NYU Data Science newsletter – April 19, 2016

GROUP CURATION: N/A

A couple of weeks ago there was a kerfuffle at Columbia, written up in the Columbia Spectator by Julie Chien. A machine learning course, taught in the CS department by Professor Satyen Kale, was assigned to “Help design RoboCop!” using Stop and Frisk data.

The title was ill-chosen. Kale meant it to be satirical, but his actual wording of the assignment didn’t make that clear at all, which is of course the danger with satire. Given the culture of CS, people misinterpreted and were outraged by it. This eventually led to an organized group of students called ColorCode to issue a statement in protest of the assignment, and then for Kale to issue an apology, after which ColorCode issued a second statement.

I’m really glad this conversation is finally happening, even if the assignment was a disaster.

In recent years, the National Science Foundation in Arlington, Virginia, has struggled with the logistics of evaluating a rising number of grant proposals that has propelled funding rates to historic lows. Annual or semiannual grant deadlines lead to enormous spikes in submissions, which in turn cause headaches for the program managers who have to organize merit review panels. Now, one piece of the agency has found a potentially powerful new tool to flatten the spikes and cut the number of proposals: It can simply eliminate deadlines.

This week, at an NSF geosciences advisory committee meeting, Assistant Director for Geosciences Roger Wakimoto revealed the preliminary results from a pilot program that got rid of grant proposal deadlines in favor of an anytime submission. The numbers were staggering. Across four grant programs, proposals dropped by 59% after deadlines were eliminated.

… Q1. What kind of companies generate Big Data, besides the Internet giants?

James Kobielus: Big data isn’t something you “generate.” Rather, the term refers to the ability to achieve differentiated value from advanced analytics on trustworthy data at any scale. In other words, it’s a best practice, not a specific type of data or even a specific scale of data (measured in volume, velocity, and/or variety).

When considered in this light, you can identify big data analytic applications in every industry.

IRCS was founded just over 25 years ago in 1990, strengthening a tradition of cross-disciplinary collaboration in Cognitive Science at Penn that dates back to the early 1960s and building on a formal program in Cognitive Science that was initiated in 1978 with support from the Alfred P. Sloan Foundation.

Also, in the academic research circle of life:

Final OK for Science and Engineering Complex in Allston (April 14, Harvard Gazette)

Awarded! New centre of excellence and research building for collective behaviour (April 18, University of Konstanz, Department of Collective Behaviour)

Amy Ingram, the artificial intelligence personal assistant from startup X.ai, sounds remarkably like a real person. The company designed her to take on the mundane tasks of scheduling meetings and e-mailing about appointments. If a bot had access to your calendar and was cc-ed on correspondence, why couldn’t it do the work for you? After she made her debut in 2014, users praised her “humanlike tone” and “eloquent manners.” “Actually better than a human for this task,” a beta tester tweeted. But what most people don’t realize about this artificial intelligence is that it isn’t totally artificial: Behind almost every e-mail is an actual human—someone like 24-year-old Willie Calvin.

Calvin, who worked as an AI trainer for X.ai before he said he quit in October, was part of the reason Amy never tripped up, sending the sort of blind response that reveals she’s a bot. The company advertises Amy as an AI personal assistant who can “magically schedule meetings,” and its software does scan e-mails and can usually guess that “tomorrow” means Tuesday. But the system isn’t yet ready to take the next step on its own. Multiple former AI trainers said that as recently as a few months ago, trainers looked over parts of almost all incoming e-mails — to evaluate what Amy guessed the user was saying— before Amy generated an auto response.

Personified user interfaces, like chat bots or agents, are the new thing once again. But despite advances in artificial intelligence, they still have many issues and drawbacks compared to direct-manipulation interfaces. There was a debate around these interfaces in the 1990s, and it seems to be bound to repeat itself.

In the last few weeks, Facebook unveiled a new push to use chat bots on Messenger, and Microsoft has a new platform for building bots. These bots are supposed to be the new way of doing everything, from delivering news (like Quartz’ recent iPhone app) to letting you order pizza, flowers, and everything else.

These bots act like people, in that they talk to you via text chat and try to understand free-form text. They don’t attempt to pass the Turing Test, but they promise to be smarter (and more attentive) than an overworked call center worker tending to a dozen people at the same time.

There has recently been an explosion of cynicism in the Twitterverse (and generally in the tech community) about machine learning and AI. This was taken up a notch after Facebook’s F8 conference earlier this week and the splashy launch of their bot platform on Messenger.

I understand the cynicism. When you dig in, many of the companies claiming to be leveraging machine learning are doing nothing of the sort. It’s easy to find companies that are building weak ML-driven user experiences, turning off early adopters. Others are building products centered around gimmicky ML features that won’t create any sort of long term barrier to competitors, frustrating would-be investors.

I think we’re probably on our way up to the peak of the AI hype cycle right now.

Ethan White, Ph.D., is an investigator in the foundation’s Data-Driven Discovery initiative and directs the Quantitative Macroecology Lab at the University of Florida. … In our first installment of Beyond the Lab, Ethan discusses his work in bringing ecology to the data-intensive era.

Data about us—where we are, what we’re buying, what we’re reading—is being collected everyday, everywhere. Our cell phones, TVs, wearables, watches and even our Facebook feeds collect data about our daily lives. The Robert Wood Johnson Foundation (RWJF) is convinced this data also contains important insights into how we live, learn, work and play—and we think harnessing these insights could lead to major improvements in the health of all Americans.

Efforts to make sense of all this personal data and unlock the knowledge within are underway.

Extension for Jupyter Notebook that enables the layout and presentation of grid-based dashboards from notebooks.

… My colleagues and I have another interesting project that I want to introduce you to as well. It’s called Big Data University and is an online educational site that provides free courses on data science, big data, and programming.