Questioning The Dotted Line: Bringing Tech to Our Level

A rendering of Teller’s graph (Source: Embedded Computing Designs)

Astro Teller, CEO of Google X, created this graph to state technological change when compared to human adaptability (I saw it originally in Thomas Friedman’s Thanks For Being Late). The dotted line is our potential as humans – to rise to the occasion as the pace of technological advances escalates.

Nate Silver puts it another way in The Signal and The Noise:

“In many ways, we are our greatest technological constraint. The slow and steady march of human evolution has fallen out of step with technological progress: evolution occurs on millenial time scales, whereas processing power doubles roughly every other year.” (see Moore’s Law)

The question we ask is how to keep up with technology. That is what the dotted line on Teller’s graph is all about. How do we rise to technology’s demand?

“It has become difficult for contemporary man to imagine development and modernization in terms of lower rather than higher energy use” Ivan Illich noted in Tools of Conviviality.

With that drive towards constant adaptation to new technologies, can there be time to slow down and contemplate the alternative? Can we have time to ask whether there is an approach other than proceeding up the dotted line of technological escalation? Can we find a way to bring technology back down to our level? Can we take the “We are here” and slide it to where human adaptability and technology meet?

L.M. Sacasas puts it eloquently in “The Tech Backlash We Really Need” (here):

“We fail to ask, on a more fundamental level, if there are limits appropriate to the human condition, a scale conducive to our flourishing as the sorts of creatures we are. Modern technology tends to encourage users to assume that such limits do not exist; indeed, it is often marketed as a means to transcend such limits. We find it hard to accept limits to what can or ought to be known, to the scale of the communities that will sustain abiding and satisfying relationships, or to the power that we can harness and wield over nature. We rely upon ever more complex networks that, in their totality, elude our understanding, and that increasingly require either human conformity or the elimination of certain human elements altogether. But we have convinced ourselves that prosperity and happiness lie in the direction of limitlessness.”

Tasting the Data Ourselves

“We only labour to stuff the memory and leave the conscience and the understanding unfurnished and void. Like birds who fly abroad to forage for grain, and bring it home in the beak, without tasting it themselves, to feed their young; so our pedants go picking knowledge here and there, out of books, and hold it at the tongue’s end, only to spit it out and distribute it abroad.”

-Montaigne, Of Pedantry (trans. Charles Cotton)

I love the analogy of knowledge being picked like grain to feed young birds. It takes on a peculiar spin when connected to cognitive systems which are at the heart of modern (and future) computing, being utilized in everything from winning Jeopardy! to looking for signs of cancer. What does cognitive computing mean anyway? The definition will help with the analogy. Senior VP & Director of IBM Research John E. Kelly III explains in “Computing, Cognition, and the Future of Knowing”:

Cognitive systems are “probabilistic, meaning they are designed to adapt and make sense of the complexity and unpredictability of unstructured information. They can ‘read’ text, ‘see’ images and ‘hear’ natural speech. And they interpret that information, organize it and offer explanations of what it means, along with the rationale for their conclusions. They do not offer definitive answers. In fact, they do not ‘know’ the answer. Rather they are designed to weigh information and ideas from multiple sources, to reason, and then offer hypotheses for consideration.”

A key part to cognitive computing is feeding the machine this unstructured information. Tons of it. “If you give the computer enough examples of what is right and what is wrong,” Thomas Friedman declares in Thank You For Being Late, “the computer will figure out how to properly weight answers, and learn by doing.”

Are we in this instance, then, the bird feeding grain to our young? Are we giving our cognitive systems batches of data without tasting it ourselves? What does it mean to taste this data anyway? How do we do so when dealing with millions upon millions of grains? How much of it should we taste before giving it off to something like IBM’s Watson?

What does it mean if we, like Montaigne’s pedants, do not taste the data for ourselves?

Laughing on the Bicycle

“This past summer, I gave a lecture at a web conference and afterward got into a fascinating conversation with a young digital design student. It was fun to compare where we were in our careers. I had fifteen years of experience designing for web clients, she had one year, and yet some how, we were in the same situation: we enjoyed the work, but were utterly confused and overwhelmed by the rapidly increasing complexity of it all. What the hell happened? ”

– Frank Chimero, “Everything Easy is Hard Again”

I happen to fall into the same boat as the young digital design student. Actually, take ten months away from her experience. That’s me – fresh meat. As you brave into a world of programming, into a world of technology, the whole scope of it all stops you in your tracks. SQL, NoSQL, JavaScript, JQuery, HTML, CSS. How can one deal with all of this stuff? Reading Chimero gave me pause for thought. It wasn’t just the beginners. Even the experienced tussle with the increasing escalation.

“I began to wonder,” Chimero writes, “if this situation was something to laugh off or take seriously. Neither of us had an answer, but a bit of time and distance has shown me that we must do both.”

The serious side seems to whack me on the head most of the time. It’s all around us. If it isn’t in trends like Moore’s Law, it’s in our pockets with smart phones. I am starting to read Thomas Friedman’s Thank You For Being Late, which begins by examining this acceleration we are living in. The striking parts so far have been from Eric Teller of Google:

“If the technology platform for society can now turn over in five to seven ears, but it takes ten to fifteen years to adapt to it, Teller explained, ‘we will all feel out of control, because we can’t adapt to the world as fast as it’s changing. By the time we get used to the change, that won’t even be the prevailing change anymore – we’ll be on to some new change.'”

And what do I focus on? The growing pains. To climb up in competency to only be thrown down the ladder seems more Sisyphean than anything. “It hurts to go from feeling like an expert to feeling like an idiot” as Derek Sivers put it in a recent post. “But it’s crucial to go through that pain, or you’ll never grow. This is a modern situation that’s here to stay. Technology will keep changing the world faster, so we’ll have to keep unlearning what we knew, and relearning anew.”

But…that seems to be the important part. I go back to Chimero – “but a bit of time and distance has shown me that we must do both.” What was the other of that “both”? To laugh. Laugh in unlearning. Laugh in increasing change. Laugh in relearning. The subtitle of Friedman’s book? “An Optimist’s Guide to Thriving in the Age of Accelerations.” Optimism in unlearning. Optimism in increasing change. Optimism in relearning. This is the side that cannot be forgotten in all the hubbub of what we feel to be a never ending race like Achilles chasing the tortoise.

Teller put it best. “[T]he new kind of stability has to be dynamic stability. There are some ways of being, like riding a bicycle, where you cannot stand still, but once you are moving it is actually easier. It is not our natural state. But humanity has to learn to exist in this state.”

Here’s to laughing on the bicycle.