BERLIN (Reuters) - Over 50 top Artificial Intelligence researchers on Wednesday announced a boycott of KAIST, South Korea's top university, after it opened what they called an AI weapons lab with one of South Korea's largest companies. The researchers, based in 30 countries, said they would refrain from visiting KAIST, hosting visitors from the university, or cooperating with its research programs until it pledged to refrain from developing AI weapons without "meaningful human control". KAIST, which opened the center in February with Hanwha Systems, one of two South Korean makers of cluster munitions, responded within hours, saying it had "no intention to engage in development of lethal autonomous weapons systems and killer robots." University President Sung-Chul Shin said the university was "significantly aware" of ethical concerns regarding Artificial Intelligence, adding, "I reaffirm once again that KAIST will not conduct any research activities counter to human dignity including autonomous weapons lacking meaningful human control." The university said the new Research Centre for the Convergence of National Defence and Artificial Intelligence would focus on using AI for command and control systems, navigation for large unmanned undersea vehicles, smart aircraft training and tracking and recognition of objects.

C3 IoT won a multi-year deal artificial intelligence and Internet of Things deal with 3M. The customer win is a big one for C3 IoT, which has been gaining momentum as an IoT platform and has some of the largest production implementations. For 3M, C3 IoT is one vendor in the mix of a multi-year business transformation effort. Read also: What is the IoT? Everything you need to know about the Internet of Things right now What is AI? Everything you need to know about Artificial Intelligence All executive guides Previously, 3M moved to a single instance of ERP via SAP. For 2017, 3M delivered sales of $31.7 billion, up 5.1 percent from 2016, and net income of $4.86 billion.

Australian authorities say they are investigating whether Facebook breached the country's privacy law when personal information of more than 300,000 Australian users was obtained by Cambridge Analytica, a Trump-linked political consulting firm, without their authorization.

Artificial intelligence researchers from nearly 30 countries are boycotting a South Korean university over concerns a new lab in partnership with a leading defence company could lead to "killer robots". More than 50 leading academics signed the letter calling for a boycott of Korea Advanced Institute of Science and Technology (KAIST) and its partner, defence manufacturer Hanwha Systems. The researchers said they would not collaborate with the university or host visitors from KAIST over fears it sought to "accelerate the arms race to develop" autonomous weapons. "There are plenty of great things you can do with AI that save lives, including in a military context, but to openly declare the goal is to develop autonomous weapons and have a partner like this sparks huge concern," said Toby Walsh, the organiser of the boycott and a professor at the University of New South Wales. "This is a very respected university partnering with a very ethically dubious partner that continues to violate international norms."

In 2015, Google's DeepMind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of 49 Atari games. However, the current manifestation of DRL is still immature, and has significant drawbacks. One of DRL's imperfections is its lack of "exploration" during the training process, especially when working with high-dimensional problems. In this paper, we propose a mixed strategy approach that mimics behaviors of human when interacting with environment, and create a "thinking" agent that allows for more efficient exploration in the DRL training process. The simulation results based on the Breakout game show that our scheme achieves a higher probability of obtaining a maximum score than does the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic method. The proposed scheme therefore can be applied effectively to solving a complicated task in a real-world application.

Around the world last year, unprovoked shark attacks (where humans do not initiate physical contact) resulted in just five fatalities–one fewer than the global average over the last decade. Meanwhile, humans carve up 100 million sharks and rays annually (a conservative estimate), causing enormous gaps in the aquatic ecosystem that directly disrupts the carbon cycle and incidentally reduces carbon capture by sea grasses, further heating the planet. Still, the fear of being bitten by a shark–literally any shark–is understandable. As rare as incidents already are, they might soon be even rarer, thanks to a new solution that comes out of Australia. Called the Clever Buoy, it's an eco-friendly ocean monitoring system that uses dual-wave sonar and artificial intelligence to detect and identify large marine life underwater more accurately than your standard fish-finder.

WrestleMania 34 is WWE's biggest show of 2018 in more ways than one. Wrestling's Super Bowl should feature an attendance of around 75,000, lasting somewhere between six and seven hours long. Every single WWE championship will be on the line Sunday night in New Orleans, and 14 matches are expected to be on the card. Below are predictions for every WrestleMania 34 match. A lot has changed since Lesnar and Reigns fought for the world title in the WrestleMania main event three years ago.

Ed DeFraine describes how AI improved managing vending machines, flanked at left by Jason Hosking of Hivery, Reza Kasravi of Coca-Cola North America and Matt Robards of Hivery. The power of artificial intelligence in automated retailing has been proven in the full line beverage vending industry, a business that requires managing product selection and service for thousands of machines across hundreds of miles. Reyes Coca Cola Bottling, an Irvine, California-based Coke bottler serving California and Nevada, presented the results of a two-year AI project that helped the company to significantly improve sales and reduce service costs. Ed DeFraine, vice president of food service and on-premise for the bottling company, explained the AI project during last week's National Automatic Merchandising Association show at the Las Vegas Convention Center. He was joined by representatives of the company's AI partner in the project, Australia-based Hivery. DeFraine told attendees that the project marks the first application of AI in the full line beverage vending industry.

Logan Marshall-Green stars in the upcoming ultra-violent sci-fi/horror/action film, Upgrade, directed by Australia's own Leigh Whannell. The film follows Marshall-Green's quadriplegic character Grey, as he's upgraded with a new form of artificial intelligence chip that restores all of his former functions – as well as turning him into a killing machine. He then uses his newfound gifts to seek revenge on the men that killed his wife. Here is another science-fiction premise that may have some actual future plausibility, with a mix of gory violence to spice up the entertainment factor. Judging by this trailer, Whannell looks to have hit the mark. While I think the red band trailer below may be revealing too much awesome action already, it does do plenty to get action/sci-fi/horror fans excited.

Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally, it provides an analytical method to update the decoding matrix which maps the labels into the original space and is used during the test phase. Experimental results show the effectiveness of this approach in terms of running times and the prediction performance over different measures. Keywords: data stream classification, multi-label data, label compression 1. Introduction Standard classification is the task of assigning the correct class to previously unknown test instances based on training instances. Training data consist of a set of features and an associated target class or class label. Many modern data mining applications, however, need to deal with more than one label per instance.