Deep neural networks obtain state-of-the-art performance on a series of tasks. However, they are easily fooled by adding a small adversarial perturbation to input. The perturbation is often human imperceptible on image data. We observe a significant difference in feature attributions of adversarially crafted examples from those of original ones. Based on this observation, we introduce a new framework to detect adversarial examples through thresholding a scale estimate of feature attribution scores. Furthermore, we extend our method to include multi-layer feature attributions in order to tackle the attacks with mixed confidence levels. Through vast experiments, our method achieves superior performances in distinguishing adversarial examples from popular attack methods on a variety of real data sets among state-of-the-art detection methods. In particular, our method is able to detect adversarial examples of mixed confidence levels, and transfer between different attacking methods.

In this paper, we study a challenging problem of how to pool multiple ride-share trip requests in real time under an uncertain environment. The goals are better performance metrics of efficiency and acceptable satisfaction of riders. To solve the problem effectively, an objective function that compromises the benefits and losses of dynamic ridesharing service is proposed. The Polar Coordinates based Ride-Matching strategy (PCRM) that can adapt to the satisfaction of riders on board is also addressed. In the experiment, large scale data sets from New York City (NYC) are applied. We do a case study to identify the best set of parameters of the dynamic ridesharing service with a training set of 135,252 trip requests. In addition, we also use a testing set containing 427,799 trip requests and two state-of-the-art approaches as baselines to estimate the effectiveness of our method. The experimental results show that on average 38% of traveling distance can be saved, nearly 100% of passengers can be served and each rider only spends an additional 3.8 minutes in ridesharing trips compared to single rider service.

Motivated by the many real-world applications of reinforcement learning (RL) that require safe-policy iterations, we consider the problem of off-policy evaluation (OPE) --- the problem of evaluating a new policy using the historical data obtained by different behavior policies --- under the model of nonstationary episodic Markov Decision Processes with a long horizon and large action space. Existing importance sampling (IS) methods often suffer from large variance that depends exponentially on the RL horizon $H$. To solve this problem, we consider a marginalized importance sampling (MIS) estimator that recursively estimates the state marginal distribution for the target policy at every step. MIS achieves a mean-squared error of $O(H^2R_{\max}^2\sum_{t=1}^H\mathbb E_\mu[(w_{\pi,\mu}(s_t,a_t))^2]/n)$ for large $n$, where $w_{\pi,\mu}(s_t,a_t)$ is the ratio of the marginal distribution of $t$th step under $\pi$ and $\mu$, $H$ is the horizon, $R_{\max}$ is the maximal rewards, and $n$ is the sample size. The result nearly matches the Cramer-Rao lower bounds for DAG MDP in \citet{jiang2016doubly} for most non-trivial regimes. To the best of our knowledge, this is the first OPE estimator with provably optimal dependence in $H$ and the second moments of the importance weight. Besides theoretical optimality, we empirically demonstrate the superiority of our method in time-varying, partially observable, and long-horizon RL environments.

An overwhelming majority (94 per cent) of UK-based financial services industry decision-makers believe that Artificial Intelligence (AI) has the most potential to revolutionise the sector over the next five years, considerably ahead of blockchain (53 per cent) and the Internet of Things (24 per cent). Intertrust, a provider of administrative services to clients operating and investing in the international business environment, surveyed UK-based executives covering the asset management, capital markets and private wealth sectors to identify the value-add delivered by new technologies now and in the future. Some 83 per cent of respondents believe that operations roles are the most likely to be replaced or dramatically changed by AI, robotics and blockchain, ahead of accounting (82 per cent), and compliance (65 per cent). While AI is seen as the clear frontrunner among disruptive technologies, its rollout is being hindered by a skills shortage. Some 41 per cent of respondents said they are struggling to recruit AI specialists, ahead of those working in data-analytics (29 per cent), cybersecurity (18 per cent) and compliance (18 per cent).

More people die in construction than in any other industry, and the number one cause of death on a job site is falling. Autodesk's latest addition to its BIM 360 suite of artificial intelligence (AI) enabled industry tools – Construction IQ – aims to reduce these tragic occurrences. It does this by predicting when falls are likely to happen – as well as any other danger to life, limb, or even just quality of work. Autodesk's data scientists hit upon the solution while looking for applications where the massive amount of data collected on modern-day construction sites could be put to use, thanks to the industry's enthusiastic adoption of mobile tools and sensing devices. "Imagine being a construction manager and having to contend with the fact that every X number of months, someone's going to die on the job – it's unfathomable to most of us in white collar jobs," says Pat Keaney, Autodesk's lead on the Construction IQ project.

A group of Australian researchers have recently developed an AI-powered smartphone app that can diagnose respiratory disorders by "listening" to the user's cough. This technology was developed by researchers at Curtin University and The University of Queensland, Australia, whose findings were published June 6 in the journal Respiratory Research. The researchers created an algorithm that can analyze coughs for features that are unique to five different diseases. This technique is similar to speech recognition technologies in that the software examines the auditory cough for characteristics specific to these conditions. This is typically done by a physician during a clinical exam, with a stethoscope being used to listen to sound produced while breathing or coughing (auscultation). The downside to this is that the patient must be in the presence of a trained professional to have their respiration sounds analyzed.

Lately it seems that AI is our knight in shining armor, the missing link between our past and our future. AI is now being used to steer the direction of hedge funds and drive much needed efficiency upgrades to our supply chains, for example. The machine learning society introduced a face recognition algorithm that is able to distinguish gender with an accuracy of up to 91%. AI is becoming so commonplace, in fact, that our electronics are often using it in the background to improve our experience, from taking a picture or securing our devices, without us even knowing it. Smaller AI startups are introducing AI services that can help sales teams surface talking points that closes deals. According to Element AI, a specialty lab in Montreal, "in the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research."

Altoida Inc., a digital health company using digital biomarkers to drive better clinical outcomes for brain diseases has raised $6.3M in Series A funding led by M Ventures, the corporate venture capital arm of the science and technology company Merck KGaA, Darmstadt, Germany. The round also includes participation from Grey Sky Venture Partners, VI Partners AG, Alpana Ventures, and FYRFLY Venture Partners. The number of people living with Alzheimer's disease is rapidly growing. In 2019 alone, Alzheimer's disease and other dementias will cost the nation $290 billion. By 2050, these costs could rise as high as $1.1 trillion.

Military pilots may soon have a new kind of wingman to depend upon: not flesh-and-blood pilots but fast-flying, sensor-studded aerial drones that fly into combat to scout enemy targets and draw enemy fire that otherwise would be directed at human-piloted aircraft. War planners see these robotic wingmen as a way to amplify air power while sparing pilots' lives and preventing the loss of sophisticated fighter jets, which can cost more than $100 million apiece. "These drone aircraft are a way to get at that in a more cost-effective manner, which I think is really a game-changer for the Air Force," says Paul Scharre, director of the technology and national security program at the Center for a New American Security, a think tank in Washington, D.C. Unlike slow-moving drones such as the Reaper and the Global Hawk, which are flown remotely by pilots on the ground, the new combat drones would be able to operate with minimal input from human pilots. To do that, they'd be equipped with artificial intelligence systems that give them the ability not only to fly but also to learn from and respond to the needs of the pilots they fly alongside. "The term we use in the Air Force is quarterbacking," says Will Roper, assistant secretary of the U.S. Air Force for acquisition, technology and logistics and one of the experts working to develop the AI wingmen.

The sprawling warehouse, which looks big enough to double as an airport hangar, is unofficially known as the "robot highway." Inside Amazon's Denver, Colo., "sortation center," an army of orange robots –– each one about the size of a large suitcase with a small conveyor belt on top –– glides across the concrete floor, picking up and then delivering packages to one of hundreds of chutes that organize each item by Zip code before they're shipped to customers. Though largely unknown to the outside world, the robots, known as Pegasus, have already logged more than 1.5 million miles of driving, according to an Amazon blog post describing work inside the warehouse. Amazon unveiled Pegasus during the keynote session at its first-ever re:MARS conference in Las Vegas this week, devoted to, in Amazon's words, "Machine Learning, Automation, Robotics, and Space." It was there that Amazon revealed that the company already has 200,000 robots working at dozens of distribution facilities around the world.