IBM Research, with the help of the University of Texas Austin and the University of Maryland, has created a technology, called BlockDrop, that promises to speed convolutional neural network operations without any loss of fidelity. This could further excel the use of neural nets, particularly in places with limited computing capability. Increase in accuracy level have been accompanied by increasingly complex and deep network architectures. This presents a problem for domains where fast inference is essential, particularly in delay-sensitive and realtime scenarios such as autonomous driving, robotic navigation, or user-interactive applications on mobile devices. Further research results show regularization techniques for fully connected layers, is less effective for convolutional layers, as activation units in these layers are spatially correlated and information can still flow through convolutional networks despite dropout.
Testing for pathogens is a critical component of maintaining public health and safety. Having a method to rapidly and reliably test for harmful germs is essential for diagnosing diseases, maintaining clean drinking water, regulating food safety, conducting scientific research, and other important functions of modern society. In recent research, scientists from University of California, Los Angeles (UCLA), have demonstrated that artificial intelligence (AI) can detect harmful bacteria from a water sample up to 12 hours faster than the current gold-standard Environmental Protection Agency (EPA) methods. In a new study published yesterday in Light: Science and Applications, the researchers created a time-lapse imaging platform that uses two separate deep neural networks (DNNs) for the detection and classification of bacteria. The team tested the high-throughput bacterial colony growth detection and classification system using water suspensions with added coliform bacteria of E. coli (including chlorine-stressed E. coli), K. pneumoniae and K. aerogenes, grown on chromogenic agar as the culture medium.
MIM Software Inc., a leading global provider of medical imaging software, announced it has received 510(k) clearance from the US Food and Drug Administration (FDA) for its deep learning auto-contouring software, Contour ProtégéAI . Contour ProtégéAI is an auto-contouring solution that seamlessly integrates into any department's workflow and can be rapidly implemented into virtually any environment. User feedback and a determination to continuously improve auto-segmentation were key drivers in developing the product. "Our customers are under continual pressure to improve their practices while facing escalating time constraints," said Andrew Nelson, Chief Executive Officer of MIM Software Inc. "Our deep learning auto-segmentation product, Contour ProtégéAI, will play a critical role in reducing the burden of contouring." Auto-contouring is an ideal use case for deep learning algorithms because it is one of the most time-consuming clinical tasks.
Everyone despises CAPTCHAs (humans, since bots do not have emotions) -- Those annoying images containing hard to read the text, which you have to type in before you can access or do "something" online. CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) were developed to prevent automatized programs from being mischievous (filling out online forms, accessing restricted files, accessing a website an incredible amount of times, and others) on the world wide web, by verifying that the end-user is "human" and not a bot. Nevertheless, several attacks on CAPTCHAs have been proposed in the past, but none has been as accurate and fast as the machine learning algorithm presented by a group of researchers from Lancaster University, Northwest University, and Peking University showed below. One of the first known people to break CAPTCHAs was Adrian Rosebrock, who, in his book "Deep Learning for Computer Vision with Python,"  Adrian goes through how he bypassed the CAPTCHA systems on the E-ZPass New York website using machine learning, where he used deep learning to train his model by downloading a large image dataset of CAPTCHA examples in order to break the CAPTCHA systems. The main difference between Adrian's solution and the solution from the research scientists from Lancaster, Northwest, and Peking, is that the researchers did not need to download a large dataset of images to break the CAPTCHAs system, au contraire, they used the concept of a generative adversarial network (GAN) to create synthesized CAPTCHAs, along with a small dataset of real CAPTCHAs to create an extremely fast and accurate CAPTCHA solver.
MIM Software Inc., a leading global provider of medical imaging software, announced today it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its deep learning auto-contouring software, Contour ProtégéAI . Contour ProtégéAI is an auto-contouring solution that seamlessly integrates into any department's workflow and can be rapidly implemented into virtually any environment. User feedback and a determination to continuously improve auto-segmentation were key drivers in developing the product. "Our customers are under continual pressure to improve their practices while facing escalating time constraints,'' said Andrew Nelson, Chief Executive Officer of MIM Software Inc. "Our deep learning auto-segmentation product, Contour ProtégéAI, will play a critical role in reducing the burden of contouring." Auto-contouring is an ideal use case for deep learning algorithms because it is one of the most time-consuming clinical tasks.
AI startup Trefos is helping foresters see the wood for the trees. Using custom lidar and camera-mounted drones, the Philadelphia-based company collects data for high-resolution, 3D forest maps. These metrics allow government agencies and the forestry industry to estimate the volume of timber and biomass in an area of forest, as well as the amount of carbon stored in the trees. With this unprecedented detail, foresters can make more informed decisions when, for example, evaluating the need for controlled burns to clear biomass and reduce the risk of wildfires. "Forests are often very dense, with a very repetitive layout," said Steven Chen, founder and CEO of the startup, a member of the NVIDIA Inception program, which supports startups from product development to deployment. "We can use deep learning algorithms to detect trees, isolate them from the surrounding branches and vines, and use those as landmarks."
Want to be a part of an elite team where our innovative technical solutions are delivered to customers that advance the state of the art while addressing long-term problems of importance to national security? At our Leidos' Multi-Spectrum Warfare Research and Analytics Systems (MSWRAS) Division, an organization in the Leidos Innovation Center (LInC), we are looking for you, our next Scientist who specializes in remote sensing data analytics. Join our team of Ph.D. level peers in designing and developing advanced technology-based solutions for contract research and development projects working in our Arlington, VA office. Fun roles you will have in this job: Describe instances of successful, proven, and demonstrable experience contributing to the technical work as part of cross-discipline teams in the development and integration of software-based solutions for competitive, contract-based applied research programs Work with teams composed of members from industry, small businesses, and academic-based researchers and should have experience working on projects focused on multiple technical fields such as machine learning, artificial intelligence, engineering, and software development and integration Describe how the work products to which they contributed had solved customers' problems in such domains as energy, health, and national security or in the commercial sector Work within the MSWRAS Division and across the LInC, performing basic and applied contract research and development projects both leading and working under the guidance of senior scientists and engineers. Processing, interpreting and analyzing large volumes of data collected by remote sensing platforms but may also include other types of phenomenological data such as field measurements, or weather data Independently design and undertake new research as well as partner in a team environment across organizations Contribute to the development of creative and innovative R&D approaches to solving major remote sensing analytics challenges and work with potential sponsors (customers or internal champions) to secure funding for new research efforts based on those topics Contribute to the productivity of teams composed of fellow researchers, data scientists, data engineers, and software engineers to execute complex R&D programs Under the guidance of a senior scientist or engineer, design and develop or integrate secure and scalable applications that are part of broader solutions, that are applicable across multiple domains.
Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.
CV is a nascent market but it contains a plethora of both big technology companies and disruptors. Technology players with large sets of visual data are leading the pack in CV, with Chinese and US tech giants dominating each segment of the value chain. Google has been at the forefront of CV applications since 2012. Over the years the company has hired several ML experts. In 2014 it acquired the deep learning start-up DeepMind. Google's biggest asset is its wealth of customer data provided by their search business and YouTube.
This opinion piece is inspired by the old Danish proverb: "Making predictions is hard, especially about the future" (1). As every reader knows, the momentum of artificial intelligence (AI) and the eventual implementation of deep learning models seem assured. Some pundits have gone considerably further, however, and predicted a sweeping AI takeover of radiology. Although many radiologists support AI and believe it will enable greater efficiency, a recent study of medical students found very different reactions (2). While such doomsday predictions are understandably attention-grabbing, they are highly unlikely, at least in the short term.