I prefer my Neural Networks solid thank you very much

Fantastic work. The relative simplicity of the model proves that this methodology is truly a step towards artificial brains. Expressivity, better causality and the many neuron inspired improvements are inspiring.

It just amazes me how the final few layers are so crucial to the objective of the neural network!

Great work,i hope that they publish the article of this model soon.

0:00: 🤖 The talk introduces the concept of liquid neural networks, which aim to bring insights from natural brains back to artificial intelligence. - 0:00: The speaker, Daniela Rus, is the director of CSAIL and has a curiosity to understand intelligence. - 2:33: The talk aims to build machine learned models that are more compact, sustainable, and explainable than deep neural networks. - 3:26: Ramin Hasani, a postdoc in Daniela Rus' group, presents the concept of liquid neural networks and their potential benefits. - 5:11: Natural brains interact with their environments to capture causality and go out of distribution, which is an area that can benefit artificial intelligence. - 5:34: Natural brains are more robust, flexible, and efficient compared to deep neural networks. - 6:03: A demonstration of a typical statistical end-to-end machine learning system is given. 6:44: 🧠 This research explores the attention and decision-making capabilities of neural networks and compares them to biological systems. - 6:44: The CNN learned to attend to the sides of the road when making driving decisions. - 7:28: Adding noise to the image affected the reliability of the attention map. - 7:59: The researchers propose a framework that combines neuroscience and machine learning to understand and improve neural networks. - 8:23: The research explores neural circuits and neural mechanisms to understand the building blocks of intelligence. - 9:32: The models developed in the research are more expressive and capable of handling memory compared to deep learning models. - 10:09: The systems developed in the research can capture the true causal structure of data and are robust to perturbations. 11:53: 🧠 The speaker discusses the incorporation of principles from neuroscience into machine learning models, specifically focusing on continuous time neural networks. - 11:53: Neural dynamics are described by differential equations and can incorporate complexity, nonlinearity, memory, and sparsity. - 14:19: Continuous time neural networks offer advantages such as a larger space of possible functions and the ability to model sequential behavior. - 16:00: Numerical ODE solvers can be used to implement continuous time neural networks. - 16:36: The choice of ODE solver and loss function can define the complexity and accuracy of the network. 17:07: ✨ Neural ODEs combine the power of differential equations and neural networks to model biological processes. - 17:07: Neural ODEs use differential equations to model the dynamics of a system and neural networks to model the interactions between different components. - 17:35: The adjoint method is used to compute the gradients of the loss in respect to the state of the system and the parameters of the system. - 18:35: Neural ODEs have high memory complexity but are more accurate than the adjoint method. - 19:17: Neural ODEs can be inspired by the dynamics of biological systems, such as the leaky integrator model and conductance-based synapse model. - 20:43: Neural ODEs can be reduced to an abstract form with sigmoid activation functions. - 21:33: The behavior of the neural ODE depends on the inputs of the system and the coupling between the state and the time constant of the differential equation. 22:26: ⚙️ Liquid time constant networks (LTCs) are a type of neural network that uses differential equations to control interactions between neurons, resulting in stable behavior and increased expressivity. - 22:26: LTCs have the same structure as traditional neural networks but use differential equations to control interactions between neurons. - 24:25: LTCs have stable behavior and their time constant can be bounded. - 25:26: The synaptic parameters in LTCs determine the impact on neuron activity. - 25:50: LTCs are a universal approximator and can approximate any given dynamics. - 26:23: Trajectory length measure can be used to measure the expressivity of LTCs. - 27:58: LTCs consistently produce longer and more complex trajectories compared to other neural network representations. 28:46: 📊 The speaker presents an empirical analysis of different types of networks and their trajectory lengths, and evaluates their expressivity and performance in representation learning tasks. - 28:46: The trajectory length of LTC networks remains higher regardless of changes in network width or initialization. - 29:04: Theoretical evaluation reveals a lower bound for expressivity of these networks based on weighted scale, biases scale, width, depth, and number of discretization steps. - 30:38: In representation learning tasks, LTCs outperform other networks, except for tasks with longer term dependencies where LSTMs perform better. - 31:13: LTCs show better performance and robustness in real-world examples, such as autonomous driving, with significantly reduced parameters. - 33:09: LTC-based networks impose an inductive bias on convolutional networks, allowing them to learn a causal structure and exhibit better attention and robustness to perturbations. 34:22: ⚙️ Different neural network models have varying abilities to learn representations and perform in a causal manner. - 34:22: The CNN consistently focuses on the outside of the road, which is undesirable. - 34:31: LSTM provides a good representation but is sensitive to lighting conditions. - 34:39: CTRNN or neural ODEs struggle to gain a nice representation in this task. - 36:07: Physical models described by ODEs can predict future evolution, account for interventions, and provide insights. - 38:36: Dynamic causal models use ODEs to create a graphical model with feedback. - 39:55: Liquid neural networks can have a unique solution under certain conditions and can compute coefficients for causal behavior. 40:18: 🧠 Neural networks with ODE solvers can learn complex causal structures and perform tasks in closed loop environments. - 40:18: Dynamic causal models with parameters B and C control collaboration and external inputs in the system. - 41:12: Experiments with drone agents showed that the neural networks learned to focus on important targets. - 41:58: Attention and causal structure were captured in both single and multi-agent environments. - 43:05: The success rate of the networks in closed loop tasks demonstrated their understanding of the causal structure. - 43:46: Complexity of the networks is tied to the complexity of the ODE solver, leading to longer training and test times. - 44:53: The ODE-based networks may face vanishing gradient problems, which can be mitigated with gating mechanisms. 45:41: 💡 Model-free inference and liquid networks have the potential to enhance decision-making and intelligence. - 45:41: Model-free inference captures temporal aspects of tasks and performs credit assignment better. - 45:53: Liquid networks with causal structure enable generative modeling and further inference. - 46:32: Compositionality and differentiability make these networks adaptable and interpretable. - 46:40: Adding CNN heads or perception modules can handle visual or video data. - 48:09: Working with objective functions and physics-informed learning processes can enhance learning. - 49:02: Certain structures in liquid networks can improve decision-making for complex tasks. Recap by Tammy AI

Sounds like an important and necessary evolution of ML. Lets see how much this can be generalized and scaled but sounds fascinating.

amazing video. thank you for uploading.

Most underrated talk. This is an actual game changer for ML.

very good, the idea for that is simple, the problem relay in puting all of it to work together, that is the good stuff

Always interesting. Thx

Amazing!!

Thanks Ramin and team. That was the most interesting and well delivered presentation on neural nets that I have ever seen, certainly a lot new to learn in there. Most impressed by the return to learning from nature and the brain and how that significantly augmented 'standard' RNNs etc. Well, there's a new standard now, and it's liquid.

man i dont work in ml field but sure this is fascinating!!!!

Awesome Thanks!

This is truly a game changer in AI, well done folks 👍

i hope that one day i'll be able to fully understand what he's talking about... but it sounds amazing and i want to play around with it!

I got so happy finding out the person who wrote this exciting paper, is also a Ramin :)

Casualty is extremely important in building Bayesian model of the world. It allows to identify correlations between events, that are useful to create a-priori statistics for reasoning, because we avoid double-counting. Single evidence and its logical consequences is not seen as many independent confirmations of hypothesis.

The "discovery" that fixed time steps for ODE work better in this case is very well known in the optimal control literature (at least by a couple of decades). Basically if your ODE solver has adaptive time steps, the exact mathematical operations performed for a given integration time interval dT can vary because a different number of internal steps is performed. This can have really bad consequences on the gradients of the final time states. There's plenty of theoretical and practical discussion in Betts' book Practical Methods for Optimal Control, chapter 3.9 Dynamic Systems Differentiation.

Could you please share the link to the original paper? Thanks

I would need a simpler version of the 22:49 diagram to understand this. Ramin says here that standard NN neurons have a recursive connection to themselves. I don't know a ton about ANNs, but I overhear from my coworkers, and I never heard of that recursive connection. Is that for RNNs? Is there a "Reaching 99% on MNIST"-simple explanation, or does this liquidity only work on time-series data?

Hi, thanks for the great video! Can you share the idea of how to visualize the activated neurons as in 6:06 in this video? It's really cool and I'm curious about it!

I've had unarticulated thoughts with resemblance to this concept for many decades, I never learned the math, so never could express my ideas. I still need someone to explain the math at a highschool level!

naming this liquid analogizes the conventional network as solid or rigid. The analogous mechanism is the utility of differential equations that represent multitudinous connections, just as water molecules can more freely interact with a greater degree of neighbors.

The video showed up in my feed randomly and I clicked on it just cause the lecturer was Iranian. But the content was so interesting that I watched it to the end. Sounds like a really evolutionary breakthrough in ML and DL. Specially with the computational power of computing systems growing every day, training/inferencing such complex network models become more possible. Great job

does anyone know about any sample code for a model like this?

Where to get the slides?

Fascinating... loved every minute.

Amazing talk

How do these compare to neural ode?

I am moved beyond description. What an amazing privilege to be alive in this day and age. The future will be great for mankind.

But why do the other methods have smaller error bands? There is further improvement that can be done here

where can i find the paper ?

Must share technology that saves lives.

That's amazing concept. We should implement it out of spite. Too often we feel our brain to be a mush. Ai should suffer that way too.

Feels like ML folks keep rediscovering things all over

Wow!

Attention map at 7:00 looks fine to me. If you do not want to wander off out of the road, you should attend to the boundary of the road. And even after you add noise at 7:30, the attention still picks up the boundary which is pretty good.

- let's make another attempt at implementing a biology-inspired neural network - proceeds implementing backprop

This guy is a genius.

this was a great talk, this could change everything

this should be the next deep learning revolution.

I'm curious would't numerical solver to ODE kill all gradients, getting error scaling exponentially as depth grows ?

Sounds interesting, but... The fundamental difference between biological and NN processing at the current state is time. While biological systems process input asynchronously, computers try to do the whole path in one tick. I believe, this must be addressed first, leading to a completely new concept of NN, where input neurons will generate a set of signals first (with some variations as per change in the physical source signals), those signals then will be accumulated by the next layer of NN, processed in the same fashion, and passed further. This way signals which will repeat over the multiple sampling ticks of the first layer will be treated with a higher trust (importance) level on the next layer.

Can't wait to do a project on LNN and add it to my resume to stand out of the crowd.

How does the attention map showing that the LNN was looking at the vanishing point mean it’s forming “better” representations? Shouldn’t “better” representations only be understood as having better performance? If it’s more explainable that’s cool but there’s ways to train CNNs that make them more explainable while hurting performance.

fathi fes medos aziza 1 said Thank you so much

What is the difference betweem these LNNs and coupled ODEs? Aren't we conflating these terms. If you drive a car with only 19, then what you have is an asynchronous network of coupled ODEs and not a neural network, the term is misleading.

Does anyone have code for an Autonomous car system, I would like to practice it. If anyone knows please share.

Great work! Does this mean all the training done for autonomous driving with the traditional NN goes to the toilet?

Why does no one in this video seem to have any plan for what they want to communicate and in what order? So hard to follow

Can someone point to a simple LTC network implementation please?

I am wondering how it does for RL scenarios!

Any association with Liquid Snake ?

Makes me wonder if the research of OpenAI and such will shift towards a multimodal language mode + universal approximators. I can imagine that the world can be differentially modelled from the pretrained weights of the collective knowledge of humanity .......... eventually.

Dam...nice.

Get this video to the FSD team.

To me it seems similar to variational inference ...

What about back propagations? Too bad he didnt finish his train of thought, this is rather an interview than source for knowledge/lesson.

good

There are so many smart people on MIT that America must be already a superintelligent nation. Please continue your work and this world will become a wonderful place to live in.

at the end of the video he says these technologies are open sourced but there are no links in the video descriptions , can we gather the relevant code sources and git hub repos?

😮

As a newbie. It is hard to grasp

...it runs on water, man.

Can this be extended to a liquid transformer model?

I'm not an expert, but this sounds to me like this an implementation of different neurotransmitters, isn't?

Ok, after half of the talk, I stopped and read the entire paper, which kind of left me disappointed. LNNs are promoted as a huge step forward, but where are the numbers to back this up? I couldn't find them in the paper, and I strongly doubt that this is the "game-changer" as some suggest.

USA: I WILL TAKE YOUR ENTIRE MODEL

Soo when AGI ?

John Venn 3 diagram class 10

루마니아의 보석

@tesla 👀

is it really reasonable to "just" model a CNN for autonomous driving? it would be better to compare liquid nets with policies trained in an RL system (where at least some underlying goal was followed), no?

Does The Brain Distribute Calculation Loads To Systemic Subsets, Initiating Feedback Loops From Other Cellular Systems With Different Calculative Specialties And Capacities, Based Upon The Types Of Contextual Information It Recieves From Extraneous Environmental Sources, Which It Then Uses To Construct Or Render The Most Relevant Context To Appropriate Consciousness Access To A Meaningful Response Field Trajectory?

1year later?

For all intents and purposes doesn't a liquid network work like a KAN? Change my mind.

To be fair though, he isn't at Stanford, so he hasn't sold out completely yet. Lets give him a chance :)

μ Muon Spec

Very sad to see AI development fall into another hole. The last 10 years were fun while it lasted. See you guys 30 years later!

Path breaking new ground forming revolutionary research work that will change the face of futures liberating Force focused On digital humanities SPINNED Technologies INNOVATIONS Spectrums.

He failed at 2 things: 1. He decided to solve differential equations. 2. He didn’t get rid of back propagation. Probably he is required to do some good math in order to publish papers and be paid a salary. As long as we have such an incentive from a scientific community, we would be stuck with suboptimal narrow AI based on statistics and back propagation.

Why is the brain a Mandelbrot set?

Not clear at all.

If liquid neural networks were a serious thing, they would have gained a lot more attention in proper ML/AI conferences, not just TEDx and the shit you are presenting here.

PLEASE.... Do you mind bothering to pronounce other languages correctly!!? (And before people jump up .. I speak multiple languages myself).

Sorry but you make so many assumptions at almost every level. You are biased and your interpretation of the functionality and eventual use of this 'computational' model has nothing to do with how true intelligence arises. Start again. And this time leave your biases where they belong... in your professors heads.

Really it is just an interview because he wants to get a job at Google and make lots of money to serve ads :)