"Nearly all of the studies published in the study period that evaluated the performance of AI algorithms for diagnostic analysis of medical images were designed as proof-of-concept technical feasibility studies and did not have the design features that are recommended for robust validation of the real-world clinical performance of AI algorithms," wrote Seong Ho Park, MD, PhD, the department of radiology and research institute of radiology at the University of Ulsan College of Medicine, Seoul, Korea, and colleagues.

Esquire Singapore is the latest entity to use artificial intelligence in creating articles with its April edition, set to come out in a few weeks, says Gary Jackson of Qualex Consulting Services. Jackson, based in Western North Carolina, is one of three men and the only American, who created the AI program, called Squire, that will create 100 pages for the magazine. He says another 100 pages will be created by humans. "It basically is fed the content and then it begins to write and you basically ask how many words do you want to be written by the topic and then it tries to put it together," said Jackson. He said it won't replace humans as there are several things people must do in order for Squire to work.

Then came Aibo, the robotic pet dog, and Pepper, the semi-humanoid robot designed to read emotions. Now, Sophia, the digital humanoid, is touring the world. All of which is to say, we've been around artificial intelligence (AI) in some form or another for years. Even so, there remains widespread fear over the potential of an impending malevolent "AI revolution." During one of my recent trips to Japan, I got a chance to interact with three Pepper robots.

With Artificial Intelligence (AI) technology leading the global fintech conversation, the United Arab Emirates is looking forward to becoming the testing ground for the worldwide development of the technology. According to a statement by the Minister of State for Artificial Intelligence, Omar Alama, the government is urging companies of the private sector to work together on understanding and promoting the technology's use, thus, transforming the country to become the world's leading AI hub. The statements came in the light of the launch of the government's latest initiative "Think AI", with accountancy and consulting firm Ernst & Young on Wednesday. Furthermore, Mr. Alama has pointed out that the initiative is part of a wider national plan to implement AI across all parts of society, starting with the introduction of workshops and panel talks that will bring together more than 100 experts in the field. In addition, a long-term vision for supporting AI expansion with the necessary infrastructure has been put in place.

News anchors must have been rather reluctant to read out the following news: Xin Xiaomeng began working as the world's first female artificial intelligence news anchor at Xinhua News Agency on Sunday, three months after a male robot joined the profession. In fact, Xin has been broadcasting news about the ongoing two sessions just like her human colleagues do. Some video clips show how Xin works. All Xinhua needs to do is to feed the English and Chinese texts into the receiver for her to read them like a normal news anchor. Unlike previous androids though, Xin does not read news like a cold machine; she reads it almost like a human being.

Google and Verily, Alphabet's life sciences and healthcare arm, have created a machine learning algorithm to help screen for diabetic retinopathy and diabetic macular edema, according to a Google blog post. The development of the algorithm has been a three-year project, which also involved the organizations conducting a global clinical research program focused on India. Verily has received a CE mark for the algorithm. Now, they've revealed the first real-world clinical use of the algorithm is happening at Aravind Eye Hospital in Madurai, India. At the hospital, the process works like this: Technicians use a fundus camera to take one image of each of the patient's eyes.

Large scale knowledge graph embedding has attracted much attention from both academia and industry in the field of Artificial Intelligence. However, most existing methods concentrate solely on fact triples contained in the given knowledge graph. Inspired by the fact that logic rules can provide a flexible and declarative language for expressing rich background knowledge, it is natural to integrate logic rules into knowledge graph embedding, to transfer human knowledge to entity and relation embedding, and strengthen the learning process. In this paper, we propose a novel logic rule-enhanced method which can be easily integrated with any translation based knowledge graph embedding model, such as TransE . We first introduce a method to automatically mine the logic rules and corresponding confidences from the triples. And then, to put both triples and mined logic rules within the same semantic space, all triples in the knowledge graph are represented as first-order logic. Finally, we define several operations on the first-order logic and minimize a global loss over both of the mined logic rules and the transformed first-order logics. We conduct extensive experiments for link prediction and triple classification on three datasets: WN18, FB166, and FB15K. Experiments show that the rule-enhanced method can significantly improve the performance of several baselines. The highlight of our model is that the filtered Hits@1, which is a pivotal evaluation in the knowledge inference task, has a significant improvement (up to 700% improvement).

The online programing services, such as Github,TopCoder, and EduCoder, have promoted a lot of social interactions among the service users. However, the existing social interactions is rather limited and inefficient due to the rapid increasing of source-code repositories, which is difficult to explore manually. The emergence of source-code mining provides a promising way to analyze those source codes, so that those source codes can be relatively easy to understand and share among those service users. Among all the source-code mining attempts,program classification lays a foundation for various tasks related to source-code understanding, because it is impossible for a machine to understand a computer program if it cannot classify the program correctly. Although numerous machine learning models, such as the Natural Language Processing (NLP) based models and the Abstract Syntax Tree (AST) based models, have been proposed to classify computer programs based on their corresponding source codes, the existing works cannot fully characterize the source codes from the perspective of both the syntax and semantic information. To address this problem, we proposed a Graph Neural Network (GNN) based model, which integrates data flow and function call information to the AST,and applies an improved GNN model to the integrated graph, so as to achieve the state-of-art program classification accuracy. The experiment results have shown that the proposed work can classify programs with accuracy over 97%.