GIFU – Six people, including children, were injured Saturday when a 4-kg drone that was distributing candy at an event in Ogaki, Gifu Prefecture, suddenly crashed into the crowd, the police said. Those hit by the drone ranged in age from 5 to 48, but most of the injuries were minor, such as scratches to foreheads and shoulders, the police said. The drone, about 85 cm in diameter and 55 cm high, was scattering sweets while hovering over a park in Ogaki as part of an event to showcase robotic technologies, the police said. The event was organized by a local tourism association. About 600 people, including about 100 children attending with their families, were on site at the time of the accident, the organizer said.

Kevin Du is travelling to the United States this week to visit Harvard Business School. But he has other things on his mind. He plans to make a side trip to other top universities and technology companies, part of his regular day job as a headhunter, looking to rope in engineers, programmers and coders to work in China. China, already the world's largest market for automatons, e-commerce and smartphones, is also the job market for artificial intelligence, big data analytics and robotics. The Chinese government has just unveiled a road map to global dominance in AI by 2030, forecasting the industry to be worth 1 trillion yuan (US$151 billion) by then.

A light blinks on the black box alerting the gymnast to begin her routine. She launches off the vault, lands and turns to salute the robotic judge. Her score is already flashing on the big screen to the Olympics crowd and to millions of viewers at home, who have followed the live scoring as the move is dissected in real time. This is not a scene from Blade Runner 2049, but a possible vision of gymnastics future as it races to include artificial intelligence in its judging system. The International Gymnastics Federation (FIG) is planning to introduce the AI technology to assist with scoring at the Tokyo 2020 Olympic Games (as long as the IOC partner's for timekeeping and results approves, the plan is good to go).

In RoboCup, several To assist automated learning of team behavior, we provide a large dataset generated using 10different leagues exist to emphasize specific research problems by using different kinds of the top participants in RoboCup 2016 or 2017. of robots and rules. There are different soccer While it is possible to use the simulator for robot leagues in the RoboCup with different types and learning, we also generate additional data that is sizes of hardware and software: small size, middle not normally available from playing other teams size, standard platform league, humanoid, 2D directly: We modified the simulator to record and 3D simulation (Kitano et al., 1997). In the data from each robots local perspective, that is, soccer simulation leagues (Akiyama et al., 2015), with the restricted views that depend on each the emphasis is on multi-robot team work with robots situation and actions, and also include partial and noisy information, in real-time.

Reinforcement Learning (RL) can model complex behavior policies for goal-directed sequential decision making tasks. A hallmark of RL algorithms is Temporal Difference (TD) learning: value function for the current state is moved towards a bootstrapped target that is estimated using next state's value function. $\lambda$-returns generalize beyond 1-step returns and strike a balance between Monte Carlo and TD learning methods. While lambda-returns have been extensively studied in RL, they haven't been explored a lot in Deep RL. This paper's first contribution is an exhaustive benchmarking of lambda-returns. Although mathematically tractable, the use of exponentially decaying weighting of n-step returns based targets in lambda-returns is a rather ad-hoc design choice. Our second major contribution is that we propose a generalization of lambda-returns called Confidence-based Autodidactic Returns (CAR), wherein the RL agent learns the weighting of the n-step returns in an end-to-end manner. This allows the agent to learn to decide how much it wants to weigh the n-step returns based targets. In contrast, lambda-returns restrict RL agents to use an exponentially decaying weighting scheme. Autodidactic returns can be used for improving any RL algorithm which uses TD learning. We empirically demonstrate that using sophisticated weighted mixtures of multi-step returns (like CAR and lambda-returns) considerably outperforms the use of n-step returns. We perform our experiments on the Asynchronous Advantage Actor Critic (A3C) algorithm in the Atari 2600 domain.

Pattern classification is the machine learning task of identifying to which category a new observation belongs, on the basis of a training set of observations whose category membership is known. This type of machine learning algorithm that uses a known training dataset to make predictions is called supervised learning, which has been extensively studied and has wide applications in the fields of bioinformatics [1], computer-aided diagnosis (CAD) [2], machine vision [3], speech recognition [4], handwriting recognition [5], spam detection and many others [6], [7], [8]. Usually, different kinds of learning methods use different models to generalize from training examples to novel test examples. As pointed out in [9], [10], one of the important invariants in these applications is the local structure: variables that are spatially or temporally nearby are highly correlated. Local correlations benefit extracting local features because configurations of neighboring variables can be classified into a small number of categories (e.g.

DUBAI – Blockchain, together with artificial intelligence, machine learning, robotics, and virtual and augmented reality, have the potential to deliver disruptive outcomes and reshape digital business in 2018. And companies that have not started the digital investment cycle are at high risk of being disrupted. This is according to the list of top IT predictions for 2018 published Saturday by Dimension Data. But the top trend for the coming year is the adoption of Blockchain - the technology behind Bitcoin - and its immense potential to disrupt and transform the world of money, business, and society using a variety of applications. Ettienne Reinecke, Dimension Data's Group Chief Technology Officer, said Blockchain has gone from strength to strength.

MUMBAI: IDFC AMC, has launched the IDFC Neo Equity Portfolio, a unique PMS powered by AI (Artificial Intelligence) and Big Data analysis.The portfolio will analyse data from multiple traditional and non-traditional sources available in the public domain to identify stock opportunities. The fund house believes explosive growth in data generation now allows tracking several activities like goods and shipping movements and traffic patterns to better estimate demand, footfalls at malls to track customer behaviour, social media sentiments, credit card spending patterns etc., all of which can play an increasingly important role in fund management. India is on the verge of a data revolution thanks to the convergence of multiple factors such as smart phone penetration, movement to a cashless economy and the implementation of GST, Aadhar and IndiaStack. This is making conditions ripe for widespread use of Artificial Intelligence to analyse vast quantities of data for meaningful predictive analysis. The IDFC Neo Equity Portfolio leverages these advanced technologies for the entire investment process including research and stock selection, portfolio optimization and risk management.

How and Why I lost my appetite for TV and Movies?

Six years after reporting its biggest-ever loss, Sony is no longer a conglomerate in freefall. Last week the Japanese group behind the Bravia TV set, the PlayStation, Beyoncé and the Spider-Man films said it was on track to set a new annual profit record – expecting to beat its previous corporate best of ¥526bn (£3.5bn) by 20%. It has been a long journey for the group after years of underperformance and missed targets, including most recently a £800m writedown of its Sony Pictures film division. But at last week's quarterly results update, the company stated that the film unit was one of the company's strongest performers and would help it beat the record profits it made in 1997-98: the year it released Men in Black, and when Steve Jobs had yet to release the Walkman-killing iPod. Now, Sony is expected to make full-year profits of £4.2bn.