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.

I-Robot_Roomba_Autonomous_FloorVac_Vacuum_Cleaner

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.

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

Passenger Drones

One of the most interesting developments in sci-tech in the past few years is the sudden interest in the concept of “passenger drones“. That appears to be their most popular name, though you may have heard of them as “drone taxis” and “autonomous flying cars”. I’ve even seen the term “pilotless helicopter” used once or twice (though drones don’t necessarily have to be rotored vehicles). For the sake of this article, I’ll stick with ‘passenger drone’.

So what exactly is a passenger drone? In short, its name gives it away— a drone that can carry passengers. Typically, drones are defined as being “unmanned aerial vehicles”. You can see the conflict in definitions, hence why some are hesitant to actually call these ‘drones’. Nevertheless, linguistic drift has changed the definition of drone and that’s something drone hobbyists have to live with.

I say this because passenger drones are based on the designs of quadcopters, now popularly referred to as ‘drones’.

But enough about the linguistics.

Passenger drones represent the closest realization of yesteryear’s dream of flying cars. They are personal vehicles that theoretically anyone can own and use with ease, and they indeed work in three dimensions*. So why should we care about them when that dream has never come true before now?

*”Three dimensions” in transportation terms refers to the inclusion of flight. “Two dimensions” refers to ground and sea travel.

Simple: your answer is in the name. Again.

This is a drone. That means you are not the one piloting the vehicle. And I don’t mean ‘you’ specifically, but ‘you’ as a human. Humans did not evolve to navigate 3D space. We can barely manage traveling in 2D space at high speeds— proto-humans never had to move any faster than their fastest sprint. This becomes obvious when you view motor vehicle statistics. In the United States of America alone, over 30,000 people die in vehicular accidents yearly.
And despite this, we are not even in the top 5 for “most killed yearly in motor accidents.” The number one country is, not surprisingly, China: they lose well over 250,000 a year in car accidents.

Worldwide, 1.25 million die every year in motor accidents. And note, that’s deaths, not casualties. All of this is evidence that humankind is simply not designed well to casually travel at speeds higher than 20 miles per hour.

To throw another dimension and another two hundred miles per hour at us would unleash gigadeaths per year until humanity as a whole finally gives up. Human extinction by flying car.

This is the chief reason why flying cars aren’t a thing. Humans simply cannot handle it. Pilots have to go through thousands of hours of training just to become proficient, and that’s with vehicles that are already highly automated.

Indeed, as of right now, the closest thing to a “flying car” is a Cessna 172.

Of course, other reasons include the fact roadable vehicles and flying vehicles require completely different designs and aerodynamics, as well as the power requirements necessary to keep such a vehicle in the air. But perhaps we could overcome these issues if only there were a way for the common person to actually survive take-off, flight, and landing without killing himself.

Drones are that solution. Take away the need for the common person to do the flying.

That’s the promise passenger drones offer us. Again, there’s still the issue that flying is inefficient, but it’s always possible that passenger drones become a common sight over cities. Perhaps they’ll be privately owned; perhaps they’ll be municipally owned and rented out for use. This remains to be seen because the idea of flying cars and personal aerial vehicles being a real thing only became feasible within the past couple of years.

As of today, 4 April 2017, the first passenger drones will enter operation in Dubai, UAE in the summer of this year.

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.

Types of Artificial Intelligence

Not all AI is created equal. Some narrow AI is stronger than others. Here, I redefine AI, separating the “weak=narrow” and “strong=general” correlation.

Let’s talk about AI. I’ve decided to use the terms ‘narrow and general’ and ‘weak and strong’ as modifiers in and of themselves. Normally, weak AI is the same thing as narrow AI; strong AI is the same thing as general AI. But I mentioned elsewhere on this wide, wild Internet that there certainly must be such a thing as ‘less-narrow AI.’ AI that’s more general than the likes of, say, Siri, but not quite as strong as the likes of HAL-9000.

So my system is this:

    • Weak Narrow AI
    • Strong Narrow AI
    • Weak General AI
    • Strong General AI
    • Super AI

Weak narrow AI (WNAI) is AI that’s almost indistinguishable from analog mechanical systems. Go to the local dollar store and buy a $1 calculator. That calculator possesses WNAI. Start your computer. All the little algorithms that keep your OS and all the apps running are WNAI. This sort of AI cannot improve upon itself meaningfully, even if it were programmed to do so. And that’s the keyword— “programmed.” You need programmers to define every little thing a WNAI can possibly do.
We don’t call WNAI “AI” anymore, as per the AI Effect. You ever notice when there’s a big news story involving AI, there’s always a comment saying “This isn’t AI; it’s just [insert comp-sci buzzword].” Problem being, it is AI. It’s just not AGI.
I didn’t use that mention of analog mechanics passingly— this form of AI is about as mechanical as you can possibly get, and it’s actually better that way. Even if your dollar store calculator were an artificial superintelligence, what do you need it to do? Calculate math problems. Thus, the calculator’s supreme intellect would go forever untapped as you’d instead use it to factor binomials. And I don’t need ASI to run a Word document. Maybe ASI would be useful for making sure the words I write are the best they could possibly be, but actually running the application is most efficiently done with WNAI. It would be like lighting a campfire with Tsar Bomba.
Some have said that “simple computation” shouldn’t be considered AI, but I think it should. It’s simply “very” weak narrow AI. Calculations are the absolute bottom tier of artificial intelligence, just as the firing of synapses are the absolute bottom of biological intelligence.
WNAI can basically do one thing really well, but it cannot learn to do it any better without a human programmer at the helm manually updating it regularly.

Strong narrow AI (SNAI) is AI that’s capable of learning certain things within its programmed field. This is where machine learning comes in. This is the likes of Siri, Cortana, Alexa, Watson, some chatbots, and higher-order game AI, where the algorithms can pick up information from their inputs and learn to create new outputs. Again, it’s a very limited form of learning, but learning’s happening in some form. The AI isn’t just acting for humans; it’s reacting to us as well, and in ways we can understand. SNAI may seem impressive at times, but it’s always a ruse. Siri might seem smart at times, for example, but it’s also easy to find its limits because it’s an AI meant for being a personal virtual assistant, not your digital waifu ala Her. Siri can recognize speech, but it can’t deeply understand it, and it lacks the life experiences to make meaningful talk anyhow. Siri might recognize some of your favorite bands or tell a joke, but it can’t also write a comedic novel or actually genuinely have a favorite band of its own. It was programmed to know these things, based on your own preferences. Even if Siri says it’s “not an AI”, it’s only using preprogrammed responses to say so.
SNAI can basically do one thing really well and can learn to do that thing even better over time, but it’s still highly limited.

Weak general AI (WGAI) is AI that’s capable of learning a wide swath of things, even things it wasn’t necessarily programmed to learn. It can then use these learned experiences to come up with creative solutions that can flummox even trained professional humans. Basically, it’s as intelligent as a certain creature— maybe a worm or even a mouse— but it’s nowhere near intelligent enough to enhance itself meaningfully. It may be par-human or even superhuman in some regards, but it’s sub-human in others. This is what we see with the likes of DeepMind— DeepMind’s basic algorithm can basically learn to do just about anything, but it’s not as intelligent as a human being by far. In fact, DeepMind wasn’t even in this category until they began using the differentiated neural computing system because it could not retain its previously learned information. Because it could not do something so basic, it was squarely strong narrow AI until literally a couple months ago.
Being able to recall previously learned information and apply it to new and different tasks is a fundamental aspect of intelligence. Once AI achieves this, it will actually achieve a modicum of what even the most cynical can consider “intelligence.”
DeepMind’s yet to show off the DNC in any meaningful way, but let’s say that, in 2017, they unveil a virtual assistant to rival Siri and replace Google Now. On the surface, this VA seems completely identical to all others. Plus, it’s a cool chatbot. Quickly, however, you discover its limits— or, should I say, its lack thereof. I ask it to generate a recipe on how to bake a cake. It learns from the Internet, but it doesn’t actually pull up any particular article— it completely generates its own recipe, using logic to deduce what particular steps should be followed and in what order. That’s nice— now, can it do the same for brownies?
If it has to completely relearn all of the tasks just to figure this out, it’s still strong narrow AI. If it draws upon what it did with cakes and figures out how to apply these techniques to brownies, it’s weak general AI. Because let’s face it— cakes and brownies aren’t all that different, and when you get ready to prepare them, you draw upon the same pool of skills. However, there are clear differences in their preparation. It’s a very simple difference— not something like “master Atari Breakout; now master Dark Souls; now climb Mount Everest.” But it’s still meaningfully different.
WGAI can basically do many things really well and can learn to do them even better over time, but it cannot meaningfully augment itself. That it has such a limit should be impressive, because it basically signals that we’re right on the cusp of strong AGI and the only thing we lack is the proper power and training.

Strong general AI (SGAI) is AI that’s capable of learning anything, even things it wasn’t programmed to learn, and is as intellectually capable as a healthy human being. This is what most people think of when they imagine “AI”. At least, it’s either this or ASI.
Right now, we have no analog to such a creation. Of course, saying that we never will would be as if we were in the year 1816 and discussing whether SNAI is possible. The biggest limiting factor towards the creation of SGAI right now is our lack of WGAI. As I said, we’ve only just created WGAI, and there’s been no real public testing of it yet. Not to mention that the difference between WGAI and SGAI is vast, despite seemingly simple differences between the two. WGAI is us guessing what’s going on in the brain and trying to match some aspects of it with code. SGAI is us building a whole digital brain. Not to mention there’s the problem of embodied cognition— without a body, any AI would be detached from nearly all experiences that we humans take for granted. It’s impossible for an AI to be a superhuman cook without ever preparing or tasting food itself. You’d never trust a cook who calls himself world-class, only come to find out he’s only ever made five unique dishes, nor has he ever left his house. For AI to truly make the leap from WGAI to SGAI, it’d need someone to experience life as we do. It doesn’t need to live 70 years in a weak, fleshy body— it could replicate all life experiences in a week if needbe if it had enough bodies— but having sensory experiences helps to deepen its intelligence.

Super AI or Artificial Superintelligence (SAI or ASI) is the next level beyond that, where AI has become so intellectually capable as to be beyond the abilities of any human being.
The thing to remember about this, however, is that it’s actually quite easy to create ASI if you can already create SGAI. And why? Because a computer that’s as intellectually capable as a human being is already superior to a human being. This is a strange, almost Orwellian case where 0=1, and it’s because of the mind-body difference.
Imagine you had the equivalent of a human brain in a rock, and then you also had a human. Which one of those two would be at a disadvantage? The human-level rock. And why? Because even though it’s as intelligent as the human, it can’t actually act upon its intelligence. It’s a goddamn rock. It has no eyes, no mouth, no arms, no legs, no ears, nothing.
That’s sort of like the difference between SGAI and a human. I, as a human, am limited to this one singular wimpy 5’8″ primate body. Even if I had neural augmentations, my body would still limit my brain. My ligaments and muscles can only move so fast, for example. And even if I got a completely synthetic body, I’d still just have one body.
An AI could potentially have millions. If not much, much more. Bodies that aren’t limited to any one form.
Basically, the moment you create SGAI is the moment you create ASI.

From that bit of information, you can begin to understand what AI will be capable of achieving.


Recap:

“Simple” Computation = Weak Narrow Artificial Intelligence. These are your algorithms that run your basic programs. Even a toddler could create WNAI.
Machine learning and various individual neural networks = Strong Narrow Artificial Intelligence. These are your personal assistants, your home systems, your chatbots, and your victorious game-mastering AI.
Deep unsupervised reinforcement learning + differentiable spiked recurrent progressive neural networks = Weak General Artificial Intelligence. All of those buzzwords come together to create a system that can learn from any input and give you an output without any preprogramming.
All of the above, plus embodied cognition, meta neural networks, and a master neural network = Strong General Artificial Intelligence. AGI is a recreation of human intelligence. This doesn’t mean it’s now the exact same as Bob from down the street or Li over in Hong Kong; it means it can achieve any intellectual feat that a human can do, including creatively coming up with solutions to problems just as good or better than any human. It has sapience. SGAI may be very humanlike, but it’s ultimately another sapient form of life all its own.

All of the above, plus recursive self-improvement = Artificial Superintelligence. ASI is beyond human intellect, no matter how many brains you get. It’s fundamentally different from the likes of Einstein or Euler. By the very nature of digital computing, the first SGAI will also be the first ASI.

Cyberkinesis

Cyberkinesis: The manipulation of digital and robotic apparatuses through one’s mind. Also known as technokinesistechnopathy, and psychotronics.

Which one is technically correct? I don’t believe it matters, though I have heard more use ‘technopathy’ to describe a superpower where one literally controls machines with their native mind while ‘cyberkinesis’ is used to describe augmentation that allows a person to do such. Thus, I tend towards ‘cyberkinesis.’

Cyberkinesis is a fun little thing; I remember a cyberkinetic toy I played with back in 2010.

 

There are also other cyberkinetic products one can purchase right now, such as Emotiv’s Insight.

So it’s not science fiction, but the applications are still rather fleeting. Fast forward a decade, when algorithms will be much more capable of deciphering our brain waves, and you’ll begin to notice that our phones have become ‘telepathy machines.’

2026 Smartphones

I remember when I first saw a smartphone. The year was 2006, and I was a relatively normal elementary school kid who had just entered 6th grade. One of my classmates was bragging about her flashy new status symbol— a BlackBerry Pearl. She was talking about how she could access this website called ‘MySpace’ and how this phone could hold about two hundred (compressed) songs.

“God Christ! Two hundred songs on a phone? Unbelievable!” I thought. At this point in my life, I was still using CD players and I owned maybe four or five CDs. This idea of having hundreds of songs at my fingertips was beyond me— let alone also being able to access the Internet in the palm of my hand.

Fast forward ten years and such a thing barely barely warrants a “meh” from me. Two hundred songs? I have over a hundred playlists, and each one averages roughly double that. But that’s a sign of the times, isn’t it? The phone I have dates from 2013, so it’s still outdated, but it’s also an order of magnitude greater than that spoiled 6th grader’s “unbelievable” phone.

But it’s still a cheap phone, all things considering. Compared to the 6th grader’s, whose parents spent a pretty penny on it, I barely gave a crap when choosing this one out. It gets things done, so I don’t care too much. However, in the future, I plan to dig into my wallet to pay for quality.

What’s my ideal smartphone?

I want something that holds 512 GB of storage and has 4 GB of RAM. The iPhone 7s Plus sounds like it’ll come very close to my ideal, so that’s why I will probably buy one. However, I might also hold out until the iPhone 8s Plus. When that happens, I’ll keep it close for roughly 7 more iPhone iterations, until about 2026.

What do I expect there to be in 2026?

Let me start by saying I expect my current ‘high end’ to be the standard. If that, of course. It would be better if there were phones that could hold upwards of 3 TB and have 32 GB of RAM.

In fact, by 2026, I wouldn’t be at all surprised if phones could hold 64 to 128 TB. What’s the use of all this space? We always ask that question.

In 2026, it’ll be common for phones to do several things

  • Holographic displays. iPhone 7 is allegedly going to achieve this this year. So holograms? A given for 2026 phones.
  • Virtual reality. Again, there are already VR-capable phones on the market (Gear VR), but if we want phones that can withstand the power of higher-end VR systems (like the Rift or Vive), we’re going to need exponentially more powerful hardware.
  • Cyberkinesis. Phones of the 2020s will be expected to have the ability to utilize texting via thinking software. I can only imagine the hardware necessary. Cyberkinesis will be highly important for several other features to work, I tell you.
  • Virtual assistants. Artificial intelligences that help you out with your every day life. This, I bet will be largely left to the Cloud, except for a few programs. The AI VAs of 2026 will seem like actual AI, rather than the glorified chat bot answering machines that are today’s VAs, and will be capable of holding whole conversations and having personalities. Think of all the basic apps you currently have, such as reminders, news, weather, calendars, etc. AI VAs will replace all of them.
  • Augmented reality. I largely doubt phones of the future will resemble the phones of today— much like the phones of today largely don’t resemble the phones of decades past. Phones will most likely transition into being terminals for augmented/hybrid reality glasses and contact lenses, rather than the multimedia machines they are today. This is actually more likely for the lenses than glasses, as some of today’s glasses (like the HoloLens) are entirely self-powered. AR glasses and lenses will benefit greatly from cyberkinesis technology.
  • 5G and 6G capabilities. 5G is set to begin around 2020, and has already been teased in several East Asian cities. The same will be true in 2026, except one generation ahead. Standard phones will be based upon the 4G network (the “slow” option), while higher end phones will casually access 5G networks, and the highest end in the most futuristic cities will play with 6G features.

These are just some of the things I expect. Mainly the bigger things, of course. 6G phones will be the shiny new toys, and I can’t even begin to imagine what they’ll be like. I strongly doubt they’ll resemble phones as we know them to be. 6G networks, however, will be mandatory for the worlds of data we’ll be sending each other.

Futuristic Realism: The Disconnect

They say the easiest way to create futuristic realism is to write Sarah, Plain and Tall and add ASIMOs, drones, and smartglasses

I want to buy some droids.

I want to buy a self-driving car.

I already have a drone, and I still plan on using it to scout out a cemetery to hunt ghosts. Ghost-hunting robots, anyone? Seriously, why haven’t any of the big ghosthunting shows thought of that yet?

And there are legit drone shows that are going to occur or have occurred. Or try floating balls that make the sounds of a city street. It’s all so sci-fi, but there isn’t really a genre to describe this. So I chose Futuristic Realism. As opposed to Hard Sci-Fi, which is mainly concerned with how well sci-fi conforms to known physics, futuristic realism is all stuff happening in a manner that feels realistic, without any flash or pomp, and feels relatable.

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Pepper isn’t the most advanced robot out there, but it still feels bizarre to see it in action.

I’ve always said that the best example of Futuristic Realism is a bit where I took “Sarah, Plain and Tall” and added robots. ASIMOs working on a farm is as Futuristic Realist as you can get. To an extent, it doesn’t matter if that farm is in the USican midwest or located on a space colony in the Keiper belt. Does the story really feel realistic?

To another extent, it does. That is more hardcore realism where the aim is to be as ’20 minutes into the future’ as possible. I suppose you can say Futuristic Realism is taking science fiction and translating it into Realistic and Literary fiction. A truer futuristic realist story about a farmer would be about that farmer’s struggle to survive a drought and dealing with some other people. A more traditional sci-fi (particularly cyberpunk) story may have him pit against a megacorporation bent on buying out the farm and tossing him to the side. Still futuristic realism, though, and depending on how you handle the story, it could lean more one way or the other. If it’s more about corporate vs the individual, alienation wrought by corporate culture, and the technology used by the corporation to push him out, it would fare better as being called cyberpunk. If it’s more about the people themselves, and just happens to feature corporate alienation, then you have something closer to pure futuristic realism. That’s why I say it’s easiest to pull of futuristic realism with a farm (or suburban) setting— it’s already much closer to individual people doing their own thing, without being able to fall back on the glittering neon cyberscapes of a city or cold interiors of a space station to show off how sci-fi/cyberpunk it is. It makes the writer have to actually work. Also, there’s a much larger clash. A glittering neon cyberscape of a megaopolis is already very science fiction (and realistic); adding sexbot prostitutes and a population fitted with smartglasses doesn’t really add to what already exists. Add sexbot prostitutes and smartglasses to Smalltown, USA, however, and you have a jarring disconnect that needs to be rectified or at least expanded upon. That doesn’t mean you can’t have a futuristic realist story in a cyberpunk city, or a space cruiser, etc. It’s just much easier to tell one in Smalltown, USA because of the very nature of rural and suburban communities. They’re synonymous with tradition and conformity, with nostalgic older years and pleasantness, of a certain quietness you can’t find in a city. Throw in technological abominations, and you realize just how timeless they are.

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It is my personal dream to own a domestic robot, and part of the reason is simply “to own a domestic robot.” I am not good with finances.

I live in a rural area. As soon as I become rich (any day now……), I’m buying a Pepper robot. Two problems? One, it’s not all that easy to become rich (but dammit, I’m gonna keep trying). Two, they don’t sell Peppers in the US. But they will. And when all this comes together, I’ll be that creepy black guy living in a trailer with a humanoid robot. I’ll be talking to Pepper while outside, in the evening. Crickets sing their songs, cicadas buzz, dusklight cools the air, I pull up a plastic chair and sit and listen to my playlists filled with stoner-rock, and watch Venus and the stars blink into the sky. Next to me, Pepper the robot. We’re just chatting, maybe chatting to the neighbors, talking about life.

That’s futuristic realism. Would it be the same without Pepper? We’d still be doing what we’re doing, but Pepper adds something. And it’s not even just Pepper. That I’m listening to music, with tens of thousands of songs, on a handheld computer that contains all the world’s knowledge, is, too, Futuristic Realism. Things that feel ripped from the pages of a cyberpunk novel, yet are part of our everyday lives, things that don’t even feel so futuristic at times, are what makes this genre work.

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Ett Bedårande Barn Av Sin Tid by Simon Stålenhag