Elon Musk, founder, CEO and lead designer at SpaceX and co-founder of Tesla, speaks at the International Space Station Research and Development Conference in Washington, U.S., July 19, 2017. A revolution in warfare where killer robots, or autonomous weapons systems, are common in battlefields is about to start. Both scientists and industry are worried. The world's top artificial intelligence (AI) and robotics companies have used a conference in Melbourne to collectively urge the United Nations to ban killer robots or lethal autonomous weapons. An open letter by 116 founders of robotics and artificial intelligence companies from 26 countries was launched at the world's biggest artificial intelligence conference, the International Joint Conference on Artificial Intelligence (IJCAI), as the UN delays meeting until later this year to discuss the robot arms race.

Bottlenose dolphins that work together with humans to catch fish have their own distinctive whistle, one that may help them recognise each other. Off Laguna, Brazil, fishers stand in a line in waist-deep water or wait in canoes while, farther out, bottlenose dolphins chase shoals of mullet to the shore. The fishers can't see the fish in the murky water, so they wait for the dolphins to give a signal -- like an abrupt dive or tail slap -- then cast their nets. Fishers catch larger and more fish when they work with dolphins. "Dolphins likely reap similar benefits," says Mauricio Cantor of the Federal University of Santa Catarina in Brazil – it might be easy for them to gobble up fish disoriented by the nets.

Artifical intelligence is set to transform the marketing and communications world even more than social media has, according to 55% of CMOs surveyed by Weber Shandwick across five markets. The agency's latest study examines current consumer knowledge and attitudes toward AI in the US, UK, Brazil, China and Canada. Of the 150 senior executives surveyed, 68% said their brand is currently selling, using or planning for business in the AI era. Moreover, nearly six in 10 believe that within the next five years, companies will need to compete in the AI space to succeed. Weber Shandwick also polled 2,100 consumers across the five markets, and found that Chinese consumers (31%) report having the strongest knowledge of AI, while UK consumers report the weakest (10%).

A growing crop of cloud platforms are targeting machine learning users with applications and services that can serve as an alternative to complicated and disparate open source tools. Some say there's cause for caution in evaluating these tools, however. "We're seeing a really massive shift of big data into the public cloud," said Brian Hopkins, an analyst at Forrester Research. "Companies that jumped on Hadoop early are now facing upgrades; companies are challenged to maintain multiple versions of Spark. In the midst of all this expensive on-premises big data, you can get in the cloud in a week or so."

The US government's mighty DARPA last year kicked off a research project designed to make systems controlled by artificial intelligence more accountable to their human users. The Defense Advanced Research Projects Agency, to use this $2.97bn agency its full name, is the Department of Defense's body responsible for emerging technology for use by the US armed forces. Significantly, it was DARPA's early funding of packet-switching network the Advanced Research Projects Agency Network (ARPANET) more than 40 years ago that helped bring about the internet. Coming bang up to date, the issue at the heart of the Explainable Artificial Intelligence (XAI) programme is that AI is starting to extend into many areas of everyday life yet the internal workings of such systems are often opaque, and could be concealing flaws in their decision-making processes. The field of AI has made great strides in the last several years, thanks to developments in machine learning algorithms and deep learning systems based on artificial neural networks (ANNs).

We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sato's distribution semantics). We focus on two semantics, respectively based on stable and on well-founded models. We show that the semantics based on stable models (referred to as the "credal semantics") produces sets of probability measures that dominate infinitely monotone Choquet capacities; we describe several useful consequences of this result. We then examine the complexity of inference with probabilistic logic programs. We distinguish between the complexity of inference when a probabilistic program and a query are given (the inferential complexity), and the complexity of inference when the probabilistic program is fixed and the query is given (the query complexity, akin to data complexity as used in database theory). We obtain results on the inferential and query complexity for acyclic, stratified, and normal propositional and relational programs; complexity reaches various levels of the counting hierarchy and even exponential levels.

SAN FRANCISCO – Researchers have discovered a disturbing pattern: dozens of ships whose GPS signals tell them they're on land -- at an airport no less -- even when they're far out to sea. An investigation released this week by the Washington D.C.-based Resilient Navigation and Timing Foundation and Windward Ltd., a maritime data and analytics company, has found multiple instances of so-called GPS spoofing in Russian waters. As recently as Monday, two vessels' GPS told them they were at Sochi Airport near the site of the 2014 Sochi Olympics, 12 miles away from the harbor where the vessels actually were. Researchers are calling these "mass GPS interferences" and they appear to be linked to the intentional transmission of false GPS signals to provide incorrect time or location information, possibly to veil certain facilities from attack. Familiar to anyone using a smartphone or built-in auto navigation system to map out a route, the satellite-based system is also the main way ships and trucking fleets find their way.

The company announced it has received €30 million ($36 million) funding from the European Investment Bank, the lending arm of the European Union. "We're proud to receive this stamp of approval from the EIB. It's the type of offer you can't refuse and it will allow us to further accelerate our growth and continue to level the playing field for small businesses, giving them access to tools to take on the big corporations," said Jacob de Geer, CEO and co-founder of iZettle, in a statement. The funding follows the startup's most recent round, which was earlier this year, when it raised $63 million at the same $500 million valuation it had in its last equity round. It appears the valuation is staying the same with this latest round: the money, as with earlier this year, is coming in the form of debt funding and will be distributed over the next three years, the company said.

Switching to efficient artificial intelligence systems has already saved Google a ton of money on its energy bills. And, it seems machine learning may also pose monetary benefits (of a different kind) for electricity providers. With power theft costing the industry roughly $96 billion in losses per year, companies could start looking to AI to help identify pilferers. A team from the University of Luxembourg has developed an algorithm that sifts through electricity meter data to detect abnormal usage. They put the system to work on info compiled from 3.6 million Brazilian households over the course of five years.

We study the quantum synchronization between a pair of two-level systems inside two coupledcavities. Using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in this digital-analog block decomposition the fundamental elements of a quantum machine learning protocol, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If we can additionally equip this algorithm with a classical feedback mechanism, which consists of projective measurements in the register, reinitialization of the register state and local conditional operations on the agent and register subspace, a powerful and flexible quantum machine learning protocol emerges. Indeed, numerical simulations show that this protocol enhances the synchronization process, even when every subsystem experience different loss/decoherence mechanisms, and give us flexibility to choose the synchronization state. Finally, we propose an implementation based on current technologies in superconducting circuits.