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.
We are entering the golden age of artificial intelligence.
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.
As most deep learning engineers know, it can take days or weeks to train a deep learning model, costing organizations considerable time and money. But what if we could speed up the process and achieve better results in the process?
2019 has gotten off to a good start for us at Determined AI. As a company focusing on accelerating deep learning model development for our users, we saw the deep learning community gathering together at the RE•WORK Deep Learning Summit and it further validated our mission.
Deep learning offers the promise of bypassing the process of manual feature engineering by learning representations in conjunction with statistical models in an end-to-end fashion.
In this post, we discuss how a technique known as warm-starting can be used to save computational resources and improve generalizability when training deep learning models.
Last week, we at Determined AI were honored to sponsor a meetup of the Women in Infrastructure group focused on ML infrastructure.
In this post, we discuss the missing key to fully leveraging your hardware investment: specialized software that understands the unique properties of deep learning workloads.
To maximize the value of your deep learning hardware, you’ll need to invest in software infrastructure. Setting up a cluster manager is an essential first step in this process, but it’s not the end of the story.
You’ve probably had some version of this debate numerous times: cloud or on-premise? Maybe you want to migrate some pieces of your application to the cloud.
Reproducing results across machine learning experiments is painstaking work, and in some cases, even impossible.
I recently spoke at the AI Conference in NYC about some of the academic research underlying our efforts at Determined AI.
Machine learning today resembles the dawn of aviation.
I recently joined the O’Reilly Data Show podcast to talk about various challenges associated with developing deep learning at scale.