MIT: “Liquid” machine-learning system adapts to changing conditions

MIT researchers have developed a neural “liquid” network
that varies its equations’ parameters, enhancing its ability to
analyze time series data. Image: Jose-Luis Olivares, MIT

MIT researchers have developed a type of neural network that
learns on the job, not just during its training phase. These
flexible algorithms, dubbed “liquid” networks, change their
underlying equations to continuously adapt to new data inputs. The
advance could aid decision making based on data streams that change
over time, including those involved in medical diagnosis and
autonomous driving.

“This is a way forward for the future of robot control,
natural language processing, video processing — any form of time
series data processing,” says Ramin Hasani, the study’s lead
author. “The potential is really significant.”

The research will be presented at February’s AAAI Conference
on Artificial Intelligence. In addition to Hasani, a postdoc in the
MIT Computer Science and Artificial Intelligence Laboratory
(CSAIL), MIT co-authors include Daniela Rus, CSAIL director and the
Andrew and Erna Viterbi Professor of Electrical Engineering and
Computer Science, and PhD student Alexander Amini. Other co-authors
include Mathias Lechner of the Institute of Science and Technology
Austria and Radu Grosu of the Vienna University of Technology.

Time series data are both ubiquitous and vital to our
understanding the world, according to Hasani. “The real world is
all about sequences. Even our perception — you’re not
perceiving images, you’re perceiving sequences of images,” he
says. “So, time series data actually create our reality.”

He points to video processing, financial data, and medical
diagnostic applications as examples of time series that are central
to society. The vicissitudes of these ever-changing data streams
can be unpredictable. Yet analyzing these data in real time, and
using them to anticipate future behavior, can boost the development
of emerging technologies like self-driving cars. So Hasani built an
algorithm fit for the task.

Hasani designed a neural network that can adapt to the
variability of real-world systems. Neural networks are algorithms
that recognize patterns by analyzing a set of “training”
examples. They’re often said to mimic the processing pathways of
the brain — Hasani drew inspiration directly from the microscopic
nematode, C. elegans. “It only has 302 neurons in its nervous
system,” he says, “yet it can generate unexpectedly complex

Hasani coded his neural network with careful attention to how C.
elegans neurons activate and communicate with each other via
electrical impulses. In the equations he used to structure his
neural network, he allowed the parameters to change over time based
on the results of a nested set of differential equations.

This flexibility is key. Most neural networks’ behavior is
fixed after the training phase, which means they’re bad at
adjusting to changes in the incoming data stream. Hasani says the
fluidity of his “liquid” network makes it more resilient to
unexpected or noisy data, like if heavy rain obscures the view of a
camera on a self-driving car. “So, it’s more robust,” he

There’s another advantage of the network’s flexibility, he
adds: “It’s more interpretable.”

Hasani says his liquid network skirts the inscrutability common
to other neural networks. “Just changing the representation of a
neuron,” which Hasani did with the differential equations, “you
can really explore some degrees of complexity you couldn’t
explore otherwise.” Thanks to Hasani’s small number of highly
expressive neurons, it’s easier to peer into the “black box”
of the network’s decision making and diagnose why the network
made a certain characterization.

“The model itself is richer in terms of expressivity,” says
Hasani. That could help engineers understand and improve the liquid
network’s performance.

Hasani’s network excelled in a battery of tests. It edged out
other state-of-the-art time series algorithms by a few percentage
points in accurately predicting future values in datasets, ranging
from atmospheric chemistry to traffic patterns. “In many
applications, we see the performance is reliably high,” he says.
Plus, the network’s small size meant it completed the tests
without a steep computing cost. “Everyone talks about scaling up
their network,” says Hasani. “We want to scale down, to have
fewer but richer nodes.”

Hasani plans to keep improving the system and ready it for
industrial application. “We have a provably more expressive
neural network that is inspired by nature. But this is just the
beginning of the process,” he says. “The obvious question is
how do you extend this? We think this kind of network could be a
key element of future intelligence systems.”

Originally published by
Daniel Ackerman, MIT News Office | January 28, 2021

This research was funded, in part, by Boeing, the National
Science Foundation, the Austrian Science Fund, and Electronic
Components and Systems for European Leadership.