Since the days of Da Vinci's "Ornithoper", mankind's greatest minds have sought inspiration from the natural world for their technological creations. It's no different in the modern world, where bleeding-edge advancements in machine learning and artificial intelligence have begun taking their design cues from the most advanced computational organ in the natural word: the human brain. Deep learning neural networks -- the likes of which power AlphaGo as well as the current generation of image recognition and language translation systems -- are the best machine learning systems we've developed to date. They're capable of incredible feats but still face significant technological hurdles, like the fact that in order to be trained on a specific skill they require upfront access to massive data sets. What's more if you want to retrain that neural network to perform a new skill, you've essentially got to wipe its memory and start over from scratch -- a process known as "catastrophic forgetting". Compare that to the human brain, which learns incrementally rather than bursting forth fully-formed from a sea of data points.

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."

It will not be the first experiment with advanced pizza delivery technology. Australia-based Domino's Pizza Enterprises, the Ann Arbor-based company's largest independent franchisee, has tested delivery to customers in New Zealand via drone and self-driving robot.

Ford Motor Co and Domino's Pizza Inc in September will begin testing Michigan consumers' reactions to having their pies delivered by self-driving vehicles, the companies said on Tuesday. It will not be the first experiment with advanced pizza delivery technology. Australia-based Domino's Pizza Enterprises, the Ann Arbor-based company's largest independent franchisee, has tested delivery to customers in New Zealand via drone and self-driving robot. In a blog post last week, Sherif Marakby, head of Ford's autonomous and electric vehicles, signaled the automaker's broader ambitions, saying Ford planned to cooperate "with multiple partners" in deploying self-driving vehicles "designed to improve the movement of people and goods." Previously, Ford executives had said the company expected to launch a self-driving shuttle for commercial ride-sharing fleets in 2021.

IBM Watson Internet of Things 1,760 views IBM Watson presents Soul Machines, LENDIT Conference 2017 (Professional Camera) - Duration: 5:12.

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.