NYU Data Science newsletter – September 4, 2015

GROUP CURATION: N/A

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

Microsoft this past spring announced Project Oxford, a set of SDKs and APIs that allow developers to build “intelligent” applications without having to learn machine learning. Using Oxford’s face, speech, and vision APIs, developers can create applications that recognize facial features, analyze images, or perform speech-to-text or text-to-speech translations.

In an interview with InfoWorld Editor at Large Paul Krill, Microsoft’s Ryan Galgon, senior program manager responsible for the Project Oxford platform and technologies, talked about the goals behind Oxford, emphasizing its potential in the Internet of things.

Here’s a caricature of a data science project: your company or client needs information (usually to make a decision). Your job is to build a model to predict that information. You fit a model, perhaps several, to available data and evaluate them to find the best. Then you cross your fingers that your chosen model doesn’t crash and burn in the real world.

We’ve discussed detecting if your data has a signal. Now: how do you know that your model is good? And how sure are you that it’s better than the models that you rejected?

… UC Berkeley is piloting a class this fall that faculty say will teach students how to engage with this information in a digitized world, where data are increasingly ubiquitous.

The new four-unit course, “Foundations of Data Science” — cross-listed as Statistics 94 and Computer Science 94 — combines introductory statistics and computational concepts with hands-on work involving hard data that brings “real-world relevance,” according to the program’s website. The course is a part of the new Data Science Education Program, a project that was initiated last year in response to strong student interest in learning programming and statistics.

… The potential for improved human intelligence is enormous. Cognitive ability is influenced by thousands of genetic loci, each of small effect. If all were simultaneously improved, it would be possible to achieve, very roughly, about 100 standard deviations of improvement, corresponding to an IQ of over 1,000. We can’t imagine what capabilities this level of intelligence represents, but we can be sure it is far beyond our own. Cognitive engineering, via direct edits to embryonic human DNA, will eventually produce individuals who are well beyond all historical figures in cognitive ability. By 2050, this process will likely have begun.