From Claire North's new novel to a 10th anniversary edition of a brilliant Adrian Tchaikovsky book, there's lots to watch out for in November's science fiction Claire North's Slow Gods follows a deep-space pilot We'll need to get our skates on if we're to keep up with all the new science fiction published in November. And I am creeped out by the idea at the heart of Grace Walker's . Everything feels frightening this month - perhaps the sci-fi world is still in Halloween mode. It sounds poignant, moving and beautiful, and without any supernatural scares. Emily H. Wilson is wild for this sci-fi novel: I've not heard our sci-fi columnist recommend a book so wholeheartedly in all the time she's written for us.

It's one thing to want to protect endangered animals, but another entirely to keep track of them. Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow. Cute they may be, but spotting them in large bodies of water is easier said than done. Since marine researchers want to do so to keep tabs on population sizes, conservation status, and their important habitat areas, that poses a bit of a problem. Fortunately, this is where Dr. Amanda Hodgson of Australia's Murdoch University comes in.

It's one thing to want to protect endangered animals, but another entirely to keep track of them. Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow. Cute they may be, but spotting them in large bodies of water is easier said than done. Since marine researchers want to do so to keep tabs on population sizes, conservation status, and their important habitat areas, that poses a bit of a problem. Fortunately, this is where Dr. Amanda Hodgson of Australia's Murdoch University comes in.

It's one thing to want to protect endangered animals, but another entirely to keep track of them. Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow. Cute they may be, but spotting them in large bodies of water is easier said than done. Since marine researchers want to do so to keep tabs on population sizes, conservation status, and their important habitat areas, that poses a bit of a problem. Fortunately, this is where Dr. Amanda Hodgson of Australia's Murdoch University comes in.

It's one thing to track endangered animals on land, but it's another to follow them when they're in the water. How do you spot individual critters when all you have are large-scale aerial photos? Queensland University researchers have used Google's TensorFlow machine learning to create a detector that automatically spots sea cows in ocean images. Instead of making people spend ages coming through tens of thousands of photos, the team just has to feed photos through an image recognition system that knows to look for the cows' telltale body shapes. An initial version could spot 80 percent of the sea cows that had been confirmed in existing photos.

Artificial-intelligence tech has been moving out of academic circles into real-world applications like screening out spam or making it look like Van Gogh painted your photo. But now it's also tapping our weakness for cute sea creatures threatened by extinction. The large marine animal is really hard to keep track of. Researchers are using drones to take aerial photos of the ocean, but detecting them in those photos is a challenging task...for humans. That's where Google's TensorFlow neural network software project comes in, developer advocate Josh Gordon explained in a story posted Wednesday to Google's machine learning blog.

Can you spot the lone dugong in the image above? Now do that with 45,000 more, and you'll have a general idea of the population of these endangered critters. If that sounds tedious, then perhaps you, like researchers at Murdoch University, would prefer to delegate the duty to a specially-trained computer. Amanda Hodgson, of the school's Cetacean Research Unit, has been using UAVs to capture images of marine animals for years, but the data piles up fast, and there are only so many grad students. Hodgson worked with computer scientist Frederic Maire, of the Queensland University of Technology, to automate the process.