Congratulations, Sir Richard Branson, on your successful space flight! (Yes, yes, I *know* it's technically just upper atmosphere, I *know* there's no path to orbit (yet) but can we give the man some credit for an awesome achievement?) And I look forward to Jeff Bezos making a similar flight later this month.
Now, I stand by my earlier statement: the way you guys are doing this, a race, is going to get someone killed, perhaps one of you guys. A rocketship is not a racecar, and moves into realms of physics where we do not have good human intuition. Please, all y'all, take it easy, and get it right.
That being said, congratulations on being the first human being to put themselves into space as part of a rocket program that they themselves set in motion. That's an amazing achievement, no-one can ever take that away from you, and maybe that's why you look so damn happy. Enjoy it!
P.S. And day 198, though I'll do an analysis of the drawing at a later time.
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.
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.
What happens when deep learning hits the real world? Find out at the Embodied AI Workshop this Sunday, June 20th! We’ll have 8 speakers, 3 live Q&A sessions with questions on Slack, and 10 embodied AI challenges. Our speakers will include:
Motivation for Embodied AI Research
Hyowon Gweon, Stanford
Peter Anderson, Google
Aleksandra Faust, Google
Anca Dragan, UC Berkeley
Chelsea Finn, Stanford / Google
Akshara Rai, Facebook AI Research
Sanja Fidler, University of Toronto, NVIDIA Konstantinos Bousmalis, Google
... came up as my wife and I were discussing the "creative hangers-on form" of Stigler's Law. The original Stigler's Law, discovered by Roger Merton and popularized by Stephen Stigler, is the idea that in science, no discovery is named after its original discoverer.
In creative circles, it comes up when someone who had little or nothing to do with a creative process takes credit for it. A few of my wife's friends were like this, dropping by to visit her while she was in the middle of a creative project, describing out loud what she was doing, then claiming, "I told her to do that."
In the words of Finn from The Rise of Skywalker: "You did not!"
In computing circles, the old joke referred to the Java programming language. I've heard several variants, but the distilled version is "He thinks he invented Java because he was in the room when someone made coffee." Apparently this is a good description of how Java itself was named, down to at least one person claiming they came up with the name Java and others disputing that, even suggesting that they opposed it, claiming instead that someone else in the room was responsible - while that person in turn rejected the idea, noting only that there was some coffee in the room from Peet's.
Hail, fellow adventurers: to prove I do something more than just draw and write, I'd like to send out a reminder of the Second Embodied AI Workshop at the CVPR 2021 computer vision conference. In the last ten years, artificial intelligence has made great advances in recognizing objects, understanding the basics of speech and language, and recommending things to people. But interacting with the real world presents harder problems: noisy sensors, unreliable actuators, incomplete models of our robots, building good simulators, learning over sequences of decisions, transferring what we've learned in simulation to real robots, or learning on the robots themselves.
The Embodied AI Workshop brings together many researchers and organizations interested in these problems, and also hosts nine challenges which test point, object, interactive and social navigation, as well as object manipulation, vision, language, auditory perception, mapping, and more. These challenges enable researchers to test their approaches on standardized benchmarks, so the community can more easily compare what we're doing. I'm most involved as an advisor to the Stanford / Google iGibson Interactive / Social Navigation Challenge, which forces robots to maneuver around people and clutter to solve navigation problems. You can read more about the iGibson Challenge at their website or on the Google AI Blog.
Most importantly, the Embodied AI Workshop has a call for papers, with a deadline of TODAY.
Call for Papers
We invite high-quality 2-page extended abstracts in relevant areas, such as:
Embodied Question Answering
Embodied Vision & Language
Accepted papers will be presented as posters. These papers will be made publicly available in a non-archival format, allowing future submission to archival journals or conferences.
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?
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.
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.
Growing up with Superman comics, Hollywood movies and Greek mythology can give you a distorted idea of the spiritual world. Colorful heroes with flashy powers hurl villains into the Phantom Zone, and a plucky bard with a fancy lyres can sing his way into hell to rescue his bride, if only he doesn't look back.
This models the afterlife as a distant but reachable part of the natural world. The word "supernatural" gets tossed around without force, because there are rules for breaking the rules: like warp drive breaking the laws of motion or the cheat codes to the Matrix, you can hack your way into and out of the afterlife.
But spirituality is not magic, and prayers aren't spells. While I've argued "spirit" isn't strictly necessary for the practice of Christianity, most theologians would agree that the supernatural realm is a reflection of the grander reality of God and operates on His will - not a set of rules that could be manipulated by Man.
Even the idea of the "afterlife" isn't necessary. We're waiting in hope for bodily resurrection. We die, and stay dead, yet our essences live on in the mind of God, to be resurrected in a future world which outstrips even our boldest imaginations (though C. S. Lewis sure tried in The Great Divorce and The Last Battle).
Death, in this view, is a one-way trajectory. It isn't likely that people are going to and returning from the afterlife, no matter how many tunnels of light are reported by hypoxia patients, because the afterlife is not a quasi-physical realm to be hacked into, but a future physical state accompanied by spiritual perfection.
So if no-one's come back from Heaven to tell us about the afterlife, how do we know to seek it?
This is not trivial for someone who teaches robots to learn. In reinforcement learning, we model decision making as Markov decision processes, a mathematical formalism in which we choose actions in states to receive rewards, and use the rewards to estimate the values of those actions to make better choices.
But if no-one has returned from a visit to the state of the afterlife, how can we estimate the reward? One typical way around this dilemma is imitation learning: the trajectories of one agent can be used to inform another agent, granting it knowledge of the rewards in states that it cannot visit.
That agent might be human, or another, more skilled robot. You can imagine it as an army of robots with walkie-talkies trying to cross a minefield: as long as they keep radioing back what they've observed, the other robots can use that information to guide their own paths, continuing to improve.
But we're back to the same problem again: there's no radio in the afterlife, no cell service in Heaven.
One-way trajectories like this exist in physics: black holes. Forget the traversable black holes you see in movies from The Black Hole to Star Trek to Interstellar: a real black hole in general relativity is defined as a region of space where trajectories go in, but do not come back out; its boundary is the event horizon.
It's called the event horizon because no events beyond the horizon affect events outside the horizon. Other than the inexorable pull to suck more world-lines in, no information comes back from the black hole: no reward is recorded for the unvisited states of the Markov decision process.
Death appears to be a black hole, literally and figuratively. We die, remain dead, and are often put in a cold dark place in the ground, communicating nothing back to the world of the living, now on a trajectory beyond the event horizon, heading to that undiscovered country of Shakespeare and Star Trek.
In our robot minefield example, that might be a mine with a radio scrambler, cutting off signals before any other robots could be told not to follow that path. But what if there was someone with a radio who was watching that minefield from above, say a rescue helicopter, signaling down the path from above?
In a world where spirituality is a reflection of the grander reality of God, there's no magical hack which can give us the ability to communicate with the afterlife. But in a world where every observed particle event has irreducible randomness, God has plenty of room to turn around and contact us.
Like a faster-than-light radio which only works for the Old Ones, we can receive information from God if and only if He chooses to. The Old Testament records many stories of people hearing the voice of God - in dreams, in waking, in writing on the wall, in voices thundering from the heaven, in whispers.
You don't need to treat the Bible like a fax from God to imagine that the information it contains could be inconceivably precious, a deposit of revelation which could never be received from any amount of human experience. No wonder the Church preserved these books and guarded them so jealously.
But even this sells short the value that we get from God incarnating as Jesus.
Jesus Christ, a human being, provides a direct model of the behavior we should follow, informed by the knowledge of Jesus God, the portion of the Trinity most directly comprehensible by us. This is the best example we could have for imitation learning: a trace of the behavior of a divinely inspired teacher.
No amount of flying around the Earth will bring someone back from the dead; there may very well be "a secret chord that pleases the Lord," but you can't sing yourself into the afterlife. Fortunately, the afterlife has already sent an emissary, showing us the behavior we need to model to follow Him there.
Wow. It's been a long time. Or perhaps not as long as I thought, but I've definitely not been able to post as much as I wanted over the last six months or so. But it's been for good reasons: I've been working on a lot of writing projects. The Dakota Frost / Cinnamon Frost "Hexology", which was a six book series; the moment I finished those rough drafts, it seemed, I rolled into National Novel Writing Month and worked on JEREMIAH WILLSTONE AND THE MACHINERY OF THE APOCALYPSE. Meanwhile, at work, I've been snowed under following up on our PRM-RL paper.
But I've been having fun! The MACHINERY OF THE APOCALYPSE is (at least possibly) spaaaace steampunk, which has led me to learn all sorts of things about space travel and rockets and angular momentum which I somehow didn't learn when I was writing pure hard science fiction. I've learned so much about creating artificial languages as part of the HEXOLOGY.
So, hopefully I will have some time to start sharing this information again, assuming that no disasters befall me in the middle of the night.
So, this happened! Our team's paper on "PRM-RL" - a way to teach robots to navigate their worlds which combines human-designed algorithms that use roadmaps with deep-learned algorithms to control the robot itself - won a best paper award at the ICRA robotics conference!
I talked a little bit about how PRM-RL works in the post "Learning to Drive ... by Learning Where You Can Drive", so I won't go over the whole spiel here - but the basic idea is that we've gotten good at teaching robots to control themselves using a technique called deep reinforcement learning (the RL in PRM-RL) that trains them in simulation, but it's hard to extend this approach to long-range navigation problems in the real world; we overcome this barrier by using a more traditional robotic approach, probabilistic roadmaps (the PRM in PRM-RL), which build maps of where the robot can drive using point to point connections; we combine these maps with the robot simulator and, boom, we have a map of where the robot thinks it can successfully drive.
We were cited not just for this technique, but for testing it extensively in simulation and on two different kinds of robots. I want to thank everyone on the team - especially Sandra Faust for her background in PRMs and for taking point on the idea (and doing all the quadrotor work with Lydia Tapia), for Oscar Ramirez and Marek Fiser for their work on our reinforcement learning framework and simulator, for Kenneth Oslund for his heroic last-minute push to collect the indoor robot navigation data, and to our manager James for his guidance, contributions to the paper and support of our navigation work.
Woohoo! Thanks again everyone!
When I was a kid (well, a teenager) I'd read puzzle books for pure enjoyment. I'd gotten started with Martin Gardner's mathematical recreation books, but the ones I really liked were Raymond Smullyan's books of logic puzzles. I'd go to Wendy's on my lunch break at Francis Produce, with a little notepad and a book, and chew my way through a few puzzles. I'll admit I often skipped ahead if they got too hard, but I did my best most of the time.
I read more of these as an adult, moving back to the Martin Gardner books. But sometime, about twenty-five years ago (when I was in the thick of grad school) my reading needs completely overwhelmed my reading ability. I'd always carried huge stacks of books home from the library, never finishing all of them, frequently paying late fees, but there was one book in particular - The Emotions by Nico Frijda - which I finished but never followed up on.
Over the intervening years, I did finish books, but read most of them scattershot, picking up what I needed for my creative writing or scientific research. Eventually I started using the tiny little notetabs you see in some books to mark the stuff that I'd written, a "levels of processing" trick to ensure that I was mindfully reading what I wrote.
A few years ago, I admitted that wasn't enough, and consciously began trying to read ahead of what I needed to for work. I chewed through C++ manuals and planning books and was always rewarded a few months later when I'd already read what I needed to to solve my problems. I began focusing on fewer books in depth, finishing more books than I had in years.
Even that wasn't enough, and I began - at last - the re-reading project I'd hoped to do with The Emotions. Recently I did that with Dedekind's Essays on the Theory of Numbers, but now I'm doing it with the Deep Learning. But some of that math is frickin' beyond where I am now, man. Maybe one day I'll get it, but sometimes I've spent weeks tackling a problem I just couldn't get.
Enter puzzles. As it turns out, it's really useful for a scientist to also be a science fiction writer who writes stories about a teenaged mathematical genius! I've had to simulate Cinnamon Frost's staggering intellect for the purpose of writing the Dakota Frost stories, but the further I go, the more I want her to be doing real math. How did I get into math? Puzzles!
So I gave her puzzles. And I decided to return to my old puzzle books, some of the ones I got later but never fully finished, and to give them the deep reading treatment. It's going much slower than I like - I find myself falling victim to the "rule of threes" (you can do a third of what you want to do, often in three times as much time as you expect) - but then I noticed something interesting.
Some of Smullyan's books in particular are thinly disguised math books. In some parts, they're even the same math I have to tackle in my own work. But unlike the other books, these problems are designed to be solved, rather than a reflection of some chunk of reality which may be stubborn; and unlike the other books, these have solutions along with each problem.
So, I've been solving puzzles ... with careful note of how I have been failing to solve puzzles. I've hinted at this before, but understanding how you, personally, usually fail is a powerful technique for debugging your own stuck points. I get sloppy, I drop terms from equations, I misunderstand conditions, I overcomplicate solutions, I grind against problems where I should ask for help, I rabbithole on analytical exploration, and I always underestimate the time it will take for me to make the most basic progress.
Know your weaknesses. Then you can work those weak mental muscles, or work around them to build complementary strengths - the way Richard Feynman would always check over an equation when he was done, looking for those places where he had flipped a sign.
Back to work!
Pictured: my "stack" at a typical lunch. I'll usually get to one out of three of the things I bring for myself to do. Never can predict which one though.