Wikipedia says of classical dialectic that the point is to generate either a refutation of one viewpoint or a synthesis — to reach a better conclusion. But it doesn’t say what form the better conclusion is in. Similarly, it says of Hegelian dialectic that the point is to reach a synthesis by combining the common points of a thesis and an antithesis.

These models of truth-finding appear to be rather limited. Besides the fact that in some sense they are specialized to dual or opposing viewpoints numbering two (or even if we extend it, a finite number), they are restricted to finding truth only in the intersection or union or some other simple-minded method of synthesis. I argue for a more general way to model truth-finding. This is inspired by engineering, as usual.

The truth in this case is like an object that lives in some high dimensional space. The viewpoints, any number of them, are projections of the truth onto different subspaces. Some subspaces may be larger than others even. The point is each “view” will see something different out of the same object.

Truth-finding is to combine the viewpoints in the sense of inverting the projections, so as to recover the original object. This is not a simple or even unique process, but there is probably a most parsimonious way to do it a la estimation. But it has the benefit of giving not only the complex and rich object in its original form or some approximation of it, but also the capability to support projection to an infinite number of other subspaces or viewpoints not present in the original argument. This is what dialectics cannot provide.

As you can see it carries on just fine and can fool a casual observer. But the longer you carry on a conversation with it the more apparent that Cleverbot is frustrating to talk to, not so much that it isn’t human — after all, all of the responses are taken from human sources. If it weren’t so good at emulating a human from which you expected more, you wouldn’t be frustrated.

Cleverbot is frustrating in two ways:

• it isn’t interesting
• it doesn’t make sense

In other words, it lacks deepness, like a shallow human. Why?

Some say that Cleverbot isn’t an AI because it’s just humans talking to humans via the intermediary of a selection algorithm. One is tempted to be dismissive like this but it’s too convenient. It places too much weight on the role of the corpus and too little on that of learning and interaction. Cleverbot clearly captured some aspect of the human intellectual process that raised it to the level of a shallow conversation partner. What it neglected though will be enlightening to the question of what is deepness.

The most obvious way in which Cleverbot is shallow is in novelty. This is a result of mimicry of others. Mimicry is of course fundamentally human because we learn by mimicking. It doesn’t preclude novelty of some sort. In fact, although it isn’t clear at what level Cleverbot replicates from the corpus, whether in phrases or entire sentences, we are forced to accept that some novelty is injected into the selection process, as responses don’t come out the same every time. However it’s not enough to make Cleverbot consistently interesting, probably because Cleverbot doesn’t synthesize new ideas, merely passing on received wisdoms at the appropriate moments. It seems deep novelty requires more atomicity, and more computation.

The more subtle way in which Cleverbot is shallow is in contextual memory. Cleverbot, probably also for computational reasons, just makes remarks in response to the most recent context. As a result, it changes topics frequently, and doesn’t pursue a line of inquiry to its satisfactory conclusion because it doesn’t know what a line of inquiry is. It can be described even as evasive or flighty. While each response usually makes sense, Cleverbot doesn’t make sense over the span of a conversation, which requires more persistence.

It seems deepness is related to computational resources. And if we believe more intelligent machines — those that exhibit deepness and frustrate us less — to be more “human,” then we must expect a high level of computational competence as a human trait. Deep conversation is a measure of that. Disturbingly, the kind of conversation that Cleverbot can make is rather the more commonplace case among humans, yet it borders on meaningless drivel because it produces nothing new. Even more disturbingly, this is despite that Cleverbot can be quite clever sometimes, being stimulated to produce some of its store of wisecracks after elicitation by a motivated human counterpart who steered it to them. It shows that it doesn’t take much to carry on one half of a conversation, and deceptively so especially if one of the two is playing the role of an intelligent “human.”

Maybe being “human” isn’t that hard. Maybe the binary identification of machine/human should be considered a necessary quantification artifact, and the Turing test really measures a gradation of deepness instead (it is a probabilistic test after all, and of course subjective). If so, let’s run the Turing test on people in a double-blind way.

Eduardo … I’m talking about taking the entire social experience of college and putting it online.

When the movie “The Social Network” came out, this line caught my attention. I’m not sure this thesis — let’s call it the “replication thesis” — was what Monsieur Zuckerberg had in mind rather than something the screenwriter came up with, but it makes sense as to what actually undergirds online social platforms of today.

In all likelihood, Zuckerberg did not at first intend Facebook to be more than its namesake — a dorm facebook. Just as, in all likelihood, Twitter was meant as no more than a status message broadcast system, at first. The fact that Facebook became something of a gathering place and Twitter became a “microblogging” service — in essence, taking over functions that used to be conducted in other ways — I think owed something to their use of a “correct” user graph for certain contexts. It was the user graph that allowed, then limited, the range of social functions that people were willing to port over to the online platform. With the undirected graph, Facebook (and clones) modeled something like a big gathering, maybe a party. With the directed graph, Twitter (and clones) modeled something a bit more nuanced, like a groupie-celebrity relationship. (Is it any surprise, then, that celebrities drove the latter’s popularity?)

But I get the sense that neither Facebook nor Twitter truly believes in the replication thesis. They’ve construed their challenge narrowly as one of periodically pushing out new “things you could do,” most of which are nowadays ignored by users, or adding more places at which you could interact, but in the same way. They don’t see that users voluntarily do on a platform only those things that are compatible with their perception of the modeled social space. You can’t push anything on them any more than you can force people to play some game at a party. Yet I see no movement to revisit the user graph and better model real social relationships with all of their complexities. If left unchanged, the inevitable result will be that the range of social functions these platforms support stagnates, and therein should lie their eventual downfall. In fact, that probably solves the supposed “mystery” of Myspace’s decline, too. It is in this context that Google+ arrives.

Why has Google+ “succeeded” in a way that Orkut and Buzz had not? For one, Google+ posits an even more complex user graph in the form of circles. It’s a colored directed graph, not only directed like Twitter, but each user’s edges to other users are colored by the circles these neighbors belong to. Instantly, the range of social functions is expanded. We don’t have to wait for new applications to take advantage of this to make the realization: imagined possibilities are sufficient. It is like going to a new space. You leave the party, and the groupie session, and come to this space that is novel in a way that is defined not by its location or the people in it, but by its different arrangement.

By accident, Google is also a provider of several useful web services. This is an enviable position from which to make suggestive proposals on new interactions that people can have on this particular user graph. And unlike on Facebook or Twitter, some of Google’s more productive collaborative applications may actually require the Google+ user graph. The former two platforms are already shackled by their user graphs to “unserious” applications that are not coincidentally popular there. However, while Google+ will have a while to go before it exhausts these unrealized possibilities, it isn’t immune to the same questions of what is a “correct” user graph model or, what “correctly” replicates the “entire social experience”. Colored directed graph models social relationships in greater detail but is this model complete, or fundamentally right?

I recall reading Zuckerberg’s recent comment in which he quipped (about Google+) that it would be too much trouble to keep track of many circles, and that Facebook Groups is more transparent in that everybody knows who else is in the group (again, like at a party). I think he’s on to something even if his own model isn’t really moving forward. The fact that you can put people into circles gives you a false sense of privacy, because there will always be common connections and information leak. For example, you can go to a particular circle’s stream page and read only the stuff shared by people in that circle. But when you comment on something there, unlike being in a cloistered room which this metaphor would have you believe, you’re actually talking to all the people whom the original author has decided to include into the conversation. You see them but you don’t really see them. It jars the mind a little bit. Will you really spend mental energy to keep track of where your comments will be seen? Not likely. So you either decide to ignore this “leak” with even more embarrassment potential than on Facebook, or you decide there is no privacy despite circles and revert, in behavioral terms, back to the Facebook model. Something is amiss.

That thing is the fact that there is no concept of “spacetime” online like in real life. Everything happens at one time and in one place despite efforts to counter it (RIAA discovered this). What you write can be read ten years later, and it can be transmitted everywhere, too. It has your identity attached to it as if you did it right then and there to all the people you have no control over.

Let’s reconsider social relationships. Circles is an attempt to model the different groups in which a person moves. These groups may be “socially independent” as Google researchers have found, but they are not disjoint. If they were or if they were nearly so, Google+ would have fully modeled the situation, so a “Colleague” circle and a “Family” circle may indeed be easy to keep track of (unless your coworker is also your brother). But once there are more than about 2-3 circles, things surely muddle up. So why do people feel the need to separate their communication to different groups in real life? Maybe it is less so about who are in a group, and more about the identity that the person wants to project in a group. The need for circles isn’t really one for categorizing other people, as much as one for categorizing your own identities. It is this need for compartmentalizing identities (code for “hiding” certain parts of a composite identity) that drives the need for privacy across group boundaries.

In my mind, a more powerful and frank model of social relationships recognizes this. It is a classic one that a generation of online users have already grown accustomed to. You shouldn’t have multiple circles for one identity, in the false hopes that they be disjoint. It defeats the whole purpose. You should have multiple disjoint identities, but easily accessible under one account. Maybe you have a “model child” identity to your family, an “industrious worker” identity to your colleagues, and I don’t know a “troll” (er, I mean “free-speech protected”) identity for your political rants. This probably requires complex per-item ACL, or simply pseudonymous identities. Some people cringe at this, but I don’t see why. Pseudonymous identities can be every bit as authentic as real ones. (Google+ has already run into a dilemma on this issue.) Moreover, each identity actually has to be accepted into your chosen cliques, so behavioral norms are already enforced intra-clique; identity labels are largely inconsequential. I think the replication thesis demands this since there is no spacetime disjointness online as exists in real life, so we can only go for identity disjointness. There are of course those (including Zuckerberg) who believe in radical unprivacy, and that is a utopian ideal, but for those opposed to it, Google+ unfortunately didn’t solve the problem.

All masculine and singular pronouns shall include the feminine, neuter and plural thereof, and vice-versa, wherever the sense of the language so requires.

Somehow the ability to turn handwritten math into MathML escaped my attention as a Windows 7 feature from trying the first beta. Finally! I’ve been waiting for this since forever… Wonder what took so long.

Next up are music notation and graphics in general*. The ultimate goal of a handwriting recognizer is of course similar to that of OCR: to turn one piece of art (for the lack of a better word) rendering to another, text included. Specifically, it should rectify all the rendering to a “typeset” form. It should intelligently recognize a host of objects with its own Visio-like templates: if I draw a resistor, it should pull out a nice schematic rendering of a resistor. If I draw a rectangle, and select “rectify”, it should make a rectangle with straight edges.

It is in some sense harder than OCR: the handwriting can vary a lot; but in other sense easier: the user is by definition actively interacting with the process. Combined with a rational input device (not a mouse, not a pen that is but a mouse emulator**), this can become the realistic next-generation human-computer interface, and tablet will take off as one of the next-generation form factors (as it almost inevitably must take off, probably in a merged tablet/ebook form). I’m not too sure what human-computer interface people are doing these days, but last I checked they were working on fanciful stuff divorced from an actual use. If it were up to me to decide, I’d explore the limits of the pen input device first, simple things often yield greater and surprising results.

* Now this is something I’ve been ranting about for years as one of these “obvious” advances that should have happened but haven’t, but it is also possible that technology has caught up enough to implement these things. Certainly the math input idea isn’t new at all. Somebody wrote an MEng thesis on it 10 years ago and I’m sure he wasn’t the first one. The general input problem is more interesting though.

** A useful pen device would forsake the god-awful mouse model, and move along the lines of some pens nowadays: with a digital eraser, with pressure sensing (these two already exist), but also with a scroll wheel to select menus or choose options, with some feedback either mechanical or like a small display. The latter can be useful, for instance, to show the color of the pen or some other such property. There is no reason why a pen cannot be as useful — indeed, more useful — than a mouse (which has become an uncomfortable and unnatural inner glove). The pen form factor has been tried for thousands of years and its usefulness should not be doubted, disdain for archaism be damned.

This reminds me of my own near death experience. It was in the Bay Area near an airport in the middle of the night, I can’t remember where now. There was a very confusing road division where there were two left turn lanes, and one of them is somehow on top of some kind of surface rail (trolley) track running parallel to the lanes. I couldn’t see clearly at all. The traffic lights were not normal traffic lights, but some kind of symbol for each lane. Anyway, there were no dividers, fences, or barriers of any kind and I somehow ended up half on the tracks waiting for the light to turn green. Then in my rear view mirror, I see the bright lights of a train coming straight behind me. As the train comes ever closer, I get a feeling just like in a dream when something unreal is happening, a feeling of … “wait, this doesn’t make any sense”. So just like you might violently snap out of a dream in cold sweat, I stepped on the gas, ran the red, and moved into the next lane on autopilot, as the train whizzes by not more than a few feet beside me.

No horns, no noise, just a quiet train in the middle of the night. The whole thing still feels like sleepwalking to me and still doesn’t make any sense, but that would have been a terrible way to die.

Here’s some random guy’s page. He’s a big proponent of using a total market portfolio: http://homepage.mac.com/j.norstad/finance/total.html

His hand-waving is almost convincing, almost. Okay, “J. Norstad”, let’s take you point by point.

Many people feel that the strongest arguments for investing in total markets are based on market history. Innumerable studies have shown that individual investors and even professionals like institutional fund managers and mutual fund managers have terrible track records trying to beat the market. For example, many studies have shown that total-market index mutual funds consistently outperform most actively managed mutual funds.

It is easy to see why most actively managed mutual funds must fail to beat their passive total-market index fund counterparts. The performance of “the market” is nothing more or less than the capitalization-weighted average of the performance of all the investors in the market. If some active managers outperform, other active managers must underperform. For every winner (a performer above the average), there must be a loser (a performer below the average). This is a matter of simple arithmetic. Index funds match the market’s performance minus their low expenses. Actively managed funds have considerably higher expenses than index funds because they must pay for expensive research into individual securities and sectors, and they must pay extra costs for frequent trading of individual securities. As a consequence, the average performance of all actively managed mutual funds must trail the performance of total-market index mutual funds by the significant difference in their expenses. John Bogle has done many studies that confirm that this is exactly what happens in practice.

The underlined part is a good observation. But to believe this weighting is efficient is suspect. Moreover, this guy likes to believe in “arithmetic”, well, precisely because this is a capitalization weighted average, it is quite possible on “arithmetic” alone for an overwhelming majority of participants (by number) to be outperforming the market by large margins, if they be of small capitalization (not saying it is so).

Bogle’s numbers are just averages. Over any given time period some active managers do in fact manage to outperform the market even if most of them do not. Is this luck or skill? If it is skill, we would expect those active managers who have outperformed in the past to continue to outperform in the future…

A few active managers like Warren Buffett and Peter Lynch have astounding track records over long periods of time. How is it possible to claim they were just lucky? Perhaps their success was indeed due to skill. Then again, perhaps even they were just lucky. After all, if you have 1,000 chimpanzees flip coins ten times in a row, it is likely that one of them will get heads all ten times. We call the chimpanzee lucky. If the chimpanzee were a mutual fund manager we would call it talented. In any case, whether it is luck or skill, these few long-term success stories are in the past. How is an investor supposed to identify the next Buffett or Lynch in advance?

This is a strong argument here. I give props.

The Efficient Market Hypothesis states that modern financial markets are “efficient.” This means that they quickly react so that prices reflect all available information. For example, new information can often cause prices to rise or fall on the world’s major stock and bond exchanges to adjust appropriately within only minutes. Prices of individual securities, market sectors and style segments, and entire stock and bond markets are therefore always “correct” in the sense that they always reflect the collective beliefs of all investors taken together as a whole about their future prospects.

I take some issue with this “correctness” argument. This collective “belief” as reflected in the price is a capitalization weighted average. Participants naturally have different objectives and beliefs, but are only able to assert their beliefs in accordance with their share in the market, which are determined by their wealth and willingness to take risk, not by any notion of correctness or diligence.

One major consequence of the EMH is that unless an investor is just plain lucky, it is impossible to exploit the market to make an abnormal profit by using any information that the market already knows. Another consequence is that for someone without any such private information, it does not make any sense to talk about “undervalued” or “overvalued” individual securities, sectors, styles, or markets.

Another consequence of the EMH is that the total market is always perfectly diversified. Other portfolios that deviate from the total-market portfolio are never “more diversified” than the total-market portfolio. In other words, it is impossible for any other portfolio to have both a higher expected return and lower risk than the total-market portfolio. (The technical way to say this is that the total-market portfolio is always located on the “efficient frontier” in academic risk/return models. This is true in both the classical single-factor risk model of Markowitz and Sharpe and in the newer three-factor risk model of Fama and French.)

There are several serious problems with this efficiency claim. First of all, any claim of market efficiency is dependent on the existence of arbitrageurs making it so. Even if risk and return of every asset can be perfectly predicted, it is not true that the market operates efficiently. This is clearly seen in various portfolio scenarios wherein a point on the efficient frontier is not obtained by averaging the assets held in the two extemal efficient points associated with minimal and maximal risk. Again, if the market consisted of two such extremal investors (each perfectly rational), the average allocation still would not be operating at an efficient point, in the absence of arbitrageurs.

Second, it seems to me the kind of arbitraging needed to achieve another point on the efficient frontier would be to borrow an inefficient portfolio, liquidate it and use proceeds to purchase the efficient portfolio of the same risk, then liquidate that at a later date, and use the proceeds to repurchase and repay the inefficient portfolio, in turn keeping the excess return. However, it isn’t clear what this “later date” would be to ensure an excess return.

Third, the market allocation that happens to optimize the needs of all arbitrageurs (each with own risk/return appetite) according to their peculiar capital investment amounts in the market has nothing to do with being efficient as an overall portfolio. It may very well be that no arbitrageur is holding the overall market portfolio, and that because it isn’t efficient.

Fourth and a major complication, forward-going risk and return are not known quantities, and are merely assessed by the participants of the market on an individual basis. So returns and risks of particular assets can at best be modeled as random variables (each with a distribution). It might make some sense for a class of participants who impute the same future risk/return on the assets to drive some arbitraging process that allocates as in the previous paragraph; but then, the distribution of efficient allocations among arbitrageurs holding different estimates on risk/return would not average to the efficient allocation given by taking the market mean future risk/return assessment of each asset as the actual risk/return. Even worse, arbitrageurs have no real way of making a survey of risk/return assessment to find the mean and they do not actually operate this way.

The EMH does not require that investors behave rationally. This is a common misconception. When faced with new information, some investors may overreact and some may underreact. Indeed, this behavior is expected and common. Markets would not behave the way they do in the real world if everyone always reacted in the same perfectly rational way to new information. All that is required by the EMH is that these overreactions and underreactions be random enough and cancel each other out enough so that the net effect on market prices cannot be exploited to make an abnormal profit. Stated another way, irrationality is irrelevant as long as it is unpredictable and unexploitable. Even the entire market can behave irrationally for a long period of time and still be consistent with the EMH, again as long as this irrational behavior is not predictable or exploitable. Thus crashes, panics, bubbles, and depressions are all consistent with a belief that markets are efficient.

It isn’t clear what is being discussed here: behavioral irrationality or underlying uncertainty. Pricing uncertainty may be unexploitable on average if fluctuations are noisy (one can always use luck), reflecting the randomness of the arrival of information, but that isn’t a sign of investor irrationality. Crashes and depressions aren’t necessarily irrational, though bubbles and panics may be. This sounds like word games, but if a certain behavior is called “irrational”, it is certainly judged against some information that contradicts it, and if that be the case, then certainly the behavior is exploitable on average (perhaps over a long time).

Here are a few examples of efficient reasons to differ from the total market:

1. A young investor with a stable job, a good education, and a solid foundation in his trade or profession feels capable of being more aggressive than average and holds, for example, an 80/20 stock/bond portfolio.

2. A retired investor with no pension uses his or her investment portfolio to supply living expenses and feels less capable of taking on risk than average. This investor, for example, might hold a 40/60 stock/bond portfolio.

–snip 10 more bullets–

Notice how these examples are in pairs. In an efficient market, for every set of investors with good reasons to deviate from the average in one direction, there is some other set of investors with equally good reasons to deviate in the opposite direction. These investors are in effect using the markets to make trades of risks which benefit both sides of the trade. Many academic theorists say that this is in fact what efficient markets are all about.

This is an almost trivial observation. These allocation strategies don’t come “in pairs”. Maybe if one presupposes everyone does indeed start with the total market, then they can arrive at their desired holdings by executing a series of pairwise trades in individual assets amongst themselves, without affecting the total market.

In the case of equal-weighting countries, people who believe that markets are efficient counter by noticing once again that if the cap-weighted international market percentage for some country is, for example, 15%, and if some behavioral analyst says that the proper weighting is some other percentage, say 10% or 20%, then the question to ask the analyst is “The international market thinks the proper weighting for this country is 15%, but you disagree – what do you know about this country’s prospects that the international market does not already know?” To an efficient market believer, equal-weighting countries or regions in international investing makes no more sense than equal-weighting sectors, styles, or even individual stocks in US investing. None of these equal-weighting strategies have anything at all to recommend themselves if markets are efficient.

Besides all the reasons prior, there are additional reasons to believe international capitalization weighting is not a priori reasonable. For one, global capital flow is far from liquid. Capital is often country-bound and filtered through currency exchange barriers and currency risk, especially in the emerging market. Some people just don’t have the choice of investing in certain markets, not because they would not like to. Secondly, information flow about global markets is not efficient. Thirdly, the objective of the investor in one country is likely to be far different from the objective of those in other countries or the country in which the capital project is taking place.

It isn’t bad to start with the total market if one is forced to be able to do only a few rough modifications to it (in a limited funds situation). However, why bother with this belief in efficiency based on the wrong assumptions, when one can pick a few assets (or asset classes as reasonable) and run optimization to find an efficient portfolio for the risk to be taken? One may need to track efficiency over time. Why deviate from the market? Because the market isn’t run by one investor like you and it isn’t going to be efficient for you or for anybody in particular. Not being able to exploit inefficiencies within the market doesn’t mean holding the market as a portfolio is efficient — those are two separate issues.

In Chinese, there is this elementary passage which plays on the homophone issue of spoken Mandarin Chinese. Every character in the entire passage is pronounced “shi” (with varying tones), but nevertheless at this level the passage would make no sense if recited. But, written in the Classical Chinese idiom, the passage makes perfect sense when read visually (and isn’t particularly difficult to comprehend even for a modern reader).

《施氏食狮史》
石室诗士施氏，嗜狮，誓食十狮。施氏时时适市视狮。十时，适十狮适市。是时，适施氏适市。氏视是十狮，恃矢势，使是十狮逝世。氏拾是十狮尸，适石室。石室湿，氏使侍拭石室。石室拭，氏始试食是十狮。食时，始识是十狮，实十石狮尸。试释是事。

This is one demonstration that written Chinese can hold significant semantic content beyond phonetic value, a mechanism that has been key for tying together diverging spoken dialects over a thousand years.