James,
It is a little hard to know where to start, to be honest. Do you have a
background in any particular area already, or are you pre-college? If
the latter, and if you are interested in the field in a serious way, I
would recommend that you hunt down a good programme in cognitive science
(and if possible do software engineering as a minor). After about three
or four years of that, you'll have a better idea of where the below
argument was coming from. Even then, expect to have to argue the heck
out of your professors, only believe one tenth of everything they say,
and discover your own science as you go along, rather than be told what
the answers are. A lot of the questions do not have answers yet.
All thinking systems do have a motivation system of some sort (what you
were talking about below as "rewards"), but people's ideas about the
design of that motivational system vary widely from the implicit and
confused to the detailed and convoluted (but not necessarily less
confused). The existence of a motivational system was not the issue in
my post: the issue was exactly *how* you design that motivation system.
Behaviorism (and reinforcement learning) was a suggestion that took a
diabolically simplistic view of how that motivation system is supposed
to work .... so simplistic that, in fact, it swept under the carpet all
the real issues. What I was complaining of was a recent revival in
interest in the idea of reinforcement learning, in which people were
beginning to make the same stupid mistakes that were made 80 years ago,
without apparently being aware of what those stupid mistakes were.
(To give you an analogy that illustrates the problem: imagine someone
waltzes into Detroit and says "It ain't so hard to beat these Japanese
car makers: I mean, a car is just four wheels and a thing that pushes
them around. I could build one of those in my garage and beat the pants
off Toyota in a couple of weeks!" A car is not "four wheels and a
thing that pushes them around". Likewise, an artificial general
intelligence is not "a set of environment states S, a set of actions A,
and a set of scalar "rewards" in the Reals".)
Watching history repeat itself is pretty damned annoying.
Richard Loosemore
James Ratcliff wrote:
Richard,
Can you explain differently, in other words the second part of this
post. I am very interested in this as a large part of an AI system.
I believe in some fashion there needs to be a controlling algorithm
that tells the AI that it is doing "Right" be it either an internal or
external human reward. We receive these rewards in our daily life, in
our jobs relationships and such, wether we actually learn from these is
to be debated though.
James Ratcliff
*/Richard Loosemore <[EMAIL PROTECTED]>/* wrote:
Will,
Comments taken, but the direction of my critique may have gotten
lost in
the details:
Suppose I proposed a solution to the problem of unifying quantum
mechanics and gravity, and suppose I came out with a solution that said
that the unified theory involved (a) a specific interface to quantum
theory, which I spell out in great detail, and (b) ditto for an
interface with geometrodynamics, and (c) a linkage component, to be
specified.
Physicists would laugh at this. What linkage component?! they would
say. And what makes you *believe* that once you sorted out the linkage
component, the two interfaces you just specified would play any role
whatsoever in that linkage component? They would point out that my
"linkage component" was the meat of the theory, and yet I had referred
to in such a way that it seemed as though it was just an extra, to be
sorted out later.
This is exactly what happened to Behaviorism, and the idea of
Reinforcement Learning. The one difference was that they did not
explicitly specify an equivalent of my (c) item above: it was for the
cognitive psychologists to come along later and point out that
Reinforcement Learning implicitly assumed that something in the brain
would do the job of deciding when to give rewards, and the job of
deciding what the patterns actually were .... and that that something
was the part doing all the real work. In the case of all the
experiments in the behaviorist literature, the experimenter substituted
for those components, making them less than obvious.
Exactly the same critique bears on anyone who suggests that
Reinforcement Learning could be the basis for an AGI. I do not believe
there is still any reply to that critique.
Richard Loosemore
William Pearson wrote:
> On 01/06/06, Richard Loosemore wrote:
>
>> I had similar feelings about William Pearson's recent message about
>> systems that use reinforcement learning:
>>
>> >
>> > A reinforcement scenario, from wikipedia is defined as
>> >
>> > "Formally, the basic reinforcement learning model consists of:
>> >
>> > 1. a set of environment states S;
>> > 2. a set of actions A; and
>> > 3. a set of scalar "rewards" in the Reals.
>> > "
>>
>> Here is my standard response to Behaviorism (which is what the above
>> reinforcement learning model actually is): Who decides when the
rewards
>> should come, and who chooses what are the relevant "states" and
>> "actions"?
>
> The rewards I don't deal with, I am interested in external brain
> add-ons rather than autonomous systems, so the reward system will be
> closely coupled to a human in some fashion.
>
> The rest of post I was trying to outline a system that could alter
> what it considered actions and states (and bias, learning algorithms
> etc). The RL definition was just there as an example to work against.
>
>> If you find out what is doing *that* work, you have found your
>> intelligent system. And it will probably turn out to be so
enormously
>> complex, relative to the reinforcement learning part shown
above, that
>> the above formalism (assuming it has not been discarded by then)
will be
>> almost irrelevant.
>
> The internals of the system will be enormously more complex compared
> to the reinforcement part I described. But it won't make that
> irrelevent. What goes on inside a PC is vastly more complex than the
> system that governs the permissions of what each *nix program can do.
> This doesn't mean the permission governing system is irrelevent.
>
> Like the permissions system in *nix the reinforcement system it is
> only supposed to govern who is allowed to do what, not what actually
> happens. Unlike the permission system it is supposed to get that from
> the affect of the programs on the environment. Without it both sorts
> of systems would be highly unstable.
>
> I see it as a necessity for complete modular flexibility. If you get
> one of the bits that does the work wrong, or wrong for the current
> environment, how do you allow it to change?
>
>> Just my deux centimes' worth.
>>
>
> Appreciated.
>
>>
>> On a more positive note, I do think it is possible for AGI
researchers
>> to work together within a common formalism. My presentation at the
>> AGIRI workshop was about that, and when I get the paper version
of the
>> talk finalized I will post it somewhere.
>>
>
> I'll be interested, but sceptical.
>
> Will
>
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Thank You
James Ratcliff
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