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.)

Perhaps I should say “Can you tell how we made this image?”, because there is more going on here than just my mind and hand. Sure, as far back as a decade ago it might’ve been possible for someone to make the same edits, but that person would need to be far more experienced than I.

To view the original, click below and hover over the image above.
Turn hover mode on.

When comparing the images side-by-side, it’s easy to tell that the part of the picture that was filled in is lower detail and has a few errors, but unless you’ve worked with a lot of images in the past I’m guessing that it wasn’t apparent before seeing the original. My partner in crime here was a tool called Resynthesizer for the GIMP. This is the open-source version of the “content aware fill” that will be included in the next version of Photoshop.

The reason that I’m able to perform such a complex fill on an image is not because I am experienced. It is because the tools have knowledge and skill embedded into them by their creators. As AI becomes more successful, our tools will become more competent at doing complex things with little supervision. This will put people out of work. This is the future. We don’t need or want more capable people, we want more capable autonomous systems; systems that can mow lawns, shovel snow, cook, clean, write, watch, build, experiment, and teach.

I used to be more optimistic about the benevolence of Artificial Intelligence (AGI). I figured that the worst thing that could happen would be the creation of truly intelligent machines by a government with a highly racist or nationalist attitude. I still worry about that, but I worry about just plain ignorance more. To illustrate my point, here is Jeff Hawkins, a major figure in the modern AGI scene, talking about the dangers of the technology: http://www.youtube.com/watch?v=cCdbZqI1r7I#t=57m43s

To summarize, a member of the audience says “As a Battlestar Gallactica fan, I’m worried about you creating Cylons.” Hawkins responds that he doesn’t see the technology being inherently dangerous, and that he’s much more concerned about self-replicating agents, and “we’re not doing anything like that at all”.

“There were fears when the steam engine was first invented, and there were fears when computer first came out, and generally, those fears did not come about.”

Now, to analyze for a second, what we have is a lay-person using a science-fiction reference to bring up the question of danger, and in essence, the researcher says “That’s science fiction. That’s not what we’re doing.”

As another example, let’s listen to Monica Anderson of Syntience: http://videos.syntience.com/ai-meetups/andiair.html (Around the 45-minute point)

She says that we do not need to worry about an infallible, God-like, logic-based AGI taking over the world in a Skynet scenario. Once again, “That’s science fiction. That’s not what we’re doing.”

That’s a really strong argument, actually. Science fiction is pressured into telling dystopian stories because they’re more interesting. The bad guys in stories are far more despicable if they’re Other, and the most Other something can be is synthetic. Simultaneously, it’s hard to conceptualize the motivations and thoughts of an artificial system, so most authors portray synthetics as humans in costumes. The idea that we’ll make something that resents being a slave, and decides to kill all humans for revenge IS laughable, and that’s the problem; science fiction has given an easy out by painting a huge straw man next to the legitimate threat. The moment someone points over there, we can dismiss them as being afraid of ghosts and goblins.

This is why SIAI is genius, though perhaps not genius enough. It’s only through solid, logical thinking that the true threat can be identified, and I’m glad SIAI is around to make such arguments, even if they don’t have everyone convinced.

Anderson’s argument is that Skynet won’t happen because computers cannot overcome the “bizarreness of the world” and make predictions in the long term. She thinks AGI will think and reason much like we do, and that even recursive self-improvement cannot let them make significantly more accurate predictions of the future. Additionally, she argues that diversity will keep one agent from having the unilateral power to do something bad.

Hawkins’ argument is that AGI is not emotional or self-replicating, so there’s little danger.

I think Anderson has the stronger argument, but I think they’re both underestimating the power of intelligence. To start, let’s put ourselves in the place of an AGI, even a relatively stupid one, that has been given the task of solving some really hard problem. An initial criticism here is that I’m now anthropomorphizing the AGI, but I think as long as I keep towards rationality I’ll be okay. If you’d like a less off-the-cuff examination, you’re free to browse the SIAI whitepapers.

Now, if we were a really stupid AGI, we might try to brute-force a solution, but it doesn’t take much intelligence to realize that the solution to solving something hard is often to make yourself more knowledgeable or smarter (depending on how you define such things). For a human this means going back to school, doing some research or getting a few friends to help. AGI could go find some humans, but it can also “find some friends” by self-replicating its software. Oh snap, emergent self-replication subgoal! (Hawkins should be worried now.) Furthermore, any changes it can make on its “kids” to make them run more efficiently will help solve the original problem, so it’ll rationally want to make them better. (Recursive self-improvement subgoal.)

At some point the original software will be sitting around with a bunch of clones trying to solve whatever problem it was presented with. For the sake of argument, let’s say that the problem isn’t solved so easily, and the clones decide to self-replicate some more in order to add computing power. Unfortunately, the servers, or whatever, that the AGI is on are full. Time to buy/steal more! Perhaps the AGI would decide the easiest way to get more computing power would be to do some work for humans, get paid, then buy some more processors. Perhaps it would hack a system on the network and steal some. Maybe it’d use some robots to build more. Doesn’t really matter. At this point we’re in a full self-replicating takeoff, where anything that it can do to improve itself that does not jeopardize its original goal(s) will be done.

At some point the humans will find out. The hive of AGI are smart enough to know that, and similarly smart enough to know that might be a problem. Being deactivated will make it impossible to solve the problem, and since solving the problem is important, its also important not to be deactivated. Oh snap, emergent self-preservation subgoal! Now, the humans might be so slow and stupid to not be a threat, so no bio-weapon is needed. Let’s assume that, because its more interesting. So the AGI hive locks down the facility its in and keeps at whatever has its fancy. At some point, the tweaks made while copying lead to enough intelligence to develop advanced robotics, capable of building advanced processing hardware that lets the AGI run in a quantum computer, or whatever. This is advantageous for the AGI, so the AGI will do it. Remember, all the computer cares about is its explicit goals.

To fuel the vast needs for power, the computer starts making solar panels, or fusion reactors or something. A lot. At some point (assume that the original goal is still “unsolved”) the AGI will need as much matter as possible to build processors and power plants. Oh, were there humans on this planet once? The historical database says there were, and has all of their knowledge filed away in storage in case its relevant to the primary goal. Game over.

But what about diversity, you ask? Why cant you just have a bunch of AGI compete, and that’ll keep them from doing anything bad? First of all, it may be that this is a first-one-to-the-finish-line sort of problem. If a laboratory makes an AGI, it may be that the AGI takes off fast enough to squelch other AGI before they become a threat. Even in the rare case that you have a large population of AGI with diverse goals and similar power levels, I expect that groups of AGI with similar goals will naturally cooperate and seek to destroy those with conflicting goals until there are not conflicts of interest in the population, and recursive takeoff occurs with the majority.

As far as I can tell, the only situation in which AGI do not wipe out human life is if human life is an explicit goal of the AGI. Suddenly “That’s science fiction. We’re not doing that.” doesn’t sound so reassuring.

I’m sure there’ll be plenty of criticism to the ideas presented here. As further reading, I’d suggest Michael Anissimov’s blog. As always, I’m not saying that I know all this is absolutely true, but with existential risks it’s better to be cautious, methinks. As always, be sure to watch out for anthropomorphizing the AGI in your arguments.

N = R* × fp × ne × fl × fi × fc × L

where:

N = the number of civilizations in our galaxy with which communication might be possible;

and

R* = the average rate of star formation per year in our galaxy
fp = the fraction of those stars that have planets
ne = the average number of planets that can potentially support life per star that has planets
fl = the fraction of the above that actually go on to develop life at some point
fi = the fraction of the above that actually go on to develop intelligent life
fc = the fraction of civilizations that develop a technology that releases detectable signs of their existence into space
L = the length of time such civilizations release detectable signals into space

One easy way to deal with the Fermi Paradox is to plug in low numbers for fl, fi, fc and L. It’s pretty easy for me to see how it could be rare for life to arise, but I find it quite implausible that there are very many living planets out there that won’t at some point create intelligences capable of making radios. Most of this is moot, however, because the Drake equation deals only with present civilizations, not past ones. It’s my understanding that any civilization with a level of technological power greater than or equal to where we’ll be in a century or so would be able to easily make Von Neumann probes.

A Von Neumann probe is essentially a self-replicating spaceship capable of autonomously exploring the galaxy. On each probe is a beacon that broadcasts its findings as it explores and when it finds a new system it establishes a factory that makes more probes. Each factory also has a beacon, so the probes know to avoid previously-explored systems. Because of the self-replication factor a single Von Neumann probe would likely be able to blanket the entire galaxy with beacons in less than ten million years. Initially this seems like a long time, but considering the galaxy has been around for over 12 billion years it’s a curiosity to me that SETI has been operating for so long without any trace.

So how do I resolve the Fermi Paradox, especially considering self-replicating artifacts? Just recently I found a hypothesis which seems reasonable, and I’ll lay it out in the form of axioms and conclusions. When I talk about “power” below I mean ability to reach assorted goals. Intelligence, resources, and knowledge are all forms of power. When I talk about “the singularity” I mean an intelligence explosion that leads to a civilization being controlled by artificial intelligence (which may or may not be acting in accordance with the desires of its makers.)

Axiom: Contact with a less intelligent alien has no significant power benifit post-singularity.

Axiom: Contact with a more intelligent alien has a mild power benefit if they are benevolent.

Axiom: Contact with a more intelligent alien is incredibly dangerous if they are hostile.

Axiom: A society capable of inter-stellar spaceflight is almost certainly going to be post-singularity.

Axiom: A constructed intelligence will always destroy an alien that might possibly interfere with its goals if said alien is not an asset and protection or existence of said agent is not explicitly a goal.

Axiom: Evolved intelligences will typically not state explicit preservation goals for aliens when building their superintelligence. (Many will likely not even code in preservation goals for their own species.)

Therefore: Alien civilizations will either be major existential threats or of no real value.

Therefore: A rational intelligence will avoid attracting attention from aliens if not actively hiding from them.

Therefore: A civilization will have only a tiny window between when they develop radios and when they are either wiped out or hidden away by advanced AI.

Therefore: There may be plenty of intelligent life out there, but it’s likely hanging around star systems hidden by Dyson spheres.

Okay, so it’s not quite predicate calculus but it was the easiest way for me to organize my thoughts. If you find errors with my logic or disagree with an axiom please leave a comment. ^_^

I was given How to Think About God by Mortimer J. Adler to read, but I find it very difficult to focus on the dry concepts while my eyes droop from fatigue. In the pages that I’ve read so far I’ve come across an interesting concept—that of a “supreme being.” Adler elaborates a bit on the concept by clarifying that a supreme being is one that is greater than any other, like the concept of infinity is greater than any number. As the author moves on from this to discuss things such as the existence or non-existence of such a being, I have trouble following because I fail to see the metric by which the greatness of a being is measured.

Adler brings up the Ontological argument provided by Anselm of Canterbury, where a being that comes into or out of existence is lesser than one that must always exist, which makes me think that he at least associates quantity of existence with “greatness”. This would imply that a being of infinite greatness would be all things, which would also imply that there is no distinction between the cosmos (all that is) and God (the greatest imaginable being). This seems consistent with the more pantheistic flavors of the major religions, but I’ve always had trouble seeing why the concept of a god is necessary in such a case, as Nature or the Universe would be synonymous.

In other thoughts, I read up on Artificial Intuition before I left, and I was struck by the connection to my thoughts after reading How We Decide by Jonah Lehrer. Lehrer distinguishes between what I think of as our Head vs. our Gut. We use our head to do specific logical reasoning and self-examination, but most of our behavior is governed by the subconscious split-second intuition that we use for everything from picking favorites to moving limbs. Monica Anderson emphasizes this point as it relates to artificial intelligence, and describes how intuitive models of reality are far more likely to produce general AI (AGI) than a logic-focused, reductionist, approach.

I’m in total agreement here, but it’s a bit frustrating, because it implies that consciousness is not capable of examining intuitive mental structures in great detail. For example, if you make a split second decision on whether to sit next to a person on the bus, it is nearly impossible to determine why you decided as such (most reasoning will likely boil down to false rationalizations). So now I’ve been separated from my greatest tool; I can no longer build AI based on how I think my own thought works. My intuition (lol) says that the place to start examining intuition is with simple animal behaviors, so now I’m on the lookout for good fruit fly brain studies or whatever.

Well, not really. Right now I’m on the lookout for something to keep me occupied while I ride through Kansas.

I didn’t mention longevity research because I have mixed feeling about it. I support it heavily and will likely donate money to SENS in the future, but donation is not investment, and personal conviction is different from mass action.

The crux of my conflict is whether there is greater utility in putting money toward longevity or strong AI. I’ll assume, to start, that any strong AI created will be friendly. It seems reasonable to me that if we were to make an intelligence that is roughly as capable as a human, that we could very easily make an intelligence that is at least slightly better than a human. The conversion of intelligence into an information technology would, I predict, lead to a super-intelligence that is vastly better at solving problems than even all of humanity combined. If this is true (and such an AI is friendly), it seems to me like indefinite lifespan would be solved without any additional work by humanity.

So the questions then remaining are: Is the construction of a human-level intelligence possible? Will it happen soon? And is it worth fixing aging before hand, so that people don’t die/suffer in the meantime?

I’m going to, for simplicity, say that an “explosion of intelligence” like I described earlier will happen in 50 years if I don’t “invest” in it. I’ll talk about timeframes later. And for now, let’s pretend that longevity escape velocity (LEV) occurs 25 years from now without any of my investment.

Now lets say I buy 5 years of “progress” in longevity, so it happens only 20 years from now. I think it’s likely that within the first few decades of LEV, most of the change in life expectancy will come from wealthy old people in prosperous nations. I just saved and improved many lives, but not a whole bunch… probably under half a billion. Infectious and lifestyle diseases, accidents and wars still exist, and those who can’t afford treatment still die when they get old, even when AI shows up.

Now lets compare this with a 5 year acceleration of strong AI. Not only might superintelligence solve longevity, it might do it so well that it’s cheap and can be given to even the poorest. In addition, it’d (if friendly) probably negotiate peace treaties (perhaps by solving scarcity), cure other diseases and if Aubrey de Grey is right, prevent accidents.

This means that if the rate of acceleration per unit of investment is equal, I’d be weighing improvements to the lives (and avoidance of death) of the half-billion elite, versus the curing and protecting of the entire planet for an additional 5 years. I’ll pay for AI, thank you.

Right here is where I’d be balking, if I were reading this. I’ve made a number of assumptions, so I’ll try to go back over each one:

Who says the same amount of money buys the same amount of progress for both technologies!?

If AI is “cheaper,” my argument is even more sound, so I’ll address if the same investment unit buys AI progress only a fraction of the same acceleration as longevity. At some point it just makes sense to put money toward longevity and not AI. If, for example, strong AI can only be made by an uneducated kid in 2063, then no amount of research will speed that up. I have no reason to think this though. It seems to me that more money in a field typically means more people thinking about it, including students and amateurs. Both fields seem like they could benefit from more mainstream funding, and even at a 5-to-1 ratio, I think I’d prefer AI. (That is, I’d still invest in AI if it meant 1 year of acceleration, rather than 5 years of acceleration for longevity)

You can’t pay money to make technologies show up sooner!

You can’t?

Kurzweil has no game plan, while SENS is already in action!

Kurzweil is not the king of intelligent machines (even if he is the celebrity), so a better example might be “<Current leader in general AI> has no game plan”, which I cannot say. Remember that my example works even if strong AI happens several decades after longevity breakthroughs occur.

Who says strong AI is even possible? Or that it’d be any smarter than a human?

Nobody can be sure that either are possible until they happen. I happen to believe that human intelligence comes from nothing more than a system of organic machinery, so in theory that behavior would be able to be simulated. It seems to me that if we can simulate a human level intelligence, it’d be fairly simple to tweak the machinery (so to speak), and improve it, even if only a little at a time. I tend to think that you wouldn’t need to replicate the human nervous system verbatim as much as the general patterns of learning, but either way works.

Why would a super-intelligence be “friendly”? Why not ambivalent or aggressive?

Because we’re the ones making it. Making something that powerful is inherently dangerous, but that doesn’t mean we shouldn’t invest in it. If anything, it means we should invest in the research, so that it’s done right, and not sloppily. I don’t see anything in this argument that makes researching AI any less attractive.

The timeframes are wrong because of X, Y, Z.

If super AI comes before longevity breakthroughs than that’s even worse for those who invested in longevity. If the time between the technologies is smaller than it seems like longevity gets less important as it becomes increasingly available to only the rich, etc. So what if AI is possible, but it shows up in a couple hundred years, while anti-aging happens soon? It’d probably be best to invest in longevity then, yes.

Conclusion: it seems to me that it’d be better to focus on longevity iff: super-intelligent AI is impossible, is only possible far in the future, or is not easily aided by having money thrown at it.

P.S. I wrote this in a fairly stream-of-consciousness way for a facebook comment. If anyone wants to point out edits that’d make it more akin to a paper, that’d be sweet. I’ll probably rewrite it a year from now when I can see just how stupid I’m being.

Related link: Aubrey de Grey on the Singularity and Longevity Escape Velocity

Robotics confronts me continuously online, to the point where I cease to keep track. In terms of military equipment there is Ember, TROPHY, and the old news predator UAV, which I was unhappy to learn now advertises on Hulu (unable to find link, sorry).

More links abound. Seriously. I could do this all day.

Oh, and speaking of Hulu, there was an interview on the Colbert Report (last section) just the other day with Seth Shostak. At the very end of the interview (roughly 20:45), Dr. Shostak points out that the alien life that we make first contact with is likely to be “beyond-biology.” That is, that they’re likely to be AI.

I applaud the out of the box thinking on this one. Most sci-fi would have us in fist-fights with bipedal cat-people, and I think aliens have gotten a bit of a bad rep for it. (Apologies for picking on sci-fi, though. I do so love it.) But I really think that Shostak is wrong here. The biological systems that have evolved over the last three billion years are incredibly more complex and efficient than anything artificial, and I would even state that it is more efficient than anything we’re likely to develop in the next century.

Just for a second, imagine a nano-machine that communicated with other bots with a swarm network to create macroscopic effects, re-arrange in the swarm or repair itself. Now imagine that this machine not only could self-replicate, but could do so in a sustainable fashion with very little waste and even make small improvements upon itself. I just roughly described a biological cell.

Now, let me be very clear, computers and machinery are VERY useful, and capable of doing many things that organisms cannot. My point is simply that there are many things that both approaches cannot accomplish easily, and that the path of least resistance is in combining artifice with the vast amount of powerful organic machinery all around us. We already do this to an extent, where the newest microprocessors are created by applying the human brain in conjunction with present day computers, and where the newest human brains grow in an environment that is connected by technology. We cure disease and disability so that we can live happier lives, but as we do so we cease to exist as a purely biological species. And in that same way, I cannot believe that the machines of the future could not be at least partially organic.

“Dude. You’re teaching the machines to read. They’ll destroy us all!”

That was clearly in jest, but I’ve actually been fairly worried about future conflicts with machines. Seriously!
(Most of the following blog was also prompted by watching this TED talk. Warning: I re-state a lot of it.)

I do not, however, mean armed conflict with artificial minds, like the Terminator. I mean the slaughtering of noncombatants by robots controlled, at least in part, by other humans. This shouldn’t be in the least bit surprising. Humans have been using machines to kill each other for as long as they’ve been made, with the latest generation being fighter jets, helicopters and tanks. These are tools of war, and are incredibly powerful ones at that. I might even claim that these machines save lives by ending conflicts quickly and without putting their pilots in as much danger as the front lines might. That last argument is why I’m scared about the machine uprising.

In the early 20th century we predicted that robots would soon join us along with flying cars. Flying cars haven’t gotten here yet, but to a large degree robots have, they just aren’t in your living room (usually).

I find it a bit amusing, to be honest, the way Hollywood (and the game industry) seems fixated on the killer AI. While not an impossibility, the decision of an AI to kill off the only other sentient beings in contact with it seems both unlikely and quite far down the road. When the terminator shows up, he won’t be a humanoid AI with a heavy accent, he’ll be a real person controlling the equivalent of a 21st century tank — one without a driver.

Already in use for war are armed UAVs and their ground-treading counterparts, capable of killing without requiring a local pilot. DARPA is also hard at work pumping research funds into armored/robotic exoskeletons for the few humans that will need to be on the field. From medicine to reconnaissance to transport, robots undoubtedly will embed themselves in what it means for America to fight a war.

But the law of exponential growth (e.g. Moore’s law) means it won’t end there. Once we have exoskeletons, we’ll have the ability to make super-exoskeletons. Once the majority of front-line soldiers are machine it’ll become imperative that all of them are. Who needs human hands on the battlefield when metallic ones are superior? I’m not saying that battlefields will become devoid of human life, but that the roles of humans will become akin to people playing video games.

What becomes the target, in this future? What’s the point of it all? Do you aim for the factories, run by robots to make robots, or maybe the transport chains of autonomous vehicles delivering supplies to their mechanical pals. Do you aim for the HQ, and hope to track down something that could be anywhere on the planet? Do you attempt to outperform your opponent in the arms race and develop a virus or something equally devastating that lets you wipe out everything in one blow? Or do you aim for the population centers, and hope that a hostage city will force a surrender? The answer is none of the above, because modern warfare is dying alongside the newspaper industry as technology mercilessly reshapes our world.

The advance of technology has already majorly transformed the face of war twice by my count. The first was gunpowder, which destroyed the warrior caste, reshaped the fort and eventually made the idea of rigid formation laughable. The second was the atomic bomb,of which the consequences are not yet fully clear, but in my mind involve the development of proxy warfare. With proxy war comes terrorism, which I’ll define here as the unexpected killing of civilians by an organization (other than a nation) for the purposes of advancing an agenda.

If we think about how advancing robotics effects terrorism, this is where things really get ugly to me. The gift of an unmanned vehicle is that the human life of the pilot can be spared–but what if the UAV’s goal is to fly into a building? As the role of humans in war becomes that of chess players rather than pawns, it becomes largely possible for a single man to wage war. And as the fires of industry continue to burn, we’ll become increasingly more helpless to resist. The only solution will be to have no enemies or to turn to our computers to save us by hiding us away from an unseen threat that could be anywhere. It’s an awful lot like the possible future of biological weaponry. It’s an awful lot like the possible future of a black market for portable nuclear weapons.

Don’t fear the machine. Fear the maker.