By 2034/35, almost 20% of Australians (6.2 million) are projected to be aged 65 or over. One sector already feeling the impact of the ageing population is construction. In Queensland, the number of construction workers aged 55 and over increased from 8% of full-time workers in 1992 to 14.2% in 2014. An ageing workforce is likely to increase the need for less physically demanding jobs or maybe technology might address this issue. Task automation and the industry's innovation culture are two of the greatest areas of uncertainty for the construction industry.

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Humans will journey to Mars in the 2030s accompanied by robots, if Nasa's ambitious plans become reality. The US space agency has launched a 1 million competition challenging engineers to develop the capabilities of humanoid robots, which will help astronauts on their long and arduous journey to the red planet. The Space Robotics Challenge will see teams program a virtual robot modelled on Nasa's advanced Robonaut R5 android, dubbed Valkyrie, to complete a series of virtual tasks that could save human crew members' lives, such as repairing leaks. Valkyrie is a six foot tall, 290 pound humanoid designed to work in extreme environments. It can'see' thanks to sensors and cameras in its head, walk, and grasp objects in its hands, which have three fingers and a thumb.

Artificial intelligence has held great promise for decades now. Since the 1950s, experts have been predicting that the day when computers would think like humans was just around the corner. By the 1970s, as one system after another failed the Turing Test, artificial intelligence research fell out of favor -- the hype largely deflated by the influential book "Perceptrons," by Seymour Papert and Marvin Minsky. Recently, however, a series of advances in computing and so-called machine learning has triggered renewed interest in the potential of artificial intelligence. And while computers might not (yet) be self-aware or able to think like human beings, they becoming very good at learning how to diagnose diseases, find and defend against malware -- and beat humans at games of chess and Go.

China's biggest search engine, Baidu, announced it will use Intel's Xeon Phi processor when the processor's release plan was disclosed on August 17 at Intel's annual developer forum in San Francisco. "When it comes to AI (artificial intelligence), Intel's Xeon Phi is a great fit," said Jing Wang, a senior vice president of Baidu, who joined Diane Bryant, executive vice president in charge of Intel's data center group, at the forum. Intel said Xeon Phi will help accelerate deep learning, a computerized technique increasingly used for tasks such as interpreting speech, identifying objects in photos and piloting autonomous vehicles. Baidu, having researched the application of artificial intelligence for years, is considering using the new chip to support its voice recognition system, called Deep Speech. Deep Speech is based on the collection of 7,000 hours of voice clips created by 9,600 people.

Stories about racist Twitter accounts and crashing self-driving cars can make us think that artificial intelligence (AI) is a work in progress. But while these headline-grabbing mistakes reveal the frontiers of AI, versions of this technology are already invisibly embedded in many systems that we use everyday. These everyday uses include everything from fraud detection systems that monitor credit card transactions to email filters that learn not to swamp your inbox with spam. You've probably already interacted with an AI system today without even knowing it and probably enjoyed the experience. One increasingly common form of AI can be found in chatbots, a type of software that lets you interact with it by having a conversation.

Facebook today is announcing that it's open-sourcing some of its latest artificial intelligence (A.I.) software for segmenting objects within images. The DeepMask, SharpMask, and MultiPathNet tools are available now on GitHub under a BSD license. It's not as if Facebook is opening up about these programs for the first time. They've been described in academic papers (specifically this one, this one, and this one). Now Facebook's Artificial Intelligence Research (FAIR) lab is connecting the dots with an extensive blog post and is also, of course, making the software available free for others to inspect and build on.

SILICON has been making our computers work for almost half a century. Whether designed for graphics or number crunching, all information processing is done using a million-strong horde of tiny logic gates made from element number 14. But silicon's time may soon be up. Moore's law – the prophecy which dictates that the number of silicon transistors on microprocessors doubles every two years – is grinding to a halt because there is a limit to how many can be squeezed on a chip. The machine-learning boom is another problem. The amount of energy silicon-based computers use is set to soar as they crunch more of the massive data sets that algorithms in this field require.

In case you missed it, there's a post on Medium by Steven Levy that explains everything you might want to know about how machine learning works at Apple. It's a fascinating account of the Apple Brain, the A.I. hidden inside your iPhone. To be honest, I've always wondered about this stuff: According to Levy's article, the "brain" is about 200MB, depending on your personal data stores, and it tracks interactions with people and acts a bit like a neural network. And yes, when Apple buys a company, it is usually doing that to hire the people. Is this the new Apple, a company that allows people to get an inside view of what they are doing?

This is because every auditory neuron is tuned to a certain range of sound, so that each neuron is more sensitive to particular types and levels of sound than others. In a new study, researchers have designed a neuromorphic ("brain-inspired") computing system that mimics this neural selectivity by using artificial level-tuned neurons that preferentially respond to specific types of stimuli. In the future, level-tuned neurons may help enable neuromorphic computing systems to perform tasks that traditional computers cannot, such as learning from their environment, pattern recognition, and knowledge extraction from big data sources. The researchers, Angeliki Pantazi et al., at IBM Research-Zurich and École Polytechnique Fédérale de Lausanne, both in Switzerland, have published a paper on the new neuromorphic architecture in a recent issue of Nanotechnology. Like all neuromorphic computing architectures, the proposed system is based on neurons and their synapses, which are the junctions where neurons send signals to each other.