I think there are a lot of interesting things about this paper. Read
this little abstract from the "discussion" which follows some
examples. They are referring to the examples:
-----------------------
the first example involves counting skills, ability to compare small num-
bers, ability to associate the words "your friend" to a known person,
ability to retrieve information about her age from the LTM, ability to
use personal pronouns. The system is able to learn how to answer this
question through a rewarding procedure, and to generalize the acquired
knowledge to similar questions involving different people with
different ages. In the second example ("how many games did you play?")
the system is able to retrieve the three games from LTM and to count
them. It is important to point out that our model does not include a
specialized structure for counting, or a specialized structure for
number comparison, or a specialized structure for mapping names into
personal pronouns...
All its abilities arise from a relatively small set of mental actions
that are compatible with psychological findings.
-----------------
What I find interesting about this is that they didn't just teach the
system to count which I suppose is not that momentus in this age.
Their system FIRST figured out there was such a need to learn how to
count, THEN it learned how to count. So it seems to have developed a
skill based upon need.
On 11/15/15, Jim Bromer <[email protected]> wrote:
> I feel that a symbolic approach would be easier to start with and it
> could be feasible with better insight and some stronger methods. I
> do, however, also feel that (what I think is) a gated recurrent
> artificial neural network with n-space mapping (or bus-state mapping)
> could be made to work, but this would in essence be very similar to a
> hybrid approach.
>
> Jim Bromer
>
> On Sat, Nov 14, 2015 at 9:57 PM, Ben Goertzel <[email protected]> wrote:
>
>> The paper is here... http://arxiv.org/abs/1506.03229
>>
>> Sensationalist media article here:
>>
>> http://www.iflscience.com/technology/scientists-create-artificial-system-capable-learning-human-language
>>
>> This is from Angelo Cangelosi (among others), who works with the iCub
>> robot and gave a keynote at AGI-12 at Oxford...
>>
>> It's very good stuff, but unlike what that news article says, this is not
>> the first time automated response-generation has been done w/ neural
>> nets.... I recall a paper by some Russian dude giving similar results in
>> the "Artificial Brains" special issue of Neurocomputing that Hugo DeGaris
>> and I co-edited some years ago...
>>
>> What distinguishes this work is more the sophistication of the underlying
>> cognitive architecture ... maybe it works better than prior NNs trained
>> for
>> dialogue-response or maybe it doesn't; careful comparison isn't given
>> (understandably -- there is no standard test corpus for this stuff, and
>> prior researchers mostly didn't open their code).... But the cognitive
>> architecture is very carefully constructed in a psychologically realistic
>> way; combined with the interesting practical results, this is pretty
>> nifty...
>>
>> The training method is interesting, incrementally feeding the system
>> facts
>> with increasing complexity, while interacting with it along the way, and
>> letting it build up its knowledge bit by bit. A couple weeks ago I
>> talked
>> to a Russian company at RobotWorld in Korea who was training a Russian
>> NLP
>> dialogue system in a similar way.... (again with those Russians!!)
>>
>> Note that with this method, the system can respond to questions involving
>> the word "dad" without really knowing what a "dad" is (e.g. without
>> knowing
>> that a dad is a human or is older than a child, etc.). This is just
>> fine,
>> and people can do this too. But we should avoid assuming that just
>> because it gives responses that, if heard from a human, would result from
>> a
>> certain sort of understanding, the system is demonstrating that same sort
>> of understanding. This system is building up question-response
>> patterns
>> from the data fed into it, and then performing some generalization. The
>> AI
>> question is whether the kind of generalization it is performing is really
>> the right kind to support generally intelligent cognition.
>>
>> My thought is that the kind of processing their network is doing,
>> actually
>> plays only a minor supporting rule in human question-answering and
>> dialogue
>> behavior. They are using a somewhat realistic cognitive architecture
>> for
>> reactive processing, and a somewhat realistic neural learning mechanism
>> --
>> but the way the learning mechanism is used within the architecture for
>> processing language, is not very much like the way the brain processes
>> language. The consequence of this difference is that their system is
>> not
>> really forming the kinds of abstractions that a human mind (even a
>> child's
>> mind) automatically forms when processing this kind of linguistic
>> information.... The result of this is that the kinds of
>> question-answering, question-asking, concept formation etc. their system
>> can do will not actually resemble that of a human child, even though
>> their
>> system's answer-generation process may, under certain restrictions, give
>> results resembling those you get from a human child...
>>
>> These observations do not really contradict anything they say in the
>> paper, at least upon my quick read....
>>
>> An interesting step, anyway...
>>
>>
>> --
>> Ben Goertzel, PhD
>> http://goertzel.org
>>
>> "The reasonable man adapts himself to the world: the unreasonable one
>> persists in trying to adapt the world to himself. Therefore all progress
>> depends on the unreasonable man." -- George Bernard Shaw
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