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Marnie Landon
Works at C 2 Infinity
Lives in Toronto
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Marnie Landon

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Edd Dumbill originally shared:
 
+Audrey Watters looks back over a year of big data and data sciece.
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Marnie Landon

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Avoiding Myopia as we jump on the Analytics Lifeboat in the vast sea of Big Data
http://www.fastcoexist.com/1679015/avoiding-short-term-thinking-in-a-world-of-big-data#comments
The promise of a world in which we collect massive amounts of data is that it will change our behavior for the better.
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Marnie Landon

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Linda Dee originally shared:
 
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Alistair Croll originally shared:
 
Tomorrow, serendipity is for the rich

I gave an Ignite presentation last night in Sebastopol (which +Edd Dumbill was referring to.) It was a bit dystopian. I'll record it (better; I screwed some of it up) but here are the speaker's notes.

It’s easy to forget that humans run on jungle-surplus hardware. Our lymbic region is wired for near-term things: remembering to breathe, avoiding sabre-toothed tigers, and so on. But we have a hard time with stuff like obesity, war, and climate change. We don’t optimize well when the feedback isn’t immediate. It’s worse if we’re bad at self-reflection: Incompetent people not only make stupid decisions but also think that they’re doing just fine, something known as the Lake Wobegon Effect.

The rich and famous have a way around their limited wetware and poor decision skills. They hire personal assistants, trainers, financial planners, and life coaches as prosthetic brains to optimize their lives. But soon, everyone will be able to afford their own personal assistant. Ubiquitous computing, Moore’s Law, a tolerance for increased personal data collection, and cheap, powerful mobility mean that what started as a phone is now much, much more.

Just take a look at the wall of the Apple store and you’ll realize that we’re equipping society with prosthetic brains, able to measure every aspect of our lives, at an astonishing rate.

Instead of trainers and coaches, we hook our lives up to the machine through sensors and APIs, then develop algorithms to optimize ourselves through constant feedback loops, part of a movement known as the Quantified Self. Today, QS focuses on basic stuff: food, sex, sleep, health. But it’s moving up maslow’s hierarchy fast: Rescuetime makes us productive; Gottafeeling catalogs our moods; New York’s School of One adapts teaching to the way each student learns best.

Companies aren’t far behind, using algorithms to hire and fire better, squeeze more miles out of truckers, identify bad insurance risks, and so on. Employees have little choice but to submit to optimization if they want the job; soon, citizens won’t be able to opt out either. Countries have to remain productive, after all, if they want to maintain their standard of living.

In the early 20th century, the entry of women into the workforce and cheap consumer credit boosted GDP. In the later 20th century, the boost came from a switch from atoms to bits, which built the Internet, flattened hierarchies, shattered distribution costs, connected the world, and monetized information.

Now we’re going to turn the Quantified Self into the Quantified Society. The optimizations that come from living algorithmically will be too good to pass up. And course, like Big Banks or Big Oil, Big Feedback will soon be too big to fail. The pressure to participate will be huge. Try to opt out, and at best you’re seen as a quaint anachronism, at worst, a traitor squandering scarce resources of your society and burdening it with chronic health issues. Unplugging is unpatriotic.

Which brings me to the risk. Algorithms are bad at exceptions. Every statistician knows that when an algorithm can’t find an underlying pattern, it can’t make good predictions. But breaking rules is how new rules are made.

We call exceptions by other names: “serendipity” or “leap of faith.” Exceptions invented the minivan and the Walkman. They gave us Kiva and Wikipedia. Exceptions, by their very nature, defy traditional analysis. Exceptions made Steve Jobs take a calligraphy class, made Feynman pick locks, made Einstein dream of trains. They’re not what the algorithm would have told you to do.

What if our dependency on feedback, algorithms, and optimization means we can’t afford serendipity? No joy rides, fatty foods, or lazy Sunday naps. And no time for the rule-breaking that breaks the mould.

Of course, today, only the 1% can afford the personal assistants and prosthetic brains that optimize their lives. This isn’t a widespread movement yet, and the technologies cost thousands of dollars. But remember: once, if you had a cellphone, it meant you were important. Today, the truly important are hard to reach, while the rest of us are issued digital tethers by our employers, expected to be on call around the clock.

In a world driven by feedback, only the privileged will be able to afford serendipity instead of slavish obedience to calculated regimen. In that world, only the rich can afford to waste time and to live sub-optimally. We need to be careful of a tomorrow in which we’re feedback slaves, with every cake, idle afternoon, and guilty pleasure calculated away by some future version of Siri, chasing ever-greater increased efficiency.
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Marnie Landon

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Edd Dumbill originally shared:
 
Feynman lecturing at the University of Auckland

Via +Timo Hannay, great stuff to settle down to this holiday.

Feynman gives us not just a lesson in basic physics but also a deep insight into the scientific mind of a 20th century genius analyzing the approach of the 17th century genius Newton.

For the young scientist, brought up in this age of hi-tech PC/Power Point-based presentations, we also get an object lesson in how to give a lecture with nothing other than a piece of chalk and a blackboard. Furthermore we are shown how to respond with wit and panache to the technical mishaps that are part-and-parcel of the lecturer's life.
View four priceless archival science video recordings from the University of Auckland (New Zealand) of the outstanding Nobel prize-winning physicist Richard Feynman - arguably the greatest science lec...
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Marnie Landon

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Edd Dumbill originally shared:
 
This is quite a nice definition of what the nebulous term "big data" means in practice to businesses:

Small businesses face big data challenges when the toolsets they have become unaffordable and too complex for them to extract meaningful results from data sets in their systems, he explained.
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Churchill Club originally shared:
 
Video from The Big Data Effect program on 12.7 is now posted to our YouTube channel:12.7.11 The Big Data Effect.
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In her circles
78 people
Have her in circles
32 people
Chuck Johnson's profile photo
Debra Woods's profile photo
Romy Astrovik's profile photo
Gina Kostanecki's profile photo
AJ Christensen's profile photo
David Carter's profile photo
Scott Mills Gray's profile photo
Stanford Center for Professional Development's profile photo
Churchill Club's profile photo
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