## The Turing moment

### November 30th, 2014

 …less histrionic… Ed Stoppard as Alan Turing in Codebreaker

We seem to be at the “Turing moment”, what with Benedict Cumberbatch, erstwhile Sherlock Holmes, now starring as a Hollywood Alan Turing in The Imitation Game. The release culminates a series of Turing-related events over the last few years. The centennial of Turing’s 1912 birth was celebrated actively in the computer science community as a kind of jubilee, the occasion of numerous conferences, retrospectives, and presentations. Bracketing that celebration, PM Gordon Brown publicly apologized for Britain’s horrific treatment of Turing in 2009, and HRH Queen Elizabeth II, who was crowned a couple of years before Alan Turing took his own life as his escape from her government’s abuse, finally got around to pardoning him in 2013 for the crime of being gay.

I went to see a preview of The Imitation Game at the Coolidge Corner Theatre’s “Science on Screen” series. I had low expectations, and I was not disappointed. The film is introduced as being “based on a true story”, and so it is – in the sense that My Fair Lady was based on the myth of Pygmalion (rather than the Shaw play). Yes, there was a real place called Bletchley Park, and real people named Alan Turing and Joan Clark, but no, they weren’t really like that. Turing didn’t break the Enigma code singlehandedly despite the efforts of his colleagues to stop him. Turing didn’t take it upon himself to control the resulting intelligence to limit the odds of their break being leaked to the enemy. And so on, and so forth. Most importantly, Turing did not attempt to hide his homosexuality from the authorities, and promoting the idea that he did for dramatic effect is, frankly, an injustice to his memory.

Reviewers seem generally to appreciate the movie’s cleaving from reality, though with varying opprobrium. “The truth of history is respected just enough to make room for tidy and engrossing drama,” says A. O. Scott in the New York Times. The Wall Street Journal’s Joe Morgenstern ascribes to the film “a marvelous story about science and humanity, plus a great performance by Benedict Cumberbatch, plus first-rate filmmaking and cinematography, minus a script that muddles its source material to the point of betraying it.” At Slate, Dana Stevens notes that “The true life story of Alan Turing is much stranger, sadder and more troubling than the version of it on view in The Imitation Game, Morton Tyldum’s handsome but overlaundered biopic.

Of course, they didn’t make the movie for people like me, that is, people who had heard of Alan Turing before. And to the extent that the film contributes to this Turing moment — leading viewers to look further into this most idiosyncratic and important person — it will be a good thing. The Coolidge Corner Theatre event was followed by commentary from Silvio Micali and Seth Lloyd, both professors at MIT. (The former is a recipient of the highest honor in computer science, the Turing Award. Yes, that Turing.) Their comments brought out the many scientific contributions of Turing that were given short shrift in the film. If only they could duplicate their performance at every showing.

Those who become intrigued by the story of Alan Turing could do worse than follow up their viewing of the Cumberbatch vehicle with one of the 2012 docudrama Codebreaker, a less histrionic but far more accurate (and surprisingly, more sweeping) presentation of Turing’s contributions to science and society, and his societal treatment. I had the pleasure of introducing the film and its executive producer Patrick Sammon in a screening at Harvard a couple of weeks ago. The event was another indicator of the Turing moment. (My colleague Harry Lewis has more to say about the film.)

To all of you who are aware of the far-reaching impact of Alan Turing on science, on history, and on society, and the tragedy of his premature death, I hope you will take advantage of the present Turing moment to spread the word about computer science’s central personage.

## Switching to Markdown for scholarly article production

### August 29th, 2014

With few exceptions, scholars would be better off writing their papers in a lightweight markup format called Markdown, rather than using a word-processing program like Microsoft Word. This post explains why, and reveals a hidden agenda as well.1

### Microsoft Word is not appropriate for scholarly article production

 …lightweight… “Old two pan balance” image from Nikodem Nijaki at Wikimedia Commons. Used by permission.

Before turning to lightweight markup, I review the problems with Microsoft Word as the lingua franca for producing scholarly articles. This ground has been heavily covered. (Here’s a recent example.) The problems include:

Substantial learning curve
Microsoft Word is a complicated program that is difficult to use well.
Appearance versus structure
Word-processing programs like Word conflate composition with typesetting. They work by having you specify how a document should look, not how it is structured. A classic example is section headings. In a typical markup language, you specify that something is a heading by marking it as a heading. In a word-processing program you might specify that something is a heading by increasing the font size and making it bold. Yes, Word has “paragraph styles”, and some people sometimes use them more or less properly, if you can figure out how. But most people don’t, or don’t do so consistently, and the resultant chaos has been well documented. It has led to a whole industry of people who specialize in massaging Word files into some semblance of consistency.
Backwards compatibility
Word-processing program file formats have a tendency to change. Word itself has gone through multiple incompatible file formats in the last decades, one every couple of years. Over time, you have to keep up with the latest version of the software to do anything at all with a new document, but updating your software may well mean that old documents are no longer identically rendered. With Markdown, no software is necessary to read documents. They are just plain text files with relatively intuitive markings, and the underlying file format (UTF-8 née ASCII) is backward compatible to 1963. Further, typesetting documents in Markdown to get the “nice” version is based on free and open-source software (markdown, pandoc) and built on other longstanding open source standards (LaTeX, BibTeX).
Poor typesetting
Microsoft Word does a generally poor job of typesetting, as exemplified by hyphenation, kerning, mathematical typesetting. This shouldn’t be surprising, since the whole premise of a word-processing program means that the same interface must handle both the specification and typesetting in real-time, a recipe for having to make compromises.
Lock-in
Because Microsoft Word’s file format is effectively proprietary, users are locked in to a single software provider for any and all functionality. The file formats are so complicated that alternative implementations are effectively impossible.

### Lightweight markup is the solution

The solution is to use a markup format that allows specification of the document (providing its logical structure) separate from the typesetting of that document. Your document is specified – that is, generated and stored – as straight text. Any formatting issues are handled not by changing the formatting directly via a graphical user interface but by specifying the formatting textually using a specific textual notation. For instance, in the HTML markup language, a word or phrase that should be emphasized is textually indicated by surrounding it with <em>…</em>. HTML and other powerful markup formats like LaTeX and various XML formats carry relatively large overheads. They are complex to learn and difficult to read. (Typing raw XML is nobody’s idea of fun.) Ideally, we would want a markup format to be lightweight, that is, simple, portable, and human-readable even in its raw state.

Markdown is just such a lightweight markup language. In Markdown, emphasis is textually indicated by surrounding the phrase with asterisks, as is familiar from email conventions, for example, *lightweight*. See, that wasn’t so hard. Here’s another example: A bulleted list is indicated by prepending each item on a separate line with an asterisk, like this:

 * First item
* Second item

which specifies the list

• First item
• Second item

Because specification and typesetting are separated, software is needed to convert from one to the other, to typeset the specified document. For reasons that will become clear later, I recommend the open-source software pandoc. Generally, scholars will want to convert their documents to PDF (though pandoc can convert to a huge variety of other formats). To convert file.md (the Markdown-format specification file) to PDF, the command

 pandoc file.md -o file.pdf

suffices. Alternatively, there are many editing programs that allow entering, editing, and typesetting Markdown. I sometimes use Byword. In fact, I’m using it now.

Markup languages range from the simple to the complex. I argue for Markdown for four reasons:

1. Basic Markdown, sufficient for the vast majority of non-mathematical scholarly writing, is dead simple to learn and remember, because the markup notations were designed to mimic the kinds of textual conventions that people are used to – asterisks for emphasis and for indicating bulleted items, for instance. The coverage of this basic part of Markdown includes: emphasis, section structure, block quotes, bulleted and numbered lists, simple tables, and footnotes.
2. Markdown is designed to be readable and the specified format understandable even in its plain text form, unlike heavier weight markup languages such as HTML.
3. Markdown is well supported by a large ecology of software systems for entering, previewing, converting, typesetting, and collaboratively editing documents.
4. Simple things are simple. More complicated things are more complicated, but not impossible. The extensions to Markdown provided by pandoc cover more or less the rest of what anyone might need for scholarly documents, including links, cross-references, figures, citations and bibliographies (via BibTeX), mathematical typesetting (via LaTeX), and much more.For instance, this equation (the Cauchy-Schwarz inequality) will typeset well in generated PDF files, and even in HTML pages using the wonderful MathJax library.$\left( \sum_{k=1}^n a_k b_k \right)^2 \leq \left( \sum_{k=1}^n a_k^2 \right) \left( \sum_{k=1}^n b_k^2 \right)$(Pandoc also provides some extensions that simplify and extend the basic Markdown in quite nice ways, for instance, definition lists, strikeout text, a simpler notation for tables.)

Above, I claimed that scholars should use Markdown “with few exceptions”. The exceptions are:

1. The document requires nontrivial mathematical typesetting. In that case, you’re probably better off using LaTeX. Anyone writing a lot of mathematics has given up word processors long ago and ought to know LaTeX anyway. Still, I’ll often do a first draft in Markdown with LaTeX for the math-y bits. Pandoc allows LaTeX to be included within a Markdown file (as I’ve done above), and preserves the LaTeX markup when converting the Markdown to LaTeX. From there, it can be typeset with LaTeX. Microsoft Word would certainly not be appropriate for this case.
2. The document requires typesetting with highly refined or specialized aspects. I’d probably go with LaTeX here too, though desktop publishing software (InDesign) is also appropriate if there’s little or no mathematical typesetting required. Microsoft Word would not be appropriate for this case either.

Some have proposed that we need a special lightweight markup language for scholars. But Markdown is sufficiently close, and has such a strong community of support and software infrastructure, that it is more than sufficient for the time being. Further development would of course be helpful, so long as the urge to add “features” doesn’t overwhelm its core simplicity.

### The hidden agenda

I have a hidden agenda. Markdown is sufficient for the bulk of cases of composing scholarly articles, and simple enough to learn that academics might actually use it. Markdown documents are also typesettable according to a separate specification of document style, and retargetable to multiple output formats (PDF, HTML, etc.).2 Thus, Markdown could be used as the production file format for scholarly journals, which would eliminate the need for converting between the authors’ manuscript version and the publishers internal format, with all the concomitant errors that process is prone to produce.

In computer science, we have by now moved almost completely to a system in which authors provide articles in LaTeX so that no retyping or recomposition of the articles needs to be done for the publisher’s typesetting system. Publishers just apply their LaTeX style files to our articles. The result has been a dramatic improvement in correctness and efficiency. (It is in part due to such an efficient production process that the cost of running a high-end computer science journal can be so astoundingly low.)

Even better, there is a new breed of collaborative web-based document editing tools being developed that use Markdown as their core file format, tools like Draft and Authorea. They provide multi-author editing, versioning, version comparison, and merging. These tools could constitute the system by which scholarly articles are written, collaborated on, revised, copyedited, and moved to the journal production process, generating efficiencies for a huge range of journals, efficiencies that we’ve enjoyed in computer science and mathematics for years.

As Rob Walsh of ScholasticaHQ says, “One of the biggest bottlenecks in Open Access publishing is typesetting. It shouldn’t be.” A production ecology built around Markdown could be the solution.

1. Many of the ideas in this post are not new. Complaints about WYSIWYG word-processing programs have a long history. Here’s a particularly trenchant diatribe pointing out the superiority of disentangling composition from typesetting. The idea of “scholarly Markdown” as the solution is also not new. See this post or this one for similar proposals. I go further in viewing certain current versions of Markdown (as implemented in Pandoc) as practical already for scholarly article production purposes, though I support coordinated efforts that could lead to improved lightweight markup formats for scholarly applications. Update September 22, 2014: I’ve just noticed a post by Dennis Tenen and Grant Wythoff at The Programming Historian on “Sustainable Authorship in Plain Text using Pandoc and Markdown” giving a tutorial on using these tools for writing scholarly history articles.
2. As an example, I’ve used this very blog post. Starting with the Markdown source file (which I’ve attached to this post), I first generated HTML output for copying into the blog using the command
pandoc -S --mathjax --base-header-level=3 markdownpost.md -o markdownpost.html

A nicely typeset version using the American Mathematical Society’s journal article document style can be generated with

pandoc markdownpost.md -V documentclass:amsart -o markdownpost-amsart.pdf

To target the style of ACM transactions instead, the following command suffices:

pandoc markdownpost.md -V documentclass:acmsmall -o markdownpost-acmsmall.pdf

 Attachments mardownpost.md: The source file for this post in Markdown format markdownpost-amsart.pdf: The post rendered using pandoc according to AMS journal style markdownpost-acmsmall.pdf: The post rendered using pandoc according to ACM journal style

## No, the Turing Test has not been passed.

### June 10th, 2014

 …that’s not Turing’s Test… “Turing Test” image from xkcd. Used by permission.

There has been a flurry of interest in the Turing Test in the last few days, precipitated by a claim that (at last!) a program has passed the Test. The program in question is called “Eugene Goostman” and the claim is promulgated by Kevin Warwick, a professor of cybernetics at the University of Reading and organizer of a recent chatbot competition there.

The Turing Test is a topic that I have a deep interest in (see this, and this, and this, and this, and, most recently, this), so I thought to give my view on Professor Warwick’s claim “We are therefore proud to declare that Alan Turing’s Test was passed for the first time on Saturday.” The main points are these. The Turing Test was not passed on Saturday, and “Eugene Goostman” seems to perform qualitatively about as poorly as many other chatbots in emulating human verbal behavior. In summary: There’s nothing new here; move along.

First, the Turing Test that Turing had in mind was a criterion of indistinguishability in verbal performance between human and computer in an open-ended wide-ranging interaction. In order for the Test to be passed, judges had to perform no better than chance in unmasking the computer. But in the recent event, the interactions were quite time-limited (only five minutes) and in any case, the purported Turing-Test-passing program was identified correctly more often than not by the judges (almost 70% of the time in fact). That’s not Turing’s test.

Update June 17, 2014: The time limitation was even worse than I thought. According to my colleague Luke Hunsberger, computer science professor at Vassar College, who was a judge in this event, the five minute time limit was for two simultaneous interactions. Further, there were often substantial response delays in the system. In total, he estimated that a judge might average only four or five rounds of chat with each interlocutor. I’ve argued before that a grossly time-limited Turing Test is no Turing Test at all.

Sometimes, people trot out the prediction from Turing’s seminal 1950 Mind article that “I believe that in about fifty years’ time it will be possible to programme computers, with a storage capacity of about $$10^9$$, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent. chance of making the right identification after five minutes of questioning.” As I explain in my book on the Test:

The first thing to note about the prediction is that it is not a prediction about the Test per se: Turing expects 70 percent prediction accuracy, not the more difficult 50 percent expected by chance, and this after only a limited conversation of five minutes. He is therefore predicting passage of a test much simpler than the Test.

Not only does the prediction not presuppose a full Turing Test, but it could well be argued that it had already come to pass with the advent of Joseph Weizenbaum’s Eliza some thirty-five years early. Weizenbaum developed a simple computer program that attempted to imitate the parroting conversational style of a Rogerian psychotherapist…. Although the methods used were quite simple – repeating the user’s question after adjusting some pronouns, throwing in a stock phrase every now and then – the result was, in its own way, extremely convincing.

Second, “Eugene Goostman” uses the very techniques that began with Weizenbaum’s  “Eliza” program from the 1960′s. We see the same tricks – repeating the judge’s statements with simple substitutions, keyword-triggered responses, falling back on vague or unresponsive replies, and the like. Those tricks are no more successful than they have been in the two decades of runnings of the Loebner Prize Competition, another ill-conceived attempt at running a Turing-like test. And there too, entrants used the trick of having their programs emulate humans with built in excuses.  “Eugene Goostman” purports to be a non-English-fluent child, rather than a fluent adult.1

In fact, anyone with the slightest familiarity with chatbots, the modern incarnations of “Eliza”, could unmask “Eugene Goostman” quickly. (The judges at the recent competition were apparently not so familiar.) Scott Aaronson has provided a transcript of his own interaction with “Eugene Goostman”, which shows the telltale signs as soon as the computer’s first reply, which blatantly ignores an utterly trivial question and tries to change the subject:

Scott: Which is bigger, a shoebox or Mount Everest?

Eugene: I can’t make a choice right now. I should think it out later. And I forgot to ask you where you are from…

Here’s an appeal to cute, vague language:

Scott: How many legs does a camel have?

Eugene: Something between 2 and 4. Maybe, three? :-))) By the way, I still don’t know your specialty – or, possibly, I’ve missed it?

And here’s the repetition of a canned response:

Scott: No, I need to know that you’re not a chatbot. Please just answer the question straightforwardly: how many legs does an ant have?

Eugene: Something between 2 and 4. Maybe, three? :-))) Oh, what a fruitful conversation;-)

Here’s a canned keyword-triggered response, nonresponsive as usual:

Scott: In your opinion, does your existence demonstrate any idea or principle that wasn’t demonstrated just as convincingly by ELIZA, Joseph Weizenbaum’s chatbot from the 1960s?

Eugene: Eliza was a break-thru. All the bots after it were nothing but weak parodies, claiming to have “revolutionary improvements”.

And here’s “Eliza”‘s trick of rewriting the judge’s utterance after pronoun replacement and (poorly executed) synonym substitution:

Scott: OK, I’ll count that as the second sensible thing you’ve said (grading generously). Don’t you think that a more convincing chatbot could be created, by using Google to trawl the entire web for plausible-sounding answers (analogous to what IBM’s Watson did)?

Eugene: Yes, I do think that a more convincing chatbot can be built by using google to trawl the all web for plausible – sounding – I hope I have right to have my own opinion. Maybe, let’s talk about something else? What would you like to discuss?

Literally every one of “Eugene”‘s responses reflects its “Eliza”-like programming. It would be amusing, if it weren’t so predictable.

In summary, “Eugene Goostman” is not qualitatively superior to other chatbots, and certainly has not passed a true Turing Test. It isn’t even close.

1. In a parody of this approach, the late John McCarthy, professor of computer science at Stanford University and inventor of the term “artifical intelligence”, wrote a letter to the editor responding to a publication about an “Eliza”-like program that claimed to emulate a paranoid psychiatric patient. He presented his own experiments that I described in my Turing Test book: “He had designed an even better program, which passed the same test. His also had the virtue of being a very inexpensive program, in these times of tight money. In fact you didn’t even need a computer for it. All you needed was an electric typewriter. His program modeled infantile autism. And the transcripts – you type in your questions, and the thing just sits there and hums – cannot be distinguished by experts from transcripts of real conversations with infantile autistic patients.”

### May 9th, 2014

 …heading to Scotland… “Arthur’s Seat in Edinburgh in Scotland, Great Britain” image from Wikimedia Commons

I’m excited to be heading to Scotland for much of June under the Distinguished Visiting Fellowship program of the Scottish Informatics and Computer Science Alliance, visiting the Universities of Aberdeen, St. Andrews, and Edinburgh, and giving talks at each. If any locals are around and would like to meet, please let me know. Here’s my provisional itinerary (to be updated as details come in):

• Aberdeen 2–4
• St. Andrews 4–8
• Edinburgh 8–11
• 6/9 12:30pm – lecture: “What’s so great about compositionality?”, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh
• 6/10 10-11:30am – seminar: The Harvard open-access initiatives, University of Edinburgh Library
Appleton Tower, University of Edinburgh
• Aberdeen 11–19
• three-part master class on synchronous grammars:
• 6/11 11:00am – tutorial: Synchronous grammars introduced
• 6/12 11:00am – seminar: What’s so great about compositionality?
• 6/13 11:00am – survey: Synchronous grammar applications in language processing
• London 19–23

[This is a transient post.]

## Can gerrymandering be solved with cut-and-choose?

### October 28th, 2013

 …how to split a cupcake… “Halves” image by flickr user Julie Remizova.

Why is gerrymandering even possible in a country with a constitutional right to equal protection?:

No State shall make or enforce any law which shall…deny to any person within its jurisdiction the equal protection of the laws.

By reshaping districts to eliminate the voting power of particular individuals, as modern district mapping software allows, some persons are being denied equal protection, I’d have thought. And so have certain Supreme Court justices.

It’s hard to know what to do about the problem. Appeals to fairness aren’t particularly helpful, since who decides what’s fair? It would be nice to think that requirements of “compact districts of contiguous territory” (as Chief Justice Harlan put it) would be sufficient. But this reduces the problem of districting to a mathematical optimization problem; James Case proposes something like minimum isoperimetric quotient tessellation of a polygon. But such purely mathematical approaches may yield results that violate our intuitions about what is fair. They ignore other criteria, such as “natural or historical boundary lines”, determined for instance by geographical features like rivers and mountains or shared community interests. These boundaries may not coincide with the mathematical optima, so any mathematical formulation would need to be defeasible to take into account such features. This leads us right back to how to decide in which cases the mathematical formulation should be adjusted: who should decide what is fair?

A comment at a ProPublica article about gerrymandering from “damien” caught my attention as a nice way out of this quandary. In essence, he proposes that the parties themselves choose what’s fair.

The first solution to gerrymandering is to have a fitness measure for a proposed districting (e.g. the sum of the perimeters), and then to allow any individual or organisation to propose a districting, with the winner having the best fitness value.

What “damien” is proposing, I take it, is the application of an algorithm somewhat like one familiar from computer science (especially cryptography) and grade school cafeterias known as “cut and choose”. How do you decide how to split a cupcake between two kids? One cuts; the other chooses. The elegance of cut-and-choose is that it harmonizes the incentives of the two parties. The cutter is incentivized to split equally, since the chooser can punish inequity.

Cut-and-choose is asymmetrical; the two participants have different roles. A symmetrical variant has each participant propose a cut and an objective third party selecting whichever is better according to the pertinent objective measure. This variant shares the benefit that each participant has an incentive to be more nearly equal than the other. If Alice proposes a cut that gives her 60% of the cupcake and Bob 40%, she risks Bob proposing a better split that gives her only 45% with him taking the remaining 55%. To avoid getting taken advantage of, her best bet is to propose a split as nearly equal as possible.

In the anti-gerrymandering application of the idea, the two parties propose districtings, which they could gerrymander however they wanted. Whichever of the two proposals has the lower objective function (lower isoperimetric quotient, say) is chosen. Thus, if one party gerrymanders too much, their districting will be dropped in favor of the other party’s proposal. Each party has an incentive to hew relatively close to a compact partition, while being allowed to deviate in appropriate cases.

A nice property of this approach is that the optimization problem doesn’t ever need to be solved. All that is required is the evaluation of the objective function for the two proposed districtings, which is computationally far simpler. (In fact, I’d guess the minimum isoperimetric quotient optimization problem might well be NP-hard.)

There are problems of course. The procedure is subject to gaming when the proposal-generating process is not private to the parties. It is unclear how to extend the method to more than two parties. Of course, the obvious generalization works once the eligible parties are determined. The hard part is deciding what parties are eligible to propose a redistricting. Most critically, the method is subject to collusion, especially in cases where both parties benefit from gerrymandering. In particular, both parties benefit from a districting that protects incumbencies for both parties. The parties could agree, for instance, not to disturb each other’s safe districts, and would benefit from observing the agreement.

Nonetheless, once districting is thought of in terms of mechanism design, the full range of previous algorithms can be explored. Somewhere in the previous literature there might be a useful solution. (Indeed, the proposal here is essentially the first step in Brams, Jones, and Klamler’s surplus procedure for cake-cutting.)

Of course, as with many current political problems (campaign financing being the clearest example), the big question is how such new mechanisms would be instituted, given that it is not in the incumbent majority party’s interest to do so. Until that’s sorted out, I’m not holding out much hope.

## For Ada Lovelace Day 2012: Karen Spärck Jones

### October 16th, 2012

 Karen Spärck Jones, 1935-2007

In honor of Ada Lovelace Day 2012, I write about the only female winner of the Lovelace Medal awarded by the British Computer Society for “individuals who have made an outstanding contribution to the understanding or advancement of Computing”. Karen Spärck Jones was the 2007 winner of the medal, awarded shortly before her death. She also happened to be a leader in my own field of computational linguistics, a past president of the Association for Computational Linguistics. Because we shared a research field, I had the honor of knowing Karen and the pleasure of meeting her on many occasions at ACL meetings.

One of her most notable contributions to the field of information retrieval was the idea of inverse document frequency. Well before search engines were a “thing”, Karen was among the leaders in figuring out how such systems should work. Already in the 1960′s there had arisen the idea of keyword searching within sets of documents, and the notion that the more “hits” a document receives, the higher ranked it should be. Karen noted in her seminal 1972 paper “A statistical interpretation of term specificity and its application in retrieval” that not all hits should be weighted equally. For terms that are broadly distributed throughout the corpus, their occurrence in a particular document is less telling than occurrence of terms that occur in few documents. She proposed weighting each term by its “inverse document frequency” (IDF), which she defined as log(N/(n + 1)) where N is the number of documents and n the number of documents containing the keyword under consideration. When the keyword occurs in all documents, IDF approaches 1 for large N, but as the keyword occurs in fewer and fewer documents (making it a more specific and presumably more important keyword), IDF rises. The two notions of weighting (frequency of occurrence of the keyword together with its specificity as measured by inverse document frequency) are combined multiplicatively in the by now standard tf*idf metric; tf*idf or its successors underlie essentially all information retrieval systems in use today.

In Karen’s interview for the Lovelace Medal, she opined that “Computing is too important to be left to men.” Ada Lovelace would have agreed.

## Talmud and the Turing Test

### June 16th, 2012

 …the Golem… Image of the statue of the Golem of Prague at the entrance to the Jewish Quarter of Prague by flickr user D_P_R. Used by permission (CC-BY 2.0).

Alan Turing, the patron saint of computer science, was born 100 years ago this week (June 23). I’ll be attending the Turing Centenary Conference at University of Cambridge this week, and am honored to be giving an invited talk on “The Utility of the Turing Test”. The Turing Test was Alan Turing’s proposal for an appropriate criterion to attribute intelligence (that is, capacity for thinking) to a machine: you verify through blinded interactions that the machine has verbal behavior indistinguishable from a person.

In preparation for the talk, I’ve been looking at the early history of the premise behind the Turing Test, that language plays a special role in distinguishing thinking from nonthinking beings. I had thought it was an Enlightenment idea, that until the technological advances of the 16th and 17th centuries, especially clockwork mechanisms, the whole question of thinking machines would never have entertained substantive discussion. As I wrote earlier,

Clockwork automata provided a foundation on which one could imagine a living machine, perhaps even a thinking one. In the midst of the seventeenth-century explosion in mechanical engineering, the issue of the mechanical nature of life and thought is found in the philosophy of Descartes; the existence of sophisticated automata made credible Descartes’s doctrine of the (beast-machine), that animals were machines. His argument for the doctrine incorporated the first indistinguishability test between human and machine, the first Turing test, so to speak.

But I’ve seen occasional claims here and there that there is in fact a Talmudic basis to the Turing Test. Could this be true? Was the Turing Test presaged, not by centuries, but by millennia?

Uniformly, the evidence for Talmudic discussion of the Turing Test is a single quote from Sanhedrin 65b.

Rava said: If the righteous wished, they could create a world, for it is written, “Your iniquities have been a barrier between you and your God.” For Rava created a man and sent him to R. Zeira. The Rabbi spoke to him but he did not answer. Then he said: “You are [coming] from the pietists: Return to your dust.”

Rava creates a Golem, an artificial man, but Rabbi Zeira recognizes it as nonhuman by its lack of language and returns it to the dust from which it was created.

This story certainly describes the use of language to unmask an artificial human. But is it a Turing Test precursor?

It depends on what one thinks are the defining aspects of the Turing Test. I take the central point of the Turing Test to be a criterion for attributing intelligence. The title of Turing’s seminal Mind article is “Computing Machinery and Intelligence”, wherein he addresses the question “Can machines think?”. Crucially, the question is whether the “test” being administered by Rabbi Zeira is testing the Golem for thinking, or for something else.

There is no question that verbal behavior can be used to test for many things that are irrelevant to the issues of the Turing Test. We can go much earlier than the Mishnah to find examples. In Judges 12:5–6 (King James Version)

5 And the Gileadites took the passages of Jordan before the Ephraimites: and it was so, that when those Ephraimites which were escaped said, Let me go over; that the men of Gilead said unto him, Art thou an Ephraimite? If he said, Nay;

6 Then said they unto him, Say now Shibboleth: and he said Sibboleth: for he could not frame to pronounce it right. Then they took him, and slew him at the passages of Jordan: and there fell at that time of the Ephraimites forty and two thousand.

The Gileadites use verbal indistinguishability (of the pronounciation of the original shibboleth) to unmask the Ephraimites. But they aren’t executing a Turing Test. They aren’t testing for thinking but rather for membership in a warring group.

What is Rabbi Zeira testing for? I’m no Talmudic scholar, so I defer to the experts. My understanding is that the Golem’s lack of language indicated not its own deficiency per se, but the deficiency of its creators. The Golem is imperfect in not using language, a sure sign that it was created by pietistic kabbalists who themselves are without sufficient purity.

Talmudic scholars note that the deficiency the Golem exhibits is intrinsically tied to the method by which the Golem is created: language. The kabbalistic incantations that ostensibly vivify the Golem were generated by mathematical combinations of the letters of the Hebrew alphabet. Contemporaneous understanding of the Golem’s lack of speech was connected to this completely formal method of kabbalistic letter magic: “The silent Golem is, prima facie, a foil to the recitations involved in the process of his creation.” (Idel, 1990, pages 264–5) The imperfection demonstrated by the Golem’s lack of language is not its inability to think, but its inability to wield the powers of language manifest in Torah, in prayer, in the creative power of the kabbalist incantations that gave rise to the Golem itself.

Only much later does interpretation start connecting language use in the Golem to soul, that is, to an internal flaw: “However, in the medieval period, the absence of speech is related to what was conceived then to be the highest human faculty: reason according to some writers, or the highest spirit, Neshamah, according to others.” (Idel, 1990, page 266, emphasis added)

By the 17th century, the time was ripe for consideration of whether nonhumans had a rational soul, and how one could tell. Descartes’s observations on the special role of language then serve as the true precursor to the Turing Test. Unlike the sole Talmudic reference, Descartes discusses the connection between language and thinking in detail and in several places — the Discourse on the Method, the Letter to the Marquess of Newcastle — and his followers — Cordemoy, La Mettrie — pick up on it as well. By Turing’s time, it is a natural notion, and one that Turing operationalizes for the first time in his Test.

The test of the Golem in the Sanhedrin story differs from the Turing Test in several ways. There is no discussion that the quality of language use was important (merely its existence), no mention of indistinguishability of language use (but Descartes didn’t either), and certainly no consideration of Turing’s idea of blinded controls. But the real point is that at heart the Golem test was not originally a test for the intelligence of the Golem at all, but of the purity of its creators.

## References

Idel, Moshe. 1990. Golem: Jewish magical and mystical traditions on the artificial anthropoid, Albany, N.Y.: State University of New York Press.

## Processing special collections: An archivist’s workstation

### May 29th, 2012

 John Tenniel, c. 1864. Study for illustration to Alice’s adventures in wonderland. Harcourt Amory collection of Lewis Carroll, Houghton Library, Harvard University.

We’ve just completed spring semester during which I taught a system design course jointly in Engineering Sciences and Computer Science. The aim of ES96/CS96 is to help the students learn about the process of solving complex, real-world problems — applying engineering and computational design skills — by undertaking an extended, focused effort directed toward an open-ended problem defined by an interested “client”.

The students work independently as a self-directed team. The instructional staff provides coaching, but the students do all of the organization and carrying out of the work, from fact-finding to design to development to presentation of their findings.

This term the problem to be addressed concerned the Harvard Library’s exceptional special collections, vast holdings of rare books, archives, manuscripts, personal documents, and other materials that the library stewards. Harvard’s special collections are unique and invaluable, but are useful only insofar as potential users of the material can find and gain access to them. Despite herculean efforts of an outstanding staff of archivists, the scope of the collections means that large portions are not catalogued, or catalogued in insufficient detail, making materials essentially unavailable for research. And this problem is growing as the cataloging backlog mounts. The students were asked to address core questions about this valuable resource: What accounts for this problem at its core? Can tools from computer science and technology help address the problems? Can they even qualitatively improve the utility of the special collections?

The clients were the leadership of Harvard’s premier Houghton and Schlesinger libraries. The students received briefings from William Stoneman, Florence Fearrington Librarian of Houghton Library, and Marilyn Dunn, Executive Director of the Schlesinger Library and Librarian of the Radcliffe Institute; toured both libraries; and met with a wide range of archivists and librarians, who were incredibly generous with their time and expertise. I’d like to express my deep appreciation and thanks to all of the library staff who helped out with the course. Their participation was vital.

The students’ recommendations centered around the design, development, and prototyping of an “archivist’s workstation” and the unconventional “flipped” collections processing that the workstation enabled. Their process involves exhaustive but lightweight digitization of a collection as a precursor to highly efficient metadata acquisition on top of the digitized images, rather than the conventional approach of  digitizing selectively only after all processing of the collection is performed. The “digitize first” approach means that documents need only be touched once, with all of the sorting, arrangement, and metadata application being performed virtually using optimized user interfaces that they designed for these purposes. The output is a dynamic finding aid with images of all documents, complete with search and faceted browsing of the collection, to supplement the static finding aid of traditional archival processing. The students estimate that processing in this way would be faster than current methods, while delivering a superior result. Their demo video (below) gives a nice overview of the idea.

The deliverables for the course are now available at the course web site, including the final report and a videotape of their final presentation before dozens of Harvard archivists, librarians, and other members of the community.

I hope others find the ideas that the students developed as provocative and exciting as I do. I’m continuing to work with some of them over the summer and perhaps beyond, so comments are greatly appreciated.

Archivist%20Video.mov

## An efficient journal

### March 6th, 2012

 “You seem to believe in fairies.” Photo of the Cottingley Fairies, 1917, by Elsie Wright via Wikipedia.

Aficionados of open access should know about the Journal of Machine Learning Research (JMLR), an open-access journal in my own research field of artificial intelligence, a subfield of computer science concerned with the computational implementation and understanding of behaviors that in humans are considered intelligent. The journal became the topic of some dispute in a conversation that took place a few months ago in the comment stream of the Scholarly Kitchen blog between computer science professor Yann LeCun and scholarly journal publisher Kent Anderson, with LeCun stating that “The best publications in my field are not only open access, but completely free to the readers and to the authors.” He used JMLR as the exemplar. Anderson expressed incredulity:

I’m not entirely clear how JMLR is supported, but there is financial and infrastructure support going on, most likely from MIT. The servers are not “marginal cost = 0″ — as a computer scientist, you surely understand the 20-25% annual maintenance costs for computer systems (upgrades, repairs, expansion, updates). MIT is probably footing the bill for this. The journal has a 27% acceptance rate, so there is definitely a selection process going on. There is an EIC, a managing editor, and a production editor, all likely paid positions. There is a Webmaster. I think your understanding of JMLR’s financing is only slightly worse than mine — I don’t understand how it’s financed, but I know it’s financed somehow. You seem to believe in fairies.

Since I have some pretty substantial knowledge of JMLR and how it works, I thought I’d comment on the facts of the matter. Read the rest of this entry »

## Tales of peer review, episode 1: Boyer and Moore’s MJRTY algorithm

### September 23rd, 2011

I’m generally a big fan of peer review. I think it plays an important role in the improvement and “chromatography” of the scholarly literature. But sometimes. Sometimes.

 The Boyer-Moore MJRTY algorithm allows efficient determination of which shape (triangle, circle, square) is in the majority without counting each shape.

This past week I was reading Robert Boyer and J Strother Moore‘s paper on computing the majority element of a multiset, which presents a very clever simple algorithm for this fundamental problem and a description of a mechanical proof of its correctness. The authors aptly consider the work a “minor landmark in the development of formal verification and automated reasoning”.

Below is the postscript to that paper, in its entirety, which describes the history of the paper including how and why it was “repeatedly rejected for publication”. (It was eventually published as a chapter in a 1991 festschrift for Woody Bledsoeten years after it was written, and is now also available from Moore’s website.)

In this paper we have described a linear time majority vote algorithm and discussed the mechanically checked correctness proof of a Fortran implementation of it. This work has a rather convoluted history which we would here like to clarify.

The algorithm described here was invented in 1980 while we worked at SRI International. A colleague at SRI, working on fault tolerance, was trying to specify some algorithms using the logic supported by “Boyer-Moore Theorem Prover.” He asked us for an elegant definition within that logic of the notion of the majority element of a list. Our answer to this challenge was the recursive expression of the algorithm described here.

In late 1980, we wrote a Fortran version of the algorithm and proved it correct mechanically. In February, 1981, we wrote this paper, describing that work. In our minds the paper was noteworthy because it simultaneously announced an interesting new algorithm and offered a mechanically checked correctness proof. We submitted the paper for publication.

In 1981 we moved to the University of Texas. Jay Misra, a colleague at UT, heard our presentation of the algorithm to an NSF site-visit team. According to Misra (private communication, 1990): “I wondered how to generalize [the algorithm] to detect elements that occur more than n/k times, for all k, k ≥ 2. I developed algorithm 2 [given in Section 3 of [9]] which is directly inspired by your algorithm. Also, I showed that this algorithm is optimal [Section 5, op. cit.]. On a visit to Cornell, I showed all this to David Gries; he was inspired enough to contribute algorithm 1 [Section 2, op. cit.].” In 1982, Misra and Gries published their work [9], citing our technical report appropriately as “submitted for publication.”

However, our paper was repeatedly rejected for publication, largely because of its emphasis on Fortran and mechanical verification. A rewritten version emphasizing the algorithm itself was rejected on the grounds that the work was superceded by the paper of Misra and Gries!

When we were invited to contribute to the Bledsoe festschrift we decided to use the opportunity to put our original paper into the literature. We still think of this as a minor landmark in the development of formal verification and automated reasoning: here for the first time a new algorithm is presented along with its mechanically checked correctness proof—eleven years after the work.

I have to think the world would have been better off if Boyer and Moore had just posted the paper to the web in 1981 and been done with it. Unfortunately, the web hadn’t been developed yet.