ML master pitches trimmed-down deep learning for the masses
In 2015, modern AI trailblazer Andrew Ng’s recipe for success was to go big on neural networks, data, and monolithic systems. Now that recipe has created a problem: the technology is dominated by only a few rich companies with the money and headcount to build such immense systems. But the landscape doesn’t need to hinge on such mainstream accessibility, according to Ng, the Baidu and Google Brain alum (and current CEO of software maker Landing. AI). Instead, he suggests an approach to make machine learning inclusive and open during a session at Nvidia’s GPU Technology Conference last week. Ng suggested building better analytical AI tools and adomain knowledge, with the goal of being able to do more with less, essentially. The key to AI accessibility is to be able to understand patterns and trends from smaller-sized datasets. “We know that in consumer internet companies you may have a billion users and a giant dataset. But when you go to other industries, the sizes are often much smaller,” said Ng. Ng referred to building AI systems in sites like hospitals, schools, or factories, which lack the resources and datasets to develop and train AI models. “AI is supposed to change all industries. We’re not yet seeing this happen at the pace we would like, and we need data-centric AI tools and principles to make AI useful for everyone… not just to large consumer internet companies,” Ng said.
