Artificial intelligence sounds like it could be hiring's Holy Grail: A completely automated system that maximizes application-sorting efficiency, minimizes HR labor and reduces the chance that bias could taint the hiring process. Despite its great potential, AI carries liability risks that HR pros must understand. Artificial intelligence software relies on computerized algorithms to sort data and analyze it quickly, without human input. Many HR departments now use AI programs to sort through résumés and applications to identify key words and phrases. Applications that meet an employer's screening criteria are then forwarded to HR for further review.

The US military is not discounting the future development of killer robots, or lethal autonomous weapon systems (LAWS), as agents in the US war machine. Artificial intelligence (AI) has shown much promise since its original inception by Alan Turing and his contemplation of machines that can learn to think and act like humans. Machine learning and its subset deep learning have inspired hope that machines can one day develop or even supersede human cognition. This is a potential technology that the Department of Defense (DoD) cannot and will not ignore. Whilst the DoD has established the Directive 3000.09,

Autonomous drones are cautious when navigating the unknown. Now MIT researchers have developed a trajectory-planning model that helps drones fly at high speeds through previously unexplored areas, while staying safe. The model -- aptly named "FASTER" -- estimates the quickest possible path from a starting point to a destination point across all areas the drone can and can't see, with no regard for safety. But, as the drone flies, the model continuously logs collision-free "back-up" paths that slightly deviate from that fast flight path. When the drone is unsure about a particular area, it detours down the back-up path and replans its path. The drone can thus cruise at high speeds along the quickest trajectory while occasionally slowing down slightly to ensure safety.

The Department for Business, Energy & Industrial Strategy (BEIS) has announced funding to boost the national artificial intelligence (AI) skills base. One of the funding packages comes from industry and government, and will see £200m going towards 1,000 PhD places focused on AI in the next five years. Students will study the application of the technology to support diagnostics in healthcare and enhance processes in industries such as aviation and car manufacturing. Separately, a further £170m will be committed to funding 1,700 places to study PhDs in biosciences. Announcing the funding, prime minister Boris Johnson said the UK must continue to be world-leading in AI and technology.

This is the second installment in a two-part series about generative adversarial networks. For the full story, be sure to also read part one. Now that we've described the origin and general functionality of generative adversarial networks (GANs), let's explore the role of this exciting new development in artificial intelligence (AI) as it pertains to cybersecurity. Perhaps the most famous application of this technology is described in a paper by researchers Briland Hitaj, Paolo Gasti, Giuseppe Ateniese and Fernando Perez-Cruz titled "PassGAN: A Deep Learning Approach for Password Guessing," the code for which is available on GitHub. In this project, the researchers first used a GAN to test against password cracking tools John the Ripper and HashCat, and then to augment the guessing rules of HashCat.

The edge is a rich source of new data, whether it's from subscribers connected to a telco's latest 5G network, or radio signals detected in a battlefield environment. Faced with the huge data volumes and an increasing demand for low-latency analysis and action, the processing needs to occur local to the source – at the edge itself. Furthermore, security, compliance and corruption risks may dictate whether the data is permitted to be moved away from the edge at all, and if allowed, in what curated form. A data center or cloud used in isolation cannot meet all these emerging needs. But most customers don't want to reinvent the wheel when analyzing data at the edge, and instead prefer to reuse proven technologies like AI, while adopting architectures that have already been honed inside a data center or cloud.

Research by the UK's Bank of England (BoE) and Financial Conduct Authority (FCA) has found that the country's financial services businesses are fast deploying machine learning (ML) technology to tackle money laundering and fraud. The survey found that ML – defined as "the development of models for prediction and pattern recognition, with limited human intervention" – is increasingly being deployed, with use expected to more than double in the next three years. As well as addressing crime, businesses are developing ML tech for customer-facing applications such as customer services and marketing. The central bank and regulator combined forces to run the survey, having pinpointed ML as a'principal driver' of how innovative technology is transforming global finance. The survey was sent to organisations such as e-money institutions, banks, financial market infrastructure firms and investment managers.

Abstract: The phenomenal growth of social media, mobile applications, sensor based technologies and the Internet of Things is generating a flood of "Big Data" and disrupting our world in many ways. Simultaneously, we are seeing many interesting developments in machine learning and Artificial Intelligence (AI) technologies and methods. In this talk I will examine the paradigm shift caused by recent developments in AI and Big Data and ways to harness their power to create a smarter enterprise computing environment. Using examples from health care, smart cities, education, and businesses in general, I will highlight challenges and research opportunities for developing an enterprise of the future. Bio: Sudha Ram is Anheuser-Busch Endowed Professor of MIS, Entrepreneurship & Innovation in the Eller College of Management at the University of Arizona.

Federal data analytics and automation efforts are still getting organized around a larger new paradigm, Gil Alterovitz, director of artificial intelligence at the Department of Veterans Affairs, said at ATARC's Oct. 24 Federal Artificial Intelligence And Data Analytics Summit. The VA is testing a number of artificial intelligence-driven projects to help patients, including programs to reduce waiting times at its facilities, predict potential suicides and monitor customer service. Todd Myers, automation lead at the National Geospatial-Intelligence Agency, said the government should look to companies like Uber and Amazon for models of how to use data to advance their missions. "These companies are successful, and the government will be successful when we break down the organizational silos" of units that may working on their own data analytics and data sets, he said. "The days of separate business units and organizations going off and doing their own thing, I think are long gone. I think the federal government is leaning hard and fast in changing that approach," Myers said.

"In order to identify the rare few drug candidates that induce desired epigenetic effects, scientists need methods to screen hundreds of thousands of potential compounds," says Alexey Terskikh, Ph.D., associate professor in Sanford Burnham Prebys' Development, Aging and Regeneration Program and senior author of the study. "Our study describes a powerful image-based approach that enables high-throughput epigenetic drug discovery." Epigenetics refers to chemical tags on DNA that allow cellular machinery greater or less access to genes -- thus altering gene expression. Nearly all changes in a cell, including reaction to a drug and environmental stress, are reflected by its epigenetic state. Several medicines that target epigenetic alterations are approved by the U.S. Food and Drug Administration (FDA) for the treatment of cancer, and researchers are working to find additional epigenetic-based therapies.