The scanR high-content screening (HCS) station provides fully automated image acquisition and data analysis. Version 3.3 improves the deep-learning technology's capabilities to reliably separate objects in biological samples using instance segmentation, the ability to detect and delineate distinct objects of interest in an image. Accurate object segmentation: raw data (left), standard threshold (middle), TrueAI instance segmentation (right). Instance segmentation reliably separates difficult-to-distinguish objects that are very close together, such as cells or nuclei in colonies or tissue. The deep-learning technology can detect cells, nuclei and subcellular objects, and extract features from a list of over 100 object parameters.

A team of researchers at UCLA developed a cancer-detection mechanism based on deep learning technologies. The research team developed a device for detecting the presence of cancer cells in the blood. The project was accomplished in collaboration with NantWorks, and it spans into new areas of cancer research. This is the most distinct function of the device, and it could facilitate quick extraction of cancer cells. This device could also help in accentuating blood cancer treatments.

Computer-aided diagnosis (CAD) in colonoscopy may provide real-time differentiation between neoplastic polyps requiring resection and non-neoplastic polyps that can safely be left in place, according to a Japanese study in Annals of Internal Medicine. This method allows the complete resection of adenomatous polyps while preventing unnecessary polypectomy of non-neoplastic polyps. As screening for colon cancer increases worldwide, this approach could result in efficiencies and cost savings. Researchers led by Yuichi Mori, MD, PhD, of Showa University Northern Yokohama Hospital in Japan, looked at 791 consecutive patients having colonoscopy at the hospital with a total of 23 endoscopists in the period June to September 2017. Indications for colonoscopy included surveillance (most common), screening, symptom investigations, and planned treatment of other polyps.

While the deep-learning technology behind many of today's AI advances seems new to most, it has been around for decades--simply needing the data and power available today to fuel it. Artificial-intelligence technology has begun to hit its stride, springing from research labs into real business and consumer applications. Earlier this year at the AI Frontiers conference in Santa Clara, California, we sat down with AI experts from some of the world's leading technology-first organizations to find out. An edited version of the experts' remarks follows the video. Li Deng, chief AI officer, Citadel: There are a few factors that really propelled AI to this current state--what many people call "the third wave." The first wave died because people were probably too naive.

Samsung was granted permission by South Korea's Ministry of Land, Infrastructure and Transport to test run self-driving cars, Yonhap News reported. Samsung's autonomous vehicle is apparently under development and based on deep-learning technology, the report said. The approval Monday by officials will allow the company to test the self-driving vehicle on the roads. Samsung wants to develop top-of-the-line sensors and computer modules backed by artificial intelligence and deep-learning technologies. That technology would improve vehicles' self-driving capabilities, even in bad weather conditions, the report said.

The artificial intelligence (AI) engine "can understand with near-human accuracy the textual content of several thousands posts per second, spanning more than 20 languages," Facebook announced in a blog post Wednesday. Facebook's newest AI system will allow it to continue to personalize content for its billion plus users, as well as weed out spam and hate speech. The engine also marks another major milestone in deep-learning technology, as Facebook, Google, and Microsoft invest heavily in teaching machines to think. "Having good machine learning models is a force multiplier for a lot of the stuff they are doing," Bradley Hayes, a postdoctoral associate at MIT and the creator of the satirical AI chatbot @DeepDrumpf, told The Christian Science Monitor last month. For Facebook, the "scalability" Mr. Hayes refers to is enormous.

Investors haven't been happy with IBM (NYSE:IBM) lately. Since Ginni Rometty started as CEO in January of 2012, IBM has had 18 consecutive quarters of year-over-year declining revenue -- essentially every quarter of her tenure. The stock has lost nearly 10% compared with a gain of 78% for the S&P 500 over the last five years. For IBM shareholders, Ginni Rometty's four-year reign as chief executive officer hasn't been anything to go to Disneyland about. But her company has become a leader in one corporate category: board members willing to shovel incentive pay at a CEO turning in a mediocre performance.

Japan lags Silicon Valley in conventional internet services but could become a global leader in the deep-learning technology of artificial intelligence, a leading AI expert has said. Yutaka Matsuo, 41, Project Associate Professor at the University of Tokyo, believes machine learning will revolutionize sectors such as image recognition. "In deep learning, Japan still has a great chance to compete," Matsuo said in an interview. He singled out Sony Corp., Ricoh Co., Olympus Corp. and Canon Inc. as companies that show promise. "Changes happening now are in image processing technology, and Japan has been good at this," he said.