"The long-term goal and true potential of AI is to replicate the complexity of human thinking at the macro level, and then surpass it to solve complex problems--problems both well-documented and currently unimaginable in nature."1 Skin cancer has reached epidemic proportions in much of the world. A simple test is needed to perform initial screening on a wide scale to encourage individuals to seek treatment when necessary. Doctor Hazel, a skin cancer screening service powered by artificial intelligence (AI) that operates in real time, relies on an extensive library of images to distinguish between skin cancer and benign lesions, making it easier for people to seek professional medical advice. Hackathons have proven to be a successful way to channel energy and technical expertise into solving very specific problems and generating bright, new ideas for applied technology.

Corrindus has raised $118 million and installed 33 systems to date. Playground Global led the round, with participation from Sony Innovation Fund and existing investor Robotics Hub. Agility's two-legged Cassie robot is already deployed in 6 research institutes. Agility is planning on using Cassie for everything from deliveries to facility inspections to hazardous search-and-rescue operations.

FOSTER CITY, Calif., March 27, 2018 -- GridGain Systems, provider of enterprise-grade in-memory computing solutions based on Apache Ignite, today announced the immediate availability of GridGain Professional Edition 2.4, a fully supported version of Apache Ignite 2.4. GridGain Professional Edition 2.4 now includes a Continuous Learning Framework, which includes machine learning and a multilayer perceptron (MLP) neural network that enable companies to run machine and deep learning algorithms against their petabyte-scale operational datasets in real-time. Companies can now build and continuously update models at in-memory speeds and with massive horizontal scalability. GridGain Professional Edition 2.4 also enhances the performance of Apache Spark by introducing an API for Apache Spark DataFrames, adding to the existing support for Spark RDDs. GridGain Continuous Learning Framework GridGain Professional Edition 2.4 now includes the first fully supported release of the Apache Ignite integrated machine learning and multilayer perceptron features, making continuous learning using machine learning and deep learning available directly in GridGain.

Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings. That said, deep learning is rapidly advancing, and it's a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology.

Researchers at Massachusetts General Hospital have developed a new medical imaging technique based on artificial intelligence designed to enable clinicians to acquire higher quality images without having to collect additional data. The AI technique--called AUTOMAP (automated transform by manifold approximation)--produces high-quality images in less time with MRI or with lower radiation doses with X-ray, CT and PET. And, as a result of its very quick processing speed, the approach could help in making real-time clinical decisions about imaging protocols while the patient is in the scanner, according to MGH researchers. A description of the technique, published last week in the journal Nature, shows dramatic differences between images reconstructed from the same data with conventional approaches compared to AUTOMAP. "What we did was condition a neural network through machine learning to recognize what makes an image an image," says Matthew Rosen, director of the Low Field MRI and Hyperpolarized Media Laboratory and co-director of the Center for Machine Learning at MGH's Athinoula A. Martinos Center for Biomedical Imaging.

Artificial intelligence is currently a white-hot buzzword in healthcare. Both in everyday conversations and in large context settings like HIMSS, the term is guaranteed to generate interest. But what can it actually achieve in the world of healthcare? This question will be a topic of discussion at the upcoming MedCity INVEST conference in Chicago. IDx chairman and CEO Gary Seamans, who will be speaking at the event, commented on the popular nature of the technology.

Artificial intelligence originally aspired to replace doctors. Researchers imagined robots that could ask you questions, run the answers through an algorithm that would learn with experience and tell whether you had the flu or a cold. However, those promises largely failed, as artificial intelligent algorithms were too rudimentary to perform those functions. Particularly tricky was the variability between people, which caused basic machine learning algorithms to miss the patterns. Eventually though, a subset of AI called deep learning became sensitive enough to recognize speech from voice data.

It was the last question of the night and it hushed the entire room. An entrepreneur expressed his aggravation about the FDA's antiquated regulatory environment for AI-enabled devices to Dr. Joel Stein of Columbia University. Stein a leader in rehabilitative robotic medicine, sympathized with the startup knowing full well that tomorrow's exoskeletons will rely heavily on machine intelligence. Nodding her head in agreement, Kate Merton of JLabs shared the sentiment. Her employer, Johnson & Johnson, is partnered with Google to revolutionize the operating room through embedded deep learning systems. To better understand the frustration expressed at RobotLab, a review of the policies of the Food & Drug Administration (FDA) relative to medical devices and software is required.

The Global Artificial Intelligence in Agriculture (AIA) Market is expected to grow at a significant CAGR of 24.3% during the forecast period. The factors driving the growth of the global AIA market are rising adoption of information management systems (IMS), automated irrigation, increasing crop productivity by implementing deep learning techniques, and increasing global population. Furthermore, growing trend of precision farming and increasing adoption of smart sensors are also fueling the demand of the global AIA market. Replacement of human labor is also expected to overcome by AIA, to minimize scarcity of physical labor. However, the high cost of collecting data of agricultural land is a major restraint of the AIA market growth.

High blood potassium levels constitute a condition known as hyperkalemia. It can be related to a number of causes, including kidney disease, dehydration, injury and diabetes and hyperkalemia can affect heartbeat rhythm. Yesterday, during the American College of Cardiology conference, AliveCor presented work done with the Mayo Clinic showing that its technology can detect hyperkalemia through EKGs. The researchers used electrocardiogram data collected from 709,000 patients over the course of 23 years, which included 2.1 million EKGs and 4 million blood potassium measurements. Two-thirds of that data were used to train a neural network to detect hyperkalemia through EKG readings.