By Jade Boyd

Researchers have demonstrated methods for both designing innovative data-centric computing hardware and co-designing hardware with machine-learning algorithms that together could improve energy efficiency by as much as two orders of magnitude.

The 24th edition of RoboCup, due to take place in Bordeaux in late June, has been postponed until 2021. Obviously an event which centres on soccer matches between opposing teams of robots is something that cannot be recreated online. However, keen to do something in place of the annual extravaganza, the organisers laid on a virtual workshop for the RoboCup humanoid community. This provided a venue for teams to present updates, discuss ideas and solve problems.

The Association for Computing Machinery (ACM) U.S. Technology Policy Committee (USTPC) released a statement on 30 June calling for “an immediate suspension of the current and future private and governmental use of FR [facial recognition] technologies in all circumstances known or reasonably foreseeable to be prejudicial to established human and legal rights.”

The second conference on learning for dynamics and control (L4DC) was held on 11-12 June. In their introduction to the conference, the organisers write that “over the next decade, the biggest generator of data is expected to be devices which sense and control the physical world.” They note that this explosion of real-time data from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. The overall goal for the conference is to create a new community of people that think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area.

Researchers have used artificial intelligence to reduce the ‘communication gap’ for nonverbal people with motor disabilities who rely on computers to converse with others.

The team, from the University of Cambridge and the University of Dundee, developed a new context-aware method that reduces this communication gap by eliminating between 50% and 96% of the keystrokes the person has to type to communicate.

This year the International Conference on Robotics and Automation (ICRA) is being run as a virtual event. One interesting feature of this conference is that it has been extended to run from 31 May to 31 August. A number of workshops were held on the opening day and here we focus on two of them: “Learning of manual skills in humans and robots” and “Emerging learning and algorithmic methods for data association in robotics”.

In February this year, the European Commission released a white paper entitled: On Artificial Intelligence – A European approach to excellence and trust. With the public consultation phase on this document now closed, CLAIRE (Confederation of Laboratories for Artificial Intelligence Research in Europe) have published their response, which largely endorses the EC plans.

Can machine learning learn new physics – or do we have to put it in by hand? A workshop organised by Ilya Nemenman (Emory University), and featuring a number of experts in the field, aimed to find out more.

The 2020 edition of the United Nations AI for Good Global Summit is in full swing and this year takes the form of a continuous virtual event. It features weekly programming across multiple formats, platforms and time-zones, including keynotes, expert webinars, project pitches, Q&As, performances, demos, interviews, networking and more.

By Bryce Benda

In the search for new medicines for diseases such as cancer, a Leiden team has developed a new workflow. This approach combines artificial intelligence (AI) with molecular modelling and is suitable for finding unknown and innovative drug structures, the researchers demonstrated.