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THE AI REVOLUTION: THE ROAD TO SUPERINTELLIGENCE

January 22, 2015 By Tim Urban




PDF: We made a fancy PDF of this post for printing and offline viewing. Buy it
here. (Or see a preview.)

Note: The reason this post took three weeks to finish is that as I dug into
research on Artificial Intelligence, I could not believe what I was reading. It
hit me pretty quickly that what’s happening in the world of AI is not just an
important topic, but by far THE most important topic for our future. So I wanted
to learn as much as I could about it, and once I did that, I wanted to make sure
I wrote a post that really explained this whole situation and why it matters so
much. Not shockingly, that became outrageously long, so I broke it into two
parts. This is Part 1—Part 2 is here.

_______________

We are on the edge of change comparable to the rise of human life on Earth. —
Vernor Vinge

 

What does it feel like to stand here?



It seems like a pretty intense place to be standing—but then you have to
remember something about what it’s like to stand on a time graph: you can’t see
what’s to your right. So here’s how it actually feels to stand there:



Which probably feels pretty normal…

_______________


THE FAR FUTURE—COMING SOON

Imagine taking a time machine back to 1750—a time when the world was in a
permanent power outage, long-distance communication meant either yelling loudly
or firing a cannon in the air, and all transportation ran on hay. When you get
there, you retrieve a dude, bring him to 2015, and then walk him around and
watch him react to everything. It’s impossible for us to understand what it
would be like for him to see shiny capsules racing by on a highway, talk to
people who had been on the other side of the ocean earlier in the day, watch
sports that were being played 1,000 miles away, hear a musical performance that
happened 50 years ago, and play with my magical wizard rectangle that he could
use to capture a real-life image or record a living moment, generate a map with
a paranormal moving blue dot that shows him where he is, look at someone’s face
and chat with them even though they’re on the other side of the country, and
worlds of other inconceivable sorcery. This is all before you show him the
internet or explain things like the International Space Station, the Large
Hadron Collider, nuclear weapons, or general relativity.

This experience for him wouldn’t be surprising or shocking or even
mind-blowing—those words aren’t big enough. He might actually die.

But here’s the interesting thing—if he then went back to 1750 and got jealous
that we got to see his reaction and decided he wanted to try the same thing,
he’d take the time machine and go back the same distance, get someone from
around the year 1500, bring him to 1750, and show him everything. And the 1500
guy would be shocked by a lot of things—but he wouldn’t die. It would be far
less of an insane experience for him, because while 1500 and 1750 were very
different, they were much less different than 1750 to 2015. The 1500 guy would
learn some mind-bending shit about space and physics, he’d be impressed with how
committed Europe turned out to be with that new imperialism fad, and he’d have
to do some major revisions of his world map conception. But watching everyday
life go by in 1750—transportation, communication, etc.—definitely wouldn’t make
him die.

No, in order for the 1750 guy to have as much fun as we had with him, he’d have
to go much farther back—maybe all the way back to about 12,000 BC, before the
First Agricultural Revolution gave rise to the first cities and to the concept
of civilization. If someone from a purely hunter-gatherer world—from a time when
humans were, more or less, just another animal species—saw the vast human
empires of 1750 with their towering churches, their ocean-crossing ships, their
concept of being “inside,” and their enormous mountain of collective,
accumulated human knowledge and discovery—he’d likely die.

And then what if, after dying, he got jealous and wanted to do the same thing.
If he went back 12,000 years to 24,000 BC and got a guy and brought him to
12,000 BC, he’d show the guy everything and the guy would be like, “Okay what’s
your point who cares.” For the 12,000 BC guy to have the same fun, he’d have to
go back over 100,000 years and get someone he could show fire and language to
for the first time.

In order for someone to be transported into the future and die from the level of
shock they’d experience, they have to go enough years ahead that a “die level of
progress,” or a Die Progress Unit (DPU) has been achieved. So a DPU took over
100,000 years in hunter-gatherer times, but at the post-Agricultural Revolution
rate, it only took about 12,000 years. The post-Industrial Revolution world has
moved so quickly that a 1750 person only needs to go forward a couple hundred
years for a DPU to have happened.

This pattern—human progress moving quicker and quicker as time goes on—is what
futurist Ray Kurzweil calls human history’s Law of Accelerating Returns. This
happens because more advanced societies have the ability to progress at a faster
rate than less advanced societies—because they’re more advanced. 19th century
humanity knew more and had better technology than 15th century humanity, so it’s
no surprise that humanity made far more advances in the 19th century than in the
15th century—15th century humanity was no match for 19th century humanity.11←
open these

This works on smaller scales too. The movie Back to the Future came out in 1985,
and “the past” took place in 1955. In the movie, when Michael J. Fox went back
to 1955, he was caught off-guard by the newness of TVs, the prices of soda, the
lack of love for shrill electric guitar, and the variation in slang. It was a
different world, yes—but if the movie were made today and the past took place in
1985, the movie could have had much more fun with much bigger differences. The
character would be in a time before personal computers, internet, or cell
phones—today’s Marty McFly, a teenager born in the late 90s, would be much more
out of place in 1985 than the movie’s Marty McFly was in 1955.

This is for the same reason we just discussed—the Law of Accelerating Returns.
The average rate of advancement between 1985 and 2015 was higher than the rate
between 1955 and 1985—because the former was a more advanced world—so much more
change happened in the most recent 30 years than in the prior 30.

So—advances are getting bigger and bigger and happening more and more quickly.
This suggests some pretty intense things about our future, right?

Kurzweil suggests that the progress of the entire 20th century would have been
achieved in only 20 years at the rate of advancement in the year 2000—in other
words, by 2000, the rate of progress was five times faster than the average rate
of progress during the 20th century. He believes another 20th century’s worth of
progress happened between 2000 and 2014 and that another 20th century’s worth of
progress will happen by 2021, in only seven years. A couple decades later, he
believes a 20th century’s worth of progress will happen multiple times in the
same year, and even later, in less than one month. All in all, because of the
Law of Accelerating Returns, Kurzweil believes that the 21st century will
achieve 1,000 times the progress of the 20th century.2

If Kurzweil and others who agree with him are correct, then we may be as blown
away by 2030 as our 1750 guy was by 2015—i.e. the next DPU might only take a
couple decades—and the world in 2050 might be so vastly different than today’s
world that we would barely recognize it.

This isn’t science fiction. It’s what many scientists smarter and more
knowledgeable than you or I firmly believe—and if you look at history, it’s what
we should logically predict.

So then why, when you hear me say something like “the world 35 years from now
might be totally unrecognizable,” are you thinking, “Cool….but nahhhhhhh”? Three
reasons we’re skeptical of outlandish forecasts of the future:

1) When it comes to history, we think in straight lines. When we imagine the
progress of the next 30 years, we look back to the progress of the previous 30
as an indicator of how much will likely happen. When we think about the extent
to which the world will change in the 21st century, we just take the 20th
century progress and add it to the year 2000. This was the same mistake our 1750
guy made when he got someone from 1500 and expected to blow his mind as much as
his own was blown going the same distance ahead. It’s most intuitive for us to
think linearly, when we should be thinking exponentially. If someone is being
more clever about it, they might predict the advances of the next 30 years not
by looking at the previous 30 years, but by taking the current rate of progress
and judging based on that. They’d be more accurate, but still way off. In order
to think about the future correctly, you need to imagine things moving at a much
faster rate than they’re moving now.



2) The trajectory of very recent history often tells a distorted story. First,
even a steep exponential curve seems linear when you only look at a tiny slice
of it, the same way if you look at a little segment of a huge circle up close,
it looks almost like a straight line. Second, exponential growth isn’t totally
smooth and uniform. Kurzweil explains that progress happens in “S-curves”:



An S is created by the wave of progress when a new paradigm sweeps the world.
The curve goes through three phases:

1. Slow growth (the early phase of exponential growth)
2. Rapid growth (the late, explosive phase of exponential growth)
3. A leveling off as the particular paradigm matures3

If you look only at very recent history, the part of the S-curve you’re on at
the moment can obscure your perception of how fast things are advancing. The
chunk of time between 1995 and 2007 saw the explosion of the internet, the
introduction of Microsoft, Google, and Facebook into the public consciousness,
the birth of social networking, and the introduction of cell phones and then
smart phones. That was Phase 2: the growth spurt part of the S. But 2008 to 2015
has been less groundbreaking, at least on the technological front. Someone
thinking about the future today might examine the last few years to gauge the
current rate of advancement, but that’s missing the bigger picture. In fact, a
new, huge Phase 2 growth spurt might be brewing right now.

3) Our own experience makes us stubborn old men about the future. We base our
ideas about the world on our personal experience, and that experience has
ingrained the rate of growth of the recent past in our heads as “the way things
happen.” We’re also limited by our imagination, which takes our experience and
uses it to conjure future predictions—but often, what we know simply doesn’t
give us the tools to think accurately about the future.2 When we hear a
prediction about the future that contradicts our experience-based notion of how
things work, our instinct is that the prediction must be naive. If I tell you,
later in this post, that you may live to be 150, or 250, or not die at all, your
instinct will be, “That’s stupid—if there’s one thing I know from history, it’s
that everybody dies.” And yes, no one in the past has not died. But no one flew
airplanes before airplanes were invented either.

So while nahhhhh might feel right as you read this post, it’s probably actually
wrong. The fact is, if we’re being truly logical and expecting historical
patterns to continue, we should conclude that much, much, much more should
change in the coming decades than we intuitively expect. Logic also suggests
that if the most advanced species on a planet keeps making larger and larger
leaps forward at an ever-faster rate, at some point, they’ll make a leap so
great that it completely alters life as they know it and the perception they
have of what it means to be a human—kind of like how evolution kept making great
leaps toward intelligence until finally it made such a large leap to the human
being that it completely altered what it meant for any creature to live on
planet Earth. And if you spend some time reading about what’s going on today in
science and technology, you start to see a lot of signs quietly hinting that
life as we currently know it cannot withstand the leap that’s coming next.

_______________


THE ROAD TO SUPERINTELLIGENCE


WHAT IS AI?

If you’re like me, you used to think Artificial Intelligence was a silly sci-fi
concept, but lately you’ve been hearing it mentioned by serious people, and you
don’t really quite get it.

There are three reasons a lot of people are confused about the term AI:

1) We associate AI with movies. Star Wars. Terminator. 2001: A Space Odyssey.
Even the Jetsons. And those are fiction, as are the robot characters. So it
makes AI sound a little fictional to us.

2) AI is a broad topic. It ranges from your phone’s calculator to self-driving
cars to something in the future that might change the world dramatically. AI
refers to all of these things, which is confusing.

3) We use AI all the time in our daily lives, but we often don’t realize it’s
AI. John McCarthy, who coined the term “Artificial Intelligence” in 1956,
complained that “as soon as it works, no one calls it AI anymore.”4 Because of
this phenomenon, AI often sounds like a mythical future prediction more than a
reality. At the same time, it makes it sound like a pop concept from the past
that never came to fruition. Ray Kurzweil says he hears people say that AI
withered in the 1980s, which he compares to “insisting that the Internet died in
the dot-com bust of the early 2000s.”5

So let’s clear things up. First, stop thinking of robots. A robot is a container
for AI, sometimes mimicking the human form, sometimes not—but the AI itself is
the computer inside the robot. AI is the brain, and the robot is its body—if it
even has a body. For example, the software and data behind Siri is AI, the
woman’s voice we hear is a personification of that AI, and there’s no robot
involved at all.

Secondly, you’ve probably heard the term “singularity” or “technological
singularity.” This term has been used in math to describe an asymptote-like
situation where normal rules no longer apply. It’s been used in physics to
describe a phenomenon like an infinitely small, dense black hole or the point we
were all squished into right before the Big Bang. Again, situations where the
usual rules don’t apply. In 1993, Vernor Vinge wrote a famous essay in which he
applied the term to the moment in the future when our technology’s intelligence
exceeds our own—a moment for him when life as we know it will be forever changed
and normal rules will no longer apply. Ray Kurzweil then muddled things a bit by
defining the singularity as the time when the Law of Accelerating Returns has
reached such an extreme pace that technological progress is happening at a
seemingly-infinite pace, and after which we’ll be living in a whole new world. I
found that many of today’s AI thinkers have stopped using the term, and it’s
confusing anyway, so I won’t use it much here (even though we’ll be focusing on
that idea throughout).

Finally, while there are many different types or forms of AI since AI is a broad
concept, the critical categories we need to think about are based on an AI’s
caliber. There are three major AI caliber categories:

AI Caliber 1) Artificial Narrow Intelligence (ANI): Sometimes referred to as
Weak AI, Artificial Narrow Intelligence is AI that specializes in one area.
There’s AI that can beat the world chess champion in chess, but that’s the only
thing it does. Ask it to figure out a better way to store data on a hard drive,
and it’ll look at you blankly.

AI Caliber 2) Artificial General Intelligence (AGI): Sometimes referred to as
Strong AI, or Human-Level AI, Artificial General Intelligence refers to a
computer that is as smart as a human across the board—a machine that can perform
any intellectual task that a human being can. Creating AGI is a much harder task
than creating ANI, and we’re yet to do it. Professor Linda Gottfredson describes
intelligence as “a very general mental capability that, among other things,
involves the ability to reason, plan, solve problems, think abstractly,
comprehend complex ideas, learn quickly, and learn from experience.” AGI would
be able to do all of those things as easily as you can.

AI Caliber 3) Artificial Superintelligence (ASI): Oxford philosopher and leading
AI thinker Nick Bostrom defines superintelligence as “an intellect that is much
smarter than the best human brains in practically every field, including
scientific creativity, general wisdom and social skills.” Artificial
Superintelligence ranges from a computer that’s just a little smarter than a
human to one that’s trillions of times smarter—across the board. ASI is the
reason the topic of AI is such a spicy meatball and why the words “immortality”
and “extinction” will both appear in these posts multiple times.

As of now, humans have conquered the lowest caliber of AI—ANI—in many ways, and
it’s everywhere. The AI Revolution is the road from ANI, through AGI, to ASI—a
road we may or may not survive but that, either way, will change everything.

Let’s take a close look at what the leading thinkers in the field believe this
road looks like and why this revolution might happen way sooner than you might
think:


WHERE WE ARE CURRENTLY—A WORLD RUNNING ON ANI

Artificial Narrow Intelligence is machine intelligence that equals or exceeds
human intelligence or efficiency at a specific thing. A few examples:

 * Cars are full of ANI systems, from the computer that figures out when the
   anti-lock brakes should kick in to the computer that tunes the parameters of
   the fuel injection systems. Google’s self-driving car, which is being tested
   now, will contain robust ANI systems that allow it to perceive and react to
   the world around it.
 * Your phone is a little ANI factory. When you navigate using your map app,
   receive tailored music recommendations from Pandora, check tomorrow’s
   weather, talk to Siri, or dozens of other everyday activities, you’re using
   ANI.
 * Your email spam filter is a classic type of ANI—it starts off loaded with
   intelligence about how to figure out what’s spam and what’s not, and then it
   learns and tailors its intelligence to you as it gets experience with your
   particular preferences. The Nest Thermostat does the same thing as it starts
   to figure out your typical routine and act accordingly.
 * You know the whole creepy thing that goes on when you search for a product on
   Amazon and then you see that as a “recommended for you” product on a
   different site, or when Facebook somehow knows who it makes sense for you to
   add as a friend? That’s a network of ANI systems, working together to inform
   each other about who you are and what you like and then using that
   information to decide what to show you. Same goes for Amazon’s “People who
   bought this also bought…” thing—that’s an ANI system whose job it is to
   gather info from the behavior of millions of customers and synthesize that
   info to cleverly upsell you so you’ll buy more things.
 * Google Translate is another classic ANI system—impressively good at one
   narrow task. Voice recognition is another, and there are a bunch of apps that
   use those two ANIs as a tag team, allowing you to speak a sentence in one
   language and have the phone spit out the same sentence in another.
 * When your plane lands, it’s not a human that decides which gate it should go
   to. Just like it’s not a human that determined the price of your ticket.
 * The world’s best Checkers, Chess, Scrabble, Backgammon, and Othello players
   are now all ANI systems.
 * Google search is one large ANI brain with incredibly sophisticated methods
   for ranking pages and figuring out what to show you in particular. Same goes
   for Facebook’s Newsfeed.
 * And those are just in the consumer world. Sophisticated ANI systems are
   widely used in sectors and industries like military, manufacturing, and
   finance (algorithmic high-frequency AI traders account for more than half of
   equity shares traded on US markets6), and in expert systems like those that
   help doctors make diagnoses and, most famously, IBM’s Watson, who contained
   enough facts and understood coy Trebek-speak well enough to soundly beat the
   most prolific Jeopardy champions.

ANI systems as they are now aren’t especially scary. At worst, a glitchy or
badly-programmed ANI can cause an isolated catastrophe like knocking out a power
grid, causing a harmful nuclear power plant malfunction, or triggering a
financial markets disaster (like the 2010 Flash Crash when an ANI program
reacted the wrong way to an unexpected situation and caused the stock market to
briefly plummet, taking $1 trillion of market value with it, only part of which
was recovered when the mistake was corrected).

But while ANI doesn’t have the capability to cause an existential threat, we
should see this increasingly large and complex ecosystem of relatively-harmless
ANI as a precursor of the world-altering hurricane that’s on the way. Each new
ANI innovation quietly adds another brick onto the road to AGI and ASI. Or as
Aaron Saenz sees it, our world’s ANI systems “are like the amino acids in the
early Earth’s primordial ooze”—the inanimate stuff of life that, one unexpected
day, woke up.


THE ROAD FROM ANI TO AGI

Why It’s So Hard

Nothing will make you appreciate human intelligence like learning about how
unbelievably challenging it is to try to create a computer as smart as we are.
Building skyscrapers, putting humans in space, figuring out the details of how
the Big Bang went down—all far easier than understanding our own brain or how to
make something as cool as it. As of now, the human brain is the most complex
object in the known universe.

What’s interesting is that the hard parts of trying to build AGI (a computer as
smart as humans in general, not just at one narrow specialty) are not
intuitively what you’d think they are. Build a computer that can multiply two
ten-digit numbers in a split second—incredibly easy. Build one that can look at
a dog and answer whether it’s a dog or a cat—spectacularly difficult. Make AI
that can beat any human in chess? Done. Make one that can read a paragraph from
a six-year-old’s picture book and not just recognize the words but understand
the meaning of them? Google is currently spending billions of dollars trying to
do it. Hard things—like calculus, financial market strategy, and language
translation—are mind-numbingly easy for a computer, while easy things—like
vision, motion, movement, and perception—are insanely hard for it. Or, as
computer scientist Donald Knuth puts it, “AI has by now succeeded in doing
essentially everything that requires ‘thinking’ but has failed to do most of
what people and animals do ‘without thinking.'”7

What you quickly realize when you think about this is that those things that
seem easy to us are actually unbelievably complicated, and they only seem easy
because those skills have been optimized in us (and most animals) by hundreds of
millions of years of animal evolution. When you reach your hand up toward an
object, the muscles, tendons, and bones in your shoulder, elbow, and wrist
instantly perform a long series of physics operations, in conjunction with your
eyes, to allow you to move your hand in a straight line through three
dimensions. It seems effortless to you because you have perfected software in
your brain for doing it. Same idea goes for why it’s not that malware is dumb
for not being able to figure out the slanty word recognition test when you sign
up for a new account on a site—it’s that your brain is super impressive for
being able to.

On the other hand, multiplying big numbers or playing chess are new activities
for biological creatures and we haven’t had any time to evolve a proficiency at
them, so a computer doesn’t need to work too hard to beat us. Think about
it—which would you rather do, build a program that could multiply big numbers or
one that could understand the essence of a B well enough that you could show it
a B in any one of thousands of unpredictable fonts or handwriting and it could
instantly know it was a B?

One fun example—when you look at this, you and a computer both can figure out
that it’s a rectangle with two distinct shades, alternating:



Tied so far. But if you pick up the black and reveal the whole image…



…you have no problem giving a full description of the various opaque and
translucent cylinders, slats, and 3-D corners, but the computer would fail
miserably. It would describe what it sees—a variety of two-dimensional shapes in
several different shades—which is actually what’s there. Your brain is doing a
ton of fancy shit to interpret the implied depth, shade-mixing, and room
lighting the picture is trying to portray.8 And looking at the picture below, a
computer sees a two-dimensional white, black, and gray collage, while you easily
see what it really is—a photo of an entirely-black, 3-D rock:

Credit: Matthew Lloyd

And everything we just mentioned is still only taking in stagnant information
and processing it. To be human-level intelligent, a computer would have to
understand things like the difference between subtle facial expressions, the
distinction between being pleased, relieved, content, satisfied, and glad, and
why Braveheart was great but The Patriot was terrible.

Daunting.

So how do we get there?

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:9



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:

Source

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.


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:

Hardware:

 * 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.

Software:

 * 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:

 

Will it be a nice God?

 

That’s the topic of Part 2 of this post.

___________

Sources at the bottom of Part 2.



If you’re into Wait But Why, sign up for the Wait But Why email list and we’ll
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If you’d like to support Wait But Why, here’s our Patreon.

Related Wait But Why Posts

The Fermi Paradox – Why don’t we see any signs of alien life?

How (and Why) SpaceX Will Colonize Mars – A post I got to work on with Elon Musk
and one that reframed my mental picture of the future.

Or for something totally different and yet somehow related, Why Procrastinators
Procrastinate

And here’s Year 1 of Wait But Why on an ebook.

--------------------------------------------------------------------------------

 1. Okay so there are two different kinds of notes now. The blue circles are the
    fun/interesting ones you should read. They’re for extra info or thoughts
    that I didn’t want to put in the main text because either it’s just
    tangential thoughts on something or because I want to say something a notch
    too weird to just be there in the normal text.

    

 2. Kurzweil points out that his phone is about a millionth the size of, a
    millionth the price of, and a thousand times more powerful than his MIT
    computer was 40 years ago. Good luck trying to figure out where a comparable
    future advancement in computing would leave us, let alone one far, far more
    extreme, since the progress grows exponentially.

    

 3. Much more on what it means for a computer to “want” to do something in the
    Part 2 post.

    

--------------------------------------------------------------------------------

 1.  Gray squares are boring objects and when you click on a gray square, you’ll
     end up bored. These are for sources and citations only.

     

 2.  Kurzweil, The Singularity is Near, 39.

     

 3.  Kurzweil, The Singularity is Near, 84.

     

 4.  Vardi, Artificial Intelligence: Past and Future, 5.

     

 5.  Kurzweil, The Singularity is Near, 392.

     

 6.  Bostrom, Superintelligence: Paths, Dangers, Strategies, loc. 597

     

 7.  Nilsson, The Quest for Artificial Intelligence: A History of Ideas and
     Achievements, 318.

     

 8.  Pinker, How the Mind Works, 36.

     

 9.  Kurzweil, The Singularity is Near, 118.

     

 10. Bostrom, Superintelligence: Paths, Dangers, Strategies, loc. 1500-1576.

     

 11. This term was first used by one of history’s great AI thinkers, Irving John
     Good, in 1965.

     

 12. Nick Bostrom, Superintelligence: Paths, Dangers, Strategies, loc. 660

     

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