GEOMETRIC DEEP LEARNING BLUEPRINT
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- เผยแพร่เมื่อ 10 พ.ค. 2024
- Patreon: / mlst
Discord: / discord
"Symmetry, as wide or narrow as you may define its meaning, is one idea by which man through the ages has tried to comprehend and create order, beauty, and perfection." and that was a quote from Hermann Weyl, a German mathematician who was born in the late 19th century.
The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact tractable given enough computational horsepower. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning and second, learning by local gradient-descent type methods, typically implemented as backpropagation.
While learning generic functions in high dimensions is a cursed estimation problem, many tasks are not uniform and have strong repeating patterns as a result of the low-dimensionality and structure of the physical world.
Geometric Deep Learning unifies a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.
This week we spoke with Professor Michael Bronstein (head of graph ML at Twitter) and Dr.
Petar Veličković (Senior Research Scientist at DeepMind), and Dr. Taco Cohen and Prof. Joan Bruna about their new proto-book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.
Enjoy the show!
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
arxiv.org/abs/2104.13478
[00:00:00] Tim Intro
[00:01:55] Fabian Fuchs article
[00:04:05] High dimensional learning and curse
[00:05:33] Inductive priors
[00:07:55] The proto book
[00:09:37] The domains of geometric deep learning
[00:10:03] Symmetries
[00:12:03] The blueprint
[00:13:30] NNs don't deal with network structure (TedX)
[00:14:26] Penrose - standing edition
[00:15:29] Past decade revolution (ICLR)
[00:16:34] Talking about the blueprint
[00:17:11] Interpolated nature of DL / intelligence
[00:21:29] Going tack to Euclid
[00:22:42] Erlangen program
[00:24:56] “How is geometric deep learning going to have an impact”
[00:26:36] Introduce Michael and Petar
[00:28:35] Petar Intro
[00:32:52] Algorithmic reasoning
[00:36:16] Thinking fast and slow (Petar)
[00:38:12] Taco Intro
[00:46:52] Deep learning is the craze now (Petar)
[00:48:38] On convolutions (Taco)
[00:53:17] Joan Bruna's voyage into geometric deep learning
[00:56:51] What is your most passionately held belief about machine learning? (Bronstein)
[00:57:57] Is the function approximation theorem still useful? (Bruna)
[01:11:52] Could an NN learn a sorting algorithm efficiently (Bruna)
[01:17:08] Curse of dimensionality / manifold hypothesis (Bronstein)
[01:25:17] Will we ever understand approximation of deep neural networks (Bruna)
[01:29:01] Can NNs extrapolate outside of the training data? (Bruna)
[01:31:21] What areas of math are needed for geometric deep learning? (Bruna)
[01:32:18] Graphs are really useful for representing most natural data (Petar)
[01:35:09] What was your biggest aha moment early (Bronstein)
[01:39:04] What gets you most excited? (Bronstein)
[01:39:46] Main show kick off + Conservation laws
[01:49:10] Graphs are king
[01:52:44] Vector spaces vs discrete
[02:00:08] Does language have a geometry? Which domains can geometry not be applied? +Category theory
[02:04:21] Abstract categories in language from graph learning
[02:07:10] Reasoning and extrapolation in knowledge graphs
[02:15:36] Transformers are graph neural networks?
[02:21:31] Tim never liked positional embeddings
[02:24:13] Is the case for invariance overblown? Could they actually be harmful?
[02:31:24] Why is geometry a good prior?
[02:34:28] Augmentations vs architecture and on learning approximate invariance
[02:37:04] Data augmentation vs symmetries (Taco)
[02:40:37] Could symmetries be harmful (Taco)
[02:47:43] Discovering group structure (from Yannic)
[02:49:36] Are fractals a good analogy for physical reality?
[02:52:50] Is physical reality high dimensional or not?
[02:54:30] Heuristics which deal with permutation blowups in GNNs
[02:59:46] Practical blueprint of building a geometric network architecture
[03:01:50] Symmetry discovering procedures
[03:04:05] How could real world data scientists benefit from geometric DL?
[03:07:17] Most important problem to solve in message passing in GNNs
[03:09:09] Better RL sample efficiency as a result of geometric DL (XLVIN paper)
[03:14:02] Geometric DL helping latent graph learning
[03:17:07] On intelligence
[03:23:52] Convolutions on irregular objects (Taco)
I cannot believe it! I am so happy that you got the whole GDL crew on ML street talk. This episode is great; thanks for your awesome content!
The amount of detail here is astounding
This has taken epic proportions, wow! 💪
Bloody hell your introductions are brilliant, they are mini documentaries.
This episode is beyond good... Finally, some visionaries turning ML from its alchemy stage into proper science!
This video really needs 100k views. Everyone working in RL research needs to view this
During my study of Statistics, ML and DL I never understood the connection between different NN architectures - they simply pop up without any history or proofs. Shut up, learn and memorize. Therefore my professors in ML do not earn my respects, because they do not real understand what they are teaching.
I like thank you for bringing of the fundamental understanding of the zoo of DNN models. I lost the intuition for long time about DNN models. Again thank you for the clarity.
I love Taco's point about the community having too heavy of a cognitive reliance on benchmark datasets and ground truth labels. I have learned this lesson to my bones recently when I developed a "best ever" model in an industrial setting, but all of the metrics showed it to be WORSE because the model was actually better than the ground truth data, and e.g., true positives were counted as false positives. When your data/labels are biased from the data collection process, you can come up with all sorts of counter-intuitive conclusions when we don't keep those biases in mind during interpretation.
+Prof. Bronsteins’ 26:03 mic drop
I couldn't agree more, this is why in an industrial setting I always take new SOTA approaches with a grain of salt. It is not to say that the results are not convincing, but there's danger in having benchmarks that have been static for years.
Benchmarks have generally been collected on a single temporal (and, at times, spatial) scale using a uniform collection method. What I found is that once you start using data that has been evolving over years and years, it becomes non-trivial to deal with the non-uniformity of the data or biases that have been induced throughout the years. I remember finding this "great" pattern in our data and only later finding out that this was simply due to one of the systems assigning a certain property to data points if the system was running out of memory #rip.
So glad to see more attention here on geometric deep learning. Thanks for sharing your chats with these fantastic thinkers!
At 2:10:43 the speaker describes how the methods used to analyze an N based model breaks down at 2N. The Theory of Complex Dynamic Systems includes the concepts “self similarity independent of scale”, fractals to relate scaled features, and non-integer dimensionality. Something worth considering …
Now getting close to the end off your video , I realize that the" architecture" is the grande unified theory. Got excited , jumped the gun...
Excellent !
thank you for all your hard work putting this together, been waiting for this to drop for a long time
Tim, your channel is the best of its kind. Kudos man, much love 🤘🏻
This is great! Thank you for making this episode :)
Great episode! Learned a lot, really appreciate all the work that makes these possible. The videos are incredibly well done! Great guest speakers too of course.
Wow, three and a half hours of amazing content. Thank you so much for making it. It's like a documentary.
Remarkable channel. Inspiring, challenging, eye opening... I have been following it for months and I love it. Thank you
One of the best TH-cam videos I’ve ever watched. Videos, not only ML videos.
Agreed
Yesss normalize awkward people in cinematic shots! I love it
Our sincere thanks to these 4 brilliant researchers:
Professor Michael Bronstein
www.imperial.ac.uk/people/m.bronstein
twitter.com/mmbronstein
Dr. Petar Veličković
twitter.com/PetarV_93
petar-v.com/
Dr. Taco Cohen
twitter.com/TacoCohen
tacocohen.wordpress.com/
Prof. Joan Bruna
twitter.com/joanbruna
cims.nyu.edu/~bruna/
References:
ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML"
th-cam.com/video/w6Pw4MOzMuo/w-d-xo.html
(Note we used some clips from this, and the graphics designer was Jakub Kuba Makowski www.linkedin.com/in/jakub-kuba-makowski-19b17143/)
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
[Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković]
geometricdeeplearning.com/
arxiv.org/abs/2104.13478
Review: Deep Learning on Sets
fabianfuchsml.github.io/learningonsets/
arxiv.org/pdf/2107.01959.pdf
AMMI Course "Geometric Deep Learning" (12 lectures)
th-cam.com/video/PtA0lg_e5nA/w-d-xo.html
Beyond the Patterns 28 - Petar Veličković - Geometric Deep Learning
th-cam.com/video/9cxhvQK9ALQ/w-d-xo.html
Neural Algorithmic Reasoning [Petar Veličković, Charles Blundell]
arxiv.org/abs/2105.02761
Equivariant convolutional networks [Taco Cohen]
pure.uva.nl/ws/files/60770359/Thesis.pdf
Solving Mixed Integer Programs Using Neural Networks
arxiv.org/abs/2012.13349
Project CETI: meaningful communication with another species
audaciousproject.org/ideas/2020/project-ceti
Discovering Symbolic Models from Deep Learning with Inductive Biases [Cranmer]
arxiv.org/pdf/2006.11287.pdf
Node2Vec
arxiv.org/pdf/1607.00653.pdf
Deepwalk
arxiv.org/pdf/1403.6652.pdf
AMMI Course "Geometric Deep Learning" - Lecture 12 (Applications & Conclusions) - Michael Bronstein
th-cam.com/video/caQV-Vb9TBw/w-d-xo.html
Graph Attentional Networks [PetarV]
arxiv.org/pdf/1710.10903.pdf
A Generalization of Transformer Networks to Graphs
arxiv.org/pdf/2012.09699.pdf
The Hardware Lottery [Sara Hooker]
arxiv.org/pdf/2009.06489.pdf
Developments in fractal geometry [Barnsley]
link.springer.com/content/pdf/10.1007/s13373-013-0041-3.pdf
Super-Resolution from a Single Image [Fractals]
www.wisdom.weizmann.ac.il/~vision/single_image_SR/files/single_image_SR.pdf
XLVIN: eXecuted Latent Value Iteration Nets
arxiv.org/pdf/2010.13146.pdf
Analogy as the Core of Cognition [Hofstadter]
worrydream.com/refs/Hofstadter%20-%20Analogy%20as%20the%20Core%20of%20Cognition.pdf
The CLRS Algorithmic Reasoning Benchmark
github.com/deepmind/clrs
WHAT CAN NEURAL NETWORKS REASON ABOUT? [Xu]
openreview.net/forum?id=rJxbJeHFPS
Graph Representation Learning [Hamilton]
www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf
Great content! It may be better to put the references in the description so they don't get pushed too far down in the comment section. I initially thought there were no references and just happened to scroll down the list of comments.
pin this comment
Again, thanks so much to the MLST team and you Tim for bringing this to us! Nothing like it, truly special stuff!
Will be very intrigued to hear the next few months of conversations while y’all chew on Hawkins vs graphs. 😆
Honestly, this channel has such a great format. It's the perfect mix between a podcast and a documentary. It reminds me a lot of Sixty Symbols or Numberphile but for ML instead of physics and maths
Epic...3 hours of pure bliss
This is the longest TH-cam video I’ve ever watched.
I've never known anyone more prepared than Tim.
😎😃
thanks so much for making this! amazing video
You really need a patreon. Would love to give you money to help make more of these documentary style parts. Excellent podcast
Thanks! I don't really want the audience to pay for the content. Most of them are poor Ph.D students! Also -- even very large channels make nothing on patreon so it's pretty pointless adding it, monetization is off too. Perhaps one day we will get a viable sponsorship offer. Right now I would rather keep the channel pure i.e. almost any amount of sponsorship money would pale into insignificance next to the effort we put into making the content and just seems out of place.
@@MachineLearningStreetTalk I understand what you're saying but I still feel like I owe it to you to tell you that I'd definitely sign up for your Patreon if you ever decided to make one
Why not just privately message the person responsible and make a massive donation- privately - if that’s the way you know to encourage someone’s work.
All grateful helpful comments are welcome,
This is on you tube.
I hope it continues to be be free for me and students forever
💰
May this podcast last for another 10 years !
why not forever?
I am enjoying it thoroughly. Its fascinating to see different perspectives from all GDL experts!
What a great episode, thank you guys so much for this!
The way this video builds up over the fundamentals totally blew my mind.
A fantastically detailed video, I'm starting a PhD project on this topic now so this is perfect to watch! Thanks.
Heard rumors about this MLST - and missed it when it came out. Looking forward to the talk!
excellent episode 👌👌 the geometric dl viewpoint is truly fascinating. More than the obvious repercussions on the ML/DL community, I also hope that it will have an impact on the theoretical statistics community's research interests as well. I'm eager to see what lies ahead 😉
The most epic video you have ever made!
As a lot of this is quite over my head. BUT, I'm using OpenAI's playground to have the concepts I don't understand explained to me. And it works extremely well. Ideas like gage symmetry and such now make sense to me. On a very broad level of course, but still. An AI explaining tome the concepts that went into it's own creation! Truly amazing!
Paradigm shift
Excellent content again. Damn another book to read !
Thanks Eric!
It's my 1st time here. Your show already brings me wow!!! Thank you!!
Thanks for podcasts. It helps me with other stuff i found to be in touch with ML, Ai, and neuroscience. Before this new hype around ML and transformers i didnt know that i will found that i will be in love with neuroscience
omg!! what a great generalization effort and applauses for the metanalogy with geometry Erlangen program.. how many problems are in some way only really the same problem!! congratulations and thank you.
Love this topic, looking forward to hear this. I think this episode is great and land at the right technical level, not as low-level as "In these paper we did these augmentation and it works well", but not too philosophically useless as the "knowledge is universal..." argument.
I really enjoyed your introduction. This is starting to be my favorite science podcast besides Lex Fridman's!
This is amazing! Thanks!
I enjoyed watching it very much!!!. Thanks.🙂
amazing stuff. thank you for the time stamps. thus i can only listen to questions relevant for my domain:=)
i love the rotational intro sequence around michael i n the tradition of a science documentary
Nice gem of a channel.
One year after, still one of my favorite episode, with the Chomski one
Absolutely amazing episode! When you eavesdrop on a topical conversation of such detail you are almost bound to pick up some wisdom and sync in with the speakers at least for some time (prerequisite you have some background)
thank you for this
Best episode so far :) !!!!
Best episode ever
Hello sir
can you group all these talks in a single youtube playlist please
thank you for the tremendous effort you are doing
I really enjoyed this one the second time around. One thing, the transformer is mostly a pyramidal graph network -- just FYI.It's a fully-connected Feed Forward NN A pyramidal graph is a type of graph that has a hierarchical structure where nodes are organized into layers. The nodes in each layer are connected to all nodes in the layer above it. Pyramidal graphs have been used in various applications such as object detection1, EEG classification2, and spatial significance exploration3
Amazing!
Love the format! Personally I'd prefer more content than more production value. If you can do both though that would be phenomenal lol
Wow, this chair looks comfortable.
hey Street Talk crew. Watching out for the next one ✌️
Something very cool on its way ;)
Graphcore should hire Tim
That was really interesting.
Love it
I can never keep up with these long episodes. It means I miss an awful lot of MLST if the guest or topic title doesn't really jump out at me. Once in a blue moon, the stars align to when the YT notification pops up and I can envisage fitting in an episode over the next couple of days in chunks. The promise of the Netflix-style Part 1 got me intrigued and it's one of those stars-aligned moments today (probably just because it's Sunday though). I think it's time to crack open the notepad and follow along. I can tell it's going to be good. Most of your stuff is just top content for us researchers in the ML domain (whether partially or fully). I really would consider trying to make smaller videos that are less intimidating length-wise, as I can more easily just ignore this entire episode, but would be much more likely to watch a relevantly-titled section I found interesting that was more around the 20-40 min mark, and thereby find myself enjoying more of your content.
That's a fair point. I sometimes have a similar issue where even if the title attracts my attention, I wont come to finish episodes, just because theyre so long. Also given the often challenging content it is hard to just resume where you stopped.
I really appreciate MLSTs effort to make it easier to absorb the content. Perhaps, shorter videos that summarize a certain topic using scenes from different epsiodes would help in that aspect. On the other hand, I assume this is a lot of work.
I hear where you all are coming from but I want to voice that the beauty of these conversations is there fullness, and distillation or bite sizing seems a big ask given the discussion is already at the edge of current understanding. I love the long form. If it could also be snackable that’d be overpowered 😅
I am so happy!!!!!!!!!!! thank tou so much
it would be extremely helpful for phd students who are very busy if you can make a highlight video of this that contains only the most eseential academical discussions done in this video.
Fantastic, thank you. (Minor improvement suggestion - might just be me, but I would prefer if the music was not constantly playing.)
A grande unified theory is needed.
Okay Tim... I have a question worthy of your intellect and imagination.
Granting arguendo the proposition that we operate within a carefully designed simulation, is it conceivable that the manifold hypothesis illustrates an intentionally implemented efficiency in our computational regime?
Otherwise put, is the low dimensionality of crucial correlations and symmetries in source data an artificially induced property of our technical methodology in some sense?
If proper pedagogy compels the sim architects to deploy logic that is never compromised by later learning--but the sim requires _some_ mechanism for invisibly adjusting the computational reach of our algorithms--then would it not be logical to establish that control inside the _dimensionality_ of our data?
Yes!
The content was great! The editing was a bit disjointed. I love long form videos like this, but a touch more planning may have made for a smoother experience.
wow....some episode !
Still trying to figure out how this guy stays so buff despite being five magnitudes more nerd than me
A thing to keep in mind is you don't want to use neural networks for things that other computer methods do much better at like sorting, computing, mapping, and information lookup. A good example is Apple's Seri.
5:55 you have written 'the WORD is full of simulac..' don't you mean the "worLd"?
btw love it so far, thank you so much for your hard work!
Whoops!!
Teaching a computer to recognize natural laws as it computes and extrapolate relevance.
I still think that coming from an understanding of a thing ,it is much easier to determine the way to think about it. BIOS
Which are rules...I'm getting interested in computer science.
Is there a theory for ‘transfer learning’ as a method for discovering a basis of symmetries for a domain?
Finally!!
A good attempt to geometrization of machine learning by extending the Erlang program using group theory to found networks that capture symmetries. But, unfortunately, although valid for artificial CNN this is only valid to simple neuron equations that only capture very superficially the signal processing of real biological neurons.
If you attack a certain kind of complexity, you need not do that on the same level.
I.e. there are cheap ways of working through certain problems. Example: to apply all the necessary facts, after you have collected all the related phenomena and then choose how to work with those first.
First the easy way, and when you are done, you apply 'all'.
No need to do too expensive computations.
To some degree.
It seems clear how to identify symmetries in problems of computer vision.
However, in games such as chess, go, etc. can the individual symmetries of space, time, power, material be extracted from a trained model?
Can a superhuman AI reveal additional symmetries which lead us to "ah-ha!" moments not experienced by any human in history?
1:59:35 Is the growth really EXPONENTIAL in a hyperbolic space ALSO? Or did Bernstein misspeak? I'm no expert here sure, just trying to understand.
- EDIT: No! Of course, if the embedding space grows exponentially, this also captures better ..what ever represented.
At 1:04:00 Joan Bruna starts talking about "scale separation", giving the example of classifying images via convolutions that have a locality bias as a necessity alongside simmetry, further going on with how there's experimental evidence of how this can hold too.
But doesn't the recent progress in transformer based image models (like ViT and newer ones) and MLP Mixer suggest that it probably doesn't matter or that it might even be constraining? After all these models even ignore translational symmetry and even remove the assumptions of permutation invariance in transformers by using positional encodings.
But when you divide images into patches, doesn't it fix the locality invariant part?
@@adikamath9740 Only if you explicitly encode it in the positional embedding, but in the usual setting you are just telling it that the image patch at position 1 is different than the one at position 2, the transformer doesn't make assumptions over attending to closer patches rather than farther ones.
If you mean that the patch embedding itself is learning locality, I'd argue that something like GPT-Image doesn't even divide the image into patches but just does it pixel based, while ViT seems to show better performance as the patches become smaller and smaller, suggesting that the ideal size would be single pixel based (but of course self attention on a million tokens would be prohibitively expensive)
I may not be understanding your question; so please excuse me if I'm being simplistic. I think that the take-away here is that if you know the symmetries, you can enhance training performance. It's as simple as that. You can avoid redundancies by constraining the input and output spaces. If the function is invariant and equivariant you only need to worry about stationarity and locality. All the rest of the spaces are scaled transforms. I might be getting this wrong as I'm just learning this, but I think I may be properly addressing your concern.
@@dr.mikeybee I agree that geometric deep learning is the right approach for when you know the exact symmetries of your problem, like encoding permutation invariance in GNNs, however the cases made by him at that point of the video were:
- CNNs are good at image tasks because they encode translational invariance *and* have a locality prior thanks to convolutions
- Making networks learn invariances is far too expensive (he gives the example of N! amount of permutations being prohibitive for data augmentation)
On the first point, CNNs are a very good approach at low data/compute regimes and they were the first successful models at their own tasks, but recent progress is showing how networks that completely avoid both the translation invariances and locality biases still perform extremely well at image recognition at larger scales, even beating CNNs in some cases (like CLIP and DALL-E showing ViT is better for their task compared to resnets)
In the second case I think that's just not true: the augmentations provided to the inputs don't have to cover all possible actions of the group considered, ViT or MLP Mixer show robustness to translation even if the amount of all possible image translations is far higher than what's obtained via augmented datasets.
I like geometric DL approaches as I think newer classes of networks are always worth checking out, however I don't really buy their argument that it's something necessary for the future, it seems that more compute and larger scales ultimately end up beating the inductive priors researchers want to encode, since the latter often risk removing information from the input.
What I found more interesting was the perspective of *hinting* symmetries rather than forcing them on the input and let the network discover approximate group actions instead, as it maps better to the concept in bayesian inference of not making your prior density absolutely 0 at spaces you don't have the absolute certainty can't represent your parameters.
@@mgostIH I agree that machines are almost always better at search than we humans, but computational restraints often force us to "stick our noses in." Moreover, I would say this will always be the case. We will always model the world at some small scale relative to the actual world. Nevertheless, if we know the data resides on a sphere, why check the whole three-dimensional space? Or if the world is a meta-graph as Stephen Wolfram suggests, etc. I think the argument here is that we need to find the right methods for these searches that begin with finding the right signal representation. Right now, I don't understand this too much at all. It just seems like where all this research is heading. The point is that like with early geometry, we have not yet generalized the postulates (priors.) And this is an attempt to do that. My intuition tells me it's correct on the whole, but for smaller problems, what you say about CNNs, for example, is true too. There are many approaches to these problems, but there may only be one optimal approach. So like using Ptolemaic system for geo-navigation, CNNs may be perfect for small problems, but I would want something more restrictive for large problems as I would want the Copernican system for navigating the solar system.
I wonder, does Hawkins "reference frames" are analogs of the symmetry group ideas? Thus, brain cortical columns encode priors and symmetries using reference frames, which leads to a robust generalization and reasoning? Does generalization is basically grid cell mapping of the internal knowledge graph? I mean, isnt these ideas very similar?
Musing about this here too. I think Hawkins’ model is one implementation of some kind of good trade off general purpose symmetry-leverage. Sort of along the fuzzy lines of analogy vs graphs/categories like was asked. Like, at the end of the day you can never depend on rigid symmetry because you’ll find a novelty that breaks it, so whatever AGI model must bend to that.
Does anybody have a clue as to where to find the result stated at 1:59:55?
Can someone ELI5 this? I understand basic ML but this was greek and Latin to me.
Hi, i learned that I can use the depth of my brain itself. Perhaps there are people who can use this idea. As far as I know, this hasn't been done. I myself try to realize whilst typing. Which might be too unclear a way of getting inside. This is THE legacy ever. Namely our brains.
2:19:17 and the next 15 seconds contains all the dreaded words of my undergraduate education in a single sentence. Anyone dare to explain what it means in ELI5 fashion.
Where do I find the learning courses that you mentioned? :)
In comments we have references th-cam.com/video/caQV-Vb9TBw/w-d-xo.html
@@MachineLearningStreetTalk Thanks a lot! For everything :€)
Was the lecture is English .. I doubt, didn't understand one little bit .. plz can you elaborate ... On the topics you have been speaking upon.
Symmetry is all you need
0:17 cool Tim became Morpheus :) is Yannic Neo?
Cellular automata. Stephen wolfram ,I think , is working on that.
wonder why the sound went out at a certain part? couldve been just my speakers.. But - im still going to do some deep lip reading operations because if its that secretive then I definitely need to know it even more lol
I hope it's just your speakers!
@@TimScarfe i think it was. speakers must have just thought the phrase "only interpolate but not extrapolate" was a noise signal and subsequently went into standby mode. LOL.
just kidding
2:03:07 The sound is temporarily missing.
If the problem of creating intelligence didn't end up becoming conquered by human ingenuity - then we weren't truly intelligent in the first place. So if we really are supposedely intelligent, we should be able to mechanize it. Thus, machine intelligence is an extension of the fact of human intelligence. If we can't do it then our intelligence should be able to be surpassed given that the speed of human learning can be beat by a machine. Either way, in the final analysis we arent the end all be all of intelligence in the universe. Nature always transcends itself
The sound effects are distracting. Thanks for the info.
the only deep question about AI is one never raised, which is whether the Galilean metaphor that the universe is a book written in mathematics is still relevant, I think it is not, the metaphor is dead. Then the question is whether AI has the potential to mutate beyond the Galilean metaphor, like the scholasticism that preceded it the worldview embedded in the metaphor has exhausted itself. Nothing in AI supports the metaphor, if anything it negates it. Which is why AI's potential to inaugurate a new era is really the only interesting philosophical question to ponder.
Jeff Hawkins is asking questions about locations of mobiles here th-cam.com/video/p_KRsF-ncJQ/w-d-xo.html that I think could eventually be answered by the future GPT3… Here is my hypothesis: I was thinking of this last week : Brain makes maps of the environment. But I was wondering how it managed mobile objects. How it worked to know where it is. It's location. And then I remembered that when I was a kid, my mom would always ask me this question when I had lost something: When did you use it the last time? The question when. And I would try to remember what happened the last days, all the chronological events with a context linked to the object. And then I would remember where it could be. I'm amazed to consider that finally, the brain has a chronological map of events and their context, remembering every sequence and is able to navigate in this chronological map of events. And every object is there, the brain has all these objects identified and painted in every events, and keep all these pictures (I say picture, but it can be linked to any senses) chronologically and can navigate in this chronological mapping, like if the events were layers it can access. Soooooo if the events are like layers that can be accessed chronologically (or you can make jumps, but knowing where it stands in the chronology), could a future GPT3 do pattern recognitions on sequences. Could it learn sequences of patterns and then make predictions?
Clicked so fast!