#3: What the Hell Am I Supposed to Do With This?

Data, data, data. We're producing more of it every day than we did in our first 200,000 years. What to do with all of this data? Well, you can mine it for intimate personal information and sell ads against it (see previous rant about ad tech). But that's the data coming from our web habits and devices.

What about the data coming from our bodies? Fitbits, food trackers, Apple Watches, blood tests, spit tests, blood pressure cuffs. Each of these has the ability to read one or more specific health stations, analyze that data on a basic level, and spit it back to you so you can move more, eat less, eat better, run faster, sleep more, take vitamin D, drink more water, etc.

But when we hear about "Big Data", what they're usually talking about is a huge collection of data points, and mining them for trends, or answers, or a diagnosis. 

Here's the issue: most data created before, I don't know, last year -- like your health records -- is created and organized on paper, or maybe digitally, but poorly. So not to a standardized, universal format (best case scenario). For example, on your medical chart, a patient outcome might just be a box on a piece of paper where your doctor just fucking scribbled "Jeffrey feels better today". Which doesn't do shit for an algorithm, really. And it's frustrating, because as you can imagine, "patient outcome" is a fairly vital piece of the puzzle when you're trying to figure out which treatment methods work, and which don't. All of which makes it relatively impossible for computers and their algorithms to make any sense of them. To get the freshest take, we've gotta corral everything into something remotely readable. THEN we can apply so-called "deep learning".

But the good news is for much of the NEW data, readable datasets are being considered ahead of time. Data from the devices above, or from self-driving cars, or all the sensors deployed across a city. We're making learning from our mistakes! I'm proud of us.

Anyways: today's links mostly focus on data, and what we're trying to do with it. And then there's some other stuff you should definitely read, too.


On to the news!

1. Remember: datasets over/before algorithms. - Edge

"In 2011, IBM’s Watson became the world Jeopardy! champion using a variant of the mixture-of-experts algorithm published twenty years earlier, but utilized a dataset of 8.6 million documents from Wikipedia, Wiktionary, Wikiquote, and Project Gutenberg updated one year prior."


2. Imagine a world where every classroom has a hand-held DNA sequencer. Imagine the REAL potential of spitballs. - The Atlantic

"If an astronaut could decipher the full genetic code of whatever’s plaguing her, she could identify the offending bug and work out if it’s vulnerable to any drugs. But until recently, this scenario would have been laughably impractical. Sequencers were all the size and weight of microwaves and fridges. They’d be impossible to cart aboard a space station, and probably wouldn’t have survived the trip."


3. Our tools to diagnose cancer on the cellular level just became 17% more accurate - Popular Science

"The microscope is called a photonic time stretch microscope, which uses nanosecond-long pulses of light broken into lines to capture images of hundreds of thousands of cells per second. Those images are fed into a computer program, which categorizes 16 of the cells’ different physical features, such as diameter, circularity, and how much light they absorb."


4. And then the inevitable question. What if there's too much data? What if we don't know what it means yet? What if it causes a panic? - Wired

"So if a woman with a family history of breast cancer finds out she carries an ATM mutation, does that completely explain her family history? Should her sisters and daughters get tested, too? Should she be offered a preemptive mastectomy? The answer with BRCA is usually yes. But with the ATM’s modest increase in risk, it’s not so obvious."


5. And now for something different: a thorough analysis of basic income. AKA What if we just gave people money? - FiveThirtyEight

"Basic income challenges our notions of the social safety net, the relationship between work and income, and how to adapt to technological change. That makes it one of the most audacious social policy experiments in modern history. It could fail disastrously, or it could change everything for the better."


6. And finally, here's a short video about the inevitable, upcoming antibiotic apocalypse. Have a great weekend!