Never Say Never

The deluge of end-of-year related media seems to take two forms: recaps of the past year and predictions for the future. (Interestingly, there aren’t a lot of year-end reports along the lines of ‘and here’s what I’m doing RIGHT NOW’) Many predictions involve technology: will 2014 be the year of the 3-D printed, cloud-neural network quantum-computing autonomous cars?!?!?! (Answer: No.)

For everybody who thinks fusion powered utopia is just around the corner, it seems there are at least ten naysayers who think technological optimists are just indulging sci-fi daydreams. Although anecdotes aren’t proof, many very smart people, through their own pessimism, have inadvertently offered up counter-evidence for a pessimistic viewpoint. Check it out:

  • “A rocket will never be able to leave the Earth’s atmosphere.” — The New York Times , 1920.
  • “There is no likelihood man can ever tap the power of the atom.” — Robert Millikan , winner of the 1923 Nobel Prize in physics, 1928.
  • “Heavier-than-air flying machines are impossible.”  — Scottish mathematician and creator of the Kelvin temperature scale William Thomson, Lord Kelvin , 1895.
  • “There is no reason anyone would want a computer in their home.” — Ken Olsen , founder and president of Digital Equipment Corporation, 1977 .

(Quotes from this long article on the Motley Fool) 

The anecdote usually mentioned in this context – the patent officer claiming in 1902 that everything useful had already been invented – is apocryphal.

You should be skeptical of any tech prediction more than five years down the road, and ‘never’ is about the most far-off prediction there is.

So how do you strike a balance between deluded optimism and stick-in-the-mud pessimism? Don’t just predict a future, choose the one you want the most.  Work backwards to the actionable item for the next year to make your future a reality starting in 2014.


Why Teachers Need to Be Human

Today a friend suggested that I type “why” into Matlab. You should too. Not only had I never tried it, but it never crossed my mind that it was something that one should try.  I’ve never wondered “what happens when I type ‘why’ in Matlab?”

That’s the value of person-to-person teaching that’s irreplaceable in textbooks and MOOCs – the human ability to recognize a problem someone doesn’t even know they have. Great teachers and entrepreneurs both possess this magically human ability. Once I know I have a problem (and how to phrase it correctly) the Internet is amazingly useful, but Dr. Google is pretty useless if you just type ‘help!’

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What would somebody from 1863 think if you told them that in the future, we would think nothing of watching a moving image of  a real man actually pouring a gallon of a metal more precious than gold down an ant hill without getting up from our desks?

He would think you were batshit crazy, that’s what.

HT Professor Mike Munger

You do what??

Everybody has passed dozens of construction sites. Usually you get a quick snapshot – heavy machinery, a half finished structure and dozens of workers. How often do you pay attention to the logos on the side of the trucks? I’ve been walking past the same construction site every day for months and the sides of the trucks tell a story about the amazing amount of specialization in construction industry companies. For example, Ithaca has its very own gravel and dirt trucking company. It’s not a company that trucks stuff and today happened to be trucking gravel. All they do is truck gravel and dirt.

And this degree of specialization is freaking everywhere, if you pay attention. These jobs and companies make so much sense when you see them, but ‘ATM repair man’ is never on a list of ‘things you could be when you grow up.’  It’s so easy to gloss over, but I think it’s worth recording the specialization that makes you step back and think ‘whoah. There’s a company that does what? Man, modern society is cool.’

Have you cleaned your filters?

One tool in the control/dynamics engineer’s toolbox is the Kalman filter. It’s one of those big intellectual hammers that makes many problems look like nails.

Put simply, a Kalman filter combines noisy external measurements with an internal simulation of the system in question in order to estimate the system’s true state. Depending on how much you trust the measurements (the covariance of those measurements), the filter will weigh the internal model and the external measurements differently.

As with a lot of these big concepts, it’s a fun thought experiment to draw an analogy from the Kalman filter to the human brain. In many different domains, we are always running an internal model of the world, and comparing our own measurements to that model. We use a pseudo-Kalman filter when we’re walking – you hold a model of the ground in your head and for the most part assume that the ground at your next step is roughly the same as the ground on your last step. We also use a pseudo-Kalman filter when we’re learning or thinking – you compare new information to the model of the world in your head. Depending on certainty in your model and trust in the source of information, you give relative weight to both and compose the two together to estimate the true state of things.

This filtering can be powerful:

Say you see a bunch of dragons out of the corner of your eye. That’s your sensor data. Your internal model of the world says the word doesn’t have dragons (alas!) For most people, both the covariance of our peripheral vision and trust in our mental model are high enough to conclude that the ‘truth’ involves a flock of geese rather than a flight of dragons. (Fun fact: a group of dragons can also be referred to as a wing, flight or weyr.)

However, the filters can also fail: When your model is worse than you think (so you trust it too much) you can slip on a patch of ice by stepping like it’s firm ground or rejecting a new source of information simply because it doesn’t square with your internal picture of the world.

The trick, both in more advanced filters and in your brain, is to correctly update both your internal model and your sensor covariance based on new information. Like many worthwhile things, that’s simple to describe but surprisingly hard to implement.

Optimism! It’s just more fun.

This week, Russ Roberts of the ever-excellent EconTalk is having a short essay contest. The prompt is to compare the visions of our technology-future as portrayed Tyler Cowen and Joel Mokyr (both guests on the podcast.)

Here’s my take on the matter, but I highly recommend you listen to both podcasts and form your own opinion.

Like the wise men and the elephant, Mokyr and Cowen focus on vastly different aspects of the same huge enterprise: progress in science and technology, past, present, and future. Mokyr focuses on the human experience – increased leisure and improved interactions – while Cowen focuses on our interaction with the world around us – gadgets and robots. A gross simplification would be to say that Mokyr focuses on the mental while Cowen looks at the physical.

Their different perspectives lead to different conclusions about the future: Mokyr is across-the-board optimistic while Cowen presents a greyer vision; he thinks some things will improve somewhat for some people. I find myself compelled by Cowen’s focus on the physical world, but convinced by Mokyr’s optimism and overall vision.

EconTalk is all about identifying biases and here are mine: I’m an aerospace engineer who also has a degree in history. Thus, I focus on how we interact with the physical world, but from more of a historical than aggregate economic perspective.

Mokyr takes a historian’s perspective and notes that there are many trends and improvements that are not captured in the data. While it ignores Cowen’s aggregate data, I find Mokyr’s (and Russ’) admonition to just ‘look around you and see the progress’ more compelling than the fact that GDP is not rising as fast and we don’t have flying cars yet.

Yes, we haven’t changed what every day technology can fundamentally do in the real world for half a century. Airplanes still fly and cars still drive the same way by exploding fuel to spin a shaft that drives turbofans or wheels. We still get to space by sitting on giant explosions. Our bathrooms are still serviced by water carrying tubes. We still build our houses out of wood, glass and concrete.

Looking at the surface, we haven’t made much progress. But delve deeper, look at the ways these familiar things do what they do and how they are made. Behind the scenes, there has been significant progress. Engines are far more efficient; pipes are made of cheaper, lighter and less-degradable plastics. On a walk today, I saw a house that had an entire corner built of only glass – impossible with the glass-making technologies of twenty years ago.

This type of progress supports Mokyr’s view that people will be able to do the same things that we could before, but ever cheaper and with more delight in our lives. My optimistic engineer’s twist on this perspective is a perhaps naïve prediction. This steady increase in freedom from drudgery will combine with a deep-seated human desire to affect the physical world and create a tipping point. Right now, we are seeing a lull in our physical progress – what Cowen identifies as ‘The Great Stagnation.’ True, there are fewer discrete jumps as we make our same planes, trains, and automobiles ever more efficient and our leisure ever longer. But (hopefully soon) these incremental improvements will add up to give us enough excess time and resources to again make discrete technological leaps. Don’t lose hope for flying cars, rocket packs, and moon vacations just yet.