
A Minimal Architecture for General Cognition [pdf] - luu
http://axon.cs.byu.edu/mrsmith/2015IJCNN_MANIC.pdf
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sago
When I was an undergrad doing AI at the start of the 90s, the 'general AI
architecture' seemed to already be a passing phase. Though many of the
proposed architectures were not symbolic, the approach felt part of the
symbolic AI bubble that had recently burst. The idea is appealing, but the
difficulty in showing success even in isolated tasks made the claims of
general architectures rather bald and self-aggrandising.

I felt at the time that it would take a lot of progress before it was
warranted. We'd need to actually develop tools that were widely applicable and
solve certain fundamental problems (the frame problem, the problem of behavior
composition, the problem of inference). I'm not entirely convinced 'deep
learning' is as widely applicable or capable as its hype, and we haven't found
too many interesting new general purpose cognitive algorithms in the last 30
years. So it still feels way too early to be talking about general models of
cognition, when we don't have convincing data that shows the generation of
cognition in any context.

An interesting paper, but it feels very 1980s, to me. At root a combination of
AI techniques glued together with hand waving and optimism.

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doublerebel
Github of MANIC implementation:
[https://github.com/mikegashler/manic/blob/master/docs/index....](https://github.com/mikegashler/manic/blob/master/docs/index.html)

Docs for implementation:
[https://htmlpreview.github.io/?https://github.com/mikegashle...](https://htmlpreview.github.io/?https://github.com/mikegashler/manic/blob/master/docs/index.html)

From docs:

 _Does it work?_ It passes some tests. More testing is still needed.

 _What is the license of this code?_ ...Creative Commons Zero public domain
dedication. In other words, you can do pretty much whatever you want with this
code.

 _Why is MANIC so slow?_ It does a lot of stuff. Your brain has about 100
billion neurons that all run in parallel. This code is doing it all in serial
on the Java Virtual Machine.

My thoughts:

Can this be trained for "abstract" knowledge (wikipedia) or does it need
specific, detailed knowledge? (the paper examples train with several related
real-world states)

Why can't this be run in parallel?

 _EDIT, additional sources:_

SVG diagram of the MANIC architecture:
[http://uaf46365.ddns.uark.edu/lab/cogarch.svg](http://uaf46365.ddns.uark.edu/lab/cogarch.svg)

2011 paper which began this work:
[http://axon.cs.byu.edu/papers/gashler2011ijcnn2.pdf](http://axon.cs.byu.edu/papers/gashler2011ijcnn2.pdf)

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joe_the_user
Hmm,

I can't tell from this description how "MANIC" solves the broad frame problem.

Another issue that I think Nicholas Cassimatis has focused on the most is that
any multi-model approach is going to need much tighter integration between
modules than ordinary software system normally have.

And altogether, the document seems little more than a series of arrows drawn
between different subsystems. It may be "correct" but so broad as to be not-
useful. I'll have to look at their implementation.

~~~
0xdeadbeefbabe
Anyone who has had a visceral reaction might agree with Cassimatis about
tighter integration between modules.

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Houshalter
It looks like they just put a planning algorithm on top of a deep learning
algorithm. I think this ignores the advancements of reinforcement learning.
And I don't think this model adds to much. A GA isn't enough to do high level
planning, and you need more than just a big neural net to get AGI.

~~~
rhaps0dy
What is a GA?

~~~
andyjohnson0
Genetic Algorithm [1]. The MANIC planner uses a GA.

[1]
[https://en.wikipedia.org/wiki/Genetic_algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm)

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iamcurious
_...if general intelligence is one of the ultimate objectives of the fields of
machine learning and artificial intelligence, then they are very much on the
right track..._

Bold assertion.

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gwern
Sounds like they've reinvented deep reinforcement learning.

~~~
joe_the_user
To be fair, the document claims not to be trying to invent anything new but
rather to be cobbling together different known algorithms into a broad
intelligence architecture.

I'd see the problem laying in these approaches not easily inter-operating with
each other rather than in the approaches themselves.

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m0llusk
Interesting, but study of human consciousness suggests that the basic model
used for human cognition is sequences of framed and situated actions. If this
is true then a different model might be better for planning and evaluating
progress regardless of goals or context.

