NYU Data Science newsletter – April 17, 2015

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Technologist Elon Musk, Bill Gates, and Steve Wozniak named artificial intelligence as one of humanity’s biggest existential risks. Will robots outpace humans in the future? Should we set limits on A.I.? Our panel of experts discusses what questions we should ask as research on artificial intelligence progresses. [audio, 28:15]

Creating and running software produces large amounts of raw data about the development process and the customer usage, which can be turned into actionable insight with the help of skilled data scientists. Unfortunately, data scientists with the analytical and software engineering skills to analyze these large data sets have been hard to come by; only recently have software companies started to develop competencies in software-oriented data analytics.

IBM Watson has taken a giant step deeper into the healthcare industry with the formation of a new business unit called Watson Health and a new cloud offering called the Watson Health Cloud.

Over the last year, we’ve spent a good chunk of time thinking about the ways in which data scientists and social scientists can work together. While we don’t always think the same way, use the same tools, or even speak the same mathematical languages, here at Civis we’re determined to leverage the strengths of both disciplines to understand, predict, and change human behavior for the better. In my talk, I go through some of the challenges and benefits of data science/social science collaborations. [video, 19:32]

I spent the usual long afternoon at work doing little but ordering tests, far more than I honestly thought any patient needed, but that’s what we do these days. Guidelines mandate tests, and patients expect them; abnormal tests mean medication, and medication means more tests.

My tally for the day: five hours, 14 reasonably healthy patients, 299 separate tests of body function or blood composition, three scans and a handful of referrals to specialists for yet more tests.

A software app developed collaboratively by the University of Leicester and High Efficiency Heating UK Ltd. automatically selects appropriate locations in UK rivers to site a large range of micro renewable hydro-power turbines in UK rivers and determines the environmental sensitivity of the location.

Data scientists solve real life business problems by applying a broad set of technical skills to explore, transform, and model data of various shapes and forms to produce actionable predictions. The Cloud Data Science Process (CDSP) describes the steps typically traversed in the course of completing a data science project. The steps in this process have also been conceptualized in both data mining and data science contexts as a life cycle or workflow.

At BigML we are excited to announce BigMLKit, a new open source framework for iOS and OS X that blends the power of BigML’s best-in-class Machine Learning platform with the ease and immediacy of Apple technologies.