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Archive for July, 2009

There’s a great deal of confusion about what is meant by the concept “computational knowledge.”

Stephen Wolfram put out a nice blog post on the question for computable knowledge.  In the beginning he loosely defines the concept:

So what do I mean by “computable knowledge”? There’s pure knowledge—in a sense just facts we know. And then there’s computable knowledge. Things we can work out—compute—somehow. Somehow we have to organize—systematize—knowledge to the point that we can build on it—compute from it. And we have to know methods and models for the world that let us do that computation.

Knowledge

Trying to define it any more rigorously than above is somewhat dubious.  Let’s dissect the concept a bit to see why.  Here we’ll discuss knowledge without getting too philosophical.  Knowledge is concepts we have found to be true and that we somewhat understand the context, use and function – facts, “laws” of nature, physical constants.  Just recording those facts without understanding context, use, and function would be pretty worthless – a bit like listening to a language you’ve never heard before.  It’s essentially just data.

In that frame of reference, not everything is “knowledge” much less computational knowledge.  How to define what is and isn’t knowledge… well, it’s contextual in many cases and gets into a far bigger discussion of epistemology and all that jive.  A good discussion to have, for sure, but will muddy this one.

Computation

What I suspect is more challenging for folks is the idea of “computational” knowledge.  That’s knowledge we can work out – generate, in a sense, from other things we already know or assume (pure knowledge – axioms, physical constants…).  Computation is a very broad concept that refers to far more than “computer” programs.  Plants, People, Planets, the Universe computes – all these things take information in (input) one form (energy, matter) and converts it to other forms (output).  And yes, calculators and computers compute… and those objects are made from things (silicon, copper, plastic…) that you don’t normally think of as “computational”… but when configured appropriately they make a “computer”.   Now to get things to compute particular things they need instructions – (we need to systemitize… or program it).  Sometimes these programs are open ended (or appear to be!).  Sometimes they are very specific and closed.  Again, here don’t think of a program as something written in Java.  DNA is an instruction set, so are various other chemical structures, and arithmetic, and employee handbooks… basically anything that can tell something else how to use/do something with input.  Some programs, like DNA, can generate themselves.  these are very useful programs.  The point is… you transform input to some output.  That’s computation put in a very basic, non technical way.  It becomes knowledge when the output  has an understandable context, use and function.

Categorizing what is computational knowledge and what is not can be a tricky task.  Yet for a big chunk of knowledge it’s very clear.

Implications and Uses

The follow on question once this is grokked — What’s computational knowledge good for?

The value end result, the computed knowledge, is determined by its use.  However, the method of computing knowledge is valuable because in many cases it is much more efficient (faster and cheaper) than waiting around for the “discovery” of the knowledge by other methods.  For example, you can run through millions of structure designs using formal computational methods very quickly versus trying to architect / design / test those structures by more traditional means.  The same could be said for computing rewarding financial portfolios, AdWords campaigns, optimal restaurant locations, logo designs and so on.  Also, computational generation of knowledge sometimes surfaces knowledge that may otherwise never have been found with other methods (many drugs are now designed computationally, for example).

Web Search

These concepts and methods have implications in a variety of disciplines.   The first major one is the idea of “web search”.  The continuing challenge of web search is making sense of the corpus of web pages, data snippets and streams of info put out every day.  A typical search engine must hunt through this VERY BIG corpus to answer a query.  This is an extremely efficient method for many search tasks – especially when the fidelity of the answer is not such a big deal.  It’s a less efficient method when the search is really a very small needle in a big haystack and/or when precision and accuracy are imperative to the overall task.  Side note: Web search may not have been designed with that in mind… however, users come more and more to expect a web search to really answer a query – often users mistake the fact that it is the landing page, the page that was indexed that is doing the answering of a query.  Computational Knowledge can very quickly compute answers to very detailed queries.  A web search completely breaks down when the user query is about something never before published to the web.  There are more of these queries than you might think!  In fact, an infinite number of them!

Experimentation

Another important implication is that computational knowledge is a method for experimentation and research.  Because it is generative activity one can unearth new patterns, new laws, new relationships, new questions, new views….  This is a very big deal.  (not that this has been possible before now… of course, computation and knowledge are not new!  the universe has been doing it for ~14 billion years.  now we coherent and tangible systems to make it easier and more useful to use formal computation for more and more tasks).

P.S.

There are a great many challenges, unsolved issues and potentially negative aspects of computational knowledge.  Formal computation systems by no means are the most efficient, most elegant, most fun ways to do some things.  My FAVORITE example and what I want to propose one day as the evolution of the Turing Test is HUMOR.  Computers and formal computation suck at humor.  And I do believe that humor can be generated formally.  It’s just really really really hard to figure this out.  So for now, it’s still just easier and more efficient to get a laugh by hitting a wiffle-ball at your dad and putting it on YouTube.

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Read a great piece today (which I found on Slashdot…) on the state of violence in video games.  It’s remarkable in that it’s author is a life long gamer (like myself) and he starts to drop some value anchors.

If we come to that, should it be illegal to simulate player imposed suffering of photorealistic humans in video games? If so, where do we draw the line with regards to realism? For example, BioShock is “OK” now, but how much more realistic will the virtual human’s appearance and behavior have to get before virtual murder is considered genuinely and irreversibly harmful for the player?

Will it matter if it’s done “by hand and knife” in a holodeck-style brain-machine interface, or if it’s executed through a 10-button game controller? Will it matter if it’s a quick death or a slow, drawn-out one? Will it matter if the human-killing enacted by the player fits the legal definition of murder or if it is done in self-defense?

I don’t know the answers to these questions, but I do know that they won’t come easy, especially if the game industry fights back against government regulation. As we grow ever closer to 100% graphical and situational realism in games, hopefully game publishers will decline to encourage the stunningly accurate simulation of gratuitous human suffering.

My concern is not that these violent simulations described will happen; they probably will at some point. I’m concerned that we as an audience will continue to consider gratuitous virtual murder a form of mainstream entertainment. The kind of violence I’m describing should be relegated to the bottom, back-corner shelf of any game store — not by law or punishment, but by consumer demand.

This is a great debate to engage in now!  We can define the values and shape our behavior.  If we don’t actively define them, it will still passively happen and we may end up having to unlearn a bunch of values.  And, as Mr. Edwards points out, we just don’t know how that will turn out.  At some point the realism of the games and the idea that you are controlling something virtual will erode and we’ll have real trouble telling the difference between what is real world behavior and what is virtual.  When and what that looks like we just can’t say.  We already have real legal and social issues regarding what happens on social networks – and those are not realistic and/or even close to as full person engaging as modern games.

I’ll give you one my own experiences… and for those that have played a first person shooter on the PC or X Box live know just how insanely over the top scary the live voice chatter between people can get.  When I was actively playing Halo 3 you would hear multiple times a session about how other players want to ass-rape, gang bang, whack and kill those fags/mutherfuckers and their mothers.   This language and threats would be made whether there was a 10 year old on the other end or a bunch of adults. I’m not using made up language here.  One time I let the audio escape out of speakers instead of my headset and it kinda freaked my wife out. “People really talk like that on there?” Yes. Yes they do.

Do I think that language itself means someone will go out and do those things? no.  Do I think repeated exposure and reinforcement that associates that langauge and winning and “earning buddies or friends” starts to seep into non-gaming behavior?  Absolutely.

I now report all language like that.  I don’t know if XBox or Microsoft aggressively pursues it.  I hope so.  One time I even tried to track down someone I thought crossed the line with another player.  This is an impossible task.

My thinking on this is related to other conversations about the impact of news media on events and the slippery evading authorities behavior encouraged during the #iranelection stuff on Twitter.

The last 12 months have been a whirl wind of big things… presidential shifts, big world events, wars, economic troubles, unemployment, technology advances, health care… just huge value disruptors.  There’s an obsession with Real Time right now.  More Data Faster!  The challenge is you can’t reflect on values in real time.  you can’t set anchors and see where you stand against them.  No, we don’t have to stop and reflect – we can keep charging ahead.  That approach will have different consequences than if we stop and reflect.  I can admit I’m a bit frightened by the consequences of this relentless acceleration towards more data faster – technical progress at all costs – we’ll sort it out later.  (And those that know me understand I’m not exactly a patient person and love change)…

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