Last year MIT researchers announced that they had built ‘liquid’ neural networks, inspired by the brains of small species. The flexibility of these ‘liquid’ neural nets yielded better decision-making for many tasks.
But these models became computationally expensive as their number of neurons and synapses needed to be increased to solve their underlying complicated math.
Now, the same team of scientists has discovered a way to alleviate this bottleneck with a new type of fast and efficient artificial intelligence algorithms.
These new network models have the same characteristics of ‘liquid’ neural nets – flexible, causal, robust, and explainable – but they are orders of magnitude faster, and scalable.MORE