As artificial intelligence continues to creep into everyday life, the Australian government has pledged $29.9 million over four years to enhance local AI capabilities. Treasurer Scott Morrison announced in Tuesday night's budget that "research in artificial intelligence" was to be included as part of the Government's $2.4 billion investment into Australia's science and technology capacity. The Department of Industry, Innovation and Science will receive the bulk of the funding ($26 million), alongside the CSIRO ($2.3 million) and the Department of Education and Training ($1.5 million). "This measure will support Cooperative Research Centre projects, PhD scholarships and school-related learning to increase knowledge and develop the skills needed for AI and machine learning," the budget papers state. Professor of Artificial Intelligence at the University of New South Wales and ACS AI Ethics Committee Member, Professor Toby Walsh, welcomed the funding, but questioned whether it was enough to poise Australia as a global leader in the field.

While the NFL draft is already over, the Boston Dynamics Atlas robot is angling for a free agent workout with its latest highlight video. We've already seen one backflipping, but considering the abilities necessary, it's nearly as impressive to see a robot running untethered across open terrain, and easily clearing a small obstacle. The stats on its 40 time and vertical aren't world-class yet, but a glance at older PETMAN walking demos from 2009 show how far the technology has come in just a few years. We're betting the next DARPA competition will look a little different from the last one. Richard's been tech-obsessed since first laying hands on an Atari joystick.

Acute kidney injury (AKI) is a common and serious complication after a surgery which is associated with morbidity and mortality. The majority of existing perioperative AKI risk score prediction models are limited in their generalizability and do not fully utilize the physiological intraoperative time-series data. Thus, there is a need for intelligent, accurate, and robust systems, able to leverage information from large-scale data to predict patient's risk of developing postoperative AKI. A retrospective single-center cohort of 2,911 adult patients who underwent surgery at the University of Florida Health has been used for this study. We used machine learning and statistical analysis techniques to develop perioperative models to predict the risk of AKI (risk during the first 3 days, 7 days, and until the discharge day) before and after the surgery. In particular, we examined the improvement in risk prediction by incorporating three intraoperative physiologic time series data, i.e., mean arterial blood pressure, minimum alveolar concentration, and heart rate. For an individual patient, the preoperative model produces a probabilistic AKI risk score, which will be enriched by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. We compared the performance of our model based on the area under the receiver operating characteristics curve (AUROC), accuracy and net reclassification improvement (NRI). The predictive performance of the proposed model is better than the preoperative data only model. For AKI-7day outcome: The AUC was 0.86 (accuracy was 0.78) in the proposed model, while the preoperative AUC was 0.84 (accuracy 0.76). Furthermore, with the integration of intraoperative features, we were able to classify patients who were misclassified in the preoperative model.

Machine learning methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. Despite the excitement surrounding machine learning methods, clarity regarding their proper use and potential pitfalls is lacking. This paper attempts to fill this gap in the literature through providing a machine learning "guide to practice" for public administration scholars and practitioners. Here, we take a foundational view of machine learning and describe how these methods can enrich public administration research and practice through their ability develop new measures, tap into new sources of data and conduct statistical inference and causal inference in a principled manner. We then turn our attention to the pitfalls of using these methods such as unvalidated measures and lack of interpretability. Finally, we demonstrate how machine learning techniques can help us learn about organizational reputation in federal agencies through an illustrated example using tweets from 13 executive federal agencies.

WASHINGTON--(BUSINESS WIRE)--The following is an opinion editorial provided by Brian Krzanich, chief executive officer of Intel Corporation. China, India, Japan, France and the European Union are crafting bold plans for artificial intelligence (AI). They see AI as a means to economic growth and social progress. Meanwhile, the U.S. disbanded its AI taskforce in 2016. The U.S. technology sector has long been a driver of global economic growth.

Are Healthcare Leaders Ready For AI-Driven Change? Last year I looked at a new report from the House of Lords that explored the'future of healthcare'. As the name of the report suggests, the nature of the work lended itself to longer-term thinking, and this is a central criticism of current NHS leadership. Whilst the Five Year Forward View published by NHS England in 2014 is widely supported, it doesn't look beyond 2019, and nothing has entered the public domain to highlight NHS policy beyond then. Unfortunately, it's not at all clear that the report fills that vacuum, despite a hugely impressive range of names summoned to give evidence to the committee.

Uber has teased a look at what its futuristic Skyport flying taxi hubs could be like when UberAir comes to life. At the firm's Elevate Summit in Los Angeles, Uber unveiled elaborate concept images of the Connect system developed by Corgan that could provide infrastructure for the vertical take-off and landing craft. The modular system can essentially be installed anywhere, be it an open site, atop a parking garage, or even on the roof of a skyscraper, according to Corgan. Uber has teased a look at what its futuristic Skyport flying taxi hubs could be like. At the firm's Elevate Summit in Los Angeles, Uber unveiled elaborate concept images of the Connect system developed by Corgan that could provide infrastructure for UberAir Uber has plans to begin its first flight demonstrations as soon as 2020, and begin taking passengers by 2023.

This analysis was first available to Bloomberg Government subscribers. The Central Intelligence Agency is looking to team up with industry experts to run a series of open-source intelligence projects using its Amazon cloud. The agency released a revised acquisition schedule on May 7 for a project known as Mesa Verde that will test the frontiers of big data and open-source intelligence. The project calls for using the CIA's C2S cloud, built by Amazon Web Services LLC, to pore through thousands of terabytes of data, including data publicly available on the Web, and apply tools such as natural language processing, sentiment analysis, and data visualization to draw conclusions others might have missed. For the three years before October 2017, the CIA's Directorate of Digital Innovation Digital Futures led a series of prototype activities to develop analytics applications for Mesa Verde, capable of techniques ranging from simple search through statistical analysis of massive amounts of data using advanced analytic algorithms, according to documents released by the agency.

NSF director outlines vision for AI that benefits the economy and U.S. workers NSF director makes statement on AI research. On May 10, 2018, the White House convened academic researchers, industry experts and federal leaders at an event on Artificial Intelligence for American Industry. National Science Foundation (NSF) Director France Córdova participated in the event and issued the following statement. Artificial intelligence (AI) is transforming every segment of American industry. It is making agriculture more precise and efficient, giving us new medical diagnostics that save lives, and creating the promise of autonomous transportation and advanced manufacturing.

Artificial intelligence (AI) has tremendous potential to benefit the American people, and has already demonstrated immense value in enhancing our national security and growing our economy. AI is quickly transforming American life and American business. It improves how we diagnose and treat illnesses, grow our food, manufacture and deliver new products, manage our finances, power our homes, and traverse our roads. Today, the White House hosted the "Artificial Intelligence for American Industry" summit, an opportunity to discuss the promise of AI and the policies we will need to realize that promise for the American people and maintain U.S. leadership in the age of artificial intelligence. "Artificial intelligence holds tremendous potential as a tool to empower the American worker, drive growth in American industry, and improve the lives of the American people," said Deputy Assistant to the President for Technology Policy Michael Kratsios.