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by   -   January 24, 2020

Is our autonomy affected by interacting with intelligent machines designed to persuade us? That’s what researchers at the University of Bristol attempted to find out through an analysis of the interaction between intelligent software agents and human users.

by   -   January 23, 2020
Anonymised data is crucial for AI to work. alphaspirit/Shutterstock

By Ara Darzi, Imperial College London

It is not often that one witnesses a transformational advance in medicine. But the application of artificial intelligence (AI) to improve the early detection of disease is exactly that.

by   -   January 22, 2020

Michael Carbin, Assistant Professor of Electrical Engineering and Computer Science at MIT, joins Michael Littman and Dave Ackley to discuss neural net lottery tickets, computing with uncertainty and more.

by and   -   January 21, 2020

AI Education Matters is a regular feature in the AI Matters newsletter. Here, Marion Neumann explains the teaching methods she uses to expose students to the ideas and working principles of AI technology.

by   -   January 20, 2020

The United States Office of Science and Technology Policy has released draft guidance for regulation that it proposes agencies must adhere to when drawing up new AI regulations for the private sector.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

by   -   January 20, 2020

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.

by   -   January 17, 2020

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.

by   -   January 16, 2020

By Aviral Kumar

One of the primary factors behind the success of machine learning approaches in open world settings, such as image recognition and natural language processing, has been the ability of high-capacity deep neural network function approximators to learn generalizable models from large amounts of data. Deep reinforcement learning methods, however, require active online data collection, where the model actively interacts with its environment.

by   -   January 14, 2020

What’s hot on arXiv? Here are the most tweeted papers that were uploaded onto arXiv during December 2019.

by   -   January 10, 2020

We have collected some of the most interesting tweets about AI from the past couple of months.

by   -   January 8, 2020

Climate change was one of the many topics covered at NeurIPS 2019 (the Thirty-third Annual Conference on Neural Information Processing Systems), with a day-long workshop dedicated to the theme. The session was organised by Climate Change AI, a group of volunteers from academia and industry that seeks to facilitate work in climate change and machine learning.

by   -   December 24, 2019

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.

by   -   December 13, 2019

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.

by   -   December 8, 2019

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.

by   -   December 7, 2019

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

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