Posts in: Blog

In this post, we explore the reasons behind it and suggest paths towards scalable training that have the potential to reliably work out of the box.

The environmental impact of artificial intelligence (AI) has been a hot topic as of late—and I believe it will be a defining issue for AI this decade.

Decades ago, Japan faced an unavoidable, long-term economic challenge. Even as its economy reached record highs in the late 1980s (fueled by strong auto sales, the rise of innovative companies like Nintendo, and real estate speculation), it was preparing for the coming day when more than a quarter of its population would be over age 65.

In the first of a series of posts, we share some thoughts on papers and blog posts that we’re reading right now that have generated some fiery internal discussion at Determined AI.

With the AI revolution solidly underway, tech’s top 5 companies are investing huge amounts of money into AI development and AI engineering talent.

In the next few years, chipmaking giants and well-funded startups will race to gain market share.

Training a massive deep neural network can be daunting. Many deep learning (DL) engineers rely on TensorBoard for visualization so that they can better understand, debug, and optimize their model code.

The general perception of cloud computing is that it makes all compute tasks cheaper and easier to manage.

Imagine a world in which gradient descent or second-order methods have not yet been invented, and the only way to train machine learning models is to tune their weights by hand.

In a previous post on “What’s the deal with Neural Architecture Search?”, Liam Li and I discussed Neural Architecture Search (NAS) as a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures.