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Saturday, April 29, 2017

Will it be a nice God!

Dear Readers: "Let's get things back into Perspective!"

 Everyone is so concerned about 'Global Warming' and what it means for human civilization, when the real uncertainty about our future rests on what 'Artificial Intelligence' will do for us ......., or to us! (This is one in a series of articles about the future of Humanity we are presenting this week!)

By Tim Urban:
Before we dive much further into this good vs. bad outcome part of the question, let’s combine both the “when will it happen?” and the “will it be good or bad?” parts of this question into a chart that encompasses the views of most of the relevant experts:

We’ll talk more about the Main Camp in a minute, but first—what’s your deal? Actually I know what your deal is, because it was my deal too before I started researching this topic. Some reasons most people aren’t really thinking about this topic:
  • As mentioned in Part 1, movies have really confused things by presenting unrealistic AI scenarios that make us feel like AI isn’t something to be taken seriously in general. James Barrat compares the situation to our reaction if the Centers for Disease Control issued a serious warning about vampires in our future.5
  • Due to something called cognitive biases, we have a hard time believing something is real until we see proof. I’m sure computer scientists in 1988 were regularly talking about how big a deal the internet was likely to be, but people probably didn’t really think it was going to change their lives until it actually changed their lives. This is partially because computers just couldn’t do stuff like that in 1988, so people would look at their computer and think, “Really? That’s gonna be a life changing thing?” Their imaginations were limited to what their personal experience had taught them about what a computer was, which made it very hard to vividly picture what computers might become. The same thing is happening now with AI. We hear that it’s gonna be a big deal, but because it hasn’t happened yet, and because of our experience with the relatively impotent AI in our current world, we have a hard time really believing this is going to change our lives dramatically. And those biases are what experts are up against as they frantically try to get our attention through the noise of collective daily self-absorption.
  • Even if we did believe it—how many times today have you thought about the fact that you’ll spend most of the rest of eternity not existing? Not many, right? Even though it’s a far more intense fact than anything else you’re doing today? This is because our brains are normally focused on the little things in day-to-day life, no matter how crazy a long-term situation we’re a part of. It’s just how we’re wired.
One of the goals of these two posts is to get you out of the I Like to Think About Other Things Camp and into one of the expert camps, even if you’re just standing on the intersection of the two dotted lines in the square above, totally uncertain.

During my research, I came across dozens of varying opinions on this topic, but I quickly noticed that most people’s opinions fell somewhere in what I labeled the Main Camp, and in particular, over three quarters of the experts fell into two Subcamps inside the Main Camp:
We’re gonna take a thorough dive into both of these camps. Let’s start with the fun one—

Why the Future Might Be Our Greatest Dream

As I learned about the world of AI, I found a surprisingly large number of people standing here:
The people on Confident Corner are buzzing with excitement. They have their sights set on the fun side of the balance beam and they’re convinced that’s where all of us are headed. For them, the future is everything they ever could have hoped for, just in time.

The thing that separates these people from the other thinkers we’ll discuss later isn’t their lust for the happy side of the beam—it’s their confidence that that’s the side we’re going to land on.

Where this confidence comes from is up for debate. Critics believe it comes from an excitement so blinding that they simply ignore or deny potential negative outcomes. But the believers say it’s naive to conjure up doomsday scenarios when on balance, technology has and will likely end up continuing to help us a lot more than it hurts us.

We’ll cover both sides, and you can form your own opinion about this as you read, but for this section, put your skepticism away and let’s take a good hard look at what’s over there on the fun side of the balance beam—and try to absorb the fact that the things you’re reading might really happen. If you had shown a hunter-gatherer our world of indoor comfort, technology, and endless abundance, it would have seemed like fictional magic to him—we have to be humble enough to acknowledge that it’s possible that an equally inconceivable transformation could be in our future.

Nick Bostrom describes three ways a superintelligent AI system could function:
  • As an oracle, which answers nearly any question posed to it with accuracy, including complex questions that humans cannot easily answer—i.e. How can I manufacture a more efficient car engine? Google is a primitive type of oracle.
  • As a genie, which executes any high-level command it’s given—Use a molecular assembler to build a new and more efficient kind of car engine—and then awaits its next command.
  • As a sovereign, which is assigned a broad and open-ended pursuit and allowed to operate in the world freely, making its own decisions about how best to proceed—Invent a faster, cheaper, and safer way than cars for humans to privately transport themselves.
These questions and tasks, which seem complicated to us, would sound to a superintelligent system like someone asking you to improve upon the “My pencil fell off the table” situation, which you’d do by picking it up and putting it back on the table.

Eliezer Yudkowsky, a resident of Anxious Avenue in our chart above, said it well:
There are no hard problems, only problems that are hard to a certain level of intelligence. Move the smallest bit upwards [in level of intelligence], and some problems will suddenly move from “impossible” to “obvious.” Move a substantial degree upwards, and all of them will become obvious.
There are a lot of eager scientists, inventors, and entrepreneurs in Confident Corner—but for a tour of brightest side of the AI horizon, there’s only one person we want as our tour guide.

Ray Kurzweil is polarizing. In my reading, I heard everything from godlike worship of him and his ideas to eye-rolling contempt for them. Others were somewhere in the middle—author Douglas Hofstadter, in discussing the ideas in Kurzweil’s books, eloquently put forth that “it is as if you took a lot of very good food and some dog excrement and blended it all up so that you can’t possibly figure out what’s good or bad.”

Whether you like his ideas or not, everyone agrees that Kurzweil is impressive. He began inventing things as a teenager and in the following decades, he came up with several breakthrough inventions, including the first flatbed scanner, the first scanner that converted text to speech (allowing the blind to read standard texts), the well-known Kurzweil music synthesizer (the first true electric piano), and the first commercially marketed large-vocabulary speech recognition. 

He’s the author of five national bestselling books. He’s well-known for his bold predictions and has a pretty good record of having them come true—including his prediction in the late ’80s, a time when the internet was an obscure thing, that by the early 2000s, it would become a global phenomenon. Kurzweil has been called a “restless genius” by The Wall Street Journal, “the ultimate thinking machine” by Forbes, “Edison’s rightful heir” by Inc. Magazine, and “the best person I know at predicting the future of artificial intelligence” by Bill Gates.

In 2012, Google co-founder Larry Page approached Kurzweil and asked him to be Google’s Director of Engineering.5 In 2011, he co-founded Singularity University, which is hosted by NASA and sponsored partially by Google. Not bad for one life.

This biography is important. When Kurzweil articulates his vision of the future, he sounds fully like a crackpot, and the crazy thing is that he’s not—he’s an extremely smart, knowledgeable, relevant man in the world. You may think he’s wrong about the future, but he’s not a fool. Knowing he’s such a legit dude makes me happy, because as I’ve learned about his predictions for the future, I badly want him to be right. And you do too.

As you hear Kurzweil’s predictions, many shared by other Confident Corner thinkers like Peter Diamandis and Ben Goertzel, it’s not hard to see why he has such a large, passionate following—known as the singularitarians. Here’s what he thinks is going to happen:


Kurzweil believes computers will reach AGI by 2029 and that by 2045, we’ll have not only ASI, but a full-blown new world—a time he calls the singularity. His AI-related timeline used to be seen as outrageously overzealous, and it still is by many,6 but in the last 15 years, the rapid advances of ANI systems have brought the larger world of AI experts much closer to Kurzweil’s timeline.

His predictions are still a bit more ambitious than the median respondent on Müller and Bostrom’s survey (AGI by 2040, ASI by 2060), but not by that much.
Kurzweil’s depiction of the 2045 singularity is brought about by three simultaneous revolutions in biotechnology, nanotechnology, and, most powerfully, AI.

Before we move on—nanotechnology comes up in almost everything you read about the future of AI, so come into this blue box for a minute so we can discuss it—
Nanotechnology Blue Box

Nanotechnology is our word for technology that deals with the manipulation of matter that’s between 1 and 100 nanometers in size. A nanometer is a billionth of a meter, or a millionth of a millimeter, and this 1-100 range encompasses viruses (100 nm across), DNA (10 nm wide), and things as small as large molecules like hemoglobin (5 nm) and medium molecules like glucose (1 nm). If/when we conquer nanotechnology, the next step will be the ability to manipulate individual atoms, which are only one order of magnitude smaller (~.1 nm).

To understand the challenge of humans trying to manipulate matter in that range, let’s take the same thing on a larger scale. The International Space Station is 268 mi (431 km) above the Earth. If humans were giants so large their heads reached up to the ISS, they’d be about 250,000 times bigger than they are now.

If you make the 1nm – 100nm nanotech range 250,000 times bigger, you get .25mm – 2.5cm. So nanotechnology is the equivalent of a human giant as tall as the ISS figuring out how to carefully build intricate objects using materials between the size of a grain of sand and an eyeball. To reach the next level—manipulating individual atoms—the giant would have to carefully position objects that are 1/40th of a millimeter—so small normal-size humans would need a microscope to see them.

Nanotech was first discussed by Richard Feynman in a 1959 talk, when he explained: “The principles of physics, as far as I can see, do not speak against the possibility of maneuvering things atom by atom. It would be, in principle, possible … for a physicist to synthesize any chemical substance that the chemist writes down…. How? Put the atoms down where the chemist says, and so you make the substance.” It’s as simple as that. If you can figure out how to move individual molecules or atoms around, you can make literally anything.

Nanotech became a serious field for the first time in 1986, when engineer Eric Drexler provided its foundations in his seminal book Engines of Creation, but Drexler suggests that those looking to learn about the most modern ideas in nanotechnology would be best off reading his 2013 book, Radical Abundance.

Gray Goo Bluer Box

We’re now in a diversion in a diversion. This is very fun.

Anyway, I brought you here because there’s this really unfunny part of nanotechnology lore I need to tell you about. In older versions of nanotech theory, a proposed method of nanoassembly involved the creation of trillions of tiny nanobots that would work in conjunction to build something. One way to create trillions of nanobots would be to make one that could self-replicate and then let the reproduction process turn that one into two, those two then turn into four, four into eight, and in about a day, there’d be a few trillion of them ready to go. That’s the power of exponential growth. Clever, right?

It’s clever until it causes the grand and complete Earthwide apocalypse by accident. The issue is that the same power of exponential growth that makes it super convenient to quickly create a trillion nanobots makes self-replication a terrifying prospect. Because what if the system glitches, and instead of stopping replication once the total hits a few trillion as expected, they just keep replicating?

The nanobots would be designed to consume any carbon-based material in order to feed the replication process, and unpleasantly, all life is carbon-based. The Earth’s biomass contains about 1045 carbon atoms. A nanobot would consist of about 106 carbon atoms, so 1039 nanobots would consume all life on Earth, which would happen in 130 replications (2130 is about 1039), as oceans of nanobots (that’s the gray goo) rolled around the planet.

Scientists think a nanobot could replicate in about 100 seconds, meaning this simple mistake would inconveniently end all life on Earth in 3.5 hours.

An even worse scenario—if a terrorist somehow got his hands on nanobot technology and had the know-how to program them, he could make an initial few trillion of them and program them to quietly spend a few weeks spreading themselves evenly around the world undetected. Then, they’d all strike at once, and it would only take 90 minutes for them to consume everything—and with them all spread out, there would be no way to combat them.

While this horror story has been widely discussed for years, the good news is that it may be overblown—Eric Drexler, who coined the term “gray goo,” sent me an email following this post with his thoughts on the gray goo scenario: “People love scare stories, and this one belongs with the zombies. The idea itself eats brains.”
Once we really get nanotech down, we can use it to make tech devices, clothing, food, a variety of bio-related products—artificial blood cells, tiny virus or cancer-cell destroyers, muscle tissue, etc.—anything really. And in a world that uses nanotechnology, the cost of a material is no longer tied to its scarcity or the difficulty of its manufacturing process, but instead determined by how complicated its atomic structure is. In a nanotech world, a diamond might be cheaper than a pencil eraser.

We’re not there yet. And it’s not clear if we’re underestimating, or overestimating, how hard it will be to get there. But we don’t seem to be that far away. Kurzweil predicts that we’ll get there by the 2020s.11 Governments know that nanotech could be an Earth-shaking development, and they’ve invested billions of dollars in nanotech research (the US, the EU, and Japan have invested over a combined $5 billion so far).

Just considering the possibilities if a superintelligent computer had access to a robust nanoscale assembler is intense. But nanotechnology is something we came up with, that we’re on the verge of conquering, and since anything that we can do is a joke to an ASI system, we have to assume ASI would come up with technologies much more powerful and far too advanced for human brains to understand.

For that reason, when considering the “If the AI Revolution turns out well for us” scenario, it’s almost impossible for us to overestimate the scope of what could happen—so if the following predictions of an ASI future seem over-the-top, keep in mind that they could be accomplished in ways we can’t even imagine. Most likely, our brains aren’t even capable of predicting the things that would happen.

What AI Could Do For Us

Armed with superintelligence and all the technology superintelligence would know how to create, ASI would likely be able to solve every problem in humanity. Global warming?

ASI could first halt CO2 emissions by coming up with much better ways to generate energy that had nothing to do with fossil fuels. Then it could create some innovative way to begin to remove excess CO2 from the atmosphere.

Cancer and other diseases? No problem for ASI—health and medicine would be revolutionized beyond imagination. World hunger? ASI could use things like nanotech to build meat from scratch that would be molecularly identical to real meat—in other words, it would be real meat. Nanotech could turn a pile of garbage into a huge vat of fresh meat or other food (which wouldn’t have to have its normal shape—picture a giant cube of apple)—and distribute all this food around the world using ultra-advanced transportation.

Of course, this would also be great for animals, who wouldn’t have to get killed by humans much anymore, and ASI could do lots of other things to save endangered species or even bring back extinct species through work with preserved DNA. ASI could even solve our most complex macro issues—our debates over how economies should be run and how world trade is best facilitated, even our haziest grapplings in philosophy or ethics—would all be painfully obvious to ASI.

But there’s one thing ASI could do for us that is so tantalizing, reading about it has altered everything I thought I knew about everything:

ASI could allow us to conquer our mortality.

A few months ago, I mentioned my envy of more advanced potential civilizations who had conquered their own mortality, never considering that I might later write a post that genuinely made me believe that this is something humans could do within my lifetime. But reading about AI will make you reconsider everything you thought you were sure about—including your notion of death.

Evolution had no good reason to extend our lifespans any longer than they are now. If we live long enough to reproduce and raise our children to an age that they can fend for themselves, that’s enough for evolution—from an evolutionary point of view, the species can thrive with a 30+ year lifespan, so there’s no reason mutations toward unusually long life would have been favored in the natural selection process. As a result, we’re what W.B. Yeats describes as “a soul fastened to a dying animal.” Not that fun.

And because everyone has always died, we live under the “death and taxes” assumption that death is inevitable. We think of aging like time—both keep moving and there’s nothing you can do to stop them. But that assumption is wrong. Richard Feynman writes:
It is one of the most remarkable things that in all of the biological sciences there is no clue as to the necessity of death. If you say we want to make perpetual motion, we have discovered enough laws as we studied physics to see that it is either absolutely impossible or else the laws are wrong. But there is nothing in biology yet found that indicates the inevitability of death. This suggests to me that it is not at all inevitable and that it is only a matter of time before the biologists discover what it is that is causing us the trouble and that this terrible universal disease or temporariness of the human’s body will be cured.
The fact is, aging isn’t stuck to time. Time will continue moving, but aging doesn’t have to. If you think about it, it makes sense. All aging is is the physical materials of the body wearing down. A car wears down over time too—but is its aging inevitable? If you perfectly repaired or replaced a car’s parts whenever one of them began to wear down, the car would run forever. The human body isn’t any different—just far more complex.

Kurzweil talks about intelligent wifi-connected nanobots in the bloodstream who could perform countless tasks for human health, including routinely repairing or replacing worn down cells in any part of the body. If perfected, this process (or a far smarter one ASI would come up with) wouldn’t just keep the body healthy, it could reverse aging. The difference between a 60-year-old’s body and a 30-year-old’s body is just a bunch of physical things that could be altered if we had the technology. 

ASI could build an “age refresher” that a 60-year-old could walk into, and they’d walk out with the body and skin of a 30-year-old. Even the ever-befuddling brain could be refreshed by something as smart as ASI, which would figure out how to do so without affecting the brain’s data (personality, memories, etc.). 

A 90-year-old suffering from dementia could head into the age refresher and come out sharp as a tack and ready to start a whole new career. This seems absurd—but the body is just a bunch of atoms and ASI would presumably be able to easily manipulate all kinds of atomic structures—so it’s not absurd.
Kurzweil then takes things a huge leap further. He believes that artificial materials will be integrated into the body more and more as time goes on. 

First, organs could be replaced by super-advanced machine versions that would run forever and never fail. Then he believes we could begin to redesign the body—things like replacing red blood cells with perfected red blood cell nanobots who could power their own movement, eliminating the need for a heart at all. 

 He even gets to the brain and believes we’ll enhance our brain activities to the point where humans will be able to think billions of times faster than they do now and access outside information because the artificial additions to the brain will be able to communicate with all the info in the cloud.

The possibilities for new human experience would be endless. Humans have separated sex from its purpose, allowing people to have sex for fun, not just for reproduction. Kurzweil believes we’ll be able to do the same with food. 

Nanobots will be in charge of delivering perfect nutrition to the cells of the body, intelligently directing anything unhealthy to pass through the body without affecting anything. An eating condom. Nanotech theorist Robert A. Freitas has already designed blood cell replacements that, if one day implemented in the body, would allow a human to sprint for 15 minutes without taking a breath—so you can only imagine what ASI could do for our physical capabilities. 

Virtual reality would take on a new meaning—nanobots in the body could suppress the inputs coming from our senses and replace them with new signals that would put us entirely in a new environment, one that we’d see, hear, feel, and smell.

Eventually, Kurzweil believes humans will reach a point when they’re entirely artificial;11 a time when we’ll look at biological material and think how unbelievably primitive it was that humans were ever made of that; a time when we’ll read about early stages of human history, when microbes or accidents or diseases or wear and tear could just kill humans against their own will; a time the AI Revolution could bring to an end with the merging of humans and AI.

This is how Kurzweil believes humans will ultimately conquer our biology and become indestructible and eternal—this is his vision for the other side of the balance beam. And he’s convinced we’re gonna get there. Soon.

You will not be surprised to learn that Kurzweil’s ideas have attracted significant criticism. His prediction of 2045 for the singularity and the subsequent eternal life possibilities for humans has been mocked as “the rapture of the nerds,” or “intelligent design for 140 IQ people.” 

Others have questioned his optimistic timeline, or his level of understanding of the brain and body, or his application of the patterns of Moore’s law, which are normally applied to advances in hardware, to a broad range of things, including software. For every expert who fervently believes Kurzweil is right on, there are probably three who think he’s way off.

But what surprised me is that most of the experts who disagree with him don’t really disagree that everything he’s saying is possible. Reading such an outlandish vision for the future, I expected his critics to be saying, “Obviously that stuff can’t happen,” but instead they were saying things like, “Yes, all of that can happen if we safely transition to ASI, but that’s the hard part.” Bostrom, one of the most prominent voices warning us about the dangers of AI, still acknowledges:
It is hard to think of any problem that a superintelligence could not either solve or at least help us solve. Disease, poverty, environmental destruction, unnecessary suffering of all kinds: these are things that a superintelligence equipped with advanced nanotechnology would be capable of eliminating.

Additionally, a superintelligence could give us indefinite lifespan, either by stopping and reversing the aging process through the use of nanomedicine, or by offering us the option to upload ourselves. A superintelligence could also create opportunities for us to vastly increase our own intellectual and emotional capabilities, and it could assist us in creating a highly appealing experiential world in which we could live lives devoted to joyful game-playing, relating to each other, experiencing, personal growth, and to living closer to our ideals.
This is a quote from someone very much not on Confident Corner, but that’s what I kept coming across—experts who scoff at Kurzweil for a bunch of reasons but who don’t think what he’s saying is impossible if we can make it safely to ASI. That’s why I found Kurzweil’s ideas so infectious—because they articulate the bright side of this story and because they’re actually possible. If it’s a good god.

The most prominent criticism I heard of the thinkers on Confident Corner is that they may be dangerously wrong in their assessment of the downside when it comes to ASI. Kurzweil’s famous book The Singularity is Near is over 700 pages long and he dedicates around 20 of those pages to potential dangers. 

I suggested earlier that our fate when this colossal new power is born rides on who will control that power and what their motivation will be. Kurzweil neatly answers both parts of this question with the sentence, “[ASI] is emerging from many diverse efforts and will be deeply integrated into our civilization’s infrastructure. Indeed, it will be intimately embedded in our bodies and brains. As such, it will reflect our values because it will be us.”

But if that’s the answer, why are so many of the world’s smartest people so worried right now? Why does Stephen Hawking say the development of ASI “could spell the end of the human race” and Bill Gates say he doesn’t “understand why some people are not concerned” and Elon Musk fear that we’re “summoning the demon”? 

And why do so many experts on the topic call ASI the biggest threat to humanity? These people, and the other thinkers on Anxious Avenue, don’t buy Kurzweil’s brush-off of the dangers of AI. They’re very, very worried about the AI Revolution, and they’re not focusing on the fun side of the balance beam. They’re too busy staring at the other side, where they see a terrifying future, one they’re not sure we’ll be able to escape.

TOMORROW THE SERIES WINDS UP WITH:  Why the Future Might Be Our Worst Nightmare

Be prepared to be amazed!

Dear Friends:

Sit back, crank up the volume, and be prepared to be amazed! (This kid is only four years old.)

Here's another song by a little four year old Asian kid.

(There must be something in the food over there!)

Stick it to the Man!

Dear Friends: "Let's get things back into Perspective!"

This is a brief story about how we are all victims and stooges of "The System" and "The Man!" (My generation didn't shout "Stick it to The Man" for no reason kids!)

So here's the story:

Image result for tax time clipartI was in the hospital for two months at the beginning of 2015 due to complications from pneumonia, which included blood clots in my lungs, organs shutting down, and all sorts of mean, nasty, terrible stuff ....., to the point that when I did arrive at the Emergency department I died a couple of times, (They obviously brought me back!) was put on life support for a month, and then intensive care for another month.  (P.S. Don't let anyone tell you that pneumonia is not serious, it is ......, and kills almost as many people as cancer.)

Anyway, because I didn't work for almost two years, my credit score went from real good, to somewhere around  600, and when it came time to buy another car this spring I managed to get approved for a VW all wheel drive Tiguan that cost around $10k.

Before I picked up the car I realized that until my income became substantial and regular I shouldn't be looking at that type of vehicle, but rather something cheaper to hold me over until business picked up again! (Which it is!)

Instead, I went to a car lot where they had a 2005 Hyundai Sonata with about 120k on it and in GREAT shape for $4000 bucks including tax, license, etc.

In other words it was "on the road" for 4 grand!!!!

This time I went to my bank to get a small loan and after a credit check I found out my credit score had fallen to 550 because the first car lot had farmed out my deal to FIVE (5) different credit companies!

The bank manager was very apologetic when he said that with a credit score under 600 (550) there was no way he could approve anything for me.

This means I might be relegated to taking a bus and not working because I need a vehicle for my job!

Like I said, let's "Stick it to the Man" because he sure is sticking it to us!

Saturday Morning Confusion about the White House Correspondents Dinner

Dear Friends: "Let's get things back into Perspective!"

After a hilarious appearance by President Obama at the White House Correspondents Dinner last year it comes as no surprise that Don the Con Trumplethinskin is a no-show this year!

An annual Washington tradition best known for celebrity guests and a comedy routine by the president continues on Saturday with neither.
The White House Correspondents Association dinner, at the Washington Hilton in D.C.’s Dupont Circle neighborhood, will be held without President Donald Trump and the high-profile guests typically invited by news outlets that organize the event. Instead, this year’s dinner will include guests like the high-school journalists who brought down their principal with a story on her faked credentials. (The kids will be guests of HuffPost.)
Trump announced he was skipping the dinner in February, making him the first president in more than three decades to break the tradition. At the time, Trump and his administration were fiercely attacking reporters and media outlets.

He described the media as “the enemy of the American people,” threatened to change libel laws so he can more easily sue news organizations, attacked the media for using unnamed sources in stories critical of his administration, and blocked certain outlets, including HuffPost, from attendance at press briefings.

Saturday’s correspondents dinner will still feature a roast of the president, delivered by “The Daily Show” correspondent Hasan Minhaj, a Muslim comedian who has condemned Trump’s anti-Muslim actions. It’s a safe bet that there will be plenty of jabs at the president’s expense.

Still, this year’s correspondents dinner will be a diminished affair compared with past years. Much of the star power typically drawn to the dinner will instead be concentrated at an alternative event thrown by “Full Frontal” host Samantha Bee.

The New Yorker and Vanity Fair have canceled the extravagant after-parties they typically hold. And Trump is holding a rally at the same time as the dinner, presumably to divert attention from the Washington event.

(On a sad side-note I was also watching several clips of the old Celebrity Roast Dinners with Dean Martin when I realized that each and every one of the people who attended those get-togethers was dead ........., except for Betty White!)

Friday, April 28, 2017

The A.I. Revolution: Immortality or Extinction?

Dear Readers: "Let's get things back into Perspective!"

 Everyone is so concerned about 'Global Warming' and what it means for human civilization, when the real uncertainty about our future rests on what 'Artificial Intelligence' will do for us ......., or to us! (This is one in a series of articles about the future of Humanity we are presenting this week!)

By Tim Urban:

We have what may be an extremely difficult problem with an unknown time to solve it, on which quite possibly the entire future of humanity depends. — Nick Bostrom

Welcome to Part 2 of the “Wait how is this possibly what I’m reading I don’t get why everyone isn’t talking about this” series.

Part 1 started innocently enough, as we discussed Artificial Narrow Intelligence, or ANI (AI that specializes in one narrow task like coming up with driving routes or playing chess), and how it’s all around us in the world today.

We then examined why it was such a huge challenge to get from ANI to Artificial General Intelligence, or AGI (AI that’s at least as intellectually capable as a human, across the board), and we discussed why the exponential rate of technological advancement we’ve seen in the past suggests that AGI might not be as far away as it seems. Part 1 ended with me assaulting you with the fact that once our machines reach human-level intelligence, they might immediately do this:
This left us staring at the screen, confronting the intense concept of potentially-in-our-lifetime Artificial Superintelligence, or ASI (AI that’s way smarter than any human, across the board), and trying to figure out which emotion we were supposed to have on as we thought about that.

Before we dive into things, let’s remind ourselves what it would mean for a machine to be superintelligent.

A key distinction is the difference between speed superintelligence and quality superintelligence. Often, someone’s first thought when they imagine a super-smart computer is one that’s as intelligent as a human but can think much, much faster2—they might picture a machine that thinks like a human, except a million times quicker, which means it could figure out in five minutes what would take a human a decade.

That sounds impressive, and ASI would think much faster than any human could—but the true separator would be its advantage in intelligence quality, which is something completely different. What makes humans so much more intellectually capable than chimps isn’t a difference in thinking speed—it’s that human brains contain a number of sophisticated cognitive modules that enable things like complex linguistic representations or longterm planning or abstract reasoning, that chimps’ brains do not.

Speeding up a chimp’s brain by thousands of times wouldn’t bring him to our level—even with a decade’s time, he wouldn’t be able to figure out how to use a set of custom tools to assemble an intricate model, something a human could knock out in a few hours. There are worlds of human cognitive function a chimp will simply never be capable of, no matter how much time he spends trying.

But it’s not just that a chimp can’t do what we do, it’s that his brain is unable to grasp that those worlds even exist—a chimp can become familiar with what a human is and what a skyscraper is, but he’ll never be able to understand that the skyscraper was built by humans. In his world, anything that huge is part of nature, period, and not only is it beyond him to build a skyscraper, it’s beyond him to realize that anyone can build a skyscraper. That’s the result of a small difference in intelligence quality.

And in the scheme of the intelligence range we’re talking about today, or even the much smaller range among biological creatures, the chimp-to-human quality intelligence gap is tiny. In an earlier post, I depicted the range of biological cognitive capacity using a staircase:3
To absorb how big a deal a superintelligent machine would be, imagine one on the dark green step two steps above humans on that staircase. This machine would be only slightly superintelligent, but its increased cognitive ability over us would be as vast as the chimp-human gap we just described. And like the chimp’s incapacity to ever absorb that skyscrapers can be built, we will never be able to even comprehend the things a machine on the dark green step can do, even if the machine tried to explain it to us—let alone do it ourselves. And that’s only two steps above us.

A machine on the second-to-highest step on that staircase would be to us as we are to ants—it could try for years to teach us the simplest inkling of what it knows and the endeavor would be hopeless.

But the kind of superintelligence we’re talking about today is something far beyond anything on this staircase. In an intelligence explosion—where the smarter a machine gets, the quicker it’s able to increase its own intelligence, until it begins to soar upwards—a machine might take years to rise from the chimp step to the one above it, but perhaps only hours to jump up a step once it’s on the dark green step two above us, and by the time it’s ten steps above us, it might be jumping up in four-step leaps every second that goes by. Which is why we need to realize that it’s distinctly possible that very shortly after the big news story about the first machine reaching human-level AGI, we might be facing the reality of coexisting on the Earth with something that’s here on the staircase (or maybe a million times higher):
And since we just established that it’s a hopeless activity to try to understand the power of a machine only two steps above us, let’s very concretely state once and for all that there is no way to know what ASI will do or what the consequences will be for us. Anyone who pretends otherwise doesn’t understand what superintelligence means.

Evolution has advanced the biological brain slowly and gradually over hundreds of millions of years, and in that sense, if humans birth an ASI machine, we’ll be dramatically stomping on evolution. Or maybe this is part of evolution—maybe the way evolution works is that intelligence creeps up more and more until it hits the level where it’s capable of creating machine superintelligence, and that level is like a tripwire that triggers a worldwide game-changing explosion that determines a new future for all living things:
And for reasons we’ll discuss later, a huge part of the scientific community believes that it’s not a matter of whether we’ll hit that tripwire, but when. Kind of a crazy piece of information.

So where does that leave us?

Well no one in the world, especially not I, can tell you what will happen when we hit the tripwire. But Oxford philosopher and lead AI thinker Nick Bostrom believes we can boil down all potential outcomes into two broad categories.

First, looking at history, we can see that life works like this: species pop up, exist for a while, and after some time, inevitably, they fall off the existence balance beam and land on extinction—

“All species eventually go extinct” has been almost as reliable a rule through history as “All humans eventually die” has been. So far, 99.9% of species have fallen off the balance beam, and it seems pretty clear that if a species keeps wobbling along down the beam, it’s only a matter of time before some other species, some gust of nature’s wind, or a sudden beam-shaking asteroid knocks it off.

Bostrom calls extinction an attractor state—a place species are all teetering on falling into and from which no species ever returns.

And while most scientists I’ve come across acknowledge that ASI would have the ability to send humans to extinction, many also believe that used beneficially, ASI’s abilities could be used to bring individual humans, and the species as a whole, to a second attractor state—species immortality. Bostrom believes species immortality is just as much of an attractor state as species extinction, i.e. if we manage to get there, we’ll be impervious to extinction forever—we’ll have conquered mortality and conquered chance. So even though all species so far have fallen off the balance beam and landed on extinction, Bostrom believes there are two sides to the beam and it’s just that nothing on Earth has been intelligent enough yet to figure out how to fall off on the other side.
If Bostrom and others are right, and from everything I’ve read, it seems like they really might be, we have two pretty shocking facts to absorb:

1) The advent of ASI will, for the first time, open up the possibility for a species to land on the immortality side of the balance beam.

2) The advent of ASI will make such an unimaginably dramatic impact that it’s likely to knock the human race off the beam, in one direction or the other. 

It may very well be that when evolution hits the tripwire, it permanently ends humans’ relationship with the beam and creates a new world, with or without humans.

Kind of seems like the only question any human should currently be asking is: When are we going to hit the tripwire and which side of the beam will we land on when that happens?

No one in the world knows the answer to either part of that question, but a lot of the very smartest people have put decades of thought into it. We’ll spend the rest of this post exploring what they’ve come up with.

Let’s start with the first part of the question: When are we going to hit the tripwire? i.e. How long until the first machine reaches superintelligence?

Not shockingly, opinions vary wildly and this is a heated debate among scientists and thinkers. Many, like professor Vernor Vinge, scientist Ben Goertzel, Sun Microsystems co-founder Bill Joy, or, most famously, inventor and futurist Ray Kurzweil, agree with machine learning expert Jeremy Howard when he puts up this graph during a TED Talk:
Howard Graph
Those people subscribe to the belief that this is happening soon—that exponential growth is at work and machine learning, though only slowly creeping up on us now, will blow right past us within the next few decades.

Others, like Microsoft co-founder Paul Allen, research psychologist Gary Marcus, NYU computer scientist Ernest Davis, and tech entrepreneur Mitch Kapor, believe that thinkers like Kurzweil are vastly underestimating the magnitude of the challenge and believe that we’re not actually that close to the tripwire.

The Kurzweil camp would counter that the only underestimating that’s happening is the underappreciation of exponential growth, and they’d compare the doubters to those who looked at the slow-growing seedling of the internet in 1985 and argued that there was no way it would amount to anything impactful in the near future.

The doubters might argue back that the progress needed to make advancements in intelligence also grows exponentially harder with each subsequent step, which will cancel out the typical exponential nature of technological progress. And so on.

A third camp, which includes Nick Bostrom, believes neither group has any ground to feel certain about the timeline and acknowledges both A) that this could absolutely happen in the near future and B) that there’s no guarantee about that; it could also take a much longer time.

Still others, like philosopher Hubert Dreyfus, believe all three of these groups are naive for believing that there even is a tripwire, arguing that it’s more likely that ASI won’t actually ever be achieved.

So what do you get when you put all of these opinions together?

In 2013, Vincent C. Müller and Nick Bostrom conducted a survey that asked hundreds of AI experts at a series of conferences the following question: “For the purposes of this question, assume that human scientific activity continues without major negative disruption. By what year would you see a (10% / 50% / 90%) probability for such HLMI4 to exist?” 

It asked them to name an optimistic year (one in which they believe there’s a 10% chance we’ll have AGI), a realistic guess (a year they believe there’s a 50% chance of AGI—i.e. after that year they think it’s more likely than not that we’ll have AGI), and a safe guess (the earliest year by which they can say with 90% certainty we’ll have AGI). Gathered together as one data set, here were the results:

Median optimistic year (10% likelihood): 2022Median realistic year (50% likelihood): 2040Median pessimistic year (90% likelihood): 2075
So the median participant thinks it’s more likely than not that we’ll have AGI 25 years from now. The 90% median answer of 2075 means that if you’re a teenager right now, the median respondent, along with over half of the group of AI experts, is almost certain AGI will happen within your lifetime.

A separate study, conducted recently by author James Barrat at Ben Goertzel’s annual AGI Conference, did away with percentages and simply asked when participants thought AGI would be achieved—by 2030, by 2050, by 2100, after 2100, or never. The results:

By 2030: 42% of respondentsBy 2050: 25%
By 2100: 20%After 2100: 10%
Never: 2%

Pretty similar to Müller and Bostrom’s outcomes. In Barrat’s survey, over two thirds of participants believe AGI will be here by 2050 and a little less than half predict AGI within the next 15 years. Also striking is that only 2% of those surveyed don’t think AGI is part of our future.

But AGI isn’t the tripwire, ASI is. So when do the experts think we’ll reach ASI?

Müller and Bostrom also asked the experts how likely they think it is that we’ll reach ASI A) within two years of reaching AGI (i.e. an almost-immediate intelligence explosion), and B) within 30 years. The results:

The median answer put a rapid (2 year) AGI → ASI transition at only a 10% likelihood, but a longer transition of 30 years or less at a 75% likelihood.
We don’t know from this data the length of this transition the median participant would have put at a 50% likelihood, but for ballpark purposes, based on the two answers above, let’s estimate that they’d have said 20 years. So the median opinion—the one right in the center of the world of AI experts—believes the most realistic guess for when we’ll hit the ASI tripwire is [the 2040 prediction for AGI + our estimated prediction of a 20-year transition from AGI to ASI] = 2060.

Of course, all of the above statistics are speculative, and they’re only representative of the center opinion of the AI expert community, but it tells us that a large portion of the people who know the most about this topic would agree that 2060 is a very reasonable estimate for the arrival of potentially world-altering ASI. Only 45 years from now.

Okay now how about the second part of the question above: When we hit the tripwire, which side of the beam will we fall to?

Superintelligence will yield tremendous power—the critical question for us is:
Who or what will be in control of that power, and what will their motivation be?

The answer to this will determine whether ASI is an unbelievably great development, an unfathomably terrible development, or something in between.
Of course, the expert community is again all over the board and in a heated debate about the answer to this question. 

Müller and Bostrom’s survey asked participants to assign a probability to the possible impacts AGI would have on humanity and found that the mean response was that there was a 52% chance that the outcome will be either good or extremely good and a 31% chance the outcome will be either bad or extremely bad.  

For a relatively neutral outcome, the mean probability was only 17%. In other words, the people who know the most about this are pretty sure this will be a huge deal. It’s also worth noting that those numbers refer to the advent of AGI—if the question were about ASI, I imagine that the neutral percentage would be even lower.

TOMORROW:  Possible Opinions on A.S.I. Arrival

Thursday, April 27, 2017

The Road to Superintelligence.

Dear Friends: "Let's get things back into Perspective!"

Everyone is so concerned about 'Global Warming' and what it means for human civilization, when the real uncertainty about our future rests on what 'Artificial Intelligence' will do for us ......., or to us! (This is one in a series of articles about the future of Humanity we are presenting this week!)

By Tim Urban:

The Road From AGI to ASI

At some point, we’ll have achieved AGI—computers with human-level general intelligence. Just a bunch of people and computers living together in equality.
Oh actually not at all.

The thing is, AGI with an identical level of intelligence and computational capacity as a human would still have significant advantages over humans. Like:
  • Speed. The brain’s neurons max out at around 200 Hz, while today’s microprocessors (which are much slower than they will be when we reach AGI) run at 2 GHz, or 10 million times faster than our neurons. And the brain’s internal communications, which can move at about 120 m/s, are horribly outmatched by a computer’s ability to communicate optically at the speed of light.
  • Size and storage. The brain is locked into its size by the shape of our skulls, and it couldn’t get much bigger anyway, or the 120 m/s internal communications would take too long to get from one brain structure to another. Computers can expand to any physical size, allowing far more hardware to be put to work, a much larger working memory (RAM), and a longterm memory (hard drive storage) that has both far greater capacity and precision than our own.
  • Reliability and durability. It’s not only the memories of a computer that would be more precise. Computer transistors are more accurate than biological neurons, and they’re less likely to deteriorate (and can be repaired or replaced if they do). Human brains also get fatigued easily, while computers can run nonstop, at peak performance, 24/7.
  • Editability, upgradability, and a wider breadth of possibility. Unlike the human brain, computer software can receive updates and fixes and can be easily experimented on. The upgrades could also span to areas where human brains are weak. Human vision software is superbly advanced, while its complex engineering capability is pretty low-grade. Computers could match the human on vision software but could also become equally optimized in engineering and any other area.
  • Collective capability. Humans crush all other species at building a vast collective intelligence. Beginning with the development of language and the forming of large, dense communities, advancing through the inventions of writing and printing, and now intensified through tools like the internet, humanity’s collective intelligence is one of the major reasons we’ve been able to get so far ahead of all other species. And computers will be way better at it than we are. A worldwide network of AI running a particular program could regularly sync with itself so that anything any one computer learned would be instantly uploaded to all other computers. The group could also take on one goal as a unit, because there wouldn’t necessarily be dissenting opinions and motivations and self-interest, like we have within the human population.10
AI, which will likely get to AGI by being programmed to self-improve, wouldn’t see “human-level intelligence” as some important milestone—it’s only a relevant marker from our point of view—and wouldn’t have any reason to “stop” at our level. And given the advantages over us that even human intelligence-equivalent AGI would have, it’s pretty obvious that it would only hit human intelligence for a brief instant before racing onwards to the realm of superior-to-human intelligence.

This may shock the shit out of us when it happens. The reason is that from our perspective, A) while the intelligence of different kinds of animals varies, the main characteristic we’re aware of about any animal’s intelligence is that it’s far lower than ours, and B) we view the smartest humans as WAY smarter than the dumbest humans. Kind of like this:
So as AI zooms upward in intelligence toward us, we’ll see it as simply becoming smarter, for an animal. Then, when it hits the lowest capacity of humanity—Nick Bostrom uses the term “the village idiot”—we’ll be like, “Oh wow, it’s like a dumb human. Cute!” The only thing is, in the grand spectrum of intelligence, all humans, from the village idiot to Einstein, are within a very small range—so just after hitting village idiot level and being declared to be AGI, it’ll suddenly be smarter than Einstein and we won’t know what hit us:

And what happens…after that?

An Intelligence Explosion
I hope you enjoyed normal time, because this is when this topic gets unnormal and scary, and it’s gonna stay that way from here forward. I want to pause here to remind you that every single thing I’m going to say is real—real science and real forecasts of the future from a large array of the most respected thinkers and scientists. Just keep remembering that.

Anyway, as I said above, most of our current models for getting to AGI involve the AI getting there by self-improvement. And once it gets to AGI, even systems that formed and grew through methods that didn’t involve self-improvement would now be smart enough to begin self-improving if they wanted to.3
And here’s where we get to an intense concept: recursive self-improvement. 

It works like this—

An AI system at a certain level—let’s say human village idiot—is programmed with the goal of improving its own intelligence. Once it does, it’s smarter—maybe at this point it’s at Einstein’s level—so now when it works to improve its intelligence, with an Einstein-level intellect, it has an easier time and it can make bigger leaps. 

These leaps make it much smarter than any human, allowing it to make even bigger leaps. As the leaps grow larger and happen more rapidly, the AGI soars upwards in intelligence and soon reaches the superintelligent level of an ASI system. This is called an Intelligence Explosion,11 and it’s the ultimate example of The Law of Accelerating Returns.

There is some debate about how soon AI will reach human-level general intelligence. The median year on a survey of hundreds of scientists about when they believed we’d be more likely than not to have reached AGI was 204012—that’s only 25 years from now, which doesn’t sound that huge until you consider that many of the thinkers in this field think it’s likely that the progression from AGI to ASI happens very quickly. Like—this could happen:

It takes decades for the first AI system to reach low-level general intelligence, but it finally happens. A computer is able to understand the world around it as well as a human four-year-old. Suddenly, within an hour of hitting that milestone, the system pumps out the grand theory of physics that unifies general relativity and quantum mechanics, something no human has been able to definitively do. 90 minutes after that, the AI has become an ASI, 170,000 times more intelligent than a human.

Superintelligence of that magnitude is not something we can remotely grasp, any more than a bumblebee can wrap its head around Keynesian Economics. In our world, smart means a 130 IQ and stupid means an 85 IQ—we don’t have a word for an IQ of 12,952.

What we do know is that humans’ utter dominance on this Earth suggests a clear rule: with intelligence comes power. Which means an ASI, when we create it, will be the most powerful being in the history of life on Earth, and all living things, including humans, will be entirely at its whim—and this might happen in the next few decades.

If our meager brains were able to invent wifi, then something 100 or 1,000 or 1 billion times smarter than we are should have no problem controlling the positioning of each and every atom in the world in any way it likes, at any time—everything we consider magic, every power we imagine a supreme God to have will be as mundane an activity for the ASI as flipping on a light switch is for us. 

Creating the technology to reverse human aging, curing disease and hunger and even mortality, reprogramming the weather to protect the future of life on Earth—all suddenly possible. Also possible is the immediate end of all life on Earth. As far as we’re concerned, if an ASI comes to being, there is now an omnipotent God on Earth—and the all-important question for us is:
NEXT:  Will it be a nice God?

Wednesday, April 26, 2017

Robbie the Robot?

Dear Friends: "Let's get things back into Perspective!"

Everyone is so concerned about 'Global Warming' and what it means for human civilization, when the real uncertainty about our future rests on what 'Artificial Intelligence' will do for us ......., or to us! (This is one in a series of articles about the future of Humanity we are presenting this week!)

By Tim Urban

First Key to Creating AGI: Increasing Computational Power

One thing that definitely needs to happen for AGI to be a possibility is an increase in the power of computer hardware. If an AI system is going to be as intelligent as the brain, it’ll need to equal the brain’s raw computing capacity.
One way to express this capacity is in the total calculations per second (cps) the brain could manage, and you could come to this number by figuring out the maximum cps of each structure in the brain and then adding them all together.

Ray Kurzweil came up with a shortcut by taking someone’s professional estimate for the cps of one structure and that structure’s weight compared to that of the whole brain and then multiplying proportionally to get an estimate for the total. Sounds a little iffy, but he did this a bunch of times with various professional estimates of different regions, and the total always arrived in the same ballpark—around 1016, or 10 quadrillion cps.

Currently, the world’s fastest supercomputer, China’s Tianhe-2, has actually beaten that number, clocking in at about 34 quadrillion cps. But Tianhe-2 is also a dick, taking up 720 square meters of space, using 24 megawatts of power (the brain runs on just 20 watts), and costing $390 million to build. Not especially applicable to wide usage, or even most commercial or industrial usage yet.

Kurzweil suggests that we think about the state of computers by looking at how many cps you can buy for $1,000. When that number reaches human-level—10 quadrillion cps—then that’ll mean AGI could become a very real part of life.
Moore’s Law is a historically-reliable rule that the world’s maximum computing power doubles approximately every two years, meaning computer hardware advancement, like general human advancement through history, grows exponentially.
Looking at how this relates to Kurzweil’s cps/$1,000 metric, we’re currently at about 10 trillion cps/$1,000, right on pace with this graph’s predicted trajectory.


So the world’s $1,000 computers are now beating the mouse brain and they’re at about a thousandth of human level. This doesn’t sound like much until you remember that we were at about a trillionth of human level in 1985, a billionth in 1995, and a millionth in 2005.

Being at a thousandth in 2015 puts us right on pace to get to an affordable computer by 2025 that rivals the power of the brain.

So on the hardware side, the raw power needed for AGI is technically available now, in China, and we’ll be ready for affordable, widespread AGI-caliber hardware within 10 years. But raw computational power alone doesn’t make a computer generally intelligent—the next question is, how do we bring human-level intelligence to all that power?

Second Key to Creating AGI: Making It Smart

This is the icky part. The truth is, no one really knows how to make it smart—we’re still debating how to make a computer human-level intelligent and capable of knowing what a dog and a weird-written B and a mediocre movie is. But there are a bunch of far-fetched strategies out there and at some point, one of them will work. Here are the three most common strategies I came across:

1) Plagiarize the brain.

This is like scientists toiling over how that kid who sits next to them in class is so smart and keeps doing so well on the tests, and even though they keep studying diligently, they can’t do nearly as well as that kid, and then they finally decide “k fuck it I’m just gonna copy that kid’s answers.” It makes sense—we’re stumped trying to build a super-complex computer, and there happens to be a perfect prototype for one in each of our heads.

The science world is working hard on reverse engineering the brain to figure out how evolution made such a rad thing—optimistic estimates say we can do this by 2030. Once we do that, we’ll know all the secrets of how the brain runs so powerfully and efficiently and we can draw inspiration from it and steal its innovations.

One example of computer architecture that mimics the brain is the artificial neural network. It starts out as a network of transistor “neurons,” connected to each other with inputs and outputs, and it knows nothing—like an infant brain. The way it “learns” is it tries to do a task, say handwriting recognition, and at first, its neural firings and subsequent guesses at deciphering each letter will be completely random.

But when it’s told it got something right, the transistor connections in the firing pathways that happened to create that answer are strengthened; when it’s told it was wrong, those pathways’ connections are weakened. After a lot of this trial and feedback, the network has, by itself, formed smart neural pathways and the machine has become optimized for the task. The brain learns a bit like this but in a more sophisticated way, and as we continue to study the brain, we’re discovering ingenious new ways to take advantage of neural circuitry.

More extreme plagiarism involves a strategy called “whole brain emulation,” where the goal is to slice a real brain into thin layers, scan each one, use software to assemble an accurate reconstructed 3-D model, and then implement the model on a powerful computer.

We’d then have a computer officially capable of everything the brain is capable of—it would just need to learn and gather information. If engineers get really good, they’d be able to emulate a real brain with such exact accuracy that the brain’s full personality and memory would be intact once the brain architecture has been uploaded to a computer.

If the brain belonged to Jim right before he passed away, the computer would now wake up as Jim, which would be a robust human-level AGI, and we could now work on turning Jim into an unimaginably smart ASI, which he’d probably be really excited about.

How far are we from achieving whole brain emulation? Well so far, we’ve not yet just recently been able to emulate a 1mm-long flatworm brain, which consists of just 302 total neurons. The human brain contains 100 billion. If that makes it seem like a hopeless project, remember the power of exponential progress—now that we’ve conquered the tiny worm brain, an ant might happen before too long, followed by a mouse, and suddenly this will seem much more plausible.

2) Try to make evolution do what it did before but for us this time.

So if we decide the smart kid’s test is too hard to copy, we can try to copy the way he studies for the tests instead.

Here’s something we know. Building a computer as powerful as the brain is possible—our own brain’s evolution is proof. And if the brain is just too complex for us to emulate, we could try to emulate evolution instead. The fact is, even if we can emulate a brain, that might be like trying to build an airplane by copying a bird’s wing-flapping motions—often, machines are best designed using a fresh, machine-oriented approach, not by mimicking biology exactly.

So how can we simulate evolution to build AGI? The method, called “genetic algorithms,” would work something like this: there would be a performance-and-evaluation process that would happen again and again (the same way biological creatures “perform” by living life and are “evaluated” by whether they manage to reproduce or not). A group of computers would try to do tasks, and the most successful ones would be bred with each other by having half of each of their programming merged together into a new computer. The less successful ones would be eliminated.

Over many, many iterations, this natural selection process would produce better and better computers. The challenge would be creating an automated evaluation and breeding cycle so this evolution process could run on its own.

The downside of copying evolution is that evolution likes to take a billion years to do things and we want to do this in a few decades.

But we have a lot of advantages over evolution. First, evolution has no foresight and works randomly—it produces more unhelpful mutations than helpful ones, but we would control the process so it would only be driven by beneficial glitches and targeted tweaks.

Secondly, evolution doesn’t aim for anything, including intelligence—sometimes an environment might even select against higher intelligence (since it uses a lot of energy). We, on the other hand, could specifically direct this evolutionary process toward increasing intelligence.

Third, to select for intelligence, evolution has to innovate in a bunch of other ways to facilitate intelligence—like revamping the ways cells produce energy—when we can remove those extra burdens and use things like electricity.

It’s no doubt we’d be much, much faster than evolution—but it’s still not clear whether we’ll be able to improve upon evolution enough to make this a viable strategy.

3) Make this whole thing the computer’s problem, not ours.

This is when scientists get desperate and try to program the test to take itself. But it might be the most promising method we have.

The idea is that we’d build a computer whose two major skills would be doing research on AI and coding changes into itself—allowing it to not only learn but to improve its own architecture

We’d teach computers to be computer scientists so they could bootstrap their own development. And that would be their main job—figuring out how to make themselves smarter. More on this later.

All of This Could Happen Soon

Rapid advancements in hardware and innovative experimentation with software are happening simultaneously, and AGI could creep up on us quickly and unexpectedly for two main reasons:

1) Exponential growth is intense and what seems like a snail’s pace of advancement can quickly race upwards—this GIF illustrates this concept nicely:

2) When it comes to software, progress can seem slow, but then one epiphany can instantly change the rate of advancement (kind of like the way science, during the time humans thought the universe was geocentric, was having difficulty calculating how the universe worked, but then the discovery that it was heliocentric suddenly made everything much easier). 

Or, when it comes to something like a computer that improves itself, we might seem far away but actually be just one tweak of the system away from having it become 1,000 times more effective and zooming upward to human-level intelligence.