Smarter Artificial Intelligence and Deep Learning Post Covid-19

MIT CSAIL project shows that neural nets contain "subnetworks" 10x smaller that can just learn just as well - and often faster These days, nearly all AI-based products in our lives rely on "deep neural networks" that automatically learn to process labeled data. For most organizations and individuals, though, deep learning is tough to break into. To learn well, neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) - and sometimes even custom-designed hardware. But what if they don't actually have to be all that big after all?