NYU Data Science newsletter – September 23, 2015

GROUP CURATION: N/A

You’ve been on the road for hours, trying to make good time back home after an exhausting weekend in Vegas. With each blink your eyes stay closed a little longer. Your head nods and then snaps back up. Before it can happen again, possibly resulting in a deadly crash, your car begins to slow down and an alarm blares to jolt you awake.

This future is getting closer to reality, according to a trio of researchers from Cornell University and Stanford University, who have created an artificial intelligence system that can predict with high accuracy what the driver of a car will do next. Applied broadly, this type of research could improve everything from the safety of our cars to the usefulness of our robots.

Please can you explain what deep learning algorithms are and how they could help to uncover disease-causing genetic mutations?

To understand deep learning in the context of genetic disease, you need to understand shallow learning first. Shallow learning relates mutations to diseases by looking for mutations that commonly occur in patients with a disease. It’s a commonly used method.

However, doing that almost always fails to identify the mutation that causes the disease, because mutations occur in clusters and the true causal one will be buried in a cluster of non-causal mutations that are all correlated with the disease.

It’s time to stop thinking about Facebook as just a social media company. Between its efforts to deliver internet service with drones, buying Oculus for virtual reality, and its continued pursuit of artificial intelligence, Facebook has quickly become one of the most advanced technology research centers in the world.

Computer software can often cost more than the machine itself. Not only is there a huge economic barrier for anyone looking to explore a new tool, program, or application, but anyone interested in taking bits and pieces of a software’s offering is deterred by exorbitant price points. Furthermore, if something ever goes wrong with an installed application, little to no support is available, other than upgrading to the next costly version. It’s no wonder that in recent years, open-sourced software has become increasingly important in the world of data science, and inter-disciplinary computer science.

Open sourced software allows for greater transparency software creation and maintenance. Users can open up a piece of software and “look under the hood” to study software’s inner workings. Users can also modify an application, allowing customization not available with traditional software. But the greatest draw of open-sourced software is its price point. With most programming languages entrenched in costly software options, Python has become the primary language of open-source solutions in data science. Most python download’s are free or low-cost, and if you have access to a computer, even through a public library, you can start programming with Python. What gives Python its life and sustainability is a community of people constantly willing to improve and maintain python-based software, and applications. Enter: SciPy.

The Division of Arts & Humanities of the College of Letters & Science at the University of California, Berkeley, invites applications for postdoctoral fellowships in the Digital Humanities.

Funded by the Andrew W. Mellon Foundation, Digital Humanities at Berkeley is designed to increase our capacity for teaching and scholarship in the digital humanities, with a focus on integrating these into the central academic enterprise of the university.

Application Deadline: Friday, January 8, 2016

The objective of this workshop is to present problems of growing relevance in healthcare and discuss how advanced machine learning techniques can be used to address them through interactions between clinicians and ML-researchers.

One of the chief concerns of the medical sciences is to use empirical observations of various measurements, symptoms, tests, treatments and outcomes to build empirical models of diseases and patient responses and then act on them, aided by life-science.

In this workshop we want to bring together clinicians with problems associated with empirical data and machine learning researchers working on healthcare solutions.

Deadline for Paper Submissions: Friday, October 23

… How did you start to incorporate data science into your political science studies?

I developed an interest in non-linear models very early on, so moving to data science was not a hard sell. And I had been doing supervised machine learning for a long time before I even knew that term existed