A quick observation: Donald Trump is not like normal people. In particular, he doesn't have any principles to speak of, that might guide him. That doesn't mean he doesn't have a method. He does, but it's local rather than global. Instead of following some hidden but stable agenda, I would suggest Trump's goal is simply to "not be boring" at Trump rallies.

According to industry estimates, the US needs about 200,000 more workers to fill current cybersecurity roles. Globally, the gap is five times higher – an estimated 1 million workers. The issue has become such a priority that President Obama made increasing the number of cybersecurity workers a key component of his multibillion-dollar Cybersecurity National Action Plan, which was introduced earlier this year. The White House said earlier this month it plans on boosting the federal cybersecurity workforce by 3,500 new hires by year's end. But as businesses compete for scarce cybersecurity talent and policymakers weigh remedies for the digital security worker shortage, the ground underneath the profession is shifting.

The rise of artificial intelligence has long stoked fears of killer robots like the "Terminator," and early versions of military automatons are already in the battlefield. Now the Navy is looking into how it can teach machines to do the right thing. "We've been looking at different ways that we can have people interact with autonomous systems," Marc Steinberg, an Office of Naval Research manager, said in a phone interview this month. The Navy is funding a slew of projects at universities and institutes that look at how to train such systems, including stopping robots from harming people. In 1979, a Ford autoworker in Michigan became the first person killed by a robot when he was struck in the head by the arm of a 1-ton production-line machine, according to Guinness World Records. More recently, police in Dallas used a robot to deliver a bomb that killed the shooter who opened fire on officers at a Black Lives Matter protest.

I'm seated in a giant ballroom where vast rows of chairs face seven glowing supercomputers. Each liquid-cooled rack of servers is lit with a different color. Though they stand on a dais at the Paris Las Vegas resort as still as statues, the computers are locked in heated battle with each other. "The race for third is very tight," says Hakeem Oluseyi, an astrophysicist, in a rousing voice. ForAllSecure's team Mayhem stands as a silent sentinel in the DARPA Cyber Grand Challenge.

Zoltan Istvan, who represents the Transhumanist Party and bills himself as "the science candidate" in the 2016 U.S. presidential election, has garnered more media coverage than many third party candidates, with recent mentions in Vocativ, The Verge, USA Today, and Pacific Standard. He also writes regularly for Motherboard and The Huffington Post. Istvan's popularity is likely due to a combination of his quirky campaign style (he drives around in a bus painted to resemble a coffin with "Science vs. The Coffin" written above the bumper) and an unconventional platform that pushes for gene editing, human life extension, and morphological freedom (the right to do anything to your body so long as it doesn't harm others). As a broader movement, transhumanism focuses on leveraging science and technology toward the ultimate goal of overcoming death, largely through as-yet-unproven methods such as mind uploading, in which a person's entire consciousness would be transferred to a digital system or machine.

The young British coder whose simple "robot lawyer" chatbot has overturned more than 170,000 unlawful parking tickets in the UK and America has set his sights on solving homelessness by providing people with basic legal advice. An update to his DoNotPay bot works by asking users a simple set of questions about their circumstances, before advising them on the best course of action--often helping them draft an effective form letter to apply to their local councils for emergency housing. Councils have to take every letter seriously, and using Freedom of Information requests, he's researched the best ways to prompt them into acting on his bot's clients' behalf. The bot's creator, Joshua Browder, a 19-year-old Brit studying at Stanford University in California, told Ars that since the update launched last Wednesday "almost every local government in the UK has signed up for the website." "I can see on the back end that they are actively trying out the service for themselves," he added.

Nasa launches million dollar contest to create'Valkyrie' AI robot that will accompany astronauts on ... What Exactly Do You Mean By AI?

WILLIAMSBURG, Va.: The Pentagon's top weapons buyer, Frank Kendall, warned today that the US might hobble itself in future warfare by insisting on human control of thinking weapons if our adversaries just let their robots pull the trigger. Kendall even worries that Deputy Defense Secretary Bob Work is being too optimistic when Work says humans and machines working together will beat robots without oversight. These are unnerving ideas -- and top Army leaders swiftly responded with concern that robots would shoot civilians if you take the human out of the loop. This is what Vice Chairman of the Joint Chiefs Paul Selva calls the Terminator Conundrum: "When do we want to cross that line as humans? And who wants to cross it first? Those are really hard ethical questions."

Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multiple standpoints, including impact on the crop commodity futures markets, formulation of agricultural policy, as well as crop insurance rating. The focus of this work is to construct a corn yield predictor at the county scale. Corn yield (forecasting) depends on a complex, interconnected set of variables that include economic, agricultural, management and meteorological factors. Conventional forecasting is either knowledge-based computer programs (that simulate plant-weather-soil-management interactions) coupled with targeted surveys or statistical model based. The former is limited by the need for painstaking calibration, while the latter is limited to univariate analysis or similar simplifying assumptions that fail to capture the complex interdependencies affecting yield. In this paper, we propose a data-driven approach that is "gray box" i.e. that seamlessly utilizes expert knowledge in constructing a statistical network model for corn yield forecasting. Our multivariate gray box model is developed on Bayesian network analysis to build a Directed Acyclic Graph (DAG) between predictors and yield. Starting from a complete graph connecting various carefully chosen variables and yield, expert knowledge is used to prune or strengthen edges connecting variables. Subsequently the structure (connectivity and edge weights) of the DAG that maximizes the likelihood of observing the training data is identified via optimization. We curated an extensive set of historical data (1948-2012) for each of the 99 counties in Iowa as data to train the model.

Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way. Conductance is a popular objective function used in many algorithms for local community detection. This paper studies a continuous relaxation of conductance. We show that continuous optimization of this objective still leads to discrete communities. We investigate the relation of conductance with weighted kernel k-means for a single community, which leads to the introduction of a new objective function, $\sigma$-conductance. Conductance is obtained by setting $\sigma$ to $0$. Two algorithms, EMc and PGDc, are proposed to locally optimize $\sigma$-conductance and automatically tune the parameter $\sigma$. They are based on expectation maximization and projected gradient descent, respectively. We prove locality and give performance guarantees for EMc and PGDc for a class of dense and well separated communities centered around the seeds. Experiments are conducted on networks with ground-truth communities, comparing to state-of-the-art graph diffusion algorithms for conductance optimization. On large graphs, results indicate that EMc and PGDc stay localized and produce communities most similar to the ground, while graph diffusion algorithms generate large communities of lower quality.