To improve evaluation efficiency, a team of researchers at Helmholtz Zentrum München and the University Hospital, LMU Munich, trained a deep neuronal network with almost 20,000 single cell images to classify them. Dr. med Karsten Spiekermann and Simone Schwarz from the Department of Medicine III, University Hospital, LMU Munich, used images which were extracted from blood smears of 100 patients suffering from the aggressive blood disease AML and 100 controls. The new AI-driven approach was then evaluated by comparing its performance with the accuracy of human experts. The result showed that the AI-driven solution is able to identify diagnostic blast cells at least as good as a trained cytologist expert. Deep learning algorithms for image processing require two things: first, an appropriate convolutional neural network architecture with hundreds of thousands of parameters; second, a sufficiently large amount of training data.

Slowly but surely, artificial intelligence is infiltrating almost every aspect of our lives. It is already busy in the background of many routine tasks, powering virtual assistants like Siri and Alexa, recommendations from Amazon and Netflix, and underpinning billions of Google searches each day. But as the technology matures, AI's impact will become more profound, and nowhere is that more apparent than in healthcare. Healthcare's data-heavy nature makes it an ideal candidate for the application of AI across multiple disciplines, from diagnosis and pathology to drug discovery and epidemiology. At the same time, the sensitivity of medical data raises fundamental questions around privacy and security.

At its AI Summit today in San Francisco, Intel touted a raft of AI training and inference hardware for deployments ranging from cloud to edge and designed to support organizations at various points of their AI journeys. The company revealed its Movidius Myriad Vision Processing Unit (VPU), codenamed "Keem Bay," for edge media, computer vision and inference applications. The company said the VPU, available the first half of 2020, incorporates "highly efficient architectural advances" and will deliver more than 10 times the inference performance of current Movidius VPUs and up to six times the power efficiency of competitor processors. Intel claimed that "early performance testing indicates that Keem Bay will offer more than 4x the inference throughput of Nvidia's similar-range TX2 SOC at one third less power, and nearly equivalent throughput of Nvidia's next higher class SOC, Nvidia Xavier, at one fifth the power. Keem Bay will also be supported by Intel's OpenVINO Toolkit for development of computer vision applications – "addresses a key pain point for developers -- allowing them to try, prototype and test AI solutions on a broad range of Intel processors before they buy hardware," according to Intel. It also will be incorporated into Intel's newly announced Dev Cloud for the Edge, launched today, designed to allow developers to test algorithms on any Intel hardware. Intel also offered the first live demonstrations and additional architectural details of its Nervana Neural Network Processors for training (NNP-T1000) and inference (NNP-I1000) ASICS for cloud and data center environments, first announced last August at the Hot Chips conference. In discussing the company's AI products roadmap (see above), Naveen Rao, corporate VP/GM of Intel's AI Products Group, said the combination of "the new Intel hardware will enable the industry to embrace much larger and more complex AI algorithms, expanding what can be achieved with AI in the cloud and data center, an edge server, or an IoT device." "With this next phase of AI, we're reaching a breaking point in terms of computational hardware and memory," said Rao. "Purpose-built hardware like Intel Nervana NNPs and Movidius Myriad VPUs are necessary to continue the incredible progress in AI.

We have now entered the era of artificial intelligence. In just a few years, the number of applications using AI has grown tremendously, from self-driving cars to recommendations from your favourite streaming provider. Almost every major research field is now using AI. Behind all this, there is one constant: the reliance, in one way or another, on deep learning. Thanks to its power and flexibility, this new subset of AI approach is now everywhere, even in ecology we show in'Applications for deep learning in ecology'.

Artificial Intelligence is still a nascent technology; much of the groundbreaking work moving the industry forward is done inside research labs. It's often from those labs that open source projects are started. That's why ODSC focuses on research at its conferences and invites the experts pushing the boundaries of AI to speak. Between the two upcoming conferences, researchers from more than 20 of the top research institutes in the country will deliver talks and lead trainings at ODSC West 2019. Institutes like Open AI, NASA's JPL, Google, MIT CSAIL, BAIR, The Turing Institute, and Max Planck - to name just a handful - are presenting at ODSC in 2019, helping us bring our community to the leading edge of AI.

Here's a look at industry specific companies that utilise various forms of artificial intelligence to solve some really interesting and particular problems for different markets. If you want to be included in any of the list don't forget to comment below. If you use Apple News or similar simple visit the site on a web browser to make comments. Imagia -- helps detect changes in cancer early Kuznech -- computer vision products range Lunit Inc. -- a range of medical imaging software Zebra Medical Vision -- medical imaging to help physicians and practitioners Aerial Achron -- automated UAV operations Airware -- drones for industrial purposes Alive.ai Developers, Studios and Consultants (only a few listed) Aitia Amplify Applied AI Blindspot Solutions Cogent Crossing Minds DSP Expert Systems Explosion Minds.ai

CNR-IAC, Rome, Italy Abstract Turbulence, the ubiquitous and chaotic state of fluid motions, is characterized by strong and statistically nontrivial fluctuations of the velocity field, over a wide range of length-and timescales, and it can be quantitatively described only in terms of statistical averages. Strong non-stationarities hinder the possibility to achieve statistical convergence, making it impossible to define the turbulence intensity and, in particular, its basic dimensionless estimator, the Reynolds number. Here we show that by employing Deep Neural Networks (DNN) we can accurately estimate the Reynolds number within 15% accuracy, from a statistical sample as small as two large-scale eddy-turnover times. In contrast, physics-based statistical estimators are limited by the rate of convergence of the central limit theorem, and provide, for the same statistical sample, an error at least 100 times larger. Our findings open up new perspectives in the possibility to quantitatively define and, therefore, study highly non-stationary turbulent flows as ordinarily found in nature as well as in industrial processes. Turbulence is characterized by complex statistics of velocity fluctuations correlated over a wide range of temporal-and spatial-scales.

Geophysics Department, Stanford University, Stanford, California, USA In this study we develop a single-station deep-learning approach for fast and reliable estimation of earthquake magnitude directly from raw waveforms. We design a regressor composed of convolutional and recurrent neural networks that is not sensitive to the data normalization, hence waveform amplitude information can be utilized during the training. Our network can predict earthquake magnitudes with an average error close to zero and standard deviation of 0.2 based on single-station waveforms without instrument response correction. We test the network for both local and duration magnitude scales and show a station-based learning can be an effective approach for improving the performance. The proposed approach has a variety of potential applications from routine earthquake monitoring to early warning systems.

--The growing role artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of vision-aided wireless communications, which aims at leveraging visual sensory information in tackling wireless communication problems. Like any new research direction driven by machine learning, obtaining a development dataset poses the first and most important challenge to vision-aided wireless communications. It is developed to be a parametric, systematic, and scalable data generation framework. It utilizes advanced 3D-modeling and ray-tracing softwares to generate high-fidelity synthetic wireless and vision data samples for the same scenes. The result is a framework that does not only offer a way to generate training and testing datasets but helps provide a common ground on which the quality of different machine learning-powered solutions could be assessed. Can we use vision to help wireless communication?

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering point cloud of each one. For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature. A three-layer CNN model is created to classify the behavior for each patient. The tracking and point clouds detection algorithm was also implemented on an mmWave radar hardware platform with an embedded graphics processing unit (GPU) board to collect Doppler pattern and run the CNN model. A training dataset of six types of behavior were collected, over a long duration, to train the model using Adam optimizer with an objective to minimize cross-entropy loss function. Lastly, the system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient's behavior in a two-patient scenario.