OVER THE past five years researchers in artificial intelligence have become the rock stars of the technology world. A branch of AI known as deep learning, which uses neural networks to churn through large volumes of data looking for patterns, has proven so useful that skilled practitioners can command high six-figure salaries to build software for Amazon, Apple, Facebook and Google. The top names can earn over $1m a year.
The standard route into these jobs has been a PhD in computer science from one of America’s elite universities. Earning one takes years and requires a disposition suited to academia, which is rare among more normal folk. Graduate students are regularly lured away from their studies by lucrative jobs.
That is changing. This month fast.ai, an education non-profit based in San Francisco, kicked off the third year of its course in deep learning. Since its inception it has attracted more than 100,000 students, scattered around the globe from India to Nigeria. The course and others like it come with a simple proposition: there is no need to spend years obtaining a PhD in order to practise deep learning. Creating software that learns can be taught as a craft, not as a high intellectual pursuit to be undertaken only in an ivory tower. Fast.ai’s course can be completed in just seven weeks.
Demystifying the subject, to make it accessible to anyone who wants to learn how to build AI software, is the aim of Jeremy Howard, who founded fast.ai with Rachel Thomas, a mathematician. He says school mathematics is sufficient. “No. Greek. Letters,” Mr Howard intones, thumping the table for punctuation.
It is working. A graduate from fast.ai’s first year, Sara Hooker, was hired into Google’s highly competitive AI residency programme after finishing the course, having never worked on deep learning before. She is now a founding member of Google’s new AI research office in Accra, Ghana, the firm’s first in Africa. In Bangalore, some 2,400 people are members of AI Saturdays, which follows the course together as a gigantic study group. Andrei Karpathy, one of deep learning’s foremost practitioners, recommends the course.
Fast.ai’s is not the only alternative AI programme. AI4ALL, another non-profit venture, works to bring AI education to schoolchildren in the United States that would otherwise not have access to it. Andrew Ng, another well-known figure in the field, has started his own online course, deeplearning.ai.
Mr Howard’s ambitions run deeper than loosening the AI labour market. His aim is to spread deep learning into many hands, so that it may be applied in as diverse a set of fields by as diverse a group of people as possible. So far, it has been controlled by a small number of mostly young white men, almost all of whom have been employed by the tech giants. The ambition, says Mr Howard, is for AI training software to become as easy to use and ubiquitous as sending an email on a smartphone.
Some experts worry that this will serve only to create a flood of dodgy AI systems which will be useless at best and dangerous at worst. An analogy may allay those concerns. In the earliest days of the internet, only a select few nerds with specific skills could build applications. Not many people used them. Then the invention of the world wide web led to an explosion of web pages, both good and bad. But it was only by opening up to all that the internet gave birth to online shopping, instant global communications and search. If Mr Howard and others have their way, making the development of AI software easier will bring forth a new crop of fruit of a different kind.