The automated nature of such attacks means that they can be launched at speeds far in excess of what humans are capable of, he said, suggesting that attacks could happen on a microsecond-by-microsecond level. "We're going to have to understand the implications of, not people-to-machine attacks, but machine-to-machine attacks," said Mr. Fanning. Some security teams are using AI defensively, but cybersecurity leaders across sectors worry that the same technology could propel sophisticated attacks that will be difficult to fend off. A congressional report published last year raised the possibility of AI-based attacks overwhelming grid defenses. Utilities need to invest in defenses and do so quickly, said Mark James, an adjunct professor of law at Vermont Law School and a co-author of a report on state power utilities' cybersecurity practices, published this month.

Today, the practical synergies between economics and computer science are flourishing. We outline some of the many opportunities for the two disciplines to engage more deeply through the new MIT Schwarzman College of Computing." Nancy L. Rose is the Charles P. Kindleberger Professor of Applied Economics and head of the MIT Department of Economics, where her research and teaching focus on industrial organization, competition policy, and the economics of regulation. David Autor is the Ford Professor of Economics and co-director of the MIT Task Force on the Work of the Future. His scholarship explores the labor market impacts of technological change and globalization, earnings inequality, and disability insurance and labor supply.

AI has the power to search through millions of genetic variants at high speed and identify likely ... [ ] causes of rare diseases, while also comparing what they find with the existing medical literature. This is greater than the population of the United States, yet the ominous figures don't end there. According to the Global Genes organization, eight out of ten rare diseases are caused by a faulty gene, yet it takes an average of 4.8 years to arrive at an accurate diagnosis. This is part of the reason why 30% of children with a rare disease won't live to see their fifth birthday. Neither is this situation helped by the fact that 95% of rare diseases lack an FDA-approved treatment.

Leaked Chinese Communist Party (CCP) documents have revealed how China uses artificial intelligence to round up Uyghurs and other ethnic minorities for detention in Xinjiang's network of mass internment camps. The classified documents, made public by the International Consortium of Investigative Journalists (ICIJ) on Nov. 24, has also uncovered the repressive inner workings of the detention camps in the troubled western region, where at least one million are believed to have been detained, according to figures quoted by the U.S. Congressional-Executive Commission on China and the United Nations. NEW: #ChinaCables is a leak of highly classified Chinese documents that expose the inner workings of mass detention camps in Xinjiang and reveal, in the government's own words, how it manages the day-to-day internment and forced indoctrination of Uighurs. In the second major leak in just days of the inner-workings of the CCP in Xinjiang, the papers--the China Cables--reveal that "Chinese police are guided by a massive data collection and analysis system that uses artificial intelligence to select entire categories of Xinjiang residents for detention." In the space of just one week, the names of hundreds of thousands of Uyghurs and other ethnic minorities in the region were issued for arrest and interrogation using data collected by mass surveillance technology, according to the ICIJ report.

Provides tools to create and manipulate probability distributions using S3. Generics random(), pdf(), cdf(), and quantile() provide replacements for base R's r/d/p/q style functions. The documentation for each distribution contains detailed mathematical notes. There are several vignettes: Intro to hypothesis testing, One-sample sign tests, One-sample T confidence interval, One-sample T-tests, Z confidence interval for a mean, One-sample Z-tests for a proportion, One-sample Z-tests, Paired tests, and Two-sample Z-tests.

Experts agree that we're headed toward a future where global leadership in artificial intelligence will translate into economic and military dominance. Unfortunately, authoritarian regimes, such as China, have inherent advantages in research and development. The training of A.I. systems requires data -- lots of it. Big data is the oil of the Digital Age and whoever has the most of it -- at the highest quality and at the lowest cost -- will have a comparative advantage. Assembling and using big data sets in developed countries, however, can be complicated, for privacy and legal reasons. For example, the European Union is considering rules giving each individual the right to control how their facial data can be used in facial recognition technology -- which will (probably) slow development.

Artificial intelligence (AI) has the potential to deliver significant social and economic benefits, including reducing accidental deaths and injuries, making new scientific discoveries, and increasing productivity.[1] However, an increasing number of activists, scholars, and pundits see AI as inherently risky, creating substantial negative impacts such as eliminating jobs, eroding personal liberties, and reducing human intelligence.[2] Some even see AI as dehumanizing, dystopian, and a threat to humanity.[3] As such, the world is dividing into two camps regarding AI: those who support the technology and those who oppose it. Unfortunately, the latter camp is increasingly dominating AI discussions, not just in the United States, but in many nations around the world. There should be no doubt that nations that tilt toward fear rather than optimism are more likely to put in place policies and practices that limit AI development and adoption, which will hurt their economic growth, social ...

HULMAN FIELD AIR NATIONAL GUARD BASE, Ind. -- New systems in the development of artificial intelligence (AI) technology for domestic response were tested by managers of the Johns Hopkins Applied Physics Lab (JHU-APL) and the Joint Artificial Intelligence Center's (JAIC) Humanitarian Assistance Disaster Relief (HADR) program. The testing took place Nov. 2 at an Indiana Unclassified Processing, Assessment and Dissemination (UPAD) site at the 181st Intelligence Wing. Intelligence Analysts assigned to the Indiana Air National Guard's 181st Intelligence Wing, 137th Intelligence Squadron UPAD, were chosen to assist in new developmental programs expected to be launched in the next year. This was the first time any of these programs or systems were tested by a UPAD site and UPAD analysts. "The AI technology uses commercial satellite static imagery, National Oceanic and Atmospheric Administration (NOAA) imagery, and MQ-9 full-motion video (FMV). Utilization of Synthetic Aperture Radar (SAR) imagery is in the works as well," said Tech. "The four lines of effort include route analysis, damage assessment, flood water detection, and fire perimeter analysis," he said.

The acquisition and labeling of training data remains one of the major challenges for the mainstream adoption of machine learning solutions. Within the machine learning research community, several efforts such as weakly supervised learning or one-shot learning have been created in order to address this issue. Microsoft Research recently incubated a group called Minimum Data AI to work on different solutions for machine learning models that can operate without the need of large training datasets. Recently, that group published a paper unveiling Icebreaker, a framework for "wise training data acquisition" which allow the deployment of machine learning models that can operate with little or no-training data. The current evolution of machine learning research and technologies have prioritized supervised models that need to know quite a bit about the world before they can produce any relevant knowledge.

The UAE government is leading the way in establishing the necessary integrated and secure data ecosystem to expedite the implementation of future technologies like Artificial Intelligence (AI) in healthcare, which use data from many disparate sources to produce unprecedented services that will transform all aspects of people's wellness and everyday life. AI and machine learning offers hope in reducing the risk and impact of cyber-attacks on patient data, but also opens doors to potential wrong doers – "The Bad Guys" – by its very nature. Security threats are, and always have been, major concerns to healthcare organizations due to the value and vulnerability of the clinical data that is being recorded and distributed. The value of the data comes from the fact that it directly affects our ability to safely treat patients. Due to its content and historical nature it can be very big, so it takes a long time to rebuild, and it contains more than just clinical data.