The following is a message I sent to a new friend, where we were discussing transhumanism, ecology, and specifically whether the transhumanist perspective is egocentric and not concerned enough about environmental health and sustainability. I’m a neohippy and environmentalist, so I could totally relate to the values expressed, but I wanted to clarify a point about egoism.

As I see things (which may certainly be wrong), there is no objective moral system. (This is what I mean when I say I’m an amoralist or moral non-realist.) What this means, essentially, is that all notions of “good” and “bad” are inventions of people. (Same with “purpose”.) This doesn’t mean that ethics don’t exist–just that they’re entirely subjective (within an ethical context something can be good or bad, but it’s probably healthy to recognize that said context is artificial).

If morals were an objectively true part of the universe, like rocks, it would make sense that so many cultures would have them, as they’d be a natural product of perception. But if ethics are invented, the question then emerges as to why they exist at all. There are plenty of good reasons to suggest that ethics are largely a product of social coercion (implicit in in-group/out-group boxing), but I think it’s pretty likely that there is a basic human desire to look at things in such ways (evolution probably played a large role here).

Regardless of origin, however, I see all people adopt and maintain ethical systems because it is in their (perceived) interests to do so. The foundation of ethics, in other words, are our goals. (I think this is the reason many people stop maintaining ethical guidelines (such as kosher, or the sabbath) when it becomes clear that they’re highly impractical.)

If ethics serve goals (be they aggregate or individual), then it makes sense to me to recognize that no intentional action can be anything other than deluded (i.e. incoherent) or self-serving, as long as we include peace-of-mind as benefit (most charity is based on this, for instance). The anthropocentric, egocentric worldview seems to me not to be bad in any way, but rather the true state of things, after we dig past ethical facades.

Now, to be egocentric doesn’t mean I have to be cruel or destructive. Empathy means that the joys of others can bring me my own joy, and I can be altruistic even fully recognizing how I’m doing it because of my own desire. Similarly, I cherish the earth and life in general, and I seek to protect the biosphere because of that internal goal.

This is another section of the same message. The recipient was interested in the concept of an intelligence explosion, so I figured I’d write something quick and simple. Unfortunately, I am incredibly verbose, and this is the result.

The term intelligence explosion is really referring to a “power explosion”. Power, here, means the ability for an agent (note: agents can be composed of sub-agents; corporations are a kind of agent) to change the world. If one agent can do x, y, and z, then it is more powerful than an agent that can only do x and y. One of the most important aspects of power is increasing one’s own abilities. As an agent becomes more powerful, they’re generally able to make themselves even more powerful. This, like all positive feedback loops, results in an exponential growth of power (an “explosion”).

Power explosions happen all the time on micro-scales. In the game Monopoly, for instance, the winning player has more ability to buy up property from competitors, thus increasing their own revenue. A few times it has also happened on macro-scales, such as when empires form. As I zoom out, it seems clear to me that the rise of humans with tools and culture are themselves a *massive* power explosion. (The Quaternary Extinction is evidence of this.)

Power explosions are limited or halted by various factors, depending on the context. The positive feedback loop of population growth is slowed/stopped by limited resources, for instance. As history and various models will show, however, nearly all limiting factors can be bypassed through sufficient knowledge
(this is what I think of as the “no true scarcity” theorem). Knowledge, then, is a kind of meta-power that can bypass normal limits. The power explosion of human civilization has been as big as it has been largely due (I think) to culture being a storage-vat for knowledge (pre-cultural people (which I’m sure many folks would just as soon not call people (though why an intelligent ape isn’t a person is beyond me)) lacked a good way to accumulate knowledge across time).

As powerful as culture has been in facilitating bypass on limits to power growth, there is one major limiting agent remaining: intelligence. As much as humans have grown more intelligent over the ages (and I think they have), we’re still mostly as smart as we were a few thousand years ago. The rate at which we can learn is not meaningfully higher.

This is due to the incredible complexity of our intelligent bits, but we’re on the cusp, I think, of understanding intelligence to the point where we can apply our power towards making ourselves smarter (IMO primarily through the creation of intelligent machines, but I recognize that an intelligence explosion could occur via other means). As intelligence is boosted by power, knowledge will be boosted by intelligence, and power will be boosted by the combination. As long as I’m correct in assuming that true scarcity is far, far beyond our current vision, this should mean an unparalleled power explosion that will almost certainly change the entire world quite rapidly into something beyond anything but wild speculation.

When some Jehovah’s Witnesses come to my door and ask how I know the earth is 4.5 billion years old, I tell them that I don’t know it’s that old; I believe it’s that old because it’s the best fit for my experiences (evidence).

But clearly it is not the best fit for their experiences, because they don’t believe it. So what do we do besides go our separate ways? I like to adopt the position that I’m wrong.

Note: I don’t think my experiences are wrong. I think my beliefs are wrong. Experiences cannot be false (though memories can).

Though this uncertainty in belief is artificial, I try and make it as real as I can. Beliefs are incredibly useful things, as you pointed out, and in this way I see them a bit like tools. If someone asks me to throw away my hammer, I would refuse, because the hammer has utility. But beliefs can be picked up again, after being discarded, with no cost, and as long as I hold onto them for fear of losing their utility, I am closed-minded and attached.

Now what do I do when presented with the Witnesses at my doorstep as I drop my beliefs? Naturally, I want to reform some beliefs, else I will be paralyzed. So I take the extra step of asking them what to believe. And without fail they will tell me something that makes no sense.

I want to take an aside here to let up on Christian creationists. I try to use the method I’ve been elaborating on *whenever* I disagree with someone and have the time to hash it out. Most of the time I fail to let go of my beliefs, and end up arguing like a fool. Often I forget to do this with the smartest people on the subjects that are most important to me. I use the Jehovah’s Witnesses as an example, because I use their presence as an opportunity to practice this technique, and am much more mindful during those times.

But the position of my new educator never makes sense to me, regardless of whether we’re talking about global warming, economics, genetic engineering, or religious apocalypse. If their position made sense to me, there wouldn’t have been a disagreement in the first place.

So, seeking to understand, I ask of the other party how they would reconcile the strange belief (theirs) with my experiences. For instance, if the earth is less than a million years old, how can I explain the light from stars that are millions of light-years away?

If I am doing my best, this question is not meant as an “attack” at all; I am genuinely curious. But it has the wonderful by-product of forcing the other party to question their beliefs. It puts us on the “same side” in a very serious way. Which is good, because we ARE. We’re both trying to know the truth (everyone is), and there is only one truth. Debate becomes collaboration if one or both parties have the ability to detach themselves from their priors.

As time goes on, either the other party will have accepted some major evidence and be significantly less confident, or I will have built a working model from their explanations. At the end of the night, I can take that model, and compare it to the one I used to have, and ask which one is better. If my model still looks better, I can even ask the other party “why do you prefer this model over my old one?” Which is a perfect segue into letting them listen and question. (Though it almost never gets that far, in my experience.)

This method, you may notice, resembles the Socratic method, as it is all about asking the right questions. But where I see the Socratic method as being a kind of rhetorical weapon, I see my method as being a tool for my own learning. It’s not just that I think I’m wrong when I disagree with someone, it’s that I often think I’m wrong when I’m by myself. I am an ignorant human, and as such will never be able to know all of what other people do. When I meet someone who disagrees, it is an opportunity for growth.

In the end, it is rare that I will accept the belief of the other party. Most of the time I pick up my old “hammer” where I dropped it. But even if the other party didn’t pay any serious attention to my questions, I have still learned much about the perspectives of others, and I get the opportunity to revisit the foundation of my own beliefs.

With regard to AI, it’s easier for me to answer direct questions rather than general interests, but I’ll do my best to summarize some of the ideas which I find most interesting, and the dilemmas I’m working on at present.

You may have noticed that I use the term Artificial General Intelligence, rather than simple AI. The “General” is used to distinguish from what is called “Narrow” AI. A good example of narrow AI is the spam filter you probably have in your email client. The spam filter has knowledge, the capacity to learn, and ability to act autonomously, but the domain to which the filter is applied is highly restricted. My interest pertains to AI that is not specific to any domain, and can solve problems in a multitude of environments. For instance, a truly general agent could drive a car, translate languages, direct air-traffic, or diagnose diseases without any special work on the behalf of a programmer.

Naturally, such a system would be equally capable of learning computer science, psychology, electrical engineering, etc. and be capable of applying them towards various goals, presumably including the design of AGI. Because of efficiencies in replication, communication, and not having to sleepication, I can imagine that it wouldn’t be long before our creations were better at creating than us. The theorized result of this would be an Intelligence Explosion, wherein ever-smarter agents would be able to build ever-smarter agents… until the bounds of their intelligence hit some sort of ceiling. I, and many others, believe this ceiling to be far beyond the intelligence of even the smartest humans, and would allow the artifical superorganism to achieve its goals to an unsurpassed degree. (Note: this doesn’t have a timeframe attached to it. It may happen in the next few decades, or the next few centuries. I don’t know.)

An emergent goal of all rational beings is self-preservation, so unless specifically counteracted in the initial design, a superintelligence would naturally be defensive at best and hostile at worst. Any possible threat, or impediment to its goals would naturally be removed. It is thus paramount that any AGI design be given the exact same goal set as the most ethical person imaginable, as anything else would likely result in vastly terrible things occurring, notably including the destruction of all evolved life.

There are some good reasons why I think that can be avoided, however, and if said superintelligence was benevolent, we would be gifted with the solution to nearly any problem we can think of today (possible exceptions include: entropy).

Naturally, I think this is a big fucking deal.

I also recognize that it makes me sound a bit crazy. I am reminded, however, of something Arthur Clarke once said:

“Trying to predict the future is a discouraging and hazardous occupation, because the prophet invariably falls between two stools(?). If his predictions sound at all reasonable, you can be quite sure that in 20 or at most 50 years, the progress of science and technology has made him seem ridiculously conservative. On the other hand, if by some miracle, a prophet could describe the future exactly as it was going take place, his predictions would sound so absurd–so far fetched–that everybody would laugh him to scorn. … So, if what I say now seems to you to be very reasonable than I will have failed completely. Only if what tell you appears absolutely unbelievable do we have any chance of visualizing the future as it really will happen.”

Because I am curious by nature, and not content to watch the future simply unfold, I find myself drawn to making it happen, or at least exploring the field with great depth. To this end, I must fully understand what it is that allows a person to solve any sort of problem they are given, and distill this quality until it can be given to a machine.

Many researchers in the field have approached this problem by trying to take the pinnacle of human thought, which in their eyes is typically logic, and reducing it to a procedure, primarily through introspection. Most early attempts at AGI followed this introspective, reductionist approach… and failed horribly. Despite pouring in buckets of “facts”, their logical machines were unable to solve anything more than toy problems.

This failure caused a collapse in the AGI field, as institutions like DARPA withdrew funding, thinking that General Intelligence was a myth. Good work continued to happen in AI, but it was applied to fields that were tractable, and would offer results. The defeat of Garry Kasparov, the chess-master, in 1997 to IBM’s Deep Blue computer is a good example of a success in narrow AI that has little to no relevance to the field of AGI.

During the AGI winter, neural nets became a hot topic, and were developed to levels of modest success (such as filtering spam). Inspired by the behavior of neurons (though not actually realistically replicating their function), neural nets provided an alternative to the reductionist logic of earlier work. By linking together simple pattern-matchers into vast webs, and letting them automatically adjust to match provided data, neural nets are capable of learning to recognize commonalities within very complex environments; such as discerning between objects when given a host of visual data.

In parallel to all this work on AI came some interesting work on psychology. The theory of Behaviorism, once championed, fell from popularity in the late 20th century, and in its place came a not very well known (even today) theory that was inspired by electrical engineering of all things, and served as a better model for behavior than anything before it (from my perspective). This theory, stated that behavior was not actually a fixed response to a given environment, but instead an attempt to change what the organism perceived. As an example, a mouse that is under a sun lamp will crawl under a rock. Earlier psychologists would claim that the sun lamp causes the mouse to move, but under the perceptual-control theory, the mouse moves so that its perception matches innate goals, namely coolness. Hence, if the mouse is removed from the perception of heat, by making the air colder for instance, it will not seek shade.

Though perceptual-control theory may seem basic at first glance, it is very powerful when closed-loop control systems are linked together to simultaneously control for complex outputs to perform one smooth action.

Words really don’t do this justice, because both neural nets and perceptual control systems are highly emergent systems; those that are simple at the unit basis, but unfold in complexity and power as these units interact and connect. To really grasp them, one must view the power of the whole. Emergent systems seem a bit like magic to me. Go examine the Mandelbrot set for a classic example.

Two major pieces of the puzzle are yet missing, however. We have perceptions and actions (and logic, but that’s not really important in the scheme of things), but we also need goals and thoughts.

Goals are what I’m working on right now, and are the last “animalistic” portion of mind that is truly necessary. I’ll talk about my work with them in a moment.

Thoughts will ultimately serve as the powerhouse for the mind, by allowing for future prediction, past inspection, and other imaginings of non-present perceptions. Imagination serves, as far as I can see, as the foundation to complex language, but I haven’t studied it enough. It does seem clear to me, however, that one cannot jump to thoughts without first having a solid “animal mind” to support them.

(I think many animals have thoughts, such as dolphins and apes (and humans), but far more have the other, more basic, systems.)

Backing up to goals, I can address some of the problems I’m working on at the moment. Specifically, while we seem to have a goal to remove certain bodily sensations (pain) and gain others (pleasure), we also notice very common abstract social goals, such as fulfillment, companionship, love, and safety of offspring. These concepts are abstract things, which must be learned through exploration, just like other abstract things like math or politics. But if they’re learned, how can they become goals? Is there some sort of homunculus that watches for the emergence of said ideas, and then hooks them up to the control systems that will implement them? Are these goals not actually innate, but instead the product of social pressures? If so, how is social pressure able to bind them? It’s all very confusing, and if you have any ideas, I’d love to hear them.

For example, if I have a stone, I might form the hypothesis that the mass of the stone is 30g. To test this, I might weigh the stone. Implicit here is the idea that what is recorded from the scale is the mass of the stone. Once I read the scale, I have the following ideas:

(A) The scale says “45g”.
(B) The mass of the stone is 30g.
(C) The scale is an accurate measure of the stone’s mass.

If all three were true, there would be a contradiction, so I can conclude that one or more of my ideas must be false. The problem with falsification is that I have no logical reason to favor A & C (false hypothesis), over A & B (bad scale), B & C (bad eyesight), or others. Ultimately, I cannot refute anything with absolute certainty, so I cannot disprove.

This difficulty can be reduced by what I like to think of as the inductivist section of critical rationalism (I’ll show why in a moment). Wikipedia says that, with respect to hypotheses, “differentiation may be made on the basis of how much subjection to criticism they have received, [and] how severe such criticism has been”. In my example, none of the three ideas has been criticised, but it’s easy to imagine a scenario where the accuracy of the scale had been previously tested.

There are, unfortunately, two problems with this reasoning: (1) the scale requires evidence to test, so we still have the “which conjecture do we accept” problem at an earlier point, and (2) we still have two conjectures to decide between (A&B). What most critical rationalists will likely turn to is the difference between unsubstantiated conjectures (B) and those based on observation. It’s important to remember that hypothesis A is still conjectural, but we can grant it a sort of “natural critisism” stemming from our perception.

Here’s the rub: lending a heigher weight to any of our conjectures still doesn’t allow any refutation to logically occur. The only way to do that would be to accept something as true after enough observation, and this is exacly what everyone does (including critical rationalists), but CR brushes off as illegitimate.

At this point, one might turn to belief weights in order to avoid having to assign a binary value to a hypothesis (and the fallacy of inference). Unfortunately, any sort of updates to belief weights requires knowledge that is assumed to be true; it doesn’t actually let you move from a state of unsubstantiated conjecture to one of informed belief. In other words, it requires prior knowledge, which, as discussed earlier, we can’t logically obtain.

As an example, let’s say I have a hypothesis that a zebra exists and then I perceive a zebra. What weight do I give my hypothesis? In order to find it, I must know how accurate my perception is. For example, if hallucinating a zebra is equally probable to seeing a real zebra, there is a 50/50 chance that the zebra actually exists. But let’s say that I am not given a value for how accurate my perception is… how do I determine the likelihood of false positives, etc? The natural answer is to make a bunch of observations, and test to see if they were “correct”… except to do that, you’d need to assume the training labels (“correct/incorrect”) were true! If you want to evaluate the accuracy of the training labels, you have to assume some other input is true. The catch 22 ensures that you cannot logically produce a factual statement (even a probabilistic one) about the world without having been given other (binary) factual statements.

Unless I’m overlooking an infallible source of knowledge, I can conclude that nobody in the entire universe has any knowledge (that is, factual data) of the universe… and never will. Not even an infinite intelligence would be able to know anything about reality.

To escape this agnosticism, I might suggest that when we look like we’re doing logic, we’re actually not (at least, not formally). For instance, if the scale reads “45g”, I might simply accept that the stone is 45g and reject my old hypothesis, not through logic, but through common sense. The problem here is that common sense is a blanket term used to describe mental tasks that are easily done by people, but we don’t understand explicitly. Doing something via “common sense” is a lot like dying from “old age”; it’s just not a useful term. To make things worse, humans generally seem to reject paradox and use deduction, so we can be confident that something very close to formal logic is going on mentally.

My theory is that ideas are not evaluated based on truth, but based on the utility that comes from predictive power. Prediction, here, is based on sensory data, as opposed to objective reality. Unlike reality, we can be sure of our sensors as long as we think of the sensors as “inputs”. Let me give some examples…

I find a stone and decide to weigh it. I predict that the measured mass of the stone will be 30g. I put the stone on a scale, and it says 45g. My prediction had a significant error, so I discard it as being non-useful. Because I’d like to be able to predict the stone’s mass I form a new prediction that the mass is 45g (informed hypothesizing). I can use my memory to test the prediction… it works! This retrospective success reinforces the expected predictive power of that hypothesis. This explains why a hypothesis that matches previously observed data is granted more weight, and why one that doesn’t is discarded (falsification).

Let me give an example. Little Andrea sees a crow that is black. She conjectures that all crows are black (or more simply: “crows are black”). She sees another black crow. Prediction reinforced. She asks her mom what color crows are. Prediction reinforced. She sees a green apple (non-black non-crow). Observation is outside prediction scope; no change. At the age of 46, Andrea meets a street performer with an albino crow. Prediction failed. She notes “Oh, how strange… a white crow!” Prediction weakened slightly, but still retained, because in the vast majority of cases it’s useful to guess that crows are black. After seeing enough white crows she may reject her initial generalization and adopt a more probabilistic one (about 90% of ravens are black), but since a probabilistic idea has intrinsically less predictive power (and is harder for humans to measure*), they are under-weighted and often avoided (leading to accident fallacies and others).

I could wander from here into my theories of semantic memory, but I’ll try to stick with critical rationalism to finish my thought. When Popper started, what he sought was to step away from justification, the practice of trying to support currently held ideas. In the service of this, the claim was made that one can disprove an idea, but not prove one. Though I’ve come to reject this claim, I don’t think that critical rationalism is a bad approach.

Justificationalism comes out of a natural tendency to want to be right, and it appeals to this bias even when a more open mind might find more effective ideas. Critical rationalism avoids this by forcing each person to listen to other arguments in order to determine how they might fail.

Critical rationalism also avoids the trap of adding weight to a theory because of selective observation. For instance, if I have the theory that “proteins are a kind of enzyme”, I might seek to “confirm” it by looking for enzymatic proteins. This will bias my data set so that it appears that the idea is effective, when it actually isn’t. Critical rationalism will naturally disrupt this bias with a second bias of seeking data that doesn’t fit the theory. Because an idea that is predictive only, say, 80% of the time isn’t very useful, this bias is helpful in pushing us towards more consistently accurate ideas.

One might suggest that when people say “truth” they mean “predictive power”. If this is true, I can easily show where Popper’s ideology fails. No prediction will be correct 100% of the time; our sensors are fallible. An idea that fails shouldn’t be rejected as “falsified” if it’s still accurate almost all of the time. F=ma is still a really important piece of knowledge. In this way, induction works.

* – Probabilistic ideas work differently for different forms of memory. Associative memory, the kind of thought that we use when making split-second decisions, is very probabilistic. For example, it is easy to have a gut feeling that a deck of cards is about half black and half red. Semantic knowledge, the kind of idea that we use consciously, doesn’t work so well with such things. I cannot imagine a person being able to tell you what proportion of a pile of cards is clubs unless they do some mental math.

Photo credit: swissbones on Flickr

Specifically, I began to notice how few links my notes contained, and the difficulty I might have in changing small details (like names) across many pages. Wouldn’t it be nice, I thought, if I were able to tell my computer about my world, and have the computer organize and maintain my notes in a way that let me quickly and easily extract and change the underlying facts. Essentially, I was thinking about automating the world-building process by reducing a world into a data and making code to manipulate and present that data.

Because I had heard good things about associative databases, I built one to handle the facts of the world and began entering items like
Kogámos is_child_of Sáríbá
(Yes, I know that’s a lot of accents). Associative databases (with exceptions, I’m sure) represent data ontologically (that is, as a collection of factual claims about entities), and in this way resemble the data structures of Good Old-Fashioned AI*. Now, before I started this project I was very skeptical about logic-based AGI, but I knew that in some domains, (such as Expert Systems) logic based narrow AI had become successful, so I set about exploring whether this was one such domain.

* – Really, this structure is common to all logic-based AI, even fuzzy-logic, modern approaches like NARS. Much of the criticism that follows can be applied to any sort of artificial reasoning system.

Before I go on, however, I’d like to share a documentary about the semantic web. It’s related, and I’ll be talking about it, but it’s 15 minutes and you could probably skip it if you’re familiar with the concept.

With the possible exception of being able to make meta-facts (that is, facts about facts), the eventual structure of the semantic web closely resembles that of an associative database. In fact, I’m sure that facebook has a piece of data saying
Max_Harms is_child_of Tracy_Harms
or something to that effect, right now. And like my project, the semantic web seeks to represent data about an entire world, not just a highly-restricted domain.

My project has some big advantages, though. The biggest is probably that all the information given might as well be coming from God. If the world-creator says that rivers run uphill, that’s just the way it works. Thus there is no need to model confidence in facts entered. Secondly, the system doesn’t actually have to act; it simply has to store and present the data in an intelligent way.

One of the early things I noticed in my exploration is just how fun it is to see the computer answer questions. Logic-based AI is very attractive because it takes hardly any work before you can ask it things like “Who is the child of Sáríbá?” and it will give you the right answer. I ended up spending lots of time asking questions that I already knew the answer to, just because it was fun whenever the computer was right.

One of the biggest hurdles in the transition from a Reductionist to a Holistic stance is that the Reductionist stance works so well for simple problems, and thus is very seductive to beginners.- Monica Anderson

The problem here is that answering simple questions based on simple rules from entered facts doesn’t mean squat. It’s about as useful as writing the information in a text file and using regex.

It gets worse than that, though. As mentioned before, worlds are messy and complicated. If the problem was as simple as generating a family tree, or making Venn diagrams, we’d be okay. But how do you represent something as nuanced as “Sally likes to spend time downtown, except on the weekend, when she finds it too crowded”? Such things are simple for us to remember because they tie into our experiences. To illustrate what I mean, imagine the similar phrase “Queue is angry about rowing in Vah, except when floating, when torque is standard.” This statement has no connection with our common experience, and because we do not understand it, we cannot store it easily. This is the same problem as that confronting the semantic web. We can pour in data until we die, and the system still wouldn’t “get it”.

If a programmer attempts to describe the world to a Logic based AI, for instance by creating ontologies, he’ll never finish the task. The world is too rich. The Cyc project – the largest and most famous AI project ever undertaken – has been trying to describe the world using predicate calculus for decades; it is the poster project for Reductionist approaches to AI. But Cyc will never approach anything worthy of the term “Intelligence”. It has been told many things and can recite many definitions but Understands nothing. This is the difference between “Instructionist” top down education and “Constructionist” bottom up learning – a distinction poorly understood even in human education.- Monica Anderson

Even in a task as basic as storing and retrieving information we still need the computer to understand what it’s doing beyond tasks of relative simplicity. This is because knowing about the world falls into a large class of problems that are fundamentally impossible without General Intelligence**. Other problems in this category include using natural language, navigating complex environments, and behaving in socially acceptable ways. As hard as we may try, until Strong AI is solved, a chatbot will always be a mimicking machine, a database will always fail in complex domains, and humans will be forced adapt to robots (instead of robots adapting to humans).

** – It may be possible for a vast enough intelligence to make a system so complex that it’d be able to handle one of these domains. I don’t think even large numbers of humans would be smart enough to make one, though.

Even with my increased dislike of logical systems, I’m quite happy with what I was able to make in the few days I spent building my database. You can check out the results yourself, or even add and delete facts. The database will be restored to a backup state twice a day (not sure what timezone my server is in), so don’t worry about making a mess.

Search commands:

• “describe X” = get all facts relating to X
• “list X is ?” = get all items that X is
• “list X is_child_of ?” = get the parents of X
• “list ? is|has X” = get everything that is or has X
• “list ? imports * ? is geographical_area” = gets geographical areas that import anything
• “infer” = list suggestions for facts, based on correlations

Source code is here. Everything I wrote (files in the root directory) is public domain. Attribution is appreciated.

(P.S. I’m a big supporter of the semantic web, and doubly so for open data. The criticisms expressed in this post are against the idea that it is possible to intelligently know lots about the world using ontological representations. Links are data, and that data is important, even if that data isn’t knowledge in itself.)