Happy new year! Here are a few stories from the data space that caught my attention recently.
It’s OK if all our jobs are belong to them
Kevin Kelly took a look at the effects of automation on society over at Wired and argues that we should welcome our forthcoming robot overlords with open arms. Kelly says that giving current work tasks, future yet-to-be-imagined tasks, and jobs we can’t do at all to machines opens up possibilities for humans to do things previously unimaginable and “will let us focus on becoming more human than we were” — much like the industrial revolution “led a greater percentage of the population to decide that humans were meant to be ballerinas, full-time musicians, mathematicians, athletes, fashion designers, yoga masters, fan-fiction authors, and folks with one-of-a kind titles on their business cards.” Kelly argues that our future employment incomes will depend on our ability to work with robots and that most of what we do won’t be possible without them.
The beginnings of the future Kelly envisions may already be coming to pass. Derrick Harris observes over at GigaOm that the big deal in big data may well be automation rather than insight. In the piece, Harris is arguing that humans are still necessary to glean the ultimate insights and to apply context, but he allows that to get there, we need machines to do the work we can’t possibly do:
“Yes, machine learning algorithms and big data technologies analyze a volume of data points that humans could never do, uncovering complex relationships the naked eye could never spot. But once the heavy lifting is done, humans come in and use their subject-matter expertise and logic to prune off bad connections, add context and maybe even inject a little serendipity into the final algorithms. Whether it’s corporate business intelligence or the consumer web, though, all of this is about automation. Data-minded people have always used data to aid in decision-making without ignoring their instincts. Big data just lets them learn a lot more, a lot faster.”
Using big data to shed light on gun violence
In the aftermath of the Newtown, Conn., elementary school shooting, much has been written about gun violence, where it stems from, and what can or should be done about it. Marc Parrish suggests in a post at The Atlantic that big data could be used to prevent such mass murders from happening in the first place by identifying likely offenders. Derrick Harris addresses Parrish’s theory in a post at GigaOm, pointing out a few flaws in his reasoning that would preclude its efficacy, namely that there’s not enough data to fuel accurate assumptions of behavior and that mass murderers don’t necessarily use their own guns to commit the crime. Harris says, however, that big data still could be employed to curb gun violence, just from a more cultural, psychological perspective. He writes:
“Perhaps — if someone were willing to undertake a massive data collection effort, carefully selecting, gathering and analyzing international data on topics such as poverty rates, mental health, gun laws, drug laws, violence in the media, known information about those who have committed murder, family composition, health care, etc. — we could actually identify commonalities or anomalies that shed some light on why certain countries have higher murder rates than others. It’s possible that Americans’ easy access to guns only facilitates a willingness to kill that has been cultivated by other factors and extends far beyond the small fraction of deaths attributable to mass murder.”
Criminal justice professor Adam Lankford argues in a post at Wired that big data and predictive analytics also could be used to better inform the response to mass shootings, and thus save more lives. Lankford points to the misinformation in the Newtown shooting — witness testimony that there were two shooters, the one who lay dead and another who fled. Lankford argues that it might have helped if responders knew “that of all active shootings that occurred in the U.S. over the last half century and yielded multiple casualties, less than 2.23 percent had been carried out by dual gunmen,” and that never in U.S. history has there been a mass murder case where one rampage shooter killed himself while the other tried to escape.
Lankford also highlights the Columbine shooting and points out that both gunmen (Columbine, he notes, was the rare dual gunman exception) had already committed suicide — unbeknownst to the police — while officers were waiting for backup and securing the school’s perimeter. Lankford argues historical data analysis of previous mass shootings can help responders predict whether or not a rampage shooter will commit suicide and how they will do it — by their own hand or by cop. “These patterns have significant implications for improving emergency response tactics and saving lives,” Lankford writes.
Study highlights data analysis opportunities, security issues
In a post at The Guardian’s Datablog, John Burn-Murdoch reviewed the International Data Corporation’s latest Digital Universe Study (PDF) and reports that “[t]he global data supply reached 2.8 zettabytes (ZB) in 2012 — or 2.8 trillion GB — but just 0.5% of this is used for analysis.” He also notes that only 3% of that 2.8 zettabytes is “tagged and ready for manipulation,” and that with global revenues tied to big data analysis estimated to hit $16.9 billion in 2015, business worldwide are facing significant opportunities.
The study also covered security measures, Burn-Murdoch reports, and found that 80% of that 2.8 zettabytes of data is unprotected. He explains that in 2012, only about a third of all data required some sort of protection, due to privacy, custody or confidentiality reasons. The study, however, showed that of that third, just 53% was found to have the required measures in place. You can read more from Burn-Murdoch’s report here, or access the study report directly here (PDF).
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