Two Japanese national research institutes said Saturday they have succeeded in using artificial intelligence to identify early-stage stomach cancer with a high accuracy rate. The breakthrough may help extend the lives of more cancer patients in Japan, where stomach cancer is one of the leading causes of death. According to Riken and the National Cancer Center, it took AI only 0.004 seconds to judge whether an endoscopic image showed early-stage cancer or normal stomach tissue. AI correctly detected cancer in 80 percent of cancer images, while the accuracy rate was 95 percent for normal tissue. The accuracy rates were as high as those of veteran doctors, the institutes said, adding that they will aim to put AI into practical use as a device to support doctors in making diagnoses.

Robots in factories have historically been unwieldy, dangerous, and confined to large industrial settings. But now, smaller collaborative robots are overcoming traditional challenges in the robotics industry. They're paving the way for robot technology that gets us much closer to our Jetsons-like future. When George C. Devol, inventor of the automatic garage door opener, pitched his programmable Unimate arm, he was initially met with skepticism. However, "the robot had one advantage immediately," said Devol. "And that is that a robot can work three shifts, or 24 hours a day." Get the free data-driven report to see how robots are revolutionizing factories and manufacturing.

The British University in Dubai has launched a bachelors degree in Artificial Intelligence, the first of its kind in the country. The programme has been developed in collaboration with the University of Edinburgh and is open to both Emiratis and expatriates. Emiratis on the course will receive scholarship from the ICT fund, while expatriates can apply for scholarships to British University in Dubai. The ICT Fund was launched by Telecommunications Regulatory Authority in 2007 to achieve rapid development within the information and communication technology sector in UAE. The four-year degree course will be starting at the end of September.

If we can protect videos, audio and photos with digital watermarking, why not AI models? This is the question my colleagues and I asked ourselves as we looked to develop a technique to assure developers that their hard work in building AI, such as deep learning models, can be protected. You may be thinking, "Protected from what?" Well, for example, what if your AI model is stolen or misused for nefarious purposes, such as offering a plagiarized service built on stolen model? This is an concern, particularly for AI leaders such as IBM. Earlier this month we presented our research at the AsiaCCS '18 conference in Incheon, Republic of Korea, and we are proud to say that our comprehensive evaluation technique to address this challenge was demonstrated to be highly effective and robust.

Traditionally, robots have been configured to perform tasks by programming them explicitly. They have been taught the intricacies of how humans communicate so that they can respond accordingly. While this process is not only tedious, it has significantly high error rates too. Especially in areas involving safety-critical tasks, the accuracy of robots is of paramount importance. This brings the need to control robots in a quick and effective manner, and brainwaves are a way out.

The U.S Department of Homeland Security had funded a research approximately 6 years ago of the virtual border agent technology, better known as AVATAR (Automated Virtual Agent for Truth Assessments in Real Time) and had tested it at the U.S Mexico border on travelers voluntarily. Canada and EU has also tested the robot like kiosk that is asking travelers a series of questions. If the trend continues, International Travelers could be speaking with kiosk to determine if they are lying on any aspect at an airport or border crossings. The technology can also be used to screen the refugees and unwanted travelers travelling to any country. It can also be used to screen the citizenship applications, processing visas and many other such inter-related services.

Artificial intelligence is coming to healthcare. In fact, it's already here, and physicians are benefiting from it, using it to streamline administrative functions and enhance the patient experience. It's getting so that even smaller practices are able to harness this emerging technology, and not only is it making physicians' lives easier, but jumping on the bandwagon is a smart move financially. Patient experience is now a metric that ties into reimbursement, and if that experience can be enhanced, then patients and their caregivers all benefit. The good news for small practices is that they don't have to wait to start implementing some aspects of AI, particularly if they're using the same electronic health records systems as some of the larger nearby providers.

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence (AI) - Global Strategic Business Report" report has been added to ResearchAndMarkets.com's offering. The report provides separate comprehensive analytics for the US, Canada, Japan, Europe, Asia-Pacific, and Rest of World. Annual estimates and forecasts are provided for the period 2015 through 2024. Market data and analytics are derived from primary and secondary research.

White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely used for biomedical image segmentation. Recently, U-net has been successfully applied to WMH segmentation. Random initialization is usally used to initialize the model weights in the U-net. However, the model may coverage to different local optima with different randomly initialized weights. We find a combination of thresholding and averaging the outputs of U-nets with different random initializations can largely improve the WMH segmentation accuracy. Based on this observation, we propose a post-processing technique concerning the way how averaging and thresholding are conducted. Specifically, we first transfer the score maps from three U-nets to binary masks via thresholding and then average those binary masks to obtain the final WMH segmentation. Both quantitative analysis (via the Dice similarity coefficient) and qualitative analysis (via visual examinations) reveal the superior performance of the proposed method. This post-processing technique is independent of the model used. As such, it can also be applied to situations where other deep learning models are employed, especially when random initialization is adopted and pre-training is unavailable.

Industrial Revolution (4IR) (Xing and Marwala, 2017), many countries (Shah et al., 2015; Ding and Li, 2015) are setting out an overarching goal of building/securing an "innovation-driven" economy. As innovation emphasizes the implementation of ideas, creativity is typically regarded as the first stage of innovation in which generating ideas becomes the dominant focus (Tang and Werner, 2017; Amabile, 1996; Mumford and Gustafson, 1988; Rank et al., 2004; West, 2002). In other words, if creativity is absent, innovation could be just luck. Though creativity can be generally understood as the capability of producing original and novel work or knowledge, the universal definition of creativity remains rather controversial, mainly due to its complex nature (Tang and Werner, 2017; Hernández-Romero, 2017). But putting it informally, by famous innovator Steve Jobs in 1995, we can think creativity like this way (Sanchez-Burks et al., 2015): "Creative people [are] able to connect experiences they've had and synthesize new things."