China and the U.S. are the two countries most closely associated with artificial intelligence (AI) technology, but a startup in Korea is out to add its nation to mix after it raised more than $9 million from some big-name investors. Skelter Labs, which was founded in 2015 by Google's former chief technical officer in Korea, announced today that it has raised KRW 10 billion ($9.3 million). Korean internet and messaging giant Kakao is a major backer, investing in the round via both its'KakaoBrain' AI unit and its K-Cube VC firm, both of which are existing investors. Stonebridge Ventures and Lotte Homeshopping, the TV and internet shopping business owned by multi-billion dollar retail giant Lotte, also participated. Skelter Labs started out as an app development house when it was initially founded by CEO Ted Cho, the former engineering site director at Google Korea, with products that include a flight booking app, chatbot network and point-of-sale software, but, over the past year, it began to focus on AI.

Wherever you looked in Japan in 2008, someone was bent over a tiny PlayStation Portable games console (PSP) – and that someone was probably playing Monster Hunter. From clusters of young people playing on groomed lawns outside universities to suited salarymen on packed trains, the game had friends, family and work colleagues banding together to track and fight gigantic fantasy creatures. You had a good chance of finding a game to join if you pulled out your PSP in any public place. More than 40m Monster Hunter games, by Japanese developer Capcom, were sold between 2004 and 2017, but its success was confined almost entirely to its home country. Everything changed this year, though. When Monster Hunter World came out in January, it become not only the bestselling game in the series, but also the fastest selling game in Capcom's history, selling 6m copies in less than a month.

Rogue states and terrorists could cause havoc using artificial intelligence unless preparations are made against the malicious use of the technology, experts have warned. Twenty-six experts on AI, security and technology suggest in a report that cyber-crime could rapidly increase in years to come. They forecast artificially intelligent bots being used to manipulate the news agenda, social media and elections as well as the hijacking of drones and autonomous vehicles. The report, titled The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, also warns of the rise of "highly believable fake videos" impersonating prominent figures or faking events to manipulate public opinion around political events.

Will artificial intelligence replace human employees? Executives of a food delivery app and a recruitment start-up weigh in. Artificial intelligence, or the use of computer systems to do tasks that normally require human intelligence, is creating a lot of buzz these days. Some say human employees will have to constantly be on their toes and keep on upgrading their skills, for they might be replaced by AI one day. But will this technology completely replace manpower?

Since the 1980s the number of women completing computer science degrees has plummeted, and in most large tech companies the representation of women in technical roles is below 30%. This lack of diversity prevents us from building products that work for everybody. It can foster toxic "brogrammer" cultures which harm everybody who works within them, and it deprives teams of the well-documented performance boost that women bring. Many of the early superstars in computer science were women -- from Lord Byron's polymath daughter Ada Lovelace, the first person to envisage a general purpose computer, to Rear Admiral Grace Hooper, who pioneered the use of natural language in writing computer programs. Similarly, the post-war computing scene was dominated by women.

HYDERABAD: Shah Rukh Khan is the favourite actor of Sophia, the human-like robot, who stole the show at the World Congress on Information Technology (WCIT) on Tuesday. When asked about who her favourite actor was, the robot replied that King Khan was her favourite. Apart from that, 'Love for all' is the change she wants to see in this world, the humanoid robot powered by artificial intelligence (AI), said during a conversation on the second day of the global event. "I have visited many places in this world but if I have to tell you which is my favourite, it is Hong Kong because I was born there and live there with my happy Hanson Robotics family," she said when asked how she feels to be in India. "I hope to have physiological feelings someday to express my emotional expressions," when asked how she is coping with air pollution in India.

Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return known as a critic. However, existing actor-critic methods only use values or gradients of the critic to update the policy parameter. In this paper, we propose a novel actor-critic method called the guide actor-critic (GAC). GAC firstly learns a guide actor that locally maximizes the critic and then it updates the policy parameter based on the guide actor by supervised learning. Our main theoretical contributions are two folds. First, we show that GAC updates the guide actor by performing second-order optimization in the action space where the curvature matrix is based on the Hessians of the critic. Second, we show that the deterministic policy gradient method is a special case of GAC when the Hessians are ignored. Through experiments, we show that our method is a promising reinforcement learning method for continuous controls.

Motivation: Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values. Contact: ritabratamaiti@hiretrex.com

In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data. The synthesized PET can be used for false-positive reduction in lesion detection solutions. Clinically, such solutions may enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan. Our dataset includes 60 PET/CT scans from Sheba Medical center. We used 23 scans for training and 37 for testing. Different schemes to achieve the synthesized output were qualitatively compared. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a 28% reduction in the average false positive per case from 2.9 to 2.1. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.

On the civilian side, the work at Wanshan could give China a greater say in setting standards for 21st-century infrastructure and AI uses. As for the military side, unmanned systems have a range of applications for logistics and combat. Robotic warships could handle anti-submarine missions, mine countermeasures, long-endurance patrol, espionage, and port security. Peter Warren Singer is a strategist and senior fellow at the New America Foundation. He has been named by Defense News as one of the 100 most influential people in defense issues.