I’ve been meaning to learn the game of “Go” (weiqi) for quite a while now, and finally got around to it now that there are some people at Harvard at beginner’s levels to play with.

The usual adage is that the rules of “Go” are simple, but the strategies are difficult. Sometimes they cite the fact that computers can’t play “Go” very well (compared to, say, chess). I’m not in a position to defend or refute this sentiment yet, but I think this stuff is easier than it first appeared to me years ago, if you think about it the right way. “Go” seems a very mathematical game, almost a counting and topological reasoning game. And all the difficulties arise out of the fact that the counting and reasoning is done in 2D (rather than 1D, which would be simple).

So there is this list of terminology like “liberties,” “eyes,” “false eyes,” “alive and dead regions,” etc., but sometimes they greatly confuse the matter, so I found it easier to recast these basic concepts in more general terms.

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There is some rumbling (here, here) regarding the formation of another tech bubble, this time riding on the so-called “Web 2.0,” i.e. social media. To that, I’ll attest that over the past year, there has been a steady stream of stealth startups of this sort arriving seemingly out of nowhere and hiring all comers.

What to make of this? Is social media any more viable than e-commerce of the first dot-com bubble?
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A prank call transcript from ZUG.

What happens when you ask a credit card company the same security questions they ask you?

VISA: …And for security, I just need your mother’s maiden name?

JOHN HARGRAVE: [I tell him] And Barry, for security purposes, I also need your mother’s maiden name.

VISA: Uh … my mother’s maiden name, sir?

JH: Uh-huh.

VISA: OK. Uh … please hold for a moment, sir.

[Hold time of 3:54]

VISA: Yes, thank you for holding. This is cardmember services, my name is Isabelle. May I have your 15-digit card number, please?

JH: Sure. [I give it to her] What happened to Barry? I was just on the phone with him and then there was a very long pause.

VISA: OK, for some reason you got transferred to the fraud department. I’m going to have to transfer you back to customer service.

JH: The fraud department?

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There was this talk (by M. Dahleh) on modeling whether distributed learning occurs in a social network, i.e., is the crowd always right? The problem model was like this: there is a “truth” which is either 0 or 1, representing some binary preference. Then in a connected graph representing a learning network, each node makes a binary decision (0 or 1 again) based on an independent noisy read on the “truth,” as well as the decisions made by some or all of its neighbors who have already made a decision. (Each nodal decision is made once and binding, so there is a predefined decision-making order among the nodes.)

This is an interesting question because at first thought, one would think that in a large enough network, a sufficient number of independent reads on the truth will occur in the aggregate to allow at least the later-deciding nodes to get a really good estimate of the truth. This is the basis of the folk belief in “wisdom of the crowd.” However, this is not what happens all the time.

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Consumer broadband modems and routers typically cannot deal with a large number of connections from multiple users, and the cheap firmware has no settings to restrict the bandwidth of each user. This is when you resort to the physical layer to help you out.

Solution: Restrict wifi to the really slow 802.11b, maybe even slower, and let the physical layer radio contention fake the effects of fair bandwidth control. Works like a charm, no more dropped connections.

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So being at the event captured in the image, I got to ask a question toward the end. Actually I asked two questions. The first was whether Watson would ring in and use the remaining 3 seconds or whatever to continue to compute. Gondek said it would if it helped. In actual competition it doesn’t appear to be the case, as the buzz-in thresholding condition ensured that further computation would not have been helpful. The second question was a follow-up on the identified weakness of Watson — learning on “common sense” knowledge. I asked what path AI research would take to tackle such knowledge, which are by its very definition, “not in the books.” Gondek said that IBM is building up semantic information (e.g. a “report” is something that can be “turned in” and “assessed,” etc.) from corpus. That wasn’t exactly what I was asking, however.

My point was whether all “knowledge” is written down. There is such a thing as experiential “knowledge,” and humans take years to learn it/be trained in it through parenting (i.e., to “mature”). If only there were a handbook on life, or that life could be learned through reading a series of textbooks, then perhaps I’d believe that the kind of general-purpose AI that most people are probably imagining (rather than an expert/Q&A system) can be achieved along the lines of current methods.

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Walking around campus these days, there are cryptic-looking things like

\(\binom{n}{2}\mathrm{.com}\) and \(\binom{n}{2} \ni \binom{i}{u}\)

obviously referring to a dating site — currently it’s restricted to MIT and Harvard students. This one tries on an idea that I’ve heard discussed numerous times in different contexts, but apparently nobody went and did it in all these years. Instead of running a matching algorithm, it asks third parties (i.e. matchmakers) as well as the interested parties themselves to suggest matches. The thing that is supposed to keep this low-risk is anonymity: a match isn’t revealed until the two primary parties involved mutually accept or their lists intersect.

As with all things that involve anonymity, this asks for trollish and antisocial behavior. I’ve already registered three aliases on moira for exactly this purpose — ok, ok, so they’ve suppressed that antic after people raised concerns, though these and other ramifications should have perhaps been worked through a bit more carefully pre-launch.

The spam potential remains. A matchmaker’s identity isn’t revealed unless both people accept her suggestion, so pranks and insults can be conducted to an extent. One way around this may be grafting social graph data onto the system for collaborative filtering (if they manage to obtain such data…). And if they do, perhaps the suggestions of more closely related people should weigh more, along with those of successful matchmakers. Perhaps there should even be more weight if multiple matchmakers concur. This is extremely intriguing, because eliminating spam is equivalent to predicting who is a likely match, and collaborative filtering for this problem is an unexplored direction.

The more fundamental question is why such a site is even necessary.
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This design is pretty good, though it would probably break prematurely. Stuff that needs to be plugged in and out repeatedly maybe should not have moving parts. More to the point, why was USB specified to be one-sided in the first place? There are no two rows of pins like in some connectors, so there is really no problem with a receptacle having contacts on two sides.

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After some recent bouts of navel-gazing, it occurred to me that I had no idea what was on the other side of the navel. The umbilical cord couldn’t have been spilling nutrients into the abdominal cavity — so there must be something connected on the other side!

Turns out there is.
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After last week’s snow, this appeared near the new Media Lab building (Amherst Street near Ames Street):

“CAL (butt-print) MIT”

I think it’s a sentence…

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