In a post from last summer, I noted how rapidly PyTorch was gaining users in the machine learning research community. At that time PyTorch was growing 194% year-over-year (compared to a 23% growth rate for TensorFlow). That post used research papers, specifically simple full-text searches of papers posted on the popular e-print service arXiv.org.
As companies increase their investments in artificial intelligence (AI), there is growing pressure on developers and engineers to deploy AI projects more quickly and at greater scale across the enterprise.
[This post originally appeared on the Anyscale blog.]
It’s clear that AI can and will have a big influence on how we develop software. By Mike Loukides and Ben Lorica. [A version of this post appears on the O’Reilly Radar.] Roughly a year ago, we wrote “What machine learning means for software development.” In that article, we talked about Andrej Karpathy’s concept of SoftwareContinue reading “The Road to Software 2.0”
We need to remember that creating fakes is an application, not a tool — and that a malicious applications are not the whole story. By Ben Lorica and Mike Loukides. [A version of this post appears on the O’Reilly Radar.] Deepfakes have been very much in the news for the past two years. It’s timeContinue reading “A world of DeepFakes”
I was able to catch the morning sessions of the PyTorch developer conference in San Francisco, and just like last year the event was well-organized and packed. The official blog post has details on the many announcements pertaining to the version 1.3 release, but here are a few that caught my eye: Growth: The scrolling listContinue reading “Notes from the second PyTorch Developer conference”
The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning. In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of manyContinue reading “Understanding deep neural networks”
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data. By Ben Lorica and Mike Loukides. [A version of this post appears on the O’Reilly Radar.] AI will have a huge impact on healthcare. It is currently moving out of the laboratory and into real-world applications for healthcareContinue reading “How New Tools In Data And AI Are Being Used In Healthcare And Medicine”
The O’Reilly Data Show Podcast: Kesha Williams on how she added machine learning to her software developer toolkit. In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learningContinue reading “Becoming a machine learning practitioner”
To successfully implement AI technologies, companies need to take a holistic approach toward retraining their workforces. Continuous learning is critical to business success, but providing employees with an easily accessible, results-driven solution they can access from wherever they are, whenever they need it, is no easy feat. Additionally, delivering valuable content in a variety ofContinue reading “How organizations are sharpening their skills to better understand and use AI”