Today during the 2019 World Innovation Forum, Partners HealthCare unveiled its selections for the fifth annual "Disruptive Dozen," an annual list of 12 emerging artificial intelligence (AI) technologies with the greatest potential to impact healthcare in the next year. The annual 12 most disruptive technologies are selected through a rigorous process by Partners HealthCare thought leaders, clinicians, and researchers. Interview results from nearly 100 experts are assembled into a field of nominated technologies. The nominated innovation must have a strong potential for significant clinical impact at some point in the next decade and offers significant patient benefit in comparison to current practices. The innovation may also have a significant benefit to the delivery/efficiency of health.

DeepMind's latest AI program is faster and better than humans in predicting the missing words in ancient Greek inscriptions. Google's artificial intelligence (AI) research arm, DeepMind, made an international name for itself in 2017 when its AlphaGo program consistently beat the world's best human Go players in the board game. Now, a new project borne out of the lab has proved that AI is also better than humans at learning words, including those long-forgotten ones dating back thousands of years. In a recent collaboration between DeepMind and the University of Oxford, a team of computer scientists trained a set of neural networks (algorithms) to recognize words inscribed on unearthed Greek stones that were between 1,500 and 2,600 years old. The neural networks were then asked to apply those learnings to predicting the missing characters or words on a new set of damaged relics.

WIRE)-- Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced Intelligent Clear-IQ Engine (AiCE) for the Aquilion PrecisionTM further expanding access to its new deep convolutional neural network (DCNN) image reconstruction technology. This technology, now available on both the Aquilion Precision and Aquilion ONE / GENESIS EditionTM premium CT systems, uses a deep learning algorithm to differentiate signal from noise so that it can suppress noise while enhancing signal, forging a new frontier for CT image reconstruction. Aquilion Precision - the world's first Ultra-High Resolution CT provides 2 times the resolution of conventional CT, revealing detail that is typically only seen in Cath labs. With AiCE, the system now enables clinicians to perform super-high resolution studies at doses equivalent to standard resolution CT (with traditional hybrid iterative reconstruction techniques). AiCE learns from the high image quality of Model Based Iterative Reconstruction (MBIR) to reconstruct CT images with improved high contrast spatial resolution*.

Machine learning methods based on artificial neural networks are fast becoming the norm with high end programing activities and work. Early restricted to research applications alone deep learning or hierarchical learning is fast being adopted by tech companies in day to day work. We have seen enormous use of machine learning algorithms that run in the backend today powering some of the most famous apps and software we use. It is because of them we are seeing intelligent systems that can predict effectively what will happen next. For instance you are typing and have activated auto keyboard it throws up potential words that you will use next.

If you've ever seen a self-driving car in the wild, you might wonder about that spinning cylinder on top of it. It's a "lidar sensor," and it's what allows the car to navigate the world. By sending out pulses of infrared light and measuring the time it takes for them to bounce off objects, the sensor creates a "point cloud" that builds a 3D snapshot of the car's surroundings. Making sense of raw point-cloud data is difficult, and before the age of machine learning it traditionally required highly trained engineers to tediously specify which qualities they wanted to capture by hand. But in a new series of papers out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), researchers show that they can use deep learning to automatically process point clouds for a wide range of 3D-imaging applications.

To grasp the idea of deep learning, imagine a family, with an infant and parents. The toddler points objects with his little finger and always says the word'cat.' As its parents are concerned about his education, they keep telling him'Yes, that is a cat' or'No, that is not a cat.' The infant persists in pointing objects but becomes more accurate with'cats.' The little kid, deep down, does not know why he can say it is a cat or not. He has just learned how to hierarchies complex features coming up with a cat by looking at the pet overall and continue to focus on details such as the tails or the nose before to make up his mind.

Called LOGAN, the deep neural network, i.e., a machine of sorts, can learn to transform the shapes of two different objects, for example, a chair and a table, in a natural way, without seeing any paired transforms between the shapes. All the machine had seen was a bunch of tables and a bunch of chairs, and it could automatically translate shapes between the two unpaired domains. LOGAN can also automatically perform both content and style transfers between two different types of shapes without any changes to its network architecture. The team of researchers behind LOGAN, from Simon Fraser University, Shenzhen University, and Tel Aviv University, are set to present their work at ACM SIGGRAPH Asia held Nov. 17 to 20 in Brisbane, Australia. SIGGRAPH Asia, now in its 12th year, attracts the most respected technical and creative people from around the world in computer graphics, animation, interactivity, gaming, and emerging technologies. "Shape transform is one of the most fundamental and frequently encountered problems in computer graphics and geometric modeling," says senior coauthor of the work, Hao (Richard) Zhang, professor of computing science at Simon Fraser University.

Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard.

In the fall of 2017, Sam Bowman, a computational linguist at New York University, figured that computers still weren't very good at understanding the written word. Sure, they had become decent at simulating that understanding in certain narrow domains, like automatic translation or sentiment analysis (for example, determining if a sentence sounds "mean or nice," he said). But Bowman wanted measurable evidence of the genuine article: bona fide, human-style reading comprehension in English. So he came up with a test. In an April 2018 paper coauthored with collaborators from the University of Washington and DeepMind, the Google-owned artificial intelligence company, Bowman introduced a battery of nine reading-comprehension tasks for computers called GLUE (General Language Understanding Evaluation). The test was designed as "a fairly representative sample of what the research community thought were interesting challenges," said Bowman, but also "pretty straightforward for humans." For example, one task asks whether a sentence is true based on information offered in a preceding sentence.