Data Science newsletter – October 28, 2016

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While interactive robots have been popularized by science fiction over the past century, Assistant Professor of Computer Science Stefanie Tellex and her research group are working to make them a reality. By focusing on robotic language, perception and action, Tellex hopes to create a robot that can assist humans in both simple and complex tasks.

“So far, however, active memory has not improved over attention for most natural language processing tasks, in particular for machine translation. We analyze this shortcoming in this paper and propose an extended model of active memory that matches existing attention models on neural machine translation and generalizes better to longer sentences. We investigate this model and explain why previous active memory models did not succeed. Finally, we discuss when active memory brings most benefits and where attention can be a better choice.”

Prof. Daniel Jurafsky, linguistics and computer science, Stanford University, described the unexpected role linguistics play in the construction of restaurant menus and police-civilian interactions at a lecture Thursday.

After Shirley Pepke was diagnosed with ovarian cancer, she had trouble finding information she needed — so she and USC physicist and data expert Greg Ver Steeg got to work.

Over a course of 15,000 tries, Google researches were about to create neural networks that could successfully write and decode a cipher that a third party could not.

In an increasingly data-driven world, students are learning to harness the power of data through a new online course that complements Columbia’s existing data science programs.

In a monumental set of experiments, spread out over nearly two decades, biologists removed genes two at a time to uncover the secret workings of the cell.

“The thing people don’t get is that cybercrime is becoming automated and it is scaling exponentially,” said Marc Goodman, a law enforcement agency adviser and the author of Future Crimes. He added, “This is not about Matthew Broderick hacking from his basement,” a reference to the 1983 movie “War Games.”

Our ultimate goals are to convince Harvard to start standardizing and organizing its data and to empower Harvard students and community members to make powerful apps, services, and policies with this data.

This Notice extends the response date for NOT-OD-17-006 “Request for information (RFI): Including Preprints and Interim Research Products in NIH Applications and Reports”. Deadline to respond is Friday, December 9.

If you would like to learn more about the PhD in Data Science program and are in the NYC area, please plan to attend our Open House below. The Open House provides an overview of the doctoral program and gives you a chance to interact with faculty and staff at the NYU Center for Data Science.

Thursday, November 10, 2016
6:00pm-7:00pm

Over the past year, our top priority for Firefox was the Electrolysis project to deliver a multi-process browsing experience to users. Running Firefox in multiple processes greatly improves security and performance. This is the largest change we’ve ever made to Firefox, and we’ll be rolling out the first stage of Electrolysis to 100% of Firefox desktop users over the next few months.

But, that doesn’t mean we’re all out of ideas in terms of how to improve performance and security. In fact, Electrolysis has just set us up to do something we think will be really big.

We’re calling it Project Quantum.

We want to teach how to integrate this into your R project and build a pipeline for forecasting the sentiment of a time series using the Forecast algorithm. Forecasting sentiment time series data is useful when there is a seasonal component in a variety of use cases such as scheduling call center employees for a retail business, understanding market sentiment for stock market prediction or adjusting your social media marketing campaigns based on sentiment forecasts.