Recent concern about harms of information technologies motivate consideration of regulatory action to forestall or constrain certain developments in the field of artificial intelligence (AI). However, definitional ambiguity hampers the possibility of conversation about this urgent topic of public concern. Legal and regulatory interventions require agreed-upon definitions, but consensus around a definition of AI has been elusive, especially in policy conversations. With an eye towards practical working definitions and a broader understanding of positions on these issues, we survey experts and review published policy documents to examine researcher and policy-maker conceptions of AI. We find that while AI researchers favor definitions of AI that emphasize technical functionality, policy-makers instead use definitions that compare systems to human thinking and behavior.

In the 70s a novel branch of statistics emerged focusing its effort in selecting a function in the pattern recognition problem, which fulfils a definite relationship between the quality of the approximation and its complexity. These data-driven approaches are mainly devoted to problems of estimating dependencies with limited sample sizes and comprise all the empirical out-of sample generalization approaches, e.g. cross validation (CV) approaches. Although the latter are \emph{not designed for testing competing hypothesis or comparing different models} in neuroimaging, there are a number of theoretical developments within this theory which could be employed to derive a Statistical Agnostic (non-parametric) Mapping (SAM) at voxel or multi-voxel level. Moreover, SAMs could relieve i) the problem of instability in limited sample sizes when estimating the actual risk via the CV approaches, e.g. large error bars, and provide ii) an alternative way of Family-wise-error (FWE) corrected p-value maps in inferential statistics for hypothesis testing. In this sense, we propose a novel framework in neuroimaging based on concentration inequalities, which results in (i) a rigorous development for model validation with a small sample/dimension ratio, and (ii) a less-conservative procedure than FWE p-value correction, to determine the brain significance maps from the inferences made using small upper bounds of the actual risk.

WASHINGTON – All but the smallest civilian drones would have to broadcast radio tracking data to ensure greater safety and prevent terrorism under a sweeping proposal unveiled by U.S. regulators Thursday. The long-awaited draft rules call for a massive new tracking network for everything from toys to larger commercial drones so that law enforcement can spot the devices flying anywhere, from congested urban areas to the most rural zones. The controversial measure by the Federal Aviation Administration, which is subject to public comment and could change before it becomes final, is a key foundation to advance drone-driven commerce, including deliveries of consumer goods by companies such as Alphabet Inc.'s Wing and Amazon.com The rules would come into full force three years after being finalized. "Remote ID technologies will enhance safety and security by allowing the FAA, law enforcement and federal security agencies to identify drones flying in their jurisdiction," Transportation Secretary Elaine Chao said in a press release.

Delivering presents to every child around the world in a single evening is an exhausting task, with only one man fit for the job - Father Christmas. But his outdated techniques seem more antiquated now than ever before. MailOnline spoke to a forward-thinking industry expert who offered Father Christmas some helpful advice to make his arduous task more efficient. Dr Carl Diver, academic lead at Manchester Metropolitan University in industry 4.0, said a hydrogen-powered sleigh, AI algorithms and elf-assisting robots could help. As well as streamlining production and making the manufacturing and delivery process more efficient, Dr Diver thinks the old methods would benefit from a sprucing up to make things easier, more cost-effective and better for the environment.

Capitalism's global success has lifted billions of people out of grinding poverty while spurring incredible technological advancements. However, this progress has not been without cost. Abundant energy from fossil fuels, global supply chains and middle-class lifestyles fueled by advancements such as on-demand electronic commerce have created complex and widespread challenges including climate change and a lack of economic inclusion. We've reached a major inflection point and the way forward – toward a more sustainable and inclusive future – requires a significant shift in mindset and behaviour at individual, institutional and industry levels. Venture capital can play an outsized role in addressing the economic, environmental, social and technological challenges we face today by returning to its roots of industrial transformation.

In the coming years, artificial intelligence will dramatically affect every aspect of human life. AI--the technologies that simulate intelligent behavior in machines--will change how we process, understand, and analyze information; it will make some jobs obsolete, transform most others, and create whole new industries; it will change how we teach, grow our food, and treat our sick. The technology will also change how we wage war. For all of these reasons, leadership in AI, more than any other emerging technology, will confer economic, political, and military strength in this century--and that is why it is essential for the United States to get it right. That begins with creating a national strategy for AI--a whole-of-society effort that can create the opportunities, shape the outcome, and prepare for the inevitable challenges for U.S. society that this new technological era will bring.

OVER THE past year, I have been collaborating with a prominent leader in the technology industry to combine his decades of experience advancing frontier technologies, on the one hand, and my decades of experience in national security decisionmaking, on the other. Together, we have been trying to understand the national security implications of China's great leap forward in artificial intelligence (AI). Our purpose in this essay is to sound an alarm over China's rapid progress and the current prospect of it overtaking the United States in applying AI in the decade ahead; to explain why AI is for the autocracy led by the Chinese Communist Party (hereafter, the "Party") an existential priority; to identify key unanswered questions about the dangers of an unconstrained AI arms race between the two digital superpowers; and to point to the reasons why we believe that this is a race the United States can and must win. First, most Americans believe that U.S. leadership in advanced technologies is so entrenched that it is unassailable. Likewise, many in the American national security community insist that in the AI arena China can never be more than a "near-peer competitor."

The drone footage shows various parts of the raid on Abu Bakr al-Baghdadi's compound in northern Syria; national security correspondent Jennifer Griffin reports from the Pentagon. At sunrise on the Friday before the killing of notorious ISIS leader Abu Bakr al-Baghdadi, our 11th Armored Blackhorse unit boarded four Black Hawk helicopters and flew in formation across the silent desert. Soon we landed near a town called Razish with reports of enemy ISIS combatants in place. Our Special Forces operation had officially begun. We drove a few miles to base camp in darkness and waited for our commanding officer to brief us.

Key point: There is an ongoing co-evolution between indispensable human cognition and decision-making and AI-enabled autonomy. As the armed soldier's clear rooms and transition from house to house in a firefight, how quickly would they need to know that groups of enemies awaited them around the next corner? Getting this information to soldiers in seconds can not only decide victory or defeat in a given battle but save lives. What if AI-enabled computer programs were able to instantly discern specifics regarding the threat such as location, weapons and affiliation by performing real-time analytics on drone feeds and other fast-moving sources of information, instantly sending crucial data to soldiers in combat? While current technology can today perform some of these functions, what if this data was provided to individual dismounted soldiers in a matter of seconds?

Stephen Chen investigates major research projects in China, a new power house of scientific and technological innovation. He has worked for the Post since 2006. He is an alumnus of Shantou University, the Hong Kong University of Science and Technology, and the Semester at Sea programme which he attended with a full scholarship from the Seawise Foundation.