Character Creator: The Game | Possibly coming as soon as next year?

Several years ago, a neat article in Forbes appeared:

The Case For ‘Character Creator: The Game’

I found it only because of my perpetual desires to find a way to design fictional characters and personas for various story ideas. I’ve been searching since 2013 for the perfect one, but they all have some shortcoming.

The most technically robust character creators were part of dedicated games, but of course you actually needed those games to get the experience. More than that, since they were parts of games, the base character creators very often did not have all possible customization items from the start— you’d have to buy and unlock more items and accessories as you played the game. What’s more, since games are often thematic, you may not be able to create the exact kind of character you want if there are any specific details in mind that aren’t also available in the creation system. But all in all, retail games have the best graphics of them all and you can usually do a lot more with them.

Free online character creators came in two packages: graphical programs and dress-up games. The former, which includes stuff like Mixamo, certainly have more ways to pose but have very few customization options since you either needed to download extra packs from their store or had to design it yourself.

The latter has always been the easiest. Dress-up games are basically just flash games where you dress up an avatar, typically designed like a doll, superhero, or anime character.

The big problems with dress-up games is that they are very often thematic and the art is dodgy— considering they’re made for flash game sites and offer little to no financial restitution for their creators, art assets are typically ready-made and low quality. There’s usually only one perspective— full-frontal or, less often, quarter-turn. And save for the best ones, you can’t edit any aspect of your character’s body outside of token masculine or feminine features since these are indeed dress-up games. In other words, you get what you pay for. You decided to go the free route instead of commissioning an artist, so you can’t complain that your character looks cheap.

With the rise of GANs, this may change in very short order. Indeed, it is entirely possible that we are within a year or two of a true “character creator: the game”. What’s more, the capabilities of such will be far beyond anything we see today even in the highest quality character creation systems.

This is due to three important factors:

  1. Text-to-image synthesis. In this theoretical game, you won’t necessarily need to fiddle with sliders, attachable items, or presets. Instead, you could type in a description into a box and near-instantly get your design as an output. Say I want to design an anime-style girl with jet-black hair, blue skin, pink eyes, and wearing such-and-such clothes with a devil tail and steampunk wings. Normally, I’d have to go through a series of different menus starting with the basic body type, then the hair, then the face, and so on and so forth. Here, that simple description alone will generate an image. If it’s not the one I want, I can keep generating them until I find one that’s at least close enough and then go in to edit the finer details if needbe.
  2. Variable artstyle or graphics. If I want to create a character in the style of the Simpsons, I either need to commission an artist who draws in that style, find a flash game that allows me to edit a character and hope they have what I want, or learn to draw myself. And what if I want another character in 3D but in a janky, Sega Saturn or PS1-style polygonal graphics? With this theoretical game, this won’t be much of a problem either. As long as you give the GAN something from your preferred style, of course, it could conceivably give you a character that’s minimalist, blocky, cel-shaded, photorealisic, and everything in between. For example, if I want to generate a character that looks as if it were drawn by the mangaka Akira Toriyama, I could. If I wanted a character “drawn” by cartoonist Tex Avery, I could very well get one. If I wanted a photorealistic avatar, I could have that as well. This could be used to create that generated comic I talked about before, and it could also theoretically be used to create character models that modders can insert into old games.
  3. Unlimited flexibility. Because of the aforementioned aspects, there’s no limit to what you can create. You wouldn’t need to worry about whether or not certain assets are in the engine— as long as you can provide the GAN with some representation of that asset, it’ll be able to translate it onto your character. For example: almost all character creators don’t have wild, electrical, Super Saiyan-esque hair. But if you can give the GAN images like this, it will remember that design and even be able to fill in the blanks if it’s not exactly what you want. What if your character is supposed to have neon glow-in-the-dark hair like this? You’re welcome, I’ve just given you all you really need for your character in the future.

The possibilities are endless. And of course, if this GAN can create a character, it can obviously create other things. But this is what I’m focusing on at the moment.

I think we’ll see early variants of it this year, building off the recently released StyleGAN network. We’ll even see some text-to-image synthesis, which is well within the powers of current day AI.

Artificial Intelligence: A Summary of Strength and Architecture

Not all AI is created equal

Types of Artificial Intelligence: Redux

Artificial intelligence has a problem: no one can precisely tell you what it is supposed to be. This makes discussing its future difficult. Current machine learning methods are impressive by the standards of what has come before, and certainly we can give various systems and networks enough power to rival and exceed human capabilities in narrow areas. The contention is whether or not these networks qualify as “artificial intelligence”.

My personal definition of artificial intelligence is a controversial one, as I am privy towards lumping even basic software calculations under the umbrella of “AI”. This is because there are essentially two separate kinds of “artificial intelligence”— there is the field of artificial intelligence research, which is a branch of computer science, and there is the popular connotations of artificial intelligence. AI is popularly known as being “computers with brains of varying intelligence”.

Business rule management systems are not commonly considered to be “artificial intelligence” in the popular imagination. Indeed, the very name conjures a sort of gunmetal-boring corporate software model. Yet BRMS software is one of the most widely commercialized forms of AI, dating back to the late ’80s.

If we limit all AI to “computers capable of creative thinking”, even many classic sci-fi depictions of AI would not qualify. Yet if my terrifying and anarchist definition became dominant, then we would have to presume that the Sumerians created the first AIs when they invented abacuses.

This is one reason why the original post on the various types of AI doesn’t work. But there are plenty more.


Another bottleneck in understanding the future of AI research is our limited imaginings of artificial general intelligence— a feat of engineering considered equal to the creation of practical nuclear fusion, room-temperature superconductors, and metallic hydrogen. As with all of these, the possibilities are much wider than we initially conceived. Yet it’s with AGI that I feel there is a great deal of hype and misunderstanding that would be more easily turned to practical breakthroughs if there were a shift in how we perceived it.

I, for one, always found it odd that we equate “artificial general intelligence” with “human-level AI” despite the fact that every animal lifeform possesses general intelligence— yet no one seriously claims that nematodes and insects are our intellectual rival.

“Surely,” I said as far back as 2012, “there has to be something that comes before human-level AI but is still well past what we have now.”

A related issue is that we compare and contrast narrow AI software with imagined general AI many decades henceward, allowing ourselves nothing to bridge the gap. There is no ‘intermediate’ AI.

All AI is either narrow and weak or general and strong. We have no popular ideas for “narrow and strong” AI despite the fact we have developed a multitude of narrow networks that have far surpassed human capabilities. We also have no popular ideas for “general and weak” AI, which is to say an AI that is capable of generalized learning but is not as intelligent as a human. This could be for a motley variety of factors, many of them coming down to basic neuroscience— for example, something that learns on a generalized level may still lack agency.

So here is a basic rundown on my revised “map” of AI, which has three degrees: ArchitectureStrength, and Ability.


Architecture is defined by an AI’s structural learning capacity and is already known by the terms “narrow AI” and “general AI“. Narrow AI describes software that is designed for one task. General AI describes software that can learn to accomplish a wide variety of tasks. Of course, this is usually synonymous with software that can learn to accomplish any task at the same level as a human being, which I’ll explain later why that isn’t necessarily always the case.

I wish to add one more category: “expert AI“. Expert artificial intelligence, or artificial expert intelligence (XAI or AXI) describes artificial intelligences that are generalized across a certain area but are not fully generalized, as I’ll explain in greater detail below. You may see it as “less-narrow AI”, with computers capable of learning a variety of like narrow tasks. AXI is very likely the next major step in AI research over the next five to ten years.


Mechanical Calculations: These are calculators and traditional computer software. They only do calculations. Addition, subtraction, multiplication, division, etc. There is no intelligence involved. Mechanical calculations can be considered the ‘DNA’ of AI, the root by which we are able to construct intelligences but by itself is not a form of AI. As aforementioned, this level starts with ancient abacuses.

Artificial Narrow Intelligence: Artificial narrow intelligence (ANI), colloquially referred to as “weak AI”, refers to software that is capable of accomplishing one singular task, whether that be through hard coding or soft learning. This describes almost all AI that currently exists, and is also possibly the most consistently underestimated technology of the past 100 years. Just about any AI you can think of, from Siri down to motion sensors, qualify as ANI. Once you program an ANI to do a certain task, it is locked into that task. Just as you cannot make a clock play music unless you reformat its gears for that purpose, you must reprogram an ANI if you want it to do something it was not programmed to do. This includes narrow machine learning networks that are limited to cohesive parameters. Machine learning involves using statistical techniques to refine an agent’s performance, and while this can be generalized for much more interesting uses, it is not magical and is natively a narrow field of AI.

Artificial Expert Intelligence: Artificial expert intelligence (AXI), sometimes referred to as “less-narrow AI”, refers to software that is capable of accomplishing multiple tasks in a relatively narrow field. This type of AI is new, having become possible only in the past five years due to parallel computing and deep neural networks. The best example is DeepMind’s AlphaZero, which utilized a general-purpose reinforcement learning algorithm to conquer three separate board games— chess, go, and shogi. Normally, you would require three separate networks, one for each game, but with AXI, you are able to play a wider variety of games with a single network. Thus, it is more generalized than any ANI. However, AlphaZero is not capable of playing any game. It also likely would not function if pressed to do something unrelated to game playing, such as baking a cake or business analysis. This is why it is its own category of artificial intelligence— too general for narrow AI, but too narrow for general AI. It is more akin to an expert in a particular field, knowledgeable across multiple domains without being a polymath. This is the next step of machine learning, the point at which transfer learning and deep reinforcement learning allow for computers to understand certain things without needing to be mechanically fed rules and capable of expanding its own hyperparameters.

Artificial General Intelligence: Artificial general intelligence (AGI), sometimes referred to as “strong AI”, refers to software capable of accomplishing any task, or at least any task accomplishable by biological intelligence. Currently, there are no AGI networks on Earth and we have no idea when we’ll create the first truly general-purpose artificial intelligence. However, AGI is a much greater qualitative improvement over AXI than AXI is over ANI— whereas AXI is multi-purpose, AGI is omni-purpose. Theoretically, a sufficiently advanced AGI is indistinguishable from a healthy adult human— and even this represents the lower end of the true capabilities of strong artificial intelligence.


Strength in AI is defined by an AI’s intellectual capacity compared to humans.

Weak Artificial Intelligence is any AI that is intellectually less capable than humans but is colloquially used to describe all narrow AI.

Strong Artificial Intelligence is any AI that is intellectually as capable or more capable than humans but is colloquially used to describe all general AI.

Because of colloquial usage, “weak” and “narrow” are interchangeable terms. Likewise, “strong” and “general” are used to mean the same thing. However, as AI progresses and increasingly capable computers leave the realm of science fiction and enter reality, we are discovering that there is a spectrum of strength even within AI architectures.

For example: we used to claim that only human-level general intelligence will be capable of defeating humans at Chess. Yet DeepBlue accomplished the task over twenty years ago and no one seriously claims that we are being ruled over by superintelligent machine overlords. People said only strong AI could beat humans at for Go, as well as for interpersonal game shows like Jeopardy. Yet “weak” narrow AIs were able to trounce humans in all these tasks and general AI is still nowhere in sight.

My belief is that nearly any task we can conceive can be accomplished by a sufficiently strong narrow intelligence, but since we conflate strong AI with general AI, we consistently blind ourselves to this truth. That’s why I’ve decided to decouple strength from architecture.

Weak Narrow Artificial Intelligence: Weak Narrow AI (WNAI) describes software that is subhuman or approaching par-human in strength in one narrow task. Most smart speakers/digital assistants like Amazon Echo and Siri occupy this stratum as they do not possess any area of ‘smarts’ that is equal to that of humans, though their speech recognition abilities does lead to us psychologically imbuing them with more intelligence than they actually possess. These are merely the most visible WNAI— most AI in the world is in this category by nature and this will always be the truth, as there is only so much intelligence you need to accomplish certain tasks. As I mentioned in the original post, you don’t need artificial superintelligence to run task manager or an industrial robot. Doing so would be like trying to light a campfire with Tsar Bomba. Interestingly, this is a lesson a lot of sci-fi overlooks due to the belief that all software in the distant future will become superintelligent, no matter how inefficient it may be.

Strong Narrow Artificial Intelligence: Strong Narrow AI (SNAI) describes software that is par-human or superhuman in strength in one narrow task. In my original post, I made the grievously idiotic mistake of conflating ‘public’ AI with SNAI, despite the fact that SNAI have essentially been around since the early 1950s— even a program that can defeat humans more than 50% of the time at tic-tac-toe can be considered a “strong narrow AI”. This is one reason why the term likely never went anywhere, as our popular idea of any strong AI requires worlds more intelligence than a tic-tac-toe lord. But strength is subjective when it comes to narrow AI. What’s strong for plastic may be incredibly weak for steel. What’s usefully strong for glass is likely far too brittle for brick. This is still true for narrow AI. Right now, SNAIs are more popularly represented by game-mastering software such as AlphaGo and IBM Watson because they require some level of proto-cognition and somewhat recognizable intellectual capability that is utterly alien compared to the likes of Bertie the Brain.

Weak Expert Artificial Intelligence: Weak expert AI (WXAI) describes software that is subhuman or approaching par-human in strength in a field of tasks. Due to expert AI still being a novel development as of the time of writing, we don’t have many examples, and ironically one of few examples we have is actually strong expert AI.  However, I can imagine WXAI as being similar to what Google DeepMind and OpenAI are currently working on with their Atari-playing programs. DeepMind in particular uses one generalized network to play a wide variety of games, as aforementioned. And while many of them have reached par-human and superhuman levels of playing, so far we have not received any word that this algorithm has achieved par-human across all Atari games. This would make it closer to approaching par-human strength. This becomes even more noticeable when taking into consideration that this network’s play experience likely has not been transferred to games from more advanced consoles such as the NES and SNES.

Strong Expert Artificial Intelligence: Strong expert AI (SXAI) describes software that is par-human or superhuman in strength in a field of tasks. Currently, the best (and probably only) known example is DeepMind’s AlphaZero network. To a layman, an SXAI will likely seem indistinguishable from an AGI, though there will still be obvious parameters it cannot act beyond. This is also likely going to be a very peculiar and frightening place for AI research, an era where AIs will begin to seem too competent to control despite their actual limitations. One major consideration is that since SXAI will have capabilities beyond one narrow field, it can’t be considered “strong” if it’s only competent in a single field. I would reckon that if it’s parhuman in 30% of all capabilities, it qualifies as SXAI.

Weak General Artificial Intelligence: Weak general AI (WGAI) describes software that is subhuman or approaching par-human in strength in general, perhaps with a stronger ability in a particular narrow field but otherwise not as strong as the human brain. Oddly enough, I’ve very rarely heard of the possibility of WGAI. If anything, it’s usually believed that the moment we create a general AI, it will rapidly evolve into a superintelligence. However, WGAI is very likely going to be a much longer-lived phenomenon than currently believed due to computational limits. WGAI is not nearly as magical as SGAI or ASI— should the OpenWorm project bear fruit, the result would be a general AI. The only difference being that it would prove to be an extraordinarily weak general AI, which gives this term a purpose. Most robots used for automation will likely lie in this category, if not SXAI, since most tasks merely require environmental understanding and some level of creative reactivity rather than higher order sapience.

Strong General Artificial Intelligence: Strong general AI (SGAI) describes software that is par-human or superhuman in strength across all general tasks. This is sci-fi AI, agents of such incredible intellectual power that they rival our own minds. When people ask of when “true AI” will be created, they typically mean this.

Artificial Superintelligence: Artificial superintelligence (ASI) describes a certain kind of strong general artificial intelligence, one that is so far beyond the capabilities of the human brain as to be virtually godlike. The point at which SGAI becomes ASI is a bit fuzzy, as we tend to think of the two much of the same way we think of the difference between stellar-mass and supermassive black holes. My hypothesis is that SGAI can still be considered superhuman and not break beyond theoretical human capabilities— the point at which SGAI becomes ASI is the exact point at which a computer surpasses all theoretical human capabilities. If you took our intelligence and brought it to as many standard deviations down the curve as genetically possible, you will eventually come across some limit. Biological brains are electrochemical in nature, and the fastest brain signal travels at around 270 miles per second. There is, in theory, a maximum human intelligence. ASI is anything beyond that point. All the heavens lie above us.


Ability in AI is defined by an AI’s cognitive capabilities, ranging from complete lack of self-awareness all the way to sapience. I did not create this list, but I find it to be extremely useful towards understanding the future development of artificial intelligence.

Reactive: AI that only reacts. It doesn’t remember anything; it only experiences what exists and reacts. Example: Deep Blue.

Limited Memory: This involves AI that can recall information outside of the immediate moment. Right now, this is more or less the domain of chatbots and autonomous vehicles.

Theory of Mind: This is AI that can understand the concept that there are other entities than itself, entities that can affect its own actions.

Sapience: This is AI that can understand the concept that it is an individual separate from other things, that it has a body and that if something happens to this body, its own mind may be affected. By extension, it understands that it has its own mind. In other words, it possesses self-awareness. It is capable of reflecting on its sentience and self-awareness and can draw intelligent conclusions using this knowledge. It possesses the agency to ask why it exists. At which point it is essentially conscious.



Artificial Intelligence: The How

Meet the Sensory Orb. It’s a flesh orb that possesses a powerful synthetic brain. There are several questions one must ask about the Sensory Orb.

Why is the Sensory Orb important? Because it’s the key to artificial general intelligence of the human variety.

You see, there are multiple strains of thought as to how to achieve AGI. Most serious computer scientists and neurologists know that it’s not something we’re likely to achieve anytime soon, but their reasoning is different from what most people might assume.

We don’t understand how intelligence or consciousness works, first and foremost. However, we can try our best at mimicking what we see. Perhaps one of these methods will work. After all, we don’t need to understand every single facet of something in order to make it work. We also expect the final leap to AGI to be accomplished by AI itself. So why is getting there so hard in the first place?

For one, we are still limited by computing power. It seems ridiculous considering how stupidly powerful computers today really are, but it’s true— while the most powerful supercomputers have exceeded the expected operations-per-second done by the brain, these computers still cost hundreds of millions of dollars. We need to bring that cost down if we want to make AI research practical.

But forget about the cost for a moment. Let’s pretend DeepMind had TaihuLight in their possession and could utilize every FLOPS for its own purposes. Would we see major breakthroughs in AI? Of course. But would we see human-level AI? Not even close.

“But they’re DeepMind! Their AI has beaten the human champion at Go a decade before the experts said it could be done! How do they still lack AGI?”

For one, that’s not entirely true— experts said a computer could become the world champion at Go by 2016 if there were sufficient funding put into the problem. And sufficient funding did indeed arrive.

But more importantly, while DeepMind’s accomplishments cannot be overstated, they haven’t actually brought us any closer to human-level AGI.

I want you to marvel at the human brain. It’s a fine thing.

Here is a metal table. On top of this metal table are two brains. One is a newborn baby’s brain, and next to it is the brain of Stephen Hawking. Don’t worry, we’ll return the brains to their rightful owners after this blog post. But I want you to think about what these brains are capable of.

The newborn brain is already a powerful computer that’s learning every single second, forming new neural pathways as it experiences life. Mr. Hawking’s brain is a triple-A machine of cosmic proportions, always thinking and never resting.

Except these two facts are dirty lies. The brains before you aren’t doing anything of the sort. The newborn baby’s brain is not forming any new connections. Hawking’s brain isn’t thinking. And why? Because they are disembodied. They are no longer experiencing any senses, and the senses necessary to make thoughts even work are no longer there. They’re both equal in terms of active intelligence— zero.

If you asked the newborn baby’s brain to add two and two, you’d just look like a fool because you’re talking to a tiny little blob of fat. Even if you asked Hawking’s brain the same question, you’d never get an answer. They can’t answer that question— they’re just brains. They don’t have ears to hear you. They don’t have eyes to see you. They don’t have mouths or hands to respond to you. You do not exist to them.

Despite what fiction may proclaim, brains are not actually ‘sentient’ without their bodies. A brain can’t “see” you or “respond” to you if you ask it a question, even if you stick it into a jar full of culture fluids.

If you hook up a screen and a keyboard to that brain, would you then have a proper sensory input in order to get the outputs of the newborn and Mr. Hawking? Of course not— brains did not evolve to be literal computers. You can’t just stick a plug into a brain and expect it to behave just like your desktop. In order to bring these two brains back to life, you’d need to construct whole bodies around their functions. And not just one or two of their functions— all of them.

So the point is: you can’t just take a human brain, set it out on a desk, and treat it like a fully-intelligent person. If you had Descartes’ Evil Demon or the Brain in a Vat, you could develop the brain until it possessed intelligence in a simulated reality, but the brain itself can do nothing for you. It sounds utterly insane to even contemplate.

Yet, for whatever reason, this is how we treat computers. We think that, if we had a computer with deep reinforcement recurrent spiked progressive neural networks and 3D graphene quantum memristors (insert more buzzwords here), we’d have AGI. In fact, you could have the servers running Skynet brought into real life, and you’d still not see AGI if your idea of making it intelligent is simply to feed it internet data.

Without sensory experiences, that computer will never achieve human-level intelligence. That’s not to say that we could achieve human-level AI today if we took ASIMO and decked it out with sensors, but the gist is that it would be foolish to ever expect synthetic intelligence surpassing humans by treating a computer to the furthest thing from human experiences.

And so we return to the Sensory Orb. The Orb itself is not natively intelligent. It’s no more intelligent than your desktop computer (circa 2027). But, unlike your desktop, it is fitted with a whole body of sensory inputs. The more it experiences, the more it body ‘evolves’ sensory outputs.

It is programmed to like being touched and tickled. Thus, if you tickle it, it will grow to like you. If you pinch its skin, it will roll away from you. Of course, it has to learn how to roll away first, but it quickly learns. If you keep pleasing it or abusing it, its visual senses will recognize you and either run to or away from you on sight. It has many preprogrammed instincts, including knowledge of “eating”. It knows how to find its charger, but if you bring it to its charger, it will grow to like you even more.

And if you teach the orb how to communicate with you through speech, you can teach it various commands. With enough training, the orb will learn to ask about itself. It can learn about other Sensory Orbs, learn about computers and flesh, learn about sensory experiences, and learn that it has its own body that allows it to ‘live’. So one day, you may be surprised if it asks about itself.

Is this human-level intelligence? Not necessarily, but it’s far closer to anything we have today. And we don’t necessarily need a real-life Sensory Orb to achieve this— a good-enough virtual simulation can also suffice. But nevertheless, the point remains: in order to achieve AGI, computers need to experience things..

Yuli’s Law: On Domestic Utility Robots

The advancement of computer technology has allowed for many sci-tech miracles to occur in the past 70 years, and yet it still seems as if we’ve hit a plateau. As I’ve explained in the post on Yuli’s Law, this is a fallacy— the only reason why an illusion of stagnation appears is because computing power is too weak to accomplish the goals of long-time challenges. That, or we have already accomplished said goals a long time ago.

The perfect example of this can be seen with personal computing devices, including PCs, laptops, smartphones— and calculators.

The necessary computing power to run a decent college-ready calculator has long been achieved, and miniaturization has allowed calculators to be sold for pennies.  There is no major quantum leap between calculators and early computer programs.

Calculating the trajectory of a rocket requires far less computing power than some might think, and this is because of the task required: guiding an object using simple algorithms. A second grader could conceivably create a program that guides a bottle rocket in a particular direction.

This is still a step up from purely mechanical systems that give the illusion of programming, but there are obvious limits.

I’ll explain these limits by using a particular example, an example that is the focus of this post: a domestic robot.  Particularly, a Roomba.


An analog domestic robot has no digital programming, so it is beholden to its mechanics. If it is designed to move in a particular direction, it will never move in another direction. In essence, it’s exactly like a wind-up toy.
I will wind up this robot and set it off to clean my floors. Thirty seconds later, it makes a left turn. After it makes this left turn, it will move for twenty seconds before making another left turn. And so on and so forth until it returns to its original spot or runs out of energy.

There are many problems with this. For one, if the Roomba runs into an obstacle, it will not move around it. It will make no attempt to avoid it a second time through. It only moves along a preset path, a path you can perfectly predict the moment you set it off. There is a way to get around this— by adding sensors. Little triggers that will force a turn early if it hits an object.


Let’s bring in a digitally programmed Roomba, something akin to a robot you could have gotten in 2005. Despite having a digital computer for a brain, it seems to act absolutely no different from the mechanical Roomba. It still gets around by bumping into things. Even though the mechanical Roomba could have been created by someone in Ancient Greece, yours doesn’t seem any more impressive on a practical level.

Thus, the robot seems to be more novel than practical. And that’s the perception of Roombas today— cat taxis that clean your floor as a bonus rather than a legitimate domestic robot.
Yet this is no longer a fair perception as the creators of the Roomba, iRobot, have added much-needed intelligence to their machines. This has only been possible thanks to increases in computing power allowing for the proper algorithms to run in real-time.

For example, a 2017-era Roomba 980 can actually “see” in advance when it’s about to run into something and avoid it. It can also remember where it’s been, recognize certain objects, among other things (though Neato’s been able to do this for a long time). Much more impressive, though still not quite what we’re looking for.

What’s going on? Why are robots so weak in an age of reusable space rockets, terabyte smartphones, and popular drone ownership?

We need that last big push. We need computers to be able to understand 3D space.

Imagine a Roomba 2000 from the year 2025. It’s connected to the Cloud and it utilizes the latest in artificial intelligence in order to do a better job than any of its predecessors. I set it down, and the first thing it begins doing is mapping out my home. It recognizes any obstacle as well as any stain— that means if it detects dog poop, it’ll either avoid it or switch to a different suction to pick it up. Once it has mapped my house, it is able to get a good feel for where things are and should be. Of course, I could also send it a picture of another room, and it will still be able to get a feel for what it will need to do even if it’s never roamed around inside before.

The same thing applies to other domestic robots such as robotic lawn mowers— you’d rather have a lawn mower that knows when to stop cutting, whether that means because it’s moving over a new terrain or because it’s approaching your child’s Slip n’ Slide. Without the ability to comprehend 3D space or remember where it’s been or where it needs to go, it’ll be stuck operating within a pre-set invisible fence.

Over all of this, there’s the promise of bipedal and wheeled humanoid robots working in the home. After all, homes are designed around the needs of humans, so it makes sense to design tools modeled after humans. But the same rules apply— no comprehension of 3D space, no dice.

In fact, a universal utility robot like a future model of Atlas or ASIMO will require greater advancements than specialized utility robots like a Roomba or Neato. They must be capable of utilizing tools, including tools they may never have used before. They must be capable of depth perception— a robot that makes the motions of mopping a floor is only useful when you make sure the floor isn’t too closer or far away, but a robot that genuinely knows how to mop is universally useful. They must be capable of understanding natural language so you can give them orders. They must be flexible, in that they can come across new and unknown situations and react to them accordingly. A mechanical robot would come across a small obstacle, fall over, and continue moving its legs. A proper universal utility robot will avoid the obstacle entirely, or at least pick itself up and know to avoid the obstacle and things like it. These are all amazingly difficult problems to overcome at our current technological level.

All these things and more require further improvements in computing power. Improvements were are, indeed, still seeing.

Mother Jones – “Welcome Robot Overlords. Please Don’t Fire Us?”

Evolution of Automation: A Technist Perspective






















































From EnergyBC:A Brief History of Energy Use





































Unexplained Mysteries of the Future

I am only a half-believer in the paranormal, so taking mysteries of the unexplained at face value smacks of the ridiculous. Yet I can never shake those doubts, hanging onto my mind like burrs.
The mammalian brain fears and seeks the unknown. That’s all I want— to know. The chance any one particular paranormal or supernatural happening is real is infinitesimal. Cryptids are usually another story, save for the most outlandish, but what likelihood is there that evolution wrought a lizard man or a moth man? Or that certain dolls are cursed?
However, I won’t cast off these reports completely until I can know for sure that they either are or are not true, as unlikely as they may be.

So here are a few words on the subject of paratechnology.

Self-Driving Cars Have Ruined The Creepiness of Self-Driving Cars

Imagine it’s a cool summer evening in 1969. You’re hanging with your mates out in the woods, minding your own business. All of a sudden, as you pass near a road, you see an Impala roll on by, creaking to a stop right as it closes in on your feet. Everything about the scene seems normal— until you realize that’s your Impala. You just saw your own car drive up to you. But that’s not what stops your heart. When you walk up to the window to see who’s the fool who tried to scare you, horror grips your heart as you realize the car was driving itself.

Needless to say, when your grandson finds the burned out shell of the car 50 years later, he doesn’t believe you when you doggedly claim that you saw the car acting on its own.

Except he would believe you if your story happened in the present day.

Phantom vehicles are a special kind of strange, precisely because you’d never expect a car to be a ghost. After all, aren’t ghosts the souls of the deceased?

(ADD moment: this is easy to rectify if you’re a Shintoist)

Nevertheless, throughout history, there have been reports of vehicles that move on their own, with no apparent driver or means of starting. The nature of these reports is always suspect— extraordinary claims require extraordinary evidence— but there’s undeniably something creepy about the idea of a self-driving vehicle.

Unless, of course, you’re talking about self-driving vehicles. You know, the robotic kind. Today, walking out in the woods and seeing your car drive up to you is still a creepy sight to behold, but as time passes, it grows less ‘creepy’ and more ‘awesome’ as we imbue artificial intelligence into our vehicles.

This does raise a good question— what happens if an autonomous car became haunted?


The Truth About Haunted Smarthouses

For thousands of years, people have spoken of seeing spectres— ghosts, phantoms, spirits, whathaveyou. Hauntings would occur at any time of day, but everyone knows of the primal fear of things that go bump in the night. It’s a leftover of the days when proto-humans were always at risk of being ambushed by hungry nocturnal predators, one that now best serves the entertainment industry.

Ghosts are scary because they represent a threat we cannot actively resist. A lion can kill you, but at least you can physically fight back. Ghosts are ethereal, and their abilities have never been properly understood. This is because we’ve never been fully sure if they’re real at all. Science tells us they’re all in our heads, but science also tells us that everything is all in our heads. Remember: ghosts are ethereal, meaning they cannot actually be caught. Thus, they cannot be studied, rendering them completely useless to science. Anything that cannot be physically examined might as well not exist. Because ghosts are so fleeting, we never even get a chance to study them, instead leaving the work to pseudoscientific “ghost hunters”.  By the time anyone has even noticed a ghost, they’ve already vanished.

Even today, in the era of ubiquitous cameras and surveillance, there’s been no definitive proof of ghosts. No spectral analysis, no tangible evidence, nothing. Why can’t we just set up a laboratory in the world’s most haunted house and be done with it? We’ve tried, but the nature of ghosts (according to those who believe) means that even actively watching out for a ghost doesn’t mean you’ll actually find one, nor will you capture usable data. Our technology is too limited and ghosts are too ghostly.

So what if we put the burden onto AI?

Imagine converting a known haunted house into a smarthouse, where sensors exist everywhere and a central computer always watches. No ghost should escape its notice, no matter how fleeting.

Imagine converting damn near every house into a smarthouse. If paranormal happenings continue evading smarthouse AIs, that casts near irrefutable doubt onto the larger ghost phenomenon. It would mean ghosts cannot actually be meaningfully measured.

Once you bring in transhumanism, the ghost phenomena should already be settled. A posthuman encountering a spectre at all would be proof in and of itself— and if it never happens— if ghosts remain the domain of fearful, fleshy biological humans— then we will properly know once and for all that the larger phenomenon truly is all in our heads.

Bigfoot Can Run, But He Can’t Hide Forever

For the same reasons listed above, cryptids will no longer be able to hide. There’s little tangible evidence suggesting Bigfoot is real, but if there’s any benefit of the doubt we can give, it’s that there’s been very little real effort to find him. If we were serious about finding Bigfoot, we wouldn’t create ‘Bigfoot whistles’ or dedicate hour-long, two hundred episode reality shows to searching for scant evidence. We would hook up the Pacific Northwest with cameras and watch them all.

Except we can’t. INGSOC could never be watching you at all times for as long as the Party lacked artificial intelligence to do the grunt-work for them. That’s true in reality as it is true in fiction— if you have a million cameras and only a hundred people watching them, you’ll never be able to find everything that goes on. You’d need to be able to watch these videos at all moments every day, without fail. Otherwise, video camera #429,133 may capture a very clear image of Bigfoot, but you’d never know.

AI could meet the challenge. And if you need any additional help, call in the robots. Whether you go for drones, microdrones, or ground-traversing models, they will happily and thanklessly search for your spooky creatures of the night.

If, in the year 2077, when we have legions of super-ASIMOs and drones haunting the world’s forests, we still have no definitive proof of a variety of our more outlandish cryptids, we’ll know for sure that they truly were all stories.

Grades of Automation

  • Grade-I is tool usage in general, from hunter-gatherer/scavenger tech all the way up to the pre-industrial age. There are little to no complex moving parts.
  • Grade-II is the usage of physical automation, such as looms, spinning jennies, and tractors. This is what the Luddites feared. There are many complex moving parts, many of which require specialized craftsmen to engineer.
  • Grade-III is the usage of digital automation, such as personal computers, calculators, robots, and basically anything we in the modern age take for granted. This age will last a bit longer into the future, though the latter ends of it have spooked quite a few people. Tools have become so complex that it’s impossible for any one person to create all necessary parts for a machine that resides in this tier.
  • Grade-IV is the usage of mental automation, and this is where things truly change. This is where we finally see artificial general intelligence, meaning that one of our tools has become capable of creating new tools on its own. AI will also become capable of learning new tasks much more quickly than humans and can instantly share its newfound knowledge with any number of other AI-capable machines connected to its network. Tools, thus, have become so infinitely complex that it’s only possible for the tools themselves to create newer and better tools.

Grades I and IV are only tenuously “automation”— the former implies that the only way to not live in an automated society is to use your hands and nothing else; the latter implies that intelligence itself is a form of automation. However, for the sake of argument, let’s keep with it.

Note: this isn’t necessarily a “timeline of technological development.” We still actively use technologies from Grades I and II in our daily lives.

Grade-I automation began the day the first animal picked up a stone and used it to crush a nut. By this definition, there are many creatures on Earth that have managed to achieve Grade-I automation. Grade-I lacks complex machinery. There are virtually no moving parts, and any individual person could create the whole range of tools that can be found in this tier. Tools are easy to make and easy to repair, allowing for self-sufficiency. Grade-I automation is best represented by hammers and wheels.

A purely Grade-I society would be agricultural with the vast majority of the population ranging from sustenance farmers to hunter-gatherer-scavengers. The lack of machinery means there is no need for specialization; societal complexity instead derives from other roles.

Grade-II automation introduces complex bits and moving parts, things that would take considerably more skill and brainpower to create. As far as we know, only humans have reached this tier— and only one species of humans at that (i.e. Homo sapiens sapiens). Grade-II is best represented by cogwheels and steam engines, as it’s the tier of mechanisms. One bit enables another, and they work together to form a whole machine. As with Grade-I, there’s a wide range of Grade-II technologies, with the most complex ends of Grade-II becoming electrically powered.

A society that has reached and mastered Grade-II automation would resemble our world as it was in the 19th century. Specialization rapidly expands— though polymaths may be able to design, construct, and maintain Grade-II technologies through their own devices, the vast majority of tools require multiple hands throughout their lifespan. One man may design a tool; another will be tasked with building and repairing it. However, generally, one person can grasp all facets of such tools. Using Grade-II automation, a single person can do much more work than they could with Grade-I technologies. In summary, Grade-II automation is the mark of an industrial revolution. Machines are complex, but can only be run by humans.

Grade-III automation introduces electronic technology, which includes programmable digital computers. It is at this point that the ability to create tools escapes the ability of individuals and requires collectives to pool their talents. However, this pays off through vastly enhanced productivity and efficiency. Computers dedicate all resources towards crunching numbers, greatly increasing the amount of work a single person can achieve. It is at this point that a true global economy becomes possible and even necessary, as total self-sufficiency becomes near impossible. While automation unemploys many as computational machines take over brute-force jobs that once belonged to humans, the specialization wrought is monumental, creating billions of new jobs compared to previous grades. The quality of life for everyone undergoes enormous strides upwards.

A society that has reached and mastered Grade-III automation would resemble the world of many near-future science fiction stories. Robotics and artificial intelligence have greatly progressed, but not to the point of a Singularitarian society. Instead, a Grade-III dominant society will be post-industrial. Even the study of such a society will be multilayered and involve specialized fields of knowledge. Different grades can overlap, and this continues to be true with Grade-III automation. Computers have begun replacing many of the cognitive tasks that were once the sole domain of humans. However, computers and robots remain tools to complete tasks that fall upon the responsibility of humans. Computers do not create new tools to complete new tasks, nor are they generally intelligent enough to complete any task they were not designed to perform. The symbol of Grade-III is a personal computer and industrial robot.

Grade-IV automation is a fundamental sea change in the nature of technology. Indeed, it’s a sea change in the nature of life itself, for it’s the point at which computers themselves enter the fray of creating technology. This is only possible by creating an artificial brain, one that may automate even higher-order skills. Here, it is beyond the capability of any human— individuals or collectives— to create any tool, just as it is beyond the capability of any chimpanzee to create a computer. Instead, artificial intelligences are responsible for sustaining the global economy and creating newer, improved versions of themselves. Because AI matches and exceeds the cognitive capabilities of humans, there is a civilization-wide upheaval where what jobs remain from the era of late Grade-III domination are then taken by agents of Grade-IV automation, leaving humans almost completely jobless. This is because our tools are no longer limited to singular tasks, but can take on a wide array of problems, even problems they were not built to handle. If the tools find a problem that is beyond their limits, they simple improve themselves to overcome their limitations.

It is possible, even probable, that humans alone cannot reach this point— ironically, we may need computers to make the leap to Grade-IV automation.

A society that has reached Grade-IV automation will likely resemble slave societies the closest, with an owner class composed of humans and the highest order AIs profiting from the labor of trillions, perhaps quadrillions of ever-laboring technotarians. The sapient will trade among themselves whatever proves scarce, and the highest functions of society will be understood only by those with superhuman intelligence. Societal complexity reaches its maximal state, the point of maximum alienation. However, specialization rapidly contracts as the intellectual capabilities of individuals— particularly individual AI and posthumans— expands to the point they understand every facet of modern society. Unaugmented humans will have virtually no place in a Grade-IV dominant society besides being masters over anadigital slaves and subservient to hyperintelligent techno-ultraterrestrials. What few jobs remain for them will, ironically, harken back to the days of Grade I and II automation, where the comparative advantage remains only due to artificial limitations (i.e. “human-only labor”).

Grade-IV automation is alien to us because we’ve never dealt with anything like it. The closest analog is biological sapience, something we have only barely begun to understand. In a future post, however, I’ll take a crack at predicting a day in the life of a person in a Grade-IV society. Not just a person, but also society at large.