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Synopsis
Our unique, innate ability as creative humans is an important
identification. It will reveal currents in automation, AI,
and innovation opportunities.
Our recent attempts to compute AI, or rather AGI (artificial
general Intelligence) are not new. They can be traced back
quite some time.
How far back depends on whom you ask, for the purpose of this
write–up I will focus on Minsky, McCarthy and MIT’s AI Lab as
the starting point (but we could easily go back to Turing and
beyond).
When Minsky and McCarthy started the lab in 1959 they were very
much set on computing general intelligence. Machines that
think, armed with consciousness and able at learning.
They sought to achieve that by a number of means. Of varying
techniques, but a shared point of view. In an interview to
Jeffery Mishlove (of Thinking Allowed), John McCarthy confessed
to:
“There are 2 ways of looking at computing artificial
intelligence, you can look at it from the point of view of
biology or point of view of computer science You could
imitative the nervous system as far as you understand the
nervous system or you can immediate human psychology as far
as you understand human psychology”
To annotate: “looking at it from the point of view of biology…”
means the kind of “wet” programming the brain does, neurons,
axons and so forth. Simulating such process is likely to be a
reference to neural networks, which are (broadly speaking) sets
of networks modeled around the way the brain works: namely
taking a stab at clustering logic in proximity and generating
hierarchies of information. “Looking at it from the point of
psychology” is equally as provoking, as it refers to human
intelligence as buckets of knowledge.
To push that slightly further before annotating, here is a
quote by Marvin Minsky, part of a monologue in Machine Dreams.
“In order (for a machine) to be intelligent we have to give
it several different kinds of thinking, when it switches from
one of those to another we will say that it is changing
emotions” “Emotion (in itself) is not a very profound thing,
it’s just a switch between different modes of operation”
This is the other way of computing a brain. Buckets of
knowledge, and sets of actions, compartmentalized in different
vertical buckets. Switched by emotions.
Beyond being offensive to humanities, this argument is also
easy to debunk using a quick thought experiment.
Imagine an intellectual person sitting in a chair, and doing
nothing at all, starring into thin air. That person is clearly
consciousness, and intelligent. Her lack of action does nothing
to rob her of the consciousness and intelligent title.
In other words, intelligence is not conditioned by action. It
need not be modeled around goals, nor operational switches.
This is an important point to stay on. This idea that we can
compute human intelligence (we can’t), or that the brain is a
computer (it’s not) is the underpinning belief that has been
fueling generations of researchers, keen and persistent in
their pursuit to compute a brain. Allan Watts refers to life,
as an on–going oscillation. A continuous frequency of the
brain.
You can imagine your intelligence (or soul, or consciousness)
as a vibrating frequency. Like the trajectory of an analog
sound wave as it travels through space. It is continuous, and
unlike a digital factor it is not broken into a set of high
fidelity instances.
A digital sound wave — say an MP3 file — is economic in storage
because it can slice the edges of the frequency range, and then
create a high res sound–alike composition.
Let’s say that I could somehow peer into your brain and start
creating a map of your intelligence. It may take me up to a few
decades, but eventually I succeeded in computationally solving
your intelligence.
The problem is that I only solved the computation of your
brain, and only at one point in time.
In other words I only solved one instance of intelligence.
Anchored to a point in time, and a subject. The brain–as is
conciseness, and intelligence–is an on–going analog frequency.
Not instance based digital permutations.
For a much wider palette of opinions on the prospect of AGI
(artificial general intelligence) I highly recommend
What To Think of Machines That Think, by John Brockman.
If you’re still reading, it is safe for me to assume that that
you’re onboard my pro–human view. That is the view that the
brain is not a computer problem, and hence we should abort the
idea of AGI. Narrow AI on the other hand is alive, and strong.
Once we accept the premise that AGI is of no use we can start
identifying the opportunities in narrow AI. Think of your
calculator as a narrow expert in calculation, think of a
medical journal scanner as the best tool we have to consume
terabytes of medical journals. And machine vision algorithms as
the best face recognizer.
We can do the same tasks the machine is doing: calculate on a
piece of paper, skim journals and classify images, but only to
a certain extent. Let’s draw this.
Intelligence on a narrow plain
On the left there is ‘0’, no intelligence is being used, and
nothing is being done. We can learn to perform a task, and as a
next step we might relay this knowledge to a machine, so it
could hyper mathematize it. We can foresee improvements in
computing power, access to data, and other technologies pushing
the machine’s ability into infinity and the unknown. ON A
NARROW DOMAIN. A singular plain. Life, as a system, contains
many of these trajectories. Hunting, flying, writing, internet
browsing, coding, dancing, driving. We learn new tasks, and
some of them graduate to automation. New domains are added,
while other become obsolete. For example self–driving car
technician, or a horseshoe maker respectively. We can imagine
the system of life — i.e. humanities — as made of an infinite
amount of these single line trajectories.
What would AGI need to achieve
Under this lens we can position AGI in the bottom right, as
holding infinite ability in infinite domains. But we
established the fallacies of that view so that leaves us with a
much more current, and useful alternative.
In the absence of AGI is it up for us to navigate in this bend.
Crossing, and linking disparate skills and disciplines. We hold
an intellectual monopoly in that regard. Machines are
incredibly capable in their unique domains but are blind to
anything else, they only compute and extend the steps we
relayed to the domain.
Another way of thinking about it, is when we come up with a new
skill or a technology, we might slowly improve it, maturing the
domain for other humans to participate in it. As part of that
on–boarding, sets of instructions need to be written. In which
point the domains is ready for a machine to excel it.
That machine is domain specific, and holds no intelligence. Its
algorithms could do a lot of things that could we never do. For
example, untangle messy data, or make assumptions about the
future. But the bend is uniquely creative, and can’t be
mechanically produced.
This is the core of it. If your product or service is narrow by
design then you’re open for a machine to excel, or replace you.
This is absolutely not limited to lower grade jobs, as the
machine hold no interest in your prestige. It really holds no
interests at all. The system can perform tasks efficiently. And
if you’re job is on a narrow domain, and can be broken into
steps then you’re not using your human advantage properly.
Our unique, innate ability as creative humans is an important
identification. It will reveal currents in automation, AI, and
innovation opportunities.
Nitzan Hermon is a designer
and researcher of AI, human machine augmentation and
language. Through his writing, academic and industry work he is
writing a new, sober narrative in the collaboration between
humans and machines.
This article originally appeared at
Everything Will Happend
