Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn and accessible to a far wider audience.

The integration of artificial intelligence and the Internet of Things introduces a wide array of connected health tools that produce a vast amount of data that must be synthesized, analyzed, stored and communicated by a robust information infrastructure. But if hospitals don't structure and store IoT patient data properly, that information could be rendered not assessable by AI tools. For starters, significant infrastructure is needed to streamline IoT-generated data to make sure it is simple to assess and manage with AI. "AI adoption and scale will be accelerated by the relatively low cost of deployment," said Rick Krohn, president of HealthSense, a connected health consulting firm. "A terabyte of storage costs less than $100, and wearable sensors and cloud infrastructure are becoming increasingly affordable. But AI requires sophisticated applications that deliver contextually aware right-place-right-time clinical decision support."

The integration of artificial intelligence and the Internet of Things introduces a wide array of connected health tools that produce a vast amount of data that must be synthesized, analyzed, stored and communicated by a robust information infrastructure. But if hospitals don't structure and store IoT patient data properly, that information could be rendered not assessable by AI tools. For starters, significant infrastructure is needed to streamline IoT-generated data to make sure it is simple to assess and manage with AI. "AI adoption and scale will be accelerated by the relatively low cost of deployment," said Rick Krohn, president of HealthSense, a connected health consulting firm. "A terabyte of storage costs less than $100, and wearable sensors and cloud infrastructure are becoming increasingly affordable. But AI requires sophisticated applications that deliver contextually aware right-place-right-time clinical decision support."

First, Google acquired a startup called DNNresearch, snapping up some of the world's foremost experts in a burgeoning field of artificial intelligence known as deep learning. Then it shelled out $400 million for a secretive deep learning startup called DeepMind. Much like Facebook, Microsoft, and others, Google sees deep learning as the future of AI on the web, a better way of handling everything from voice and image recognition to language translation. But there's one notable deep learning company that Google hasn't yet bought. It's called Clarifai, and it may remain as an independent operation.

In Leena Krohn's novella "Datura, or A Figment Seen by Everyone," the narrator, who works for a paranormal-news magazine, transcribes the inscrutable fifteenth-century text known as the Voynich manuscript while slowly poisoning herself with the seeds from a datura plant. Datura is known to cause delirium and dissociation, but it may also ease the symptoms of asthma, which the narrator has. Though she is skeptical of supernatural phenomena, the datura slowly undermines that skepticism; each day seems to bring one serendipitous event after another, not to mention mild hallucinations. The narrator describes feeling as though meaning is floating on the surface of things, untethered from their physical reality. "What does the word refer to," she asks, in a deconstructionist turn, "does it really signify anything at all?"

In recent years, the field of academic publishing has ballooned to an estimated 30,000 peer-reviewed journals churning out some 2 million articles per year. While this growth has led to more scientific scholarship, critics argue that it has also spurred increasing numbers of low-quality "predatory publishers" who spam researchers with weekly "calls for papers" and charge steep fees for articles that they often don't even read before accepting. Ten years ago, a few students at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) had noticed such unscrupulous practices, and set out to have some mischievous fun with it. Jeremy Stribling MS '05 PhD '09, Dan Aguayo '01 MEng '02 and Max Krohn PhD '08 spent a week or two between class projects to develop "SCIgen," a program that randomly generates nonsensical computer-science papers, complete with realistic-looking graphs, figures, and citations. SCIgen emerged out of Krohn's previous work as co-founder of the online study guide SparkNotes, which included a generator of high-school essays that was based on "context-free grammar."