Stupefication and the Epistemological Assumption

Dreyfus attacks several of the foundational presuppositions of AI in his book What Computers Can’t Do.

1) The Biological Assumption — That we act, on the biological level, according to formal rules, i.e., that our brain is a digital computer and our minds are analogous to software.

2) The Psychological Assumption — That, regardless of whether our brains are digital computers, our minds function by performing calculations, i.e., by running algorithms.

3) The Epistemological Assumption — That regardless of whether our brains are digital computers or whether our minds run algorithms, the things our minds do can be described according to formal rules (and hence, by algorithms.) This is, naturally, a weaker assumption, yet one required by the idea of stupefication.

4) The Ontological Assumption — That “the world can be exhaustively analysed in terms of context free data or atomic facts” (205).

The epistemological assumption is the one that we ought to be concerned with at the moment, as evidenced by this quotation on the matter:

“[The question] is not, as Turing seemed to think, merely a question of whether there are rules governing what we should do, which can be legitimately ignored. It is a question of whether there can be rules even describing what speakers in fact do” (203).

In light of the previous post on descriptive rules, we can posit that stupefication requires a kind of epistemological assumption: that mental tasks like that of playing chess and (perhaps) of communicating in a natural language can be described by formal rules, even if those formal rules have nothing to do with what we happen to be doing while performing that task.

In Dreyfus’s book, he undermines the epistemological assumption (along with the three other assumptions) by showing that they cannot be held in all cases and with regard to all human activities. However, I don’t think this is necessarily very crippling. Even if there can be no comprehensive set of rules or formal systems that fully describe all intelligent human behaviour, AI is hardly done for. The questions merely change from ones like “Can we make a formal system that fully describes task X?” to “How close can we get to describing task X in a formal system?” And this may well put us back in Turing’s court, where the benchmark is how many people the formal system can fool.

In other words, I’m questioning the validity of this proposition:

“[The] assumption that the world can be exhaustively analyzed in terms of context free data or atomic facts is the deepest assumption underlying work in AI and the whole philosophical tradition” (Dreyfus, 205).

Dreyfus is probably right in terms of the majority of the research that has been done in AI over the past century. But this ontological assumption need not be the “deepest assumption” underlying projects that seek to stupefy. For in stupefication, one takes up the mantel of the epistemological assumption while relegating the ontological assumption to a hypothesis that must be empirically verified, not necessarily assumed — and certainly not assumed “exhaustively,” as Dreyfus suggests.

Citations:

Dreyfus, Hubert L. What Computers Can’t Do. New York : Harper and Row, 1979

Descriptive (Not Prescriptive) Rules

“Consider the planets. They are not solving differential equations as they swing around the sun. They are not following any rules at all; but their behavior is nonetheless lawful, and to understand their behavior we find a formalism — in this case — differential equations — which expresses their behavior according to a rule” (Dreyfus, 189).

In other words, rules are descriptive, not prescriptive. Given the proper descriptive rules, computer scientists and mathematicians can model the movements of the planets, even though the planets never do any mathematical calculations. In a similar way, given the proper descriptive rules, computer scientists might be able to model the movements of a human chess player and (perhaps) the “movements” of a human interlocutor. But the planets, the chess player, and the interlocutor need not have anything whatsoever to do with the formal systems that describe them. This is the fallacy that stupefication helps us skirt and which traditional GOFAI often fails to skirt.

Thus, the kind of language games mentioned at the end of my previous post, and which we’ll talk about later, need not be games that human beings play and need not be governed by rules that govern human linguistic practices.

Citations:

Dreyfus, Hubert L.  What Computers Can’t Do. New York : Harper and Row, 1979.

Games and the Problem of Relevance

Hubert Dreyfus wrote his book What Computers Can’t Do long before Luciano Floridi came onto the scene. Yet the following point seems specifically constructed to shed light on the problem of relevance (mentioned in this post):

“As long as the domain in question can be treated as a game, i.e., as long as what is relevant is fixed, and the possibly relevant factors can be defined in terms of context-free primitives, then computers can do well in the domain” (Dreyfus, 27).

Dreyfus doesn’t expound upon exactly what kinds of games he has in mind; but I think it’s safe to say that he isn’t talking about all games. After all, there are certainly games like soccer (which is analogue) and nomic (which is unstable) that would foil a computer readily.

But there are certain games with qualities that make them ideal domains for attack by projects in artificial intelligence. Chess is one of these games. Let us try to itemize the qualities that make such games so conducive to formalization:

1) Such games consist of states.

2) Such games have rules that govern changes in state.

3) Such games are stable, i.e., the rules either stay constant or change only in correspondence with other rules that do stay constant.

4) Such games are transparent, i.e., the rules can be known because they are simple enough to understand.

5) Such games have a bounded set of rules, i.e., the rules can be itemized because they are finite in number.

6) Such games have a bounded set of states, i.e., the number of possible game states is finite, even if astronomical.

7) Such games have winning conditions that can be assessed from within the system itself, i.e., there are rules that can designate some states as won and others as lost. (Note: we can weaken this condition to include games that cannot be won or lost; but there must still exist rules that designate some states as better than others or worse than others, in order for such games to be conducive to productive computational analysis.)

To wrap all of this into a tidy package: such games (considered to be a collection of states, transition rules, and evaluation rules) must be representable as a finite state machine. If so, then they can be represented syntactically. And algorithms can be written for their governance.

Bear in mind, however, that this is a necessary condition, not a sufficient one. The above criteria merely distinguish games that can be formalized from ones that can’t. But within the set of games that can be formalized, there can (and most likely do) exists games with such complex states or such complex transition rules that they are computationally intractable. So we must add another necessary condition:

8) Such games must be tractable, i.e., not only must they have a finite number of states, transition rules, and evaluation rules; these states and rules must be few enough and simple enough to effectively compute the state-to-state transitions required for playing the game.

But even this addition doesn’t guarantee that the game will be a domain in which artificial intelligence projects can thrive. Formalization and tractability don’t imply that an artificially intelligent system (or its creators) will be capable of applying heuristics and/or strategic rules necessary for a high level of play.

Nonetheless, considering Deep Blue’s success in the face of so much skepticism, a little optimism might be in order if the above conditions happen to be met.

In closing, my food-for-thought question of the day is, “Can linguistic domains be transformed into games that meet the above criteria?” I think we’ll visit Wittgenstein soon. He has quite a bit to say about language games.

Citations:

Dreyfus, Hubert L. What Computers Can’t Do. New York : Harper and Row, 1979.

The Problem of Relevance

Let me start by giving a quotation we’ve dealt with before:

“[For an AI project to be successful, it is necessary that] all relevant knowledge/understanding that is presupposed and required by the successful performance of the intelligent task, can be discovered, circumscribed, analyzed, formally structured and hence made fully manageable through computable processes” (Floridi, 146.)

Floridi’s use of the word “relevant” is suspect here. He gives us no indication of whether he means “that which is relevant to us when performing a task X” or “that which is relevant to a computer when performing the stupefied version of task X.” Considering that Floridi advocates a non-mimetic approach to artificial intelligence, I think we should assume that he means the latter.

But this leaves the would-be creators of an artificially intelligent system in a pickle:

Q: How do we stupefy task X?

A: You need to make a new task or series of tasks which require less intelligence overall.

Q: Ah. How do we do that?

A: Luciano Floridi might suggest (given the above quotation) that you need to first discover, circumscribe, analyze, and formally structure all the knowledge relevant to the stupefied task.

Q: But how do I know what’s relevant before the stupefied task even exists?

In other words, I know what’s relevant to me when, say, playing chess. But I also know that the stupefied task of chess-playing bears little resemblance to the task I perform when playing chess. So I can conclude that the things relevant to the stupefied task of chess-playing might be things that aren’t at all relevant to me when playing chess.

Here’s a more formal statement of the problem of relevance:

How can we discover rules for governing a formal system that does X if we don’t yet know how the formal system will do X?

I don’t have an answer except to say that this problem reveals that the task of stupefication — in addition to being one that can require tremendous human intelligence — is also one that can require a great deal of human creativity. I think you have to say, “Hmmm. Maybe a chess-playing machine could benefit from a complicated formula for weighing the comparative values of a chess position’s material, structural, and temporal imbalances.” Human beings don’t use such a strict formula, so it’s simply an educated guess that a computer could effectively put one into practice. The reason the guess is educated, though, is that the following mental maneuver gets performed: “If a human being could evaluate a position based on a complicated formula, precisely and accurately calculated, she might play a better game of chess.” This hypothetical reasoning is where the creative act resides.

Of course, some such guesses are more educated than others. For example, it makes sense to assume that a computer could benefit (at least in the opening phase of a chess game) from the ability to reference the moves of a few thousand grandmaster games and to copy those moves in its own games. This assumption makes sense because a human too would benefit from such grandmaster assistance — which is why this activity is generally frowned upon by chess tournament administrators.

Unfortunately, the problem of relevance (and the creativity required to surmount it) can suddenly become critical when the domain of the problem is such that we cannot reason by analogy, cannot consider that which is relevant to us and hypothesize that something similar might be relevant to a formal system. For example, when trying to stupefy linguistic tasks instead of chess-playing tasks, we are immediately confronted by the problem that we don’t even know what’s relevant to us when we speak – let alone what might be relevant to a formal system which functions nothing like us. Why do we say what we say? How do we know what we say is correct? How do we understand each other? These are questions that have been attacked in countless ways by philosophers for over two-thousand years.

And the upshot of the problem of relevance is that, even if some lucky philosopher happened to have gotten it right, happened to have discovered the essence of language and how we use it, that answer may or may not have anything to do with how we might go about creating a formal system that mimics human linguistic practices.

Citations:

Floridi, Luciano. Philosophy and Computing: An Introduction. London and New York: Routledge, 1999.

Primary Data, Metadata, and the Topology of the Internet

I’ve used the terms “primary data” and “metadata” a few times, and I’m likely to continue doing so. Here are Floridi’s definitions:

Primary data – “These are what we ordinarily mean by, and perceive as, the principal data stored in the [database system], e.g. a simple array of numbers, or the contents of books in a library. They are the data an information management system is generally designed to convey to the user in the first place” (108).

Metadata – “These data are secondary indications about the nature of the primary data. They enable the [database management system] to fulfil its tasks by describing essential properties of the primary data, e.g. location, format, updating, availability, copyright restrictions, etc. In our example, they could be library records, or the page of an Internet search engine” (108).

Also relevant is the following claim: “Primary data need metadata in order to be manageable” (112).

Metadata is what makes a relational database “relational.” It serves to link information A with information B (by providing information C, which often says something as simple as “A and B are related.”) As such, we are all very familiar with metadata (even if we don’t use the term “metadata.”) Relational databases make heavy use of metadata to manages the messages in your gmail account, the blog posts on this and similar webpages, and the comments attached to various blog posts. Indeed, the fact those comments are “attached” is maintained by metadata that stipulates an attachment relation between a comment and a post.

And of course, the internet itself is filled with primary data that wouldn’t be navigable without metadata. It The blue links you follow in your web browser are, of course, implicit statements of relation between the link-text and another location on the internet.

Notice, incidentally, that when we talk about a system rich with metadata (like the internet), a topological way of speaking creeps into our voices. We begin using words like “location” and “navigate.”

Floridi sees this clearly:

The space of reason and meaning — including the narrative and symbolic space of human memory — is now externalised in the hypertextual infosphere, and this brings about four more consequences concerning the rhetoric of spatiality. (1) A linear narrative, which is necessarily associated with time, makes room for a multi-linear narrative that is naturally associated with space. In the past, writers constructed their narrative space virtually within the mind of the reader. Now writer and reader live in a common infosphere and the former no longer needs to weave the narrative, diachronically, within the mind of the latter, as an ongoing textual Web, since all signifieds can co-exist synchronically outside, in the public and intersubjective environment represented by the hypertextual infosphere. (2) In this public domain, writing and reading become spatial gestures, and if time still plays a role, this is only as far as the fictional time of narrative is replaced by the real time of information transmission and retrieval. (3) Consequently, a whole new vocabulary develops, one based on extensional concepts borrowed from the various sciences of space: cartography, geography, topology, architecture, set theory, geology and so forth. (4) It follows that logic, broadly understood as the science of timelessness, hence as intrinsically a topo-logy, tends to displace history, broadly understood as the science of the timed, i.e. a chrono-logy, and we have seen in the previous chapter how this radical topologisation of the infosphere may unfortunately lead to the paradox of a forgetful memory (130).

Citations:

Floridi, Luciano. Philosophy and Computing: An Introduction. London and New York: Routledge, 1999.

Databases

I’ve pasted below yet another wonderful insight by Floridi. The quotation should be understood in the context of a discussion regarding the historical emergence of knowledge as a cumulative, social, and intersubjective substance — capable of continual growth due to its ability to be recorded, passed on, and synthesized. The following is commentary about the state of that ever growing body of knowledge prior to the advent of the database system (and, of course, prior to Wikipedia.)

“New knowledge could obviously be found; centuries of successful accumulation prove it unequivocally. Yet the new world represented by the human encyclopaedia had become as uncontrollable and impenetrable as the natural one, and a more sophisticated version of Meno’s paradox could now be formulated. How can a single scholar or scientist find the relevant information he requires for his own work? Moreover, what about ordinary people in their everyday lives? Meno could indeed ask: “how will you find, Socrates, what you know is already in the ever growing human encyclopaedia? Where can you find the path through the region of the known? And if you find what you are searching for, what will save you from the petrifying experience of das historische Wissen?” (95)

(Das historische Wissen is a phrase coined by Nietszche in response to Goethe’s statement that, “If I had actually been aware of all the [literary] efforts made in the past centuries, I would never have written a line, but would have done something else” [93.])

The following quotations suggest to me that Floridi believes the prevalence of database systems to have negated the pre-database state in which “the new world represented by the human encyclopaedia had become as uncontrollable and impenetrable as the natural one.” He suggests (correctly, to be sure) that “we all let our computers search, at fantastic speeds, for the required needles in those huge, well-ordered, electronic haystacks that are our databases” (97). And he says, “the growth of knowledge has followed the path of fragmentation of the infosphere and has been held in check by the new version of Meno’s paradox until the second half of the twentieth century, when information technology has finally provided a new physical form for our intellectual environment and hence the medium and the tools to manage it in a thoroughly new way, more economically and efficiently and in a less piecemeal way” (97; my emphasis).

I, however, am reluctant to say, simply because Google and Wikipedia now facilitate the quick retrieval of information, that today’s hyper complex and constantly shifting storehouses of knowledge are any less uncontrollable and impenetrable than they have been historically. Complex information management systems have certainly changed the way information is managed; but that doesn’t necessarily mean that our information storehouses have become more manageable. In other words, the fact that we have revved up the speed at which we can access information (and the speed at which we can change it, tag it, categorize it, relate it, synthesize it, use it, cite it, and undermine it) may have actually increased the complexity of our knowledge aggregations. Let me try my hand at a new, post-second-half-of-the-twentieth-century version of Meno’s paradox:

He might ask, “How can you find, Socrates, the knowledge you seek, given that if you find it, you cannot guarantee that it will remain in the same form, and in the same context, and related to the same metadata in which you found it? How can you choose a path through the region of the known, given that there are now billions of paths to choose from?” Indeed, Floridi is probably correct that, at one time, there was simply so much knowledge (codified in so many books) that the “path through the region of the known” was hard to find; but in postmodern times, we have a related problem: lightning fast search engines and effective database management systems have given us a surplus of paths through the known. Instead of drowning in books, we drown in hyperlinks. Instead of losing ourselves in the land of primary data, we find ourselves lost in the land of metadata. In postmodernity, the internet provides a thoroughly linked and navigable map of things that would, historically, have been housed in countless physical libraries. But the map happens to be just as complicated as the territory it is supposed to help us navigate, and any sufficiently complicated map begins to alter the nature of the territory itself. Borges gives a wonderful example of this in the following short story:

On Exactitude in Science

. . . In that Empire, the Art of Cartography attained such Perfection that the map of a single Province occupied the entirety of a City, and the map of the Empire, the entirety of a Province. In time, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Empire whose size was that of the Empire, and which coincided point for point with it. The following Generations, who were not so fond of the Study of Cartography as their Forebears had been, saw that that vast Map was Useless, and not without some Pitilessness was it, that they delivered it up to the Inclemencies of Sun and Winters. In the Deserts of the West, still today, there are Tattered Ruins of that Map, inhabited by Animals and Beggars; in all the Land there is no other Relic of the Disciplines of Geography.

In a similar way, the landscape of the internet now lies atop a vast sea of knowledge, spanning it so completely that we can scarcely interact with discrete, linear information anymore. Information always comes with its complex arrays of relations, fibers connecting it to countless other pieces of information. Information now has place. Where did you find it on the internet? What other quasi-discrete information surrounds it? In other words, the internet (viewed as a map) doesn’t just indicate place, it assigns place and even creates place-ness for our accumulated stores of human knowledge.

Such is the world of the aptly named “relational database.”

Citations:

Floridi, Luciano. Philosophy and Computing: An Introduction. London and New York: Routledge, 1999.

Notes on Floridi’s Five

Luciano Floridi gives five “crucial conditions that make AI projects more or less successful” (146).

1) Effective-computability (see Chapter 2);

2) Epistemic-independence, i.e. whether no knowledge/understanding is relevant, or alternatively whether all relevant knowledge/understanding that is presupposed and required by the successful performance of the intelligent task, can be discovered, circumscribed, analyzed, formally structured and hence made fully manageable through computable processes.

3) Experience-independence, i.e. whether the task is based on universal and “timeless” instructions carried out by the system, or alternatively whether all practical experience, both relevant as a background condition and necessary for the successful performance of the intelligent task can be discovered, circumscribed, analyzed, formally structured and hence made fully manageable through computable processes.

4) Body-independence, i.e. whether the intelligent task can be performed by a disembodied, stand-alone intelligence, or alternatively whether all “perceptual intelligence”, both relevant as a background condition and necessary for the successful performance of the intelligent task can be discovered, circumscribed, analyzed, formally structured and hence made fully manageable through computable processes.

5) Context-freedom, i.e. whether the context is irrelevant, or alternatively whether all relevant information concerning the context within which an intelligent task is performed, and which indeed make the task intelligent, can be discovered, circumscribed, analyzed, formally structured and hence made fully manageable through computable processes.

Citations:

Floridi, Luciano. Philosophy and Computing: An Introduction. London and New York: Routledge, 1999.