[ivan.moony]

Interested in symbolic AI (GOFAI)?

I think this might be a place to start off. I just defined on a paper an inference engine based entirely on implication. Input and output forms are defined, detailed pseudocode prototype (65 lines) is already written, Javascript implementation and bugs extermination is pending.

If we manage to get a logical representation of a chatbot input, we can pass it through this engine to derive implicitly contained information and answer questions.  Also, a logical expert system could be based on this engine. The only built in rule of inference is modus ponens that says from `A -> B` and `A` follows `B`. All other rules are combined and derived from this one, and this includes even and/or/not combinations. If we fill in a falsehood that we make sure to always fail, we have a functionally complete combination of implication and negation. Further, if we add a few axioms, we have a real implicational logic and a form of theorem prover. Developing the thought further, a search for proofs is basically automated algorithm construction... Well, all of this is just a part of a theory that always looks like a rainbow to me, but it still has to be seen how many rainy days there are on a way.

In short, Esperas is planed to be a Javascript programming library for logical bottom-up reasoning that derives conclusions based on input. If you are interested, a short read about Esperas is placed here. I'll try to implement it and inform you about the progress as soon as possible.



I would still like to see how to fuse this approach with neural networks. Maybe condition-consequence pairs from this kind of system can be seen as action-reaction pairs in trained NN?

[important edit]
Old Esperas project is renamed to Implika project. The new Implika link is here, while Esperas turned into another project based on top-down parsing technology.

[LOCKSUIT]

Cool.

My friend once told me a year ago that even my idea needed what a NN has - it knows exactly how close cat=dog.....not just cat=dog. Rather cat=dog ex. 68%. The way to store all these is exactly what W2V does - it uses a net and is efficient. We may be stuck with NNs being a part of AGI.

[AndyGoode]

We may be stuck with NNs being a part of AGI.

No way, if I understand you correctly. UMSs--Uncertainty Management Systems--are standard in rule-based expert systems, and do the estimations of likelihoods of inferences and matches that you mention. Neural networks also can only estimate, only in a different way.

[LOCKSUIT]

Every single word connects to every other single word.........with a =s score weight.........that's a lot of connections and weights. And you need them to be precise to progress further plus utilize the translation.

[ivan.moony]

We have NN today because they were proven to be successful in their simplest forms, in the most primitive organisms before 5.4 billion years ago. All this time the Nature had time to upgrade it and fine tune it, while we got the finished stuff to analyze today. I bet there are other ways to recognize and produce informations, both statistical and strict, but right now we have a finished work that weights 5.4 billion years. I only wish if there was a way to use and analyze it without abusing any living. 

[LOCKSUIT]

Although still crossing the ethical beliefs, it'd be more logically harmless and brave to take any and every dead person (young humans/animals) and experiment/study. Though they would not be alive.
Many are already are dying in pain, it's the right thing to do.

Alternatively you can take live cells.

I still believe I could make Cryonics work with all the time I'd put into it. I think it looks easy to test, ....so many things to compare what improves it!! Lots of tests could be done. Just take clumps of cells.

[HS]

Volunteers.

[LOCKSUIT]

All ya gotta do is get a Cryonics Degree, get a lab, and make a hugggge notepad or whiteboard and fill it up with a bizzilion ideas (with tests). Take out some 20 years and we'll get there in no-time. Put the chips down and you will find the gloryful gates to a utopia with much, much more chips than 80years will feed you. That sadistic high you get when eating is nothing, it will be again. An endless feast.

[AndyGoode]

I would still like to see how to fuse this approach with neural networks. Maybe condition-consequence pairs from this kind of system can be seen as action-reaction pairs in trained NN?

I refrained from getting into this topic because I would probably come across as too negative. Specifically, in the 1990s, when neural networks were the latest and greatest hope for AI, many dozens, if not thousands, of software engineers realized that since symbolic AI and neural networks have different strengths and weaknesses, a combined system might be the ultimate solution since each weakness of one paradigm would be covered by the other paradigm, so they published numerous articles proposing 'hybrid systems' composed of rules *and* neural networks. I got tired of seeing so many articles on such proposals at every conference, and to my knowledge, not one of those proposals has survived as a system that is still being used today, which is what I predicted back then.

So yes, there exist numerous ways to create hybrid systems, like the way you suggest, but ultimately something critical is missing from both paradigms, so I don't believe you'll ever get general intelligence arising from hybrid system approaches. That's where I'm so extremely negative that it doesn't even interest me to discuss the details of the pros and cons of those paradigms. I don't want to detract from your project, though, since it's a solid, legitimate, straightforward project, so you should probably just ignore my views on this topic and keep working on that, if that's what interests you.

----------

(p. 36)
Classical AI
techniques are best suited for natural language processing, planning, or explicit reasoning,
whereas neural networks are best suited for lower-level perceptual processes, pattern
matching, and associative memories.

Haykin, Simon. 1994. Neural Networks: A Comprehensive Foundation. New York, New York: Macmillan College Publishing Company.

[HS]

I think two different systems just sitting beside each-other would have a data transmission bottleneck, and might even require a third, translator unit. There's got to be a way of combining them that's not janky. Maybe symbolic logic could be transmitted within a neural network, so it accomplishes high and low level tasks in parallel.

[ivan.moony]

I managed to do the normalization, it seems it does the correct thing, but I encountered some problems with partial inference and schematic variables before even beginning implementing them. It seems that the system would only answer yes/no if specific consequences could be paired with assumptions, but wouldn't actually generate an answer of what follows from some assumptions. It would be just a prover, not a generator.

Here is the normalization testing interface: https://e-teoria.github.io/Esperas/test/

[LOCKSUIT]

In my research I have evidence suggesting generating=proving. Are you really sure yours can prove but not generate? How so...

[8pla.net]

JavaScript is actually a good choice but may limit your project to the client side  web browser which is constantly changing it seems.  So, you may want to consider a server side language such as PHP which changes more slowly, and it has neural networks or expert systems already written. 

I don't believe JavaScript does.  Of course, you may post the results from the JavaScript to PHP on the server side, but that could  slow things down.  There are many PHP tutorials, references and examples that are easy to find to help with your project.  But, this is just my opinion. As I said, JavaScript is a great choice especially with HTML5 which is a nice feature.

[goaty]

This picture is fractal data/code,   Ive put a lot of exploration into this datastructure of this picture!  =)

The main problem is you run out of page space by 4 scopes -  you cant even continue drawing it!

[goaty]

I think two different systems just sitting beside each-other would have a data transmission bottleneck, and might even require a third, translator unit. There's got to be a way of combining them that's not janky. Maybe symbolic logic could be transmitted within a neural network, so it accomplishes high and low level tasks in parallel.

Any program can be put into a perceptron feedforward structure,   they are just data I/O!!   its just in general that anything can go into one.
Im meaning you can probably put both systems into the same net,  doesn't matter what they are.