Big Data, Machine Learning, Google Translate--the data-driven algorithmic black box--
"... New, large, cheap data sets and powerful analytical tools will pay dividends – nobody doubts that. And there are a few cases in which analysis of very large data sets has worked miracles. David Spiegelhalter of Cambridge points to Google Translate, which operates by statistically analysing hundreds of millions of documents that have been translated by humans and looking for patterns it can copy. This is an example of what computer scientists call “machine learning”, and it can deliver astonishing results with no preprogrammed grammatical rules. Google Translate is as close to theory-free, data-driven algorithmic black box as we have – and it is, says Spiegelhalter, “an amazing achievement”. That achievement is built on the clever processing of enormous data sets...."
(read more here: FT.com)
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Causation, Sampling Bias, Big Problems With Big Data
Problems With Big Data?
The promise that “N = All”, and therefore that sampling bias does not matter, is simply not true in most cases that count. As for the idea that “with enough data, the numbers speak for themselves” – that seems hopelessly naive in data sets where spurious patterns vastly outnumber genuine discoveries. “Big data” has arrived, but big insights have not. The challenge now is to solve new problems and gain new answers – without making the same old statistical mistakes on a grander scale than ever. (source infra)
Big data: are we making a big mistake? - FT.com: "...Big data is a vague term for a massive phenomenon that has rapidly become an obsession with entrepreneurs, scientists, governments and the media... As with so many buzzwords, “big data” is a vague term, often thrown around by people with something to sell... Consultants urge the data-naive to wise up to the potential of big data. A recent report from the McKinsey Global Institute reckoned that the US healthcare system could save $300bn a year – $1,000 per American – through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes. But while big data promise much to scientists, entrepreneurs and governments, they are doomed to disappoint us if we ignore some very familiar statistical lessons. “There are a lot of small data problems that occur in big data,” says Spiegelhalter. “They don’t disappear because you’ve got lots of the stuff. They get worse.”.... Who cares about causation or sampling bias, though, when there is money to be made?...There’s a huge false positive issue...“We have a new resource here,” says Professor David Hand of Imperial College London. “But nobody wants ‘data’. What they want are the answers.” To use big data to produce such answers will require large strides in statistical methods....we’re flying a little bit blind at the moment...." (read more at the link above)
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The promise that “N = All”, and therefore that sampling bias does not matter, is simply not true in most cases that count. As for the idea that “with enough data, the numbers speak for themselves” – that seems hopelessly naive in data sets where spurious patterns vastly outnumber genuine discoveries. “Big data” has arrived, but big insights have not. The challenge now is to solve new problems and gain new answers – without making the same old statistical mistakes on a grander scale than ever. (source infra)
Big data: are we making a big mistake? - FT.com: "...Big data is a vague term for a massive phenomenon that has rapidly become an obsession with entrepreneurs, scientists, governments and the media... As with so many buzzwords, “big data” is a vague term, often thrown around by people with something to sell... Consultants urge the data-naive to wise up to the potential of big data. A recent report from the McKinsey Global Institute reckoned that the US healthcare system could save $300bn a year – $1,000 per American – through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes. But while big data promise much to scientists, entrepreneurs and governments, they are doomed to disappoint us if we ignore some very familiar statistical lessons. “There are a lot of small data problems that occur in big data,” says Spiegelhalter. “They don’t disappear because you’ve got lots of the stuff. They get worse.”.... Who cares about causation or sampling bias, though, when there is money to be made?...There’s a huge false positive issue...“We have a new resource here,” says Professor David Hand of Imperial College London. “But nobody wants ‘data’. What they want are the answers.” To use big data to produce such answers will require large strides in statistical methods....we’re flying a little bit blind at the moment...." (read more at the link above)
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Mobile Technology, Big Data, Health
After decades as a technological laggard, medicine has entered its data age. Mobile technologies, sensors, genome sequencing, and advances in analytic software now make it possible to capture vast amounts of information about our individual makeup and the environment around us. The sum of this information could transform medicine, turning a field aimed at treating the average patient into one that’s customized to each person while shifting more control and responsibility from doctors to patients.(source infra)
Can Mobile Technologies and Big Data Improve Health? | MIT Technology Review: ".....The question is: can big data make health care better? “There is a lot of data being gathered. That’s not enough,” says Ed Martin, interim director of the Information Services Unit at the University of California San Francisco School of Medicine. “It’s really about coming up with applications that make data actionable.”
"The business opportunity in making sense of that data—potentially $300 billion to $450 billion a year, according to consultants McKinsey & Company—is driving well-established companies like Apple, Qualcomm, and IBM to invest in technologies from data-capturing smartphone apps to billion-dollar analytical systems. It’s feeding the rising enthusiasm for startups as well. Venture capital firms like Greylock Partners and Kleiner Perkins Caufield & Byers, as well as the corporate venture funds of Google, Samsung, Merck, and others, have invested more than $3 billion in health-care information technology since the beginning of 2013—a rapid acceleration from previous years, according to data from Mercom Capital Group...."(read more at link above)
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Can Mobile Technologies and Big Data Improve Health? | MIT Technology Review: ".....The question is: can big data make health care better? “There is a lot of data being gathered. That’s not enough,” says Ed Martin, interim director of the Information Services Unit at the University of California San Francisco School of Medicine. “It’s really about coming up with applications that make data actionable.”
"The business opportunity in making sense of that data—potentially $300 billion to $450 billion a year, according to consultants McKinsey & Company—is driving well-established companies like Apple, Qualcomm, and IBM to invest in technologies from data-capturing smartphone apps to billion-dollar analytical systems. It’s feeding the rising enthusiasm for startups as well. Venture capital firms like Greylock Partners and Kleiner Perkins Caufield & Byers, as well as the corporate venture funds of Google, Samsung, Merck, and others, have invested more than $3 billion in health-care information technology since the beginning of 2013—a rapid acceleration from previous years, according to data from Mercom Capital Group...."(read more at link above)
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Palantir, Propeller, Data
Palantir snaps up Propeller — its second deal this week: ".... The two deals raise the question as to whether Palantir is embarking on an acquisition spree of consumer-facing apps. I’ve reached out to the company for more color, (update: the company declined to comment further) but until then, have some rampant speculation: The simple explanation seems to be Palantir has decided, ten years in, that it’s easier to buy talented data teams than to recruit them. The complex explanation, oversimplified: Palantir needs to enter new markets leading up to its IPO, as the slow sales cycle of its current markets — government, military and finance — isn’t appealing to Wall Street. Palantir is known for its secrecy (it even counts the CIA among its investors), but in recent months the company has tamped down IPO speculation, telling CNBC it has no plans to go public...."
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