The International Telecommunications Union (ITU), the United Nations specialised agency for information and communication technologies (ICTs), has launched a new ITU Focus Group to establish a basis for ITU standardisation to assist machine learning (ML) in bringing more automation and intelligence to ICT network design and management. Machine learning algorithms are helping operators to make smarter use of network-generated data. These algorithms enable ICT networks and their components to adapt their behaviour autonomously in the interests of efficiency, security and optimal user experience. Fixed and mobile networks generate a huge amount of data both at the network infrastructure level and at the user/customer level, which contain a lot of useful information such as data on location, mobility and call patterns. New ML methods for big data analytics in communication networks can extract relevant information from the network data, and then leverage this knowledge for autonomic network control and management as well as service provisioning.

World's first artificial intelligence politician, named SAM was created by Nick Gerritsen, a 49 year old entrepreneur in New Zealand.the Gerritsen said: "There is a lot of bias in the'analogue' practice of politics right now." "There seems to be so much existing bias that countries around the world seem unable to address fundamental and multiple complex issues like climate change and equality." It is not legal for AI to contest elections. "SAM is an enabler and we plan to operate within existing legal boundaries,"

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8x faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8x speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets.

The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups. In this context, a number of recent studies have focused on defining, detecting, and removing unfairness from data-driven decision systems. However, the existing notions of fairness, based on parity (equality) in treatment or outcomes for different social groups, tend to be quite stringent, limiting the overall decision making accuracy. In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and propose preference-based notions of fairness -- given the choice between various sets of decision treatments or outcomes, any group of users would collectively prefer its treatment or outcomes, regardless of the (dis)parity as compared to the other groups. Then, we introduce tractable proxies to design margin-based classifiers that satisfy these preference-based notions of fairness. Finally, we experiment with a variety of synthetic and real-world datasets and show that preference-based fairness allows for greater decision accuracy than parity-based fairness.

The video of the Boston Dynamics robot called Atlas performing some impressive jumps and one stuck landing after a back flip that made the rounds online last week made its way to Elon Musk over the weekend. Musk, an outspoken advocate for regulation on robotics and artificial intelligence development, said the impressive moves the robot performs in the video are "nothing." He retweeted the video and said, "This is nothing. In a few years, that bot will move so fast you'll need a strobe light to see it. He clarified to one confused follower that a strobe light would be necessary because looking at the robot any other way would just look like a blur of movement. After warning his followers about how quickly the bot could end up a threat he tweeted again about the need for regulation. "Got to regulate AI/robotics like we do food, drugs, aircraft & cars.

Relatives of two people killed in the strike sued the United States, claiming it was the actions of the U.S. that killed their family members, who were innocent civilians. Faisal bin Ali Jaber filed a wrongful death lawsuit against then-President Barack Obama in 2015. His nephew Waleed, 26, and brother-in-law Salem, a father of seven, were killed in the attack along with three others, Al Jazeera reported.

A US $5,000 fee may sound like a steep price to pay for most ordinary deliveries. But it's a price the U.S. Marine Corps Warfighting Laboratory would gladly pay for a disposable glider drone that could deliver 320 kilograms (700 pounds) of supplies to ground troops at remote outposts or in the middle of a warzone. Earlier this month, the Marines awarded a contract to kickstart the second phase of flight testing for such battlefield delivery drones. The recent contract given to the Yates Electrospace Corporation aims for flight tests to refine the design and construction of the company's "Silent Arrow" glider drones that resemble sleek missiles with extendable wings. The ultimate goal of the Marine Corps Warfighting Laboratory's TACtical Air Delivery (TACAD) program is to develop a disposable glider drone that can deliver the 320-kilogram payload to within 45 meters of any given target site on Earth--and for a price that could make military resupply costs at least 10 times cheaper.

Four4Four Science: 'Titanic iceberg' photo; doctors' hologram house calls, canine DNA, insect naming rights It was famously described as unsinkable, but in April 1912 the mighty Titanic struck an iceberg and disappeared beneath the frigid waters of the North Atlantic. It was many decades until it would be discovered in 1985, and even longer before we knew the true story behind what led to its discovery. Filmmaker James Cameron's epic Titanic blockbuster was released 20 years ago this year, but at the time it was scarcely known that RMS Titanic's discovery was largely the result of a secret Cold War military expedition. Details of the story had trickled out, but it wasn't until the past decade that the United States navy became comfortable to reveal the finer details of the search, according to Robert Ballard, the oceanographer who discovered RMS Titanic. He met with the navy in 1982 to request funding to develop the robotic submersible technology he needed to find the sunken vessel, National Geographic reported.

A man who flew a drone and dropped anti-media propaganda flyers into the stands of two NFL games was arrested Sunday for flying a drone within five miles of an airport. The man, who was not identified by police, sent his drone over Levi's Stadium in Santa Clara and the Coliseum in Oakland. The man dropped the flyers over Levi's Stadium first during the second quarter of a game between the San Francisco 49er and the Seattle Seahawks. He then headed 30 miles north and dropped pamphlets over a game between the Oakland Raiders and Denver Broncos, according to police. Both stadiums fall within five miles of an airport, a no-fly zone for drones.

The court said Monday it would not take up the case. The U.S. Court of Appeals for the District of Columbia Circuit ruled earlier this year that the case had been properly dismissed. The appeals court said taking up the case would require it to second-guess the wisdom of a military action, which it said courts could not review.