Apple is about to take to the stage and reveal the future of the iPhone – and a whole lot more. But what might really show the future of the company might be what it chooses not to announce. The iPhone is its biggest and most important product – but not, at least this year, the one that will really show off its future. As well as keeping a close eye on the iPhone and everything else that Apple chooses to unveil this week, there is plenty more to come. Here's a look at what Apple won't announce this week – and why it matters. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

New robots can learn old tricks. Bats use sound to navigate their surroundings in the dark and now a robot called Robat can do the same. Robat is a four-wheeled autonomous robot, equipped with a speaker to mimic a bat's mouth, and two microphones, positioned on the left and right, to mimic a bat's ears. As it moves around, Robat's speakers produce a high-frequency chirp every half a metre. It can then identify the position of obstacles by calculating the delay between making this sound and the echo returning, and any differences between the two microphones.

A Japanese airline wants to send you to space. Well, not you exactly, but a robot avatar that you can control in real time, while seeing through its eyes and feeling what it feels through haptic feedback. All Nippon Airlines (ANA) and the Japan Aerospace Exploration Agency (JAXA) have just announced the Avatar X programme, an initiative to build these advanced humanoid drones and send them to space. "The giant leap will be in bringing human consciousness and presence to a remote location," says Kevin Kajitani, … To continue reading this premium article, subscribe for unlimited access. Existing subscribers, please log in with your email address to link your account access.

Tsipi Shaish, a 59-year-old grandmother, knows exactly when she became an artist: when she was diagnosed with Parkinson's disease in 2006. Before her trembling hands brought her to a neurologist, she had lived "a routine life," she says. She worked a boring job at an insurance company for 25 years and focused on raising her two kids. She never took art lessons, and beyond the occasional museum visit, never gave any thought to art. Now she talks proudly of her own paintings that have hung in Paris and New York City galleries. "I go to the canvas because I feel curious, I feel an uncontrollable urge," she says.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Built by Engineered Arts, 2 Mesmer Heads perform a synchronised sequence. One is complete with lifelike skin and hair, the other is showing it's mechanical workings.

Square Enix's mysterious Left Alive project has been pushed back from an ambiguous "2018" release date to February 28th, 2019 in Japan. The delay isn't a huge surprise given how little we've seen of the game. Square Enix did, however, show a new cinematic trailer today during Sony's pre-Tokyo Game Show event. Set in the fictional city of Nova Slava, it follows three characters as they try to endure a futuristic warzone filled with soldiers and mechs. The teaser had no gameplay (boo) but did show some impressive-looking robots sliding around.

Samsung now has an artificial intelligence center in New York City -- its third in North America and sixth in total -- with an eye on robotics; a first for the company. It opened in Chelsea, Manhattan on Friday, walking distance from NYU (home to its own AI lab) boosting Samsung's hopes for an academic collaboration. The electronics giant is no stranger to academic team-ups: its UK research center is housed in Cambridge and led by the head of the university's Microsoft lab, Professor Andrew Blake. The same goes for rivals Sony, which tapped Carnegie Mellon for robotics research, and Toyota, which has poured a billion dollars into a research center for droids next to Stanford University. Samsung has quietly been investing in fledgling robotics startups (including the makers of a bot that cares for the elderly) through its SamsungNext Q Fund.

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we evaluate a prototype naive combination of the two methods, which refines the final segmentation. All methods were trained on 32 patients, and the evaluation was performed on a pure test set of 73 cases. Results show low lesion-wise false positives (30%) for the deep learning architecture, whereas the shallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).

How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes. Classification algorithms tasked with predicting outcomes accurately from these datasets tend to replicate these biases. We advocate a causal modeling approach to learning from biased data and reframe fair classification as an intervention problem. We propose a causal model in which the sensitive attribute confounds both the treatment and the outcome. Building on prior work in deep learning and generative modeling, we describe how to learn the parameters of this causal model from observational data alone, even in the presence of unobserved confounders. We show experimentally that fairness-aware causal modeling provides better estimates of the causal effects between the sensitive attribute, the treatment, and the outcome. We further present evidence that estimating these causal effects can help us to learn policies which are both more accurate and fair, when presented with a historically biased dataset.

In this paper, we consider the problem of distinguishing between authorized and unauthorized touchscreen and smart phone device users by leveraging a learned gesture classification profile combined with gesture anomaly detection. As touchscreen devices become more ubiquitous and the information stored within them becomes increasingly personal and valuable, the incentive and reward for circumventing existing security mechanisms has increased substantially. Given known vulnerabilities in existing biometric and non-biometric authentication methods such as fingerprint scanners, facial recognition, tokens, and pass codes; the development of an effective authentication approach that goes beyond the'something you know / have / are' paradigm is needed. In this paper, we propose models that accurately predict users based on touchscreen gesture patterns and detect anomalies in these patterns as a versatile approach to augment existing security methods and provide a method of continuous authentication. Touchscreen gesture are collected from a set of users from a capacitive sensor array to simulate a smart phone. Features include the pressure measured at the two dimensional (X,Y) coordinates on the sensor for each gesture, velocity at different instances of the gesture, and the duration of the gesture. We then demonstrate how logistic regression, support vector machines (SVM), and multiple Gaussian processes can be used to classify and predict the user creating the gesture. Our intent is to determine the extent to which supervised and unsupervised learning approaches can be successfully leveraged across multiple domains to limit the impact of unauthorized touchscreen device usage, quantify touchscreen security weaknesses and vulnerabilities, and potentially inform touchscreen device security design. Scenarios where our analysis may be useful include high security use cases where continuous authentication is required in the finance, transportation, public safety sectors.