The protective ozone layer in the upper atmosphere could be restored within several decades, scientists said Monday, as recent rogue emissions of ozone-depleting chemicals from China have been largely eliminated. From a report: In a United Nations-sponsored assessment, the scientists said that global emissions of CFC-11, a banned chemical that has been used as a refrigerant and in insulating foams, had declined since 2018 after increasing for several years. CFC-11 and similar chemicals, collectively called chlorofluorocarbons, destroy ozone, which blocks ultraviolet radiation from the sun that can cause skin cancer and otherwise harm people and other living things. The scientists said that if current policies remained in place, ozone levels between the polar regions should reach pre-1980 levels by 2040. Ozone holes, or regions of greater depletion that appear regularly near the South Pole and, less frequently, near the North Pole, should also recover, by 2045 in the Arctic and about 2066 in Antarctica.

"Things continue to trend in the right direction," said Stephen A. Montzka, a research chemist with the National Oceanic and Atmospheric Administration and one of the report's authors. Dr. Montzka led a 2018 study that alerted the world that CFC-11 emissions had been increasing since 2012 and that they appeared to come from East Asia. Investigations by The New York Times and others strongly suggested that small factories in Eastern China disregarding the global ban were the source. The new emissions had threatened to undermine the Montreal Protocol, the treaty negotiated in the 1980s to phase out the use of chlorofluorocarbons in favor of more benign chemicals after it was discovered that chlorofluorocarbons were depleting atmospheric ozone.

An anonymous reader shares a report: Artificial intelligence is now being used in an ever-expanding array of products: cars that drive themselves; robots that identify and eradicate weeds; computers able to distinguish dangerous skin cancers from benign moles; and smart locks, thermostats, speakers and digital assistants that are bringing the technology into homes. At Georgia Tech, students interact with digital teaching assistants made possible by AI for an online course in machine learning.

The expanding applications for AI have also created a shortage of qualified workers in the field. Although schools across the country are adding classes, increasing enrollment and developing new programs to accommodate student demand, there are too few potential employees with training or experience in AI. That has big consequences. Too few AI-trained job-seekers has slowed hiring and impeded growth at some companies, recruiters and would-be employers told Reuters. It may also be delaying broader adoption of a technology that some economists say could spur U.S. economic growth by boosting productivity, currently growing at only about half its pre-crisis pace.

[...] U.S. government data does not track job openings or hires in artificial intelligence specifically, but online job postings tracked by jobsites including Indeed, Ziprecruiter and Glassdoor show job openings for AI-related positions are surging. AI job postings as a percentage of overall job postings at Indeed nearly doubled in the past two years, according to data provided by the company. Searches on Indeed for AI jobs, meanwhile increased just 15 percent.

An anonymous reader quotes a report from ExtremeTech: Remember how that Google neural net learned to tell the difference between dogs and cats? It's helping catch skin cancer now, thanks to some scientists at Stanford who trained it up and then loosed it on a huge set of high-quality diagnostic images. During recent tests, the algorithm performed just as well as almost two dozen veteran dermatologists in deciding whether a lesion needed further medical attention. The algorithm is called a deep convolutional neural net. It started out in development as Google Brain, using their prodigious computing capacity to power the algorithm's decision-making capabilities. When the Stanford collaboration began, the neural net was already able to identify 1.28 million images of things from about a thousand different categories. But the researchers needed it to know a malignant carcinoma from a benign seborrheic keratosis. Dermatologists often use an instrument called a dermoscope to closely examine a patient's skin. This provides a roughly consistent level of magnification and a pretty uniform perspective in images taken by medical professionals. Many of the images the researchers gathered from the Internet weren't taken in such a controlled setting, so they varied in terms of angle, zoom, and lighting. But in the end, the researchers amassed about 130,000 images of skin lesions representing over 2,000 different diseases. They used that dataset to create a library of images, which they fed to the algorithm as raw pixels, each pixel labeled with additional data about the disease depicted. Then they asked the algorithm to suss out the patterns: to find the rules that define the appearance of the disease as it spreads through tissue. The researchers tested the algorithm's performance against the diagnoses of 21 dermatologists from the Stanford medical school, on three critical diagnostic tasks: keratinocyte carcinoma classification, melanoma classification, and melanoma classification when viewed using dermoscopy. In their final tests, the team used only high-quality, biopsy-confirmed images of malignant melanomas and malignant carcinomas. When presented with the same image of a lesion and asked whether they would "proceed with biopsy or treatment, or reassure the patient," the algorithm scored 91% as well as the doctors, in terms of sensitivity (catching all the cancerous lesions) and sensitivity (not getting false positives).