Artificial intelligence in medicine is currently in the infancy stage of development, but in 10 to 20 years, the capability of the technology will catch up to the hype, a speaker said at Octane's virtual Ophthalmology Technology Summit. "In the future, ophthalmologists will have to learn about AI, or you'll be vulnerable to ophthalmologists who actually know AI," keynote speaker Anthony Chang, MD, MBA, MPH, MS, chief intelligence and innovation officer at Children's Hospital of Orange County, said at the meeting. The essence of AI in medicine is moving away from evidence-based medicine to achieve precision medicine and population health. A huge information and knowledge gap must be made up by intelligence-based medicine rather than evidence-based medicine, Chang said. "This is important when we think about precision medicine, when we have so many layers of information and data that need to be gathered to make the best decision for each individual patient," he said.

Huyn Kim is the CEO and Co-Founder of Superb AI, a company that provides a new generation machine learning data platform to AI teams so that they can build better AI in less time. The Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers and data annotators create efficient training data workflows. What initially attracted you to the field of AI, Data Science and Robotics? As an undergraduate majoring in Biomedical Engineering at Duke, I was passionate about genetics and how we can engineer our DNA to cure diseases or create genetically engineered organisms. I remember one wet-lab experiment distinctly that kept failing for like 6 months straight. The most frustrating part of it was that there was a lot of repetitive manual work, and in hindsight that was probably the root of some many potential errors.

Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images. This technique, in combination with citizen science, is expected to yield further discoveries in the future. A research group, consisting of astronomers mainly from the National Astronomical Observatory of Japan (NAOJ), applied a deep-learning technique, a type of AI, to classify galaxies in a large dataset of images obtained with the Subaru Telescope. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological classification.

Researchers at the University of Notre Dame are developing a new technique using artificial intelligence (AI) that would improve CT screening to more quickly identify patients with the coronavirus. The new technique will reduce the burden on the radiologists tasked with screening each image. Testing challenges have led to an influx of patients hospitalized with COVID-19 requiring CT scans which have revealed visual signs of the disease, including ground glass opacities, a condition that consists of abnormal lesions, presenting as a haziness on images of the lungs. "Most patients with coronavirus show signs of COVID-related pneumonia on a chest CT but with the large number of suspected cases, radiologists are working overtime to screen them all," said Yiyu Shi, associate professor in the Department of Computer Science and Engineering at Notre Dame and the lead researcher on the project. "We have shown that we can use deep learning -- a field of AI -- to identify those signs, drastically speeding up the screening process and reducing the burden on radiologists."

Artificial intelligence and machine learning bring exponential changes to the way physical security processes input from video cameras and sensors. Data is the fuel that feeds AI, and cameras provide massive amounts of video for review. AI's deep learning algorithms automatically detect differences between human and vehicle movements as opposed to animals, blowing leaves or reflections of light. One result is a tremendous reduction in false alarms and potentially related fines. We view AI as an added layer of security, helping, not replacing, humans to do a better job of securing people and assets.

Every package you'll see is free and open source software. Thank you to all the folks who create, support, and maintain these projects! If you're interested in learning about contributing fixes to open source projects, here's a good guide. And If you're interested in the foundations that support these projects, I wrote an overview here. Pandas is a workhorse to help you understand and manipulate your data.

A new language model, OpenAI's GPT-3, is making waves for its ability to mimic writing, but it falls short on common sense. Some experts think an emerging technique called neuro-symbolic AI is the answer.

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Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images. This technique, in combination with citizen science, is expected to yield further discoveries in the future. A research group, consisting of astronomers mainly from the National Astronomical Observatory of Japan (NAOJ), applied a deep-learning technique, a type of AI, to classify galaxies in a large dataset of images obtained with the Subaru Telescope. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological classification.