Wallops Island--a remote, marshy spit of land along the eastern shore of Virginia, near a famed national refuge for horses--is mostly known as a launch site for government and private rockets. But it also makes for a perfect, quiet spot to test a revolutionary weapons technology. If a fishing vessel had steamed past the area last October, the crew might have glimpsed half a dozen or so 35-foot-long inflatable boats darting through the shallows, and thought little of it. But if crew members had looked closer, they would have seen that no one was aboard: The engine throttle levers were shifting up and down as if controlled by ghosts. The boats were using high-tech gear to sense their surroundings, communicate with one another, and automatically position themselves so, in theory, .50-caliber

LAS VEGAS, September 03, 2019 /PRNewswire-PRWeb/ -- HORIZONS HOLDINGS INTERNATIONAL CORP (HZHI) is pleased to announce that its subsidiary, PAI-TECH ARTIFICIAL INTELLIGENCE LTD has signed an OEM distribution agreement with REAL TIME LTD. to develop and distribute products based on PAI's Bot Operating System Standard (PAI-BOSS). Real Time ltd. has already developed a distributed and automated solution for the HR field, HR-BOT, that has been installed in the Israeli government and is going to be sold worldwide to enterprise corporations as well as governments. Tamir Fridman, PAI-Tech's Chief Executive Officer, commented: "We are grateful for this amazing cooperation with Real Time ltd. and are so pleased that they chose PAI-BOSS to be their infrastructure for their new products. Real Time ltd. took a step towards distributed computing which has enabled them to adopt artificial intelligence. With this partnership, we'll soon be able to see many distributed solutions used in major enterprises, as well as governments within Israel and worldwide."

What is behind Artificial Intelligence? Artificial intelligence can be defined as a science that modeling intelligent human behavior. This definition may have one significant drawback -- the concept of intelligence is difficult to explain in principle. The problem of defining artificial intelligence comes down to the problem of defining intelligence in general: is it something in common, or does this term combine a set of disparate abilities, and even more as individual or even collective abilities? To what dimension can intelligence be created? Is it possible to judge the presence of intelligence only by the observed behavior? What is human intelligence and especially the intelligence of a machine or program?

While AI has dominated news headlines over the past year or so, the majority of announcements and research has been around the ethics of the technology and how to manage or avoid bias in data. In April 2019, a fortnight after it was launched, Google scrapped its independent group set up to oversee the technology corporation's efforts in AI tools such as machine learning and facial recognition. The Advanced Technology External Advisory Council (ATEAC) was shut down after one member resigned and there were calls for Kay Coles James, president of conservative thinktank The Heritage Foundation to be removed after "anti-trans, anti-LGBTQ and anti-immigrant" comments, as reported by the BBC. Google told the publication that it had "become clear that in the current environment, ATEAC can't function as we wanted. We'll continue to be responsible in our work on the important issues that AI raises, and will find different ways of getting outside opinions on these topics." The big tech example Many industry experts expressed confusion at the decision and referred to Google as being naïve.

The Ministry of Climate Change and Environment, MOCCAE, organised a special event on Monday to highlight the role of Artificial Intelligence, AI, in tackling food waste. The event witnessed the launch of new AI-enabled product'Vision' that allows kitchens to automatically track food wastage by leveraging AI. A food tech company Winnow launched the product that aims to help chefs easily identify what items are routinely wasted and adjust their purchasing lists and menus to cut down on costs. Headed by Dr. Thani bin Ahmed Al Zeyoudi, Minister of Climate Change and Environment; and Omar bin Sultan Al Olama, Minister of State for Artificial Intelligence; the event brought together leading government and private sector entities to sign the UAE Food Waste Pledge. The ministry in cooperation with Winnow, launched the pledge initiative in mid-2018.

Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. You are almost guaranteed to find one above 0.999999. This is best illustrated in may article How to Lie with P-values (also discussing how to handle and fix it.) This is being done on such a large scale, I think it is probably the main cause of fake news, and the impact is disastrous on people who take for granted what they read in the news or what they hear from the government.

It's already getting tough to discern real text from fake, genuine video from deepfake. Now, it appears that use of fake voice tech is on the rise too. That's according to the Wall Street Journal, which reported a case of voice fraud -- aka vishing (short for "voice phishing") -- that cost a company $243,000. Find out at TNW's Hard Fork Summit In March, criminals sought the help of commercially available voice-generating AI software to impersonate the boss of a German parent company that owns a UK-based energy firm. They then tricked the latter's chief executive into urgently wiring said funds to a Hungarian supplier in an hour, with guarantees that the transfer would be reimbursed immediately.

Imagine an advanced fighter aircraft is patrolling a hostile conflict area and a bogie suddenly appears on radar accelerating aggressively at them. The pilot, with the assistance of an Artificial Intelligence co-pilot, has a fraction of a second to decide what action to take – ignore, avoid, flee, bluff, or attack. The costs associated with False Positive and False Negative are substantial – a wrong decision that could potentially provoke a war or lead to the death of the pilot. What is one to do…and why? No one less than the Defense Advanced Research Projects Agency (DARPA) and the Department of Defense (DoD) are interested in not only applying AI to decide what to do in hostile, unstable and rapidly devolving environments but also want to understand why an AI model recommended a particular action.

In this issue of Maine Policy Review, authors provided insights into pressing concerns for Maine's policymakers, business leaders, educators, and citizens. They highlight research regarding critical challenges faced by Maine--and the nation--in industry, workforce, health, education, and politics. This "Maine Policy Minute" provides a synopsis of the authors and arguments contained in Volume 28, Number 1 of Maine Policy Review. In, "Local Politics from Away," transplanted Mainer and college student Matthew Bourque reflects on the strength and character of Maine's political traditions including independent thinking and politicians who put ideas over political party. Joseph W. McDonnell, a professor of public policy in management at the Muskie School at the University of Southern Maine, argues in "Maine's Workforce Challenges in an Age of Artificial Intelligence" that to accommodate changes wrought by increased automation and the move towards artificial intelligence, Maine needs to upgrade the skills of its workforce in what is becoming a rapidly changing economy.

One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it. However, to date, analyses of this approach crucially rely on the convexity and smoothness of the objective function. We give two algorithms that extend this approach substantially. The first algorithm requires nothing except boundedness of the loss function, and operates over a discrete domain. Its privacy and accuracy guarantees hold even without assuming convexity. The second algorithm operates over a continuous domain and requires only that the loss function be bounded and Lipschitz in its continuous parameter. Its privacy analysis does not even require convexity. Its accuracy analysis does require convexity, but does not require second order conditions like smoothness. We complement our theoretical results with an empirical evaluation of the non-convex case, in which we use an integer program solver as our optimization oracle. We find that for the problem of learning linear classifiers, directly optimizing for 0/1 loss using our approach can out-perform the more standard approach of privately optimizing a convex-surrogate loss function on the Adult dataset.