NYU Data Science newsletter – October 12, 2015

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Technology is getting smarter, faster. Are you? Experts including the authors of The Second Machine Age, Erik Brynjolfsson and Andrew McAfee, examine the impact that “thinking” machines may have on top-management roles.

In early September I gave a keynote at Kiwi PyCon in New Zealand on effective learning for programmers. There were two pieces to the talk: one about mindset, and one about particular strategies we can use. The text below is an aspirational and lightly edited transcript of the mindset piece of that talk. There’s also a video available if you’d like to see the strategies piece.

There is a lot of debate about whether college is worth the investment, and many discount the value of a liberal arts degree at any price. While professional or pre-professional degrees, such as business or pre-law, often lead to high starting salaries, a January 2014 analysis of census data reports that “at peak earnings ages (56-60 years) workers who majored as undergraduates in the humanities or social sciences earn annually on average about $2,000 more than those who majored as undergraduates in professional or pre-professional fields.”

For success in the Second Machine Age, students would be well served to seek a liberal arts foundation coupled with 21st century skills. Joseph Aoun, president of Northeastern University and my employer, believes combining a liberal arts background with methods from domains such as analytics, statistics, and coding provides students with “a more expansive skill set that integrates both broad-based concepts and technical, quantitative content, leading to the deeper learning of each.” A successful graduate will be able to blend both sets of skills, enabling them to build on their foundation of knowledge even as new technologies and tools make their initial training obsolete.

… In analyses run by a single team, researchers take on multiple roles: as inventors who create ideas and hypotheses; as optimistic analysts who scrutinize the data in search of confirmation; and as devil’s advocates who try different approaches to reveal flaws in the findings. The very team that invested time and effort in confirmation should subsequently try to make their hard-sought discovery disappear.

We propose an alternative set-up, in which the part of the devil’s advocate is played by other research teams.

The White House’s Precision Medicine Initiative (PMI) was received with industry applause as soon as it was announced at the beginning of 2015, and the enthusiasm for tailored treatments and more accurate, personalized therapies is just beginning. As the nation waits for Congress to approve funding for the PMI included in the proposed 2016 federal budget, academic institutions, medical centers, and researchers are taking advantage of various other funding opportunities from the National Institutes of Health that promote the expansion of foundational knowledge for precision medicine applications.

“We have an incredible opportunity to advance research and make new medical breakthroughs through precision medicine, which tailors disease prevention and treatment to individuals based on genetics, environment and lifestyle,” said Department of Health and Human Services Secretary Sylvia Burwell.

Big data is coming to the university system to implement statistical methods for determining how students will fare in college and change the way campus decisions are made.

USF is one of the universities currently working on using data to help students maintain higher grades, graduate on time and find well-paying jobs after graduation. To achieve its goal, the university is using predictive analytics, a type of statistical analysis that is meant to predict the future, rather than analyze the past.

In the first fractions of a second after the Big Bang, the cosmos expanded exponentially, then became a blistering soup of fundamental particles and energy, and eventually cooled to a point where protons and electrons could combine to form neutral hydrogen. A few hundred million years later, the universe was filled with billowing clouds of hydrogen gas, entering what cosmologists call the cosmic dark ages.

Then everything changed. About 500 million years after the Big Bang, intense UV radiation suddenly began to burn into the gas, creating massive hollow holes that grew and merged. The energy onslaught continued for nearly a half-a-billion more years, ripping the neutral hydrogen back into its constituent protons and electrons and transforming the entire universe. This was the start of the era of reionization, a strange and important epoch that nonetheless remains poorly understood.

Cosmologists think that during reionization, the first stars and galaxies switched on and enormous black holes consumed anything within their reach.

The traditional pressure in academia for faculty to “publish or perish” advances knowledge in established areas. But it also might discourage scientists from asking the innovative questions that are most likely to lead to the biggest breakthroughs, according to a new study spearheaded by a UCLA professor.

Jeopardy! champion Ken Jennings and cutting-edge quiz-playing AI QANTA go head-to-CPU in quiz bowl

UW Computer Science and Engineering is pleased to host an event for both trivia aficionados and those interested in artificial intelligence: a quiz bowl match pitting a computer against the most successful Jeopardy! player of all time.

On Friday evening, a team of researchers will debut its computerized question-answering system (QANTA) in a tossup-only competition against Ken Jennings.

New tools allow researchers to share and study sensitive data safely by applying ‘differential privacy.’

Several companies are now using language-and-image-processing tech to spot what we humans can’t—or won’t.

… brain-mapping research, also known as connectomics, is currently one of the hottest fields in neuroscience, indeed in all science. Funding for connectomics-related efforts such as the EU’s Human Brain Project and the US-supported Human Connectome Project and BRAIN Initiative amounts to well over $100 million annually.

Much of this research has been aimed at gathering relatively low-resolution structural and functional data on the brain, such as from magnetic resonance imaging. But arguably the most exciting connectomics research now is aimed at producing very high-resolution maps (“reconstructions”) of brain tissue—maps that are detailed enough to reveal the actual synapses, or connection-points between neurons.