This shot of Christian on In Deep captures the relentless tech of what is probably the hardest double-black technical trail on Whistler.

The AWS Deep Learning AMIs (DLAMI) for Ubuntu and Amazon Linux are now pre-installed and fully configured with Open Neural Network Exchange (ONNX), enabling model portability across deep learning frameworks. In this blog post we’ll introduce ONNX, and demonstrate how ONNX can be used on the DLAMI to port models across frameworks.

A powerful, under-explored tool for neural network visualizations and art.

Julia Hirschberg sent this along to the natural language processing mailing list at Columbia:

As we’ve discussed before (see also here), one of the difficulties of moving from our current system of review of scientific journal articles, to a new model of post-publication review, is that any major change seems likely to break the current “gift economy” system in which thousands of scientists put in millions of hours providing free reviews. And these reviews can be pretty good. Doing pre-publication reviews at the request of a journal editor: that seems like an obligation, and it’s helped along by pressure from all those associate editors. Remove that system of social obligation, just tell people they can do post-publication reviews when they want, and you’ll see a lot less reviewing. And the reviewing that does get done will be disproportionately by people with an axe to grind. So that could be a problem.

In the article, “Testing the role of convergence in language acquisition, with implications for creole genesis,” Marlyse Baptista et al. write:

We announced the AWS DeepLens Inclusivity Challenge two weeks ago, and how time has flown! In this first challenge, we invite you to create an AWS DeepLens project that fosters inclusion for people of all abilities and helps individuals overcome barriers associated with special needs. For each project that meets the participation criteria, we will make a donation of $249 (the retail price of an AWS DeepLens) to the Northwest Center to help further support their mission of advancing equal opportunities for children and adults with disabilities.

We are excited to share the latest report and prototype from our machine intelligence R team: Multi-Task Learning.

我没有看到每个州最常见的工作，而是找到了前五位的视野。您仍然可以看到全国最受欢迎的职业 - 司机，收银员和零售工人 - 但在第一排之后，您会看到更多区域和州特定职位。

If you’re after a quick overview of some of the services available in Azure to build AI-enabled applications, you might want to check out the 6-minute video below. It provides a tour of three services: