Welcome to the Future


Welcome to the future, ladies and gentlemen. Here in the future, the obscure television shows of my childhood rate an entire section in the local bookstore, which combines books, games, music, movies, and even vinyl records with a coffeehouse and restaurant.


Here in the future, the heretofore unknown secrets of my discipline, artificial intelligence, are now conveniently compiled in compelling textbooks that you can peruse at your leisure over a cup of coffee.


Here in the future, genre television shows play on the monitors of my favorite bar / restaurant, and the servers and I have meaningful conversations about the impact of robotics on the future of labor.


And here in the future, Monty Python has taken over the world.

Perhaps that explains 2016.

-the Centaur

The Two Fear Channels


Hoisted from a recent email thread with the estimable Jim Davies:

“You wrote to me once that the brain has two fear channels, cognitive and reactive. Do you have a citation I can look at for an introduction to that idea?”

So I didn’t have a citation off the top of my head, though I do now – LeDoux’s 1998 book The Emotional Brain – but I did remember what I told Jim: that we have two fear channels, one fast, one slow. The fast one is primarily sensory, reactive, and can learn bad associations which are difficult to unlearn, as in PTSD (post-traumatic stress disorder); the slow one is more cognitive, deliberative, and has intellectual fear responses.

It turns out that it ain’t that simple, but I was almost right. Spoiling the lead a bit, there are two conditioned fear channels, the fast “low road” and slow “high road” and they do function more or less as I described: the low road has quick reactions to stimuli, a direct hotline from sensory processing in your thalamus to the amygdala which is a clearinghouse for emotional information; the high road involves the sensory cortex and confirms the quick reaction of the low road. The low road’s implicated in PTSD, though PTSD seems to involve broader areas of brain damage brought on by traumatic events.

Where that needs tweaking is that there’s also a third fear channel, the instructed or cognitive fear channel. This allows us to become scared if we’re told that there’s a tiger behind a door, even if we haven’t seen the fearsome beast. This one relies on an interaction between the hippocampus and the amygdala; if your hippocampus is damaged, you will likely not remember what you’re told, whereas if your amygdala is damaged, you may react appropriately to instruction, but you might not feel the appropriate emotional response to your situation (which could lead you to make poor choices).

So, anyway, that’s the gist. But, in the spirit of Check Your Work, let me show my work from my conversation with Jim.

Ok, I have an answer for you (description based on [Gazzaniga et al 2002], though I found similar information in [Lewis et al 2010]).

There are two fear channels: one involving fast sensory processing and one involving slower perceptual information. Based on the work of LeDoux [1996] these are sometimes called the “low road” (quick and dirty connection of the thalamus to the amygdala, a crude signal that a stimulus resembles a conditioned stimulus) and the “high road” (thalamus to sensory cortex to amygdala, a more refined signal which is more reliable); both of these channels help humans learn implicit conditioned fear responses to stimuli.

This “low road” and “high road” concept was what my understanding of PTSD is based on, that individuals acquire a fast low-road response to stimuli that they cannot readily suppress; I don’t have a reference for you, but I’ve heard it many times (and it’s memorably portrayed in Born on the Fourth of July when veterans in a parade react to firecrackers with flinches, and later the protagonist after his experience has the same reaction). A little research seems to indicate that PTSD may actually involve events traumatic enough to damage the amygdala or hippocampus or both, but likely involving other brain areas as well ([Bremner 2006], [Chen et al 2012]).

There’s a couple more wrinkles. Even patients with amygdala damage have unconditioned fear responses; conditioned responses seem to involve the amygdala [Phelps et al 1998]. Instructed fear (warning a subject about a loud noise that will follow a flashing light, for example) seems to involve the hippocampus as well, though patients with amygdala damage don’t show fear responses even though they may behave appropriately when instructed (e.g., not showing a galvanic skin response even though they flinch [Phelps et al 2001]). This amygdala response can influence storage of emotional memories [Ferry et al 2000]. Furthermore, there’s evidence the amygdala is even involved in perceptual processing of emotional expression [Dolan and Morris 2000].

So to sum, the primary reference that I was talking about was the “low road” (fast connection from thalamus to amygdala, implicated in fast conditioned fear responses and PTSD, though PTSD may involve trauma-induced damage to more brain areas) and “high road” (slow reliable connection from thalamus to sensory cortex to amygdala, implicated in conditioned fear responses), but there’s also a “sensory” path (conditioned fear response via the thalamus to the amygdala, with or without the sensory cortex involvement) vs “cognitive” path (instructed fear response via the hippocampus, which functions but shows reduced emotional impact in case of amygdala damage).

Hope this helps!

Bremner, J. D. (2006). Traumatic stress: effects on the brain. Dialogues in clinical neuroscience, 8(4), 445.

Chen, Y., Fu, K., Feng, C., Tang, L., Zhang, J., Huan, Y., … & Ma, C. (2012). Different regional gray matter loss in recent onset PTSD and non PTSD after a single prolonged trauma exposure. PLoS One, 7(11), e48298.

Dolan, R. J., & Morris, J. S. (2000). The functional anatomy of innate and acquired fear: Perspectives from neuroimaging. Cognitive neuroscience of emotion, 225-241.

Ferry, B., Roozendaal, B., & McGaugh, J. L. (1999). Basolateral amygdala noradrenergic influences on memory storage are mediated by an interaction between β-and α1-adrenoceptors. The Journal of Neuroscience, 19(12), 5119-5123.

Gazzaniga, M.S., Ivry, R.B., & Mangun, G.R. (2002) Cognitive Neuroscience – The Biology of the Mind (2e) W. W. Norton & Company.

LeDoux, J. (1998). The emotional brain: The mysterious underpinnings of emotional life. Simon and Schuster.

Lewis, M., Haviland-Jones, J. M., & Barrett, L. F. (Eds.). (2010). Handbook of emotions. Guilford Press.

Phelps, E. A., LaBar, K. S., Anderson, A. K., O’connor, K. J., Fulbright, R. K., & Spencer, D. D. (1998). Specifying the contributions of the human amygdala to emotional memory: A case study. Neurocase, 4(6), 527-540.

Phelps, E. A., O’Connor, K. J., Gatenby, J. C., Gore, J. C., Grillon, C., & Davis, M. (2001). Activation of the left amygdala to a cognitive representation of fear. Nature neuroscience, 4(4), 437-441.

-the Centaur
Pictured: a few of the books I looked at to answer Jim’s question.

“Sibling Rivalry” returning to print


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

All the Transitions of Tic-Tac-Toe, Redux

What was supposed to be a quick exercise to help me visualize a reinforcement learning problem has turned into a much larger project, one which I’m reluctantly calling a temporary halt to: a visualization of all the states of Tic-Tac-Toe.

What I found is that it’s surprisingly hard to make this work: all the states want to pile on top of each other, and there are a few subtleties to representing it correctly. To make it work, I had to separately represent board positions – the typical X’es and Oh’s used in play – from game states, such as Start, X Wins, O Wins, and Stalemate.

The Mathematica for this is gnarly and a total hack; it probably could be made more efficient to process all 17,000+ transitions of the game, and I definitely need to think of a way to make each state appear in its own, non-overlapping position. But that will require more thought than my crude jitter function above, the time it takes to run each render is way too long to quickly iterate, and I have a novel to finish. I don’t want to get stuck in a grind against a game known for its stalemate.

Ugh. You can see the jumble there; it’s hard to see which transitions lead to X’s or O’s victory and which lead to stalemate. I have ideas on how to fix this, but I want my novel done more and first, dag nab it. So let me give you all the transitions of Tic-Tac-Toe in their full glory (22.8mb). I could say more about this problem – or I can say what I have, call it victory, and move on.

On to the novel. It’s going well.

-the Centaur

I just think they don’t want AI to happen

Hoisted from Facebook: I saw my friend Jim Davies share the following article:

The momentous advance in artificial intelligence demands a new set of ethics … In a dramatic man versus machine encounter, AlphaGo has secured its third, decisive victory against a renowned Go player. With scientists amazed at how fast AI is developing, it’s vital that humans stay in control.

I posted: “The AI researchers I know talk about ethics and implications all the time – that’s why I get scared about every new call for new ethics after every predictable incremental advance.” I mean, Jim and I have talked about this, at length; so did my I and my old boss, James Kuffner … heck, one of my best friends, Gordon Shippey, went round and round on this over two decades ago in grad school. Issues like killbots, all the things you could do with the 99% of a killbot that’s not lethal, the displacement of human jobs, the potential for new industry, the ethics of sentient robots, the ethics of transhuman uplift, and whether any of these things are possible … we talk about it a lot.

So if we’ve been building towards this for a while, and talking about ethics the whole time, where’s the need for a “new” ethics, except in the minds of people not paying attention? But my friend David Colby raised the following point: “I’m no scientist, but it seems to me that anyone who doesn’t figure out how to make an ethical A.I before they make an A.I is just asking for trouble.”

Okay, okay, so I admit it: my old professor Ron Arkin’s book on the ethics of autonomous machines in warfare is lower in my stack than the book I’m reading on reinforcement learning … but it’s literally in my stack, and I think about this all the time … and the people I work with think about this all the time … and talk about it all the time … so where is this coming from? I feel like there’s something else beneath the surface. Since David and I are space buffs, my response to him was that I read all these stories about the new dangers of AI as if they said:

With the unexpected and alarming success of the recent commercial space launch, it’s time for a new science of safety for space systems. What we need is a sober look at the risks. After all, on a mission to Mars, a space capsule might lose pressure. Before we move large proportions of the human race to space, we need to, as a society, look at the potential catastrophes that might ensue, and decide whether this is what we want our species to be doing. That’s why, at The Future of Life on Earth Institute, we’ve assembled the best minds who don’t work directly in the field to assess the real dangers and dubious benefits of space travel, because clearly the researchers who work in the area are so caught up with enthusiasm that they’re not seriously considering the serious risks. Seriously. Sober. Can we ban it now? I just watched Gravity and I am really scared after clenching my sphincter for the last ninety minutes.

To make that story more clear if you aren’t a space buff: there are more commercial space endeavors out there than you can shake a stick at, so advances in commercial space travel should not be a surprise – and the risks outlined above, like decompression, are well known and well discussed. Some of us involved in space also talk about these issues all the time. My friend David has actually written a book about space disasters, DEBRIS DREAMS, which you can get on Amazon.

So to make the analogy more clear, there are more research teams working on almost every possible AI problem that you can think of, so advances in artificial intelligence applications should not be a surprise – and the risks outlined by most of these articles are well known and discussed. In my personal experience – my literal personal experience – issues like safety in robotic systems, whether to trust machine decisions over human judgment, and the potential for disruption of human jobs or even life are all discussed more frequently, and with more maturity, than I see in all these “sober calls” for “clear-minded” research from people who wouldn’t know a laser safety curtain from an orbital laser platform.

I just get this sneaking suspicion they don’t want AI to happen.

-the Centaur

All the States of Tic-Tac-Toe

Screenshot 2016-03-12 15.06.34.png

NOT the most elegant Mathematica, but trying to do clever things with NestList was a pain. And my math was creating duplicate transitions, which is why the other graphs were so dense – and the layer size needed to be tweaked a bit to show both the starting and ending states more clearly. But, after some cleanup, it worked, after a bit of churning (click the image for a larger size):

All the States of Tic Tac Toe.png

I Am Easily Amused

Screenshot 2016-03-12 14.24.06.png

More seriously, what I’m trying to do is improve my understanding of state spaces. Below’s yet another visualization of the first four stages of tic-tac-toe, trying to get at how the states reconverge.

TicTacToe v1.png

You can see the structure even better without the board visualizations, but if you do it’s just a graph and you no longer know what it is that you’re seeing. More thought is required on how to visualize this (and the real problems I’m tackling behind this, for my day job).

-the Centaur

Visualizing Cellular Automata


SO, why’s an urban fantasy author digging into the guts of Mathematica trying to reverse-engineer how Stephen Wolfram drew the diagrams of cellular automata in his book A New Kind of Science? Well, one of my favorite characters to write about is the precocious teenage weretiger Cinnamon Frost, who at first glance was a dirty little street cat until she blossomed into a mathematical genius when watered with just the right amount of motherly love. My training as a writer was in hard science fiction, so even if I’m writing about implausible fictions like teenage weretigers, I want the things that are real – like the mathematics she develops – to be right. So I’m working on a new kind of math behind the discoveries of my little fictional genius, but I’m not the youngest winner of the Hilbert Prize, so I need tools to help simulate her thought process.

And my thought process relies on visualizations, so I thought, hey, why don’t I build on whatever Stephen Wolfram did in his groundbreaking tome A New Kind of Science, which is filled to its horse-choking brim with handsome diagrams of cellular automata, their rules, and the pictures generated by their evolution? After all, it only took him something like ten years to write the book … how hard could it be?

Deconstructing the Code from A New Kind of Science, Chapter 2

Fortunately Stephen Wolfram provides at least some of the code that he used for creating the diagrams in A New Kind of Science. He’s got the code available for download on the book’s website, wolframscience.com, but a large subset is in the extensive endnotes for his book (which, densely printed and almost 350 pages long, could probably constitute a book in their own right). I’m going to reproduce that code here, as I assume it’s short enough to fall under fair use, and for the half-dozen functions we’ve got here any attempt to reverse-engineer it would end up just recreating essentially the same functions with slightly different names.
Cellular automata are systems that take patterns and evolve them according to simple rules. The most basic cellular automata operate on lists of bits – strings of cells which can be “on” or “off” or alternately “live” or “dead,” “true” and “false,” or just “1” and “0” – and it’s easiest to show off how they behave if you start with a long string of cells which are “off” with the very center cell being “on,” so you can easily see how a single live cell evolves. And Wolfram’s first function gives us just that, a list filled with dead cells represented by 0 with a live cell represented by 1 in its very center:

In[1]:= CenterList[n_Integer] := ReplacePart[Table[0, {n}], 1, Ceiling[n/2]]

In[2]:= CenterList[10]
Out[2]= {0, 0, 0, 0, 1, 0, 0, 0, 0, 0}

One could imagine a cellular automata which updated each cell just based on its contents, but that would be really boring as each cell would be effectively independent. So Wolfram looks at what he calls “elementary automata” which update each cell based on their neighbors. Counting the cell itself, that’s a row of three cells, and there are eight possible combinations of live and dead neighbors of three elements – and only two possible values that can be set for each new element, live or dead. Wolfram had a brain flash to list the eight possible combinations the same each way every time, so all you have are that list of eight values of “live” or “dead” – or 1’s and 0’s, and since a list of 1’s and 0’s is just a binary number, that enabled Wolfram to represent each elementary automata rule as a number:

In[3]:= ElementaryRule[num_Integer] := IntegerDigits[num, 2, 8]

In[4]:= ElementaryRule[30]
Out[4]= {0, 0, 0, 1, 1, 1, 1, 0}

Once you have that number, building code to apply the rule is easy. The input data is already a string of 1’s and 0’s, so Wolfram’s rule for updating a list of cells basically involves shifting (“rotating”) the list left and right, adding up the values of these three neighbors according to base 2 notation, and then looking up the value in the rule. Wolfram created Mathematica in part to help him research cellular automata, so the code to do this is deceptively simple…

In[5]:= CAStep[rule_List, a_List] :=
rule[[8 – (RotateLeft[a] + 2 (a + 2 RotateRight[a]))]]

… a “RotateLeft” and a “RotateRight” with some addition and multiplication to get the base 2 index into the rule. The code to apply this again and again to a list to get the history of a cellular automata over time is also simple:

In[6]:= CAEvolveList[rule_, init_List, t_Integer] :=
NestList[CAStep[rule, #] &, init, t]

Now we’re ready to create the graphics for the evolution of Wolfram’s “rule 30,” the very simple rule which shows highly complex and irregular behavior, a discovery which Wolfram calls “the single most surprising scientific discovery [he has] ever made.” Wow. Let’s spin it up for a whirl and see what we get!

In[7]:= CAGraphics[history_List] :=
Graphics[Raster[1 – Reverse[history]], AspectRatio -> Automatic]

In[8]:= Show[CAGraphics[CAEvolveList[ElementaryRule[30], CenterList[103], 50]]]


Uh – oh. The “Raster” code that Wolfram provides is the code to create the large images of cellular automata, not the sexy graphics that show the detailed evolution of the rules. And reading between the lines of Wolfram’s end notes, he started his work in FrameMaker before Mathematica was ready to be his full publishing platform, with a complex build process producing the output – so there’s no guarantee that clean simple Mathematica code even exists for some of those early diagrams.

Guess we’ll have to create our own.

Visualizing Cellular Automata in the Small

The cellular automata diagrams that Wolfram uses have boxes with thin lines, rather than just a raster image with 1’s and 0’s represented by borderless boxes. They’re particularly appealing because the lines are white between black boxes and black between white boxes, which makes the structures very easy to see. After some digging, I found that, naturally, a Mathematica function to create those box diagrams does exist, and it’s called ArrayPlot, with the Mesh option set to True:

In[9]:= ArrayPlot[Table[Mod[i + j, 2], {i, 0, 3}, {j, 0, 3}], Mesh -> True]


While we could just use ArrayPlot, it’ s important when developing software to encapsulate our knowledge as much as possible, so we’ll create a function CAGridGraphics (following the way Wolfram named his functions) that encapsulates the knowledge of turning the Mesh option to True. If later we decide there’s a better representation, we can just update CAMeshGraphics, rather than hunting down every use of ArrayPlot. This function gives us this:

In[10]:= CAMeshGraphics[matrix_List] :=
ArrayPlot[matrix, Mesh -> True, ImageSize -> Large]

In[11]:= CAMeshGraphics[{CenterList[10], CenterList[10]}]


Now, Wolfram has these great diagrams to help visualize cellular automata rules which show the neighbors up top and the output value at bottom, with a space between them. The GraphicsGrid does what we want here, except it by its nature resizes all the graphics to fill each available box. I’m sure there’s a clever way to do this, but I don’t know Mathematica well enough to find it, so I’m going to go back on what I just said earlier, break out the options on ArrayPlot, and tell the boxes to be the size I want:

In[20]:= CATransitionGraphics[rule_List] :=
   ArrayPlot[{#}, Mesh -> True, ImageSize -> {20 Length[#], 20}] &, rule]}]]

That works reasonably well; here’ s an example rule, where three live neighbors in a row kills the center cell :

In[21]:= CATransitionGraphics[{{1, 1, 1}, {0}}]

Screenshot 2016-01-03 14.19.21.png  

Now we need the pattern of digits that Wolfram uses to represent his neighbor patterns. Looking at the diagrams and sfter some digging in the code, it seems like these digits are simply listed in reverse counting order – that is, for 3 cells, we count down from 2^3 – 1 to 0, represented as binary digits.

In[22]:= CANeighborPattern[num_Integer] :=
Table[IntegerDigits[i, 2, num], {i, 2^num – 1, 0, -1}]

In[23]:= CANeighborPattern[3]
Out[23]= {{1, 1, 1}, {1, 1, 0}, {1, 0, 1}, {1, 0, 0}, {0, 1, 1}, {0, 1, 0}, {0, 0,
1}, {0, 0, 0}}

Stay with me – that only gets us the first row of the CATransitionGraphics; to get the next row, we need to apply a rule to that pattern and take the center cell:

In[24]:= CARuleCenterElement[rule_List, pattern_List] :=
CAStep[rule, pattern][[Floor[Length[pattern]/2]]]

In[25]:= CARuleCenterElement[ElementaryRule[30], {0, 1, 0}]
Out[25]= 1

With all this, we can now generate the pattern of 1′ s and 0′ s that represent the transitions for a single rule:

In[26]:= CARulePattern[rule_List] :=
Map[{#, {CARuleCenterElement[rule, #]}} &, CANeighborPattern[3]]

In[27]:= CARulePattern[ElementaryRule[30]]
Out[27]= {{{1, 1, 1}, {0}}, {{1, 1, 0}, {1}}, {{1, 0, 1}, {0}}, {{1, 0, 0}, {1}}, {{0,
   1, 1}, {0}}, {{0, 1, 0}, {1}}, {{0, 0, 1}, {1}}, {{0, 0, 0}, {0}}}

Now we can turn it into graphics, putting it into another GraphicsGrid, this time with a Frame.

In[28]:= CARuleGraphics[rule_List] :=
GraphicsGrid[{Map[CATransitionGraphics[#] &, CARulePattern[rule]]},
Frame -> All]

In[29]:= CARuleGraphics[ElementaryRule[30]]

Screenshot 2016-01-03 14.13.52.png

At last! We’ ve got the beautiful transition diagrams that Wolfram has in his book. And we want to apply it to a row with a single cell:

In[30]:= CAMeshGraphics[{CenterList[43]}]

Screenshot 2016-01-03 14.13.59.png

What does that look like? Well, we once again take our CAEvolveList function from before, but rather than formatting it with Raster, we format it with our CAMeshGraphics:

In[31]:= CAMeshGraphics[CAEvolveList[ElementaryRule[30], CenterList[43], 20]]

Screenshot 2016-01-03 14.14.26.png

And now we’ ve got all the parts of the graphics which appear in the initial diagram of this page. Just to work it out a bit further, let’s write a single function to put all the graphics together, and try it out on rule 110, the rule which Wolfram discovered could effectively simulate any possible program, making it effectively a universal computer:

In[22]:= CAApplicationGraphics[rule_Integer, size_Integer] := Column[
CAEvolveList[ElementaryRule[rule], CenterList[size],
   Floor[size/2] – 1]]},

In[23]:= CAApplicationGraphics[110, 43]

Screenshot 2016-01-03 14.14.47.png

It doesn’ t come out quite the way it did in Photoshop, but we’ re getting close. Further learning of the rules of Mathematica graphics will probably help me, but that’s neither here nor there. We’ve got a set of tools for displaying diagrams, which we can craft into what we need.

Which happens to be a non-standard number system unfolding itself into hyperbolic space, God help me.

Wish me luck.

-the Centaur

P.S. While I’ m going to do a standard blogpost on this, I’ m also going to try creating a Mathematica Computable Document Format (.cdf) for your perusal. Wish me luck again – it’s my first one of these things.

P.P.S. I think it’ s worthwhile to point out that while the tools I just built help visualize the application of a rule in the small …

In[24]:= CAApplicationGraphics[105, 53]

Screenshot 2016-01-03 14.14.58.png

… the tools Wolfram built help visualize rules in the very, very large:

In[25]:= Show[CAGraphics[CAEvolveList[ElementaryRule[105], CenterList[10003], 5000]]]



That’s 10,000 times bigger – 100 times bigger in each direction – and Mathematica executes and displays it flawlessly.

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


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

Welcome to 2016


Hi, I’m Anthony! I love to write books and eat food, activities that I power by fiddling with computers. Welcome to 2016! It’s a year. I hope it’s a good one, but hope is not a strategy, so here’s what I’m going to do to make 2016 better for you.

First, I’m writing books. I’ve got a nearly-complete manuscript of a steampunk novel JEREMIAH WILLSTONE AND THE CLOCKWORK TIME MACHINE which I’m wrangling with the very excellent editor Debra Dixon at Bell Bridge Books. God willing, you’ll see this come out this year. Jeremiah appears in a lot of short stories in the anthologies UnCONventional, 12 HOURS LATER, and 30 DAYS LATER – more on that one in a bit.

I also have completed drafts of the urban fantasy novels SPECTRAL IRON and HEX CODE, starring Dakota Frost and her adopted daughter Cinnamon Frost, respectively. If you like magical tattoos, precocious weretigers, and the trouble they can get into, look for these books coming soon – or check out FROST MOON, BLOOD ROCK and LIQUID FIRE, the first three Dakota books. (They’re all still on sale, by the way).

Second, I’m publishing books. I and some author/artist friends in the Bay Area founded Thinking Ink Press, and we are publishing the steampunk anthology 30 DAYS LATER edited by Belinda Sikes, AJ Sikes and Dover Whitecliff. We’re hoping to also re-release their earlier anthology 12 HOURS LATER; both of these were done for the Clockwork Alchemy conference, and we’re proud to have them.

We’re also publishing a lot more – FlashCards and InstantBooks and SnapBooks and possibly even a reprint of a novel which recently went out of print. Go to Thinking Ink Press for more news; for things I’m an editor/author on I’ll also announce them here.

Third, I’m doing more computing. Cinnamon Frost is supposed to be a mathematical genius, so to simulate her thought process I write computer programs (no joke). I’ve written up some few articles on this for publication on this blog, and hope to do more over the year to come.

Fourth, I’m going to keep doing art. Most of my art is done in preparation for either book frontispieces or for 24-Hour Comics Day, but I’m going to step that up a bit this year – I have to, if I’m going to get (ulp) three frontispieces done over the next year. Must draw faster!

Finally, I’m going to blog more. I’m already doing it, right now, but one way I’m trying to get ahead is to write two blog posts at a time, publishing one and saving one in reserve. This way I can keep getting ahead, but if I fall behind I’ve got some backlog to fall back on. I feel hounded by all the ideas in my head, so I’m going to loose them on all of you.

As for New Year’s Resolutions? Fah. I could say “exercise more, blog every day, and clean up the piles of papers” but we all know New Year’s Resolution’s are a joke, unless your name is Jim Davies, in which case they’re performance art.

SO ANYWAY, 2016. It’s going to be a year. I hope we can make it a great one!

-the Centaur

Pictured: The bookshelves of Cafe Intermezzo in the Atlanta airport, one place where I like to write books and eat food.