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Who is the Centaur and What is his Library?

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Hi, I'm Anthony Francis, and I teach robots to learn, particularly deep reinforcement learning for robot navigation as well as the intersection of memory, emotion, and planning for contextual control

I write urban fantasy about a woman who can bring her tattoos to life and steampunk about women scientists and adventurers, as well as space opera featuring a young centauress explorer. I also draw a webcomic about a girl who can travel to any possible story.

On this site, I also have resources on how to become a better writer, on how to overcome writer's block, on the science of airships, my thoughts on how religion intersects with artificial intelligence, and even a collection of recipes and thoughts on food.

If you're looking for a good place to get started, my first novel, FROST MOON, won an EPIC Ebook award, and my team's work on PRM-RL won the ICRA 2018 Best Paper Award. Otherwise, I hope while you are here in the Library that you find something informative, interesting or at least entertaining!

-the Centaur

P.S. This is a "sticky" post designed to introduce the blog; keep scrolling down for more recent content, or check out the site menu, tags or categories to explore more.

My Novels and Nano

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SO! I love to write, and four of my novels are published - FROST MOON, BLOOD ROCK, LIQUID FIRE, about magical tattoo artist Dakota Frost, and JEREMIAH WILLSTONE AND THE CLOCKWORK TIME MACHINE, about steampunk heroine Jeremiah Willstone. You can read about the published ones at my Novels page, but even though life got a bit away from me this year, I haven't stopped writing - I have six more finished novels in the editing queue, not to mention half a dozen more in process. And every single one of these novels, published or not, was largely written in National Novel Writing Month in November (or its sister challenge Camp Nanowrimo in April and July). Nanowrimo is a 501(c)(3)that helps people find their creative voices - and certainly helped me transition from mostly not-writing to writing over a million words of fiction! (Way over, now). Every year, I donate to the Nanowrimo foundation to help them not just keep the lights on but to support young writers everywhere with their Young Writers Program. This year, consider helping them bring literacy and creativity to more people all around the world! -the Centaur

GROW, Every Day

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So I read a lot and write a lot and occasionally edit what I write and even more rarely, something gets sent to an editor and turned into a publication. But this seems slow, for always there are more thoughts that I have in my head than I seem to have time to put onto a page and share. Also I seem to get stuck in ruts. Actually I like ruts - I’m in one now, eating near my house and bank and pet food store at a restaurant with really good iced tea - but only ruts that are good for getting things done, like ruts in a well-trodden road. When ruts leave you spinning your wheels in the mud, it’s time for a change. This can be as simple as engaging the locking hubs on your stuck all-wheel drive truck to get out of the mud, but you have to know that those locks are there to engage them. (True story). So in order to grow, you need to learn. But if you learn, and you don’t tell anyone, then when you die, what you learn is gone. Fortunately, at the dawn of history humans learned how to speak to the dead, if only the dead are first willing to share, through their stories. I don’t recommend waiting until you’re dead to tell your story. (Most people find that disturbing). Instead, it’s better to organize your thoughts - to reflect on what happened, what you’ve learned, and to package it the lesson with its context so it’s easy to share, like knowledge in a little case. But I don’t do that all that well. I read for entertainment, and I occasionally write things down, but I rarely reflect, and I even more rarely share. But in my attempt to grow, I’ve read some things that made me think, and it made me want to find a way to make me share. I like to LEARN, of course, usually some technical material related to writing or my job, and I’m now consciously reading books to GROW, like Art Matters by Neil Gaiman or It’s Not How Good You Are but How Good You Want To Be by Paul Arden. But I don’t take time to ORGANIZE those thoughts, nor do I seem to take time to SHARE them. That made me think. I already take time to LEARN and GROW. I’ve already decided I need to take more time to ORGANIZE my thoughts, to be a better scientist. Can I also take some time to SHARE? Maybe I can put all those together into, like, an acronym! And that acronym will help me do it! Okay, then, let’s go: LEARN-GROW-ORGANIZE-SHARE. LGOS! Well. That’s a terrible acronym. Alright, alright, if first you don’t succeed, go home and rethink your life. Or something like that, like rethink your acronym. LEARN and GROW need to come before ORGANIZE and SHARE, but G is a better thing to start a word with, as GL or GR is a more common start than LG. And I’m doing it to GROW. So perhaps it could be GROW LEARN, or GROW READ, naturally followed by ORGANIZE. That gets us GRO, which followed by S for SHARE is one letter short of GROSS; but what if instead we got to the point, and said what we have to do: WRITE. So, here’s what I recommend to you (well, actually, to me): take some time every day to
  • GROW yourself by
  • READing to learn,
  • ORGANIZing your thoughts, and share them by
  • WRITING
GROW-READ-ORGANIZE-WRITE: GROW. Why, that’s nicely recursive: GROW to GROW! Since it is recursive, let me try this GROW thing out with this very GROW thought. There. How did it go? -Anthony

How to be a Better Writer

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A notebook in a bookstore coffeehouse, with coffe. About four years ago, one of my colleagues at work found out I was a writer and asked, "So, tell me Anthony, how can I be a better writer?" I don't claim any special wisdom in this department, but I do claim two things: first, that I have opinions about the matter, and second, that I wrote a long email to my friend about it, an email which I thought I'd posted on my blog. Unfortunately, after an extensive search, I wasn't able to find the post. Now, I could attempt to clean this email up prior to posting it here, but I'm afraid that if I do that, I'll just end up going several years without posting it. SO! Here's that email, largely unedited, on "How to be a better writer!" Sorry it took so long to respond to your question about how to be a better writer - I thought I wrote an article on this on my blog, or perhaps in an email to a friend, but if so, I couldn't find it. Then I tried to write a long response, but that turned into something book length. So let me give you the short version.
  • First, just write! That's the best thing anyone can do to become a better writer. Ten thousand hours of practice can build mastery in almost any skill, so the first thing you can do to help yourself is to write regularly - preferably, about whatever comes to mind, so you're not trying to practice when you're on the spot.
  • Try morning pages. The best tool I know to help people get into the habit of writing is to write morning pages - writing, each day, ideally when you get up, three pages in a notebook. Write bla bla bla if you have to - you'll get bored of it quickly, and will write what comes to mind.
  • Take a creativity course. The book The Artist's Way is one of the most famous of these, and it's what inspires me to suggest morning pages. Actually, I've never finished this course - I always get so energized just trying it that I get sucked off into my own projects. Try one that works for you.
  • Read more than you write. You can't consciously choose the words that come out as you write them; they come from your subconscious. So it's important to feed your subconscious with a lot of interesting material to help you generate a lot of interesting material of your own.
  • Read great writing of the type you want to create. What you enjoy reading most might not be the writing you want to emulate most, so hunt down the great writers of the type of writing you're aiming for, read them, and try to figure out what you like about them - and what makes them tick.
  • Read great books on writing. The first two I always recommend to people are Ayn Rand's (yes, that Ayn Rand) The Art of Fiction and The Art of Nonfiction. More than any book I've ever read, the Art of Fiction boils down what makes good fiction writing. John Gardner's On Being a Novelist is another great, but there are so many of these it's hard to pick one.
  • Read great books on style. The two I recommend to people the most are The Elements of Style by Strunk and White and Building Great Sentences by Brooks Landon. Strunk and White is the classic, and Building Great Sentences is its antidote. If you have to pick one, pick Building Great Sentences - hands down.
  • Do writing exercises. There are many, many of these - The Artist's Way has some, at Barnes and Noble you can find dozens of books like 500 Writing Prompts or Creativity Bootcamp that have others; the important thing is to try different writing styles on.
  • Try timed challenges. Write to the End (writetotheend.com) tries 20 minute writing challenges; Shut Up and Write ( meetup.com/shutupandwritesfo ) tries (I think) an hour; National Novel Writing Month (nanowrimo.org) tries 50,000 words in a month. These cure you of the notion you need to wait for your muse.
  • Join a writing group. Not a critique group - those are dangerous until you get more confidence in and acceptance of your own writing (and a thicker skin). I already mentioned Write to the End and Shut Up and Write, but there are many more (even some at Google, such as the Creative Writing Lunch).
  • Take on writing projects. Write novels, write stories, write essays, write memoirs, write documentation, write songs, write plays, write poetry, write haiku, write impenetrable postmodern explorations of what it means for something to be writing - but take on a writing project that has a beginning, middle, and end ...
  • Finish what you write! This is so important I wanted to write this earlier, but the problem is, it depends on what you're writing for. If you just want to improve your skill, reading Strunk and White might do it - but if you want your writing to go further, you need to finish what you write.
  • Don't edit while you write! Some people do this very well, but most people have two modes: producing text, and refining text. Unless you're very confident in your ability to not rework the first paragraph of something forever, make sure you first finish, then edit. But before you do that ...
  • Let your manuscripts cool off. It's hard to have perspective right after you've finished something. At least sleep on it, if you have time; ideally, come back to a story after a week or two and see if what you wrote before still makes sense to you and does what you wanted it to. In the meantime ...
  • Work on something else. Start something new. Creating a new work has an almost magical way of solving problems you have in the work you have cooling on the back burner. Your skills improve, you're not invested in your old ideas, and you come back with a fresh start.
  • Revise your work! Give your manuscript at least a once over. I guarantee, it's not perfect. The books Self Editing for Fiction Writers or The Elements of Editing can help you with this task. It's worth working on something a bit until you can't see anything obviously wrong to it.
  • Share your work with a friendly audience. You're not ready for a critique group yet; they're often way too harsh. What you want are three friendly reviewers: a coach to help with your skills, a critic to help find flaws, and a cheerleader to praise goodness - and if the cheerleader complains, listen very closely to them.
  • Revise your work again before sending it out. Listen to your friendly critics. Revise your work. Make it the best it can be. Then you're ready to send it out - to a critique group if you have to and if you have one, but ideally, to where you want the work received or published.
  • Keep your work circulating until sold. This may not apply to bloggers, writers of memoirs, and internal communications, but if you've got something you want to send to an external audience, send it to as many places as you can. Some great books went to dozens of publishers before getting accepted.
  • Don't argue with your critics. Whether it's a friend, a critique group, or an editor, they're not critiquing you to hurt your feelings. Listen carefully, and perhaps if there's some small misconception, feel free to clear it up, but ask yourself - why wasn't your story so clear that they got it the first time?
  • Solve the problems your critics raise, but don't feel compelled to use their solutions. Humans are great at confabulating fake reasons for the feelings they have. Don't feel the need to use every suggestion your critics raise - but if two or more have problems at the same spot, listen closely.
  • Learn from your genre. Whether it's writing a thesis, writing documentation, or writing science fiction stories, there are documents out there on the pitfalls of the genre and the techniques from success, from How to Complete and Survive a Doctoral Dissertation to the Evil Overlord List.
  • Learn from the style guide. If you're aimed at a particular market, whether it's a science fiction magazine accepting William Shunn's document format, or a book publisher who wants the Chicago Manual of Style, or it's the American Psychological Association, read the style book. With a grain of salt, of course.
  • Learn from publication. Once something is published, take a look at the published work. I can guarantee you, you'll find something about it you'd do differently now, whether it's a typo or a new way to phrase things. Think carefully about this difference and what it can teach you.
  • Find a great critique group. By this point, you've been exposed to enough information to have your own opinions and to make up your own mind - and that's the right time to engage a whole bunch of other opinionated, thoughtful people to get their ideas of how to improve your work.
  • Find a great workshop. These are harder to get into, but put you in touch with great writers of your particular genre or style and can really take you to the next level, if that's what you want.
  • Find a great program - or embark on a great project. If you really want to be a writer, some people suggest a MFA program or other longer-term, intensive course. I simply prefer to take on little projects like 21 book urban fantasy series; these force you to learn some of the same things. :-D
Well, that's about it for the short version. As I said ... the long version's probably a book. :-) I hope this helps! Please feel free to ask me more questions!
And there you have it. I hope that's not a repeat!
-the Centaur

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

 

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

Don’t Fall Into Rabbit Holes

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SO! There I was, trying to solve the mysteries of the universe, learn about deep learning, and teach myself enough puzzle logic to create credible puzzles for the Cinnamon Frost books, and I find myself debugging the fine details of a visualization system I've developed in Mathematica to analyze the distribution of problems in an odd middle chapter of Raymond Smullyan's The Lady or the Tiger. I meant well! Really I did. I was going to write a post about how finding a solution is just a little bit harder than you normally think, and how insight sometimes comes after letting things sit. But the tools I was creating didn't do what I wanted, so I went deeper and deeper down the rabbit hole trying to visualize them. The short answer seems to be that there's no "there" there and that further pursuit of this sub-problem will take me further and further away from the real problem: writing great puzzles! I learned a lot - about numbers, about how things could combinatorially explode, about Ulam Spirals and how to code them algorithmically. I even learned something about how I, particularly, fail in these cases. But it didn't provide the insights I wanted. Feynman warned about this: he called it "the computer disease", worrying about the formatting of the printout so much you forget about the answer you're trying to produce, and it can strike anyone in my line of work. Back to that work. -the Centaur

My Daily Dragon Interview in Two Words: “Just Write!”

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So at Dragon Con I had a reading this year. Yeah, looks like this is the last year I get to bring all my books - too many, to heavy! I read the two flash fiction pieces in Jagged Fragments, "If Looks Could Kill" and "The Secret of the T-Rex's Arms", as well as reading the first chapter of Jeremiah Willstone and the Clockwork Time Machine, a bit of my and Jim Davies' essay on the psychology of Star Trek's artificial intelligences, and even a bit of my very first published story, "Sibling Rivalry". I also gave the presentation I was supposed to give at the SAM Talks before I realized I was double booked; that was "Risk Getting Worse". But that wasn't recorded, so, oh dang, you'll have to either go to my Amazon page to get my books, or wait until we get "Risk Getting Worse" recorded. But my interview with Nancy Northcott for the Daily Dragon, "Robots, Computers, and Magic", however, IS online, so I can share it with you all. Even more so, I want to share what I think is the most important part of my interview:
DD: Do you have any one bit of advice for aspiring writers? AF: Write. Just write. Don’t worry about perfection, or getting published, or even about pleasing anyone else: just write. Write to the end of what you start, and only then worry about what to do with it. In fact, don’t even worry about finishing everything—don’t be afraid to try anything. Artists know they need to fill a sketchbook before sitting down to create a masterwork, but writers sometimes get trapped trying to polish their first inspiration into a final product. Don’t get trapped on the first hill! Whip out your notebook and write. Write morning pages. Write diary at the end of the day. Write a thousand starts to stories, and if one takes flight, run with it with all the abandon you have in you. Accept all writing, especially your own. Just write. Write.
That's it. To read more, check out the interview here, or see all my Daily Dragon mentions at Dragon Con here, or check out my interviewer Nancy Northcott's site here. Onward! -the Centaur    

“Sibling Rivalry” returning to print

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sibling-rivalry-cover-small.png Wow. After nearly 21 years, my first published short story, “Sibling Rivalry”, is returning to print. Originally an experiment to try out an idea I wanted to use for a longer novel, ALGORITHMIC MURDER, I quickly found that I’d caught a live wire with “Sibling Rivalry”, which was my first sale to The Leading Edge magazine back in 1995. “Sibling Rivalry” was borne of frustrations I had as a graduate student in artificial intelligence (AI) watching shows like Star Trek which Captain Kirk talks a computer to death. No-one talks anyone to death outside of a Hannibal Lecter movie or a bad comic book, much less in real life, and there’s no reason to believe feeding a paradox to an AI will make it explode. But there are ways to beat one, depending on how they’re constructed - and the more you know about them, the more potential routes there are for attack. That doesn’t mean you’ll win, of course, but … if you want to know, you’ll have to wait for the story to come out. “Sibling Rivalry” will be the second book in Thinking Ink Press's Snapbook line, with another awesome cover by my wife Sandi Billingsley, interior design by Betsy Miller and comments by my friends Jim Davies and Kenny Moorman, the latter of whom uses “Sibling Rivalry” to teach AI in his college courses. Wow! I’m honored. Our preview release will be at the Beyond the Fence launch party next week, with a full release to follow. Watch this space, fellow adventurers! -the Centaur

Why yes, I’m running a deep learning system on a MacBook Air. Why?

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deeplearning.png Yep, that’s Python consuming almost 300% of my CPU - guess what, I guess that means this machine has four processing cores, since I saw it hit over 300% - running the TensorFlow tutorial. For those that don’t know, "deep learning” is a relatively recent type of learning which uses improvements in both processing power and learning algorithms to train learning networks that can have dozens or hundreds of layers - sometimes as many layers as neural networks in the 1980’s and 1990’s had nodes. For those that don’t know even that, neural networks are graphs of simple nodes that mimic brain structures, and you can train them with data that contains both the question and the answer. With enough internal layers, neural networks can learn almost anything, but they require a lot of training data and a lot of computing power. Well, now we’ve got lots and lots of data, and with more computing power, you’d expect we’d be able to train larger networks - but the first real trick was discovering mathematical tricks that keep the learning signal strong deep, deep within the networks. The second real trick was wrapping all this amazing code in a clean software architecture that enables anyone to run the software anywhere. TensorFlow is one of the most recent of these frameworks - it’s Google’s attempt to package up the deep learning technology it uses internally so that everyone in the world can use it - and it’s open source, so you can download and install it on most computers and try out the tutorial at home. The CPU-baking example you see running here, however, is not the simpler tutorial, but a test program that runs a full deep neural network. Let’s see how it did: Screenshot 2016-02-08 21.08.40.png Well. 99.2% correct, it seems. Not bad for a couple hundred lines of code, half of which is loading the test data - and yeah, that program depends on 200+ files worth of Python that the TensorFlow installation loaded onto my MacBook Air, not to mention all the libraries that the TensorFlow Python installation depends on in turn … But I still loaded it onto a MacBook Air, and it ran perfectly. Amazing what you can do with computers these days. -the Centaur

Overcoming Writer’s Block

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(Self-deprecating note: this blogpost is a rough draft of an essay that I’m later planning to refine for the Write to the End site, but I’ve been asked to share it, so I finished it up and am sharing it as is. When the full article is cleaned up, I’ll link to it … but in the meantime, enjoy, and try not to wince too much).

So for way of introduction to the Write to the End group, I’ve been asked by a lot of people recently “How can I become a better writer?” — a question for which I’ve generated a bit of stock advice I frequently sum up as, “Just Write!” But, when I dug a little deeper, I found almost half of the people asking me that question were really asking the question, “How can I overcome Writer’s Block?” Well, I have some theories. And I’m going to tell you about them. But more importantly, I’ve got some techniques which I’m going to share with you, and even better, I’m using one of them right now: if you sit down to write and get writer’s block, then write down very explicitly why you sat down to write, and what kind of writing you hope to have produced when you get up again. If you don’t know why you want to write, and you don’t know what you hope to produce before you get up again, congratulations! You’re done. Get up from the page and go have a soda, something really nice, not diet, like with Italian flavoring or an ice cream float. If you do know what you want to write, or what you want to have written, congratulations! Actually writing that down can get you … um … on the order of 227 words, according to Scrivener’s count, probably 300 by the time you’re done. The hope is that getting yourself writing ANYTHING will get your pen moving, and saying what you want to write will get you rolling in the right direction; however, if you finish saying what you want to write and remain stuck, then be really explicit about what you want to say next and what you feel is your barrier to writing more. That’s the big thing I want to leave with you: if you have writer’s block trying to write something, you can overcome it by either describing what you want to write, or why you want to write it, and springboarding off that with more questions and answers, until, in the end, you’re just writing.

Huh. A notch over 350 words. I underestimated.

Now, I know some of the people who are reading this are technical writers, and so I want to warn you up front that there’s a problem with my approach that doesn’t apply to fiction writers: describing what you want to write is not a substitute for the thought that needs to go into the technical meat of whatever it is you’re writing. For example, if you’re trying to, say, write a design doc for your teammates, you may think that outlining the project, its goals, its problems, and its possible solutions is enough to make a design doc—but it’s not. That’s what a fiction writer calls an outline. While there are fiction stories that are essentially nothing but outlines, and even more that are outlines in narrative, fiction generally isn’t an outline, but is instead people in places, talking and doing things, told in a particular way — or what we technically call character and setting, dialogue and action, and scene and narration wrapped in that stylistic veneer we call voice. But technical writers, we can get tricked by outlines of technical items into thinking we’ve said something about a problem — so it is really critical that after you get a rough outline down that you go back over it, extract the important ideas, to think about they fit together, and to identify the key ideas that are not obvious about the problem — and those key ideas are what should go into your design doc or project proposal or product requirements document or launch announcement or marketing communication or scientific paper or anything else. The value of your document is not the structure of the problem, which is often well known, but the original thought that you bring to the table.

And that brings us to the primary reason for writer’s block, at least for experienced writers, that is: not having thought clearly enough about what comes next.

But wait! Because I’m writing this extemporaneously — a big-ass word for saying I’m pulling this out of my orifice — I’ve forgotten to tell you about the other kinds of writer’s block, which is somewhat important in case you’re possibly getting bored and want a quick way to figure whether slogging on through the desert of this essay in search of water that will quench your particular search is a vain hope or not, but which is actually far more important because some of those kinds of writer’s block can KILL YOU. Well, actually, no, that’s not very likely, but they can get you to kill your story and end up back at stage one.

So how can you get blocked? Let’s tick a few of these off so we can hold your interest while I drag out the big red warning sign. First, sometimes writer’s block is caused because you just don’t want to write — Ayn Rand used to call this “white sneakers disease” because she knew a writer who’d’ve rather cleaned their sneakers than write. Ayn Rand thought that, technically speaking, this wasn’t a block, but nevermind, since people have developed a good technique for resolving “white sneaker’s disease,” and that technique is called BIC — Butt In Chair. If you think you want to write, and you are not writing, then stop whatever you are doing, go put your butt in chair in front of a pen, piece of paper and writing surface, and sit there until you get bored enough to write something, or find that you cannot and AHA NOW this is writer’s block, congratulations, move on to the techniques for tackling writer’s block proper.

Second, as I said earlier, experienced writers can have writer’s block because they haven’t thought through what comes next. Third, inexperienced writers can have writer’s block because they’re cognitively overwhelmed — which is the real point of this essay, and which is why I started the essay off with one paragraph specifically tackling this problem in case that was all that you read, but, don’t fear, if “inexperienced writer staring at a blank page feeling just that, a blank” describes you, then hang in there, I’m writing this essay specifically for you and will come back to this in detail.

But the fourth kind is the real dangerous kind of writer’s block, a particular kind of voluntary writer’s block which can hit writers of any stripe, both unmotivated and motivated, inexperienced and experienced; in fact, it almost hit me writing the second section of this essay, and if I’d given into it, I never would have written the words you’re reading right now — because I would have spent the same time editing the first section of this essay, and that right there is Writer’s Block of the Fourth Kind: editing while you write.

Trying to edit while you write is particularly dangerous for reasons I’ll get back to when I explain Why Novices Feel Fear At The Dreaded Blank Page, but the more immediate reason is that you can spend arbitrary amounts of time editing without adding to your draft. Now, there are some writers who edit while they write all the time — especially poets, who may spend as much time working over ten words as it takes me to write a thousand words —but right there that shows you that if you’re trying to cough up a ten thousand word story, it doesn’t behoove you to drill down on a perfect first sentence. There’s a reason we call our writing group Write to the End: it’s because we believe you should finish what you start before you try to edit it, or you will never finish anything at all.

Okay, that’s a first pass at why Writer’s Block of the Fourth Kind is dangerous: it can stall you out, and worse, trick you into thinking you’re actually writing. But what if you don’t have anything to edit? What if you’re suffering from Writer’s Block of the Third Kind, the Dreaded Fear of the Blank Page? This feeling of blankness is the feeling you get when you’re cognitively overwhelmed, and to understand the reasons I separate it out from Writer’s Block of the Second Kind, AKA Not Thinking Through Your Shit, we need to talk a little bit about cognitive psychology — specifically, working memory and cognitive skill acquisition.

You see, when a writer sits down at the page, we may imagine we’re creating worlds — but we’re not gods, and can hold only a finite amount of information in our heads at one time. Our working memories can only manipulate a handful of chunks of discrete information at a time — famously estimated in cognitive psychology as a short term memory holding roughly seven plus or minus two items. Of course, it ain’t that simple when you dig into the details, but as a rough rule of thumb, it holds — and that explains both writer’s block for experienced writers and the Dreaded Fear of the Blank Page for inexperienced ones.

When faced with a blank page, you can easily see how you could get blocked not knowing why you want to write, or what you want to write about, or what’s the meat behind the structure of the idea — there’s just nothing in your short term memory to put on the page. But why do so many inexperienced writers who know the answers to all these questions nevertheless come to me complaining that they feel a blank when sitting down at the page? Well, that’s easy: I’m a psychic magnet for those kinds of problems — just kidding. The real reason is that inexperienced writers have, by definition, a set of skills which are not fully developed — and we don’t actually have short term memories that hold information, we have working memories which are both the product of and are used by our skills.

Yes, that’s right — I tricked you! I started talking about working memory, then smoothly slipped to talking about short term memory in the same sentence, because for a long time cognitive psychologists made the same mistake. We imagined that humans had a short term memory like a buffer that passively held information, like a briefcase, but when you carry through the implications that model breaks down, and that’s not really how the cortex of the brain is organized anyway. It’s better to think of the brain’s fixed storage capacity as less a passive buffer and more of an active internal dashboard reporting the state all the brain’s cognitive systems. Now, there are no photogenic cartoon characters monitoring that dashboard like in Inside Out—in part because of licensing issues with Pixar, but mostly because it would involve an infinite regress—if there’s a little character monitoring your internal dashboard, who’s monitoring their internal dashboards? Cognitive psychologists call that homunculus fallacy, and so a better image of the mental stage of the mind is an empty spherical cockpit filled with instruments projecting their findings to each other. Your consciousness is just the part of your mind that is easily accessible to other parts of your mind. For example, you can recognize a person’s face, but unless there are really obvious features, like Salvador Dali’s mustache that points all the way up to your eyeballs, you can’t describe a face in sufficient detail for someone else to recognize it, because the details of your facial recognition system aren’t accessible to conscious awareness.

In most animals, the instruments of the cockpit are fixed by the design of the system, like the gas gauge on your car, which reports the status of your fuel tank, or the flashing light on the fast return switch of your TARDIS, which shows that the Ship is trying to return to its previous destination.What distinguishes humans is that many of its screens are programmable, the same way your car’s GPS can update itself when the manufacturer pushes an update, or the way your TARDIS reconfigures its controls to match your personality every time you regenerate. Over time, the systems of the cockpit collect information, slowly improving over time with respect to the problems for which they were designed, like a GPS picking up new roads. But the human mind isn’t a car, with an army of of engineers designing updates that get pushed to it over a wireless network, or a TARDIS, with a billion years of engineering designed into its architectural reconfiguration system to help it adapt. No, the human mind has to update itself from scratch, often adapting to skills for which it has no evolutionary precedent — like, for example, writing.

You’ve got dials on your dashboard for hunger, sound, even speech, but writing is something humans made up from whole cloth. And when you’ve got to learn a skill for which you’ve got no precedent, no inbuilt system that can just pick up new roads, your mind has to fall back on more powerful general problem solving techniques. These techniques involve representing the information we know about a problem explicitly, collecting the implications of that knowledge from our long term memory, and putting all that data together into new conclusions. Once again, the components of your dashboard notice these leaps from information to conclusion, storing it to make it available to solve new problems. This process is called automatization, and it’s called that because it’s transforming explicit information that you’re representing in your conscious dashboard into skilled knowledge you can use automatically without conscious awareness.

You’d think that automatization wouldn’t help you, since you’re trying to store new information, but all you have are existing systems - but one of the fundamental tricks of computing is that any sufficiently powerful process can simulate just about any other process, and the cockpit of your glorious machine—in which all the systems you’ve accumulated over a billion years of evolution can talk to each other—certainly qualifies as a very powerful process that can simulate almost anything. SO, if you keep learning basic facts about a new skill, and keep storing them in whatever systems you have that are even remotely compatible, over time, your overall cognitive system will learn a new, automatic skill—but hang on. To represent the information about a problem, to dredge up its implications, and draw conclusions, your mind needs scratchspace—temporary storage to hold this information so your general problem solving processes can work it over, and that information must be accessible your conscious awareness. Learning a new cognitive skill needs your dashboard. It needs your highly limited working memory.

But wait! Weren’t we using that to hold what we wanted to write about?

Exactly. Now you’re starting to see the problem.

As a novice writer, you may know how to physically write—how to generate words on the page in response to prompts, like writing down items for a grocery list for your spouse in response to spoken requests, or writing down the contents of a shipment from the Queen of Sheba as it comes off the boat—but when you’re writing an article or story, what you’re actually doing is the separate and more complex task of composition — the task of creating new sequences of words. Take a simple example, composing your Captain’s Log. You can’t just hit a button on the Captain’s Chair and start jabbering about what happened on the planet: the task involves creating a specific set of words in a specific sequence which is stereotyped. You start with “Captain’s Log”, followed by the stardate, followed by a sentence reporting the location or situation, followed by one or two more sentences discussing the key questions of the mission and whatever red-shirted disposable crewmembers were eaten by the monster of the week. That structure itself is information, information which you need to call to mind, somehow, in order to organize the words that you speak, and if you’ve been rattled by a bunch of red-shirted disposable crewmembers being eaten by the monster of the week, you might have trouble gathering your thoughts. An experienced Starfleet captain like Picard or Kirk, however, will have no trouble—because for them, the structure of the log is automatic.

The way that cognitive skill learning works is through the transformation of declarative knowledge to procedural knowledge: that is, the process of automatization takes information you express explicitly and turns it into information that’s the output of a skill. That means if you are skilled at a task, you don’t need to pay attention to it: the actions of the task will happen, well, automatically; but that also means that if you are not skilled at a task, you’re relying on your general purpose processing power to perform it—and that the information you need to perform the task will compete with what you know about the task.

The problem is even worse because the act of writing relies on many sources of knowledge. Let’s review for a moment what some of those are, and I’ll throw in some you may have not thought of yet:

  • Purpose: Why you’re writing (for creative expression, because your boss asked you)
  • Goal: What you want your writing to do (to be fun, to help your teammates, etc)
  • Content: What you want to write about (the specific information you contribute)
  • Form: What kind of thing you’re writing (a story, an article, a blogpost)
  • Style: What tone of voice you want to use (lighthearted, formal, quirky)

Each of these is better thought of as a skill for generating answers to questions, rather than a source for information—and if you’re not practiced at the skill, you’ll have to store information about it in working memory, competing—but wait a minute, let’s go back to content for a moment. Think about it. To answer the question about what you want to write, you need to generate several pieces of information:

  • Content: What you want to write about
  • Structure: What topics do you need to cover?
  • Questions: What questions should your piece answer?
  • Ideas: What do you think about the questions?
  • Answers: How does that translate into answers?

I’m not trying to be pedantic here—I’m making an important point, or I think I am. What you want to say involves several kinds of information: the general topic of your piece, the specific issues you want to address, whatever thoughts you have, and how to express them—but each of these types of information is, itself, a skill, which, if it is not practiced, will compete with whatever it is you have to say.

This is why inexperienced writers dread The Blank Page: because they’re actually drawing on half a dozen skills, none of which are practiced, and those are driving their ideas straight out of their head. This is why my wife, who’s a great artist but not an experienced writer, a woman who’s put a great deal of thought into eco-friendly art, who knows why she wants to write, what she wants to accomplish, and can easily spend forty-five minutes talking to me about her ideas, can nonetheless get totally stymied when she sits down to write, staring at the blank page. And this is why I separate the Writer’s Block of the Third Kind—the inexperienced author’s Dread of the Blank Page—from the simpler Writer’s Block of the Second Kind—the experienced author’s Lack of Shit Together—because if an experienced author is willing to sit down and think hard about their problem, once they get their ideas, their skills will take straight over—but if an inexperienced author tries the same thing, their very skills may drive their ideas right out their heads.

That’s why inexperienced writers may need different tools to write other than “Just Write” or “Butt in Chair” or “Stop and Think”. In cognitive skills acquisition, one way you can teach a complicated skill is to teach it in parts—we call this scaffolding. Rather than try to become a great basketball player all at once, you instead practice dribbling, taking shots, holding the ball, playing one-on-one, then pickup games—slowly building up a body of skills that eventually become the foundation for real mastery. Writing is the same way; if you’re having trouble getting started, focusing on sub-skills and developing them can give you the scaffolding you need to get started.

One scaffolding technique I’ve recommended to people is morning pages—a technique recommended in The Artist’s Way to write three pages longhand the first thing in the morning. There are a lot of reasons to do this beyond scaffolding, but it gets you past the problem of composition by giving you a safe environment to write, and it can also help you express your ideas. If even this is too hard, you may be blocked on the simple act of writing, and I recommend you try writing “bla bla bla” until you get bored with it. This doesn’t work for everyone, but you could also try the “Finding Forrester” technique of taking an existing story and typing it in until you get tired of their words and start writing your own.

Another scaffolding technique is what I call the inventory method. I hinted at this at the start of the article: ask yourself explicitly the questions you need to perform the task of composition:

  • Why do you want to write?
  • What do you want your writing to accomplish?
  • What should people learn or feel after reading your article or story?
  • What is the most important specific idea that you contribute to this topic?

And so on, and so on, with the whole list of questions that I had earlier.

If even this is too hard, there’s another method I call the one page assessment. Get a piece of lined composition paper—and I mean this literally, this is for totally blocked people, so I want you to literally do these steps physically—and draw a line down its center so it has two columns. On the left, write out, one per line, the numbers one through ten, and then the words “Who what when where why how;” on the right, write out the days of the week and the months of the year. Now, for the numbers one through ten, write the top ten most important thing about your project—these can be single words or sentences, but rack your brain until you can get ten single words—and then write brief answers to each of the “Who what why …” questions below. When you’re done with that, for each day of the week or month of the year, write something significant about your project—either in the story you’re telling, or about when you as a person can work on it, or whatever (you can also do this with other breakdowns, like states or countries or oceans or planets—whatever categories work for you). When you’ve filled the sheet, pick the five things most important from the page, flip it over, write down these five as your headings, and try to write at least one sentence about each of the five things you picked.

The purpose of this exercise is to take away the need to do composition AND the need to generate questions, just focusing you in a very general, nonthreatening way on properties that affect your problem. If you make it through the page, consider doing it again, with your own headings this time. Process repeats, until you’re generating full outlines.

On the note of outlines, the technique I used for my first novel was what I called a recursive hierarchical outline. I knew I wanted to write a novel about a genetically engineered centaur, so I wrote that sentence down in a Microsoft Word document. Then I copied that sentence, italicized it, and wrote a paragraph about that sentence detailing the plot. Then I copied that paragraph, italicized it, broke it into sentences as new headings, and expanded each of those sentences into a paragraph. I repeated the process until I had a good outline; then I expanded it further until I had sections and finally paragraphs—at which point, I just started writing.

Another way to get at this information that’s locked in your head is the interview method—having a trusted friend ask you questions, and either writing down your answers or recording it for transcription later.

Finally, Bjarne Stroustrup, the creator of C++, recommends the template method—if you want to write an article on a topic, find a similar article to use as a template, and use that to help establish your questions and find the rough structure of your outline. Since he built a whole career around basically doing that to C by turning it into C++, and since he’s done it with several books and articles since then to great effect, I guess this approach has worked well for him.

The point of giving all these potential scaffolding techniques is that each writer is different, and no technique is guaranteed to work for you. We can see why this is—everyone has a slightly different set of internal equipment, and even for equipment that’s the same, everyone has a different history of learning and a different set of skills that work with facility, or not, on any given problem.

So, to sum up, the ways of tackling writer’s block are:

  • Writer’s Block of the First Kind: What We Have Here is a Failure to Motivate.
    Solution: Butt In Chair
  • Writer’s Block of the Second Kind: Not Thinking Through Your Shit.
    Solution: Stop and Think
  • Writer’s Block of the Third Kind: The Dreaded Blank Page.
    Solution: Cognitive Scaffolding
  • Writer’s Block of the Fourth Kind: Editing While You Write.
    Solution: Write to the End, then Edit

So now you see why I sum up my writing advice as “Just write—bla bla bla if you have to so your pen’s moving—because the more you write, the easier it gets, and the better you get; but if you sit down to write and get writer’s block, then write down very explicitly why you sat down to write, and what kind of writing you hope to have produced when you get up again, and then you’ll know how to proceed.” This sums up all of the problems in one Butt in Chair, provides a Cognitive Scaffold, incorporates Stop and Think—in fact, it tackles just about everything except the editing bit, which might be summed up as “Don’t critique yourself, finish your damn story!” And as for that bit …

That’s why I go to a writing group called Write to the End.

—The Centaur

Context-Directed Spreading Activation

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netsphere.png Let me completely up front about my motivation for writing this post: recently, I came across a paper which was similar to the work in my PhD thesis, but applied to a different area. The paper didn’t cite my work – in fact, its survey of related work in the area seemed to indicate that no prior work along the lines of mine existed – and when I alerted the authors to the omission, they informed me they’d cited all relevant work, and claimed “my obscure dissertation probably wasn’t relevant.” Clearly, I haven’t done a good enough job articulating or promoting my work, so I thought I should take a moment to explain what I did for my doctoral dissertation. My research improved computer memory by modeling it after human memory. People remember different things in different contexts based on how different pieces of information are connected to one another. Even a word as simple as ‘ford’ can call different things to mind depending on whether you’ve bought a popular brand of car, watched the credits of an Indiana Jones movie, or tried to cross the shallow part of a river. Based on that human phenomenon, I built a memory retrieval engine that used context to remember relevant things more quickly. My approach was based on a technique I called context directed spreading activation, which I argued was an advance over so-called “traditional” spreading activation. Spreading activation is a technique for finding information in a kind of computer memory called semantic networks, which model relationships in the human mind. A semantic network represents knowledge as a graph, with concepts as nodes and relationships between concepts as links, and traditional spreading activation finds information in that network by starting with a set of “query” nodes and propagating “activation” out on the links, like current in an electric circuit. The current that hits each node in the network determines how highly ranked the node is for a query. (If you understand circuits and spreading activation, and this description caused you to catch on fire, my apologies. I’ll be more precise in future blogposts. Roll with it). The problem is, as semantic networks grow large, there’s a heck of a lot of activation to propagate. My approach, context directed spreading activation (CDSA), cuts this cost dramatically by making activation propagate over fewer types of links. In CDSA, each link has a type, each type has a node, and activation propagates only over links whose nodes are active (to a very rough first approximation, although in my evaluations I tested about every variant of this under the sun). Propagating over active links isn’t just cheaper than spreading activation over every link; it’s smarter: the same “query” nodes can activate different parts of the network, depending on which “context” nodes are active. So, if you design your network right, Harrison Ford is never going to occur to you if you’ve been thinking about cars. I was a typical graduate student, and I thought my approach was so good, it was good for everything—so I built an entire cognitive architecture around the idea. (Cognitive architectures are general reasoning systems, normally built by teams of researchers, and building even a small one is part of the reason my PhD thesis took ten years, but I digress.) My cognitive architecture was called context sensitive asynchronous memory (CSAM), and it automatically collected context while the system was thinking, fed it into the context-directed spreading activation system, and incorporated dynamically remembered information into its ongoing thought processes using patch programs called integration mechanisms. CSAM wasn’t just an idea: I built it out into a computer program called Nicole, and even published a workshop paper on it in 1997 called “Can Your Architecture Do This? A Proposal for Impasse-Driven Asynchronous Memory Retrieval and Integration.” But to get a PhD in artificial intelligence, you need more than a clever idea you’ve written up in a paper or implemented in a computer program. You need to use the program you’ve written to answer a scientific question. You need to show that your system works in the domains you claim it works in, that it can solve the problems that you claim it can solve, and that it’s better than other approaches, if other approaches exist. So I tested Nicole on computer planning systems and showed that integration mechanisms worked. Then I and a colleague tested Nicole on a natural language understanding program and showed that memory retrieval worked. But the most important part was showing that CDSA, the heart of the theory, didn’t just work, but was better than the alternatives. I did a detailed analysis of the theory of CDSA and showed it was better than traditional spreading activation in several ways—but that rightly wasn’t enough for my committee. They wanted an example. There were alternatives to my approach, and they wanted to see that my approach was better than the alternatives for real problems. So I turned Nicole into an information retrieval system called IRIA—the Information Retrieval Intelligent Assistant. By this time, the dot-com boom was in full swing, and my thesis advisor invited me and another graduate student to join him starting a company called Enkia. We tried many different concepts to start with, but the further we went, the more IRIA seemed to have legs. We showed she could recommend useful information to people while browsing the Internet. We showed several people could use her at the same time and get useful feedback. And critically, we showed that by using context-directed spreading activation, IRIA could retrieve better information faster than traditional spreading activation approaches. The first publication on IRIA came out in 2000, shortly before I got my PhD thesis, and at the company things were going gangbusters. We found customers for the idea, my more experienced colleagues and I turned the IRIA program from a typical graduate student mess into a more disciplined and efficient system called the Enkion, a process we documented in a paper in early 2001. We even launched a search site called Search Orbit—and then the whole dot-com disaster happened, and the company essentially imploded. Actually, that’s not fair: the company continued for many years after I left—but I essentially imploded, and if you want to know more about that, read “Approaching 33, as Seen from 44.” Regardless, the upshot is that I didn’t follow up on my thesis work after I finished my PhD. That happens to a lot of PhD students, but for me in particular I felt that it would have been betraying the trust of my colleagues to go publish a sequence of papers on the innards of a program they were trying to use to run their business. Eventually, they moved on to new software, but by that time, so had I. Fast forward to 2012, and while researching an unrelated problem for The Search Engine That Starts With A G, I came across the 2006 paper “Recommending in context: A spreading activation model that is independent of the type of recommender system and its contents” by Alexander Kovács and Haruki Ueno. At Enkia, we’d thought of doing recommender systems on top of the Enkion, and had even started to build a prototype for Emory University, but the idea never took off and we never generated any publications, so at first, I was pleased to see someone doing spreading activation work in recommender systems. Then I was unnerved to see that this approach also involved spreading activation, over a typed network, with nodes representing the types of links, and activation in the type nodes changing the way activation propagated over the links. Then I was unsettled to see that my work, which is based on a similar idea and predates their publication by almost a decade, was not cited in the paper. Then I was actually disturbed when I read: “The details of spreading activation networks in the literature differ considerably. However, they’re all equal with respect to how they handle context … context nodes do not modulate links at all…” If you were to take that at face value, the work that I did over ten years of my life—work which produced four papers, a PhD thesis, and at one point helped employ thirty people—did not exist. Now, I was also surprised by some spooky similarities between their systems and mine—their system is built on a context-directed spreading activation model, mine is a context-directed spreading activation model, theirs is called CASAN, mine is embedded in a system called CSAM—but as far as I can see there’s NO evidence that their work was derivative of mine. As Chris Atkinson said to a friend of mine (paraphrased): “The great beam of intelligence is more like a shotgun: good ideas land on lots of people all over the world—not just on you.” In fact, I’d argue that their work is a real advance to the field. Their model is similar, not identical, and their mathematical formalism uses more contemporary matrix algebra, making the relationship to related approaches like Page Rank more clear (see Google Page Rank and Beyond). Plus, they apparently got their approach to work on recommender systems, which we did not; IRIA did more straight up recommendation of information in traditional information retrieval, which is a similar but not identical problem. So Kovács and Ueno’s “Recommending in Context” paper is a great paper and you should read it if you’re into this kind of stuff. But, to set the record straight, and maybe to be a little bit petty, there are a number of spreading activation systems that do use context to modulate links in the network … most notably mine. -the Centaur Pictured: a tiny chunk of the WordNet online dictionary, which I’m using as a proxy of a semantic network. Data processing by me in Python, graph representation by the GraphViz suite’s dot program, and postprocessing by me in Adobe Photoshop.

The Dark Labyrinths of Lovecraft and Borges

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Borges and Lovecraft v2.png In many ways, Howard Philips Lovecraft and Jorge Luis Borges are different. Howard Philips Lovecraft wrote dark, atmospheric American horror at the dawn of the twentieth century. Jorge Luis Borges, born ten years later, wrote learned, ethnic Argentinian magic realism.

Lovecraft toiled in obscurity, writing for pulps; Borges was crowned with every prize the literary world has to offer short of the Nobel. Lovecraft was a high school dropout; Borges was a renowned professor of literature.

But in many ways, Howard Philips Lovecraft and Jorge Luis Borges are similar.

There’s the obvious: both the dropout and the professor were masters of erudition, capable of bring a vast number of literary techniques to their stories. Both focused largely on stories that were deeply regional, steeped in the lore of the cultures that they loved. And both were obsessed with odd details: for Borges, the labyrinth, the knife, and the tango; for Lovecraft, tangled streets, dark forests, and fishy odors.

But the important similarities between Lovecraft and Borges run far deeper.

Borges plays games with the infinite, constructing labyrinths of time and symbols that dig at the foundations of our concepts of thought and identity. His most famous story, “The Library of Babel,” imagines an infinite library filled with useless books, whose meaning might only be discerned by the allseeing eye of a god—a story that plays with ideas of faith in a random universe.

Lovecraft plays games with the cosmos, constructing vistas of time and space that threaten the foundations of our concepts of safety and knowledge. His most famous story, “The Call of Cthulhu,” imagines an undersea city inhabited by an enormous monster, whose existence threatens the sanity of humanity—a story that plays with ideas of fear and cosmic insignificance.

Borges and Lovecraft are similar, but not identical.

In Borges, the supernatural rarely breaks into the natural world openly, and when it does, it happens in dreams and visions or subtle events. The supernatural is subtle, but the meaning is not: Borges often tells us his aim directly in his stories, frequently writing them like essays that explore their own morals, or examining their meaning in conversations with himself. Borges plumbs the depths of human thought through stories that show us the vast scale of conceptual space. Throughout his work is a taste of nihilism: humans seeking meaning in a meaningless cosmos.

In Lovecraft, the supernatural manifests in dreams and vision and subtle events, but it always breaks into to the natural world openly. The supernatural is not subtle, but the meaning is: Lovecraft rarely tells us his aim directly in his stories, instead writing essays that explain their morals, or examining their meaning in letters to friends. He explores the cosmic through metaphor. Lovecraft plumbs the depths of human insignificance through stories that show us the vast scale of physical space. Throughout his work is a taste of nihilism: humans seeking sanity in an inhuman cosmos.

Lovecraft and Borges are two sides of the same coin.

They write about the same terrors. In Borges, the monsters swim beneath the surface, their shapes only dimly suggested by the churning existential confusion left in their wakes. In Lovecraft, the monsters break the surface, turn their dripping, shaggy visages towards the horrified faces of his protagonists, and show us that if we could truly see what Borges only hints at, we would surely go mad.

-the Centaur

Credits: public domain images of Lovecraft and Borges both from Wikimedia Commons; composition by me.