Liquid Neural Networks | Ramin Hasani | TEDxMIT
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- čas přidán 18. 01. 2023
- Liquid neural networks are a class of AI algorithms that can learn to stay adaptable even after training. Liquid neural networks are inspired by how brain cells communicate with each other. They are robust to perturbations, they do not need to be large to generate interesting behavior, and show promise in learning necessary skills from data to perform well beyond their training data. Liquid neural networks have the potential to alleviate many sociotechnical challenges of large-scale machine learning systems, such as interpretability, accountability, fairness, and carbon footprint. Ramin Hasani is a Principal AI and Machine Learning Scientist at the Vanguard Group and a Research Affiliate at CSAIL MIT. Ramin’s research focuses on robust deep learning and decision-making in complex dynamical systems. Previously he was a Postdoctoral Associate at CSAIL MIT, leading research on modeling intelligence and sequential decision-making, with Prof. Daniela Rus. He received his Ph.D. degree with distinction in Computer Science at Vienna University of Technology (TU Wien), Austria (May 2020). His Ph.D. dissertation and continued research on Liquid Neural Networks got recognized internationally with numerous nominations and awards such as TÜV Austria Dissertation Award nomination in 2020, and HPC Innovation Excellence Award in 2022. He has also been a frequent TEDx Speaker. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at www.ted.com/tedx
Well done Ramin, shows nicely that robustness and explainabability (re-traceability, interpretability) are among the core future research topics - a clear thumbs-up from me 🙂
Congratulations on such disruptive and provocative invention! I would like to go deeper on how this technology works and help to understand decisions that prevent the occurrence of errors, like human errors or false positives, how the model allows the logic the liquid network to take the right decision and the machine learning algorithm help to understand the logic and the learning process.
Things are changing so fast
this is mind blowing
This is really amazing… thank you for sharing about it… liquid neural networks…
Just listened to your interview with Marketplace Tech!
That graph at 2:53 matches the shape of the notional Dunning-Krueger effect graph. Simple patterns are enough early on to get the 80% solution, but as you learn more, you realize there's more you don't know. Only with real expertise do you start to get the best outputs. I think we're seeing the same effect here.
very interesting perspective thanks
Yeah, tons of people are going to think ML is easy after watching this video.
Sounds interesting!
Is the liquid neural network spoken about here the same as the paper Hasani published called Liquid Time-Constant Networks?
Very interresting 🤔🤔
I often think about the kind of dissonance that must exist in an ML researcher's mind when enthusiastically training agents to navigate the real world, with a sterile academic mindset, knowing that the technology will inevitably used for indiscriminate violence in the years to come.
Is there a Python library available for NLP tasks? :)
Totally
I'd like to know how they actually work. Feels like no resource explains it.
Brilliant work, hope to see it in a transformer model for NLP soon!!
These don't work with the transformer . They have a real time "liquid" algorithm. This has the potential to become the future of AI, I have been watching the progress over the past year and its impressive to say the least
It's unclear whether "Liquid" NNs have direct applicability to NLP. LNNs are designed to process time-based information (audio, video, but also potentially typed/spoken text/NLP). One difficulty: in NLP, the time "intervals" are not fixed (between tokens).
As to transformers, LNNs are an alternative architecture. Also, both transformers & LNNs use "attention" mechanisms -- so both may be complementary or both may contain mutually exclusive/incompatible elements.
@@keep-ukraine-free528 Last I heard they are planning on working on a NLP solution full generative AI. It would be nice if they can work between the two. Do you know if the team of 4 are the only ones working on this ? I would think more people would wanna jump on it
@@keep-ukraine-free528 I wonder if the function of brainwaves, in humans, is to synchonise it from a time based method to a static method. Text is still sequential so should be adaptable from time-based methods.... I often wonder how humans read so slowly, and so few books, while transformers must read millions of books, millions of times.... there must be a fundamental inefficiency at present in the transformer model (because by rights they should be already better read than every human in history)
@@shawnvandever3917 It should work, as a tranformer model is effectively still convertible to a vision model... it is just a matter of converting the information from bytes to text or photo... The transformer model should be able to operate on raw bytes and make predictions.
Now my only question is whether these LNNs are as scalable or capable of being put into an architecture that is as scalable as a transformer?
Your question may be based on a misunderstanding many have about information processed by artificial & biological neural networks. NNs must use one of many approaches to processing information, since information is multi-dimensional (in space & time). Liquid NNs are best for processing time-based data (audio, video, dancing/robotics, even typed/spoken language). So your question is equivalent to asking: "can an LLN *_listen_* to a photo and tell me what music it shows?". The question (yours, and this one) don't make sense -- because photos don't have info encoded in the time-domain. This is why different parts of our brain -- and why different artificial neural networks -- use very different structures (topologies/designs - "algorithms").
So one answer to your question is that transformers and "LNN"s already share a critical characteristic -- they both deal in (have & use) "attention". They already share that part.
And yes, "LLN"s do scale, in fact better than traditional deep neural networks since the "liquid" part (which is the "decision-making" part, the most important part) is usually tiny (in his talk, Hasani said their LLN had only 19 "liquid" neurons). Isn't it great that tiny scales so well!!?!
The driving example seems to mostly have attention in the far distance, ignoring directly infront and on the sides, which would suggest it would ignore the person or car coming from the side or obstacles directly infront. The example works well with a clear uninterrupted path ahead.
could be that if training data with close proximity obstacles and humans is introduced (maybe regular dashcam footage), the neural network pays attention to closer stuff aswell
That may be because there are no objects on the road to pay attention to. Perhaps the attention map would change if there were, it would be interesting to see.
i too prefer having a fruit fly brain sized network rather than a datacenter sized neural network fetching my beer
Just to clarify a misconception ,humans are animals... it makes no sense to say "looking brains but not even human brains, animal brains"
It may be noteworthy that this design was found with a human brain and not with a deep neural network. Giving all the hype it seems necessary to point this out.
This design was inspired from a small worm's "brain" (nervous system), called the nematode (C. elegans), which has only approx. 300 neurons. So no, not from the human brain. The speaker Ramin Hasani has explained this before. His team's work shows the power of modeling artificial neural networks from even very simple biological systems. The features exhibited by their "liquid" neural networks exist (but using different molecules/structures) in most simple-to-complex animals, including humans.
@@keep-ukraine-free528 He said "WITH a human brain", so I think it means a human found it 😃
19 Neurons for lane keeping is just clickbait. All the heavy lifting is done in the perception layers in your 3 convolutional layers and the condensed sensory neurons.
FYI What he is showing is that the 19 ltc neurons are replacing the original 4000 fully connected neurons presented in other videos.
@@PhilF. Not really. The 19 neurons in the 2 final "liquid" layers are not replacing neurons that comprise the CNNs. Those 19 are the "only" ones making the output decision (of where to set the steering angle). Yet those 19 definitely need the 1000s of neurons in the CNN. He's made a semantic distinction that the 19 are doing the decision-making. Some may argue that the 1000s of neurons in the CNN are "helping" (doing all preprocessing for) those 19 in the final stage.
@@PhilF.Agree, but still he didn't say that explicitly in the video, "we used only 19 neurons to process the output of the convolutional network" it's something more fair from my perspective.
@@TheNanobot when I saw the videos I donwloaded the github library to test it. It's great.
Read the papers instead of coming off so strong.
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