The United States Office of Science and Technology Policy has released draft guidance for regulation that it proposes agencies must adhere to when drawing up new AI regulations for the private sector. The document includes 10 “Principles for the Stewardship of AI Applications”.

The NeurIPS outstanding paper awards highlight some of the most notable papers at the conference. Find out who won the awards and read summaries of their work below.

It has been reported that Sweden is extending its investment in AI. An extra SEK 1.3 billion has been pledged that will extend the current programme by another three years, until 2029.

In late December, the Montreal Institute for Learning Algorithms (MILA) hosted a two-hour debate between Gary Marcus and Yoshua Bengio.

Thanks to those that sent us AI-themed holiday videos, images, and stories. Here’s a sample to get you into the spirit this season.

It’s not common that an artificial intelligence conference sells out as fast as a music festival, but as the Neural Information Processing Systems (NeurIPS) conference found out last year, it’s certainly possible.

Running for its thirty-third year, the conference introduced several new ideas to help with its exploding growth, increasing calls for diversity and inclusiveness, and the general rising interest in artificial intelligence.

Couldn’t make it to NeurIPS this week in Vancouver? Or simply unable to make your way to every exciting talk through the maze of 16k participants? Luckily, many of the sessions are available online.

To get started, we’ve embedded the invited talks below.

The 33rd annual Conference on Neural Information Processing Systems (NeurIPS), happening this week in Vancouver, brings together more than 10k researchers and practitioners from all fields engaged in fundamental work in Machine Learning and Artificial Intelligence.

That’s right! You better not run, you better not hide, you better watch out for brand new AI-themed holiday material on AIhub!

By Sudeep Dasari

This post is cross-listed at the SAIL Blog and the CMU ML blog.

In the last decade, we’ve seen learning-based systems provide transformative solutions for a wide range of perception and reasoning problems, from recognizing objects in images to recognizing and translating human speech. Recent progress in deep reinforcement learning (i.e. integrating deep neural networks into reinforcement learning systems) suggests that the same kind of success could be realized in automated decision making domains. If fruitful, this line of work could allow learning-based systems to tackle active control tasks, such as robotics and autonomous driving, alongside the passive perception tasks to which they have already been successfully applied.