This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repeating spike-patterns concealed in high rates of overall presynaptic activity. One-shot construction of neurons with synapse weights calculated as estimates of converged STDP outcomes results in immediate selective detection of hidden spike-patterns. The capability of continual learning is demonstrated through the successful one-shot detection of new sets of spike-patterns introduced after long intervals in the simulation time. Simulation expansion (Lightheart et al., 2013) has been proposed as an approach to the development of constructive algorithms that are compatible with simulations of biological neural networks. A simulation of a biological neural network may have orders of magnitude fewer neurons and connections than the related biological neural systems; therefore, simulated neural networks can be assumed to be a subset of a larger neural system. The constructive algorithm is developed using simulation expansion concepts to perform an operation equivalent to the exchange of neurons between the simulation and the larger hypothetical neural system. The dynamic selection of neurons to simulate within a larger neural system (hypothetical or stored in memory) may be a starting point for a wide range of developments and applications in machine learning and the simulation of biology.

The generation of preferences represented as CP-nets for experiments and empirical testing has typically been done in an ad hoc manner that may have introduced a large statistical bias in previous experimental work. We present novel polynomial-time algorithms for generating CP-nets with n nodes and maximum in-degree c uniformly at random. We extend this result to several statistical cultures commonly used in the social choice and preference reasoning literature. A CP-net is composed of both a graph and underlying cp-statements; our algorithm is the first to provably generate both the graph structure and cp-statements, and hence the underlying preference orders themselves, uniformly at random. We have released this code as a free and open source project. We use the uniform generation algorithm to investigate the maximum and expected flipping lengths, i.e., the maximum length over all outcomes o and o', of a minimal proof that o is preferred to o'. Using our new statistical evidence, we conjecture that, for CP-nets with binary variables and complete conditional preference tables, the expected flipping length is polynomial in the number of preference variables. This has positive implications for the usability of CP-nets as compact preference models.

Starting next month, Australia will deploy drones powered by artificial intelligence to patrol Australian beaches for sharks. As reported by Reuters, publicly available videos are used to train the system's algorithms and differentiate sharks from other marine creatures, surfers, swimmers and boats. "Studies have shown that people have a 20-30 percent accuracy rate when interpreting data from aerial images to detect shark activity. Detection software can boost that rate to 90 percent," said Dr. Nabin Sharma, a research associate at the University of Technology Sydney's School of Software. "It's not about replacing human beings all together, it's about assisting human beings to get the work done in a better way with more accuracy. That's what the application is meant for."

Whether or not shark attacks are a major problem in Australia (spoiler alert: they're not), the Australian government has devoted an enormous amount of resources into trying to mitigate the risk of sharks near popular beaches. They've tried nets to keep the sharks out, they've tried electronic gadgets to dissuade them, and they've tried lots of different ways of killing them, without much in the way of evidence that any of it is particularly effective. After six months of trials, the latest and most robot-y idea is about to be implemented: drones will start patrolling some Australian beaches next month, using cameras and some AI-backed image analysis software to spot lurking sharks much better than humans can. We can manage a 20-30 percent accuracy rate, which means both identifying other things as sharks (kinda bad) and misidentifying sharks as other things (way worse). As with many tasks of this kind, a machine learning system does much better: once it's been trained on labeled aerial videos of sharks, whales, dolphins, surfers, swimmers, boats, and whatever else, the software is 90 percent accurate at telling humans to panic because there's a shark somewhere.

Pioneers from the worlds of artificial intelligence and robotics – including Elon Musk and Deepmind's Mustafa Suleyman – have asked the United Nations to ban autonomous weapon systems. A letter from the experts says the weapons currently under development risk opening a "Pandora's box" that if left open could create a dangerous "third revolution in warfare". The open letter coincides with the International Joint Conference on Artificial Intelligence, which is currently being held in Melbourne, Australia. Ahead of the same conference in 2015, the Telsa founder was joined by Steven Hawking, Steve Wozniak and Noam Chomsky in condemning a new "global arms race". Suggestions that warfare will be transformed by artificially intelligent weapons capable of making their own decisions about who to kill are not hyperbolic.

Drones are harnessing artificial intelligence to detect sharks approaching Australian beaches. Starting next month, Little Ripper drones will be able to monitor sharks in real-time with approximately 90 percent accuracy. By contrast, humans are only about 20 to 30 percent accurate when spotting sharks. "It's not about replacing human beings all together, it's about assisting human beings to get the work done in a better way with more accuracy," Dr. Nabin Sharma, a research associate at the University of Technology Sydney's School of Software said in an interview with Reuters. "That's what the application is meant for."

Passengers' baggage is collected by robots, they relax in a luxurious waiting area complete with an indoor garden before getting a face scan and swiftly passing through security and immigration -- this could be the airport of the future. It's a vision that planners hope will become reality as new technology is rolled out, transforming the exhausting experience of getting stuck in lengthy queues in ageing, overcrowded terminals into something far more pleasant. The Asia-Pacific has been leading the way but faces fierce competition from the Middle East as major hubs compete to attract the growing number of long-haul travellers who can choose how to route their journey. The Asia-Pacific has been leading the way toward the airports of the future. The regions'are the two leading pockets of technology growth because they are really competing to be the global hubs for air transportation,' Seth Young, director of the Center for Aviation Studies at Ohio State University, told AFP. 'If I'm going to fly from New York to Bangalore, do I transfer through Abu Dhabi or Dubai or do I transfer through Hong Kong?

With shark attacks increasing every year, more vigilance is needed, especially at beaches popular for surfing. Since it has become evident that human vigilance is just not enough, technology is coming to the fore -- starting next month, the Australian government will deploy artificially intelligent'Little Ripper' drones on the country's beaches for enhanced surveillance. Little rippers cost $250,000 and can stay in the air for two and a half hours at a time. In case of an imminent attack, the drone will carry inflatable rafts and GPS beacons to aid rescuers. They will monitor the beaches using on board cameras.

Microsoft's speech recognition efforts have hit a significant milestone. It can now transcribe human speech with a 5.1% error rate, Microsoft technical fellow Xuedong Huang wrote in a blog post -- the same error rate as humans. Microsoft actually thought it hit this point last year, when it reached 5.9%, the word error rate it had measured for humans. But then other researchers carried out separate studies and pegged the human error level as slightly lower, 5.1%. But it has now achieved this -- reducing its error rate by 12%, and using AI techniques like "neural-net based acoustic and language models."

At best, they'll accurately pinpoint sharks 30 percent of the time -- not very helpful for swimmers worried about stepping into the water. Australia, however, is about to get a more reliable way of spotting these undersea predators. As of September, Little Ripper drones will monitor some Australian beaches for signs of sharks, and pass along their imagery to an AI system that can identify sharks in real-time with 90 percent accuracy. Humans will still run the software (someone has to verify the results), but this highly automated system could be quick and reliable enough to save lives. The detection AI is a quintessential machine learning system.