I used to be a computer scientist. I could respectably program in more than ten languages. I pondered the theoretical limits of computation and ways to overcome or exploit them. I tinkered solutions to arcane problems in artificial intelligence. For a short time, I even searched for the Holy Grail.

You see, there’s a kind of Holy Grail in computer science. No one has been able to write an algorithm that can compute this particularly hard set of problems (known as NP-complete) in a reasonable amount of time. A reasonable amount of time means less than a billion years.

One example is the so-called Traveling Salesman problem. What is the most efficient route a traveling salesman can take to visit all the cities in his sales area, (efficient meaning the least amount of driving time)? You can figure out the optimal route for a handful of cities, but as soon as you get above ten or so, the complexity of the problem grows astronomical. Analogous problems show up for routing traffic on the internet, routing airplanes between airports, coloring world maps, or whenever you go on a run of errands and want to be efficient with your driving time.

The Holy Grail algorithm would make this optimization problem tractable. No one knows if it exists. No one has been able to prove that it doesn’t, though they’ve tried. What we do know is that if a solution is found to any of the NP-complete problems, we’ll be able to solve all of them. That would mean instant fame, fortune and a shiny pedestal in computer history.

I once worked for an eccentric and brilliant Computer Science professor (let’s call him Ben) who was determined, in his own way, to find the Holy Grail algorithm. Ben had a hilarious sense of humor, was a great teacher, and could read Yoga sutras in the original Sanskrit. Working in the field of Artificial Intelligence, Ben had developed ingenious methods for getting computers to learn. He made money on the stock market with an automatic trading AI he’d written. He created a chess playing AI that started out with zero knowledge of the game but learned to play respectable chess after losing (and learning from) thousands and thousands of games. Most chess-playing AIs are pre-programmed with sophisticated models of chess strategy.

Yet despite all his practical successes in Artificial Intelligence solving heuristic problems that demanded merely excellent solutions as opposed to optimal ones, Ben had Holy Grail on the brain. He was obsessed with a particular NP-complete problem known as the 8-puzzle or N-puzzle, the old sliding tile game where you have to slide the scrambled tiles back into order. Ben felt that existing algorithms weren’t making optimal use of past experience – they were ignoring valuable lessons learned in the early stages of problem solving that could more quickly lead to a solution later on. He called it a waste of computation. And he was convinced he could discover a new path to solving the 8-puzzle and thereby find the Grail.

Of all my time spent working in Computer Science, it was probably the series of months I spent working with Ben, searching for the Holy Grail algorithm, that led me to realize it wasn’t the field for me. This is not to say it wasn’t time well-spent. I actually loved the work. It was fascinating and fed the part of me that sought beauty in the mathematical and abstract. I just eventually came to the conclusion that I was more interested in human beings than computers. And of course, I continue to be fascinated by the combination of the two. Still, my time with Ben was illuminating, and I think it’s a story worth sharing.

We would meet once a week or so. In order to gain some new foothold in solving the 8-puzzle – which begins in a random state and ends up in exactly one solution state – I must have dreamt up and coded a million ways to map out its strange, mountainous landscape; to find a way to see the end from wherever you happened to be starting from. It’s sort of like being trapped inside a garden maze with someone calling to you from the exit. You follow the sound of the voice and seem to be making headway, but over and over you come to a dead end that stops just short of the exit. So you turn around and try a different route. In other words, the voice calling to you is not enough. You need to be able to look down a path without walking down it and somehow know if it’s promising or not. If you can figure out a way to do that, you’ve solved NP-complete and found the Holy Grail.

Each meeting with Ben went much the same. I would share what I’d tried that week and point out the flaw I’d discovered in our earlier reasoning. Over the course of an hour, we’d inevitably have a leap of insight into a promising new technique to try out. We were back on the brink of Eureka! As the meeting wound down, the conversation would drift… to chess, metaphysics, Sanskrit, stock market prediction, yoga, or the philosophy of J. Krishnamurti.

One afternoon in his office, we sat talking while chainsaws buzzed gratingly outside Ben’s window. Every ten minutes or so, an enormous redwood tree would come crashing down. They were clearcutting to make room for the brand new Engineering building. As it fell, each tree made the most horrific moaning sound, something like a blue whale dying, not a redwood. I made a remark about the beautiful and sad old trees. Ben turned and looked out the window.

“I know, it’s terrible. What a waste of computation.”

Later that week, I’d be up at night coding our latest wizardry, some little algorithmic sleight-of-hand we’d dreamed up. As always, without fail, another flaw in our reasoning would stare back from the terminal screen in the strange half-light of dawn. Another devil in the details. Another promising path that dead-ended just short of the exit.

The weeks slid by. During the drifting, philosophical epilogues to our meetings, I began to wonder if Ben secretly knew our Holy Grail search was futile. Was he playing a sophisticated joke on me? Was the 8-puzzle merely a kind of algorithmic Zen koan intended to reveal the limits of my own mind, in order for me to let go of it? Was our collaboration an allegory for the futility of striving after rational answers to absurd questions? Was he trying to tell me that life was purposeless?

After precisely one too many of those Holy Grail nights, I found myself at a crossroads. I took a good long look down the path I was about to take – without walking down it. And you know what? It didn’t look promising. A waste of computation, really.

Edward Tufte at Stanford

Edward Tufte spoke at Stanford this afternoon and I had the pleasure of being in attendance. It was an unconventional talk, as far as academic lectures go, for Tufte was speaking “in the first person” about his own life: his origins in rural Nebraska, his education and formative years, his mentors who influenced his thinking, and the turning points that signaled moments of profound reorientation. As Tufte noted, for a sample size of N=1, the estimated variance is infinite; so other sources should be consulted.

Tufte has had a remarkable career and speaks as someone who appears to have found the courage to follow his bliss, leaving a tenured professorship at Yale to self-publish his famous books on visual information, go on speaking tours, and make large-scale landscape art in his Connecticut backyard.

There were three big lessons I took away from his talk.

Contribute to forever knowledge.

The most important decision a researcher makes is choosing what problem to focus on. One should choose problems that are not only profoundly important, but ones for which good progress is possible. It’s worth nothing to work on grand problems and make no progress. Tufte’s own compass for this decision: contribute to Forever Knowledge. That is, create knowledge that will be universally useful to humankind in any time or place in human history. Tufte ditched his career as a political economy theorist because he found he was working on only temporarily important problems, things he decided were not worth his “time, energy, passion and mind.”

Be self-exemplifying.

In whatever one’s work, be not only a great communicator of ideas and practices, be an exemplar of those same practices and this will communicate the value of what you are saying far better than anything else. Tufte’s books are not only superb treatises on the visual display of information, they are also exemplary demonstrations of clear visual communication.

Strive for personal utopia.

Here again, Tufte is, as he presents it, self-exemplifying. While utopian cultures may be unattainable, you can pretty well approximate an ideal life through clarity of purpose, courage to act on that purpose, and, most importantly, doing what you love.

The poem Tufte opened his talk with was excerpted from T. S. Eliot’s Four Quartets, fitting for a self-reflection.

Time present and time past
Are both perhaps present in time future
And time future contained in time past.
If all time is eternally present
All time is unredeemable.
What might have been is an abstraction
Remaining a perpetual possibility
Only in a world of speculation.
What might have been and what has been
Point to one end, which is always present.
Footfalls echo in the memory
Down the passage which we did not take
Towards the door we never opened
Into the rose-garden. My words echo
Thus, in your mind.
But to what purpose
Disturbing the dust on a bowl of rose-leaves
I do not know.
Other echoes
Inhabit the garden. Shall we follow?

This is a 1728 Geometry text.

Geometry was the network science of its day, with its richly visual mathematical aesthetic. This is the sort of beautiful abstraction that would drive someone to spend years of life teasing out the endless permutations of a set of axioms. 1728 was the height of the Age of Enlightenment, long before Godel came and tread on the dreams of the humble mathematical ascetic.

Today, researchers of all stripes learn an unspoken rule: beautiful visualization of data makes for “sexy” science. In other words, cool information graphics lead to tenure. It’s partly because visual communication is simply more compelling and has a wider mass appeal. That’s why networks research shows up in the New York times: because it has sexy graphics, not because it’s going to catch terrorists.

It’s no wonder that someone like Edward Tufte, an authority on the visual display of quantitative information, is a kind of cult hero. His books are the Kama Sutra of information graphics.

The tag cloud displayed on my name tag at Stanford University’s H-STAR faculty retreat.

I created tag clouds for every professor’s name tag to visualize their research interests based on research statements and resumes. It was such a treat to watch these great minds interacting and using the tag clouds as launching points for conversations.

I made the tag clouds at TagCrowd. Check it out and play with creating your own clouds from your papers, resume, poetry, chat logs, or whatever suits your fancy.

Despite anonymizing user id’s, the search queries often include information that make it easy to associate them with a person. The query data include social security numbers, credit card numbers, porn queries, evidence of intent to engage in criminal activities, etc.

AOL has since removed the data, but it’s spreading like wildfire over the internet on mirrors and torrents. I was able retrieve a complete copy of it (2 gigabytes, uncompressed) in about an hour.

As a scientist who does research that could would really benefit from data like this, I can tell you: this is big. Big and dirty.

Ethically speaking…should we, as researchers, ignore that this data exists or deal with it pragmatically as an unfortunate accident?

On one hand this is extremely useful and compelling data for a host of social and computer sciences; on the other, it is an unequivocally criminal violation of ethical standards.

Given the Google subpoena, big brother NSA, and the ethical debates about scientific research this story is provoking in mass media, this feels like a watershed moment.

No one can ever create a ‘clean’ version of this data since it could always be traced back to the original, identifiable information.

Here’s a possible scenario:

Most scientists will hesitate to research it, but some rebels will and no doubt find interesting, at-first-unpublishable, results. Sooner or later, something will get published, and then the floodgates will open. Because something can’t be unethical if everyone is doing it.

Right?

Many people complain that nowadays there is too much choice. 20 ways of having your coffee, 50 types of ketchup, ten political parties in countries of less than 10 million [...] Too much choice overloads our cognitive abilities. Fifty years ago, George Miller published a paper showing that people tended to be able to keep in their minds only seven plus-minus two things at a time. In other words, after more or less seven types of fries, we lose track of what is going on…

Another salient example of overwhelming choice is the simple over-abundance of possible life paths. For many young people growing up in this globalized age — where economic niches are more plentiful than ever, and where transportation could probably get you to 99% of the towns on Earth in less than a week — the question is a daunting one: where to place yourself in this wide world? Compare this plenitude of options with those of an average human a few centuries ago. Most likely, you would inherit the occupation of your parents. Most likely, you would never travel further than the next town. For us post-moderns, the adjacent possible has expanded a million-fold and we are confronted with a dizzying array of decisions which, in sequence, will create our lives.

Is there something wrong with having a lot of possible niches? At first glance, it seems like a low-stress, non-competitive environment of abundance. Is this how the First World appears to the Third World? At second glance, it taxes our cognitive resources to be forced to consider so many options all the time in our quest to maximize utility. A related question is: in a directed search where we uncover the options one-by-one, at what point should we give up and go with the best-so-far?

When am I better off just picking my ketchup at random? When should I stop comparison shopping and just buy? When should I stop searching for the “perfect woman” and just settle down? It’s our old friend, the law of Diminishing Returns. At some point, the cost of evaluating more options will outweigh the possible slight increase in utility.

Take it from me, someone apt to be indecisive: better to eat than starve to death looking for the best deal. Better to be happy with a satisfactory decision than to stress out trying to make an optimal one.

Network analysts use random networks as the standard by which to measure order in the networks they study. That’s because a random network is the graph-theoretic way of saying structure doesn’t matter. If the network structure you’re studying is significantly different from the random net, most likely it can’t be explained by chance alone; it has order, pattern, maybe even complexity. In other words, for the purposes of studying whatever system produced that structure, the network matters, i.e. it’s worth paying attention to.

And in the games of life and science, what matters most is knowing what is worthy of your thought and attention, and what is not.

For this reason and more, I have high hopes for the work Marko Rodriguez is doing around the scholarly communication process:

The general purpose of the scholarly communication process is to support the creation and dissemination of ideas within the scientific community. [...] This paper describes an associative network composed of multiple scholarly artifacts that can be used as a medium for supporting the scholarly communication process.

- A Multi-Graph to Support the Scholarly Communication Process

My hope is that emerging scientific research tools such as these will help support a shift to a more meritocratic social system in science, where the incentives to research and publish are more closely aligned to the “true aims of science”. Specifically, I would hope that the future network structure of the scientific communication process will represent more a knowledge structure than a social structure.

A paper’s authors, acknowledgements and references are all relational data which point to people or artifacts which gave some contribution to the finished product. It’s for inconsistent, socially-constructed reasons that we separate and prioritize these resources. Like when decisions about who gets the first author slot are based on seniority instead of contribution.

Authors, references and acknowledgements are rightly distinguished as different types of resources but they all share the value of being a relational input to a paper. It seems to me that the network created by, for example, a single ranked list of each paper’s human and artifactual inputs, would more faithfully represent the living knowledge network instead of merely the social structure of the authors (coauthorship net) or the relatedness of topics (citation net). For instance, a reference may have been more valuable to the paper’s creation than one of the five co-authors, or an acknowledged party could have been more informative than many of the thirty references.

If the true aim of science is the quest for knowledge, not the quest for tenure, then it makes sense to give credit where credit is due, and accurately weight the importance of all information inputs to a given paper.