NYU Data Science newsletter – December 10, 2015

GROUP CURATION: N/A

Neural networks provide a vast array of functionality in the realm of statistical modeling, from data transformation to classification and regression. Unfortunately, due to the computational complexity and generally large magnitude of data involved, the training of so called deep learning models has been historically relegated to only those with considerable computing resources. However with the advancement of GPU computing, and now a large number of easy-to-use frameworks, training such networks is fully accessible to anybody with a simple knowledge of Python and a personal computer. In this post we’ll go through the process of training your first neural network in Python using an exceptionally readable framework called Chainer.

It has only been a few days since the announcement of Swift going open source and the activity around the project has been incredible. When Apple revealed that Swift would be open source at WWDC earlier this year, I do not think anyone anticipated a release like this.

Researchers at the University of Washington say they can use phone records to help humanitarian efforts in developing countries. The key is the different cell phone habits of wealthier and poorer people. [audio, 1:25]

There are many factors contributing to the adoption of machine learning technologies in enterprise environments. The rapid evolution of the machine learning stacks, the adoption of big data technologies as well as the explosion in the volumes of data processed by organizations are just some of the elements that are conspiring to embrace more advance data analysis techniques. For a technology movement to become transformational trend in the enterprise, it has to combine a strong technical value proposition with elements such as distribution, market maturity or cost of adoption. From everything we can see, machine learning has all the ingredients to become the next power the next wave of innovation in the enterprise.

During the last two years, the Google Quantum AI team has made progress in understanding the physics governing quantum annealers. We recently applied these new insights to construct proof-of-principle optimization problems and programmed these into the D-Wave 2X quantum annealer that Google operates jointly with NASA. The problems were designed to demonstrate that quantum annealing can offer runtime advantages for hard optimization problems characterized by rugged energy landscapes.

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We found that for problem instances involving nearly 1000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing. It is more than 108 times faster than simulated annealing running on a single core. We also compared the quantum hardware to another algorithm called Quantum Monte Carlo. This is a method designed to emulate the behavior of quantum systems, but it runs on conventional processors. While the scaling with size between these two methods is comparable, they are again separated by a large factor sometimes as high as 108.

The launch of the Alan Turing Institute will provide data analytics capability to the UK’s data sciences community, analysing data and gaining a new understanding that leads to decisions and actions.

Currently, there is little exchange between the different communities interested in the domain of bibliometrics. A recent conference aimed to bridge this gap. Peter Kraker, Katrin Weller, Isabella Peters and Elisabeth Lex report on the multitude of topics and viewpoints covered on the quantitative analysis of scientific research. A key theme was the strong need for more openness and transparency: transparency in research evaluation processes to avoid biases, transparency of algorithms that compute new scores and openness of useful technology.

We are pleased to announce that the lessons for our instructor training course are now available online and in this GitHub repository. They have evolved a lot in the three and a half years since we started running this class, and more improvements would be welcome: please file issues or send pull requests to fix, extend, or otherwise improve the material.

We’re excited and happy to start the application process for 2016. Applications are now open for Fellows, Mentors, Project Managers, and Project Partner Organizations. We expect to take around 42 fellows, 4-6 Technical Mentors, 3 Project Managers, and 12 Project Partners. We encourage everyone to read the FAQ before applying. Most questions people ask us are answered there. If you don’t find an answer to a question you have, feel free to email us.

Deadline to apply is Monday, February 1, 2016.