As the success of the iPhone X's Face ID confirms, lots of us are thrilled to bits at the idea of a machine that can identify us based on our facial features. But how happy would you be if a computer used your facial features to start making judgments about your age, your gender, your race, your attractiveness, your trustworthiness, or even how kind you are? Chances are that, somewhere down the line, you'd start to get a bit freaked out. Especially if the A.I. in question was using this information in a way that controlled the opportunities or options that are made available to you. Exploring this tricky (and somewhat unsettling) side of artificial intelligence is a new project from researchers at the University of Melbourne in Australia. Taking the form of a smart biometric mirror, their device uses facial-recognition technology to analyze users' faces, and then presents an assessment in the form of 14 different characteristics it has "learned" from what it's seen.

Neuro-inspired recurrent neural network algorithms, such as echo state networks, are computationally lightweight and thereby map well onto untethered devices. The baseline echo state network algorithms are shown to be efficient in solving small-scale spatio-temporal problems. However, they underperform for complex tasks that are characterized by multi-scale structures. In this research, an intrinsic plasticity-infused modular deep echo state network architecture is proposed to solve complex and multiple timescale temporal tasks. It outperforms state-of-the-art for time series prediction tasks.

Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition to heart-rate, speed, altitude etc. Recently, machine learning techniques have provided huge success in a wide variety of areas where large amounts of data ("big data") is available. In this paper, we perform a pilot experiment on machine learning to model physical response in elite cyclists. As a first experiment, we show that it is possible to train a LSTM machine learning algorithm to predict the heart-rate response of a cyclist during a training session. This work is a promising first step towards developing more elaborate models based on big data and machine learning to capture performance aspects of athletes.

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM

Google is working on some nifty new features for Google Maps, including a short list of your favorite places, the possibility of a'virtual positioning system' and more. Assistant is coming to Google Maps in a big way, with a ton of new shortcuts, as well as the ability for the digital assistant to text your friend when you're on your way. Google is rolling out a tool called'Your Match', which uses machine learning to determine your location and interests, serving up targeted suggestions for new businesses opening up in your area and more.

Developed by the University of South Australia in partnership with the University of Stuttgart, Flinders University and the Max Planck Institute for Informatics in Germany, the research uses state-of-the-art machine-learning algorithms to demonstrate a link between personality and eye movements. Findings show that people's eye movements reveal whether they are sociable, conscientious or curious, with the algorithm software reliably recognising four of the Big Five personality traits: neuroticism, extroversion, agreeableness, and conscientiousness. Researchers tracked the eye movements of 42 participants as they undertook everyday tasks around a university campus, and subsequently assessed their personality traits using well-established questionnaires. UniSA's Dr Tobias Loetscher says the study provides new links between previously under-investigated eye movements and personality traits and delivers important insights for emerging fields of social signal processing and social robotics. "There's certainly the potential for these findings to improve human-machine interactions," Dr Loetscher says. "People are always looking for improved, personalised services.

Artificial Intelligence will be central to solving humanity's grand challenges Imagine a world void of antibiotic-resistant superbugs, where those once-invincible critters were no longer given a chance to grow because infection patterns in patients were identified and treated before any symptoms appeared. Or what if safety-critical industries like healthcare or transportation were virtually zero risk, thanks to automated decision making and effective pattern recognition by robotic systems? What if cancer found its cure, crime was accurately intercepted, and democracy was given the tools to empower each and every citizen? And what if this was just a fraction of what AI will offer humanity down the line? The promise of AI is the promise of transformative technology which has unfathomable potential to dramatically improve the lives of millions, if not billions, of humans on planet earth.

Which AI applications are playing a role in automation or augmentation of the retail process? How are retail companies using these technologies to stay ahead of their competitors today, and what innovations are being pioneered as potential retail game-changers over the next decade? Innovation is a double-edged sword, and as with any innovation results are a mixed bag. While many AI applications have yielded increased ROI--this case study of AI in retail marketing segmentation is one example--others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers. Below are 10 brief use cases across five retail domains or phases.

Social robot company Jibo is sadly running on fumes after burning through nearly $73 million in funding. In a story first reported by BostInno and since confirmed by The Robot Report, Jibo has laid off the majority of its workforce to enable "additional time to secure additional funding or pursue an exit." Jibo was once heralded as "the first social robot for the home." Founded in 2012 by famed MIT roboticist Cynthia Breazeal, Jibo successfully raised over $3.5 million when its Indiegogo campaign ended in 2014. At the time, Breazeal promised to usher in a new age of social robotics.

"The only thing constant in life is change," said 17th century French thinker François de La Rochefoucauld. The Internet of Things explained: What the IoT is, and where it's going next. That goes for double in the tech industry. By 2006, the people of the world were doing 100 million Google searches every day--which, by the way, we thought was an enormous number at the time. Now, we're doing 4.5 billion searches a day--45 times as many--which shows just how far and wide the digital revolution is still sweeping the planet and embedding itself deeper and deeper into our everyday lives.