Data Science newsletter – November 27, 2016

GROUP CURATION: N/A

Intel is committed to AI and is making major investments across technology, training, resources and R&D to advance AI for business and society. We have a commitment to our partners, the industry as a whole and our global society to accelerate AI development, deliver end-to-end solutions, and lead the next generation of computing transformations. Within our industry, only Intel can make and deliver upon this commitment because of our comprehensive technology portfolio – an unparalleled portfolio developed through acquisition and innovation.

A graduate student tracks the spread of viruses from bats to humans in Madagascar

We chatted with [Naveen] Rao again in the lead-up to Intel’s AI Day last week to get a better sense of what an integrated product might look like—and what could shift the tides for deep learning algorithmically and architecture-wise. At the high level, he pointed to the expected performance gains with a coupled Knights Mill and Nervana product. “We are not just talking about something that is going to be 10% better here; we’re setting bar really high for what will be possible in terms of a processor designed for neural networks. This represents a true commitment to the deep learning community and the space in general from Intel, and that is exciting from an industry standpoint,” he says. “This is only the beginning, but we are enabling an order of magnitude greater performance in the coming year and by 2020, two orders of magnitude…This will be based on a combination of silicon, software, and algorithmic innovation.”

The first effect of machine intelligence will be to lower the cost of goods and services that rely on prediction. This matters because prediction is an input to a host of activities including transportation, agriculture, healthcare, energy manufacturing, and retail.

When the cost of any input falls so precipitously, there are two other well-established economic implications. First, we will start using prediction to perform tasks where we previously didn’t. Second, the value of other things that complement prediction will rise.

Vancouver, BC, Canada Panel discussion: How do we grow BC’s data science IQ? Wednesday, November 30,at 6 p.m., UBC Robson Square, Room C300 (800 Robson Street) [free, registration requested]

Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.

A new private research institute financed by the billionaire James H. Simons in New York will develop software tools and apply cutting edge computing techniques to science often not possible in academia and industry.

Gallstones

When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated photo tagging to self-driving cars.

More recently we’ve also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I’ll try to summarize what CNNs are, and how they’re used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I’ll start there, and then slowly move towards NLP.

Learn Python 3 by solving problems online

How can one determine whether an unresponsive patient is conscious or not? Of all the information processing in your brain that can be measured with modern sensors, which corresponds to information that you are subjectively aware of and which is unconscious? A theory that has garnered much recent attention proposes that the answer involves measuring a quantity called integration that quantifies the extent to which information is interconnected into a unified whole rather than split into disconnected parts. Unfortunately, proposed measures of integration are too slow to compute in practice from patient data. In this paper, I explore and classify existing and novel integration measures by various desirable properties, and derive useful exact and approximate formulas that can be applied to real-world data from laboratory experiments without posing unreasonable computational demands. This improves the prospects of making fascinating questions and theories about consciousness experimentally testable. [full text]

While writing Developing Data Products I concocted a little scheme for showing examples of Shiny applications in a bookdown book that I think is interesting enough to share. This method should work with any Rmd file, not just bookdown books. Shiny applications are usually composed of two R files which must be named ui.R and server.R. In order to teach students about the structure of both of these files I want to show the students the code for each file, I want them to be able to see a screenshot of the app, and I want students to be able to run the app using shiny::runGitHub().

Baidu, the most widely used search engine in China, announced today that it is making available to developers four speech application programming interfaces (APIs).

Image-to-image translation using conditional adversarial nets