Video conference app Zoom illegally shared personal data with Facebook, even if users did not have a Facebook account, a lawsuit claims. The app has experienced a surge in popularity as millions of people around the world are forced to work from home as part of coronavirus containment measures. The lawsuit, which was filed in a California federal court on Monday, states that the company failed to inform users that their data was being sent to Facebook "and possibly other third parties". It states: "Had Zoom informed its users that it would use inadequate security measures and permit unauthorised third-party tracking of their personal information, users... would not have been willing to use the Zoom App." The allegations come amid a flurry of questions surrounding Zoom's privacy policies, with the Electronic Frontier Foundation recently warning that the app allows administrators to track the activities of attendees.

Apple is rolling out a new feature called "battery health management" that will change how laptops charge themselves. The update will mean that laptops may not charge themselves all the way up all of the time, if the computer believes doing so will protect the life of the battery. It aims to avoid a problem that means fully charging a laptop's battery puts a strain on it, because of the chemicals inside. Leaving a computer charged up in this way can therefore reduce its capacity, leading the battery life to fall over time. Instead, if the computer believes that it is not likely to need 100 per cent battery in the future, it will only charge up some of the way.

This post is part of our symposium on legal, operational, and ethical questions on the use of AI and machine learning in armed conflict.] I am grateful for the invitation to contribute to this online symposium. The preservation of international legal responsibility and agency concerning the employment of artificial-intelligence techniques and methods in relation to situations of armed conflict presents an array of pressing challenges and opportunities. In this post, I will seek to use one of the many useful framings in the ICRC's 2019 "Challenges" report's section on AI to widen the aperture further in order to identify or amplify four areas of concern: detention, humanitarian services, uninhabited military maritime systems, and legal advice. While it remains critical to place sufficient focus on weapons and, indeed, on the conduct of hostilities more widely, we ought to consider other (sometimes-related) areas of concern as well.

The University of Oxford's Professor Rebecca Williams will deliver the closing keynote at this year's LegalEdCon, a virtual event, taking place on Thursday 14 May. Williams will use the slot to announce the findings of Oxford's'Unlocking the Potential of Artificial Intelligence for English Law' research project. She will focus in particular on the future of legal education in relation to changes to the legal job market resulting from implementation of lawtech, changes in the business models of law firms and developments in the law brought about by technology. Williams, along with fellow Oxford Uni akamdeics Ewart Keep and Václav Janeček, are responsible for the legal education stream of Oxford's UK Research and Innovation (UKRI)-funded AI for English Law project. The project as a whole brings together researchers from computer science, law, economics, education and the Saïd Business School to examine the potential and limitations of using AI in support of legal services.

Intelligence and espionage services need to embrace artificial intelligence (AI) in order to protect national security as cyber criminals and hostile nation-states increasingly look to use the technology to launch attacks. The UK's intelligence and security agency GCHQ commissioned a study into the use of AI for national security purposes. It warns that while the emergence of AI create new opportunities for boosting national security and keeping members of the public safe, it also presents potential new challenges, including the risk of the same technology being deployed by attackers. "Malicious actors will undoubtedly seek to use AI to attack the UK, and it is likely that the most capable hostile state actors, which are not bound by an equivalent legal framework, are developing or have developed offensive AI-enabled capabilities," says the report from the Royal United Services Institute for Defence and Security Studies (RUSI). "In time, other threat actors, including cybercriminal groups, will also be able to take advantage of these same AI innovations".

All Arm trademarks featured in this course are registered or unregistered trademarks of Arm Limited (or its subsidiaries) in the US or elsewhere. Welcome to the Deep Learning From Ground Up on ARM Processors course. We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch on our microcontrollers. We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network.

Experts from the private sector and leading research labs in the US and Europe have joined forces to create a toolkit for turning AI ethics principles into practice. The preprint paper, published last week, advocates paying people for finding risks of bias in artificial intelligence (AI) systems – adapting a model used to check the security of new computer systems, in which hackers are paid'bounties' for identifying weaknesses. The paper also proposes better linking independent third-party auditing operations and government policies to foster a market in regulatory systems, and suggests that governments increase funding for researchers in academia to verify performance claims made by industry. The 80-page paper, Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims, has been put together by AI specialists from 30 organisations including Google Brain, Intel, OpenAI, Stanford University and the Leverhulme Centre for the Future of Intelligence. "In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, there is a need to move beyond [ethics] principles to a focus on mechanisms for demonstrating responsible behaviour," the executive summary reads.

Our comprehensive guide to how chatbots will develop in 2020 and beyond. Artificial intelligence is the hottest talking point for business users looking to improve their efficiency, deliver new ideas and take the next steps in the transition to a digital enterprise. AI and chatbots are helping democratise business, empower startups and help build new partnerships, something that every organisation needs to prepare for. "Every business is a technology business" was one of the mantras of the decade just concluded. Every company across every vertical and market started working and communicating with smartphones, using cloud services to open up their data and adopted as-a-service solutions to reduce the cost of doing business and broaden their business base and the opportunities for workers. Ten years ago, specialists were needed to manage databases and build websites. Now anyone with a plan can build an entire company out of off-the-shelf parts, sell across the world without leaving their desk. They can pick advice from a huge range of sources to grow the business and partner with a massive range of organisations to deliver whatever they sell. Now as we move into the 2020s, enterprises and startups alike are taking the next step, adopting AI and bringing smart services into their organisations. It has already started with chatbots and analytics tools, but is already expanding to business-enabling technology, using a mix of machine learning, deep learning, computer vision, natural language processing, machine reasoning (MR), and deep or strong AI. Companies will continue to deploy AI for intelligent robotic process automation, computer vision tasks, and machine learning applications.

In the summer of 1956, a small group of mathematicians and computer scientists gathered at Dartmouth College to embark on the grand project of designing intelligent machines. The ultimate goal, as they saw it, was to build machines rivaling human intelligence. As the decades passed and AI became an established field, it lowered its sights. There were great successes in logic, reasoning, and game-playing, but stubborn progress in areas like vision and fine motor-control. This led many AI researchers to abandon their earlier goals of fully general intelligence, and focus instead on solving specific problems with specialized methods.

Responsible Artificial Intelligence (AI) proposes a framework that holds all stakeholders involved in the development of AI to be responsible for their systems. It, however, fails to accommodate the possibility of holding AI responsible per se, which could close some legal and moral gaps concerning the deployment of autonomous and self-learning systems. We discuss three notions of responsibility (i.e., blameworthiness, accountability, and liability) for all stakeholders, including AI, and suggest the roles of jurisdiction and the general public in this matter.