We seek to determine the effectiveness of using location-based social media to predict the outcome of the 2016 presidential election. To this aim, we create a dataset consisting of approximately 3 million tweets ranging from September 22nd to November 8th related to either Donald Trump or Hillary Clinton. Twenty-one states are chosen, with eleven categorized as swing states, five as Clinton favored and five as Trump favored. We incorporate two metrics in polling voter opinion for election outcomes: tweet volume and positive sentiment. Our data is labeled via a convolutional neural network trained on the sentiment140 dataset. To determine whether Twitter is an indicator of election outcome, we compare our results to the election outcome per state and across the nation. We use two approaches for determining state victories: winner-take-all and shared elector count. Our results show tweet sentiment mirrors the close races in the swing states; however, the differences in distribution of positive sentiment and volume between Clinton and Trump are not significant using our approach. Thus, we conclude neither sentiment nor volume is an accurate predictor of election results using our collection of data and labeling process.

How the Army conducts rifle marksmanship training is undergo-ing a number of positive changes. Despite this, challenges to con-ducting and coordinating this critical training remain. One chal-lenge to assessing training effectiveness is a lack of persistent records of soldier performance; too often soldier data are purged shortly after training events for convenience and in order to en-sure privacy. This paper reports on our efforts to research the fea-sibility of collecting, analyzing, and storing data from multiple training systems, in order to accelerate and improve marksman-ship training. We do this through the use of cognitive, psychomo-tor, and affective constructs; and the use of predictive modeling techniques in order to forecast marksmanship qualification scores.These models successfully predicted scores on a 40-point scalewith a root mean square error (RMSE) of less than three, using models that are robust to changing input variables. Future im-provements and directions for this research are also discussed.

Causal inference from observation data often assumes "strong ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that combines unsupervised machine learning and predictive model checking to perform causal inference in multiple-cause settings. The deconfounder infers a latent variable as a substitute for unobserved confounders and then uses that substitute to perform causal inference. We develop theory for when the deconfounder leads to unbiased causal estimates, and show that it requires weaker assumptions than classical causal inference. We analyze its performance in three types of studies: semi-simulated data around smoking and lung cancer, semi-simulated data around genomewide association studies, and a real dataset about actors and movie revenue. The deconfounder provides a checkable approach to estimating close-to-truth causal effects.

A Tesla sedan with a semi-autonomous Autopilot feature rear-ended a fire department truck at 60 mph (97 kph) apparently without braking before impact on May 11, 2018, but police say it's unknown if the Autopilot feature was engaged. SAN FRANCISCO -- Data from the computer brain of a Tesla Model S that crashed in Utah last week confirms that the $100,000 sedan was in Autopilot mode, police in South Jordan said Wednesday. Information recovered by Tesla engineers and shared with South Jordan police confirms many of the details the driver, a 28-year-old woman from Lehi, Utah, shared with investigators after her car slammed into a stopped fire truck at 60 mph. She also said she had been distracted by her phone. Earlier Wednesday, the National Highway Traffic Safety Administration said it was sending investigators to Utah and would "take appropriate action based on its review."

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. If you're still using a Netflix login that belongs to the parents of your ex's roommate, then you know how easy it is to stay digitally signed on to the past. But with the proliferation of internet-connected home devices and an increasing amount of our personal information left in digital trails online, changing your passwords and application permissions after a breakup needs to become common sense, argues Rachel Withers. You also might want to re-evaluate your security habits if you're a PGP user. As Josephine Wolff explains, recent news of security vulnerabilities in the encrypted email program provides more evidence you should swap your encrypted email for more secure messaging services.

Have you ever wondered what AI or Artificial Intelligence is all about? It's the deliberate reprogramming of the human race to think and to act according to controllers who want to have absolute control over every aspect of our abilities to think and to act other than the way they approve and program us to act! Is that happening already with some of the counter-culture crimes in order to steer humans into a'mental slave corral'? Following are excerpts taken from the source link below, which indicates certain aspects of where the NWO controllers are directing the future of humankind into becoming technological slaves to Artificial Intelligence, which most tech-addicted humans are not aware of, in my opinion. CG are the initials for Corey Goode, a person who worked with high tech and had extremely highly classified credentials, as I understand, which included interactions with extraterrestrial beings of higher intelligence than we, who are working with the U.S. tech companies and government agencies on ET projects.

And this is especially true in China, the country in which I live and work for more than 20 years. China has become the first country for e-commerce, and it could soon become the most powerful country for AI in the world. The Chinese government is rolling out very large-scale data analytics systems to improve governance at the smallest level. This is the development the country is putting into action. I think this is real change, it's real revolution!

The Army is now crafting early requirements for what is expected to be a new attack helicopter -- beyond the Apache -- with superior weapons, speed, maneuverability, sensor technology and vastly-improved close-combat attack capability. "We know that in the future we are going to need to have a lethal capability, which drives us to a future attack reconnaissance platform. The Apache is the world's greatest but there will come a time when we look at leap ahead technology," Army Vice Chief of Staff Gen. James McConville told a small group of reporters. A future attack-reconnaissance helicopter, now in its conceptual phase, is a key part of a wide-spanning, multi-aircraft Army Future Vertical Lift (FVL) program. FVL seeks a family of next-generation aircraft to begin emerging in the 2030s, consisting of attack, utility and heavy-class air assets.

Vlocity, Inc., a cloud software company, announced the launch of automated claims features in their apps. The launch includes end-to-end management of property and casualty (P&C) insurance claims for policyholders, agents and claims handlers, and enables dynamic, digital claims interactions from any device. New features include peril-driven adjudication and an adjuster workbench that enhance a carrier's ability to run their entire business on Salesforce. Carriers can download pre-configured claims processes from Vlocity's Insurance Process Library and leverage a modern, optimized user experience. Carriers, if they prefer, can create a completely new experience from scratch in a code-free environment using Vlocity's intuitive design interface.

On December 31, 2015, I published my original call to arms for society's rational regulation of artificial intelligence before it is too late. I explained certain reasons why someone who is against solving problems through regulation would propose precisely that mechanism to help hedge the threats created by AI, and announced my proposed legislation: The Artificial Intelligence Data Protection Act (AIDPA). Since 2015, we have witnessed AI's rapidly evolving national and international growth and adoption that will soon impact every phase of mankind's life, from birth to death, sex to religion, politics to war, education to emotion, jobs to unemployment. Three of many recent developments confirm why now is the time for the AIDPA: (1) a McKinsey study from late 2017 determined that up to 800 million workers worldwide may lose their jobs to AI by 2030, half of contemporary work functions could be automated by 2055 and other recent studies suggest as many as 47 percent of U.S. jobs could be threatened by automation or AI over the next few decades; (2) AI has now created IP with little or no human involvement and continues to be programmed, tested and used to do so; see my Twitter for a library of media reports on AI-created IP; (3) tech giants and regulators are starting to acknowledge that industries that create and use AI should be at least partially responsible for minimizing the impact of AI-displaced workers. Now – and not later -- society must address AI's legal, economic and social implications with regard to IP and employment.