The rapid development of foundation models is driving a sea change in the way machine learning (ML) technologies are developed. They promise to unlock

Safety Is All You Need

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2023-01-23 19:30:06

The rapid development of foundation models is driving a sea change in the way machine learning (ML) technologies are developed. They promise to unlock the great technological transformations of the coming decades, but they also represent single points of failure, trained on planetary scale datasets that are unavailable to the people building on top of them. A safety-first mindset must permeate ML development if these systems are to be deployed at scale. The notion of alignment needs its engineering counterpart: it is more urgent than ever to invest in the engineering processes needed to build ML systems in line with our expectations.

I’ve recently been reflecting on the progress of deep learning over the last decade, and have been humbled by the pace and breadth of change. Over the last years, I recall many discussions about the future of artificial intelligence (AI) in which I was often the skeptic. I failed to foresee how significant the advances would be and how quickly they would come. This is really what it feels like to be in the middle of a rapidly evolving, exponentially growing movement. I understood how hard it is to grasp exponential growth.

When I started my PhD in 2014, I was still computing gradients by hand, and remember tedious hours debugging them with finite differences. I also built large recurrent networks for language, which were hard to scale without knowing how to implement CUDA kernels at the time.