Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.

Jaguar Land Rover is working on a raft of technology that could enable its future production cars to drive autonomously off-road as well as on-road. The research project aims to make JLR's self-driving cars viable in a wide range of on- and off-road driving environments and conditions. To enable autonomous all-terrain capability, JLR is working on new sensing technologies to provide the high levels of artificial intelligence required for the car to plan the route it should take. New surface identification and 3D path sensing systems use camera, ultrasonic, radar and lidar sensors to give the car a 360-degree view of the world around it. JLR says the combined power of the sensors is so advanced that the car could determine road surface characteristics, down to the width of a tyre, even in rain and falling snow, to plan its route.

As SAILORS returns for the second summer, the new campers are giddy with excitement. After grabbing breakfast and getting to know one another, the girls situate themselves in a lecture room in the Gates Computer Science building at Stanford University. Professor Fei-Fei Li, director of the SAILORS program and the AI Lab as a whole, warmly welcomes the campers to the summer program, imparting the grounds on which the idea of an all-girls, two-week research-intensive program came about just two years ago. Though Professor Li acknowledges the recent talk of the possibility of AI becoming the "terminator next door" that some critics of the field fear, that was exactly what swayed her, along with co-director Olga Russakovsky, to feel the desperate need of bringing more females into the field of AI. Because, as Prof. Li puts it, when we have women who gravitate AI towards humanity–women who are compassionate, who care about AI safety–the potential benefits from the societal impact far outweigh the prospect of AI coming to dominate the world.

IBM's Watson has been turning out inventive culinary creations for quite some time. Now a London company wants to use AI to improve the beers they brew. A collaboration between a machine learning company and a creative firm has led to an AI application that even the least coherent of frat boys can get behind. IntelligentX co-founder Dr. Rob McInerney refers to what they're doing as "creativity structured by data." That combination, he says, allows them to improve their brews "generation after generation" -- something they hint at right on their labels.

I was recently consulting with a publishing company that is exploring various ways to digitize and contextualize its content. Knowing that some of the company's competitors had signed deals with IBM's Watson, I asked several executives why they had not done a Watson deal themselves. "We think that the market for AI software is rapidly commoditizing, and we believe we can assemble the needed capabilities ourselves at much lower cost," was this company's party line. Some particularly knowledgeable managers mentioned that they expected the company would instead make use of open source cognitive software made available from various providers. These potential open source providers are not small vendors; they include, for example, Google, Facebook, Microsoft, Amazon, and Yahoo.

Phone (US): 1 (425) 440-5100 ... (long distance cost may apply) (1C4A) Unfamiliar with how to do this on Skype? Add the contact "join.conference" to your skype contact list first. To participate in the teleconference, make a skype call to "join.conference", then open the dial pad (see platform-specific instructions below) and enter the Conference ID: 843758# when prompted. You can indicate that you want to ask a question verbally by clicking on the "hand" button, and wait for the moderator to call on you; or, type and send your question into the chat window at the bottom of the screen. Just add the room as a buddy - (in our case here) summit_20160317@soaphub.org ... Handy for mobile devices!

A few years back, artificial intelligence reached the point at which it could recognise objects in images and answer questions about them. But it turns out that when an AI looks at an picture, it sees totally different things to humans. In experiments conducted at Facebook and Virginia Tech, researchers found significant differences between what humans and computers looked at when asked a simple question about an image. Lawrence Zitnick and a team of computer vision experts first asked human workers on Amazon's Mechanical Turk platform to answer basic questions about a photo. The photo began blurred, but the worker could click around to sharpen it in different areas.

A basic problem in information theory is that of transmitting information reliably over a noisy channel. An analogous problem in computing machines is that of reliable computing using unreliable elements. This problem has been studies by von Neumann for Sheffer stroke elements and by Shannon and Moore for relays; but there are still many open questions. The problem for several elements, the development of concepts similar to channel capacity, the sharper analysis of upper and lower bounds on the required redundancy, etc. are among the important issues. Another question deals with the theory of information networks where information flows in many closed loops (as contrasted with the simple one-way channel usually considered in communication theory).

The race to acquire artificial intelligence talent has inverted the "laws" of M&A, with pre-revenue AI firms such as UK-based Magic Pony being sold to Twitter for about 10m per employee. Magister Advisors, the global M&A advisory firm to the technology industry, notes that AI firms without revenues are more valuable than those with, as buyers look for pristine competitive advantage, and that Britain is amongst top tier for AI innovation. Twitter just paid 150m for 14-person Magic Pony, a UK-based AI visual search company barely anyone had heard of before the deal. At 10m per employee it marks a high water mark in AI for what is essentially a team acquisition. Magister has tracked 26 AI driven deals since 2014 in the US, Europe and Israel, 11 of which involved companies with less than 50 employees which were acquired largely, or entirely, for the team and capability. Across all 11 deals, the median price paid per employee has reached 2.4m, meaning a high quality AI company with 40 employees would be valued at near 100m - even if it had little or no revenue.