You might think that every choice a writer makes is highly deliberate, part of a grand master plan that only she is entirely aware of. We certainly felt that way while reading Ancillary Justice, Ann Leckie's award-winning sci-fi novel about a spaceship AI that gets trapped in a single body and seeks revenge on her enemies. There are so many details embedded in the world-building--from a fixation on gloves and tea to the use of "she" for all genders--that Leckie surely had reams of unpublished backstory (or at least a yarn chart) to explain and keep track of it all. But as we found out in our WIRED Book Club conversation with the author, Leckie takes a far less structured approach when it comes to creating new universes. Was this series always a trilogy in your mind? Though I'm not sure why, to be honest.

Every field of scientific study has its own holy grail. For particle physicists, it's the quest for physics beyond the Standard Model; for astrophysicists, it is the hunt for the elusive dark matter and dark energy; and for neuroscientists, it is explaining how inanimate matter becomes conscious. Obviously, the first step in understanding the mysteries of consciousness would be to map the tool through which it manifests itself -- the brain. On Wednesday, a team of neuroscientists released an extremely detailed map of terra incognita of the human brain -- the cerebral cortex. The cerebral cortex is the brain's outermost layer and is responsible for sensory perception, language, attention, tool use and abstract thinking.

WASHINGTON – Neuroscientists acting as cartographers of the human mind have devised the most comprehensive map ever made of the cerebral cortex, the part of the brain responsible for higher cognitive functions such as abstract thought, language and memory. Using MRI images from the brains of 210 people, the researchers said on Wednesday they were able to pinpoint 180 distinct areas in the cerebral cortex, the brain's thin, wrinkly outermost layer made of so-called gray matter. These areas were present in both the left and right hemispheres of the cerebral cortex. More than half, 97 of them, were previously unknown. The researchers nailed down the specific function of some of the areas, but said they were only scratching the surface on understanding what all of the areas did.

The idea of managing human capacity is fundamental to how firms move forward with their organization strategy. What a sustainable philosophy requires is the open recognition of a changing workforce, and the understanding that even the ability to manage human capacity needs to be supplemented with an open mind and forward thinking. Artificial Intelligence (AI), computerization, and automation, devalue certain human skills, and in time will completely overhaul the job market. At this point it will be our uniquely human potential that is valued in workplace. Working with human strengths and deficiencies in order to get the best out of your company is going to be a defining aspect of keeping up with the pace of innovation and change in the next few decades.

The development of digital health technology causes many concerns regarding bioethics. Here are 10 examples why people should not be afraid of, but rather embrace the advancements of such technologies. The fear from the unknown is as old as mankind itself, thus the fear from technological development has the same age as advancement itself. When the telephone was introduced to Sweden in the late 1800s, people were afraid that the contents of the lines would spill out in some way if there was a break and many elderly persons refused to touch a telephone for fear of electrical shock. The fear is even scarier when it comes to one's health. Shortly after Wilhelm Conrad Röntgen introduced his discovery about the X-Ray, people got scared that it might read their thoughts, and they were afraid that such omnipotent gaze will see through their body and soul. Merchants even offered X-Ray proof underwear.

The aim of the research was to discover how intellect works in the human brain and thereby inform how Artificial Intelligence (AI) works in the future. The study used MRI scans to observe thousands of brains and was only possible due to recent developments in brain imaging techniques. They conclusively found that the more variable a person's brain is - how frequently you can change neural connections with the other parts of the brain over minutes or seconds - and the more frequently these parts connect with each other, the higher a person's IQ and creativity. The discovery of these dynamic functions inside the brain could be applied to building artificial networks that mimic the brain, with the ability to learn and adapt – a current obstacle in the effectiveness of AI. Not only does this study have implications for technology, it is important in understanding mental health.

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Numerous companies in the healthcare space are experimenting with artificial intelligence, but what does the future hold in this sphere? You'll probably know IBM's supercomputer, Watson, from its 2011 appearance on Jeopardy. Up against two of the US quiz show's longest-running and highest-earning contestants, Watson clinched a 1m prize after answering a series of quick-fire general knowledge questions. It wasn't a close call either – at the final score, Watson's total was 31,547 ahead of its rivals' combined. But in the five years since, IBM's supercomputer has been working towards another goal, one far more lucrative than the Jeopardy!

Machine-learning approaches are becoming commonplace in the neuroimaging literature as potential diagnostic and prognostic tools for the study of clinical populations. However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. We systematically evaluated the classifier performance using back-to-back structural MRI in two field strengths (3- and 7-T) to discriminate patients with schizophrenia (n 19) from healthy controls (n 20). Gray matter (GM) and white matter images were used as inputs into a support vector machine to classify patients and control subjects.

The media interest surrounding deep learning has grown exponentially in the last few years. But what does it actually mean, and how will it change business and society? Deep learning is a subset of machine learning that refers to mapping artificial neural networks to recreate some of the same processes that the human brain performs, and using algorithms with speech, images and text, to recognize, identify and understand patterns in the data. Although this sounds simple, it involves complex processes and functions – but once trained, the application of deep learning algorithms could be world changing. For instance, a machine that learns like a human, but can rapidly process thousands of images and recognize patterns, is already showing promise for applying deep learning to medical imaging.