direct link to the nature paper: https://www.nature.com/articles/s42256-022-00556-7

>”The new machine-learning models we call 'CfC's' replace the differential equation defining the computation of the neuron with a closed form approximation, preserving the beautiful properties of liquid networks without the need for numerical integration," says MIT Professor Daniela Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and senior author on the new paper. "CfC models are causal, compact, explainable, and efficient to train and predict. They open the way to trustworthy machine learning for safety-critical applications."

Cool, excited to see what comes after this.

!RemindMe 3 years

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Fantastic work, a major advance for AI.

Can someone ELI5 these liquid networks?

u gonna be hit with a slew of reminders in the coming years

Please. We need a scientist in here. It sounds awesome - presumably emulating neuron function with AI is a huge deal?

> CfCs could bring value when: (1) data have limitations and irregularities (for example, medical data, financial time series, robotics and closed-loop control, and multi-agent autonomous systems in supervised and reinforcement learning schemes, (2) the training and inference efficiency of a model is important (for example, embedded applications) and (3) when interpretability matters.

Something akin to the cerebellum it seems. It is better suited for continuous motor control (and some other tasks). Yet another component for the human-level AI.

My 50% AGI estimation went down from 2033 to 2030

I think I have the training to do this (math + BME undergrad, in grad school for comp bio), but currently busy with some work. If nothing posted in 2 days send me a reminder and I’ll try.

!RemindMe 2 Years

The article talks about continuous time networks. Those networks deal with processes that are better approximated as smooth changes than as a sequence of discrete steps. Something like baseball vs chess.

A liquid time-constant network is one possible implementation of a continuous time network.

As far as I understand liquid time-constant networks can adjust their "jerkiness" (time-constant) depending on circumstances. That is they can adjust how fast they change their outputs in reaction to a sudden change in input. To be clear it's not a reaction time (the time it takes for the network to begin changing it's output).

For example, if you are driving on an icy road when it's snowing, you don't want to hit the brakes all the way down when you think for a split second that you noticed something ahead. But you may want to do it in good visibility conditions on a dry road.

😎

Hard

Could this lead to more fluid motor control in robots?

I am studying some history of Neural Networks. Is it related somehow with the different approach from Rashevskij's group and Mc Culloch - Pitts neuron? I know that both Pitts and McCulloch developed from Rashevsy research on the brain, but while the latter was using differential equations, the great innovation of Pitts' neuron was to use the approach of discrete quanta of time. This simplified logic allowed the coding of logic formula into neuron and then both Von Neumann computer and Neural Network theory as we know it.

Is this paper an attempt to retrieve Rashevky's approach? To write continuous time-dependent equations?

It really is amazing how naive you are.

That wasn't very nice said in this context

I mean if I'm reading this right this is potentially huge right?

I have some background in math, but I don't know much about the history of computational neurobiology. Sorry

Yes it opens up the possibility for large scale networks that use this type of formulation. So it's hugeness will be dependent on how useful larger versions of those networks turn out to be.