The worldwide market for radiology-specific deep learning will soar from around $40 million next year to some $300 million by 2021, largely on the wings of increasing demand for imaging combined with radiologist shortages like the one Scotland is facing. The projection comes from the U.K.-based healthcare market research firm Signify Research. "Radiology is evolving from a largely descriptive field to a more quantitative discipline," a Signify analyst says in a press release. "Intelligent software tools that combine quantitative imaging and clinical workflow features will not only enhance radiologist productivity but also improve diagnostic accuracy." Meanwhile the release, sent to publicize Signify's full report on the topic, notes that doubts over how deep learning arrives at its radiological diagnoses "could lead to legal implications. Whilst none of these problems are insurmountable, healthcare providers are likely to take a'wait and see' approach before investing in deep learning-based solutions."

Have you ever wondered how Facebook collects all the data it has to feed you with the content it presumes you'll like and keep you coming back for more? Well, now there's an app that can answer these questions. Available for free, Data Selfie is an open-source Chrome extension that helps you discover how machine learning algorithms track and process your Facebook activity, and gain insights about your personality and habits. Last year, Facebook's VP of Design thought the TNW Conference main stage was the best she'd ever been on. To accomplish this, the nifty extension monitors your Facebook interactions for patterns and then crunches the collected data into insightful reports. Data Selfie essentially tracks your activity – what you look at, how long you look at it, what you like, what you click and what you type – and then applies natural language processing and machine learning algorithms from IBM Watson and the University of Cambridge to turn this data into insight.

The robots are coming… if you believe everything you read in the news. According to some, AI technology will transform our healthcare system, break into the banking industry, and take over our jobs. But even if that's rue, is it really as scary as it seems? You may not know it, but Artificial Intelligence is already a massive part of our everyday lives – and has been for thirty-plus years. Over the coming weeks, we'll be running a series of blogs, looking at the role of Artifical Intelligence in our modern world.

We are going to move towards a world where I open an app where I talk to my device and I get an answer. I ask a question and I get an answer. I don't do a lot of work, I don't navigate too much. I'm not given too many options.That's going to be fueled by A.R., in particular, and I think A.I. is going to help make a world where you're sort of spending less time being inundated with sort of content and noise and more time sort of focusing on quality and the answers. There might be a moment when Tinder is just so good at predicting the few people that you're interested in, and Tinder might do a lot of the leg work in organizing a date, right?

A Boom with a View is a column about startups and the technology industry, written by Erin Griffith. Find them all here: fortune.com/boom. There's an easy way to tell when the hype around a technology trend has peaked. Ask yourself the following: Are the smartest venture capitalists complaining about valuations? Are big tech companies snapping up startups so young they can barely be considered real businesses?

When Anders Sandberg was a kid in the 1980s, he enjoyed making simulations on his Sinclair ZX81, mocking up mini solar systems. Later, he graduated to designing artificial neural networks that use learning algorithms inspired by the brain. "Some people relax by watching television. I program simulations while listening to philosophy lectures," Sandberg chuckles. One day back in 1999, he recalls, he deleted a copy of a neural network on his computer and got a "tinge of bad conscience." He couldn't help worrying: "Have I just killed a little creation?"

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

Want to use artificial intelligence (AI) to train autonomous drones and self-driving cars the way Microsoft does? Microsoft recently shared its open source system for performing such trainings, including its AI-based platform AirSim. The project, called the Aerial Informatics and Robotics Platform, aims to provide simulation tools and resources to help in the training of robots, drones, and other hardware that can operate autonomously, according to a Microsoft blog post. By improving the way that these devices and vehicles can navigate their environment, the hope is that that can eventually shed their "emerging technology" title. By using virtual simulators, like AirSim, a team is able to collect valuable data and train the systems more efficiently before they take them out of the lab.

FinovateEurope 2017: what awaits in the post-bank world? Last week's FinovateEurope was full of the broad range of exciting and innovative companies that we have come to expect at the show. Although the 72 companies showcasing their solutions operated right across the fintech landscape, they all sing from a common hymn sheet of improving the customer experience. Rapidly advancing applications of artificial intelligence (AI), conversational user interfaces and other emerging technologies are driving a shift away from the conventional financial services approach to a more engaging and personalised customer journey. What that actually means and how that is done was explored in a workshop run by R/GA London at the conference.

Last winter, as meteorologists warned of a monster El Niño, researchers at the Nature Conservancy in California prepared to mobilize. El Niño promised to bring in king tides that would raise the sea level by as much as one foot above normal during high tide, causing flooding along the coastline that researchers could study as a preview of climate change-induced sea level rise. But when a king tide arrives, it floods lots of pockets along the coastline at once. So they decided to try a new, distributed surveillance strategy: commercial drones, co-opted from a gung-ho statewide network of citizen scientists. The plan had a lot of advantages.