ahmed shoukry
@ashoukry
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www.scribd.com/read/374714284/The-Fourth-Age-Smart-Robots-Conscious-Computers-and-the-Future-of-Humanity
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www.scribd.com/read/410105719/Conscious-A-Brief-Guide-to-the-Fundamental-Mystery-of-the-Mind
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www.scribd.com/read/603191099/What-Makes-Us-Human-An-Artificial-Intelligence-Answers-Life-s-Biggest-Questions
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time.com/6132544/artificial-intelligence-humor/
Nov 21, 2022
thedecisionlab.com/biases/planning-fallacy
Nov 20, 2022
getting sunlight and water, and will perform actions to do
so.
Where will the AGI get its goals? Right now, we give
computer programs their goals, such as identifying email
spam or finding grammatical errors. But what about an
AGI? Again, Stephen Hawking explains the issue well:
“Once machines reach the critical stage of being able to
evolve themselves, we cannot predict whether their goals
will be the same as ours.”
Further, as Hawking alludes
Technical Challenges
The combination of narrow AI and physical robots is more
than the sum of its parts.
I maintain that things have only
really changed three times in human history. Each time
was due to technology. Not just a single technology, but
groups of interrelated technologies that changed us in
fundamental and permanent, even biological, ways. That’s
it. Just three big changes so far.
This book is about the fourth one.
The First Age: Language and Fire
While no one knows when isolated individual humans first
harnessed the power of fire, we have pretty good evidence
that around 100,000 years ago we gained widespread
mastery of it. And it is easy to see in the much more recent
Greek myth of Prometheus an eons-old memory of how
dramatically fire changed us. Fire was the original multi-
function technology. It provided light, and, because ani-
mals feared it, it also provided safety. Its portability meant
that humans could migrate to colder climates and bring
warmth with them. But far and away, its greatest benefit
was that it let us cook food.
The vastly more powerful technology of language al-
lowed us to exchange information.
During this time, the First Age (the roughly 100,000
years in which we lived as hunter-gatherers with both lan-
guage and fire), what was life like?
The Second Age: Agriculture and Cities
After about 100,000 years of humans chatting away while
they hunted and gathered their way through the day, some-
thing dramatic happened that profoundly altered humans
and our society once again: we invented agriculture. The
Second Age began just 10,000 short years ago, when the
human population of the planet was about four million
people, a little more than the current population of Los
Angeles. In
Without it, we would perish.
Weapons for organized warfare are another technology
that came along because of the city. They were invented
out of necessity, because the city concentrated wealth and
needed to be defended. The earliest cities were often
walled, which is achieved only through great effort and ex-
pense, implying that the risks of invasion were real, or at
least perceived to be.
As a result of agriculture and cities, humanity had indi-
vidual private ownership of land for the first time. Hu-
mans, being territorial, have probably always defended a
loosely defined area they regarded as their own, but we
have archaeological evidence from the beginning of the
Second Age that borders were often well defined. The
philosopher Jean-Jacques Rousseau thought of this prac-
tice as the beginning of our modern world, and stated that
“the first man who, having fenced in a piece of land, said
‘This is mine,’ and found people naïve enough to believe
him, that man was the true founder of civil society.”
Agriculture and privately owned land ended the
economic equality of the First Age. The natural inequality
of ability, birth, and luck led to unequal accumulations of
wealth. Although coinage in the modern sense didn’t exist
at this time, the idea of wealth certainly did. One could
own land, cattle, and silos for storing grain. That wealth
could be accumulated indefinitely, with no upper limit on
how rich a person could be. Since land could be farmed
and cattle could reproduce, early wealth was income pro-
ducing. As such, holdings of wealth tended to grow. Given
that wealth could be passed down from generation to
generation, it could accumulate and compound over mul-
tiple lifetimes.
Sadly, it was in the Second Age that the practice of
human enslavement began. S
The Third Age: Writing and Wheels
Fire let us cook food,
Like the other pivotal technologies we have explored,
writing also had concurrent new technologies it helped
bring about or promote. The first of these was the wheel,
which came along at the same time, about five thousand
years ago. The wheel and writing go together like PB and J,
for as a pair they increased commerce, aided the flow of
information, and promoted travel. Writing meant that
rulers could create legal codes, but it was the wheel that al-
lowed those codes to be distributed and enforced across a
wide area.
Early
into the period, but money in a dozen other forms, from
gold and silver to shells and salt, appeared all over the
world early in the Third Age.
The Fourth Age: Robots and AI
Although
2030.
That is the story that gets us from the waning centuries
of the Third Age to the doorstep of the Fourth Age. Each
new age saw technology outsourcing and enhancing func-
tions of our physical or mental life. We used fire to help
with digestion, writing to augment our memories, the
wheel to spare our backs and legs. In our time, we have
created a device, a mechanical brain, that is so versatile
that it can be programmed to solve virtually a limitless
number of problems that we ask it to. We are now devel-
oping artificial intelligence, a method to teach that device
to operate on its own, and through the power of robotics,
we have begun to give it mobility and ways to interact with
the physical world. We will use computers and robots to
outsource more and more of our thoughts and actions,
presumably as much as we possibly can. This is a real
change, and it marks the dawn of a new age, the Fourth
Age. The
Why are these questions important? They are the issues
on which hinge much of what we are about to discuss.
These include what artificial intelligence will be capable of,
whether computers can become conscious, and if robots
will take all our jobs.
What Is the Composition of the Universe?
the three reasons why this view is unset-
tling to many people:
First, it is hard to work free will into a world of simple
cause and effect.
Second, it means we are nothing more than big walking
bags of chemicals and electrical impulses. D
The modern defense of dualism is best expressed by
the “Mary’s room” problem,
What Are We?
Next question: What exactly are we? Again, a multiple-
choice question, with three possible answers: machines,
animals, or humans.
The first possible answer is that we are machines. This
is the simplest, most straightforward answer. We are a
bunch of parts that work together to achieve an end. We
have a power source and an exhaust system. We self-repair
and can reprogram ourselves to do a variety of different
tasks.
With regards to life, this view holds that it too is simply
a mechanistic process. Consciousness as well. To those
who hold this view, all this is painfully apparent, and they
do not wince when they read Kurt Vonnegut’s thoughts on
this question:
I had come to the conclusion that there was nothing sacred
about myself or any human being, that we were all ma-
chines.… I no more harbored sacredness than did a Pon-
tiac, a mousetrap, or a South Bend Lathe.
Your second choice is that we are animals. Often this
view sees an inorganic, mechanical world that is a com-
pletely different thing from the biological, living world. Life
makes us different from machines. Maybe our bodies are
machines, but “we” are animals that inhabit those ma-
chines.
This
We share a huge amount of DNA with every living thing
on the planet, including plants. This notion is profound,
and it is one best expressed by the author Matt Ridley in
just four words: “All life is one.” Beyond this idea of unity,
we share as much as 99 percent of our genome with a sin-
gle species: chimpanzees. So as machines and animals,
we are strikingly similar to chimps, with only a rounding
error of difference. But viewed through another lens, we
are absolutely nothing like chimps. And whatever that lens
is, that is what makes us human. H
The Egyptians, for instance, who saved
all of a person’s body parts during mummification be-
cause he or she would need them in the afterlife, threw the
brain out as useless, thinking it was just a goo that kept
the blood cool. Aristotle
Let’s tackle our question, then: What is your “self”?
There are three possible choices: a clever trick of your
brain; an emergent mind; or your soul.
The first option is that it is a trick of your brain.
“self” is.
The second option is that your “self” is an emergent
mind.
Emergence is a fascinating phenomenon. At its sim-
plest, emergence is when a group of things interact with
each other, and through that interaction, the collective
whole gains characteristics that none of the individual
things has.
Humans are clearly emergent things. You are made of
forty trillion cells. They all go about their daily business
doing their jobs, getting married, having kids, and then
dying. And all along, they have absolutely no idea you
exist, or that they are even part of something else. But you,
and all your abilities and attributes, are not simply the abil-
ities and attributes of a single one of your cells multiplied
by forty trillion. You are not simply the cumulative result of
your biological processes, the sum of your individual
parts. Not a single one of those forty trillion cells has a
sense of humor, and yet somehow you do. Somehow there
is an “I” that arises from all the activity of those forty tril-
lion cells doing their respective things. We call that emer-
gence. W
The final option is that your “self” is your soul. The
majority of people probably believe this. Why do I say
that? Religious belief, while not universal, is certainly the
norm. Poll after poll after poll shows that an overwhelming
(75 percent or more) portion of Americans believe in God
What exactly is artificial intelligence? This
But—and this is really important—there are two com-
pletely different things people mean today when they talk
about artificial intelligence. There is “narrow AI” and there
is “general AI.” The kind of AI we have today is narrow AI,
also known as weak AI. It is the only kind of AI we know
how to build, and it is incredibly useful. Narrow AI is the
ability for a computer to solve a specific kind of problem
or perform a specific task. The other kind of AI is referred
to by three different names: general AI, strong AI, or arti-
ficial general intelligence (AGI). Although the terms are
interchangeable, I will use AGI from this point forward to
refer to an artificial intelligence as smart and versatile as
you or me. A
So how does AI work? Very broadly speaking, there are
three different approaches to how to build AI. Let’s say you
want to make an AI that tells farmers when to plant their
seeds. The first approach would be classic AI. We call this
classic AI because in the earliest days of AI research, this
is the approach that computer scientists thought would
work the best. Classic AI involves thinking through all the
factors (soil type, crop, rainfall, etc.) and building a model
that takes those factors, weighs them accordingly, and
makes a suggestion.
The second approach is called an expert system. An ex-
pert system is developed by taking a hundred of the best
farmers and getting them to write down every rule about
planting they know. You then arrange those rules in a way
that someone can enter their relevant variables and the
system will make a suggestion based on those rules.
The third approach is machine learning. Machine
learning refers to a process by which you take all the data
concerning when everyone planted and what the yields
were and you get a computer to build rules that, in retro-
spect, would have maximized those yields. T
Robots
I learned the story
gadget.
By the time the twentieth century rolled around, robots
were completely creations of science. The word itself is
derived from a Slavic word for slavery, and it was coined
by the Czech author Karel Čapek in 1920 for his play
R.U.R., the title of which stands for “Rossum’s Universal
Robots.” In
What are our fears about robots? There are a few. The
primary fear is that robots will compete with us in the job
market and that we will lose to them. To date, robots have
been an economic boon for humans, freeing us up to do
ever more complicated and valuable jobs. But what hap-
pens at the end of that curve? What happens when a robot
can do many or most jobs better than a person?
This fear is compounded by a basic characteristic of
manufacturing. Over time, prices tend to go down as the
quality of goods produced goes up. So robots will become
better and cheaper, indefinitely. Surely, we fear, at some
point robot labor will be both cheaper and better than
human labor. Will the less skilled be locked out of the job
market forever? If a fast-food robot costs $10,000, would a
business rather pay a human a $15 minimum wage—or a
$10 minimum wage, or any minimum wage? A massive
displacement of this sort would represent a dramatic shift
of economic power away from labor and toward those who
own the robots.
Another widespread concern is the potential for a
WALL-E future. In that world, we become permanently
sedentary when we no longer have to work. On top of that,
our brains atrophy as well, since the machines maintain
themselves, as well as everything else.
Some fear that we will form emotional attachments to
robots so strong that they will supplant bonds between
humans. You d
And of course, the ultimate fear is the robot uprising.
Although
transfer learning? If I show you an item, let’s say, a small
statuette of a falcon about a foot tall, and then I show you
a dozen photos and ask you to spot the falcon in them,
you wouldn’t have any trouble. Even if the falcon is half
obscured by a tree or underwater or upside down or lying
on its side or has peanut butter smeared on its head. I
would wager that although you have never seen a falcon
statuette with peanut butter smeared on its head before,
you could still recognize the falcon. This is because hu-
mans can take a lifetime of experience of seeing things ob-
scured by objects, underwater, or covered in substances
like peanut butter, and apply that knowledge to a new task.
That’s transfer learning, and we don’t understand how we
do it, let alone how to teach a computer to do it.
But let’s say we solved that problem. We haven’t and
likely won’t any time soon. But let’s say we have and the AI
can take what it learns in one area and apply it elsewhere.
We still aren’t very far along, because the AI doesn’t know
how to improvise. Everyone, of every skill level, can impro-
vise in a way far beyond any machine. If
This is not only true in
manufacturing but also everywhere else. Say you are an
attorney. You are a generalist. You make a good living. But
how do you increase the rate you can charge? You spe-
cialize, perhaps in copyright law. Beyond that, you might
specialize even further. And yet in doing so, you sow the
seeds of your own occupational destruction. Ironically, the
more you specialize, the easier you will be to replace with
a machine. The more you increase your mastery of an
ever-smaller domain, the easier it is to instantiate that
knowledge in a computer program. A hunter-gatherer is
much harder to build a computer replacement for than an
X-ray technician, because the technician does just one nar-
row thing.
Ken
The first challenge robots have is figuring out where
they are. This
Touch is a big challenge for robots. The
cascade. Digital systems are
generally more brittle than analog ones. Delete a word
from The Great Gatsby and you still have a masterpiece.
Delete a character from a compressed file and you have…
alphabet soup. A missed hyphen won’t make a human ex-
plode. We tend to be wrong lots of times but just by a little
bit. Machines fail less often but to more catastrophic
ends. So we should be mindful of how and where we
implement technology.
Will Robots Take All Our Jobs?
The public discourse
Robots and AI will take all the jobs. “
Robots and AI will take none of the jobs. T
Most of the people holding this view believe that all
human work will eventually be done by machines, and that
the only economically profitable work left will be places
where humans subjectively prefer hiring another human,
even though the machine is objectively better. At
including creativity.
The
ASSUMPTION 6: It would be economically
practical to build such mechanical humans.
It
ASSUMPTION 9: Humans lack the ability to
find other tasks that machines cannot do.
The final assumption is that humans lack the ability to
discover new jobs that can’t be done by machines.
ASSUMPTION 1: Machines and technology
cause a net loss of jobs.
The accusation that technology is a net job destroyer has
been argued for a long time. In the 1580s, William Lee in-
vented the stocking frame knitting machine. He pulled a
few strings and arranged to give a demonstration of his
device to Queen Elizabeth in the hopes of obtaining a
royal patent. The queen thought the device cunning but
remonstrated Lee, saying, “Consider thou what the inven-
tion could do to my poor subjects. It would assuredly
bring to them ruin by depriving them of employment, thus
making them beggars.” Lee, in fact, had to leave England
because of the anger of the hosiers.
As rapid
ne thing. But our
versatility borders on the infinite. The greatest under-
utilized resource in all of the world is human potential.
And the more technology we can employ in our pursuits,
the more we can do, and thus, generally speaking, the
higher our wages will be.
Certainly we have seen technology reduce the need for
workers in particular sectors. In just the twentieth century,
agriculture went from being 40 percent of our jobs to 2
percent. And in just the back half of the twentieth century,
manufacturing jobs went from 30 percent of our economy
to 10 percent. In that century, we saw entire new profes-
sions created and then destroyed. The churn of jobs al-
most boggles the mind. Just mentally compare the work-
force of 1900 with the workforce of 2000. I calculate that
the half-life of a job in a modern economy is about fifty or
so years. From 1900 to 1950, probably half the jobs van-
ished, mostly in farming. From 1950 to 2000, another half,
many of which were manufacturing. And most, if not all, of
this disruption was caused by technology. To believe that
technology is a net destroyer of jobs, one must explain the
fact that all this disruption happened during a period of
full employment, rising gross national product, and rising
wages. (The Great Depression, a decade that defies the
trend, was not caused by technology, but rather by
macroeconomic forces.)
But
ASSUMPTION 2: Too many jobs will be de-
stroyed too quickly.
The “47 percent [or 45 percent] of jobs will vanish”
interpretation doesn’t even come close to passing the sniff
test. Humans, even ones with little or no professional
training, have incredible skills we hardly ever think about.
Let’s look closely at two of the jobs at the very top of Frey
and Osborne’s list: short-order cook and waiter. Both have
94 percent chance of being computerized
specials thrown in. The point is that
those who think so-called low-skilled humans are easy tar-
gets for robot replacement haven’t fully realized what a
magnificently versatile thing any human being is and how
our most advanced electronics are little more than glori-
fied toaster ovens.
While it is clear
We have this sense that more jobs are being destroyed
than created because the destruction of jobs is more obvi-
ous and easier to see. Whe
ASSUMPTION 4: Low-skilled workers will be
the first to go.
ASSUMPTION 5: There won’t be enough
jobs for these workers in the future.
The assumptions
employment?
Often, the analysis you hear goes along these lines:
“The new jobs are too complex for less-skilled workers.
For instance, if a new robot replaces a warehouse worker,
tomorrow the world will need one less warehouse worker.
Even if the world also happened to need an additional
geneticist, what are you doing to do? Will the warehouse
worker have the time, money, and aptitude to train for the
geneticist’s job?”
No. The warehouse worker doesn’t become the geneti-
cist. What actually happens is this: A college biology pro-
fessor becomes the new geneticist; a high-school biology
teacher takes the college job; a substitute elementary
teacher takes the high school job; and the unemployed
warehouse worker becomes a substitute teacher. This is
story of progress. When a new job is created at the top,
everyone gets a promotion. The question is not “Can a
warehouse worker become a geneticist” but “Can everyone
do a job a little harder than the one they currently do?” If
the answer to that is yes, which I emphatically believe,
then we want all new jobs to be created at the top, so that
everyone gets a chance to move up a rung on the ladder of
success.
Possibility Three: The Machines Take None of
the Jobs
Possibility three
ASSUMPTION 1: There are many jobs that
machines will not ever be able to do.
At this point,
Well, we don’t have to speculate, because the setup
is identical to the practice of outsourcing jobs to other
countries where wages are lower but educational levels are
high. Ten million, in fact, is the lowest estimate of the
number of jobs relocated offshore since 2000. And yet the
unemployment rate in 2000 was 4.1 percent and in 2017 it
is 4.9 percent. Real
world.
That is what tech disruption looks like. We have seen
thousands of such events happen in just the last few
years. We buy fewer DVDs and spend that money on dig-
ital streaming. The number of digital cameras we are buy-
ing is falling by double digits every year, but we spend that
money on smartphones instead. The amount being spent
on ads in printed phone directories is falling by $1 billion a
year in the United States. Businesses are spending that
money elsewhere. We
The truth of the matter is simple: We have decided that
we would rather work more hours and purchase more of
the “wants” we have in life. We have opted to collectively
stay late at the office instead of growing and peeling our
own potatoes. We all want a higher standard of living—
and that desire is what creates most of the jobs. As long as
you want more income, you will likely find a way to use
your skills to add value somewhere, and that action is what
creates a job.
Don’t get me wrong
have ever more.
Why don’t we work less? Keynes, it seems, saw a few
answers. According to him, it could be that our inability to
collectively chill is a memory of poorer times, and it will
take several generations for us to adapt to being part of the
leisure class. Or maybe “keeping up with the Joneses” is
so deeply ingrained in us that we can never rest. As
Are There Robot-Proof Jobs?
Jobs Robots Can Do but Probably Never Will: Some jobs
are quite secure and are accessible to a huge range of the
population, regardless of intellect, educational attainment,
or financial resources, because although a robot could do
them, it doesn’t make economic sense for them to do so.
Think of all of the jobs people will need for the next hun-
dred years, but only very occasionally.
I live
Jobs That Need a High Social IQ: Some jobs that require
high-level interaction with other people, and they usually
need superior communication abilities as well. Event plan-
ner, public relations specialist, politician, hostage nego-
tiator, and director of social media are just a few examples.
Think of jobs that require empathy or outrage or passion.
Jobs Done On-Site: On-site jobs will be difficult to be done
with robots. Robots work well in perfectly controlled env-
ironments, such as factories and warehouses, and not in
ad hoc environments like your aunt Sue’s attic. Forest
rangers and electricians are a couple of jobs like this that
come to mind, but there are many more.
Jobs That Require Creativity or Abstract Thinking: It will be
hard if not impossible for computers to be able to do jobs
that require creativity or abstract thinking, because we
don’t really even understand how humans do these things.
Possible jobs include author (yay!), logo designer, com-
poser, copywriter, brand strategist, and management con-
sultant.
Jobs No One Has Thought of Yet: There are going to be
innumerable new jobs created by all this new technology.
Given that a huge number of current jobs didn’t exist be-
fore 2000, it stands to reason that many more new profes-
sions are just around the corner. The market research
company Forrester forecasts that within the next decade,
an astonishing 12.7 million new US jobs will be created
building robots and the software that powers them.
Quiz: Can a Robot Do Your Job
It turns out that the economic benefits of new tech-
nology help the rich more than they help the poor. How?
There are three different ways that economic gains from
technology are distributed, and only one of them helps the
poor.
AGI
At this point in our narrative, the AGI isn’t conscious.
Because it is not conscious, it cannot experience the world
and it cannot suffer. So an AGI in and of itself would not
cause an existential crisis, a deep reflection about what
makes humans special. But it would prompt us to ask two
questions: “Is the AGI alive?” and “What are humans for?”
With regard to the first question, whether an AGI is
alive, the answer is not obvious. Consciousness is not a
prerequisite for life. In fact, an incredibly low percentage of
living things are conscious. A tree is alive, as is a cell in
your body, but we don’t generally regard them as con-
scious.
So what makes something alive?
with AGI we are talking about machines going from
computing something to understanding something.
Joseph Weizenbaum
There is no middle ground here. Either AGI is possible
or it isn’t. The chasm that divides the two viewpoints
couldn’t be wider, because it has to do with our core be-
liefs about the nature of reality, the identity of the self, and
the essence of being human. There is no re
And finally, many in the industry are almost giddy with
optimism about AI. Kevin Kelly is one of them. He believes
that AI will “enliven inert objects, much as electricity did
more than a century ago. Everything that we formerly elec-
trified we will now cognitize. Th
would have to clear. Tur-
ing, whom we discussed in chapter 4, was an early com-
puter pioneer. A genius by any definition of the word, he
was instrumental in cracking the Nazis’ Enigma code,
which is said to have shortened World War II in Europe by
four years. Regarded today as the father of AI, Turing, in a
1950 paper, posed the question of “can machines think?”
and suggested a thinking test we now call the Turing test.
There are varying versions of it, but here are the basics:
You are in a room alone. There are two computer termi-
nals. You can type questions on them. On one, the ques-
tions will be answered by a computer. On the other one,
by a person. You have five minutes. Try to figure out which
is which. If a machine can trick you into picking it 30 per-
cent of the time, Turing argued that you must say that the
machine is thinking, because it is able to duplicate the
capabilities of a person who is thinking. It doesn’t matter,
in Turing’s view, that the machine is doing its thinking dif-
ferently than a human does. He predicted machines would
accomplish this by the year 2000.
The 30 percent
AGI and Ethics
An AGI presents two distinct ethical challenges. The first is
how to make an AGI that behaves ethically. Assuming that
we are the ones setting its goals, we would want its values
to align with ours and act both ethically and humanely.
The word “humane” is simply a variant form of “human.”
The meaning of the word encapsulates us at our best, not
at our average. But how can we do that? Technical issues
aside, in simple English, how would we teach a machine to
act ethically?
Isaac Asimov took an early stab at it in a short story he
wrote in 1942, which he later developed into his I, Robot
series of books. Asimov coined three laws programmed
into every robot designed to ensure that the interests of
the robots never collided with that of humans. The laws
were:
A robot may not injure a human being or, through inac-
tion, allow a human being to come to harm.
A robot must obey orders given it by human beings ex-
cept where such orders would conflict with the First Law.
A robot must protect its own existence as long as such
protection does not conflict with the First or Second Law.
While his three laws
First,
humans don’t have a shared agreement on what consti-
tutes an ethical standard. In fact,
As you might guess, this can go on ad infinitum.
A project backed by the Future of Life Institute to work
on the problem of instilling ethics in an AGI described the
challenge this way: “Some AI systems do generate deci-
sions based on their consequences, but consequences are
not all there is to morality. Moral judgments are also af-
fected by rights (such as privacy), roles (such as in fami-
lies), past actions (such as promises), motives and inten-
tions, and other morally relevant features. These diverse
factors have not yet been built into AI systems.”
This is a hard problem
AGI.
Earlier, I told the story of the early AI pioneer Joseph
Weizenbaum, who later turned against the idea of AI when
he saw how people interacted in an emotional way with
ELIZA, his simple AI therapist. He wrote a landmark 1976
book called Computer Power and Human Reason in which
he maintains that any job that requires true empathy, such
as eldercare aide, soldier, or even customer service
epresentative, should never be done by a computer. He
believed that extensive interactions with machines emu-
lating human empathy would make us all feel more iso-
lated and devalued.
His
Free Will
Sentience
Consciousness
. The difference between a
truly vegetative patient and one with a minimal level of
consciousness is medically tiny and hard to discern, but
ethically enormous. I
Back in 1970, Gordon Gallup Jr., a psychologist at the
University at Albany, had an ingenious insight, which is
today regarded as the gold standard for measuring self-
awareness. It is called the mirror test, and
Because of this, some
believe you cannot have consciousness without language.
If this is true
So where do we leave this? If you answered the ques-
tion about what you are with “machine” or “animal,” you
are probably fine seeing consciousness not in a binary
sense, but along a continuum. In other words, some
things can be a little conscious, some things a lot. If this is
the case, then you are likely to believe in animal con-
sciousness. If you answered that you are “human,” then
you may see consciousness as binary—you have it or you
don’t—and animals may simply not have it.
Beyond
Nietzsche is always a good place to start. He believed
you have only the rights you can take. People claim the
rights that we have because we can enforce them. C
Let’s examine the eight theories.
Theory 1: Weak Emergence
In his book How to Create a Mind, Ray Kurzweil envisions
the brain as a collection of about 100,000 different pro-
cesses arranged hierarchically. Each process knows how
to do one small thing. One of the 100,000 might just be to
recognize the letter A, and one beneath it might exist only
to recognize the crossbar in that letter. When you read a
book, a gazillion things happen in your brain as all these
processes fire, at an unimaginable speed, enabling you to
piece together and make sense of the world around you.
Consciousness, Kurzweil believes, is “an emergent prop-
erty of a complex physical system,” and he believes that it
can be duplicated in a computer. Kurzweil explicitly ad-
dresses the question of machine consciousness, stating,
“A computer that is successfully emulating the complexity
of a human brain would also have the same emergent con-
sciousness as a human.” Of course, that is total conjec-
ture, but he may be right.
Theory 2: Strong Emergence
Weak emergence is a universally agreed-upon concept.
Strong emergence is something that may very well not
exist. And
property is completely inexplicable as simply the inter-
action of the parts. There is a break in physics, or some-
thing is missing. There is no way to explain the whole as
simply being the sum of the parts.
For
Theory 4: Quantum Phenomenon
Another variant of the “physical property of matter” theory
is that consciousness is a quantum phenomenon. The
noted mathematician
Theory 5: Consciousness Is Fundamental
A third variant of physical property theories is that con-
sciousness is a fundamental force of the universe.
Theory 6: Consciousness Is Universal
The next theory is that consciousness is universal. How is
that different from saying it is foundational? Universal
simply means it is everywhere. DNA is universal to life on
earth, but DNA is not a fundamental force beyond our
understanding.
Saying consciousness
Theory 7: A Trick of the Brain
Maybe with our first six theories, we are overthinking it,
and consciousness is instead simple brain activity. This
theory is espoused by Daniel C. Dennett, of Tufts Univer-
sity, who thinks the whole question is a bit ridiculous.
Theory 8: Something Spiritual
The final theory is that consciousness is something spir-
itual or otherworldly. Those who see themselves as dual-
ists might find a home here.
Humanity, Redefined?
In 1991, the anthropologist Donald Brown published a
book called Human Universals. Human universals, he said,
“comprise those features of culture, society, language,
behavior, and psyche for which there are no known excep-
tion.” In other words, wherever you find humans, you find
these behaviors. He identified sixty-seven, including gift
giving, joking, religious ritual, soul concepts, faith healing,
eschatology (beliefs about how the world will end), hair-
styles, athletic sports, and bodily adornment.
What
Leisure Time
Earlier, we discussed Keynes’s view that in the future, we
will work just fifteen hours a week. I suggested that if his-
tory is any guide, we will still continue to work more than
we need to, not to satisfy basic needs, but to obtain an
ever-growing list of wants. That being said, in the Fourth
Age we will certainly still have leisure time, and maybe
even more of it.
How we’ve chosen to use this tabula rasa of a tech-
nology reveals a great deal about us, most of which is
good.
We Want to Express Ourselves
We Want to Engage with Each Other
It turns out we don’t just want to express ourselves but en-
gage with each other as well. Each day, 1.3 billion people
log into Facebook.
We Want to Help Each Other
In addition to engaging, we want to help each other as
well. All over the Internet,
Those are six of the things we learned about ourselves in
the last couple of decades. I
Jack Kennedy captured this sentiment
over a decade before Voyager’s launch when he said, “Our
most basic common link is that we all inhabit this planet.
We all breathe the same air. We all cherish our children’s
future. And we are all mortal.”
We began
These include what artificial intelligence will be capable of,
whether computers can become conscious, and if robots
will take all our jobs.
How did we use all the new calories we were able to
consume? We used this new energy to grow our brains to
unprecedented complexity. In a short period, we grew to
have three times the number of neurons as gorillas or
chimpanzees. S
Another gift of language is stories. Stories are central to
humanity, for they gave form to human imagination,
The first of these was the city, which came about
as agriculture required that humans settle down in one
place. This practice was almost entirely new. Early cities,
such as Çatalhüyük, Jericho, and Abu Hureyra, were often
located near rivers for access to water and fertile farmland,
and had markets, homes, and temples. It was during the
Second Age that we began using opium, gambling with
dice, and wearing makeup and gold jewelry.
The Third Age began just five thousand years ago when
writing was likely first invented by the Sumerians, a people
who lived in the southern part of present-day Iraq. It
seems also to have been developed independently at
around the same time in Egypt and China; some scholars
give the “earliest writing” award to the Chinese. It would
later be developed independently in what is modern-day
Mexico. Writing changed humanity because for the first
time what a person knew could live after him or her, per-
fectly preserved. Knowledge could be flawlessly copied
and transported around the world. Ideas could live outside
a human mind!
None
inheritance.
Money appeared during the Third Age as well. Stamped
coins as we have today wouldn’t be developed until well
With writing, the wheel, and money all coming on the
scene concurrently, the basic ingredients needed to make
the nation-state and empires were in place. This is when
we saw the first large civilizations blossom all over the
world, independently and virtually simultaneously. China,
the Indus Valley, Mesopotamia, Egypt, and Central Amer-
ica all became home to large, cohesive, and prosperous
nations.
What is the composition of the universe? There are two
schools of thought.
The first is that everything in the universe is composed
of a single substance, atoms. This is known as monism,
from the Greek monos, meaning “one.” Monists believe
that everything in the universe is governed by the same set
of physical laws, and that those laws are largely known to
us today. N
Third, it is hard to coax a universal moral code out of
that viewpoint. Killing a person doesn’t seem to have any
more moral consequence than smashing a boulder.
color? Did she learn anything
after seeing color for the first time? If you believe she did,
if you think that experiencing something is different than
knowing something, then you are a dualist. If she learned
anything new, then it means that there is something that
happens in experience that is beyond the physical uni-
verse, beyond simply knowing a thing. Whatever Mary
learned about color when she saw it for the first time is
something that is outside the realm of physics. What is
that thing? How would you express it in an equation? Or
even words, for that matter? Holding this belief will have
profound implications on your conceptions of what a
computer can and cannot do.
Another
The final choice is that we are humans. Ev
What Is Your “Self”?
So now we come to our third and final foundational ques-
tion: What is your “self”? When you look in a mirror and
see your own eyes, you recognize yourself in there. What is
the thing that looks back at you? What is that voice in your
head that talks to you? What is the “I” that you mean when
you say, “Oh, I understand”?
Given that your cells
replace themselves, you literally aren’t the same matter
you were a decade ago. But is that you? Given that your
brain cells don’t regenerate, or regenerate relatively little, it
might be tempting to say that you are your brain cells.
However, while the cells don’t regenerate, they constantly
change in their relationship with each other. So it is hard
to pin down exactly what “you” are.
So what is this trick? It has two parts. First, your brain
gets inputs from all kinds of senses. Y
Our desire for robots has long been to build them to do
the three D kinds of jobs. These are jobs that are dirty,
dangerous, and dull. You could add another four Ds: dis-
liked, demeaning, draining, and detestable. We want to
give all these jobs to robots, who don’t mind doing them.
Some
The first problem AI robots have is seeing. We can put
a great camera in a robot, but that just gets us data.
The physical world is a
difficult place for an AI robot unless it is performing purely
repetitive motions inside a controlled environment, such
as a factory floor. In that environment,
Then, of course, there is the challenge of powering the
robot, especially if you want the mobility that batteries en-
able.
Robots and AI will take some of the jobs. T
Possibility One: The Machines Take All the
Jobs
Consider
There are nine assumptions underlying this argu-
ment.
ASSUMPTION 1: Humans are machines.
ASSUMPTION 2: Since humans are ma-
chines, we can build a mechanical one.
ASSUMPTION 3: Mechanical humans would
have the full range of our mental abilities,
ASSUMPTION 4: This conscious machine
would want to do our dirty work; and,
ASSUMPTION 5: Whether it is willing or not,
we will compel it to, creating de facto
mechanical slaves.
Likewise, th
ASSUMPTION 7: Machines would become
so inexpensive or efficient that they would be
cheaper to deploy than human labor.
ASSUMPTION 8: The programming cost to
teach the machine a new skill plus the cost of
running the machine will always be less than
the labor costs of paying a human to do it.
The assumptions machines
we are machines, that computers will continue to
improve, and that the cost of building technology will
continue to fall. Those three ideas taken together mean
that sooner or later, the machines will surpass us in every-
thing.
Possibility Two: The Machines Take Some of
the Jobs
Possibility
ASSUMPTION 3: Not enough new jobs will
be created quickly enough.
This is not always the case. From a robot’s point of
view, which of these jobs requires more skill: a waiter or a
highly trained radiologist who interprets CT scans? A
ASSUMPTION 2: There are, in effect, an infi-
nite number of jobs.
In 1940, only about 25 percent of women in the United
States participated in the workforce. Just forty years later,
that percentage was up to 50 percent. In that span of time,
33 million women entered the workforce. Where did those
jobs come from? Of
o offer another interpretation, perhaps we are driven
toward perpetual progress by our mild discontent with the
present. No matter how good things may be, we can al-
ways picture them a little better, and the drive to relent-
lessly move forward and upward is our distinctive charac-
teristic. Maybe
But if possibility two or possibility three comes to pass,
then there will be robot-proof jobs. What will they be? A
good method for evaluating any job’s likelihood of being
automated is what I call the “training manual test.” Think
about a set of instructions needed to do your job, right
down to the most specific part. How long is that docu-
ment? Think
There are many of these jobs: repairing antique clocks,
leveling pier-and-beam houses, and restoring vintage gui-
tars, just to name a few. Just
Now to the fears. Even though the AGI is not con-
scious, it will have goals. Goals do not require conscious-
ness at all. The goal of a virus is to get inside a cell and
wreak havoc. The goal of a white blood cell is to in turn
hunt that virus down. Genes are described as having the
goal of reproducing themselves. Plants have the goal of
Further, Weizenbaum makes a distinction between
deciding and choosing, and suggests that computers
should do only the former. Deciding is computational, like
deciding which is the shortest route to work. Yet he be-
lieves that machines should never choose. A human might
choose to run his car into a tree to avoid a child, but that
is a human prerogative, and Weizenbaum felt deeply that
delegating this to machines in no way ennobled the ma-
chine but rather debased the human. Weizenbaum felt that
our temptation to delegate core parts of humanity to
computers indicated “atrophy of the human spirit that
comes from thinking of ourselves as computers,” a phrase
that harkens back to our foundational question about what
exactly we are.
Additionally,
However, strong emergence says that the emergent
Theory 3: Physical Property of Matter
Others believe you don’t need any elaborate emergent phe-
nomenon to explain consciousness. On
Why do I say “be human” here? Why would we even
consider a conscious computer to be human? Humans
are, well, humans. Us. Biological. DNA based, and all that.
Why would there even be a temptation to regard a con-
scious computer as human? Simple: because humans
have never really defined themselves by biology, but by
ability. Tool using
We Are Creative
Who would have ever guessed how creative we all are? The
Internet has unlocked our creative floodgates. Each sec-
ond, fifty thousand photos are uploaded to Facebook.
for our more powerful
brains led to our creation of another new technology: lan-
guage. Language was the great leap that the historian Will
Durant says “made us human.”
The second technological advance to come along with
agriculture was the division of labor. W
Writing is the great dividing point in human
history. The First and Second Ages are, by definition, pre-
historic. History begins five thousand years ago with the
Third Age.
Our world up to recent times has been a Third Age
world. While incredible innovation has occurred along the
way, such as the development of the steam engine, the
harnessing of electrical power, and the invention of mov-
able type, these were not fundamental changes in the na-
ture of being human in the way language, agriculture, and
writing were. The signature innovations within the Third
Age have been evolutionary more than revolutionary. T
A modern proponent of monism is Francis Crick, who
offered his “astonishing hypothesis” that “you, your joys
and your sorrows, your memories and your ambitions,
your sense of personal identity and free will, are in fact no
more than the behavior of a vast assembly of nerve cells
and their associated molecules.”
A
The other school of thought is a position known as
dualism. Dualists believe that the universe is made of two
(or more) things. Yes, there are atoms, but there is also
something else.
There is a temptation to spiritualize this position, while
concurrently casting monism as the rational, modern view.
And while it is true that those who believe in God or the
soul or ghosts or “life forces” are certainly dualists, the
dualism tent is much larger and includes many viewpoints
that eschew the spiritual. Atheism and theism are beliefs
about God; monism and dualism are beliefs about the na-
ture of reality.
What
ight? Your brain has
figured out this cool trick whereby it can blend them into
one single mental experience. It combines them all to-
gether. You see
Your ability
to, in an instant, identify the architectural style of a house,
identify a dove in flight, tell the twins you know apart, or
perform any of a hundred other similar tasks that you do
effortlessly is the envy of AI programmers everywhere.
progress, robots, outside of factories, are still
novelties, facing a laundry list of challenges, including
locomotion, sensing, and the manipulation of their envi-
ronment.
Another big problem robots face is interacting with ob-
jects. W
the Moravec paradox. Hans Moravec was among
those who noted that it is easier to do hard, brainy things
with computers than “easy” things. It is easier to get a
computer to beat a grandmaster at chess than it is to get
one to tell the difference between a photo of a dog and one
of a cat.
Waiters’
But of course, unemployment never went up outside of
the range of the normal economic ebb and flow. So what
happened? Were 33 million men put out of work with the
introduction of this large pool of labor? Did real
Jobs We Won’t Want Robots to Do: Th
Now to the fears. Even though the AGI is not con-
scious, it will have goals. Goals do not require conscious-
ness at all. The goal of a virus is to get inside a cell and
wreak havoc. The goal of a white blood cell is to in turn
hunt that virus down. Genes are described as having the
goal of reproducing themselves. Plants have the goal of
We Want to Have an Impact
The Internet inspires and empowers many to work for a
better world.
Now the second half. At the same time that all this is
going on, the different parts of your brain are chugging
along, doing their respective things. Some
consider the DARPA Robotics
Challenge, which took place from 2012 to 2015. In 2015,
the finals were held. Erik Sofge, writing for Popular Science,
summed it up by saying that “the biggest and most well-
funded international robotics competition in years was a
failure.
NFL football player,
ballerina, spirit guide, priest, and actor, just to name a few
We Want to Know the Truth
Much consternation and handwringing has occurred over
the idea that the Internet allows you to lock yourself in an
information bubble and see only facts that support your
views.
. And the brain has figured out that the best way to
handle all this noise is to let just one part of the brain at a
time “have the floor.” So
Unpredictable Jobs: Some jobs are so unpredictable