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[ by Charles Cameron — from artificial intelligence to the Council of Nicea in one easy blog post ]
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It seems fairly easy for a human to tell likes from dislikes, but for a computer to tell likes from unlikes appears to be a far trickier business. Consider the following DoubleQuote in the Wild, which I found in David Berreby‘s Nautilus piece, Artificial Intelligence is Already Weirdly Inhuman:

You might think these two images are the same. Or that they’re a little different, as the images from your left and right eyes always are, but that if you squint at them just right they will merge into a single image with a vivid sense of depth, like a movie seen with 3-D glasses. You mivght even think the differences between them are a matter of steganography, encoding some IS battle plan under cover of a diggie pic.

But you are unlikely, I suggest, to think the image on the left is of a dog, while that on the right is of an ostrich. Which is what “artificial intelligence”, in the form of a neural net, figured out.

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And how different are they “in fact”?

The middle image here shows the amount of variation in pixels between the two outer images:

The image on the right — the one the neural net iodentified as an ostrich — is an example of what the researchers, Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow and Rob Fergus, call an “adversarial example”.

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It’s not my intent to dismiss neural nets by any means: I have one myself.

What interests me, though, as someone preoccupied with analogy and metaphor — with likeness and unlikeness — is the deep question of what likeness and unlikeness mean.

That question lies at the heart of my DoubleQuotes and HipBone Games.

Back in my Oxford days, my tutor in Dogmatic Theology had me thinking about the difference between the two words Homoousion and Homoiousion, homoousion meaning of the same essence, and homoiousion of similar essence. The distinction was important in Patristic theology, the questionn being whether the Son and Holy Spirit were of the same essence as the Father (one God in three Persons) or of similar essence (three Persons in one God).

You’ll see from the way that I’ve phased the distinction in brackets (one God in three Persons vs three Persons in one God) that I find the distinction itself less than helpful — and I said so in the essay I read my tutor. Those who hold the Three Persons are the same God (the homoousios doctrine) are saying they are both similar as to the recognition of their common Godness and dissimilar as to the recognition of their separate Personhood, whereas those who hold that they are of similar essence (homoiousios) are, perhaps unexpectedly, also saying they are similar but different: it’s all a matter of emphasis.

My tutor, much to my surprise and delight, mentioned that he had made the same point in a paper he had recently published in, if I recall, the Journal of Theological Studies, and gave me a signed offprint.

Similarity and dissimilarity, likeness and unlikeness appear to me to find themselves on a spectrum which approximates closely to identity at one end — but if two things are identical, how can they be two? — and absolute distinction at the other.

Yet the difference beween homoousion and homoiousion was decided in favor of homoousion at the Council of Nicea, a decision which one writer calls a “bloodless intellectual victory over dangerous error” and “of far greater consequence to the progress of true civilization, than all the bloody victories Constantine and his successors.”

And okay, there’s more to it, as always…

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Dogs and ostriches, apples and oranges — what’s the diff, eh?

And G*d knows best.

[ by Charles Cameron — a computer “plays” Mario Bros while a robot demonstrates “Bushido” ]
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Two stunning videos:

and:

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Justin Seitz turned me onto the Super Mario World video with this tweet:

This AI Used 'Neuroevolution' to Teach Itself How to Play 'Super Mario World' http://t.co/WQEcWswTm6 <- coolest article of 2015 so far

— Justin Seitz (@jms_dot_py) June 16, 2015

I’m one of those obstructionists who wouldn’t say the AI was “playing” — but that’s the hard problem in consciousness for you. I wonder what my friends Mike Sellers and Chris Bateman would have to say..

And no, I wouldn’t call the robotic slicing and dicing “Bushido” either. From the Hagakure:

There is something to be learned from a rainstorm. When meeting with a sudden shower, you try not to get wet and run quickly along the road. But doing such things as passing under the eaves of houses, you still get wet. When you are resolved from the beginning, you will not be perplexed, though you will still get the same soaking. This understanding extends to everything.

Has the Yaskawa Bushido Project learned from rainstorms?

Indirectly, I suppose. But there you go.

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Interesting that John Holland, the “father of genetic algorithms”, described his life’s work as a Glass Bead Game, eh?

[ by Charles Cameron — who just realized the Lotto would be as hard for a machine to beat as his own HipBone Games ]
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A friend pointed me to the Washington Post’s story of a chess grandmaster caught cheating with help from an iPhone today, and the phrase “a simple cellphone can transform anybody into a grandmaster” struck home immediately. Not possible, I tweeted, with games of analogy, HipBone, Sembl &c, the human being, not the computer, is key — in games, intel analysis, etc. Big data crunches well, I continued, but the human mind digests!

Y’see, I’m somewhat proud of the fact that my HipBone Games would be more difficult than Chess — more difficult even than Go — for a computer like IBM’s Watson to play at grandmaster level.

And then my eye was caught by the parallel story of an IT specialist who goes up for trial on Monday on charges of “fixing” himself a winning Lotto ticket.

Lotto is way downmarket compared to Chess. no? And the prize for the winner of the 17th annual Dubai Open Chess Tournament was $12,000, whereas the lucky Lotto winner’s grab netted him $14.3 million. Go figure, and use your smartphone if you wish.

Could Watson beat the Lotto? I don’t believe it could. Does that make Lotto more upscale — more demanding, more difficult or an AI to fudge — than my HipBone Games? Maybe it does : (

Ah well.

In any case, human beats random number generator — score yet another win for the human mind!

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Sources:

Washington Post, Chess

International Business Times, Lotto

[ by Charles Cameron — a man vs machine contest, with the betting shops favoring.. ]
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The semi-finals have been conducted, contested and concluded, with judges Elon Musk:

Elon Musk thinks artificial intelligence is more dangerous than nuclear weapons: http://t.co/zrHsCc6gky pic.twitter.com/VMra2PIpan

— Motherboard (@motherboard) March 30, 2015

and The Republicans:

Republicans see Obama as more imminent threat than Putin: Reuters/Ipsos poll – Reuters http://t.co/QLUajZ4SCP

— Blogs of War (@BlogsofWar) March 30, 2015

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The final round is upon us.

In a definitive Man vs Machine match to be adjudicated by The Turn of Events, we shall see whether artifical intelligence, slouching towards Bethlehem, is more dangerous than the sitting President, suffering under — or perhaps liberated by — the two-term limit on his office..

Who or what will win the Most Dangerous of All belt, and end-of-the world cash prize that goes with it?

According to noted statistician Nate Silver of FiveThirtyEight..

[ by Charles Cameron — who believes that detours are the spice of life ]
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Craig Kaplan:

Maurits Escher:

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There’s a fasacinating article about Craig Kaplan and his work with tiling that I came across today, Crazy paving: the twisted world of parquet deformations — I highly recommend it to anyone interested in pattern — and I highly recommend anyone uninterested in pattern to get interested!

Kaplan himself is no stranger to Escher’s work, obviously enough — he’s even written a paper, Metamorphosis in Escher’s Art — the abstract reads:

M.C. Escher returned often to the themes of metamorphosis and deformation in his art, using a small set of pictorial devices to express this theme. I classify Escher’s various approaches to metamorphosis, and relate them to the works in which they appear. I also discuss the mathematical challenges that arise in attempting to formalize one of these devices so that it can be applied reliably.

I mean Kaplan no dishonor, then, when I say that his algorithmic tilings, as seen in the upper panel above, still necessarily lack something that his mentor’s images have, as seen in the lower panel — a quirky willingness to go beyond pattern into a deeper pattern, as when the turreted outcropping of a small Italian town on the Amalfi coast becomes a rook in the game of chess…

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Comparing one with the other, I am reminded of the differences between quantitative and qualitative approaches to understanding, of SIGINT and HUMINT in terms of the types of intelligence collected — and at the philosophical limit, of the very notions of quantity and quality.