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Posts tagged as “Intelligence”

Day 057

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Turing Drawing Alan Turing, rendered over my own roughs using several layers of tracing paper. I started with the below rough, in which I tried to pay careful attention to the layout of the face - note the use of the 'third eye' for spacing and curved contour lines - and the relationship of the body, the shoulders and so on. Turing Rough 1 I then corrected that into the following drawing, trying to correct the position and angles of the eyes and mouth - since I knew from previous drawings that I tended to straighten things that were angled, I looked for those flaws and attempted to correct them. (Still screwed up the hair and some proportions). Turing Rough 2 This was close enough for me to get started on the rendering. In the end, I like how it came out, even though I flattened the curves of the hair and slightly squeezed the face and pointed the eyes slightly wrong, as you can see if you compare it to the following image from this New Yorker article: Turing Photo -the Centaur

Free Will and the Halting Problem

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turing headshot Lent is when Christians choose to give things up or to take things on to reflect upon the death of Jesus. For Lent, I took on this self-referential series about Lent, arguing Christianity is following Jesus, and that following role models are better than following rules because all sets of rules are ultimately incompete. But how can we choose to follow Jesus? To many Christians, the answer is simple: "free will." At one Passion play (where I played Jesus, thanks to my long hair), the author put it this way: "You are always choose, because no-one can take your will away. You know that, don't you?" Christians are highly attached to the idea of free will. However, I know a fair number of atheists and agnostics who seem attached to the idea of free will being a myth. I always find this bit of pseudoscence a bit surprising coming from scientifically minded folk, so it's worth asking the question. Do we have free will, or not? Well, it depends on what kind of free will we're talking about. Philosopher Daniel Dennett argues at book length that there are many definitions of "free will", only some varieties of which are worth having. I'm not going to use Dennett's breakdown of free will; I'll use mine, based on discussions with people who care. The first kind of "free will" is undetermined will: the idea that "I", as consciousness or spirit, can make things happen, outside the control of physical law. Well, fine, if you want to believe that: the science of quantum mechanics allows that, since all observable events have unresolvable randomness. But the science of quantum mechanics also suggests we could never prove that idea scientifically. To see why, look at entanglement: particles that are observed here are connected to particles over there. Say, if momentum is conserved, and two particles fly apart, if one goes left, the other must go right. But each observed event is random. You can't predict one from the other; you can only extract it from the record by observing both particles and comparing the results. So if your soul is directing your body's choices, we could only tell by recording all the particles of your body and soul and comparing them. Good luck with that. The second kind of "free will" is instantaneous will: the idea that "I", at any instant of time, could have chosen to do something differently. It's unlikely we have this kind of free will. First, according to Einstein, simultaneity has no meaning for physically separated events - like the two hemispheres of your brain. But, more importantly, the idea of an instant is just that - an idea. Humans are extended over time and space; the brain is fourteen hundred cubic centimeters of goo, making decisions over timescales ranging from a millisecond (a neuron fires) to a second and a half (something novel enters consciousness.) But, even if you accept that we are physically and temporally extended beings, you may still cling to - or reject - an idea of free will: sovereign will, the idea that our decisions, while happening in our brains and bodies, are nevertheless our own. The evidence is fairly good that we have this kind of free will. Our brains are physically isolated by our skulls and the blood-brain barrier. While we have reflexes, human decision making happens in the neocortex, which is largely decoupled from direct external responses. Even techniques like persuasion and hypnosis at best have weak, indirect effects. But breaking our decision-making process down this way sometimes drives people away. It makes religious people cling to the hope of undetermined will; it makes scientific people erroneously think that we don't have free will at all, because our actions are not "ours", but are made by physical processes. But arguing that "because my decisions are made by physical processes, therefore my decisions are not actually mine" requires the delicate dance of identifying yourself with those processes before the comma, then rejecting them afterwards. Either those decision making processes are part of you, or they are not. If they're not, please go join the religious folks over in the circle marked "undetermined will." If they are, then arguing that your decisions are not yours because they're made by ... um, the decision making part of you ... is a muddle of contradictions: a mix of equivocation (changing the meaning of terms) and a category error (mistaking your decision making as something separate from yourself). But people committed to the non-existence of free will sometimes double down, claiming that even if we accept those decision making processes as part of us, our decisions are somehow not "ours" or not "free" because the outcome of our decision making process is still determined by physical laws. To someone working on Markov decision processes - decision machines - this seems barely coherent. The foundation of this idea is sometimes called Laplace's demon - the idea that a creature with perfect knowledge of all physical laws and particles and forces would be able to predict the entire history of the universe - and your decisions, so therefore, they're not your decisions, just the outcome of laws. Too bad this is impossible. Not practically impossible - literally, mathematically impossible. To see why, we need to understand the Halting Problem - the seemingly simple question of whether we can build a program to tell if any given computer program will halt given any particular input. As basic as this question sounds, Alan Turing proved in the 1930's that this is mathematically impossible. The reason is simple: if you could build an analysis program which could solve this problem, you could feed itself to itself - wrapped in a loop that went forever if the original analysis program halts, and halts if it ran forever. No matter what answer it produces, it leads to a contradiction. The program won't work. This idea seems abstract, but its implications are deep. It applies to not just computer programs, but to a broad class of physical systems in a broad class of universes. And it has corollaries, the most important being: you cannot predict what any arbitrary given algorithm will do without letting the algorithm do it. If you could, you could use it to predict whether a program would halt, and therefore, you could solve the Halting Problem. That's why Laplace's Demon, as nice a thought experiment as it is, is slain by Turing's Machine. To predict what you would actually do, part of the demon would have to be identical to you. Nothing else in the universe - nothing else in a broad class of universes - can predict your decisions. Your decisions are made in your own head, not anyone else's, and even though they may be determined by physical processes, the physical processes that determine them are you. Only you can do you. So, you have sovereign will. Use it wisely. -the Centaur Pictured: Alan Turing, of course.

Jesus and Gödel

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kurt godel Yesterday I claimed that Christianity was following Jesus - looking at him as a role model for thinking, judging, and doing, stepping away from rules and towards principles, choosing good outcomes over bad ones and treating others like we wanted to be treated, and ultimately emulating what Jesus would do. But it's an entirely fair question to ask, why do we need a role model to follow? Why not have a set of rules that guide our behavior, or develop good principles to live by? Well, it turns out it's impossible - not hard, but literally mathematically impossible - to have perfect rules, and principles do not guide actions. So a role model is the best tool we have to help us build the cognitive skill of doing the right thing. Let's back up a bit. I want to talk about what rules are, and how they differ from principles and models. In the jargon of my field, artificial intelligence, rules are if-then statements: if this, then do that. They map a range of propositions to a domain of outcomes, which might be actions, new propositions, or edits to our thoughts. There's a lot of evidence that the lower levels of operation of our minds is rule-like. Principles, in contrast, are descriptions of situations. They don't prescribe what to do; they evaluate what has been done. The venerable artificial intelligence technique of generate-and-test - throw stuff on the wall to see what sticks - depends on "principles" to evaluate whether the outcomes are good. Models are neither if-then rules nor principles. Models predict the evolution of a situation. Every time you play a computer game, a model predicts how the world will react to your actions. Every time you think to yourself, "I know what my friend would say in response to this", you're using a model. Rules, of a sort, may underly our thinking, and some of our most important moral precepts are encoded in rules, like the Ten Commandments. But rules are fundamentally limited. No matter how attached you are to any given set of rules, eventually, those rules can fail you, and you can't know when. The iron laws behind these fatal flaws are Gödel's incompleteness theorems. Back in the 1930's, Kurt Gödel showed any set of rules sophisticated enough to handle basic math would either fail to find things that were true, or would make mistakes - and, worse, could never prove that they were consistent. Like so many seemingly abstract mathematical concepts, this has practical real-world implications. If you're dealing with anything at all complicated, and try to solve your problems with a set of rules, either those rules will fail to find the right answers, or will give the wrong answers, and you can't tell which. That's why principles are better than rules: they make no pretensions of being a complete set of if-then rules that can handle all of arithmetic and their own job besides. They evaluate propositions, rather than generating them, they're not vulnerable to the incompleteness result in the same way. How does this affect the moral teachings of religion? Well, think of it this way: God gave us the Ten Commandments (and much more) in the Old Testament, but these if-then rules needed to be elaborated and refined into a complete system. This was a cottage industry by the time Jesus came on the scene. Breaking with the rule-based tradition, Jesus gave us principles, such as "love thy neighbor as thyself" and "forgive as you wish to be forgiven" which can be used to evaluate our actions. Sometimes, some thought is required to apply them, as in the case of "Is it lawful to do good or evil on the Sabbath?" This is where principles fail: they don't generate actions, they merely evaluate them. Some other process needs to generate those actions. It could be a formal set of rules, but then we're back at square Gödel. It could be a random number generator, but an infinite set of monkeys will take forever to cross the street. This is why Jesus's function as a role model - and the stories about Him in the Bible - are so important to Christianity. Humans generate mental models of other humans all the time. Once you've seen enough examples of someone's behavior, you can predict what they will do, and act and react accordingly. The stories the Bible tells about Jesus facing moral questions, ethical challenges, physical suffering, and even temptation help us build a model of what Jesus would do. A good model of Jesus is more powerful than any rule and more useful than any principle: it is generative, easy to follow, and always applicable. Even if you're not a Christian, this model of ethics can help you. No set of rules can be complete and consistent, or even fully checkable: rules lawyering is a dead end. Ethical growth requires moving beyond easy rules to broader principles which can be used to evaluate the outcomes of your choices. But principles are not a guide to action. That's where role models come in: in a kind of imitation-based learning, they can help guide us by example until we've developed the cognitive skills to make good decisions automatically. Finding role models that you trust can help you grow, and not just morally. Good role models can help you decide what to do in any situation. Not every question is relevant to the situations Jesus faced in ancient Galilee! For example, when faced with a conundrum, I sometimes ask three questions: "What would Jesus do? What would Richard Feynman do? What would Ayn Rand do?" These role models seem far apart - Ayn Rand, in particular, tried to put herself on the opposite pole from Jesus. But each brings unique mental thought processes to the table - "Is this doing good or evil?" "You are the easiest person for yourself to fool" and "You cannot fake reality in any way whatsoever." Jesus helps me focus on what choices are right. Feynman helps me challenge my assumptions and provides methods to test them. Rand is benevolent, but demands that we be honest about reality. If two or three of these role models agree on a course of action, it's probably a good choice. Jesus was a real person in a distant part of history. We can only reach an understanding of who Jesus is and what He would do by reading the primary source materials about him - the Bible - and by analyses that help put these stories in context, like religious teachings, church tradition, and the use of reason. But that can help us ask what Jesus would do. Learning the rules are important, and graduating beyond them to understand principles is even more important. But at the end of the day, we want to do the right thing, by following the lead of the man who asks, "Love thy neighbor as thyself." -the Centaur Pictured: Kurt Gödel, of course.

What is “Understanding”?

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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.

Never Give Up

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So, 2019. What a mess. More on that later; as for me, I've had neither the time nor even the capability to blog for a while. But one thing I've noticed is, at least for me, the point at which I want to give up is usually just prior to the point where I could have my big breakthrough. For example: Scrivener. I had just about given up on Scrivener, an otherwise great program for writers that helps with organizing notes, writing screenplays, and even for comic book scripts. But I'd become used to Google Docs and its keyboard shortcuts for hierarchical bulleted lists, not entirely different from my prior life using hierarchical notebook programs like GoldenSection Notes. But Scrivener's keyboard shortcuts were all different, and the menus didn't seem to support what I needed, so I had started trying alternatives. Then I gave on more shot at going through the manual, which had earlier got me nothing.At first this looked like a lost cause: Scrivener depended on Mac OS X's text widgets, which themselves implement a nonstandard text interface (fanboys, shut up, sit down: you're overruled. case in point: Home and End. I rest my case), and worse, depend on the OS even for the keyboard shortcuts, which require the exact menu item. But the menu item for list bullets actually was literally a bullet, which normally isn't a text character in most programs; you can't access it. But as it turns out, in Scrivener, you can. I was able to insert a bullet, find the bullet character, and even create a keyboard shortcut for it. And it did what it was supposed to! Soon I found the other items I needed to fill out the interface that I'd come to know and love in Google Docs for increasing/decreasing the list bullet indention on the fly while organizing a list: Eventually I was able to recreate the whole interface and was so happy I wrote a list describing it in the middle of the deep learning Scrivener notebook that I had been working on when I hit the snag that made me go down this rabbit hole (namely, wanting to create a bullet list): Writing this paragraph itself required figuring out how to insert symbols for control characters in Mac OS X, but whatever: a solution was possible, even ready to be found, just when I was ready to give up. I found the same thing with so many things recently: stuck photo uploads on Google Photos, configuration problems on various publishing programs, even solving an issue with the math for a paper submission at work. I suspect this is everywhere. It's a known thing in mathematics that when you feel close to a solution you may be far from it; I often find myself that the solution is to be found just after the point you want to give up. I've written about a related phenomenon called this "working a little bit harder than you want to" but this is slightly different: it's the idea that your judgment that you've exhausted your options is just that, a judgment. It may be true. Try looking just a bit harder for that answer. -the Centaur Pictured: a photo of the Greenville airport over Christmas, which finally uploaded today when I went back through the archives of Google Photos on my phone and manually stopped a stuck upload from December 19th.

Robots in Montreal

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A cool hotel in old Montreal.

"Robots in Montreal," eh? Sounds like the title of a Steven Moffat Doctor Who episode. But it's really ICRA 2019 - the IEEE Conference on Robotics and Automation, and, yes, there are quite a few robots!

Boston Dynamics quadruped robot with arm and another quadruped.

My team presented our work on evolutionary learning of rewards for deep reinforcement learning, AutoRL, on Monday. In an hour or so, I'll be giving a keynote on "Systematizing Robot Navigation with AutoRL":

Keynote: Dr. Anthony Francis
Systematizing Robot Navigation with AutoRL: Evolving Better Policies with Better Evaluation

Abstract: Rigorous scientific evaluation of robot control methods helps the field progress towards better solutions, but deploying methods on robots requires its own kind of rigor. A systematic approach to deployment can do more than just make robots safer, more reliable, and more debuggable; with appropriate machine learning support, it can also improve robot control algorithms themselves. In this talk, we describe our evolutionary reward learning framework AutoRL and our evaluation framework for navigation tasks, and show how improving evaluation of navigation systems can measurably improve the performance of both our evolutionary learner and the navigation policies that it produces. We hope that this starts a conversation about how robotic deployment and scientific advancement can become better mutually reinforcing partners.

Bio: Dr. Anthony G. Francis, Jr. is a Senior Software Engineer at Google Brain Robotics specializing in reinforcement learning for robot navigation. Previously, he worked on emotional long-term memory for robot pets at Georgia Tech's PEPE robot pet project, on models of human memory for information retrieval at Enkia Corporation, and on large-scale metadata search and 3D object visualization at Google. He earned his B.S. (1991), M.S. (1996) and Ph.D. (2000) in Computer Science from Georgia Tech, along with a Certificate in Cognitive Science (1999). He and his colleagues won the ICRA 2018 Best Paper Award for Service Robotics for their paper "PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning". He's the author of over a dozen peer-reviewed publications and is an inventor on over a half-dozen patents. He's published over a dozen short stories and four novels, including the EPIC eBook Award-winning Frost Moon; his popular writing on robotics includes articles in the books Star Trek Psychology and Westworld Psychology. as well as a Google AI blog article titled Maybe your computer just needs a hug. He lives in San Jose with his wife and cats, but his heart will always belong in Atlanta. You can find out more about his writing at his website.

Looks like I'm on in 15 minutes! Wish me luck.

-the Centaur

 

Information Hygiene

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Our world is big. Big, and complicated, filled with many more things than any one person can know. We rely on each other to find out things beyond our individual capacities and to share them so we can succeed as a species: there's water over the next hill, hard red berries are poisonous, and the man in the trading village called Honest Sam is not to be trusted.

To survive, we must constantly take information, just as we must eat to live. But just like eating, consuming information indiscriminately can make us sick. Even when we eat good food, we must clean our teeth and got to the bathroom - and bad food should be avoided. In the same way, we have to digest information to make it useful, we need to discard information that's no longer relevant, and we need to avoid misinformation so we don't pick up false beliefs. We need habits of information hygiene.

Whenever you listen to someone, you absorb some of their thought process and make it your own. You can't help it: that the purpose of language, and that's what understanding someone means. The downside is your brain is a mess of different overlapping modules all working together, and not all of them can distinguish between what's logically true and false. This means learning about the beliefs of someone you violently disagree with can make you start to believe in them, even if you consciously think they're wrong. One acquaintance I knew started studying a religion with the intent of exposing it. He thought it was a cult, and his opinion about that never changed. But at one point, he found himself starting to believe what he read, even though, then and now, he found their beliefs logically ridiculous.

This doesn't mean we need to shut out information from people we disagree with - but it does mean we can't uncritically accept information from people we agree with. You are the easiest person for yourself to fool: we have a cognitive flaw called confirmation bias which makes us more willing to accept information that confirms our prior beliefs rather than ones that deny it. Another flaw called cognitive dissonance makes us want to actively resolve conflicts between our beliefs and new information, leading to a rush of relief when they are reconciled; combined with confirmation bias, people's beliefs can actually be strengthened by contradictory information.

So, as an exercise in information hygiene for those involved in one of those charged political conversations that dominate our modern landscape, try this. Take one piece of information that you've gotten from a trusted source, and ask yourself: how might this be wrong? Take one piece of information from an untrusted source, and ask yourself, how might this be right? Then take it one step further: research those chinks in your armor, or those sparks of light in your opponent's darkness, and see if you can find evidence pro or con. Try to keep an open mind: no-one's asking you to actually change your mind, just to see if you can tell whether the situation is actually as black and white as you thought.

-the Centaur

Pictured: the book pile, containing some books I'm reading to answer a skeptical friend's questions, and other books for my own interest.

Surfacing

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An interpretation of the rocket equation.

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.

Thor's Hammer space station.

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.

The Modanaqa Abugida.

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.

Gabby in the emergency room.

Oh dag nabbit! (He's going to be fine).

-the Centaur

I<tab-complete> welcome our new robot overlords.

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Hoisted from a recent email exchange with my friend Gordon Shippey:
Re: Whassap? Gordon: Sounds like a plan. (That was an actual GMail suggested response. Grumble-grumble AI takeover.) Anthony: I<tab-complete> welcome our new robot overlords.
I am constantly amazed by the new autocomplete. While, anecdotally, autocorrect of spell checking is getting worse and worse (I blame the nearly-universal phenomenon of U-shaped development, where a system trying to learn new generalizations gets worse before it gets better), I have written near-complete emails to friends and colleagues with Gmail's suggested responses, and when writing texts to my wife, it knows our shorthand! One way of doing this back in the day were Markov chain text models, where we learn predictions of what patterns are likely to follow each other; so if I write "love you too boo boo" to my wife enough times, it can predict "boo boo" will follow "love you too" and provide it as a completion. More modern systems use recurrent neural networks to learn richer sets of features with stateful information carried down the chain, enabling modern systems to capture subtler relationships and get better results, as described in the great article  "The Unreasonable Effectiveness of Recurrent Neural Networks". -the<tab-complete> Centaur  

PRM-RL Won a Best Paper Award at ICRA!

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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! -the Centaur