As cyber threats in the government become more complex and increase in number, it's critical that our defenses advance too. Some of those advances, such as automation, artificial intelligence and machine learning, may also mean a change in the skill sets required of cyber personnel. Although automation may strike fear in the hearts of some cyber professionals, the trend isn't just about replacing federal employees with computers. It's about leveraging software to do certain tasks for humans so they can focus on the tasks that computers can't perform, such as advanced analysis or other more technical cyber tasks. One thing is for certain: The role of the cyber professional is evolving.

The stochastic gradient (SG) method is the signature strategy for solving stochastic and finite-sum minimization problems. In this iterative approach, each step to update the solution estimate is obtained by taking a negative multiple of an unbiased gradient estimate. With careful choices for the stepsize sequence, the SG method possesses convergence guarantees and has been employed to great success for solving various types of problems, such as those arising in machine learning. One disadvantage of the SG method is that stochastic gradients, like the gradients that they approximate, possess no natural scaling. By this, we mean that in order to guarantee convergence, the algorithm needs to choose stepsizes in a problem-dependent manner; e.g., common theoretical guarantees require that the stepsize is proportional to 1/L, where L is a Lipschitz constant for the gradient of the objective function.

The company will initially operate out of a facility near Tinker Air Force Base and focus on engineering and production planning. In six months, the company wants to move into a 75,000 square-foot facility.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Forget about Silicon Valley, Big Pharma, and our nation's fabled industrial conglomerates--the most formidable science and tech incubator in the history of the world is the U.S. government. But maintaining, nurturing, and continuing to develop our public "knowledge infrastructure" in and around government has become increasingly challenging. In a time of crippling political divisiveness, when the very notion of expertise has come under siege, how can we ensure that our lawmakers and citizens can continue to rely on the robust research, knowledge, and expertise-enhancing infrastructure built over the past two centuries? Join Future Tense and TechCongress on Thursday, Feb. 8, in Washington, D.C. to consider whether our democracy will have the capacity in the future to address such complex issues as genetic engineering, artificial intelligence, and climate change adaptation--and whether our quest for technological and scientific advancement continues to be subjected to sound democratic governance.

One of the biggest news items in the digital workplace this week was the announcement of IBM and Salesforce's deepening partnership, with new integrations between IBM Cloud, Watson Services, Salesforce Quip and Service Cloud Einstein. The two companies announced their initial partnership last March. It envisaged a combination of Armonk, New York-based IBM Watson's capabilities with San Francisco-based Salesforce's CRM data to allow businesses to glean insights across structured and unstructured data. As part of this announcement, Salesforce named IBM a preferred cloud services provider, making IBM the third major provider to be named "preferred." The company first named AWS its preferred cloud provider in May 2016.

Last Thanksgiving, I watched my father-in-law evaluate over one hundred exams for the high school class he teaches on the U.S. government. They were mostly short answer questions: matching different provisions of the U.S. Constitution, and explaining the contents of the Bill of Rights. The grading was tedious and time consuming, and took him hour after hour during what should have been a holiday. I started to wonder whether there could be a faster way. Automatic computer grading could do exactly that, learning from previous answers and getting better as it goes -- and it is already being used in some universities and for large online courses (MOOCs).

The Situation: FDA has been grappling with regulation of rapidly advancing digital products, including artificial intelligence. While Congress and FDA have provided recent clarifications, regulatory questions remain. The Result: Both the 21st Century Cures Act and recent FDA activities provide important, but incomplete, insight regarding regulation of health products utilizing artificial intelligence. Looking Ahead: Digital health product manufacturers should recognize how recent regulatory changes may affect them, and that FDA is engaging industry to further refine its oversight approach. Artificial intelligence ("AI") can be broadly defined as a device or product that can imitate intelligent behavior.

William Gibson wrote that the future is here, just not evenly distributed. The phrase is usually used to point out how the rich have more access to technology, but what happens when the poor are disproportionately subject to it? In Automating Inequality, author Virginia Eubanks argues that the poor are the testing ground for new technology that increases inequality. The book, out this week, starts with a history of American poorhouses, which dotted the landscape starting in the 1660s and were around into the 20th century. From there, Eubanks catalogues how the poor have been treated over the last hundred years, before coming to today's system of social services that increasingly relies on algorithms.

Prime minister Theresa May has set out her ambition for the UK to harness the power of technology, but warned of the dangers of unethical use of digital technologies and platforms. Get an expert look at the government's ideas for a prosperous post-Brexit Britain as well as its ambitious 5G strategy. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

By training satellites to look in the right direction using machine learning techniques, NASA is able to discover brand new exoplanets.