A pair of researchers associated with the U.S. Air Force want to give nuclear codes to an artificial intelligence. Air Force Institute of Technology associate dean Curtis McGiffin and Louisiana Tech Research Institute researcher Adam Lowther, also affiliated with the Air Force, co-wrote an article -- with the ominous title "America Needs a'Dead Hand'" -- arguing that the United States needs to develop "an automated strategic response system based on artificial intelligence." In other words, they want to give an AI the nuclear codes. And yes, as the authors admit, it sure sounds a lot like the "Doomsday Machine" from Stanley Kubrick's 1964 satire "Dr. The "Dead Hand" referenced in the title refers to the Soviet Union's semiautomated system that would have launched nuclear weapons if certain conditions were met, including the death of the Union's leader. This time, though, the AI-powered system suggested by Lowther and McGiffin wouldn't even wait for a first strike against the U.S. to occur -- it would know what to do ahead of time. "[I]t may be necessary to develop a system based on artificial intelligence, with predetermined response decisions, that detects, decides, and directs strategic forces with such speed that the attack-time compression challenge does not place the United States in an impossible position," they wrote. The attack-time compression is the phenomenon that modern technologies, including highly sensitive radar and near instantaneous communication, drastically reduced the time between detection and decision time. The challenge: modern weapon technologies, particularly hypersonic cruise missiles and vehicles, cut the window even further. "These new technologies are shrinking America's senior-leader decision time to such a narrow window that it may soon be impossible to effectively detect, decide, and direct nuclear force in time," Lowther and McGiffin argue. The idea is to use an AI-powered solution to negate any surprise capabilities or advantages of retaliatory strikes of the enemy. It would replace what Lowther and McGiffin describe as a "system of systems, processes and people" that "must inevitably be capable of detecting launches anywhere in the world and have the ability to launch a nuclear strike against an adversary." Not surprisingly, points out Bulletin of the Atomic Scientists editor Matt Field, handing over the nuclear codes to an AI could have plenty of negative side effects. One of them is automation bias, as Field points out in his piece. People tend to blindly trust what machines are telling them, even favoring automated decision-making over human decision-making. And then there's the simple fact that the AI doesn't have much data to run on, Field argues. That means that most of the data fed to the AI would be simulated data. Strangelove" is anything to go by, as long as all major world powers are made aware of the automated system, it could keep them from attacking the United States.

As data and the business problems that can be addressed by it proliferate, our ability to analyze them is falling behind. We don't have enough data scientists, we can't create enough good models, and we can't get them into production. Enter automated machine learning (AutoML), which offers substantial potential for solving the problem. This powerful set of tools can help with a wide variety of ML activities, including preparing data for analysis, performing feature engineering, automatically generating well-fitting models using the best algorithm, and generating code or APIs to help deploy the model into production. AutoML has been around in some form since the mid-1990s, but it didn't really take off until the past few years.

One of the biggest legal problems protecting AI users in the coming years will be accountability – dealing with the opacity of the black box and explaining decisions made by machine thinking. Understanding the logic behind an AI finding is not an issue where AI is assisting in spotting real-world risks that affect individuals – such as the current use of AI in radiology, where failure to use AI radiology analysis may soon be considered malpractice. As long as the AI is accurate and productive in showing where cancer may exist, we don't care how the machine picked that specific spot on the x-ray, we are just happy to have another tool that helps save lives. But where the AI proposes treatments or outcomes, your clients – healthcare and otherwise – will need to be ready to defend those decisions. This means an entirely different baseline organization and feature set for than the AI currently envisioned or in use.

Unfamiliar faces aren't welcome at Beijing public housing projects. To prevent illegal subletting, many have facial recognition systems that allow entry only to residents and certain delivery staff, according to state news agency Xinhua. Each of the city's 59 public housing sites is due to have the technology by year's end. Artificial intelligence startup Megvii mentioned a similar public housing security contract in an unspecified Chinese city in filing for an initial public offering in Hong Kong last week. The Chinese startup, best-known for facial recognition, touts its government dealings, including locking down public housing to curb subletting, as a selling point to potential investors.

Rajeev Sambyal is Managing Director at State Street where he leads the development of State Street VerusSM, a new AI platform that combines machine learning, natural language processing, and human intelligence to explore connection between global news, events and user portfolios. His current area of interest is looking at alternative data sets combined with market data to help make informed decisions. Rajeev received his Masters degree in Technology Management from Columbia University as well as a Masters in Information Technology from Symbiosis University, India.

Next month, the brightest minds in machine learning, artificial intelligence, and advanced analytics are descending on San Jose, California for the 2019 O'Reilly Artificial Intelligence Conference. Perennially one of the top conferences in the field, the O'Reilly AI Conference is unique in its focus on bridging tech and business to "push the boundaries of AI" and transform industries. We're looking forward to the show and joining the discussion about how businesses can put their data to work more effectively - without having to hire highly specialized talent. If you're headed to San Jose, we'd love to meet and learn how you're tackling these very challenges. There are multiple ways to engage with the Sisu team at the O'Reilly AI Conference, from our booth in the Expo Hall (#217) to an Executive Briefing on usable machine learning from our CEO Peter Bailis.

Swedish autonomous driving software developer Zenuity has become the first automotive company to team up with CERN, the European Organization for Nuclear Research, in the development of fast machine learning for self-driving cars. A fundamental challenge in the development of autonomous vehicles (AV) is the interpretation of the huge quantities of data generated by normal driving conditions, such as identifying pedestrians and vehicles with the sensors on the car, including cameras, lidar and radars. Addressing these issues is crucial for the development of safe AVs and is a key part of Zenuity's long-term ambition to speed up the development of vehicles that will completely eliminate collisions and associated injuries and fatalities. Zenuity hopes that this collaboration with CERN will ultimately help it develop AV that can reach decisions and make predictions more quickly, thus avoiding accidents. One of the main quests at CERN is to study the standard model of particle physics by collecting large quantities of data originating from particle collisions produced by CERN's Large Hadron Collider (LHC) located at its laboratory on the Swiss-French border.

These robotic partners were hailed as'the future of work', particularly in the manufacturing sector, but concerns about widespread robotic implementation are rife. Figures released by the Office for National Statistics (ONS) in March claim that 1.5 million people in England alone are at risk of losing their jobs to automation, suggesting that these fears are justified. However, automation can actually help manufacturers thrive and survive many of the workforce challenges they currently face. These include the'crippling skills shortage', which continues to blight the industry, and is putting workforces under increased pressure. At the same time, tougher immigration rules, associated with the UK's imminent departure from the EU, means that working in the UK will become less attractive or accessible for foreign nationals.

Intelligence is the ability to gather information and apply it to the human experience. This was true when silicon was just a shiny rock, and it's true now that machines are becoming more intelligent. Businesses today need to deliver a different kind of intelligence: a high emotional quotient (EQ), which Harvard theorist Howard Gardner describes as the ability to understand what motivates another person and how to meet their needs. EQ (otherwise known as emotional intelligence) is mostly used to describe people--a friend's ability to empathize with a difficult situation, a manager adapting her approach to an employee's work style, or a salesperson relating to a potential buyer. It turns out that EQ is also important for businesses.