By Eliza Kosoy, Jasmine Collins and David Chan

Despite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data. Children’s learning is supported by highly efficient, hypothesis-driven exploration: in fact, they explore so well that many machine learning researchers have been inspired to put videos like the one below in their talks to motivate research into exploration methods.

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to John Havens about “Ethically aligned design & applied AI ethics”.

In this series of educational blog posts, we highlight components of data analysis by focusing on 7 topics. Each topic is based on key papers, book chapters or blog posts that we have discussed in class. For each topic, we highlight pitfalls to watch out for and propose solutions when possible, some inspired by the literature and others by class discussion. We invite the readers to share their comments with us to help us improve the posts.

With this year’s International Conference on Machine Learning (ICML) being over, it is time to have another instalment of this series. Similar to last year’s post, I shall cover several papers that caught my attention because of their use of topological concepts—however, unlike last year, I shall not restrict the selection to papers using topological data analysis (TDA).

Researchers have used a combination of AI and quantum mechanics to reveal how hydrogen gradually turns into a metal in giant planets.

Dense metallic hydrogen – a phase of hydrogen which behaves like an electrical conductor – makes up the interior of giant planets, but it is difficult to study and poorly understood. By combining artificial intelligence and quantum mechanics, researchers have found how hydrogen becomes a metal under the extreme pressure conditions of these planets.

Building the New Economy is a work in progress book edited (and in large part authored) by Alex Pentland, Alexander Lipton, and Thomas Hardjono. It lays out a vision for a new economy, with data and AI at its heart, that is more resilient to crises.

By Misha Laskin, Aravind Srinivas, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel

A remarkable characteristic of human intelligence is our ability to learn tasks quickly. Most humans can learn reasonably complex skills like tool-use and gameplay within just a few hours, and understand the basics after only a few attempts. This suggests that data-efficient learning may be a meaningful part of developing broader intelligence.

Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Mary Gray about “Ghost work and the role of compassion in tech ethics”.

Recent advances in computer vision have revolutionized many areas of research including robotics, automation, and self-driving vehicles. The self-driving car industry has grown markedly in recent years, in no small part enabled by use of state-of-the-art computer vision techniques. However, there remain many challenges in the field. One of the most difficult problems in autonomous driving is perception. Once autonomous vehicles have an accurate perception of the world around them, planning and control become easier. This article primarily focuses on perception with computer vision and capabilities of computer vision and neural networks for use in fully autonomous self-driving vehicles.