Search Engine Journal: A Beginner’s Guide to SEO in a Machine Learning World. “When thinking about the rise of machine learning as it relates to SEO, we can be faced with a frightening scenario, depending on the type of SEO professional you are.” I am not, obviously, an SEO professional. However, I do think it’s important to know about SEO, because SEO is part of why you find the content you do when you search online!
The Register: AI threatens yet more jobs – now, lab rats: Animal testing could be on the way out, thanks to machine learning. “Machine learning algorithms can help scientists predict chemical toxicity to a similar degree of accuracy as animal testing, according to a paper published this week in Toxicological Sciences. A whopping €3bn (over $3.5bn) is spent every year to study how the negative impacts of chemicals on animals like rats, rabbits or monkeys.”
Pete Warden: What Image Classifiers Can Do About Unknown Objects. “A few days ago I received a question from Plant Village, a team I’m collaborating with about a problem that’s emerged with a mobile app they’re developing. It detects plant diseases, and is delivering good results when it’s pointed at leaves, but if you point it at a computer keyboard it thinks it’s a damaged crop. This isn’t a surprising result to computer vision researchers, but it is a shock to most other people, so I want to explain why it’s happening, and what we can do about it.”
MIT Technology Review: Given a satellite image, machine learning creates the view on the ground. “Leonardo da Vinci famously created drawings and paintings that showed a bird’s eye view of certain areas of Italy with a level of detail that was not otherwise possible until the invention of photography and flying machines. Indeed, many critics have wondered how he could have imagined these details. But now researchers are working on the inverse problem: given a satellite image of Earth’s surface, what does that area look like from the ground? How clear can such an artificial image be?”
The Next Web: A beginner’s guide to AI: Neural networks. “Artificial intelligence has become a focal point for the global tech community thanks to the rise of deep learning. The radical advance of computer vision and natural language processing, two of AI’s most important and useful functions, are directly related to the creation of artificial neural networks. For the purpose of this article we’ll refer to artificial neural networks as, simply, neural networks. But, it’s important to know that deep learning techniques for computers are based on the brains of humans and other animals.”
Stanford News: Stanford AI recreates chemistry’s periodic table of elements. “It took nearly a century of trial and error for human scientists to organize the periodic table of elements, arguably one of the greatest scientific achievements in chemistry, into its current form. A new artificial intelligence (AI) program developed by Stanford physicists accomplished the same feat in just a few hours.”
Benedict Evans: Ways to think about machine learning. “We’re now four or five years into the current explosion of machine learning, and pretty much everyone has heard of it. It’s not just that startups are forming every day or that the big tech platform companies are rebuilding themselves around it – everyone outside tech has read the Economist or BusinessWeek cover story, and many big companies have some projects underway. We know this is a Next Big Thing.”