So, for those of us without a doctorate in Really Hard Math, differential equations are formulas that can describe the state of a system at various discrete points or steps throughout the process. For example, if you have a robot arm moving from point A to B, you can use a differential equation to know where it is in between the two points in space at any given step within the process. However, solving these equations for every step quickly gets computationally expensive as well.
Imagine if you have an end-to-end neural network that receives driving input from a camera mounted on a car. The network is trained to generate outputs, like the car's steering angle. In 2020, the team solved this by using liquid neural networks with 19 nodes, so 19 neurons plus a small perception module could drive a car. A differential equation describes each node of that system.
By solving this equation at the neuron-level, the team is hopeful that they’ll be able to construct models of the human brain that measure in the millions of neural connections, something not possible today. The team also notes that this CfC model might be able to take the visual training it learned in one environment and apply it to a wholly new situation without additional work, what’s known as.
All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission. All prices are correct at the time of publishing.MIT solved a century-old differential equation to break 'liquid' AI's computational bottleneck
“So how do you build AI systems that can learn robust, general concepts that remain valid outside the context of their training data?”
maybe useful for navigating end points for space travel like warp speed
'They open the way to trustworthy machine learning' - That line scared me a little, lol.