Is the Internet Remaking Us? Part II

Does the Internet make us smarter or dumber?

@smalljones dug up the following news articles in response to a discussion on this topic that will be held in our department this Friday afternoon. It is pure coincidence that the topic of the discussion is whether or not the Internet is remaking us — a topic I blogged on earlier today and last week (re: the shortcomings of multitasking for most technology users). I wanted to share these gems for those of you who have the attention span to read two news articles and watch a video!

JUNE 4, 2010
Does the Internet Make You Smarter?
Amid the silly videos and spam are the roots of a new reading and writing culture, says Clay Shirky.

The present is, as noted, characterized by lots of throwaway cultural artifacts, but the nice thing about throwaway material is that it gets thrown away. This issue isn’t whether there’s lots of dumb stuff online—there is, just as there is lots of dumb stuff in bookstores. The issue is whether there are any ideas so good today that they will survive into the future. Several early uses of our cognitive surplus, like open source software, look like they will pass that test.

JUNE 5, 2010
Does the Internet Make You Dumber?
The cognitive effects are measurable: We’re turning into shallow thinkers, says Nicholas Carr.

What we seem to be sacrificing in all our surfing and searching is our capacity to engage in the quieter, attentive modes of thought that underpin contemplation, reflection and introspection. The Web never encourages us to slow down. It keeps us in a state of perpetual mental locomotion.

It is revealing, and distressing, to compare the cognitive effects of the Internet with those of an earlier information technology, the printed book. Whereas the Internet scatters our attention, the book focuses it. Unlike the screen, the page promotes contemplativeness.


Clay Shirky looks at “cognitive surplus” — the shared, online work we do with our spare brain cycles. While we’re busy editing Wikipedia, posting to Ushahidi (and yes, making LOLcats), we’re building a better, more cooperative world.

Thanks, B.W. for the video link.

What are your thoughts on this topic? Do you believe the Internet is making us smarter…or dumber?

Is the Internet Remaking Us? Part I

We wonder, as the sum of all our knowledge and memories is uploaded, converted into bits, tagged and indexed, are we sacrificing what makes us human? Or evolving what it means to be human?” Jordan Clarke asks in his animation below, entitled Internet. The tagline for the animation is that it is “a visual metaphor of a strange and a seemingly organically created place called the internet”.

I thought this was and is an interesting question — are we evolving as humans, or sacrificing our human-ness? I blogged recently about how research indicates multitaskers still can’t multitask. Researchers’ study results continue to reveal that technology is re-wiring our brains. Researchers aren’t sure how this is being done, much less whether or not this is good for us or bad for us as a species.

As Jordan says, “All we know is that the Internet is here now, and although it is us who made it, it is the Internet that is remaking us.

What is your opinion of Jordan’s statements and the continuing research results? Do you believe we are sacrificing what it means to be human in pursuit of technology, or do you think we are merely evolving as humans?

Data Envisioned as Flowing through a Cityscape

Or, should I say, a cityscape envisioned as data?

The animation below is called Data; it was created by Carine Bigot (@c4rin3). It shows data flowing through the streets of a city, with an accompanying futuristic soundtrack. As you watch the animation you will see the data flow from above and around the “buildings”, as though you are flying. There are some nice 3D effects in a 2D-scape.

So, do you think this is a city envisioned as data, or data envisioned as a city? I couldn’t quite decide.

The Humanities Take on Data Mining via Google Books

binary dataThe Humanities are “Going Google”, according to Marc Parry of The Chronicle, in a piece he wrote a few weeks ago.

The gist of the article is that some Humanities scholars are very interested in data mining the texts scanned in for the Google Books Project.

Why do they want to use Big Data mining techniques to scan through entire corpuses of novels from a particular period? “The data are important because scholars can use these macro trends to pinpoint evolutionary mutants like Sir Walter Scott”, one scholar noted.

Some critics rightfully ask, what will this tell us that we don’t already know?

Their answer is that computers won’t destroy interpretation. They’ll ground it in a new type of evidence.

Still, sitting in his darkened office, Mr. Moretti is humble enough to admit those “cellars of culture” could contain nothing but duller, blander, stupider examples of what we already know. He throws up his hands. “It’s an interesting moment of truth for me,” he says.

(I think this is a backhanded critique of “research” in general, so I had a good laugh when I read this paragraph.)

Other takeaways — Google Books was not built for data mining, it was built to create content to sell ads against. It was built with the intention that each book will be read, one at a time, not data mined. The interfaces aren’t there for this kind of mining, and the metadata is poor to say the least. (Then again, metadata is generally inadequate; this problem is so “known” I won’t provide a citation!)

What do you think are the moral, legal, and scholarly implications (if any) of Google turning over thousands of scanned books to a handful of scholarly institutions, such as Stanford, for data mining?

The Multiple Aspects of Data Science

binary dataEarlier this month, Nathan Yau at FlowingData posted Mike Loukides‘ analysis of data science from O’Reilly Radar. I finally found some time to read it.

I really enjoyed the post. The author entitled it, “What is data science?“, and covered the various aspects of the newbie field, primarily from a commercial point of view. He examined: what is data science?; where data comes from; working with data at scale; making data tell its story; and, data scientists. His analysis is that the, “future belongs to the companies and people that turn data into products”.

I thought he did an excellent job of discussing that particular field. The one aspect he did not mention as being part of data science has to do with my field: managing the data over the indefinite long-term. The long-term storage and accessibility of the data is just as important as how you use it and what you find in it. But then again, I’m biased. I would like data scientists to examine the following questions as part of their job.

  • What do you keep?
  • For how long?
  • What do you throw out?
  • Can you legally throw it out?
  • If not, how do you provide access to it for the indefinite long-term?

My point is that the perception that because storage is cheap and keeps getting cheaper, we should just, “keep everything”, isn’t cost-effective. Think, for a moment, about a company that has yottabytes of data, of which only 90% is being used. Should you pay to store and migrate that data? What if all companies are paying to store and provide access to data, 90% of which isn’t used, and they are not legally (or morally, for that matter) required to keep that information. Should they? What about the costs of creating the electricity itself, plus the cost of purchasing it, plus the costs related to buying new machines, and the human time involved in migrating the data every few years?

The long-term archiving of data, in my opinion, needs to be as much a part of data science as the analysis and creative use of the data.

Now that I’ve gotten off my data archive soapbox, I’m going to throw out some of my favorite quotes from the article.

We’ve all heard a lot about “big data,” but “big” is really a red herring. Oil companies, telecommunications companies, and other data-centric industries have had huge datasets for a long time. And as storage capacity continues to expand, today’s “big” is certainly tomorrow’s “medium” and next week’s “small.” The most meaningful definition I’ve heard: “big data” is when the size of the data itself becomes part of the problem. We’re discussing data problems ranging from gigabytes to petabytes of data. At some point, traditional techniques for working with data run out of steam.

Which is why you need Information Scientists. :-)

Data science isn’t just about the existence of data, or making guesses about what that data might mean; it’s about testing hypotheses and making sure that the conclusions you’re drawing from the data are valid.

Data science requires skills ranging from traditional computer science to mathematics to art.

According to DJ Patil, chief scientist at LinkedIn (@dpatil), the best data scientists tend to be “hard scientists,” particularly physicists, rather than computer science majors. Physicists have a strong mathematical background, computing skills, and come from a discipline in which survival depends on getting the most from the data. They have to think about the big picture, the big problem. When you’ve just spent a lot of grant money generating data, you can’t just throw the data out if it isn’t as clean as you’d like. You have to make it tell its story. You need some creativity for when the story the data is telling isn’t what you think it’s telling.

Entrepreneurship is another piece of the puzzle. Patil’s first flippant answer to “what kind of person are you looking for when you hire a data scientist?” was “someone you would start a company with.” That’s an important insight: we’re entering the era of products that are built on data. We don’t yet know what those products are, but we do know that the winners will be the people, and the companies, that find those products.

Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdiscplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”

The part of Hal Varian’s quote that nobody remembers says it all:
The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.

What skills and knowledge do you think make a good data scientist? Do you think the ability to manage data for the long-term should also be a required skill?

Multitaskers Still Unable to Multitask Well

Multi-tasking testMatt Richtel at The New York Times wrote a piece a couple of weeks ago called, “Hooked on Gadgets, and Paying a Mental Price“. He profiled the Campbell family, who live outside of San Francisco, to demonstrate the toll the constant barrage of data via smartphones, computers, and the iPad, takes on both individuals and the family unit. Each family member cannot pay attention to what is going on around them in the present, either with tasks at hand or with other family members who are physically present. They all have a hard time unplugging, even on vacation. Each of them pays a price. The children, with their school work and grades. The adults, with the quality of their work output.

The author examined the scientific research behind high- and low-multitasking. The gist of it is that research results indicate that high multitaskers score low on attention and memory. The author writes: “While many people say multitasking makes them more productive, research shows otherwise. Heavy multitaskers actually have more trouble focusing and shutting out irrelevant information, scientists say, and they experience more stress.” As well, “…scientists are discovering that even after the multitasking ends, fractured thinking and lack of focus persist. In other words, this is also your brain off computers.”

Scientists believe our brains our being re-wired by technology, but they aren’t sure how, and if our brains are being rewired, whether or not that is good for us or bad for us as a species.

The good news regarding our brain rewiring is that, “imaging studies show the brains of Internet users become more efficient at finding information. And players of some video games develop better visual acuity.”

Apparently, only 3% of the population are “super-taskers” that can handle multiple information streams well. I long ago put myself in the corral of low multitaskers. I limit my distractions, or I cannot concentrate on the task at hand, whether that is research or baking cookies. The article includes two tests to gauge your level of concentration & focus, as well as how well you juggle tasks. I have focus and concentration, but I am also a “low multitasker”.

While these results aren’t new, I still find them a nice reinforcement of my own mode of working. I prefer to concentrate on thing at at time, and unplug from technology when I can. I do find my concentration and thinking abilities improve when I simply shut off Skype, email, etc., and concentrate on my work for a set period of time. To me, it is the same as shutting your office door to focus on work. As for the Campbells, in my opinion, they need to lock up their technology and take a break from it all!

What do you think about multitasking? Are you more or less efficient with multiple distractions around you? Do you take breaks from technology, or are you always on?