Make tags not trees – filesystem idea based on tags instead of hierarchical directories

Until recently, it was easier to find something amidst the five zillion pages on the web than it was to find something on your own hard disk. It would be faster to Google for something than to burrow through subdirectories looking for it.

Could this be because the files on my hard disk are poorly organized? Bah. Maybe so. But that’s not my fault – it’s more or less inevitable once you have a lot of files, because hierarchical filesystems require each file to live in a single location. If I download a paper on memory for a class, should I organize by:

  • the context, e.g. the name of the class, lumping together all my writings and reading materials from that context together – ~/psy330/reading/
  • or by the type – things I’ve written vs reading materials – ~/reading/psy330/
  • or by good/bad or date produced or something else entirely?

Whichever decision you make, there’ll be times when you’ll wish things were organized some other way. This is why tagging is so popular. It’s because things inherently belong to multiple categories. And, because tagging is easy.
Google Desktop, Spotlight, Beagle and other offerings have helped considerably with all this. If you want to locate a single file, and you can’t remember where you put it, then full-text search is the way to go. But let’s consider the case where you have files that you want to treat as related, even if their contents aren’t obviously similar. We want this all the time. Take the reading list for a particular course or project as an example. This is why we needed directories and filing cabinets in the first place.
My proposal here is to replace the hierarchical filesystem with a completely flat space and lots of tags. Each file would be tagged with one or more tags, just like on The ‘save as’ dialog would look a little different. Instead of a list of directories that you can burrow into, there’d be a list of tags. When saving a file, you’d select as few or as many as you like, give the file a name just as now, and you’re done. To open a document, you filter using some tags, watching the list of files that match being winnowed down, and select from an alphabetized list. Or, use wildcards to winnow down by filename directly. Or some combination.
Converting an existing hierarchical filesystem would be easy in most cases. You could just grab all the subdirectory names in a path and treat them like unordered words in a bag. Let’s keep the same ‘/’ file separator we’re used to, but change its implicit meaning from ‘contains-this-directory’ to ‘and-also-this-tag’, so:

  • ~/reading/psy330/hippocampus/blah.pdf

would now be equally accessible from:

  • ~/reading/psy330/hippocampus/blah.pdf
  • ~/psy330/reading/hippocampus/blah.pdf
  • ~/reading/hippocampus/psy330/blah.pdf

All these locations would end up meaning the same thing. In this way, a subdirectory is really a conjunction of tags. In our simple example of storing .doc and .pdf files for documents and reading materials for a class, we’d simply tag some of them ‘doc’ and some of them ‘reading’, and give them both the ‘psy330’ tag for the class.
Upon looking at this, it’s clear you’ve lost some information, but I don’t think it’s information we’d miss much. The assumption underlying a lot of this is that where we now have hierarchy, we could manage just as well with intersecting sets, which would require considerably less effort to memorize.
There are, inevitably, unanswered questions and lurking gotchas.

  • I think we’d probably want to create a default/preferred way of expressing things, so that tags with more items or that are more discriminative go on the left, or something akin.
  • You shouldn’t need to specify all the tags for a given file. Just enough to specify it uniquely, given its filename. So, if there are no other blah.pdf files in the ‘reading’ tag, then you should probably be able to access it straightforwardly at ~/reading/blah.pdf though this has the unfortunate implication that if you were to add a new blah.pdf that also had a ‘reading’ tag, the above location would become ambiguous.
    If there are multiple blah.pdf files in the reading tag, then the system would need to prompt you with a list of tags that would help disambiguate them. Wikipedia’s interface might have some lessons about disambigation that could be learned from.
  • At this stage, a tags-not-trees system seems better-suited for home directories (‘My Documents’ for Windows users) than system directories. In home directories, most of the organization is human-generated and needs to be human-readable, whereas /etc directories are mostly machine-generated to be uncomplicatedly machine-readable.
  • The only way metadata-entry systems work is if they require little work on the user’s part. The nice thing about tagging is that it should be relatively easy for the computer to make guesses about which tags you’ll want to put something in, based on your tagging of previous files. So when you click ‘save as’, it will prompt you with a list of tags that it thinks you’ll want to use, ordered in terms of certainty. You delete a couple, add a couple more, and leave the rest in place.
    This is not a trivial problem, but you’ll have a large corpus from which to do your Bayesian learning (or whatever). And you can seed the corpus from day one with information from the existing file hierarchy, and with some clustering applied to the full text of the files.
    This is the kind of problem that machine learning can really help with. There’s a decent amount of data, it’s getting feedback on each guess from the user and it’s doesn’t matter if it’s occasionally off-base because it’s only making suggestions.

I like this idea. I even think it might work, though I admit to feeling a little unsettled by the notion that all the files on my hard disk would effectively live in one place. Well, that’s not strictly true. Our notion of ‘space’ in filesystems would have to warp a little. It’s easy enough to imagine a filesystem now as a ramifying rabbit warren. This would require us to think of file locations in terms of boolean queries, and I can’t come up with a nice metaphor. I think it’s easy enough to grasp, but there’s nothing outside the computer that implements tags, because they inherently incorporate the idea of superposition (one thing existing in multiple places).
I would love to see a FUSE implementation of this. It would have to be open source and run on Linux, and I’d consider trying it. The closest I’ve seen (from this list) are:

  • OpenomyFS – propietary and web-based. Otherwise, looks interesting
  • TagsFs – seems to be focused on mp3 tags
  • RelFS – a full relational database
  • LFS – the most interesting of the bunch

If it turns out that any of those projects are alive and easy to try, I’d be pretty gung-ho about it.

UPDATE: there are some great links and comments below, and also at:

The Turing tournament – a proposal for a reformulation of the Turing Test

  1. Introduction
  2. Describing the Turing Tournament
  3. Comparing the Turing Test and the Turing Tournament
  4. Devising new rules, and non-linguistic competitors
  5. But is it intelligent?

MH: Are you a computer?

Dell: Nope.

MH: You’d be surprised how many fall for that one.

Dell: Not me.


MH: What’s fifty-six times thirty-three?

Dell: One thousand eight hundred forty-eight.

MH: You’re pretty fast!

Dell: Those are my favorite numbers.

— from


The Turing Test was designed to be an operational test of whether a machine can think. In Stuart Shieber‘s words:

“How do you test if something is a meter long? You compare it with an object postulated to be a meter long. If the two are indistinguishable with regard to the pertinent property, their length, then you can conclude that the tested object is the given length. Now, how do you tell if something is intelligent? You compare it with an entity postulated to be intelligent. If the two are indistinguishable with regard to the pertinent properties, then you can conclude that the tested entity is intelligent (pg 1).”

In order for a machine to be deemed intelligent according to the Turing Test, we would determine whether human judges could reliably distinguish a human from the machine after some lengthy text-only conversation. I don’t think a machine is going to pass it any time soon, and when it does, it’ll be pretty self-evident that we’re dealing with a machine that can think.

Anyone who disagrees that a full and proper Turing Test is a stringent enough test of intelligence should read Robert French‘s excellent discussion of the kinds of very human and culturally rooted subcognitive processes that would have to going on in the machine in order for it to pass. His criticism is that the Turing Test “provides a guarantee not of intelligence but of culturally-oriented human intelligence”, i.e. that it sets the bar too high, or too narrowly. This is a subtler variant of the obvious point that human beings who don’t speak English would fail a Turing Test with English-speaking judges. In other words, the Turing Test is a necessary but not sufficient test of intelligence, because you would have to have a certain subcognitive makeup in order to pass it, on top of being intelligent.

The beautiful thing about the Turing Test is that there’s nothing about it that’s specific to machines. Indeed, Turing’s original idea for the Imitation Game, as he termed it, was based on a parlour game where the judge attempted to distinguish male from female players. This essay is an attempt to broaden the scope of the Turing Test from being a binary and culturally-rooted test of human intelligence to something vaguer and less unidimensional.

Let’s make this idea somewhat more concrete, and considerably more vivid. Imagine that a small, slimy green-headed alien lands on your lawn right now, travelling in a spaceship the size of a Buick. Assume that the alien demonstrates its extraterrestrial credentials to your satisfaction by whisking you to its home planet and back before breakfast. It bats away the impact of a few .357 rounds with its forcefield and patiently replicates household objects for your amusement. It would seem niggardly to refuse a being that has mastered faster-than-light travel the ascription of intelligence when most humans can’t tie their shoelaces in the morning without a dose of caffeine. So we might be moved to patch the Turing Test in some ad hoc manner to read:

“Any entity that cannot be reliably distinguished from a human after a lengthy text-only conversation, OR that has mastered faster-than-light travel and can withstand a .357 round at close up, can be considered to be intelligent.”

It’s clear that this lacks the pithy generality of Turing’s original formulation, and we’d have to do quite a lot more work to restrict the scope of the above to exclude asteroids. Perhaps over time, our super-intelligent alien will learn to speak English with a flawless cockney accent, and will pass the standard Turing Test, rendering this discussion moot. But in the meantime, before it has learned to speak a human language, we are faced with a manifestly intelligent being that fails our gold standard test for intelligence. The background aim of this whole essay will be to consider a new version of the Turing Test that overcomes the inherent human- and language-specific parochialism of the original. That way, our intelligent alien might pass, without having to learn to speak English with a cockney accent.

Along the way, it may be that our reformulated test provides a more constructive goal and yardstick by which to direct and evaluate progress in AI research than the standard Turing Test. Perhaps its primary limitation is that it’s difficult to restrict the difficulty or scope without losing everything that’s interesting about the test. And since even our current best efforts are a long way from success, the gradient of improvement is almost flat in every direction, making it difficult to discern when progress is being made in the right direction. This makes it difficult for machines to bootstrap themselves by training against each other, requiring lots of labour-intensive profiling against humans. Finally, the current test is very language-orientated, and undesirably emphasizes domain knowledge,

Describing the Turing Tournament

I’ll term this new version of the Turing Test the ‘Turing Tournament’, to reflect its competitive round robin form. Like the original Turing Test, the Turing Tournament will not yield a definitive, objective yes/no answer, but rather a ranking of the entrants, where the human players provide a benchmark. A lot of the details I’m proposing will probably need considerable refinement. Here are the organizing principles of a Turing Tournament:

  • The organizers of each tournament decide what the domain of play will be, e.g. a chessboard, text, a paint program, a 3D virtual reality environment, binary numbers, or some multidimensional analogue stimuli.
  • Every ‘player’ (within which I’m subsuming both human and machine variants) is competing in a round robin competition, and will play every other player twice, once as the ‘teacher’ and once as the ‘student’.
  • Every bout will have two players, a teacher and a student. Play proceeds in turns, with the teacher going first. Play terminates when the allotted time has been exceeded, or when some terminating criterion specified by the teacher has been satisfied. Neither player will know the identity of the other player.
  • Before the bouts begin, every player is given access to the domain of play so that they can construct their own set of rules that will operate when they are the teachers in a bout.
  • The organizers of each tournament determine the scoring for bouts that terminate relative to bouts whose time elapses. We will consider some possible scoring systems later.

These sound like strange rules. What kind of games could be played? Why does each teacher get to set their own rules? Do teachers get rewarded or punished if a student is able to reach criterion for their bout?

I think the easiest way to illustrate what I have in mind is with a concrete example. Imagine the following scenario:

  • A big room with lots of people sitting at computers. The people are the human players. The machine players are sitting inside a big server at the back of the room.
  • The domain for this competition is a Go board, a 19×19 checkerboard with black and white pieces. Although all bouts in this tournament will take place on a Go board, the rules and goals of each bout will be up the teacher of that bout.
  • Let us peer over the shoulder of a human player, currently in the role of student, trying to determine what the rules of the bout are, and play so that the bout terminates before running out of time. Neither we nor they know whether the other player is human or machine.
  • The board is blank initially.
  • As always, the teacher makes the first move. They place a horizontal line of 19 black pieces in the bottom row of the board.
  • Now it is the student’s turn. They have no idea how the bout is scored, what the aim is, what constitutes a legal move, how many moves there will be or whether there will be multiple sub-bouts. All of that is up to the teacher.

    Working on the assumption that the teacher wants the student to play white, the student lays down a single white piece in the top left corner.

  • The teacher removes the white piece, and replaces it with a horizontal row of white pieces just above the existing horizontal black line, and another horizontal row of black pieces above that. So now there are three rows of pieces filling up from the bottom of the board: black, white and then black.
  • The student decides that the removal of their white piece in the corner was a signal that its future moves should consist of placing an entire row of pieces on the board at a time. The student tries placing an entire row of white pieces in the top row of the board.
  • The teacher again removes all the student’s pieces, and replaces them with another row of white pieces and another row of black pieces. The bottom of the board consists of black, white, black, white and black stripes.
  • The student reasons that its next move should be to place a row of white pieces above the most recent row of black pieces to continue the stripy pattern.
  • Gratifyingly, the teacher leaves the row of white pieces in place, and adds a black row above it, as expected.
  • The two players continue to take turns until all but the top row has been filled with alternating black and white rows.

    Now, it is once more the teacher’s turn, and the student wonders whether the last row will be filled in. Instead, the board blanks again, and the teacher places a vertical column of white pieces on the right hand side.

  • The student tries tentatively to place an adjacent column of black pieces, deciding that this sub-bout involves creating black and white vertical stripes, with the black and white players reversed.
  • As it turns out, this assumption appears to be correct, since the teacher does not remove the student’s pieces, and together they quickly build up an alternating vertical stripe that moves leftwards.
  • When only the last column remains to be filled in by the teacher, the bout has reached criterion, and the student moves on to the next bout, with a different player.
  • Upon inspecting the scores later, our human player (the ‘student’ in the bout just described) finds that they had scored highly on that bout, but not as high as some. Some of the machine players had failed to see a pattern at all, and had been putting pieces down more or less at random. These players did the worst, since the scoring for this tournament is a function of the total number of turns taken to finish the bout, as well as the number of errors made by the student. (need to clarify???)

    Like our hero, the best players at this bout had also quickly deduced that the pattern involved stripes. Their extra insight came after a few turns, where they tried placing multiple stripes down at once. As it turns out, there was nothing in the rules set by the teacher prohibiting this, and so they finished more quickly, earning a higher score.

    It seems reasonable to imagine that most humans would quickly figure out the stripy pattern, and some would eventually think to lay down multiple stripes at a time. Might a machine? Perhaps soon.

This is intended as a toy example. The rules of the bout are pretty simple, but I think they would discriminate somewhat between intelligent and not-so-intelligent players. The key point to note is that each player would play twice against every other player, once as the teacher and once as the student playing within the teacher’s rules. Perhaps some bouts are too hard, and some are too easy. But en masse, the rankings should discriminate quite finely between players, even between human players. The exact details of the scoring, especially how teachers are scored, and how teachers pre-specify their rules, are clearly going to be crucial. It will suffice for now to say that students should probably get points for satisfying the criterion of a bout quickly, and teachers should be rewarded for devising discriminative games, that is, games that only intelligent players can solve. I will defer further discussion of these topics until later.

Comparing the Turing Test and the Turing Tournament

In discussing the idea of an Inverted Turing Test (more below) Robert French states that:

“All variations of the original Turing Test, at least all of those of which I am currently aware, that attempt to make it more powerful, more subtle, or more sensitive can, in fact, be done within the framework of the original Turing Test.”

Is the same true of the Turing Tournament? I think the answer is both yes and no. In fact, you could think of a Turing Tournament as a kind of generalization of the Turing Test. That is, the original Turing Test could be treated (more or less) as a Turing Tournament where the domain of play is restricted to text, and the bouts terminate if the teachers/judges are satisfied they are talking to a human. It wouldn’t be quite the same, since here the players would double up as judges and the judges would double up as players. In other words, the machines would also themselves be making judgements about the humanness of both each other and the humans – an ‘Inverted Turing Test’. In its current formulation, where every player plays every other player as both teacher and student (i.e. judge and player), a Turing Tournament would really be a strange hybrid of both the Inverted and standard Turing Tests.

The idea of an Inverted Turing Test has been proposed before:

“Instead of evaluating a system’s ability to deceive people, we should test to see if a system ascribes intelligence to others in the same way that people do … by building a test that puts the system in the role of the observer … [A] system passes [this Inverted Turing Test] if it is itself unable to distinguish between two humans, or between a human and a machine that can pass the normal Turing Test, but which can discriminate between a human and a machine that can be told apart by a normal Turing Test with a human observer.”

French ingeniously showed that this Inverted Turing Test could be simulated within a standard (if somewhat convoluted) Turing Test. In contrast, it seems clear that an unrestricted Turing Tournament could not be fully simulated by a Turing Test because the potential domains of play are limitless. So although one might imagine instantiating the Go domain by communicating using grid references within a standard Turing test, it seems clear that there would be no way to run a domain of play such as a 3D virtual reality environment within a standard Turing Test using text alone. The principle advantage of widening the domain of play from text-only in this way is to allow players to pass some kinds of Turing Tournaments without speaking English, or any language at all. As a result, it seems reasonable to think of the Turing Tournament as (more or less) a superset of the Turing Test, or if the reader prefers, at least a redescription of it with unrestricted domains of play. I find this Ouroborean quality quite pleasing. [is it really ouroborean???] Either way, we can agree that most of the original Test’s merits and stringency should still be present in the Tournament version, depending on the way a particular Tournament’s domain of play and restrictions are set up. This does raise the important question of whether a Tournament victory would be as convincing a demonstration of intelligence as a victory in a standard Turing Test – I will return to this below.

French also shows that the Inverted Turing Test could be passed by a simple and mindless program that would take advantage of the very subcognitive demands that make the original test so parochial and difficult to pass. In short, the machine could have a pre-prepared list of questions that have been shown to weed out machines in the past, such as

“On a scale of 0 (completely implausible) to 10 (completely plausible), please rate:

  • ‘Flugblogs’ as a name Kellogg’s would give to a new breakfast cereal.
  • ‘Flugblogs’ as the name of a new computer company.
  • ‘Flugblogs’ as the name of big, air-filled bags worn on the feet and used to walk on water.
  • ‘Flugly’ as the name a student might give its favorite teddy bear.
  • ‘Flugly’ as the surname of a bank accountant in a W.C. Fields movie.
  • ‘Flugly’ as the surname of a glamorous female movie star.”

By pre-testing lots of humans and machines to figure out what kinds of things people say, and machines fail to say, a simple but well-prepared machine could draw up a ‘Human Subcognitive Profile’. By comparing this to the responses of players, it would be an extremely effective judge in an Inverted Turing Test. There are two reasons why this strategy would not work in a Turing Tournament:

a) In the above specification, none of the players know which domain they will be playing in until the competition begins officially (after which the designer is barred from tweaking his machine). As a result, it would be impossible for the designer to create Human Subcognitive Profiles for every possible domain that the machine might find itself playing in a Tournament.

This same effect could perhaps be wrought in a standard Test by restricting the domain of conversation, but not telling the players before the competition begins what that domain will be.

b) In order to be successful, players have to be good as both teachers and students. As mentioned above, this is akin to holding both a standard and Inverted Turing Test. Even if the domain was known in advance, and even if it was possible to draw up a Human Subcognitive Profile for that domain somehow, such a machine would be exposed as a student.

Lastly, French asks whether the standard Turing Test might be modified to forbid the kind of subcognitive questions that underly its cultural and species-specific parochialism. He concludes that the kinds of questions that probe “intelligence in general … are the very questions that will allow us, unfailingly, to unmask the computer”.

He may well be right. However, it may be that moving out of the text domain will dramatically reduce the scope of possible subcognitive shibboleths that human teachers could employ. Having said that, there will still be many possibilities for rules that would place human student-players at a big advantage. For instance, in the case of the Go domain, a cunning human teacher could choose to play by the rules of Connect4, which other humans might be much quicker to fathom. In the case of some kind of Photoshop canvas domain, humans could spell out words cursively, outwitting even the most seasoned OCR software. If there’s any kind of free-text involved, any of the subcognitive tricks designed for the standard Turing Test might be employed. In the case of a 3D virtual environment, human student-players will have a huge edge, though perhaps 2D or high-D worlds would level the playing field. One might hope that imaginative specification of domains could minimize such advantages, and that after 10 years of such competitions, machine programmers will almost certainly know to build in pre-loaded expert knowledge of Connect4, for instance, but the problem will clearly still remain.

[N.B. In order to ensure that the scales aren’t conversely weighted too heavily against human players, it seems reasonable to allow all human players the use of a laptop throughout the Tournament.]

Maybe instead we should accept the possibility of subcognitive shibboleths, and embrace their utility instead as a means of cataloguing different kinds of conceptual schemes. There is a presumption inherent in the standard Turing Test that smartness can be measured on a one-dimensional continuum ranging from rocks to rocket scientists. In the case of the aliens that have travelled 4 million light years in a space ship built out of genetically-engineered quantum nanobits and powered by fermented mango juice, we could be pretty sure they’re intelligent, even if they were never to get the hang of English. It’s just that their conceptual schemes are different. In this case, we may find that there are cases where they think more like machines than like humans. Or possibly more like dolphins, or African grey parrots, or white mice or marmosets. If we’re able to set up a domain in a Tournament that everyone can play in, then we can expect that human student-players may not necessarily come out on top in all respects, even within the animal kingdom. We will return to this idea when we discuss Turing Tournaments between groups of individuals.

Devising new rules, and non-linguistic competitors

Besides extending the domain of play beyond text, the principle innovation of the Turing Tournament is in casting every player as both student and teacher.

It is clear enough what is required of the student player. When the bout begins, they have some idea of the kinds of interactions, puzzles and patterns that the domain presents. By interacting with the teacher player, they have to somehow fathom what the aim (i.e. terminating criterion) of the current bout is, and attempt to satisfy that. It might involve placing pieces on the board in some complex pattern, learning the structure of a maze, guessing at the next number in a sequence or optimizing some noisy function. Depending on the tournament, they may or may not be given feedback after each move:

  • If they’re given a running score, they can attempt to learn how to maximise that reward.
  • If no reward is given, but the teacher corrects incorrect moves, then the learning by imitation can be seen as a kind of supervised mapping or reconstructive learning problem.
  • There may even be cases where no feedback is given whatsoever, such as when the bout requires the student to guess the next number in some sequence.

It is the teacher’s job to come up with new and inventive rules for bouts that challenge the student-players, and also to perhaps lead the student in the right direction. For the Tournament to work as intended, teachers should be intending to come up with the most discriminative bout rules they can.

Getting the incentive structure for the teachers right is therefore key. I expect that early scoring structures will contain loopholes that ingenious machine designers can exploit, but that over time, scoring structures that serve their purpose in a robust way will emerge. If our goal is to discriminate humans from machines, then this simple scoring system may work:

  • If the student ‘wins’ (i.e. satisfies the terminating criterion) a bout, whether human or machine, then they get a point, otherwise they get nothing.
  • If a human student wins a bout, then the teacher gets a point, otherwise they get nothing
  • If a machine student wins a bout, then the teacher loses a point, otherwise they get nothing.
  • Total player score: the sum of the player’s scores as a student and their scores as a teacher

    [There may need to be some weighting/normalization if the number of human and machine players is unequal.]

In effect, we’re rewarding players that seem human, and can devise rules that discriminate whether other players are human. This Tournament setup is the combo standard/Inverse Turing Test described above, that would not differ all that wildly in principle from the standard Turing Test if played in a text domain. Such Tournaments would encourage the kinds of subcognitive or culturally-rooted human-parochialism that we’re trying to avoid.

Perhaps this more general scheme will work instead:

  • If the student wins, whether human or machine, then they get a point, otherwise they get nothing.
  • To calculate the student’s score: at the end of all the bouts, count the number of bouts that each player won as a student. Calculate the mean number of bouts won. For each student, subtract this mean value from the number of bouts they won. This will mean that a very average player will have zero student points, a good player will have a positive number of points, and a poor player will actually have negative student points.

    In other words:

    c_m = sigma_n^N( W_nm ) – sigma_n^N( sigma_m^N( W_nm )) / 2N


    c_m = the overall student score for player m

    N = the number of players

    W_nm = 1 if student m won in their bout with teacher n

    W_nm = 0 if student m lost in their bout with teacher n

  • To calculate the teacher’s score: if the student wins a bout, then add the student’s student score (which may be negative) to the teacher’s teacher score. If the student loses the bout, then the teacher gets nothing.

    In other words:

    p_n = sigma_m^N( W_nm * c_m )


    p_n = the overall teacher score for player n

    N = the number of players

    W_nm = 1 if student m won in their bout with teacher n

    W_nm = 0 if student m lost in their bout with teacher n

    [It may be that W_nm should be -1 if the student lost]

  • Total player score: the sum of the player’s teacher score and student score

    [There may need be some normalization to ensure that the teacher and student scores are weighted equally.]

What’s the point of all this complexity? If you’re a teacher, then you do best if you can design your rules such that only above-average players (whether human or machine) win in your bouts. There’s actually a penalty if you make your rules so easy that everyone can figure them out, and you’ll get zero points if no one can figure them out at all. When you’re a student, you want to be as smart as you can, and when you’re a teacher, you want to be as discriminative as you can. En masse, the community of competitors are striving to do as well as they can and to evaluate each other as well as they can.

Inventively devising rules to favour intelligent over non-intelligent participants requires sufficient representational power to understand, let alone manipulate, your own rules, a rich theory of mind, as well as a generative good taste. Consider a Tournament played in the simple domain consisting solely of letterstring analogy problems, where the student is faced with problems such as:

“I change abc into abd. Can you ‘do the same thing’ to ijk?”

or in non-linguistic terms:

abc —> abd; ijk —> ?

Reasonable responses include ijl, ijd, or even abd.

Let us imagine that a player as cunning as Douglas Hofstadter has devised the following problem:

abc —> abd; mrrjjj —> ?

Peer at this for a moment – you won’t appreciate that this is somewhat fiendish unless you try it for a while yourself. Any ideas?

There’s no obvious pattern to the letters chosen on the right hand side, so mrrkkk seems kind of lame, and abd always feels lame. Well, how about if you try this one first:

abc —> abd; abbccc —> ?

Though your first thought may have been abbddd, doesn’t abbdddd seem so much nicer? It’s as though the successorship sequence of letters needs to be reflected in the increasing length of the letter groups (to use the FARG’s terminology). Now, let us turn back to:

abc —> abd; mrrjjj —> ?

Doesn’t mrrjjjj seem like a nice, reasonable solution now? Would you have considered it so nice before the previous example. Probably almost as nice. Would you have thought of it on your own, without the previous example? Probably, given some head-scratching.

The point of this digression is to point out how an imaginative teacher can guide, plant ideas, manipulate, prime, coax and lead the student by example in such a way that an intelligent player would almost certainly get the right answer, but there are almost no extant machines that would stand a chance. Besides having the sheer representational flexibility to deal with even barebones analogies such as the one above, a really intelligent player would be using the first few turns to gauge the teacher, get a sense of what kinds of solutions are admissible, and would probably be relying on Gricean maxims wherever possible.

What if your alien doesn’t know anything about Gricean maxims? Or doesn’t understand concepts like tournaments, rules, intelligence, machines, scores or games? We’ve finished outlining how a Tournament might be run that might require less domain knowledge and linguistic ability than the standard Turing test. But one striking pragmatic problem remains, which becomes apteacher when we consider our newly-arrived green visitor. If the alien doesn’t speak English, how are we going to explain the idea of the Turing Tournament to him so that he can participate?

Following Minsky, I think that we will be able to converse with aliens to some degree, provided they are motivated to cooperate, because we’ll both think in similar ways in spite of our different origins. Every evolving intelligence operates within spatial and temporal constraints, suffers from a scarcity of resources (and presumably, competition), must develop symbols and rules, and must have thought about computation and machine learning in order to be able build spaceships. Perhaps notions of games, intelligence, scores and tournaments are only relevant in a society of individual entities that compete with each other for resources, and that maybe a hive mind or single monolithic being or other unimaginable entity might not need such concepts. In that case, you wouldn’t have any more luck using the standard Turing Test on such a being.

Will we have much more luck with machines? Not unless we start small. At the moment, the state of the art in artificial intelligence wouldn’t do very well in most of the domains we’ve discussed, and would struggle especially when trying to generate new rules of its own. Sadly, very few researchers have focused on generative heuristics to curiously discover things that are interesting simply for their own sake, such as Lenat‘s Automated Mathematician that sought interesting mathematical concepts. In order to stand a chance in a Turing Tournament, much work needs to be done on curiously discovering interesting things that could serve as the basis for a rule set in a new domain. Good, that is, discriminative, rule sets for a Turing Tournament bout might consist of a difficult but ultimately guessable sequence of numbers based on a funny arithmetical pattern, or the kind of letterstring analogy problem that elicits an ‘aha’. Better still, teacher players that can lead an intelligent student player down a suggestive road towards the terminating criterion through tutorial or warm-up sub-bout problems will be at a tremendous advantage, where half the problem for the student consists of figuring out what their goal is supposed to be.

But is it intelligent?

Let us recall Shieber’s pithy test for intelligence:

“Now, how do you tell if something is intelligent? You compare it with an entity postulated to be intelligent.”

We’ve replaced that with an intellectual obstacle course. As teachers devising rules for their bouts, we are effectively asking players to define their own micro-test of intelligence (since being able to do this is surely a sign of good taste?). They must then be able to convey the parameters of that test such that other intelligent student players can figure out how to pass it, perhaps by creating lead-up sub-bouts, internalizing what the student player is probably thinking and so guiding the student players’ intuitions in the right direction. Finally, as students, the players must demonstrate in turn that they can flexibly assimilate what their goal should be, and then be able to get to it.

So although we might imagine some narrow machines that could best humans in certain kinds of puzzles or computation, but it seems less likely that a brute force machine player would also do well on Bongard problems, letterstring analogies, or be able to devise ingenious, fun and discriminative rules for bouts. This new generative aspect is intended to tap into the kind of creative, generative, playful, inventive or aesthetic faculty that humans display, as well as the ability to form a rich internal model of the student player’s state of confusion, and guide them towards a solution. In this respect, it borrows the idea of a dialog or gentle interrogation from the original Test, but allows for the translation of that dialog to new domains.

Bringing this back to Turing’s original question, we can finally ask, ‘if a machine were to score higher than some of the humans in a Turing Tournament, would we definitely be willing to call it intelligent?’ The answer could depend on a few factors:

  • Let us assume that the Tournament is well-planned, that the human competitors are well-chosen, that no independent experts can find any scoring loopholes or weaknesses in the organization of the Tournament, and that the result is replicable. If any of these conditions are not met, we will not consider the Tournament to be well-run.
  • If the domain is too restrictive, then there may be a dearth of interesting rule sets that can be devised. In this case, a good player won’t do much better than a poor player, and this wouldn’t be an interesting result.
  • Even if the domain is a rich one, such as letterstring analogy problems, it could be that a highly specialized program like Copycat could outperform many humans. Unless the success is relatively domain-general, then you’ve shown what you probably knew already, i.e. the machine is exhibiting some domain-specific proto-intelligence.
  • At that point, we would probably want to analyze the machine’s performance. Did it do better as a teacher or student? Was it simply very good at certain kinds of problems? Was there some simple trick to its way of devising problems which, once exposed, would clue in future human players in a rerun of the Tournament?
  • Could it pass a standard Turing Test?

Let us imagine that a machine is designed which is a poor teacher player, but a good student player, particularly in a couple of abstract limited-interaction domains like letter strings, number sequences, Go boards and cryptography, but that it can’t pass the Turing Test. Is it intelligent? Somewhat? We’ve forfeited the no-frills and no-free-parameters yes/no answer that you get from a Turingf Test, but we now have a much richer set of data with which to try and place this machine in the space of all possible minds. We have a more finely-graded multi-dimensional scale. Our machines can bootstrap themselves by competing amongst themselves without human intervention – specialist teacher machines that are good at discovering generative heuristics can be used to train specialist student machines that are good at problem solving, and vice versa. So in forfeiting our neat yes/no answer, we’ve gained a great deal.

Perhaps most importantly for the field of AI, we can now attempt to scale the enormous subcognitive iceberg of the mind incrementally, using ever more complex Turing Tournaments as yardsticks. In time, perhaps this will lead back towards the Turing Test as the final summit.

see also: AlienIntelligenceLinks

Paul Graham, Joel Spolsky and Steve Yegge and the Law of Increasing Returns

I’ve read almost everything these three guys (PG, JS, SY) have written. I think it’s because I get an unshakable feeling of rightness and convergence when I read their stuff that I’ve been trying to pin down. Some fairly obvious commonalities between them include:

But most of all, I think the key tenet that binds them together is an awareness of the Law of Increasing Returns. They each buy into the idea that:

  • a really smart person
  • a powerful programming language
  • a beautifully-architected office
  • an uninterrupted 3-day period

is worth 10

  • Joes
  • Blubs
  • cubicles
  • half-hour slots between errands.

PG’s essay on taste is perhaps the most ardent tribute to the Law of Increasing Returns. He catalogues the hallmarks of good design, and though he doesn’t say it, the key point of all this is that they add non-linearly. I’m still thinking about this.

He doesn’t say much about how one can hone one’s taste. I think there’s a Vonnegut quote to the effect that the only way to learn to tell good painting from bad is to look at thousands and thousands of good ones, and it will become obvious to you.

Interestingly, while I was trawling for links for this essay, I noticed that the three of them read each other:

I feel a little less clever now that it’s clear that a lot of other people are reading all of them too:


Reading about how to avoid procrastinating is amongst my very favourite ways of procrastinating. It’s a lot easier than whatever you’re supposed to be doing, and neatly neutralises the guilt that you’d otherwise feel with a seductive promise that in the long run, this will prove to be the most useful hour you’ve ever spent.

There appear to be at least two main schools of thought regarding procrastination. There are certainly those who treat it as an evil that can be combatted, either head-on or deviously, but there are also those that embrace some degree of procrastination in the service of sifting project-wheat from errand-chaff.

There are people who spring out of bed at 5am, chanting ‘get thee behind me, Slashdot’, who are all too willing to tell you how to ‘maximise your productivity’. Steve Pavlina‘s intoxicating account of how he ostensibly graduated from college in CS in three semesters is the best example of this. Look how easy life is if you don’t waste any time whatsoever, he whispers to you. He’s either making it all up, or a superman, but he does tell an interesting story. And his polyphasic sleep experiment is worth a read.

Then there’s this bit of mental judo for using procrastination as a force for good. Basically, the idea behind ‘structured procrastination’ is this:

“Procrastinators seldom do absolutely nothing; they do marginally useful things, like gardening or sharpening pencils or making a diagram of how they will reorganize their files when they get around to it. Why does the procrastinator do these things? Because they are a way of not doing something more important. If all the procrastinator had left to do was to sharpen some pencils, no force on earth could get him do it. However, the procrastinator can be motivated to do difficult, timely and important tasks, as long as these tasks are a way of not doing something more important.”

Continue procrastinating. But instead of reading The Onion, procrastinate by doing something you’ll have to do eventually. This may not be the thing you should be doing most of all, but it’s better than nothing. And it won’t feel as much like work, because you still get to feel that you’re avoiding the thing you’re not supposed to be avoiding. Everyone’s a winner.

In opposition to this idea, Paul Graham argues that there are good and bad forms of procrastination:

“There are three variants of procrastination, depending on what you do instead of working on something: you could work on (a) nothing, (b) something less important, or (c) something more important. That last type, I’d argue, is good procrastination.”

He and Joel Spolsky are in remarkably close agreement on this (and related issues). Difficult and important things, like research, need big chunks of time and get completely minced by interruptions and any kind of task-switching. If blowing off a few errands means that you don’t get knocked out of the zone, and work solidly on a hard problem for three days straight, then that’s the way to be. And often, the things that you’re procrastinating about will disappear of their own accord – that’s a sure sign they weren’t that important to begin with.

I think there’s a final point to remember about procrastinators, as people. It is possible to be very successful and still procrastinate horrendously. For this to work, you need constructive panic. People who constructively panic thrill a little in the throes of that total focus you get when you realize that you have no time left to waste. You have exactly as much time remaining as you need to get things done, assuming you sleep as little as humanly possible, and view the whole world through a hole the size of a pinprick with the unblinking eye of your deadline staring back at you. Procrastination brought you here, and constructive panic will get you out.

Michael Behe, ‘Darwin’s Black Box’

I’ve been reading a provocative book by Michael Behe called Darwin’s Black Box. In short, he’s arguing that Darwinism goes a long way to explaining why the various forms of life are the way they are, but is completely unable to address some of our questions and issues, specifically how life arose in the first place and how a number of low-level biochemical structures and systems came to be.

His argument hinges around the idea of irreducible complexity. Take the example of a mousetrap – a mousetrap is irreducibly complex because you need a base, a hammer, a spring, a catch to hold the hammer back and some cheese to tempt the mouse with. If you get rid of any one of those components, you have a wholly non-functional mousetrap. In fact, if the base isn’t sturdy enough, the hammer heavy enough but not too heavy, the spring springy enough etc., then the thing probably won’t work either. It is irreducibly complex because it requires a number of special components to be together, configured correctly and each meeting certain criteria, otherwise you have a paperweight that would not pass on its genes. There is no way to start with just one or even two of those components lying around and progress through a monotonically beneficial series of minor mutations to get to a mousetrap.

He tries to show in a series of unnecessarily detailed but very readable chapters that e.g. the clotting of blood, the immune system, cellular cilia and flagella (propulsion mechanisms) and vesicular transport are all examples of very complicated and irreducibly complex mechanisms at too small a level for science in Darwin’s time to know about. It would be like an internal combustion engine evolving. And he points out that even if you got a working combustion engine going, unless it goes at a certain minimum speed (say tens or hundreds of revolutions per minute), you might as well not bother.

Sometimes he overstates his case. The immune system example is interesting, but you might imagine that the original immune system started with just a handful of hardwired responses and *somehow*, god knows how, it became more and more general. But his point still stands that no one has a really good, convincing answer or even reassuringly specific set of speculations about how the antibodies, t cells, b cells, the cells that puncture unwelcome invaders etc. all came to be at once. Without a minimum functioning set of interacting components, you have roadkill – and it’s impossible to imagine how to get from rocks past roadkill in a series of small, always-beneficial mutations.

I haven’t finished the book yet, but very nearly. What I’m curious about is where he’s going to leave the reader. It really seems as though he’s pushing for god, or at least some kind of godly ‘intelligent designer’ to step in, since he’s quite certain that no form of evolution of natural search could do the job. I’m reluctant to accept this conclusion. I can’t help but feel that this tells us that the DNA programming language is cleverer still than we thought. On the one hand, nature’s machines are gradually transformable, robust to damage, distributed and parallel like the brain, and yet on the other hand, they can involve long strings of essential components that interract in irreducibly complex and fragile ways.

My only response at the moment is to try and think about how these flagella and immune systems and biochemical cascades get represented in DNA. On the one hand, they have to be represented in a precise, error-free, symbolic way, and on the other hand, they have to be mutable and robust so that tiny copying errors lead to beneficial mutations much more often than chance would have us believe. Because, at root, though Behe fails to really put it in these terms, he’s arguing that an evolutionary search through a space large enough to encompass the kinds of biochemical mechanisms he discusses would simply be too too large unless there was some very powerful representation or clever pruning or mid-life self-organisation going on. He briefly discusses Kaufmann’s ideas about complexity and catalysed self-organisation, but he doesn’t really understand them, and neither do I, and he says that they’re mathematical models rather than actual, nitty-gritty biochemical stories.
I keep coming back to the idea of DNA as a very clever, very high level programming language (at least some of the time) – in such a hypothetical language, if we had rules to say that any legal source code constructs a relatively viable creature, and the mutations are such that only legal source code can come from legal source code, then you could mess with it all you like and you’d still have lots of organisms that more or less worked. It wouldn’t be like adding random characters in the middle of your C++ source code, and hoping they fix your bug and double the performance. However, it looks as though the mutations are more often than not random – the cosmic rays aren’t very discriminating. And it makes no sense to imagine that there could be a programming language whose legal source code only ever built viable organisms. That would be like a language that only ever produced true and interesting statements. But one might imagine that if DNA is a high-level programming language, then it could have found ways to program its own copying mechanisms to ensure that a mutation gives rise to legal code more often than not ( ensuring that the crossover points happen in roughly the right places), and that that source code operates at the level of objects (like classes in C++ or Java) so that you can build increasing levels of abstraction. So you might imagine a line of DNA++ that says ‘if A and B then not C’ or ‘if B>5, then do X’. That way, messing with the contents of the slots, i.e. which objects go where, you could relatively rapidly build up a few lines of code that do something cool, e.g. only start to clot when there’s tissue damage and not that much clotting already. When that works, you treat that paragraph as an object/function by marking the start and end with a tag that says “useful code – don’t mutate unless you’re feeling really capricious”, and then build a more sophisticated clotting mechanism around or on top of that that facilitates healing once the immediate danger of bleeding to death is over.
In the case of bacteria, you have absolutely shitloads of them wandering around, and if you’re mutating at the level of swapping functions and objects in and out, rather than individual characters, then you could search through a pretty large source code space pretty efficiently, building increasing levels of abstraction in small steps. Moreover, if one bacterium solves the problem of vesicular transport, and the other one figures out how to propel itself, you just combine the two bits of code together.

What’s the alternative? God? A planet that’s much older than we realise? Some proto form of life that solved all the biochemical problems for us, then died out or went to live like a hermit in the middle of a volcano? Aliens? Could our hindbrains be unconsciously directing things from above? Nah.

Direct democracy

So I’m trying to imagine what the future will be like. We know that democracy is the worst form of government except all the others, but does that mean we’re stuck with it in its present form? The game for today is to imagine some incremental improvements to our current democratic system. For the time being, I’m lumping the US and the UK together, along with much of the rest of the world and ignoring the real differences between them.

Let’s take an example of an idea that’s part habit and part policy that I would imagine would substantially change the way democracy feels to the average punter. I just got back from Switzerland, and I was astonished to find that they have referenda many times a year, especially at the local level, but even up to the national level too. Switzerland is divided into cantons (much like states in the US), and then seemingly subdivided at almost the village level. So, as far as I can tell, people will be given ballots at least a couple of times a year on with a list of 10 or so questions that they can vote on, which range (as far as I can tell) from whether to have red or blue bunting at the village fair to whether or not Switzerland should adopt the Euro. This is in stark contrast to the UK, say, where asking the public’s opinion about something straight out is considered risque and undesirable.

Does this more referendum-driven approach work for Switzerland? Even if it does, would it work in the UK or the US? I’m not really in a position to answer either question, but I’ll briefly consider them both and then play around with an idea that grew out of them.

Who was the Swiss economist (SHB???) who argued that the level of direct democracy in the different cantons was correlated with happiness? This could just be because the richer cantons have smarter, happier people, and so trust them to vote more often, and they make better judgements, and feel more enfranchised. The point is – i don’t know how with just 20 or 30 (???) cantons, he could possibly hope to control for how many people, how many questions, what types of questions, wealth, education, blah blah…

One argument for why greater levels of direct democracy might improve the way the country is run is based on the Condorset Theorem (or Paradox???). Put simply – if I’m 51% likely to make the right decision, and you’re 51% likely to make the right decision (and we’re statistically independent), then if we pick the majority view, then the more people the better. Of course, we aren’t all statistically independent of each other because it sometimes feels like most of Britain votes whichever way The Sun tells them to, and if we take the alternative case where we’re each only 49% likely to make the right decision, then the more of us vote together, the worse we’ll all be.

I’m now rehashing the argument that SHB set out when we talked about this last week, when he pointed out that maybe the real reason why we like democracy is not because it’s the most efficient system for generating correct decisions, but because it offers things that we prize more highly…???

Let’s try taking things to extremes. Let’s imagine that the various employees and, especially, directors, of Diebold have been ruthlessly and publicly culled, and that electronic voting machines are considered magically safe. Or, better still, that the servers on the government’s web site are powered by ground-up unicorn horns, and so we can all vote online with impunity. I’m back to imagining a world where machines do all the dull, dirty and dangerous jobs, leaving us with swathes of leisure time in each week to write poetry, read the newspaper and vote on every little decision.

But I’ve only ever lived in democracy-by-proxy. We have elected representatives for a number of reasons, including:
they spend all their time becoming experts and specialists in various areas, so that they can make informed decisions
elections are expensive in time and money to organise and participate in
they’re wiser, more compassionate and fairer than the average joe
However, if people have enough leisure time to familiarise themselves fully with the issues, and elections are run at the click of the button then we’re left to face the real question: do we actually want people to decide for themselves?
In a funny kind of way, democracy today rests on an assumed inequality and friction: only the rich and educated have a real chance at getting voted into a position of power in the current system, so even if the hoi-pelloi get to choose between them, there are so many gilded hoops separating the terraced houses and secondary comprehensives from Downing Street that there’s little chance of a complete upset in a general election. But if you were to offer people frequent referenda in a frictionless way (assuming everyone has or is given a PC and broadband), I imagine government would be faced with a popular voice become more garrulous and much more hearty in its demands.

Let me give one good reason why more direct and interactive democracy is A Good Thing. Right now, you have one yay-or-nay every 5 years or so. There are really only two parties which stand a chance of getting into power, and tactical voting??? coalitions??? so if you want to maximise the impact of your vote on the election, you end up voting for one of them. So you have to compress all of the past decisions, rhetoric, promises and stances into the lesser of two evils. It’s like collapsing an intricately high dimensional space onto a line. Usually, that line runs the continuum from left to right. But there are many, many issues which could run orthogonal to that line. Authoritarianism/liberalism, adopting the Euro, gun laws, the Iraq war, money on roads or public transport etc. Right now, I vote based on the two or three most important issues, because those are the ones where I feel strongly about having my voice heard. And even if those handful all fall on the same side of the 1D line, I can’t signal my views about any of the myriad other orthogonal issues through my vote. In reality, I accept that things are more complicated than this, because decisions are rarely binary and because we vote in large part about what we think personally about the major figures and their likely future policy decisions.

Of course, one issue is that people might be expected to vote selfishly. Why should the 45 million people who only ever visit London once a year want to pay billions of pounds to improve the Underground? Why do I care if the Post Office delivers to the Orkney Islands? This is probably why the Swiss referenda tend to occur more at the local level than the national level.

Nobody seems to want to improve the world

Let’s play Design Your Own Utopia. You have a magic wand. You can’t just get rid of all the people you don’t like or banish the problem of scarcity, but you can assume that over the next 25-250 years, some things will be possible that haven’t even been glimpsed yet.

This is a fun game because it involves positive visualisation, which my doctor says is good for me, and because it’s considerably harder than you think it should be. After you’ve made the obvious first steps of ensuring year-long sunshine and unbalancing the gender ratio in whichever direction suits you, things start to get very tricky very quickly.

The idea of making the world a substantially better place makes people very uncomfortable. Everybody wants the world to be a little bit better. We’d all like an extra £5 in our pockets and for the trains to run on time, but people get nervous when considering a substantially different world. They don’t like the idea that their routine could be unrecognisably different, or that technology would encroach on our lives more, that privacy might be burnt on the altar of accessibility or security, and at the deepest level, there’s a real concern that suffering is so fundamental a part of what it is to be human that to try and banish it wholesale undermines everything that we care about most in ourselves. This is a paradoxical thought – in making things much better, everything that’s really special withers and dies.
People don’t want to live in a perfect world. If you offer them the possibility of solving many of life’s ills, they look at you with a sort of blank, worried despair – if I don’t have to work and slave, what am I supposed to do with myself?They don’t want They don’t want the capacity to lie taken away from them, or the ability to read people’s minds. They don’t want a chip in their heads that will make them less likely to get angry, or ensure that they never forget anything. And I can understand why. I don’t want my sense of autonomy trodden on by a device that won’t feel like part of me, and above all, I want to preserve every mote of the sovereignty I have over my own mind.So what I’m really trying to do is imagine the utopia that I’d like to live in. And it’s surprisingly difficult, if you consider human nature to be animal, selfish and rigid, as a rule.
So here are my starting assumptions. Robots will do more of the dull, dirty and dangerous jobs. Not all, because some of them are very difficult, and not all jobs, because there will probably always be reasons to have humans in the loop. Besides, a robot that can act as CEO is presumably going to require the same human rights as a human, and I don’t want to get into that here.

Consequently, unemployment will rise to unheard of levels, and levels of absolute wealth will continue to rise.

Why and the religious sense

The religious sense is (statistically) so common, in man, and I would bet, in any intelligent species, because it arises from a fundamental misconception of the world. This is the Hermeneutic Misconception, the result of layers of abstraction peeling away the particular to reveal the patterns underneath, the regularities that support life and give intelligence its advantage, and eventually lead to the invention of words like ‘why’ as part of the (for a while) fruitful search for ‘meaning’ in the world around us. Eventually the word-symbols trick us into asking, ‘Why are we here?’, ‘Why does anything exist?’ or ‘What is the meaning of life, the universe and everything?’ Douglas Adams wrily hints at the vague emptiness of the question by gesturing so broadly – ‘life, the universe and everything [else that we can’t name]’, i.e. what is the meaning of the ineffable or the uncapturable? Because we are an embodied species with real concerns, we reify this mystery, we call it God. This is the Hermeneutic Misconception, that there is something to understand beyond our understanding, that if we could only try a little harder, we could make sense of, and discover the true answer to, the question ‘Is there a God?’. We can no more make sense of, nor find the true answer to, the question, ‘What colour is God?’ or ‘When is time?’ or, simply ‘Why???’.
We have learned throughout prehistory that some forces or effects that seem out of our control can be affected by our actions, though we do not always understand quite how, and we know that we can best predict and manipulate the world around us by attributing intentions, beliefs and desires to the people, animals and inanimate objects (‘The water wants to go back to the sea’), until we see a humanly-comprehensible mentality even where it is not instrumentally helpful for us to think of things in this way, in terms of Mother Nature, the Greek or Hindu gods, cars and computers, and the universe.
‘How’ is a much safer question. It admits a reliabilist answer, and a how-dogma can always be trumped by a more successful, rich + predictive or explanatory one – that is, as long as it isn’t shackled to a why-dogma, a metaphysics whose dictates (or more specifically, whose advocates) sulk and pout violently when undermined by new evidence. The only healthy how-dogma is a humble science, a willingness to consider no view too sacred to be usurped by a better one. The issue becomes then how strongly we should understand the claims made by such a science – are scientific theories true (at least until proven false), or merely instrumental? Can it accept that there are truths that we cannot know or understand?
A strong candidate for such an unknowable/incomprehensible domain concerns the origin of everything. ‘How did anything come to be?’ The problem is that while this question appears to avoid the traps surrounding its sister-question, ‘Why did anything come to be?’ or even ‘Why are things as they are, rather than different?’ or ‘Could things have been/be different?’, they still get ushered in through the back door… For the most part though, I feel safer with how-questions because I feel as though they permit definite answers – ‘like this’, ‘these are the hypotheses’ or ‘we don’t know’. Of course, that’s not always true – there’s quite a lot of speculation even in how-questions 😦

The Open Manifesto

I don’t like the word ‘player’. Instead, I think I’d almost rather be called a ‘slut’. Except, like player, it has very gender-specific connotations, and so simplifications – but opposite ones. While a player is active, wilful, manipulative, objectifying, a slut is passive, a manipulated object lacking the will to guide his/her own behaviour.

So, because people are complicated, and should be able to convey their independence of mind and moral values, I propose a new, still politically uncorrect but at least gender-neutral moniker. Following the nounification of the adjective ‘gay’, and the attempt of the same with the word ‘bright’, I’d like to propose ‘open’ as the self-describing word for those of low moral fibre, embracing a high sex drive, a shameless sense of humour, an indulgence in whatever works for them, and an understanding that sexual orientation is a (possibly multi-dimensional) continuum.

Sex is fun. It shouldn’t be a source of guilt. But, to avoid anyone getting hurt, it requires honesty, in order to be fair, fun and free. In short, you have to be open.

Trophic levels, trade and the Terminator

As an AI aficionado, I’ve had my fair share of debates about the Terminator scenario. Perhaps blindly, I’m optimistic about the possibility of being enslaved or eradicated by robot overlords. Here’s one possible response.

Why would the machines be so obssessed with the idea of dominating or eradicating us? They’ll occupy a completely different ecological niche. They’ll almost certainly have entirely different energy/resource requirements, be free from human claustrophobia and may not even be embodied at all. They won’t care if the earth runs out of resources because they’ll just photosynthesise or transduce faeces. So, even if it turns out that aggression is a necessary component of intelligence, it just doesn’t make sense that they’ll want to wipe us off the face of the earth any more than we’re determined to wipe bacteria off the face of the earth.

Unfortunately though, that’s a somewhat spurious parallel. We aren’t in competition with bacteria – in fact, we depend on them. In contrast, there’s less reason to suppose that intelligent machines will depend on us. In that case, the better parallel might be the relationship we have with chimpanzees. That would be more cause for concern.

So the best case scenario would be mutual dependence and integration with the machines. It’s no coincidence that trading partners rarely go to war with one another. However, we’re going to have to find something we’re better than machines at in order to have something to trade. Perhaps we could elevate captchas to a form of poetry?