A woman uses an ATM with facial recognition technology during the presentation of the new service by CaixaBank in Barcelona on February 14, 2019. So, I just got the new iPhone 11 Pro. I have to say, I pretty much love the facial recognition unlock feature. And no, Apple is not paying me to say that. Prior, I was a facial recognition skeptic.

Aviso, the pioneer in AI-powered guided selling, announced reaching a slew of corporate milestones in Q3 2019. These include further customer momentum building on recent funding by Storm Ventures, winning recognition as a major category leader, making key new executive hires, and expanding both in the US and internationally. Aviso's headcount has increased by 30% under new CEO Trevor Templar, and revenues are up over 100% year on year. The company expanded with both new and existing clients in Q3, including major Fortune 500 corporations such as Honeywell and MongoDB. Aviso also won recognition as an industry leader for the third year running in G2 Crowd's annual tech-sector power ranking.

Clustering tries to find structure in data by creating groupings of data with similar characteristics. The most famous clustering algorithm is likely K-means, but there are a large number of ways to cluster observations. Hierarchical clustering is an alternative class of clustering algorithms that produce 1 to n clusters, where n is the number of observations in the data set. As you go down the hierarchy from 1 cluster (contains all the data) to n clusters (each observation is its own cluster), the clusters become more and more similar (almost always). There are two types of hierarchical clustering: divisive (top-down) and agglomerative (bottom-up).

NASA's mission of exploration requires leveraging new ways to utilize and learn from the unprecedented amount of data that space-based observation platforms generate. New capabilities are needed, ranging from onboard autonomy for robotic spacecraft to techniques for understanding the world and universe where we live. Artificial Intelligence (AI) and data science are rapidly becoming integral to NASA's future to drive automation and interpretation. AI is a collection of advanced technologies that allow machines to think and act, through sensing, comprehending, interacting, and learning. AI's foundations lie at the intersection of several traditional fields - Philosophy, Mathematics, Economics, Neuroscience, Psychology, and Computer Science.

Imitation learning has allowed recent advances in learning robotic manipulation tasks but has been limited due to the scarcity of high quality demonstrations to learn from. RoboTurk is a system that help solve this problem by enabling the rapid crowdsourcing of high-quality demonstrations. This allows the creation of large datasets for manipulation tasks that we show improves the quality of imitation learning policies. We have recently extended the RoboTurk platform to work with real robots and presented this work at IROS 2019. Click the link above to learn more.

Eisai Inc., the U.S. subsidiary of Eisai Co., Ltd., today announced the launch of Ella the Jellyfish, the first Amazon Alexa skill designed for those affected by Lennox-Gastaut Syndrome (LGS). Available free of charge, the Alexa skill was created with input from children living with LGS, a rare and severe form of childhood-onset epilepsy, their families and caregivers. When a child has a debilitating disease, everyday life for them, their families and caregivers can be challenging. LGS can be characterized by frequent and unpredictable seizures, limited speech and mobility, cognitive impairment, and developmental delays. Approximately 70 percent of patients with LGS will show cognitive impairment at diagnosis and more than 50 percent suffer behavioral issues including hyperactivity, sleep disturbances, aggression, and autistic symptoms.

Marlborough medical device maker Hologic announced Tuesday it has received U.S. Food & Drug Administration approval for its 3DQuorum Imaging Technology to reduces image interpretation time. The company said when combined with its high-resolution imaging technology, this new technology reduces the number of images needing to be reviewed without compromising image quality or accuracy through using artificial intelligence to find the best images. The number of images to be reviewed is reduced by 66%, according to Hologic, saving an average of one hour per eight hours of image interpretation time. The technology is available for use with existing and future Hologic 3D mammography systems.

This article originally appeared on aedit.com From smart speakers in the operating room to virtual diagnosis and treatment plans, The AEDITION looks at a few of the ways artificial intelligence is shaking up the medical industry. We may not be at the point where you overhear your surgeon saying, "Hey, Google, pass the scalpel," but artificial intelligence (AI) is gradually making its way into the healthcare industry and, by extension, dermatology and plastic surgery practices, too. Even in its limited use, AI is already helping providers offer their patients better care -- whether it's pre-op, in the OR, or during the recovery process. Here are three ways artificial intelligence is shaking up medicine.

Researchers have used a deep-learning algorithm to detect lung cancer accurately from computed tomography scans. The results of the study indicate that artificial intelligence can outperform human evaluation of these scans. The condition is the leading cause of cancer-related death in the U.S., and early detection is crucial for both stopping the spread of tumors and improving patient outcomes. As an alternative to chest X-rays, healthcare professionals have recently been using computed tomography (CT) scans to screen for lung cancer. In fact, some scientists argue that CT scans are superior to X-rays for lung cancer detection, and research has shown that low-dose CT (LDCT) in particular has reduced lung cancer deaths by 20%.

UCLA researchers have developed an artificial intelligence system that could help pathologists read biopsies more accurately and to better detect and diagnose breast cancer. The new system, described in a study published today in JAMA Network Open, helps interpret medical images used to diagnose breast cancer that can be difficult for the human eye to classify, and it does so nearly as accurately or better as experienced pathologists. "It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments," said Dr. Joann Elmore, the study's senior author and a professor of medicine at the David Geffen School of Medicine at UCLA. A 2015 study led by Elmore found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year. That earlier research revealed that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (a noninvasive type of breast cancer), and that incorrect diagnoses were given in about half of the biopsy cases of breast atypia (abnormal cells that are associated with a higher risk for breast cancer).