Wednesday, June 3, 2015
Turing and Machine Minds
In 1950, mathematician Alan M. Turing proposed a test for
machine consciousness. If a human
interrogator could not distinguish between the responses of a real human being
and a machine built to hold conversations, then we would have no reason, other
than prejudice, for not admitting that the machine was in fact conscious and
thinking. http://orium.pw/paper/turingai.pdf
I won’t debate the merits or sufficiency of the Turing Test
here. But I will use it to introduce
some clarifications into the AI discussion.
Turing thought that if a machine could do some of the things we do, like
have conversations, that would be an adequate indicator of the presence of a
mind. But we need to get clear on the
goal in building an artificial intelligence.
Human minds are what we have to work with as a model, but not everything
about them is worth replicating or modeling.
For example, we are highly prone to confirmation bias, we have loss
aversion, and we can only hold about 7-10 digits (a phone number) in short term
working memory. Being able to
participate in a conversation would be an impressive feat, give the subtleties
and vagaries of natural language. But it’s
a rather organic, idiosyncratic, and anthropocentric task. And we might invest substantial effort and
resources into replicating contingent, philosophically pointless attributes of
the human mind instead of fully exploring some of the possibilities of a new,
artificial mind of a different sort. Japanese
researchers, for example, have invested enormous amounts of money and effort
into replicating subtle human facial expressions on robots. Interesting for parties maybe, but we
shouldn’t get lost up side tributaries as we move up the river to the source of
mind.
One of the standard objections to Turing’s thesis is
this: But a Turing machine/Artificial
intelligence system can’t/doesn’t have _______________, where we insert one of
the following:
a. make mistakes. (Trivial to build in, but inessential and
unimportant.)
b. have emotions (Inessential,
and philosophically and practically uninteresting.)
c. fall in love
(Yawn.)
d. care/want (Maybe
this is important. Perhaps having goals
is essential/interesting. It remains to
be seen if this cannot be built into such a system. More on goals later.)
e. freedom (Depends
on what you mean by freedom. Short
answer: there don’t appear to be any
substantial reasons a priori why an artificial system cannot be built that has “freedom”
in the sense that’s meaningful and interesting in humans. See Hume on freewill. http://www.iep.utm.edu/freewill/
f. produce original ideas.
( What does original mean? A new
synthesis of old concepts, contents, forms, styles? That’s easy.
Watson, IBM’s jeopardy dominating system is being used to make new
recipes, and lots of innovate, original solutions to problems.)
g. creativity (What
does this mean? produce original new
ideas? See above. Complex systems, such as Watson, have
emergent properties. They are able to
lots of new things that their creators/programmers did not foresee.)
h. do anything that it’s not programmed to do. (“Programmed” is outdated talk here. More later on connectionist systems. Can sophisticated AI programs do
unpredictable things now? Yes. Can they now do things that the designers
didn’t anticipate? Yes. Will they do more in the future as the
technology advances? Yes.)
i. feel pleasure or pain
(I’ll concede, for the moment, that building an artificial system that
has this capacity is a ways off technologically. And I’ll concede that it’s a very interesting
philosophical question. I won’t concede
that building this capacity in is impossible in principle. And we must also ask why is it important? Why do we need an AI to have this capacity?)
j. intelligence
k. consciousness
l. understand
m. qualitative or phenomenal states (See Tononi, Koch, and McDermott)
I think objections a-h miss the point entirely. I take it that for a-h, the denial that a
system can be built with the attribute is either simply false, will be proven
false, or the attribute isn’t interesting or important enough to warrant the
attention. i through m, however, are
interesting. And there’s a lot more to
be said about them. For each, we will
need more than a simple denial without argument. We need an argument with substantial
principled, non-prejudicial reasons for thinking that these capacities are beyond
the reach of technology. (In general,
history should have taught us to be very skeptical of grumbling naysaying the
form of “This new-fangled technology will never be able to X.” But one of the things I’m going to be doing
in the blog in the future is caching out in much more detail what the terms
intelligence, consciousness, understand, and phenomenal states should be taken
to mean in the AI project context, and working out the details of what we might
be able to build.
But more importantly, I think the list of typical objections
to Turing’s thesis raises this question:
just what do we want one of these things to do? Maybe someone wants to simulate a human mind
to a high degree of precision. I can
imagine a number of interesting reasons to do that. Maybe we want to model up the human neural
system to understand how it works. Maybe
we want to ultimately be able to replicate or even transfer a human
consciousness into a medium that doesn’t have such a short expiration
date. Maybe we want to build helper
robots that are very much like us and that understand us well. Maybe a very close approximation of a human
mind, with some suitable tweaks, could serve as a good, tireless, optimally
effective therapist. (See the early AI
experiments with a therapy program.)
But the human brain is a kludge. It’s a messy, organic amalgam of a lot of
different models and functions that evolved under one set of circumstances that
later got repurposed for doing other things.
The path that led from point A to point B, where B is the set of
cognitive capacities we have is convoluted, circuitous, full of fits and
starts, peppered with false starts, tradeoffs, unintended consequences, byproducts,
and the like.
A partial list of endemic
cognitive fuckups in humans from Kahneman and Tversky (and me): Confirmation Bias, Sunk Cost Fallacy, Asch
Effect, Availability Heuristic, Motivated Reasoning, Hyperactive Agency
Detection, Supernaturalism, Promiscuous Teleology, Faulty Causal Theorizing, Representativeness
Heuristic, Planning Fallacy, Loss Aversion, Ignoring Base Rates, Magical
Thinking, and Anchoring Effect.
So with all of that said, again, what do we want an AI to
do? I don’t want one to make any of the
mistakes on the list just above. And I think
that we shouldn’t even be talking about mistakes, emotions, falling in love,
caring or wanting, freedom, or feeling pleasure of pain. What these things show incredible promise at
doing is understanding complex, challenging problems and then devising
remarkable and valuable solutions to them. Watson, the Jeopardy dominating system built
by IBM, has been put to use devising new recipes. Chef Watson is able to interact with would be
chefs, compile a list of preferred flavors, textures, or ingredients, and then
create new recipes, some of which are creative, surprising, and quite
good. The tamarind-cabbage slaw with
crispy onions is quite good, I hear. But
within this seemingly frivolous application of some extremely sophisticated
technology, there is a more important suggestion. Imagine that Watson’s ingenuity is put to
work in a genetics lab, in a cancer research center, in an engineering firm
building a new bridge, or at the National Oceanic and Atmospheric
Administration predicting the formation and movement of hurricanes. I submit that building a system that can
grasp our biggest problems, fold in all of the essential variables, and create
solutions is the most important goal we should have. And we should be injecting huge amounts of
our resources into that pursuit.
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