In a move that could help win over some skeptics about the value and efficacy of AI in clinical care, The American Diabetes Association, in its new set of clinical standards, recognizes the use of autonomous artificial intelligence for the screening of some medical conditions. WHY IT MATTERS The ADA's new 2020 Standards of Medical Care in Diabetes includes language noting that "AI systems that detect more than mild diabetic retinopathy and diabetic macular edema authorized for use by the FDA represent an alternative to traditional screening approaches." The clinical standards – published earlier this month in the peer-reviewed journal Diabetes Care – represent a new source for evidence-based best practices, consulted by hospitals and health systems, physicians, insurers and quality organizations. While acknowledging that autonomous AI can be an alternative to traditional screening, however, the ADA specifies that it feels the "benefits and optimal utilization of this type of screening have yet to be fully determined." In addition, it cautions that "artificial intelligence systems should not be used for patients with known retinopathy, prior retinopathy treatment, or symptoms of vision impairment."

On December 20, President Donald Trump signed into the law the historic $738 billion National Defense Authorization Act (NDAA) for Fiscal Year 2020, which contains a provision that requires the "establishment of deepfakes prize competition" to foster research on deepfake detection technologies, in addition to other comprehensive provisions considering the threat deepfakes pose to national security. Ironically, the day before, Rep. Jennifer Wexton (D-VA) introduced legislation that would require the director of the National Science Foundation (NSF) to establish prize competitions to incentivize new research into the development of innovative new technologies to detect deepfakes was introduced last week Wexton's bill, HR 5532, was referred to the House Committee on Science, Space, and Technology. "Deepfakes pose a serious threat to our national security, and there are significant challenges in our ability to effectively identify this manipulated content," Wexton said in a statement announcing her legislation, which as of this reporting has no co-sponsors. Wexton explained that by "establishing prize competitions in this critical field of research will help spur greater innovation and research into technologies that can detect deepfakes. With this bill, we will expand the tools available to address this growing threat to our democracy."

Indonesia's infamous red tape has been a persistent barrier to foreign investment into the country. Jokowi is embracing artificial intelligence (AI) as a potential solution. In a bid to cut red tape and strengthen the flow of foreign investment into Indonesia, President Joko Widodo, affectionately known as Jokowi, has ordered its government agencies to flatten the civil service from four tiers to two and adopt artificial intelligence (AI) to replace jobs where possible. The move is part of the president's strategy to push for investment and bureaucratic reform. Jokowi, whose second five-year term began in October, has found gross domestic product (GDP) growth stubborn, hovering around 5% since he took office.

Despite achieving state-of-the-art performance across many domains, machine learning systems are highly vulnerable to subtle adversarial perturbations. Although defense approaches have been proposed in recent years, many have been bypassed by even weak adversarial attacks. An early study~\cite{he2017adversarial} shows that ensembles created by combining multiple weak defenses (i.e., input data transformations) are still weak. We show that it is indeed possible to construct effective ensembles using weak defenses to block adversarial attacks. However, to do so requires a diverse set of such weak defenses. In this work, we propose Athena, an extensible framework for building effective defenses to adversarial attacks against machine learning systems. Here we conducted a comprehensive empirical study to evaluate several realizations of Athena. More specifically, we evaluated the effectiveness of 5 ensemble strategies with a diverse set of many weak defenses that comprise transforming the inputs (e.g., rotation, shifting, noising, denoising, and many more) before feeding them to target deep neural network (DNN) classifiers. We evaluate the effectiveness of the ensembles with adversarial examples generated by 9 various adversaries (i.e., FGSM, CW, etc.) in 4 threat models (i.e., zero-knowledge, black-box, gray-box, white-box) on MNIST. We also explain, via a comprehensive empirical study, why building defenses based on the idea of many diverse weak defenses works, when it is most effective, and what its inherent limitations and overhead are.

Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very specialized, and few organizations and select highly trained professionals have the wherewithal, in terms of money, manpower, and might, to chart the future. However, concentration of power can lead to marginalization, causing severe inequalities. Regulatory agencies and governments across the globe are creating national policies, and laws around these technologies to protect the rights of the digital citizens, as well as to empower them. Even private, not-for-profit organizations are also contributing to democratizing the technologies by making them \emph{accessible} and \emph{affordable}. However, accessibility and affordability are all but a few of the facets of democratizing the field. Others include, but not limited to, \emph{portability}, \emph{explainability}, \emph{credibility}, \emph{fairness}, among others. As one can imagine, democratizing AI is a multi-faceted problem, and it requires advancements in science, technology and policy. At \texttt{mlsquare}, we are developing scientific tools in this space. Specifically, we introduce an opinionated, extensible, \texttt{Python} framework that provides a single point of interface to a variety of solutions in each of the categories mentioned above. We present the design details, APIs of the framework, reference implementations, road map for development, and guidelines for contributions.

With artificial intelligence becoming all the rage across the HR world, there clearly appears to be rewards from using AI to find and land the best talent. Do AI-based tools in recruiting and hiring really outperform human decision-making? And if they do, could they potentially expose HR and employers to the same types of discrimination issues that can impact hiring driven by people, not algorithms? Right now, the legal landscape in the U.S. has yet to catch up those critical considerations. In Europe, for instance, the U.K.'s Information Commissioner's Office recently released guidance for organizations about transparency within AI decision-making.

The graph represents a network of 3,290 Twitter users whose tweets in the requested range contained "govtech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 23 December 2019 at 07:09 UTC. The requested start date was Monday, 23 December 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 13-day, 9-hour, 49-minute period from Monday, 09 December 2019 at 01:50 UTC to Sunday, 22 December 2019 at 11:39 UTC.

Leading consumer-electronics trade show CES is facing criticism after picking Ivanka Trump to serve as its keynote speaker next month in Las Vegas, after years of being accused of overlooking the role of women in technology. The selection of Donald Trump's daughter and top adviser exposes the vast electronics expo to charges that when, finally, it invites a female keynote speaker, it invites one with limited experience in technology. "This is a terrible choice on so many levels but also – what an insult to the YEARS AND YEARS of protesting how few women were invited to keynote & being told it was a pipeline problem while similarly-situated men were elevated," tech commentator Rachel Sklar tweeted. "There are so many great, qualified women. CES 2020, formally known as the Consumer Electronics Show, confirmed this week that Trump will join a keynote discussion on jobs and the future of work alongside Gary Shapiro, president of CTA, the firm that produces the event considered the largest of its kind globally. "I am excited to join this year for a substantive discussion on the how the government is working with private-sector leaders to ensure American students and workers are equipped to thrive in the modern, digital economy," Trump said in a statement. While the president's daughter has been involved in White House efforts to boost the economic empowerment of women and their families, and spoke at the Global Entrepreneurship Summit in The Hague this year, her engagement with technology is limited. Shapiro said in a statement. "We welcome her to the CES keynote stage, as she shares her vision for technology's role in creating and enabling the workforce of the future." "If they can have a female 007, they can have equally badass female keynote speakers in the tech sector," said Cindy Chin, CEO of the consultancy CLC Advisors and founder of Women on the Block, an organisation that promotes the empowerment and inclusion of women in technology. "There needs to be more systematic representation of speakers across the board and not just for keynotes," Chin says. "It would be better if the background of the keynote speaker actually fit the industry it is serving and inspirational rather than talking heads and political." Given her relative lack of experience, the selection of Ivanka Trump for CES only serves to highlight an enduring gender-bias at tech, electronics, and mobile industries – a bias that endures despite women controlling $29tn of spending worldwide, Chin said. Of all consumer electronics, over 61% of sales are initiated by women including the rise of mobile, AI and machine learning products," said Chin, a member of Nasa's Women in Data open innovation program.

AI has enormous potential when it comes to the healthcare field, capable of improving diagnoses and finding new, more effective drugs. However, as a piece in Scientific American recently discussed, the speed with which AI is penetrating the healthcare field also opens up many new challenges and risks. Over the course of the past five years, the US Food and Drug Administration has approved over 40 different AI products. However, as reported by Scientific American, none of the products cleared for sale in the US have had their performance evaluated in randomized controlled clinical trials. Many AI medical tools don't even require approval by the FDA.