A gamer known as Stallion83 has blasted through the record for the highest gamerscore in the world, becoming the first person to reach two million. Gamerscore is an achievements system on Xbox that tracks how people are progressing in games. As players make their way through, they are given points – and most players will have a few thousand, or a little more. But Stallion83, whose real name is Ray Cox, had already broken the record to be the first person to achieve one million points. And now he has done it again, becoming the first person in the world to get two million.

This project demonstrated a methodology to estimating cooperate credibility with a Natural Language Processing approach. As cooperate transparency impacts both the credibility and possible future earnings of the firm, it is an important factor to be considered by banks and investors on risk assessments of listed firms. This approach of estimating cooperate credibility can bypass human bias and inconsistency in the risk assessment, the use of large quantitative data and neural network models provides more accurate estimation in a more efficient manner compare to manual assessment. At the beginning, the model will employs Latent Dirichlet Allocation and THU Open Chinese Lexicon from Tsinghua University to classify topics in articles which are potentially related to corporate credibility. Then with the keywords related to each topics, we trained a residual convolutional neural network with data labeled according to surveys of fund manager and accountant's opinion on corporate credibility. After the training, we run the model with preprocessed news reports regarding to all of the 3065 listed companies, the model is supposed to give back companies ranking based on the level of their transparency.

In this paper we are interested in the prediction of preterm birth based on diagnosis codes from longitudinal EHR. We formulate the prediction problem as a supervised classification with noisy labels. Our base classifier is a Recurrent Neural Network with an attention mechanism. We assume the availability of a data subset with both noisy and clean labels. For the cohort definition, most of the diagnosis codes on mothers' records related to pregnancy are ambiguous for the definition of full-term and preterm classes. On the other hand, diagnosis codes on babies' records provide fine-grained information on prematurity. Due to data de-identification, the links between mothers and babies are not available. We developed a heuristic based on admission and discharge times to match babies to their mothers and hence enrich mothers' records with additional information on delivery status. The obtained additional dataset from the matching heuristic has noisy labels and was used to leverage the training of the deep learning model. We propose an Alternating Loss Correction (ALC) method to train deep models with both clean and noisy labels. First, the label corruption matrix is estimated using the data subset with both noisy and clean labels. Then it is used in the model as a dense output layer to correct for the label noise. The network is alternately trained on epochs with the clean dataset with a simple cross-entropy loss and on next epoch with the noisy dataset and a loss corrected with the estimated corruption matrix. The experiments for the prediction of preterm birth at 90 days before delivery showed an improvement in performance compared with baseline and state of-the-art methods.

Motivated by Shannon's model and recent rehabilitation of self-supervised artificial intelligence having a "World Model", this paper propose an unified intelligence-communication (UIC) model for describing a single agent and any multi-agent system. Firstly, the environment is modelled as the generic communication channel between agents. Secondly, the UIC model adopts a learning-agent model for unifying several well-adopted agent architecture, e.g. rule-based agent model in complex adaptive systems, layered model for describing human-level intelligence, world-model based agent model. The model may also provide an unified approach to investigate a multi-agent system (MAS) having multiple action-perception modalities, e.g. explicitly information transfer and implicit information transfer. This treatise would be divided into three parts, and this first part provides an overview of the UIC model without introducing cumbersome mathematical analysis and optimizations. In the second part of this treatise, case studies with quantitative analysis driven by the UIC model would be provided, exemplifying the adoption of the UIC model in multi-agent system. Specifically, two representative cases would be studied, namely the analysis of a natural multi-agent system, as well as the co-design of communication, perception and action in an artificial multi-agent system. In the third part of this treatise, the paper provides further insights and future research directions motivated by the UIC model, such as unification of single intelligence and collective intelligence, a possible explanation of intelligence emergence and a dual model for agent-environment intelligence hypothesis. Notes: This paper is a Previewed Version, the extended full-version would be released after being accepted.

Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers. Motivated by such systems, we consider a setting where each worker is asked to rank a fixed (small) number of alternatives and, then, a positional scoring rule is used to compute the aggregate ranking. Among the apparently infinite such rules, what is the best one to use? To answer this question, we assume that we have partial access to an underlying true ranking. Then, the important optimization problem to be solved is to compute the positional scoring rule whose outcome, when applied to the profile of individual rankings, is as close as possible to the part of the underlying true ranking we know. We study this fundamental problem from a theoretical viewpoint and present positive and negative complexity results and, furthermore, complement our theoretical findings with experiments on real-world and synthetic data.

A quick search on Google will show that robot cleaners and dog poo don't go well together, yet none of the big players out there have offered a solution (Sony's Aibo doesn't count). Another pain point that has put me off from acquiring a mopping robot is the fact that the mop -- often a piece of detachable fabric -- requires manual cleaning, which is rather awkward even if there's no faecal smearing. To my surprise, it was a Chinese startup at TechCrunch Shenzhen that seems to have it all figured out. Veniibot, a Chengdu-based team of over 20 employees, unveiled its Venii N1 mopping and sweeping hybrid robot at the TechCrunch event earlier this week. While you may not have heard of this startup before, its talents were hired from the likes of ZTE, Baidu, Motorola, Foxconn, Ecovacs and more.

How good are you at lying? Could you fool a friend? We've recently learned that the EU plans to trial lie detectors equipped with artificial intelligence, or "deception detection", at border control to combat crime and terrorism. This got Jordan Erica Webber and Graihagh Jackson wondering about lying – how people learn to tell fibs, whether an AI machine can pick up subtle clues and cues, and if so, can we trust its judgment? In this special collaboration between the Guardian's Chips with Everything and Science Weekly podcasts, Webber and Jackson are joined by the social and forensic psychologist Dr Paul Seager, from the University of Central Lancashire, and a reader in computational intelligence, Dr Keeley Crockett from Manchester Metropolitan University.

An eerie robot with the face of a small child can make realistic-looking facial expressions. Creepy footage shows Affetto, an android with just a head and no body mimic human expressions like smiling and frowning. The robot was made by researchers from Osaka University in Japan who say it could open the door for androids to have'deeper interactions with humans'. Affetto, who has flesh-coloured skin on its face, can mimic a range of human expressions with incredible accuracy. An eerie robot with the face of a small child can make realistic-looking facial expressions.

From across the room, his Amazon Alexa device says, "Deepak, as of now here's your daily reflection." It continues in Mr. Chopra's voice saying, "Topic: Creating a joyful energetic body." Mr. Chopra suddenly shouts, "OK stop!" Alexa doesn't listen, so Mr. Chopra stands up and walks toward it. "I never listen to my own voice, ever," he says. "It makes me too self-conscious."

John Lewis's website has broken in the middle of Black Friday. The British retailer – which like just about every major shop in the UK and US is running a series of deals and offers to mark the day – seems to have been overwhelmed by the interest and its website has broken down. "Sorry about the wait, please try again soon," a message on the site reads. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. 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 The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.