This guest post is part of the Festschrift of the Blogosphere celebrating HealthBlawg's Tenth Blogiversary. Festschrift posts are appearing throughout the month of June 2016. A recent article in The Commonwealth Fund blog, "Envisioning a Digital Health Advisor," raises the question of being able to use smartphone apps to get real-time, accurate and personalized guidance for health concerns. While one can envision the convenience, affordability and peace of mind that would result from their use, such services face a number of hurdles before they become reality. As a result, the "digital revolution" has not yet greatly affected most people's interactions with the health care system.

Northwestern University's Ken Forbus is closing the gap between humans and machines. Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. "In terms of thinking like humans, analogies are where it's at," said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern's McCormick School of Engineering. "Humans use relational statements fluidly to describe things, solve problems, indicate causality, and weigh moral dilemmas."

Marko Balabanovic, Chief Technology Officer at Digital Catapult, writes about the barriers facing machine learning companies, particularly when they are looking to scale. Machine learning techniques, within the field of Artificial Intelligence, are becoming increasingly effective and important for data innovators. The major challenges facing fast-growing organisations have been well documented in the Scale-Up Report, and include recruiting skilled employees, building leadership capability, accessing customer and finance, and navigating infrastructure. However, for companies whose products and services use machine learning, we see two more specific barriers: access to skilled machine learning specialists, and access to large pools of data with which to train their algorithms. Both are exacerbated by the dominant position of the "GAFA" major internet companies (Google, Apple, Facebook and Amazon), who are rapidly acquiring large machine learning teams and have many advantages in acquiring training data through the data and channels they already control.

Equity deals to startups in artificial intelligence -- including companies applying AI solutions to verticals like healthcare, advertising, and finance as well as those developing general-purpose AI tech -- increased nearly 6x, from roughly 70 in 2011 to nearly 400 in 2015. Q1'16 saw a new peak in deal activity to the category. So far in 2016 (as of 6/15/2016), over 200 AI-focused companies have raised nearly 1.5B in equity funding. Our analysis includes all equity funding rounds and convertible notes.

The most important subpackages provide implementations of boilerplate code that is relevant to machine-learning problems. These include computer vision, natural language processing, and speech processing. Other subpackages may be smaller and focus on more specific problems or even specific data sets.

Over the past year, questions about how emerging technologies will impact employment have taken on a new tenor. Will robots take over our jobs? One thing is indisputable: automation and artificial intelligence (AI) will displace workers in the IT and business process outsourcing services industry. Such tectonic shifts have occurred every few decades over the last two centuries. With each wave of new technology and each accompanying paradigm shift, jobs have disappeared.

In a recent interview at the Code Conference in California, technology entrepreneur Elon Musk suggested we are living inside a computer simulation. On first hearing, this claim seems far-fetched. But could there be some substance to Musk's thinking? As founder of a number of high-profile companies, such as Tesla and Space X, Musk's business interests lie firmly in leading technologies. Key to his claim is that computer games have evolved rapidly over the past 40 years to the point that, inside the next few years, they will be fully immersive, with a computer-generated and controlled world seamlessly merged with the physical world.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

There's a chance it could bring the mood down. Having chosen your shiny new driverless car, only one question remains on the order form: whether your spangly, futuristic vehicle be willing to kill you? To buyers more accustomed to talking models and colours, the query might sound untoward. But for manufacturers of autonomous vehicles (AVs), the dilemma it poses is real. If a driverless car is about to hit a pedestrian, should it swerve and risk killing its occupants?

In catch-22 traffic emergencies where there are only two deadly options, people generally want a self-driving vehicle to, for example, avoid a group of pedestrians and instead slam itself and its passengers into a wall, a new study says. But they would rather not be travelling in a car designed to do that. The findings of the study, released on Thursday in the journal Science, highlight just how difficult it may be for auto companies to market those cars to a public that tends to contradict itself. Related: Statistically, self-driving cars are about to kill someone. "People want to live a world in which everybody owns driverless cars that minimize casualties, but they want their own car to protect them at all costs," Iyad Rahwan, a co-author of the study and a professor at MIT, said.