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Posts published in “Philosophy”

RIP Jeff Bezos (and/or Richard Branson)

centaur 0
rip jeff bezos You know, Jeff Bezos isn’t likely to die when he flies July 20th. And Richard Branson isn’t likely to die when he takes off at 9am July 11th (tomorrow morning, as I write this). But the irresponsible race these fools have placed them in will eventually get somebody killed, as surely as Elon Musk’s attempt to build self-driving cars with cameras rather than lidar was doomed to (a) kill someone and (b) fail. It’s just, this time, I want to be caught on record saying I think this is hugely dangerous, rather than grumbling about it to my machine learning brethren. Whether or not a spacecraft is ready to launch is not a matter of will; it’s a matter of natural fact. This is actually the same as many other business ventures: whether we’re deciding to create a multibillion-dollar battery factory or simply open a Starbucks, our determination to make it succeed has far less to do with its success than the realities of the market—and its physical situation. Either the market is there to support it, and the machinery will work, or it won’t. But with normal business ventures, we’ve got a lot of intuition, and a lot of cushion. Even if you aren’t Elon Musk, you kind of instinctively know that you can’t build a battery factory before your engineering team has decided what kind of battery you need to build, and even if your factory goes bust, you can re-sell the land or the building. Even if you aren't Howard Schultz, you instinctively know it's smarter to build a Starbucks on a busy corner rather than the middle of nowhere, and even if your Starbucks goes under, it won't explode and take you out with it. But if your rocket explodes, you can't re-sell the broken parts, and it might very well take you out with it. Our intuitions do not serve us well when building rockets or airships, because they're not simple things operating in human-scaled regions of physics, and we don't have a lot of cushion with rockets or self-driving cars, because they're machinery that can kill you, even if you've convinced yourself otherwise. The reasons behind the likelihood of failure are manyfold here, and worth digging into in greater depth; but briefly, they include:
  • The Paradox of the Director's Foot, where a leader's authority over safety personnel - and their personal willingness to take on risk - ends up short-circuiting safety protocols and causing accidents. This actually happened to me personally when two directors in a row had a robot run over their foot at a demonstration, and my eagle-eyed manager recognized that both of them had stepped into the safety enclosure to question the demonstrating engineer, forcing the safety engineer to take over audience questions - and all three took their eyes off the robot. Shoe leather degradation then ensued, for both directors. (And for me too, as I recall).
  • The Inexpensive Magnesium Coffin, where a leader's aesthetic desire to have a feature - like Steve Job's desire for a magnesium case on the NeXT machines - led them to ignore feedback from engineers that the case would be much more expensive. Steve overrode his engineers ... and made the NeXT more expensive, just like they said it would, because wanting the case didn't make it cheaper. That extra cost led to the product's demise - that's why I call it a coffin. Elon Musk's insistence on using cameras rather than lidar on his self-driving cars is another Magnesium Coffin - an instance of ego and aesthetics overcoming engineering and common sense, which has already led to real deaths. I work in this precise area - teaching robots to navigate with lidar and vision - and vision-only navigation is just not going to work in the near term. (Deploy lidar and vision, and you can drop lidar within the decade with the ground-truth data you gather; try going vision alone, and you're adding another decade).
  • Egotistical Idiot's Relay Race (AKA Lord Thomson's Suicide by Airship). Finally, the biggest reason for failure is the egotistical idiot's relay race. I wanted to come up with some nice, catchy parable name to describe why the Challenger astronauts died, or why the USS Macon crashed, but the best example is a slightly older one, the R101 disaster, which is notable because the man who started the R101 airship program - Lord Thomson - also rushed the program so he could make a PR trip to India, with the consequence that the airship was certified for flight without completing its endurance and speed trials. As a result, on that trip to India - its first long distance flight - the R101 crashed, killing 48 of the 54 passengers - Lord Thomson included. Just to be crystal clear here, it's Richard Branson who moved up his schedule to beat Jeff Bezos' announced flight, so it's Sir Richard Branson who is most likely up for a Lord Thomson's Suicide Award.
I don't know if Richard Branson is going to die on his planned spaceflight tomorrow, and I don't know that Jeff Bezos is going to die on his planned flight on the 20th. I do know that both are in an Egotistical Idiot's Relay Race for even trying, and the fact that they're willing to go up themselves, rather than sending test pilots, safety engineers or paying customers, makes the problem worse, as they're vulnerable to the Paradox of the Director's Foot; and with all due respect to my entire dot-com tech-bro industry, I'd be willing to bet the way they're trying to go to space is an oversized Inexpensive Magnesium Coffin. -the Centaur P.S. On the other hand, when Space X opens for consumer flights, I'll happily step into one, as Musk and his team seem to be doing everything more or less right there, as opposed to Branson and Bezos. P.P.S. Pictured: Allegedly, Jeff Bezos, quick Sharpie sketch with a little Photoshop post-processing.

It’s been a long time since I’ve thrown a book …

taidoka 0
chuck that junk Yeah, so that happened on my attempt to get some rest on my Sabbath day. I'm not going to cite the book - I'm going to do the author the courtesy of re-reading the relevant passages to make sure I'm not misconstruing them, but I'm not going to wait to blog my reaction - but what caused me to throw this book, an analysis of the flaws of the scientific method, was this bit: Imagine an experiment with two possible outcomes: the new theory (cough EINSTEIN) and the old one (cough NEWTON). Three instruments are set up. Two report numbers consistent with the new theory; the third one, missing parts, possibly configured improperly and producing noisy data, matches the old. Wow! News flash: any responsible working scientist would say these results favored the new theory. In fact, if they were really experienced, they might have even thrown out the third instrument entirely - I've learned, based on red herrings from bad readings, that it's better not to look too closely at bad data. What did the author say, however? Words to the effect: "The scientists ignored the results from the third instrument which disproved their theory and supported the original, and instead, pushing their agenda, wrote a paper claiming that the results of the experiment supported their idea." Pushing an agenda? Wait, let me get this straight, Chester Chucklewhaite: we should throw out two results from well-functioning instruments that support theory A in favor of one result from an obviously messed-up instrument that support theory B - oh, hell, you're a relativity doubter, aren't you? Chuck-toss. I'll go back to this later, after I've read a few more sections of E. T. Jaynes's Probability Theory: The Logic of Science as an antidote. -the Centaur P. S. I am not saying relativity is right or wrong, friend. I'm saying the responsible interpretation of those experimental results as described would be precisely the interpretation those scientists put forward - though, in all fairness to the author of this book, the scientist involved appears to have been a super jerk.  

Day 051

centaur 0
mount tabor sketch Mount Tabor, sketched to commemorate the transfiguration of Jesus, that moment when Jesus is transformed on a mountaintop as he communes with Moses and Elijah, and Peter somehow loses a screw and decides it's a great time to start building houses. As Reverend Karen of St. Stephens in-the-Field and St. John the Divine memorably said in today's sermon, this was the moment that the disciples went from knowing Jesus only as a human teacher they admired to seeing him as touched with divinity. (And speaking as a religious person from a scientific perspective, this is a great example of why there always will be a gap between science and religion: even if the event actually happened exactly as described, we're unlikely to ever prove so scientifically, since it is a one-time event that cannot be probed with replicable experiments; the events of the day, even if true, really do have to be taken purely on faith. This is, of course, assuming that tomorrow someone doesn't invent a device for reviewing remote time). Roughed on Strathmore, then rendered on tracing paper, based on the following shot taken in 2011: צילם: אלי זהבי, כפר תבור, CC BY 2.5 , via Wikimedia Commons (Author: Eli Zehavi, Kfar Thabor) I mean, look at that. That mountain is just begging for God do something amazing there. And if God doesn't want it, the Close Encounters mothership and H.P. Lovecraft are top of the waitlist. It really is proving useful to ink my own rough sketches by hand, then to trace my own art. It is interesting to me though how I vertically exaggerated the mountain when I drew it, which probably explains why a few things kept not lining up the way that I wanted them to. Still ... Drawing every day. -the Centaur P.S. And yes, I accidentally drew the Ascension rather than the Transfiguration, which I guess is fine, because the Mount of Olives looks harder to draw. Check out that 2,000 year old tree though.

What is “Understanding”?

taidoka 1
When I was growing up - or at least when I was a young graduate student in a Schankian research lab - we were all focused on understanding: what did it mean, scientifically speaking, for a person to understand something, and could that be recreated on a computer? We all sort of knew it was what we'd call nowadays an ill-posed problem, but we had a good operational definition, or at least an operational counterexample: if a computer read a story and could not answer the questions that a typical human being could answer about that story, it didn't understand it at all. But there are at least two ways to define a word. What I'll call a practical definition is what a semanticist might call the denotation of a word: a narrow definition, one which you might find in a dictionary, which clearly specifies the meaning of the concept, like a bachelor being an unmarried man. What I'll call a philosophical definition, the connotations of a word, are the vast web of meanings around the core concept, the source of the fine sense of unrightness that one gets from describing Pope Francis as a bachelor, the nuances of meaning embedded in words that Socrates spent his time pulling out of people, before they went and killed him for being annoying. It's those connotations of "understanding" that made all us Schankians very leery of saying our computer programs fully "understood" anything, even as we were pursuing computer understanding as our primary research goal. I care a lot about understanding, deep understanding, because, frankly, I cannot effectively do my job of teaching robots to learn if I do not deeply understand robots, learning, computers, the machinery surrounding them, and the problem I want to solve; when I do not understand all of these things, I stumble in the dark, I make mistakes, and end up sad. And it's pursuing a deeper understanding about deep learning where I got a deeper insight into deep understanding. I was "deep reading" the Deep Learning book (a practice in which I read, or re-read, a book I've read, working out all the equations in advance before reading the derivations), in particular section 5.8.1 on Principal Components Analysis, and the authors made the same comment I'd just seen in the Hands-On Machine Learning book: "the mean of the samples must be zero prior to applying PCA." Wait, what? Why? I mean, thank you for telling me, I'll be sure to do that, but, like ... why? I didn't follow up on that question right away, because the authors also tossed off an offhand comment like, "XX is the unbiased sample covariance matrix associated with a sample x" and I'm like, what the hell, where did that come from? I had recently read the section on variance and covariance but had no idea why this would be associated with the transpose of the design matrix X multiplied by X itself. (In case you're new to machine learning, if x stands for an example input to a problem, say a list of the pixels of an image represented as a column of numbers, then the design matrix X is all the examples you have, but each example listed as a row. Perfectly not confusing? Great!) So, since I didn't understand why Var[x] = XX, I set out to prove it myself. (Carpenters say, measure twice, cut once, but they'd better have a heck of a lot of measuring and cutting under their belts - moreso, they'd better know when to cut and measure before they start working on your back porch, or you and they will have a bad time. Same with trying to teach robots to learn: it's more than just practice; if you don't know why something works, it will come back to bite you, sooner or later, so, dig in until you get it). And I quickly found that the "covariance matrix of a variable x" was a thing, and quickly started to intuit that the matrix multiplication would produce it. This is what I'd call surface level understanding: going forward from the definitions to obvious conclusions. I knew the definition of matrix multiplication, and I'd just re-read the definition of covariance matrices, so I could see these would fit together. But as I dug into the problem, it struck me: true understanding is more than just going forward from what you know: "The brain does much more than just recollect; it inter-compares, it synthesizes, it analyzes, it generates abstractions" - thank you, Carl Sagan. But this kind of understanding is a vast, ill-posed problem - meaning, a problem without a unique and unambiguous solution. But as I was continuing to dig through the problem, reading through the sections I'd just read on "sample estimators," I had a revelation. (Another aside: "sample estimators" use the data you have to predict data you don't, like estimating the height of males in North America from a random sample of guys across the country; "unbiased estimators" may be wrong but their errors are grouped around the true value). The formula for the unbiased sample estimator for the variance actually doesn't look quite the matrix transpose - but it depends on the unbiased estimator of sample mean. Suddenly, I felt that I understood why PCA data had to have a mean of 0. Not driving forward from known facts and connecting their inevitable conclusions, but driving backwards from known facts to hypothesize a connection which I could explore and see. I even briefly wrote a draft of the ideas behind this essay - then set out to prove what I thought I'd seen. Setting the mean of the samples to zero made the sample mean drop out of sample variance - and then the matrix multiplication formula dropped out. Then I knew I understood why PCA data had to have a mean of 0 - or how to rework PCA to deal with data which had a nonzero mean. This I'd call deep understanding: reasoning backwards from what we know to provide reasons for why things are the way they are. A recent book on science I read said that some regularities, like the length of the day, may be predictive, but other regularities, like the tides, cry out for explanation. And once you understand Newton's laws of motion and gravitation, the mystery of the tides is readily solved - the answer falls out of inertia, angular momentum, and gravitational gradients. With apologies to Larry Niven, of course a species that understands gravity will be able to predict tides. The brain does do more than just remember and predict to guide our next actions: it builds structures that help us understand the world on a deeper level, teasing out rules and regularities that help us not just plan, but strategize. Detective Benoit Blanc from the movie Knives Out claimed to "anticipate the terminus of gravity's rainbow" to help him solve crimes; realizing how gravity makes projectiles arc, using that to understand why the trajectory must be the observed parabola, and strolling to the target. So I'd argue that true understanding is not just forward-deriving inferences from known rules, but also backward-deriving causes that can explain behavior. And this means computing the inverse of whatever forward prediction matrix you have, which is a more difficult and challenging problem, because that matrix may have a well-defined inverse. So true understanding is indeed a deep and interesting problem! But, even if we teach our computers to understand this way ... I suspect that this won't exhaust what we need to understand about understanding. For example: the dictionary definitions I've looked up don't mention it, but the idea of seeking a root cause seems embedded in the word "under - standing" itself ... which makes me suspect that the other half of the word, standing, itself might hint at the stability, the reliability of the inferences we need to be able to make to truly understand anything. I don't think we've reached that level of understanding of understanding yet. -the Centaur Pictured: Me working on a problem in a bookstore. Probably not this one.

Work, Finish, Publish!

taidoka 0
So I think a lot about how to be a better scientist, and during my reading I found a sparkly little gem by one of the greatest experimentalists of all time, Michael Faraday. It's quoted in Analysis and Presentation of Experimental Results as above, but from Wikiquote we get the whole story:
"The secret is comprised in three words — Work, finish, publish." His well-known advice to the young William Crookes, who had asked him the secret of his success as a scientific investigator, as quoted in Michael Faraday (1874) by John Hall Gladstone, p. 123
Well said. The middle part often seems the hardest for many people, in my experience: it's all too easy to work on something without finishing it, or to rush to publish something before it's really ready. The hard part is pushing through all three in the right order with the appropriate level of effort. -the Centaur Pictured: Michael Faraday, Photograph by Maull & Polyblank. Credit: Wellcome Collection. CC BY.

The Sole Test of Any Idea

taidoka 0
Inspirational physicist Richard Feynman once said "the sole test of any idea is experiment." I prefer the formulation "the sole test of any idea open to observation is experiment," because opening our ideas to observation - rather than relying on just belief, instrumentation, or arguments - is often the hardest challenge in making progress on otherwise seemingly unresolvable problems. -the Centaur