Excuse me, do you think that a cs degree is mandatory when researching an
agi system??

On Tue, May 17, 2022, 03:56 Linas Vepstas <[email protected]> wrote:

>
>
> On Mon, May 16, 2022 at 12:03 AM nugi nugroho <[email protected]> wrote:
>
>> I think you are right, that system is way more complicated to build. But,
>> do you think that Link-grammar nature can be used to steal ideas from deep
>> learning to symbolic learning. (I dunno, probably just for fun, not really
>> matters)
>>
>
> Short answer is "yes", although I have not thought very much about this.
> Other tasks seem much more important to me right now, although clearly,
> most mainstream thinkers are very interested in coupling symbolic systems
> to deep learning.  Perhaps I should say: everyone else is thinking about
> this, perhaps my time is best spent working on something that everyone else
> is ignoring?
>
> --linas
>
>
>> Pada tanggal Min, 10 Apr 2022 pukul 02.41 Linas Vepstas <
>> [email protected]> menulis:
>>
>>> Hi Nugi,
>>>
>>> I have an idea for how to segment an image into parts, and recognize
>>> those parts. For example, given a car, to recognize where the wheels are,
>>> where  the tires on the wheels are, the windshields, the roof, the
>>> door-handles, and then to be able to labels these parts (with words, for
>>> instance) -- Likewise for faces (eyes, nose, mouth...) or any other
>>> real-world object.  This provides a "grammatical structure" to the object.
>>> It is more than just "image segmentation"; rather, it is a way of "seeing"
>>> the structure, the part-whole relationship.  Once this grammar is found,
>>> one can reason about objects, talk about objects, etc.
>>>
>>> The PDF in the email chain explains how to do this. Writing the code
>>> would take a long time, as would running the experiments.  You are welcome
>>> to read about it, and think about it, but I don't think it is a good
>>> project for a student: it would take many years of full-time hard work to
>>> accomplish this.
>>>
>>> --linas
>>>
>>> On Fri, Apr 8, 2022 at 5:51 PM nugi nugroho <[email protected]> wrote:
>>>
>>>> Could it be possible to make some 3d model of an object, Isolate it,
>>>> create another corpus for object recognition by inputting the video
>>>> rendering result to the system to be able to recognize the object in 2d. I
>>>> wonder if this is possible without inputting a 3d model to the new corpus
>>>> and just the blank video input and by some iteration it will be able to
>>>> identify the isolated object in some noisy environment. If this is possible
>>>> then the system could theoretically learn to recognize objects from a
>>>> youtube video by passing the video and the subtitle(for now the subtitle is
>>>> created from a neural network since I have no idea how to create the audio
>>>> corpus). Usually the cooking channels from youtube are explaining about the
>>>> ingredients like apples and moving them as they cook it, so I think that I
>>>> could be used as the input to teach the model. I know this is a huge and
>>>> very difficult thing to do.
>>>>
>>>> The thing that I know is I don't know a lot about how to use this
>>>> technique for computer vision, I was still dumb. I still don't have any
>>>> idea how to isolate each object in the video since this shouldn't be
>>>> hardcoded but learned.
>>>>
>>>> Pada tanggal Jum, 8 Apr 2022 pukul 16.06 nugi nugroho <
>>>> [email protected]> menulis:
>>>>
>>>>> I still do not understand the system completely, but I wonder if this
>>>>> system is theoretically capable of creating 3d models from video input 
>>>>> that
>>>>> the cameramen rotate around an object at a certain angle. I was thinking 
>>>>> of
>>>>> creating a corpus capable of converting video to 3d models for further
>>>>> processing by reasoning algorithms. I think AGI needs to have the skills 
>>>>> to
>>>>> think at least about the 3d world(just my naive assumption though).  This
>>>>> was the first idea that popped into my head but, well, I still need to
>>>>> prepare for my university exam so I can't learn faster than my current 
>>>>> pace
>>>>> and my current skills are not sufficient to realize that idea for now. I
>>>>> hope that I can make some contribution to the project at the end of this
>>>>> year, but I cannot promise.
>>>>>
>>>>> Pada tanggal Rab, 6 Apr 2022 pukul 01.04 Linas Vepstas <
>>>>> [email protected]> menulis:
>>>>>
>>>>>> Hi Craig, (and Ivan)
>>>>>>
>>>>>> Replying publicly to a private email:
>>>>>>
>>>>>> On Mon, Apr 4, 2022 at 4:00 PM Craig Bosco <[email protected]>
>>>>>> wrote:
>>>>>>
>>>>>>> the only way forward is to crowdsource work and ideas.
>>>>>>>
>>>>>> ...
>>>>>>
>>>>>>> everyone will ultimately benefit from the OpenCog platform as it
>>>>>>> gains ease-of-use and sophistication.
>>>>>>>
>>>>>>
>>>>>> The OpenCog "platform" is both broad and deep; to discuss all aspects
>>>>>> of it would be boiling the ocean. Unless, that is, you want to work on 
>>>>>> deep
>>>>>> and basic infrastructure.  One such would be converting the AtomSpace 
>>>>>> into
>>>>>> a commercially viable platform that ordinary developers would want to use
>>>>>> on a day-by-day basis. Having this would attract public attention, 
>>>>>> although
>>>>>> it would not much advance the overall AGI research goals.
>>>>>>
>>>>>> One way to convert the AtomSpace into a commercially viable product
>>>>>> would be to allow it to store generic JSON or similar (generic
>>>>>> s-expressions, generic YAML, or even generic python or a json-like subset
>>>>>> of python. Or all of the above.) This is "commercially appealing" because
>>>>>> there already is a company that does this (grakn.ai, but since
>>>>>> renamed to some other name I can't recall) and there are several other
>>>>>> graph-database companies that offer something similar.  I've taken some
>>>>>> small steps in this direction, but abandoned them as they seemed like a
>>>>>> distraction from the main topic of AGI research.
>>>>>>
>>>>>> The above might be appealing because it is a fairly well-defined,
>>>>>> clear-cut project. It does not require arcane theory, or deep
>>>>>> experimentation. It's mostly a matter of roll-up-your-sleeves and write
>>>>>> code, which is exactly the kind of thing most programmers enjoy. Take a
>>>>>> sketch, and turn it into a polished product.
>>>>>>
>>>>>> As to AGI research: the stuff I'm working on now is very theory-laden
>>>>>> and complex; I now realize that I should not much expect anyone to 
>>>>>> follow,
>>>>>> although a shout out to Amir who continues to surprise me regularly. He's
>>>>>> on the right track.
>>>>>>
>>>>>> As to AGI research that you or Ivan could work on (... if only Ivan
>>>>>> stopped skimming emails, and actually paid attention to what was written 
>>>>>> in
>>>>>> them...) there is brand-new green-field development on audio and video
>>>>>> processing.  Green-field, in that not much code has been written, and so
>>>>>> you don't have to modify a large complex existing code-base. It does,
>>>>>> however, require interfacing into large and complex existing systems. The
>>>>>> path is fairly straight-forward; see attached PDF. The work, however, is
>>>>>> definitely challenging: it will require some hard thinking and lots of
>>>>>> work. It's not "just programming", it's architecture and exploration.  
>>>>>> Some
>>>>>> of that work is grunt-work, e.g. collecting a suitable corpus of images.
>>>>>> Some is just painful: running CPU-intensive jobs for days on end.
>>>>>>
>>>>>> PDF:
>>>>>> https://github.com/opencog/learn/blob/master/learn-lang-diary/agi-2022/grammar-induction.pdf
>>>>>>
>>>>>> -- Linas
>>>>>>
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>>>>>> .
>>>>>>
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>>>> .
>>>>
>>>
>>>
>>> --
>>> Patrick: Are they laughing at us?
>>> Sponge Bob: No, Patrick, they are laughing next to us.
>>>
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>
>
> --
> Patrick: Are they laughing at us?
> Sponge Bob: No, Patrick, they are laughing next to us.
>
>
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