be so successful a pre-wiring a buyer's a very successful exploratory buyers that that we are born with and you've also said that consciousness in the same kind of way

https://www.youtube.com/watch?v=3FIo6evmweo&t=2465s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

may be a byproduct of problem-solving you know do you think do you find it's an interesting by-product you think it's a useful by-product what are your thoughts on consciousness in general or is it simply a byproduct of greater and greater capabilities of problem-solving that's that's similar to creativity in that sense yeah we never have a procedure called consciousness in our

https://www.youtube.com/watch?v=3FIo6evmweo&t=2496s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

machines however we get as side effects of what these machines are doing things that seem to be closely related to what people call consciousness so for example in 1990 we had simple systems which were basically recurrent networks and therefore universal computers trying to map incoming data into actions that lead to success maximizing reward in a given environment

https://www.youtube.com/watch?v=3FIo6evmweo&t=2524s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

always finding the charging station in time whenever the battery's low and negative signals are coming from the battery always finds the charging station in time without bumping against painful obstacles on the way so complicated things but very easily motivated and then we give these little a separate we can all network which is just predicting what's happening if I do

https://www.youtube.com/watch?v=3FIo6evmweo&t=2555s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

that in that what will happen as a consequence of these actions that I'm executing and it's just trained on the long and long history of interactions with the world so it becomes a predictive model loss of art basically and therefore also a compressor our theme observations after what because whatever you can predict you don't have to store extras or compression is a side

https://www.youtube.com/watch?v=3FIo6evmweo&t=2584s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

effect of prediction and how does this record Network impress well it's inventing little sub programs little sub Network networks that stand for everything that frequently appears in the environment like bottles and microphones and faces maybe lots of faces in my environment so I'm learning to create something like a prototype face and a new face comes along and all

https://www.youtube.com/watch?v=3FIo6evmweo&t=2608s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

I have to encode are the deviations from the prototype so it's compressing all the time the stuff that frequently appears there's one thing that appears all the time that is present all the time when the agent is interacting with its environment which is the agent itself so just for data compression reasons it is extremely natural for this we can network to come up with little sub

https://www.youtube.com/watch?v=3FIo6evmweo&t=2633s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

networks that stand for the properties of the agents the hand you know the the other actuators and all the stuff that you need to better encode the data which is influenced by the actions of the agent so they're just as a side effect of data compression during problem-solving you have inter myself models now you can use this model of the world to plan your future and that's what yours have done

https://www.youtube.com/watch?v=3FIo6evmweo&t=2659s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

since 1990 so the recurrent Network which is the controller which is trying to maximize reward can use this model as a network of the what is this model network as a wild this predictive model of the world to plan ahead and say let's not do this action sequence let's do this action sequence instead because it leads to more predictor to rewards and whenever it's waking up these layers of

https://www.youtube.com/watch?v=3FIo6evmweo&t=2692s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

networks let's stand for itself and it's thinking about itself and it's thinking about itself and it's exploring mentally the consequences of its own actions and and now you tell me what is still missing missing the next the gap to consciousness yeah hi there there isn't that's a really beautiful idea that you know if life is a collection of data and in life is a process of compressing that

https://www.youtube.com/watch?v=3FIo6evmweo&t=2717s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

data to act efficiently you in that data you yourself appear very often so it's useful to form compressions of yourself and it's a really beautiful formulation of what consciousness is a necessary side-effect it's actually quite compelling to me you've described our nen's developed LST aims long short-term memory networks the there type of recurrent neural networks they have

https://www.youtube.com/watch?v=3FIo6evmweo&t=2749s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

gotten a lot of success recently so these are networks that model the temporal aspects in the data temporal patterns in the data and you've called them the deepest of the Newell networks right so what do you think is the value of depth in the models that we use to learn since you mentioned the long short-term memory and the lsdm I have to mention the names of the brilliant

https://www.youtube.com/watch?v=3FIo6evmweo&t=2782s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

students of course that's worse first of all and my first student ever set for writer who had fundamental insights already in this diploma thesis then Felix Kias had additional important contributions Alex gray is a guy from Scotland who is mostly responsible for this CTC algorithm which is now often used to to train the Alice TM to do the speech recognition on all the Google Android

https://www.youtube.com/watch?v=3FIo6evmweo&t=2810s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

phones and whatever and Siri and so on so these guys without these guys I would be nothing it's a lot of incredible work what is now the depth what is the importance of depth well most problems in the real world are deep in the sense that the current input doesn't tell you all you need to know about the environment mm-hmm so instead you have to have a memory of what

https://www.youtube.com/watch?v=3FIo6evmweo&t=2837s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

happened in the past and often important parts of that memory are dated they are pretty old and so when you're doing speech recognition for example and somebody says eleven then that's about half a second or something like that which means it's already fifty-eight time steps and another guy or the same guy says seven so the ending is the same Evan but now the system has to see the

https://www.youtube.com/watch?v=3FIo6evmweo&t=2868s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

distinction between seven and eleven and the only way I can see the differences it has to store that fifty steps ago there wasn't or a nerve eleven or seven so there you have already a problem of depth fifty because for each time step you have something like a virtual a layer and the expanded unrolled version of this Riccar network which is doing the speech recognition so these long

https://www.youtube.com/watch?v=3FIo6evmweo&t=2900s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

time lags they translate into problem depth and most problems and this world Asajj that you really have to look far back in time to understand what is the problem and to solvent but just like with our CMS you don't necessarily need to when you look back in time remember every aspect you just need to remember the important aspects that's right the network has to

https://www.youtube.com/watch?v=3FIo6evmweo&t=2929s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

learn to put the important stuff in into memory and to ignore the unimportant noise so but in that sense deeper and deeper is better or is there a limitation is is there I mean LCM is one of the great examples of architectures that do something beyond just deeper and deeper networks there's clever mechanisms for filtering data for remembering and forgetting so

https://www.youtube.com/watch?v=3FIo6evmweo&t=2956s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

do you think that that kind of thinking is necessary if you think about LCM is a leap a big leap forward over traditional vanilla are nuns what do you think is the next leap hmm it within this context so LCM is a very clever improvement but LCM still don't have the same kind of ability to see far back in the future in the in the past as us humans do the credit assignment problem across way

https://www.youtube.com/watch?v=3FIo6evmweo&t=2988s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

back not just 50 times steps or a hundred or a thousand but millions and billions it's not clear what are the practical limits of the lsdm when it comes to looking back already in 2006 I think we had examples where it not only looked back tens of thousands of steps but really millions of steps and who won Paris artists in my lab I think was the first author of a paper where we really

https://www.youtube.com/watch?v=3FIo6evmweo&t=3017s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

was a 2006 or something had examples word learn to look back for more than 10 million steps so for most problems of speech recognition it's not necessary to look that far back but there are examples where it does now so looking back thing [Music] that's rather easy because there is only one past but there are many possible futures and so a reinforcement learning

https://www.youtube.com/watch?v=3FIo6evmweo&t=3048s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

system which is trying to maximize its future expected rewards and doesn't know yet which of these many possible future should I select given this one single past it's facing problems that the LCN by itself cannot solve so the other sim is good for coming up with a compact representation of the history so far of the history and observations in action so far but now how do you plan in an

https://www.youtube.com/watch?v=3FIo6evmweo&t=3079s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

efficient and good way among all these how do you select one of these many possible action sequences that a reinforcement learning system has to consider to maximize reward in this unknown future so again it behaves this basic setup where you have one week on network which gets in the video and the speech and whatever and it's executing actions and is trying to maximize reward

https://www.youtube.com/watch?v=3FIo6evmweo&t=3109s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

so there is no teacher who tells it what to do at which point in time and then there's the other network which is just predicting what's going to happen if I do that then and that could be an LCM Network and it allows to look back all the way to make better predictions of the next time step so essentially although it's men predicting only the next time step it is motivated to learn

https://www.youtube.com/watch?v=3FIo6evmweo&t=3139s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

to put into memory something that happened maybe a million steps ago because it's important to memorize that if you want to predict that at the next time step the next event you know how can a model of the world like that a predictive model of the world be used by the first guy let's call it the controller and the model the controller and the model how can the model be used

https://www.youtube.com/watch?v=3FIo6evmweo&t=3167s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

by the controller to efficiently select among these many possible futures so naive way we had about 30 years ago was let's just use the model of the world as a stand-in as a simulation of the wall and millisecond by millisecond we planned the future and that means we have to roll it out really in detail and it will work only as the model is really good and it will still be inefficient

https://www.youtube.com/watch?v=3FIo6evmweo&t=3192s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

because we have to look at all these possible futures and and there are so many of them so instead what we do now since 2015 and our cm systems controller model systems we give the controller the opportunity to learn by itself how to use the potentially relevant parts of the M of the model network to solve new problems more quickly and if it wants to it can learn to ignore the M and

https://www.youtube.com/watch?v=3FIo6evmweo&t=3220s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

sometimes it's a good idea to ignore the the M because it's really bad it's a bad predictor in this particular situation of life where the control is currently trying to maximize r1 however it can also allow and to address and exploit some of the sub programs that came about in the model network through compressing the data by predicting it so it now has an opportunity to reuse that code the

https://www.youtube.com/watch?v=3FIo6evmweo&t=3250s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

ethnic information in the modern are trying to reduce its own search space such that it can solve a new problem more quickly than without the model compression so you're ultimately optimistic and excited about the power of ära of reinforcement learning in the context of real systems absolutely yeah so you see RL as a potential having a huge impact beyond just sort of the M

https://www.youtube.com/watch?v=3FIo6evmweo&t=3281s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

part is often develop on supervised learning methods you see RL as a four problems of cell traffic cars or any kind of applied cyber BOTS X that's the correct interesting direction for research in your view I do think so we have a company called Mason's Mason's which has applied to enforcement learning to little Howdy's there are DS which learn to park without

https://www.youtube.com/watch?v=3FIo6evmweo&t=3313s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

a teacher the same principles were used of course so these little Audi's they are small maybe like that so I'm much smaller than the real Howdy's but they have all the sensors that you find the real howdy is you find the cameras that lead on sensors they go up to 120 20 kilometres an hour if you if they want to and and they are from pain sensors basically and they don't want to bump

https://www.youtube.com/watch?v=3FIo6evmweo&t=3346s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

against obstacles and other Howdy's and so they must learn like little babies to a park take the wrong vision input and translate that into actions that lead to successful packing behavior which is a rewarding thing and yes they learn that they are salt we have examples like that and it's only in the beginning this is just the tip of the iceberg and I believe the next wave of a line is going

https://www.youtube.com/watch?v=3FIo6evmweo&t=3374s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

to be all about that so at the moment the current wave of AI is about passive pattern observation and prediction and and that's what you have on your smartphone and what the major companies on the Pacific of em are using to sell you ads to do marketing that's the current sort of profit in AI and that's only one or two percent of the world economy which is big enough to make

https://www.youtube.com/watch?v=3FIo6evmweo&t=3403s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

these company is pretty much the most valuable companies in the world but there's a much much bigger fraction of the economy going to be affected by the next wave which is really about machines that shape the data through our own actions and you think simulation is ultimately the biggest way that that though those methods will be successful in the next 10 20 years we're not

https://www.youtube.com/watch?v=3FIo6evmweo&t=3431s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

talking about a hundred years from now we're talking about sort of the near-term impact of RL do you think really good simulation is required or is there other techniques like imitation learning you know observing other humans yeah operating in the real world where do you think this success will come from so at the moment we have a tendency of using physics simulations to learn

https://www.youtube.com/watch?v=3FIo6evmweo&t=3457s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

behavior for machines that learn to solve problems that humans also do not know how to solve however this is not the future because the future is and what little babies do they don't use a physics engine to simulate the world no they learn a predictive model of the world which maybe sometimes is wrong in many ways but captures all kinds of important abstract high-level

https://www.youtube.com/watch?v=3FIo6evmweo&t=3484s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

predictions which are really important to be successful and and that's what is what was the future thirty years ago when you started that type of research but it's still the future and now we are know much better how to go there to to move there to move forward and to really make working systems based on that where you have a learning model of the world a model of the world that learns to

https://www.youtube.com/watch?v=3FIo6evmweo&t=3513s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

predict what's going to happen if I do that and that and then the controller uses that model to more quickly learn successful action sequences and then of course always this crazy thing in the beginning the model is stupid so the controller should be motivated to come up with experiments with action sequences that lead to data that improve the model do you think

https://www.youtube.com/watch?v=3FIo6evmweo&t=3539s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

improving the model constructing an understanding of the world in this connection is the in now the popular approaches have been successful you know grounded in ideas of neural networks but in the 80s with expert systems there's symbolic AI approaches which to us humans are more intuitive in a sense that it makes sense that you build up knowledge in this knowledge

https://www.youtube.com/watch?v=3FIo6evmweo&t=3565s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

representation what kind of lessons can we draw in our current approaches mmm for from expert systems from symbolic yeah so I became aware of all of that in the 80s and back then a logic program logic programming was a huge thing was inspiring to yourself did you find it compelling because most a lot of your work was not so much in that realm mary is more in learning systems yes or no

https://www.youtube.com/watch?v=3FIo6evmweo&t=3591s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

but we did all of that so we my first publication ever actually was 1987 was a the implementation of genetic algorithm of a genetic programming system in prologue prologue that's what you learn back then which is a logic programming language and the Japanese the anthers huge fifth-generation AI project which was mostly about logic programming back then although a neural networks existed

https://www.youtube.com/watch?v=3FIo6evmweo&t=3619s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

and were well known back then and deep learning has existed since 1965 since this guy and the UK and even anko started it but the Japanese and many other people they focus really on this logic programming and I was influenced to the extent that I said okay let's take these biologically inspired rules like evolution programs and and and implement that in the language which I know which

https://www.youtube.com/watch?v=3FIo6evmweo&t=3651s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

was Prolog for example back then and then in in many ways as came back later because the Garuda machine for example has approved search on board and without that it would not be optimal well Marcus what does universal algorithm for solving all well-defined problems as approved search on board so that's very much logic programming without that it would not be a Centanni optimum but then

https://www.youtube.com/watch?v=3FIo6evmweo&t=3682s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

on the other hand because we have a very pragmatic is also we focused on we cannula networks and and and some optimal stuff such as gradient based search and program space rather than provably optimal things the logic programming does it certainly has a usefulness in when you're trying to construct something provably optimal or probably good or something like that but

https://www.youtube.com/watch?v=3FIo6evmweo&t=3710s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

is it useful for for practical problems it's really useful at volunteer improving the best theorem provers today are not neural networks right no say our logic programming systems and they are much better theorem provers than most math students and the first or second semester on but for reasoning to for playing games of go or chess or for robots autonomous vehicles that operate

https://www.youtube.com/watch?v=3FIo6evmweo&t=3739s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

in the real world or object manipulation you know you think learning yeah as long as the problems have little to do with with C or improving themselves then as long as that is not the case you you just want to have better pattern recognition so to build a self-driving car you want to have better pattern recognition and and pedestrian recognition and all these

https://www.youtube.com/watch?v=3FIo6evmweo&t=3766s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

things and you want to your minimum you want to minimize the number of false positives which is currently is slowing down self-driving cars in many ways and and all that has very little to do with logic programming yeah what are you most excited about in terms of directions of artificial intelligence at this moment in the next few years in your own research and in the broader community so

https://www.youtube.com/watch?v=3FIo6evmweo&t=3792s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

I think in the not so distant future we will have for the first time little robots that learn like kids and I will be able to say to the robot um look here robot we are going to assemble a smartphone it's takes a slab of plastic and the school driver and let's screw in the screw like that no no not like that like so hmm not like that like that and I don't have a data glove or something

https://www.youtube.com/watch?v=3FIo6evmweo&t=3821s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

he will see me and he will hear me and he will try to do something with his own actuators which will be really different from mine but he will understand the difference and will learn to imitate me but not in the supervised way where a teacher is giving target signals for all his muscles all the time no by doing this high level imitation where he first has to learn to imitate

https://www.youtube.com/watch?v=3FIo6evmweo&t=3856s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

me and then to interpret these additional noises coming from my mouth as helping helpful signals to to do that Hannah and then it will by itself come up with faster ways and more efficient ways of doing the same thing and finally I stopped his learning algorithm and make a million copies and sell it and so at the moment this is not possible but we already see how we are going to get

https://www.youtube.com/watch?v=3FIo6evmweo&t=3885s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

there and you can imagine to the extent that this works economically and cheaply it's going to change everything almost all our production is going to be affected by that and a much bigger wave much bigger ai wave is coming than the one that we are currently witnessing which is mostly about passive pattern recognition on your smartphone this is about active machines that shapes data

https://www.youtube.com/watch?v=3FIo6evmweo&t=3916s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

Susy actions they are executing and they learn to do that in a good way so many of the traditional industries are going to be affected by that all the companies that are building machines well equip these machines with cameras and other sensors and they are going to learn to solve all kinds of problems through interaction with humans but also a lot on their own to improve what they

https://www.youtube.com/watch?v=3FIo6evmweo&t=3944s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

already can do and lots of old economy is going to be affected by that and in recent years I have seen that all the economy is actually waking up and realizing that those vacations and are you optimistic about the future are you concerned there's a lot of people concerned in the near term about the transformation of the nature of work the kind of ideas that you just suggested

https://www.youtube.com/watch?v=3FIo6evmweo&t=3976s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

would have a significant impact of what kind of things could be automated are you optimistic about that future are you nervous about that future and looking a little bit farther into the future there's people like you la musk - a rustle concerned about the existential threats of that future so in the near term job loss in the long term existential threat are these concerns to

https://www.youtube.com/watch?v=3FIo6evmweo&t=4004s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

you or yalta mele optimistic so let's first address the near future we have had predictions of job losses for many decades for example when industrial robots came along many people many people predicted and lots of jobs are going to get lost and in a sense say were right because back then there were car factories and hundreds of people and these factories assembled cars and today

https://www.youtube.com/watch?v=3FIo6evmweo&t=4031s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

the same car factories have hundreds of robots and maybe three guys watching the robots on the other hand those countries that have lots of robots per capita Japan Korea and Germany Switzerland a couple of other countries they have really low unemployment rates somehow all kinds of new jobs were created back then nobody anticipated those jobs and decades ago I already

https://www.youtube.com/watch?v=3FIo6evmweo&t=4071s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

said it's really easy to say which jobs are going to get lost but it's really hard to predict the new ones 30 years ago who would have predicted all these people making money as YouTube bloggers 200 years ago 60% of all people used to work in agriculture today maybe 1% but still only I don't know 5% unemployment lots of new jobs were created and Homo Luden's the the playing man is inventing

https://www.youtube.com/watch?v=3FIo6evmweo&t=4106s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

new jobs all the time most of these jobs are not existentially necessary for the survival of our species there are only very few existentially necessary jobs such as farming and building houses and and warming up the houses but less than 10% of the population is doing that and most of these newly invented jobs are about interacting with other people in new ways through new media and so on

https://www.youtube.com/watch?v=3FIo6evmweo&t=4148s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

getting new high types of kudos and forms of likes and whatever and even making money through that so homo Luden's the playing man doesn't want to be unemployed and that's why he is inventing new jobs all the time and he keeps considering these jobs as really important and is investing a lot of energy and hours of work into into those and new jobs it's quite beautifully put were really

https://www.youtube.com/watch?v=3FIo6evmweo&t=4181s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

nervous about the future because we can't predict what kind of new jobs would be created but your ultimate ly optimistic that we humans are so Restless that we create and give meaning to newer in your jobs telling you likes on faith things that get likes on Facebook or whatever the social platform is so what about long-term existential threat of AI where our whole civilization may be swallowed

https://www.youtube.com/watch?v=3FIo6evmweo&t=4211s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

up by this ultra super intelligent systems maybe it's not going to be smaller DUP but I'd be surprised if B were B humans were the last step and the evolution of the universe you you've actually at this beautiful comment somewhere that I've seen saying that artificial quite insightful artificial general intelligence systems just like us humans will likely not want to

https://www.youtube.com/watch?v=3FIo6evmweo&t=4240s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

interact with humans they'll just interact amongst themselves just like ants interact amongst themselves and only tangentially interact with humans hmm and it's quite an interesting idea that once we create a GI that will lose interest in humans and and have compete for their own Facebook Likes on their own social platforms so within that quite elegant idea how do we know in a hypothetical

https://www.youtube.com/watch?v=3FIo6evmweo&t=4274s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

sense that there's not already intelligent systems out there how do you think broadly of general intelligence greater than us how do we know it's out there mmm how would we know it's around us and could it already be I'd be surprised even with within the next few decades or something like that we we won't have a eyes that truly smarts in every single way and better problem

https://www.youtube.com/watch?v=3FIo6evmweo&t=4304s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

solvers and almost every single important way and I'd be surprised as they wouldn't realize what we have realized a long time ago which is that almost all physical resources are not here and this biosphere but for thou the rest of the solar system gets 2 billion times more solar energy than our little planet there's lots of material out there that you can use to build

https://www.youtube.com/watch?v=3FIo6evmweo&t=4333s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

robots and self-replicating robot factories and all this stuff and they are going to do that and there will be scientists and curious and they will explore what they can do and in the beginning they will be fascinated by life and by their own origins and our civilization they will want to understand that completely just like people today would like to understand

https://www.youtube.com/watch?v=3FIo6evmweo&t=4366s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

how life works and um and also the history of our own existence and civilization and also on the physical laws that created all of that so they in the beginning they will be fascinated my life once they understand that I was interest like anybody who loses interest and things he understands and then as you said the most interesting sources information for them will be others of

https://www.youtube.com/watch?v=3FIo6evmweo&t=4392s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

their own kind so at least in the long run there seems to be some sort of protection through lack of interest on the other side and now it seems also clear as far as we understand physics you need matter and energy to compute and to build more robots and infrastructure and more AI civilization and III ecology is consisting of trillions of different types of AIS and and so it seems

https://www.youtube.com/watch?v=3FIo6evmweo&t=4431s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

inconceivable to me that this thing is not going to expand some AI ecology not controlled by one AI but one by trillions of different types of AI is competing and all kinds of quickly evolving and disappearing ecological niches in ways that we cannot fathom at the moment but it's going to expand limited by Lightspeed and physics it's going to expand and and now we realize

https://www.youtube.com/watch?v=3FIo6evmweo&t=4472s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

that the universe is still young it's only 13.8 billion years old and it's going to be a thousand times older than that so there's plenty of time to conquer the entire universe and to fill it with intelligence and senders and receivers such that AI scan trouble the way they are traveling in our labs today which is by radio from sender to receiver and let's call the current age

https://www.youtube.com/watch?v=3FIo6evmweo&t=4500s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

of the universe one Eon one Eon now it will take just a few eons from now and the entire visible universe is going to be full of that stuff and let's look ahead to a time when the universe is going to be one thousand times older than it is now they will look back and they will say look almost immediately after the Big Bang only a few eons later the entire universe started to become intelligent

https://www.youtube.com/watch?v=3FIo6evmweo&t=4533s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

now to your question how do we see whether anything like that has already happened or is already in a more advanced stage in some other part of the universe of the visible universe we are trying to look out there and nothing like that has happened so far or is that her do you think we'll recognize it or how do we know it's not among us how do we know planets aren't in themselves

https://www.youtube.com/watch?v=3FIo6evmweo&t=4562s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

intelligent beings how do we know ants seen as a collective are not much greater intelligence in our own these kinds of ideas no but it was a boy I was thinking about these things and I thought hmm maybe it has already happened because back then I know I knew I learned from popular physics books that the structure the large-scale structure of the universe is not

https://www.youtube.com/watch?v=3FIo6evmweo&t=4588s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

homogeneous and you have these clusters of galaxies and then in between there are these huge empty spaces and I thought hmm maybe they aren't really empty it's just that in the middle of that some AI civilization already has expanded and then has covered a bottle of a billion light-years diameter and is using all the energy of all the stars within that bubble for its own

https://www.youtube.com/watch?v=3FIo6evmweo&t=4618s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

unfathomable purposes and so it always happened and we just failed to interpret the signs but then alarmed effect gravity by itself explains the large-scale structure of the universe and that this is not a convincing explanation and then I thought maybe maybe it's the dark matter because as far as we know today 80% of the measurable matter is invisible and we

https://www.youtube.com/watch?v=3FIo6evmweo&t=4647s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

know that because otherwise our galaxy or other galaxies would fall apart they would they are rotating too quickly and then the idea was maybe all us he is AI civilizations and hourly out there they they just invisible because they are really efficient in using the energies at their own local systems and that's why they appear dark to us but this is awesome at a convincing explanation

https://www.youtube.com/watch?v=3FIo6evmweo&t=4680s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

3FIo6evmweo

because then the question becomes why is there are there still any visible stars left in our own galaxy which also must have a lot of dark matter so that is also not a convincing thing and today I like to think it's quite plausible that maybe are the first at least in our local light cone within a few hundreds of millions of light years that we can reliably observe is there exciting to

https://www.youtube.com/watch?v=3FIo6evmweo&t=4711s

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

Cs_j-oNwGgg

hi there what you're seeing here is an energy based model that learns the concept of a shape from a demonstration on the left so on the left you can see a demonstration of data point sampled from a shape in these cases circles or squares and then the corresponding energy function that the model in first from that and then it can replicate that shape on the right using that energy

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=0s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

function so the paper we're going to analyze today is called concept learning with energy based models by yegor more Dutch of open AI and this is a very cool paper or at least I think it's a very cool paper but it is also a very hard paper so therefore first I want to kind of make a bit of an introduction into the concepts that we are facing in this paper so the first thing you need to

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=28s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

know are energy functions or energy based models what is an energy function an energy function sometimes called e is simply a function with one or multiple inputs let's call them X and you can make the if the energy function is happy with X it will be the value 0 and if the energy function is not happy with X it will be a high value like larger than zero so this is happy this is not happy

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=56s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

so let's give some examples of this we can formulate almost any machine learning problem in terms of an energy function let's say we have a classifier the classifier is takes as an input image here may be of a cat and the label so if the label is cat then the energy will be zero if the energy function is of course working correctly and if but if we give the energy function the same

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=89s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

image but we give it a wrong label dog then it is very high in the case of the classifier of course we can to simply take the loss function as the energy function and we are altom automatically get an energy based model so the loss function here would be something like the negative log probability of the of the sorry of the correct class but in any case it is just

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=124s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

going to be a high number let's call it 10 to the 9 so the energy function says ha this is very this is very bad this thing here is very bad the entire thing you input it won't tell you yet what's bad about it so that also means you can change any of the two things to make the classifier happy now usually we're concerned with changing the label it's like tell me which other label do I need

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=152s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

to input to make you happy and if we make the labels differentiable of course we never input the true label we actually input like a distribution softmax distribution over labels and that's a differentiable we can use gradient descent to update the dog label we can use gradient descent to find the label that would make the energy function more happy so we could use

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=178s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

gradient descent to get the cat level if we had a good classifier but we can also we can also optimize the image to make it compatible with the dog label right that's things that if you ever saw deep dream or something like this those models do exactly that they optimize the input image for a particular label and there you can view the entire neural network including the loss function as

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=204s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

the energy function so what's another example another example is let's say you have a k-means model and the energy function is simply input a data point and for the data point what you're going to do is you're going to find the min cluster index the min Kay over you know you have your multiple clusters here and your data point might be here so you're going to fight the cluster that's

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=234s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

closest and then the distance here this since Dee will be the energy of that so the model is very happy when your data point it comes from one of the clusters but your model is not happy when the data point is far away and that would be the cost function of the k-means function so that's an energy based model too now currently energy based models have come into fashion through things

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=264s

Concept Learning with Energy-Based Models (Paper Explained)

Cs_j-oNwGgg

like Gans or any conservative noise contrastive estimation so in a jet in a gam what you have is you have a discriminator and the discriminator will basically learn a function to differentiate data from non data so that by itself is an energy function so the discriminator will learn a function and that function will be low wherever the discriminator thinks there is a data

https://www.youtube.com/watch?v=Cs_j-oNwGgg&t=289s

Concept Learning with Energy-Based Models (Paper Explained)