The plan aims to use AI to boost competitiveness and productivity and address issues such as social inequality. The Brazilian government has taken another step towards the creation of public policies around artificial intelligence (AI). A national AI strategy will be created as a response to the worldwide race for leadership in the field and the need to discuss the future of work, education, tax, research and development as well as ethics as the application of related technologies becomes more pervasive. A public consultation has been launched to gather input around how AI can solve the country's main issues, identify priority areas of focus for the development and use of the technologies, as well as limits for it. According to the summary on the purpose of the consultation, which ends on January 31, 2020, the government understands that AI can bring improvements to the country's competitiveness and productivity, as well as the provision of public services, quality of life and to reduce social inequality in the southern hemisphere's biggest economy.

Successful deployment of machine learning algorithms in healthcare requires careful assessments of their performance and safety. To date, the FDA approves locked algorithms prior to marketing and requires future updates to undergo separate premarket reviews. However, this negates a key feature of machine learning--the ability to learn from a growing dataset and improve over time. This paper frames the design of an approval policy, which we refer to as an automatic algorithmic change protocol (aACP), as an online hypothesis testing problem. As this process has obvious analogy with noninferiority testing of new drugs, we investigate how repeated testing and adoption of modifications might lead to gradual deterioration in prediction accuracy, also known as ``biocreep'' in the drug development literature. We consider simple policies that one might consider but do not necessarily offer any error-rate guarantees, as well as policies that do provide error-rate control. For the latter, we define two online error-rates appropriate for this context: Bad Approval Count (BAC) and Bad Approval and Benchmark Ratios (BABR). We control these rates in the simple setting of a constant population and data source using policies aACP-BAC and aACP-BABR, which combine alpha-investing, group-sequential, and gate-keeping methods. In simulation studies, bio-creep regularly occurred when using policies with no error-rate guarantees, whereas aACP-BAC and -BABR controlled the rate of bio-creep without substantially impacting our ability to approve beneficial modifications.

Two of the most widely quoted and shared poems in the closing years of this decade were William Butler Yeats's "The Second Coming" ("Things fall apart; the centre cannot hold"), and W.H. Auden's "September 1, 1939" ("Waves of anger and fear / Circulate over the bright / And darkened lands of the earth"). Yeats's poem, written just after World War I, spoke of a time when "The best lack all conviction, while the worst / Are full of passionate intensity." Auden's poem, written in the wake of Germany's invasion of Poland, described a world lying "in stupor," as democracy is threatened and "the enlightenment driven away." Apocalypse is not yet upon our world as the 2010s draw to an end, but there are portents of disorder. The hopes nourished during the opening years of the decade -- hopes that America was on a progressive path toward growing equality and freedom, hopes that technology held answers to some of our most pressing problems -- have given way, with what feels like head-swiveling speed, to a dark and divisive new era.

Neuromorphic engineering, also known as neuromorphic computing,[1][2][3] is a concept developed by Carver Mead,[4] in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system.[5] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors,[6] spintronic memories,[7] threshold switches, and transistors.[8] A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.[9]

The federal government on Monday announced it will invest AU$7.5 million for research into the use of artificial intelligence (AI) in healthcare. "Artificial intelligence will be critical in transforming the future of healthcare through improved preventive, diagnostic, and treatment approaches," a statement from acting Minister for Health Anne Ruston said. The new funding will be dispensed via grants to researchers through the Medical Research Future Fund. The government hopes the cash will be used to fully understand the potential benefits of AI in healthcare. "AI for better health, aged care, and disability services was recently identified as one of the top three areas where Australia is well positioned to transform existing industries and build new ones, including opportunities to export solutions worldwide," Ruston's statement continued.

As of late, a number of hot topics have arisen in data policy, notably: how to ensure data privacy for individuals; the role of government in the regulation of technology; and how best to effectively and ethically leverage big data. At the forefront of these discussions is regulation of artificial intelligence (AI). As governments race to regulate AI, they should proceed with caution and seek to balance the needs of society and the private sector. Despite its recent prevalence in public discussion, AI is not a new topic. Industry leaders, such as Jonathan Zittrain, have commented on the generative Internet and how such systems are facilitating new kinds of control. Similarly, Timnit Gebru, cofounder of Black in AI, discussed the diversity crisis facing AI systems.

AI that can scan human beings for their emotional state is already being used to vet job seekers, test criminal suspects for signs of deception, and set insurance prices – but just cause AI can read our emotions…should it?

The Defense Department's central artificial intelligence development effort wants to build an artificial intelligence-powered drone swarm capable of independently identifying and tracking targets, and maybe even saving lives. The Pentagon's Joint Artificial Intelligence Center, or JAIC, issued a request for information to find out if AI developers and drone swarm builders can come together to support search and rescue missions. Search and rescue operations are covered under one of the four core JAIC research areas: humanitarian aid and disaster relief. The program also works on AI solutions for predictive maintenance, cyberspace operations and robotic process automation. The goal for the RFI is to discover whether industry can deliver a full-stack search and rescue drone swarm that can self-pilot, detect humans and other targets and stream data and video back to a central location.

Michael Horowitz, an author of the report, said: "While so much about it is uncertain, Russia's willingness to explore the notion of a long-duration, underwater, uninhabited nuclear delivery vehicle in Status-6 shows that fear of conventional or nuclear inferiority could create some incentives to pursue greater autonomy." Countries may also consider building more Ai into their early warning systems, but the report notes that these systems have historically proved to pose further risks. A salient example can be found in 1983, where a Soviet officer, Lt. Col. Stanislav Petrov, had to ignore audio-visual warnings that US missiles were inbound. Boris Johnson's £239m life-saving pledge to detect dementia [LATEST] Artificial Intelligence: Why humans could one day marry robots [UPDATE] Brexit boost: Tory reveals how UK will become'science superpower' [ANALYSIS]

Erick Trickey is a writer in Boston. For a year, Rodriguez has worked 40-hour weeks as an apprentice test technician, examining IBM mainframes to confirm they work before shipping them to customers. In January, she'll move to a permanent position with a future salary that she says is "definitely much more than I ever thought I'd be making at 19." Rodriguez's opportunities with IBM came to her thanks to her high school, Newburgh Free Academy P-TECH. It's part of an innovative public-school model that combines grade 9-12 education with internships and tuition-free community college. P-TECH, which stands for Pathways in Technology Early College High School, has spread to 10 states and 17 countries since its founding in Brooklyn in 2011. The P-TECH network is growing fast.