As part of the G7's health track artificial intelligence (AI) governance workstream 2021, member states committed to the creation of 2 deliverables on the subject of governance: These papers are complementary and should therefore be read in combination to gain a more complete picture of the G7's stance on the governance of AI in health. This paper is the result of a concerted effort by G7 nations to contribute to the creation of harmonised principles for the evaluation of AI/ML-enabled medical devices, and the promotion of their effectiveness, performance, safety and ethicality. A total of 3 working group sessions were held to reach consensus on the content of this paper. The rapid emergence of AI/ML-enabled medical devices provides novel challenges to current regulatory and governance systems, which are based on more traditional forms of Software as a Medical Device (SaMD). Regulators, international standards bodies[footnote 2] and health technology assessors across the world are grappling with how they can provide assurance that AI/ML-enabled medical devices are safe, effective and performant – not just under test conditions but in the real world.

Humans invented artificial intelligence, so it is an unfortunate reality that human biases can be baked into AI. Businesses that use AI, however, do not need to replicate these historical mistakes. Today, we can deploy and scale carefully designed AI across organizations to root out bias rather than reinforce it. This shift is happening now in consumer lending, an industry with a history of using biased systems and processes to write loans. For years, creditors have used models that misrepresent the creditworthiness of women and minorities with discriminatory credit-scoring systems and other practices. Until recently, for example, consistently paying rent did not help on mortgage applications, an exclusion that especially disadvantaged people of color.

Fox News Flash top headlines are here. Check out what's clicking on FoxNews.com. It's called a biometric gate check -- more commonly known as facial recognition technology. U.S. Customs and Border Protection (CBP) has used it to process more than 100 million travelers at airports in the U.S. But now a bipartisan pair of U.S. senators is asking how the data is being used and trying to determine if it's an invasion of privacy, similar to daily life in communist China.

Founder and CEO at Fusemachines, Adjunct Associate Professor at Columbia University -- on a mission to democratize Artificial Intelligence. Artificial Intelligence (AI) is a complex subject, inspiring awe in some and concern from others. This complexity makes the job of instituting AI training programs in underserved areas, where knowledge of the subject is usually far and few between, a hefty undertaking. In my last two articles, I covered the need to bring AI training to underserved America and outlined the types of training that would be beneficial. In this article, I will share a step-by-step approach for establishing training in underserved markets by breaking down the audience group into education, business and government.

As a new round of registration is set to commence, the Ministry of Finance is planning to use artificial intelligence to ensure that the state welfare cards issued this year are given only to eligible low-income earners. The Ministry plans to implement new technology to cut down on fraud and to keep urgent financial assistance money from going to those who are not in need at the expense of those who are. The new artificial intelligence system will connect the systems of multiple state agencies to do real-time verification of anyone who applies for state welfare. The Deputy Finance Minister says that linking and cross-referencing all these agencies will easily filter out applicants who do not qualify for benefits. The state welfare is intended for only the lowest earners without significant savings to support them in lean times.

Hot on the heels of the UK's National AI Strategy - launched in September last year - comes the AI Standards Hub, a new government initiative, proposed in the Strategy, which aims to shape global standards for the technology. Britain's Alan Turing Institute, the London-based AI and data science organization founded in 2015, will lead the pilot, with support from the British Standards Institution (the BSI) and metrology institute the National Physical Laboratory. Three august and widely respected bodies, backed by the Department for Digital, Culture, Media and Sport (DCMS) and the UK's Office for AI, which sits across DCMS and what is still called the Department for Business, Energy, and Industrial Strategy (BEIS), even though the Prime Minister scrapped the Industrial Strategy last year - arguably the one bit of government that had been working. That aside, the move adds some much-needed substance to Whitehall claims of world leadership in AI and the UK being a "science and technology superpower". It does this by seeking to focus the debate on standards and regulation at global scale.

Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning are emerging as an important research direction. However, most existing KBQA datasets focus primarily on generic multi-hop reasoning over explicit facts, largely ignoring other reasoning types such as temporal, spatial, and taxonomic reasoning. In this paper, we present a benchmark dataset for temporal reasoning, TempQA-WD, to encourage research in extending the present approaches to target a more challenging set of complex reasoning tasks. Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata. The TempQA-WD dataset is available at https://github.com/IBM/tempqa-wd.

Deep learning (DL) has proven to be an effective machine learning and computer vision technique. DL-based image segmentation, object recognition and classification will aid many in-situ Mars rover tasks such as path planning and artifact recognition/extraction. However, most of the Deep Neural Network (DNN) architectures are so complex that they are considered a 'black box'. In this paper, we used integrated gradients to describe the attributions of each neuron to the output classes. It provides a set of explainability tools (ET) that opens the black box of a DNN so that the individual contribution of neurons to category classification can be ranked and visualized. The neurons in each dense layer are mapped and ranked by measuring expected contribution of a neuron to a class vote given a true image label. The importance of neurons is prioritized according to their correct or incorrect contribution to the output classes and suppression or bolstering of incorrect classes, weighted by the size of each class. ET provides an interface to prune the network to enhance high-rank neurons and remove low-performing neurons. ET technology will make DNNs smaller and more efficient for implementation in small embedded systems. It also leads to more explainable and testable DNNs that can make systems easier for Validation \& Verification. The goal of ET technology is to enable the adoption of DL in future in-situ planetary exploration missions.

EXPRESSING A DISEASE: Want to know whether you have Covid-19 or even Alzheimer's? Artificial intelligence might soon have an answer just by listening to your voice. Leading researchers are developing technology that sorts through evidence of so-called vocal biomarkers to hone in on medical conditions that might not be detectable during routine office visits or exams. "This line might seem to have been lifted from a Star Trek script," said Bertalan Meskó, director of the Medical Futurist Institute. "But we are close to having such conversations with our computers."

Most people are unable to tell they are watching a "deepfake" video even when they are informed that the content they are watching has been digitally altered, research suggests. The term "deepfake" refers to a video where artificial intelligence and deep learning – an algorithmic learning method used to train computers – has been used to make a person appear to say something they have not. Notable examples of it include a manipulated video of Richard Nixon's Apollo 11 presidential address and Barack Obama insulting Donald Trump – with some researchers suggesting illicit use of the technology could make it the most dangerous form of crime in the future. In the first experiment, conducted by researchers from the University of Oxford, Brown University, and the Royal Society, one group of participants watched five unaltered videos, while another watched four unaltered videos and one deepfake – with viewers asked to detect which one is false. The researchers used videos of Tom Cruise created by VFX artist Chris Ume, which have seen the American actor performing magic tricks and telling jokes about Mikhail Gorbachev in videos uploaded to TikTok.