The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide. Standard Bayesian inference algorithms are computationally expensive, however, making their direct application to large datasets difficult or infeasible. Recent work on scaling Bayesian inference has focused on modifying the underlying algorithms to, for example, use only a random data subsample at each iteration. We leverage the insight that data is often redundant to instead obtain a weighted subset of the data (called a coreset) that is much smaller than the original dataset. We can then use this small coreset in any number of existing posterior inference algorithms without modification. In this paper, we develop an efficient coreset construction algorithm for Bayesian logistic regression models. We provide theoretical guarantees on the size and approximation quality of the coreset -- both for fixed, known datasets, and in expectation for a wide class of data generative models. Crucially, the proposed approach also permits efficient construction of the coreset in both streaming and parallel settings, with minimal additional effort. We demonstrate the efficacy of our approach on a number of synthetic and real-world datasets, and find that, in practice, the size of the coreset is independent of the original dataset size. Furthermore, constructing the coreset takes a negligible amount of time compared to that required to run MCMC on it.

As we are in the beginning of tax season, millions of Americans are eager to learn what this tax season holds for them. For many Americans tax season is officially their Christmas -- a big refund is what they are anticipating. For others, they just want to break even so they do not owe any money to the IRS. Wherever you fall along these lines, you want to make sure that your taxes are accurately being prepared to ensure that you experience no issues with the IRS. H&R Block has introduced a new partnership with IBM Watson to bring its customers a new and improved tax preparation experience.

Public sector agencies must adopt emerging technologies – including machine learning, artificial intelligence, and biometrics – to attract and retain more technically adept employees, a new report from Accenture recommends. It said this is critical to addressing a widening skills gap and strong competition from a better financed private sector. According to the report, "Emerging Technologies in Public Service," the need to attract technically proficient employees is becoming even more urgent as the existing workforce continues to age, creating an irrevocable loss of institutional knowledge unless action is taken now. The report emphasized that hiring and developing people with the necessary skills, including the need for emerging technology specialists, is one of the top three challenges across all industries and countries today," the report noted. "The very concept of work is being redefined as different generations enter and exit the workforce in a rapidly changing technological landscape," said Terry Hemken, who leads Accenture's Health & Public Service Analytics Insights for Government business. "Government leaders must make every effort to reskill their people to be relevant in the future and ready to adapt to change." Survey respondents said emerging technologies will augment existing roles rather than replace them. Automating tasks, whether through artificial intelligence, machine learning or other technologies, frees up employees to focus on activities that are more critical and more closely aligned with citizen needs, according to the research. In fact, eight in 10 respondents said that implementing emerging technologies will improve job satisfaction and can aid staff retention, partly by automating certain repetitive tasks and making others more aligned with citizens' direct needs. Nearly 60 percent of respondents also said that being able to implement projects using emerging technologies would require significant investment in reskilling existing staff. "Responsive and responsible leaders must ensure that their people are relevant and adaptable to keep pace with technology," Hemken said. "Creating the future workforce now is the responsibility of the very highest levels of an organization.

'The National Highway Traffic Safety Admnnistration, under the U.S. Department of Transportation, has issued its' official report on the crash of a Tesla so-called "Auto-pilot" vehicle which crashed into a big rig last summer. "Problem Description: The Automatic Emergency Braking (AEB) or Autopilot systems may not function as designed, increasing the risk of a crash."'

Comparing the World Economic Forum at Davos and President Donald Trump's inaugural speech last week is like a rare observation of two parallel universes colliding. One is a gathering of the world's economic, banking and technology elite. The other was a speech wrapped in doses of hyperbole- a pugilistic, nationalistic call to arms against the elite that painted an unbearable American dystopia. At Davos, the talk of artificial intelligence was dominated not by business benefits and lucrative efficiencies, but the consequences for the humans caught on the wrong side of technological progress. They are only now starting to consider the changes they are inflicting upon the world.

Chess enthusiasts watch World Chess champion Garry Kasparov on a television monitor as he holds his head in his hands at the start of the sixth and final match 11 May 1997 against IBM's Deep Blue computer in New York. Kasparov lost this match in just 19 moves giving overall victory to Deep Blue with a score of 2.5-3.5 (STAN HONDA/AFP/Getty Images) This week's milestones in the history of technology include the invention of the integrated circuit, the first singing telegram, and the first widely-publicized triumph of the machines over humans. Jack Kilby of Texas Instruments (TI) files for a patent on the integrated circuit. For this invention he received the 2000 Nobel Prize for Physics. The notion of an integrated circuit was there.

Robert O. Work, the veteran defense official retained as deputy secretary by President Trump, calls them his "A.I. dudes." The breezy moniker belies their serious task: The dudes have been a kitchen cabinet of sorts, and have advised Mr. Work as he has sought to reshape warfare by bringing artificial intelligence to the battlefield. Last spring, he asked, "O.K., you guys are the smartest guys in A.I., right?" No, the dudes told him, "the smartest guys are at Facebook and Google," Mr. Work recalled in an interview. The United States no longer has a strategic monopoly on the technology, which is widely seen as the key factor in the next generation of warfare.

Every Artificial Intelligence researcher has probably a vision of how intelligent machines will reshape the world. My idealistic view is one in which automation maximizes people's freedom by giving us more time to concentrate on the things we enjoy the most. Research in all areas is astonishing: we live better and longer, and our planet has started to heal. Robots lead to superlative boosts in productivity, and an unprecedented abundance reduces tensions amongst people. We have more time to learn and inform us, and machines enable us to manage much more knowledge, so we take better decisions.

On the Stanford University campus, you could practically throw a rock and hit 100 graduate students who are building apps that enable people to communicate more effectively. But Terry Winograd is particularly enthusiastic about the app one of his graduate students, Catalin Voss, is working on. Voss, a native of Germany who completed his bachelor's and master's degrees last June at the age of 21, is working on an app that deploys Google Glass, linked to a smartphone, to help autistic children recognize human emotions through facial expressions. Venture capitalists weren't interested, even though Voss had created and sold a startup that used eye-tracking technology to monitor attentiveness to a Toyota subsidiary while still a freshman. But Terry Winograd was interested. "It runs, it has AI [artificial intelligence]," says Winograd, who 20-odd years ago advised another graduate student on the then nascent field of searching the World Wide Web. "It's at a stage where we've actually put 30 devices into homes. Our goal is to have 100 in the trial." Voss says his objective is to build a medical product that insurers will be willing to pay for. "We want to prove the investors wrong, who didn't believe in it, and build an aid for people with autism, and other mental disorders as well," he says. "We believe we've built a fairly holistic system for mental health." Winograd was Voss's first choice for an advisor even though the 70-year-old professor retired from teaching three years ago.

Last summer at the Black Hat cybersecurity conference, the DARPA Cyber Grand Challenge pitted automated systems against one another, trying to find weaknesses in the others' code and exploit them. "This is a great example of how easily machines can find and exploit new vulnerabilities, something we'll likely see increase and become more sophisticated over time," said David Gibson, vice president of strategy and market development at Varonis Systems. His company hasn't seen any examples of hackers leveraging artificial intelligence technology or machine learning, but nobody adopts new technologies faster than the sin and hacking industries, he said. "So it's safe to assume that hackers are already using AI for their evil purposes," he said. "It has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade," said Don Maclean, chief cybersecurity technologist at DLT Solutions.