eWeek: How AI Data Actually Moves from Collection to Algorithm

eWeek: How AI Data Actually Moves from Collection to Algorithm. “Though excitement about AI and ML is legitimately growing, we hear little about how the data actually goes from collection to algorithm. By examining the process behind building hypothetical machine learning models, we can look at what important processes are often glossed over in articles extolling the virtues of AI.”

TechSpot: Security researchers fake cancerous nodes in CT scans with machine learning

TechSpot: Security researchers fake cancerous nodes in CT scans with machine learning. “We expect that when we have a CT or MRI scan that the results are accurate. After all we are talking about equipment that can cost millions of dollars and radiologists with years of training and sometimes decades of experience. However, hospital security can be lax and researchers have now shown they can fake CT and MRI scans using a generative adversarial network (GAN).”

MIT Technology Review: Machine learning is making pesto even more delicious

MIT Technology Review: Machine learning is making pesto even more delicious. “What makes basil so good? In some cases, it’s AI. Machine learning has been used to create basil plants that are extra-delicious. While we sadly cannot report firsthand on the herb’s taste, the effort reflects a broader trend that involves using data science and machine learning to improve agriculture.”

Harvard Gazette: Tapping the collective mind

Harvard Gazette: Tapping the collective mind. “If done right, artificial intelligence could drastically reduce both systemic glitches and errors in the decision-making of individual clinicians, according to commentary written by scientists at Harvard Medical School and Google. The article, published April 4 in The New England Journal of Medicine, offers a blueprint for integrating machine learning into the practice of medicine and outlines the promises and pitfalls of a technological advance that has captivated the imaginations of bioinformaticians, clinicians, and nonscientists alike.”

Department of Energy: Department of Energy Announces $20 Million to Develop Artificial Intelligence and Machine Learning Tools

Department of Energy: Department of Energy Announces $20 Million to Develop Artificial Intelligence and Machine Learning Tools. “Today, the U.S. Department of Energy’s (DOE’s) Advanced Research Projects Agency-Energy (ARPA-E) announced up to $20 million in funding to accelerate the incorporation of machine learning and artificial intelligence into energy technology and product design processes.”

Tech Xplore: Computing scientists use machine learning to track health trends on Twitter

Tech Xplore: Computing scientists use machine learning to track health trends on Twitter. “A new machine learning tool, developed by University of Alberta computing scientists, sifts through millions of Twitter posts to help understand health and wellness trends in Alberta and across Canada.”

Pioneer Interview: Lenny Bogdonoff (Pioneer)

Pioneer: Pioneer Interview: Lenny Bogdonoff. “Lenny Bogdonoff, a New York-based software engineer and graffiti artist, is creating the world’s first digital genealogy of street art. He played and won a Pioneer tournament while developing a set of machine learning tools for his project, Public Art. By gathering photos from around the internet and using machine learning models to identify street art, Public Art aims to digitally preserve murals around the world. “