Last year's Castle Fire in California's Sierra Nevada is estimated to have killed more than 10 percent of the world's giant sequoias, the tallest trees on earth. Sequoias can live through many fires over life spans that last thousands of years; their bark is fire-resistant and they rely on fire to reproduce. But as climate change intensifies, wildfires are growing larger and more intense. According to state officials, six of the seven largest wildfires in California history took place roughly within the past year. To help restore fire-ravaged forests and temper the effects of climate change, a handful of young companies want to scatter seeds from drones.
Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure-;which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge. In the Nature Communications study, NYU researchers and collaborators in the U.S. and Taiwan tackled this challenge using machine learning, a type of artificial intelligence used to detect patterns in data.
Many experts believe that the Earth is quickly approaching various "tipping points" beyond which the effects of climate change could become irreversible. Researchers at the University of Waterloo in Canada are currently developing an artificial intelligence algorithm that could assess climate change tipping points to act as an early warning system. Some tipping points that are often associated with irreversible climate change include: the melting of the Arctic permafrost, leading to the release of huge amounts of methane in the atmosphere and rapid heating; the disintegration of ice sheets, causing sea-level changes; or the breakdown of oceanic current systems, spurring severe fluctuations in weather patterns. In order to detect in advance the possible emergence of such tipping points, scientists have created a deep-learning algorithm and trained it on what they characterize as a "universe of possible tipping points," including over half a million of possible models. Additionally, they have also tested it on specific real-world tipping points in different systems, including historical climate-related changes.
Powered by artificial intelligence, a new lung cancer blood test developed at Johns Hopkins, combined with other metrics, correctly identified 94% of cancer cases in almost 800 patients. The lung cancer blood test, published in Nature Communications, searches for tiny fragments of DNA released by the tumor cells. The AI looks for patterns in this shattered DNA, rather than looking for specific pieces of cancer DNA like other blood tests in development, New Atlas explained. Lung cancer kills the most people in the world, the authors note, "largely due to the late stage at diagnosis where treatments are less effective than at earlier stages" -- and lung cancer rates are increasing, worldwide. "We believe that a blood test, or'liquid biopsy,' for lung cancer could be a good way to enhance screening efforts, because it would be easy to do, broadly accessible, and cost-effective," study first author Dimitrios Mathios said. The DNA difference: Blood tests for cancer typically focus on finding pieces of mutated tumor DNA.
Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure--which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge.
Although data engineers and data scientists have overlapping skill sets, they fulfill different roles within the fields of big data and AI system development. Data scientists develop analytical models, while data engineers deploy those models in production. As such, data scientists focus primarily on analytics, and data engineers focus more heavily on programming. To launch your data career, you'll need both theoretical knowledge and applied skills. Bootcamp programs like Springboard's Data Science Career Track and Data Engineering Career Track can help make you job-ready through hands-on, project-based learning and one-on-one mentorship.
AI or artificial intelligence could predict startup success to an impressive 90% accuracy, a study using machine learning models that look into tons of companies showed. As per Embroker, startups turn out to be a complete failure in most cases. To be precise, about 90% of them do not become successful. What's more, about 10% of startups end up being a failure every year, regardless of what industry it is in--whether it is from tech or retail. Not to mention that failure began at roughly the second to the fifth year of the firm. However, CBInsights learned in its recent data that 42% of the unsuccessful startups are due to misreading the market demand.
A s the United States emerges from the pandemic, many state and local government agencies are struggling to hire and retain workers. Not only have many retirement-age employees decided to accelerate their plans to retire, but agencies face stiff competition for workers from the private sector. As a result, for a number of key positions, especially those in IT, many government agencies are receiving fewer qualified applicants than the number of jobs available. To address this challenge, government agencies should start making use of AI tools to improve how they acquire and retain workers. A growing number of tools make use of AI to help organizations recruit and hire talent more effectively.
Over the last couple of years, there has been much discussion about the benefits of artificial intelligence (AI) for improving healthcare. But how much of this is true and how much simply hype? Is the technology really a godsend to radiologists and other healthcare professionals, or is it making their lives more difficult? There is no doubt that AI-based image recognition technology has improved enormously in recent years. Many researchers and companies are now working on different types of programs with a view to improving speed, accuracy and costs of cancer screening.
A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows. When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists' ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent. Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the team's AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The team's report publishes online Sept. 24 in the journal Nature Communications. "Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign," says study senior investigator Krzysztof Geras, PhD.