When Honda Motor Co. launched the latest version of its N-Box a year ago, it promoted features on the pint-sized minicar such as error-detecting pedals, automatic emergency braking and moveable seats, part of a push to market the vehicle to young families. But a drastically different demographic has made the N-Box the country's best-selling passenger vehicle: roughly half the owners of the most recent model are 50 or older. Automakers had hoped high-tech options would attract younger buyers to kei cars (minicars) even as the number of Japanese drivers under 30 has slid nearly 40 percent since 2001. Instead, with a price tag starting around ¥850,000 and low ownership taxes, minicars have gained a more loyal following among the rapidly growing population of elderly Japanese, many of whom are on fixed incomes. "After their children are grown and leave home, more people are looking to downsize from larger family cars to more compact ones," said Kiminori Murano, managing director at Tortoise, a dealership specializing in minicars in Yamato, Kanagawa Prefecture.

Rogue drones have nearly caused air accidents, have been used as offensive weapons, to deliver drugs to prisoners, and to spy on people. So how can we fight back? This summer a packed Airbus A321 came within 100ft (30m) of disaster after encountering a drone at 15,500ft. And the number of near-misses of this sort has trebled over the last three years, with 92 incidents reported last year in the UK alone. Dozens were classified as involving a serious chance of a collision.

Analytics and artificial intelligence practitioners have come together to form a non-profit organisation called the Association of Data Scientists in India (ADaSci). The body aims to support all those working in data science, analytics, big data, AI and machine learning. Originally envisioned by Analytics India Magazine, ADaSci aims to foster a community of industry leaders, researchers and subject matter experts, while supporting the next generation of data scientists through new ideas and research. Its initial goals are to promote collaboration among data science decision makers and stakeholders, serve as a forum for research, and address data science challenges faced by organisations. It will also offer opportunities for networking, organise national and international conferences, and publish in peer reviewed journals, while providing analytics certification and continuing education, to help members with their career development. Bhaskar Gupta (pictured), founder and CEO, Analytics India Magazine, comments: 'Analytics and AI has the potential to be the next game-changer for the industry, but the space lacks a non-profit body or an umbrella society to serve the needs of analytics professionals in India.

At a warehouse in Tokyo's Ariake district once mainly staffed by people, robots are now doing the work of inspecting and sorting the clothing housed there by Japanese retailer Uniqlo. The company recently remodeled the existing warehouse with an automated system created in partnership with Daifuku, a provider of material handling systems. Now that the system is running, the company revealed during a walkthrough of the new facility, Uniqlo has been able to cut staff at the warehouse by 90%. The warehouse can now also operate 24 hours a day. The robotic system is designed to transfer products delivered to the warehouse by truck, read electronic tags attached to the products and confirm their stock numbers and other information. When shipping, the system wraps products placed on a conveyor belt in cardboard and attaches labels to them.

Complex industrial systems are continuously monitored by a large number of heterogenous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. The paper proposes an integrated automatic unsupervised feature learning approach for fault detection that uses healthy conditions data only for its training. The approach is based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and comprises stacked autoencoders performing unsupervised feature learning, and a one-class classifier monitoring the variations in the features to assess the health of the system. This study provides a comprehensive evaluation of HELM fault detection capability compared to other machine learning approaches, including Deep Belief Networks. The performance is first evaluated on a synthetic dataset with typical characteristics of condition monitoring data. Subsequently, the approach is evaluated on a real case study of a power plant fault. HELM demonstrates a better performance specifically in cases where several non-informative signals are included.

The Universal Turing Machine (TM) is a model for VonNeumann computers --- general-purpose computers. A human brain can inside-skull-automatically learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a machine can accomplish the same. This theoretical work shows how the Developmental Network (DN) can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM --- Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This letter also extends the Church-Turing thesis to automatic programming for general purposes and sketchily proved it.

Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper introduces a system model that incorporates temporal convolutions with both long term and short term time dependencies. The proposed network learns salient features and complex temporal variations in sensor values, and predicts the RUL. A data augmentation method is used for increased accuracy. The proposed method is compared with several state-of-the-art algorithms on publicly available datasets. It demonstrates promising results, with superior results for datasets obtained from complex environments.

Predicting movement of objects while the action of learning agent interacts with the dynamics of the scene still remains a key challenge in robotics. We propose a multi-layer Long Short Term Memory (LSTM) autoendocer network that predicts future frames for a robot navigating in a dynamic environment with moving obstacles. The autoencoder network is composed of a state and action conditioned decoder network that reconstructs the future frames of video, conditioned on the action taken by the agent. The input image frames are first transformed into low dimensional feature vectors with a pre-trained encoder network and then reconstructed with the LSTM autoencoder network to generate the future frames. A virtual environment, based on the OpenAi-Gym framework for robotics, is used to gather training data and test the proposed network. The initial experiments show promising results indicating that these predicted frames can be used by an appropriate reinforcement learning framework in future to navigate around dynamic obstacles.

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We believe that, in order for RL agents to comprehend utterances from human colleagues, RL agents must infer the mental states that people attribute to them because people sometimes infer an interlocutor's mental states and communicate on the basis of this mental inference. This paper proposes PublicSelf model, which is a model of a person who infers how the person's own behavior appears to their colleagues. We implemented the PublicSelf model for an RL agent in a simulated environment and examined the inference of the model by comparing it with people's judgment. The results showed that the agent's intention that people attributed to the agent's movement was correctly inferred by the model in scenes where people could find certain intentionality from the agent's behavior.

Imagine if your neighborhood grocery or convenience store offered one-hour, on-demand fulfillment -- not through intermediaries like Postmates or Instacart, but entirely in-house -- and made a profit on every order. As fantastical as the idea might seem, that's the promise of CommonSense Robotics, an Israeli micro-fulfillment startup that today launched its first autonomous sorting and shipping center in downtown Tel Aviv. "We started CommonSense Robotics because as consumers we wondered: Why can't we get our online orders faster and cheaper than going to the store? We're excited to see this groundbreaking technology finally serving real customers to allow for fast, inexpensive deliveries of high-quality products," said Elram Goren, CEO and cofounder of CommonSense Robotics, adding, "It's a true win-win for both retailers and consumers." CommonSense's fulfillment center -- which it claims is the world's smallest -- measures just 6,000 square feet in total.