Imagine, for a moment, the near future Amazon dreams of. Every morning, you are gently awakened by the Amazon Halo Rise. From its perch on your nightstand, the round device has spent the night monitoring the movements of your body, the light in your room, and the space's temperature and humidity. At the optimal moment in your sleep cycle, as calculated by a proprietary algorithm, the device's light gradually brightens to mimic the natural warm hue of sunrise. Your Amazon Echo, plugged in somewhere nearby, automatically starts playing your favorite music as part of your wake-up routine.

BROOKLYN, New York, Tuesday, September 3, 2019 - As artificial intelligence and data science enable computer tools to make predictions previously made by skilled humans, a different knowledge gap looms: These black-box tools often offer highly trained medical personnel little understanding of their inner workings. Equally little understood: how deploying these tools affects experts' work practices, perceptions of the value of work, and the expert-patient relationship. Researchers from New York University and Georgia Tech are conducting foundational research to understand and improve expert work in an age of data-intensive enhanced cognition, especially in healthcare, where new technologies are rapidly being deployed. The National Science Foundation recently awarded the team $2 million for the four-year project, which is expected to transform the future of expert work through a combined redesign of technology, workflow, and interactions. "Better understanding of how new technologies impact healthcare expert work will lead to more effective use of healthcare technologies, a healthier and better-informed population, and the more efficient use of human capabilities in restructured healthcare occupations," said NYU Tandon School of Engineering Professor of Technology Management and Innovation Oded Nov, the principal investigator.

This week in Las Vegas, Amazon rolled out dozens of new features, upgrades, and new products at AWS re:Invent. Here's a quick roundup of news out of the annual conference that may matter to members of the AI community. A disproportionate amount of money is spent on inference versus training when it comes to AI models, AWS CEO Andy Jassy said, and GPUs can be terribly inefficient. To address these issues, Amazon custom-designed a chip named Inferentia due out next year and created Elastic Inference, a service that identifies parts of a neural network that can benefit from acceleration. To speed up training of AI models, Amazon introduced AWS-Optimized TensorFlow, which can train a model with the ResNet-50 benchmark in 14 minutes.

IMAGE: The image shows how an AI tool analyzes a slice of cancerous tissue to create a map that tells apart two lung cancer types, with squamous cell carcinoma in red,... view more A new computer program can analyze images of patients' lung tumors, specify cancer types, and even identify altered genes driving abnormal cell growth, a new study shows. Led by researchers at NYU School of Medicine and published online in Nature Medicine, the study found that a type of artificial intelligence (AI), or "machine learning" program, could distinguish with 97 percent accuracy between adenocarcinoma and squamous cell carcinoma--two lung cancer types that experienced pathologists at times struggle to parse without confirmatory tests. The AI tool was also able to determine whether abnormal versions of 6 genes linked to lung cancer--including EGFR, KRAS, and TP53--were present in cells, with an accuracy that ranged from 73 to 86 percent depending on the gene. Such genetic changes or mutations often cause the abnormal growth seen in cancer, but can also change a cell's shape and interactions with its surroundings, providing visual clues for automated analysis. Determining which genes are changed in each tumor has become vital with the increased use of targeted therapies that work only against cancer cells with specific mutations, researchers say.

Social media giant Facebook and NYU School of Medicine's Department of Radiology have collaborated to examine how artificial intelligence can boost the speed of MRI scans. The project, dubbed fastMRI, will focus first on altering how MRI machines operate. Usually, scanners gather raw numerical data and turn it into cross-sectional images of internal body structures. When more data has to be captured, the scan takes longer. Overall, the scan can take anywhere from 15 minutes to more than an hour.

Evidence that Facebook has ambitions beyond its core business of social networking emerged this week, with technology development that stands to benefit society as a whole. Facebook has collaborated with NYU School of Medicine's Department of Radiology to launch fastMRI, a new research project that will investigate the use of artificial intelligence (AI) to make magnetic resonance imaging (MRI) scans up to 10 times faster. If the project is successful, it will make MRI technology available to more people, expanding access to this key diagnostic tool, according to an announcement on Code, Facebook's development blog. MRI scanners provide doctors and patients with images that typically show a greater level of detail in soft tissues -- such as organs and blood vessels -- than is captured by other forms of medical imaging. But they are relatively slow, taking anywhere from 15 minutes to over an hour, compared with less than a second or up to a minute, respectively, for X-ray and CT scans.

MRI scans may some day be available for a lot more people in need. Facebook on Monday said it's teaming up with NYU School of Medicine's Department of Radiology to launch "fastMRI," a collaborative research project that aims to use artificial intelligence to make MRI -- magnetic resonance imaging -- 10 times faster. Doctors and radiologists use MRI scanners to produce images that show in detail a patient's organs, blood vessels, bones, soft issues and such, which helps doctors diagnose problems. However, completing a MRI scan can take from 15 minutes to over an hour, according to Facebook's blog post. It also limits how many scans the hospital can do in a day.

AI research group DeepMind created the new tech that is able to spot key signs of eye disease just as well as the world's top doctors. Facebook is working with the NYU School of Medicine to shorten the length of time patients must spend in MRI scanners. Facebook's Artificial Intelligence Research (FAIR) group and the medical school's radiology department are investigating whether artificial intelligence can make magnetic resonance imaging scans up to 10 times faster. Such a development would not only reduce patient discomfort – scans can last 15 minutes to an hour – but also free up MRIs for more patients. "If this effort is successful, it will make MRI technology available to more people, expanding access to this key diagnostic tool," the research team says in a blog post on the Facebook website.

Magnetic resonance imaging is an invaluable tool in the medical field, but it's also a slow and cumbersome process. It may take fifteen minutes or an hour to complete a scan, during which time the patient, perhaps a child or someone in serious pain, must sit perfectly still. NYU has been working on a way to accelerate this process, and is now collaborating with Facebook with the goal of cutting down MRI durations by 90 percent by applying AI-based imaging tools. It's important at the outset to distinguish this effort from other common uses of AI in the medical imaging field. An X-ray, or indeed an MRI scan, once completed, could be inspected by an object recognition system watching for abnormalities, saving time for doctors and maybe even catching something they might have missed.