Artificial Intelligence (A.I.) is having a moment, albeit one marked by crucial ambiguities. Cognoscenti including Stephen Hawking, Elon Musk and Bill Gates, among others, have recently weighed in on its potential and perils.

Here’s a game: which of these poems was written by a human, and which by a computer? Answer: the first one is a computer, the second one is Gertrude Stein. You can find both of these poems on the website Bot or Not, “a Turing Test for poetry” created by Oscar Schwartz and Benjamin Laird.

In central London this spring, eight of the world’s greatest minds performed on a dimly lit stage in a wood-panelled theatre. An audience of hundreds watched in hushed reverence.

It used to be simpler. You woke up, and there was one newspaper you could read. It printed on pulp and delivered to your driveway. You got in your car, and there was a Sears strategically located within reasonable distance from your home on the highway.

Turing Collages

‘An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside, although he may still be able to some extent to predict his pupil’s behaviour.’ Alan Turing, Computing Machinery and Intelligence (1950)

Replenishing each worn-out piece of its glimmering hull, one by one, the day arrives when the entire ship of Argo has been displaced – each of its parts now distinct from those of the ‘original’ vessel. For Roland Barthes, this myth exposes two modest activities:

Substitution (one part replaces another, as in a paradigm) Nomination (the name is in no way linked to the stability of the parts) 1

The discrete breaches the continuous in the act of nomination. Take for instance the spectrum of colours, the extension of which ‘is verbally reduced to a series of discontinuous terms’ 2 such as red, green, lilac or puce. Each colour has no cause but its name. By being isolated in language the colour ‘blue’ is allowed to exist, but its existence is an act of linguistic and, some would argue, perceptual severance. The city of Hull, the phrase “I will”, the surface of an ice cube and an image compression algorithm are entities each sustained by the same nominative disclosure: a paradox of things that seem to flow into one another with liquid potential, but things, nonetheless, limited by their constant, necessary re-iteration in language. There is no thing more contradictory in this regard than the human subject, a figure Barthes’ tried to paradoxically side-step in his playful autobiography. Like the ship of Argo, human experience has exchangeable parts, but at its core, such was Barthes’ intention, ‘the subject, unreconciled, demands that language represent the continuity of desire.’ 3

In an esoteric paper, published in 1930, Lewis Richardson teased out an analogy between flashes of human insight and the spark that leaps across a stop gap in an electrical circuit. The paper, entitled The Analogy Between Mental Images and Sparks, navigates around a provocative sketch stencilled into its pages of a simple indeterminate circuit, whose future state it is impossible to predict. Richardson’s playful label for the diagram hides a deep significance. For even at the simplest binary level, Richardson argued, computation need not necessarily be deterministic.

The discrete and the continuous are here again blurred by analogy. Electricity flowing and electricity not flowing: a binary imposition responsible for the entire history of information technology.

1 Roland Barthes, Roland Barthes (University of California Press, 1994), 46.

2 Roland Barthes, Elements of Semiology (Hill and Wang, 1977), 64.

3 Paul John Eakin, Touching the World: Reference in Autobiography (Princeton University Press, 1992), 16.

What is the relationship between the ritual and the algorithm? Are all rituals algorithmic? I'm interested in any papers or books/chapters written about this. I'm writing about Google's search algorithm at the moment, and I can't help get this ritual thing out of my head. It feels like an important meeting point between humans and machines.

When I say 'ritual' I mean the enactment of a set of actions with traditional and symbolic value.

When I say 'algorithm' I am talking about computers of course (a step-by-step procedure for calculation), but I am also interested in an algorithm as a list of well-defined instructions passed on to another entity in order to execute a specific procedure in precise detail. That entity might be a human.

Are all rituals algorithmic?

The best conflation I can think of is the Japanese Tea Ceremony. To generalise, I see this as a highly specific algorithm taken to the absolute limits of human cultural perfection.

Many thanks in advance!

The differences, across a number of different brands of automated essay scoring software (AES) and essay types, were minute. “The results demonstrated that over all, automated essay scoring was capable of producing scores similar to human scores for extended-response writing items,” the Akron researchers write, “with equal performance for both source-based and traditional writing genre.”

“In terms of being able to replicate the mean [ratings] and standard deviation of human readers, the automated scoring engines did remarkably well,” Mark D. Shermis, the dean of the college of education at Akron and the study’s lead author, said in an interview.

This is the raw text output of a chat session with a bot I modified to act as an interlocutor. I use our conversation, which revolves around the history of spam, particularly algorithmic filtering, litspam, and the theories of Wiener and Turing, as a way of putting forward the outlines of new, machine-driven forms of language for which spam was the testing ground.

In case you haven’t heard, the newest champion of "Jeopardy!," the popular TV game show, is a computer. Watson, an enormous computer developed by researchers at IBM, was pitted against the two previous human champions, Brad Rutter and Ken Jennings. At the end of the first round, aired on Valentine’s Day, Jennings and Watson were tied for first place. But Watson trounced both humans in the next round, despite making some odd mistakes. And he won the second game, aired on February 16, suggesting the first victory was more than just beginner’s luck. When the IBM computer Deep Blue beat chess champion Garry Kasparov in 1997, it was not doing anything qualitatively different from an ordinary calculator. It was just calculating really quickly—running through all the possible chess moves in response to the previous move by Kasparov and picking the one most likely to succeed. That’s just the sort of problem that a fast-enough computer running the right algorithm was bound to solve.

Pac-Man is one of the most iconic video games of all time, and most people (even non-gamers) have at least a passing familiarity with it. The purpose of the game is very simple — the player is placed in a maze filled with food (depicted as pellets or dots) and needs to eat all of it to advance to the next level. This task is made difficult by four ghosts that pursue Pac-Man through the maze. If Pac-Man makes contact with any of the ghosts, the player loses a life and the positions of Pac-Man and the ghosts are reset back to their starting locations, though any dots that were eaten remain so. 

The story of Google’s algorithm begins with PageRank, the system invented in 1997 by cofounder Larry Page while he was a grad student at Stanford. Page’s now legendary insight was to rate pages based on the number and importance of links that pointed to them — to use the collective intelligence of the Web itself to determine which sites were most relevant. It was a simple and powerful concept, and — as Google quickly became the most successful search engine on the Web — Page and cofounder Sergey Brin credited PageRank as their company’s fundamental innovation.

But that wasn’t the whole story. “People hold on to PageRank because it’s recognizable,” Manber says. “But there were many other things that improved the relevancy.” These involve the exploitation of certain signals, contextual clues that help the search engine rank the millions of possible results to any query, ensuring that the most useful ones float to the top.

Web search is a multipart process. First, Google crawls the Web