Researchers from the Centre for Audio, Acoustics and Vibration at the University of Technology Sydney are exploring technology for those wanting a quieter life! Reporting in the journal Scientific Reports (a Nature Springer publication), the team of Tong Xiao, Xiaojun Qiu and Benjamin Halkon highlight the positive impacts for health and wellbeing of their 'virtual Active Noise Control/Cancellation (ANC) headphone' and its enhanced ability to reduce ambient noise. By integrating laser-based technology -- which can deal with high frequencies -- into headrests they eliminate the need for users to wear head/ear phones or buds. So, in an open plan or home office, you can cancel out colleagues' chatter, ringing phones, the neigbour's mower, the dog barking, and the kettle whistling while you work without the discomfort / inconvenience of a set of headphones... And, in enclosed spaces such as cars and aircraft, the virtual headset can significantly reduce all the extraneous noises that can enter the environment, decreasing distractions and making work/rest easier. For machinery and equipment operators, it provides a solution that reduces fatigue often caused by enclosed [...]

So, you need a robot that climbs stairs. What shape should that robot be? Should it have two legs, like a person? Or six, like an ant? Choosing the right shape will be vital for your robot's ability to traverse a particular terrain. And it's impossible to build and test every potential form. But now an MIT-developed system makes it possible to simulate them and determine which design works best. You start by telling the system, called RoboGrammar, which robot parts are lying around your shop -- wheels, joints, etc. You also tell it what terrain your robot will need to navigate. And RoboGrammar does the rest, generating an optimized structure and control program for your robot. The advance could inject a dose of computer-aided creativity into the field. "Robot design is still a very manual process," says Allan Zhao, the paper's lead author and a PhD student in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He describes RoboGrammar as "a way to come up with new, more inventive robot designs that could potentially be more effective." [...]

If you’ve been thinking about playing with an Nvidia single-board computer for an AI task, but you’re not quite ready to part with your cash for something like the Jetson Nano just yet, here’s an application-level emulator of the hardware you can tinker with. It's the Jetson AI-Computer Emulator, an open-source project created by machine-learning software engineer Tea Vui Huang. It basically provides the same Python interfaces you'd expected on a Jetson system, specifically the Inference and Utilities API, meaning it's possible to prototype applications in Python and run and test them with the emulator, and once it all works out or you get used to the interfaces, you can perhaps buy a Jetson and run your code on that system, where it will talk to Nvidia's libraries. These libraries use the onboard hardware to accelerate operations such as inference and decision making by trained neural networks. That means you can prototype your application on your PC and later run it on the board to see how it fares with Nvidia's CUDA-based acceleration. All you need to do is [...]

A rectangular robot as tiny as a few human hairs can travel throughout a colon by doing back flips, Purdue University engineers have demonstrated in live animal models. Why the back flips? Because the goal is to use these robots to transport drugs in humans, whose colons and other organs have rough terrain. Side flips work, too. Why a back-flipping robot to transport drugs? Getting a drug directly to its target site could remove side effects, such as hair loss or stomach bleeding, that the drug may otherwise cause by interacting with other organs along the way. The study, published in the journal Micromachines, is the first demonstration of a microrobot tumbling through a biological system in vivo. Since it is too small to carry a battery, the microrobot is powered and wirelessly controlled from the outside by a magnetic field. "When we apply a rotating external magnetic field to these robots, they rotate just like a car tire would to go over rough terrain," said David Cappelleri, a Purdue associate professor of mechanical engineering. "The magnetic field also [...]

We all react to stress in different ways. A sudden loud noise or flash of light can elicit different degrees of response from people, which indicates that some of us are more susceptible to the impact of stress than others. Any event that causes stress is called a "stressor." Our bodies are equipped to handle acute exposure to stressors, but chronic exposure can result in mental disorders, e.g. anxiety and depression and even physical changes, e.g. cardiovascular alterations as seen in hypertension or stroke-disorders. There has been significant effort to find a way to identify people who would be vulnerable to develop stress-related disorders. The problem is that most of that research has relied on self-reporting and subjective clinical rankings, or exposing subjects to non-naturalistic environments. Employing wearables and other sensing technologies have made some headway in the elderly and at-risk individuals, but given how different our lifestyles are, it has been hard to find objective markers of psychogenic disease. Approaching the problem with VR Now, behavioral scientists led by Carmen Sandi at EPFL's School of Life Sciences have [...]

The development of a new method to make non-volatile computer memory may have unlocked a problem that has been holding back machine learning and has the potential to revolutionize technologies like voice recognition, image processing and autonomous driving. A team from Sandia National Laboratories, working with collaborators from the University of Michigan, published a paper in the peer-reviewed journal Advanced Materials that details a new method that will imbue computer chips that power machine-learning applications with more processing power by using a common material found in house paint in an analog memory device that enables highly energy-efficient machine inference operations. "Titanium oxide is one of the most commonly made materials. Every paint you buy has titanium oxide in it. It's cheap and nontoxic," explains Sandia materials scientist Alec Talin. "It's an oxide, there's already oxygen there. But if you take a few out, you create what are called oxygen vacancies. It turns out that when you create oxygen vacancies, you make this material electrically conductive." Those oxygen vacancies can now store electrical data, giving almost any device more computing [...]

Microsoft has built a desktop app called Lobe that can be used to train object-recognition models without having to write a single line of code. Getting that model into an application, though, will require some programming. Crafting AI apps in the real world requires developers to not only understand inference but also training, and it can all seem overwhelming. Before your model can make decisions from arbitrary input data, it has to be trained: that involves collecting, organizing, and processing data to teach your model, running the training process, testing it, and so on. Lobe tries to take all this training and testing faff away: it doesn’t require any technical know-how, and is free to use. The app, available for Windows and macOS, uses transfer learning to train off-the-shelf ResNet-50 V2 and MobileNetV2 image-recognition models using images supplied and labeled by the user. When this trained model is shown subsequent images, it can make a good guess what the label should be. For example, if you feed Lobe a set of images of birds of prey, with their correct [...]

They call it artificial intelligence -- not because the intelligence is somehow fake. It's real intelligence, but it's still made by humans. That means AI -- a power tool that can add speed, efficiency, insight and accuracy to a researcher's work -- has many limitations. It's only as good as the methods and data it has been given. On its own, it doesn't know if information is missing, how much weight to give differing kinds of information or whether the data it draws on is incorrect or corrupted. It can't deal precisely with uncertainty or random events -- unless it learns how. Relying exclusively on data, as machine-learning models usually do, it does not leverage the knowledge experts have accumulated over years and physical models underpinning physical and chemical phenomena. It has been hard to teach the computer to organize and integrate information from widely different sources. Now researchers at the University of Delaware and the University of Massachusetts-Amherst have published details of a new approach to artificial intelligence that builds uncertainty, error, physical laws, expert knowledge and missing [...]

A UBC Okanagan researcher has developed a way to predict the future health of the planet's coral reefs. Working with scientists from Australia's Flinders' University and privately-owned research firm Nova Blue Environment, biology doctoral student Bruno Carturan has been studying the ecosystems of the world's endangered reefs. "Coral reefs are among the most diverse ecosystems on Earth and they support the livelihoods of more than 500 million people," says Carturan. "But coral reefs are also in peril. About 75 per cent of the world's coral reefs are threatened by habitat loss, climate change and other human-caused disturbances." Carturan, who studies resilience, biodiversity and complex systems under UBCO Professors Lael Parrott and Jason Pither, says nearly all the world's reefs will be dangerously affected by 2050 if no effective measures are taken. There is hope, however, as he has determined a way to examine the reefs and explore why some reef ecosystems appear to be more resilient than others. Uncovering why, he says, could help stem the losses. "In other ecosystems, including forests and wetlands, experiments have shown that diversity [...]

The brain detects 3D shape fragments (bumps, hollows, shafts, spheres) in the beginning stages of object vision -- a newly discovered strategy of natural intelligence that Johns Hopkins University researchers also found in artificial intelligence networks trained to recognize visual objects. A new paper in Current Biology details how neurons in area V4, the first stage specific to the brain's object vision pathway, represent 3D shape fragments, not just the 2D shapes used to study V4 for the last 40 years. The Johns Hopkins researchers then identified nearly identical responses of artificial neurons, in an early stage (layer 3) of AlexNet, an advanced computer vision network. In both natural and artificial vision, early detection of 3D shape presumably aids interpretation of solid, 3D objects in the real world. "I was surprised to see strong, clear signals for 3D shape as early as V4," said Ed Connor, a neuroscience professor and director of the Zanvyl Krieger Mind/Brain Institute. "But I never would have guessed in a million years that you would see the same thing happening in AlexNet, which is [...]