Our Biggest Existential Threat is...Google?

A while back I was walking through the parking lot at the Derby Street shops in Hingham and I saw my first, real-life Tesla. My timing was impeccable, as I gawked at the car a very pleasant older woman approached - it was her car - and we started a conversation. She gave me a complete tour of the car, what was great about it, why she bought it, the works. Toward the end of our conversation I mentioned that I thought Elon Musk was the successor to the recently departed Steve Jobs - a brilliant mind that married technology and design and appeal and might just change the world. I'll never forget what she said, "Oh, I think Musk is going to make Jobs look like a piker." (a piker is a gambler who only places small bets or someone who does things in a small way) While I didn't necessarily agree, I loved the way she phrased it - and I've been watching Elon Musk more carefully since.

So - what does this have to do with big data? I'm getting there. Clearly, Musk is not a luddite. He's involved in solar energy, electric cars, reusable rockets, and plans to bring humans to Mars to name a few. When Vanity Fair interviews Musk and he warns,“I don’t think anyone realizes how quickly artificial intelligence is advancing, particularly if [the machine is] involved in recursive self-improvement … and its utility function is something that’s detrimental to humanity, then it will have a very bad effect.”

He continued with the more dire warning: “If its [function] is just something like getting rid of email spam and it determines the best way of getting rid of spam is getting rid of humans …”

I know it's easy to dismiss this as the ravings of a lunatic - but he's not, not really. He's a visionary - a man who embraces the future, who is leading us there. But he's also a realist - and he realizes that poorly written sentient code could be our undoing. In a followup article with Computerworld, Andrew Moore - Dean of Computer Science at Carnegie Mellon offered, "At first I was surprised and then I thought, 'this is not completely crazy,' I actually do think this is a valid concern and it's really an interesting one. It's a remote, far future danger but sometime we're going to have to think about it. If we're at all close to building these super-intelligent, powerful machines, we should absolutely stop and figure out what we're doing."

So now you think either a) I'm a loon or b) what does this have to do with Google? Let us, for a moment, assume I'm not a loon. When you consider what it would take for sentient machines to cause problems it doesn't seem that intimidating. You could just pull the plug, right? Withhold energy, and no matter how malicious they were, they would just cease. Some little factory in Arizona becomes self-aware - we just cut the power, cut the internet, and wait for the problem to resolve itself. Here's where it gets interesting. What if it was one of the world's largest global resources that began to learn?

[duh duh daaaahhh]

Here's where Google comes in (finally). Last year Google acquired Deepmind Technologies, a London-based artificial intelligence firm. A recent BetaBeat article suggest that Google is interested in using this investment to have computers begin to program themselves. Which brings us back to the title. Say it's not a factory in Arizona that becomes self-aware. Instead, consider it's the Google network, or all of IBM's networks and servers taken over by a malicious Watson of the future. If these massive big-data projects begin to think and learn and adapt - are we letting the genie out of the bottle?

Don’t Let Perfect Be The Enemy of the Good!

The definition of irony includes 'an outcome of events contrary to what was, or might have been, expected'.  Take, for example, a group of people who use bicycles to offset the modern convenience of cars because they care about the environment and/or their health.  Ironically, they use technology (in the form of the Strava app) to navigate in a world that's not always welcoming.  The result?  The city of Portland (Oregon, not the much cooler Maine/New Hampshire one) "licensed a Strava metro data set of 17,700 riders and 400,000 bike trips around Portland. That adds up to 5 million BMTs (bicycle miles traveled) logged in 2013 alone"  (source: TheVerge).  The city will use that data to make bicycling safer in the city, managing infrastructure changes based on riding patterns.

There are some privacy concerns, but the company says the data has been "anonymized" and users have the ability to opt-out.  The city and the company admit the data isn't perfect, but my favorite line from the article (and an aphorism I quote often) - don't let perfect be the enemy of the good.  Simply, the city has an opportunity to begin shaping policy based on quantifiable data.  Is the data perfect?  No.  Is the data skewed (toward smartphone owners)?  A bit.  Can design decisions consider the data on 5 million miles of bicycle travel in a single year.  Absolutely!

Big data can be scary, creepy, Orwellian?  Big data can also improve the pulse of a city, and make it a little safer for bicycles to commute downtown.

Don’t Fall in Love Just BECAUSE It’s Big Data

In the YouTube video ‘The Data Scientist’s Toolbox’, one of the salient statements for me was “Be careful not to fall in love with it just BECAUSE it’s Big Data“.  This week I came across Kaleev Leetaru’s ForeignPolicy.com article Why Big Data Missed the Early Warning Signs of Ebola.  Mr. Leetaru begins with media reports citing Harvard’s HealthMap program picking up early reports of a mystery hemorrhagic fever 9 days before the World Health Organization.  He goes on to state that HealthMap was simply picking up on tweets and retweets of a newswire article (in French) reporting on a press conference held by the Guinea Department of Health.  Further, he criticizes the program for not being able to read anything but English.  He’s missing the point.

To begin with, there have been at least 10 outbreaks of Ebola in the past 10 years, so early detection of this disease was more of a test case for the software.  I don’t think anybody was surprised by an Ebola outbreak in March when the Harvard program detected chatter.  Certainly this outbreak has become a major new story with global implications, but in March – it was just another Ebola story.

What I think Mr. Leetaru is really missing is that despite it catching reporting of official statements – the program worked!  The news was detected, the system mined data from multiple sources and detected something unusual.  Could it be improved upon?  Certainly.  Is translation a missing component?  No doubt.  Still, think of the success of the program.  Health officials at the CDC could be notified days before the WHO report was able to work it’s way through bureaucracies.  Virologists who specialize in hemorrhagic fevers could be notified and placed on alert or begin communicating with colleagues in African countries.

The title Why Big Data Missed the Early Warning Signs of Ebola is misleading.  It didn’t miss the signs, it picked them up – it just happened that it picked up on official, remote, regional, page 52 below the fold news items – and in this, it succeeded.  They Harvard team is doing ground-breaking work.  If it’s not perfect yet we cannot fault them – imagine what their software will be doing in 2 years.  in 5?  In 10?  They are creating a system that will one day (in the not distant future) link what seem like unrelated news stories into the beginnings of new epidemics.  They will provide researchers invaluable data on when and where outbreaks began, helping to more quickly locate patient 0s and determine the source of infections.

Big Data didn’t miss the signs, it just wasn’t the first one to see them.  One of these days – it will be.