Meta's 'data2vec' is a step toward One Neural Network to Rule Them All

The race is on to create one neural network that can process multiple kinds of data -- a more-general artificial intelligence that doesn't discriminate about types of data but instead can crunch them all within the same basic structure. The genre of multi-modality, as these neural networks are called, is seeing a flurry of activity in which different data, such as image, text, and speech audio, are passed through the same algorithm to produce a score on different tests such as image recognition, natural language understanding, or speech detection. And these ambidextrous networks are racking up scores on benchmark tests of AI. The latest achievement is what's called "data2vec," developed by researchers at the AI division of Meta (parent of Facebook, Instagram, and WhatsApp). The point, as Meta researcher Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli reveal in a blog post, is to approach something more like the general learning ability that the human mind seems to encompass.