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Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

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Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#61

Post by Eridrus »

YeGoblynQueenne wrote: post #11
jonathanstrange wrote: post #6

Maybe good AI requires a combination of symbolic approaches for the knowledge representation, deep parsing for NLP and semantics/discourse models, and machine learning for pattern recognition tasks of any kind. The currently trendy "AI" looks more like massive data mining with powerful ML to me, that's very good for certain tasks but brings us nowhere near real AI. The knowledge representation problem has not yet bee…

>> Generally speaking, logical modelling is too limited for good concept representations In principle, First Order Logic and equivalent languages can represent anything that can be represented in natural language. The problem is that in practice it is very hard to transfer into a logic language all the knowledge you might need for a useful system. In fact, this was one motivation for at least one branch of machine le…

One problem with first order logic is that it is too powerful.

There is a direct trade-off between the expressiveness of your representation and your ability to reason about it.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#62

Post by tramGG »

jonathanstrange wrote: post #6

Maybe good AI requires a combination of symbolic approaches for the knowledge representation, deep parsing for NLP and semantics/discourse models, and machine learning for pattern recognition tasks of any kind. The currently trendy "AI" looks more like massive data mining with powerful ML to me, that's very good for certain tasks but brings us nowhere near real AI. The knowledge representation problem has not yet bee…

> Maybe good AI requires a combination of symbolic approaches for the knowledge representation, deep parsing for NLP and semantics/discourse models, and machine learning for pattern recognition tasks of any kind.

I totally agree.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#63

Post by sixdimensional »

This discussion reminds me of NASA's CLIPS [1], particularly FuzzyCLIPS. I know that is basically a rule engine/expert system basis at its core, but FuzzyCLIPS was an interesting variant that try to do inference off of CLIPS rules and facts.

I always wondered if rules and facts can be seen as high-order abstractions that represent the low-level data that is encoded into neural nets. Would that mean that a system of symbolic logic, or rules and facts can in fact accomplish the same thing, just in a different, higher order way?

Then when you get into combining fuzzy with known, it's like different organs in the brain combining to serve different functions. When I know a rule and recall it, I believe it to be true and act immediately on it. When I'm not sure, I get fuzzy and try to deduce it. The more data and inputs I have about a given environmental condition, problem, subject, input, fact, etc., the easier it becomes to "reason" using fuzzy links to try to work out the answer. Unless I get information overload or the complexity exceeds one human brain's capability.

I've always felt that techniques are complementary and probably we employ multiple subsystems in our brains together to act like humans.

[1] https://en.m.wikipedia.org/wiki/CLIPS

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#64

Post by randcraw »

garmaine wrote: post #33
goatlover wrote: post #22

Earlier quoted context omitted.

> In principle, First Order Logic and equivalent languages can represent anything that can be represented in natural language. I doubt this is the case since natural language makes a lot of use of metaphors, and our concepts are probably mostly based on the types of bodies we have and how we interact with the world. See embodied cognition and writings of Lakoff and Mark Johnson.

Look into the field of computational semantics. There is absolutely nothing in your comment which cannot be represented in existing logics. Metaphors and such are no problem. There is ambiguity in natural languages so the determination of underlying meaning is at best probabilistic, but we have ways of handling that too.

Lexical and computational semantics may be superior to FOL, (for machine translation, at least) but they've been around now for a very long time without showing much promise toward thawing past AI Winters. I've not heard anyone propose their adoption as the missing next step toward modeling general knowledge across all AI problem spaces.

In fact, populating a LS/CS knowledgebase with requisite facts was a game stopper when they were first invented and still pose challenges of scale that remain unsolved decades later. If it accomplished anything, Cyc proved that manual curation alone of a rich database of propositions is depressingly infeasible. So how does one automatically curate a general fact KB, no matter what semantic model the facts take? I don't think expressiveness of the propositional representation will ever decide that question.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#65

Post by EGreg »

jonathanstrange wrote: post #6

Maybe good AI requires a combination of symbolic approaches for the knowledge representation, deep parsing for NLP and semantics/discourse models, and machine learning for pattern recognition tasks of any kind. The currently trendy "AI" looks more like massive data mining with powerful ML to me, that's very good for certain tasks but brings us nowhere near real AI. The knowledge representation problem has not yet bee…

Let me suggest something that will turn everything upside down...

How much of our “logical reasoning” about the world is just really strong correlations?

Look, AlphaGo Zero beat the best chess programs by just precomputing MCTS. What does that say about finding the optimal solution to a problem?

It seems MCTS beats even AlphaBeta search, by far.

So when you talk about applying rules, you’re using a system that tried to reduce the system to a few rules and maybe recursively use them. That may work for some things. Certainly it seems to work amazingly for physics. But that’s the unreasonable effectiveness of mathematics.

Ultimately what if I suggested that “understanding something” with huge vectors or monte carlo search is actually far deeper understanding than a few rules?

I am talking about the actual territory not the map. Yes the model is neat and tidy but the model is just simple enough so that humans can understand!

What if an AI can no more explain its solutions to a human than a human can explain human decisions to a cat?

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#66

Post by randcraw »

EGreg wrote: post #65
jonathanstrange wrote: post #6

Maybe good AI requires a combination of symbolic approaches for the knowledge representation, deep parsing for NLP and semantics/discourse models, and machine learning for pattern recognition tasks of any kind. The currently trendy "AI" looks more like massive data mining with powerful ML to me, that's very good for certain tasks but brings us nowhere near real AI. The knowledge representation problem has not yet bee…

Let me suggest something that will turn everything upside down... How much of our “logical reasoning” about the world is just really strong correlations? Look, AlphaGo Zero beat the best chess programs by just precomputing MCTS. What does that say about finding the optimal solution to a problem? It seems MCTS beats even AlphaBeta search, by far. So when you talk about applying rules, you’re using a system that tried…

That's like saying that causation doesn't matter; correlation will suffice. But the causal directionality of separating subject from object in a relation is essential to forming or sharing an idea usefully. In the synthetic game worlds you mention, causality is decided by play sequence. But at no point does the agent ever have to employ abstraction, or 'think about thinking', which diminishes the needed depth of thought greatly.

No, probability alone simply isn't expressive enough to support higher cognition. At best it will deliver a purely reactive agent, no more cognizant than Kahnemann's knee-jerk level of thinking.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#67

Post by randcraw »

YeGoblynQueenne wrote: post #12
sgt101 wrote: post #3

Why prolog? Why not PRISM or problog?

This is a valid question, I don't know why you (originally) got downvoted. The answer I think, for the applications in industry at least, is that PRISM and other probabilistic programming langauges are not as developed as Prolog, a language that has been around for a good four decades now. Swi-Prolog in particular, is a free and open-source Prolog interpreter with an IDE, a graphical package and a veritable somrgasbo…

But its not clear to me that encoding productions in source code adds any value over encoding them in the various other forms used by expert systems of old. Prolog also compels binary constraint satisfaction, recursive descent parsing, backtracking, and depth-first resolution — all undesirable constraints that are easily avoided using other fact representations or resolution engines.

Until the startup in question can make a compelling case for using prolog, much less avoiding the brittleness inherent in expert systems' resolution model, count me a disbeliever that their 'better way' really is.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#68

Post by garmaine »

randcraw wrote: post #64
garmaine wrote: post #33

Earlier quoted context omitted.

Look into the field of computational semantics. There is absolutely nothing in your comment which cannot be represented in existing logics. Metaphors and such are no problem. There is ambiguity in natural languages so the determination of underlying meaning is at best probabilistic, but we have ways of handling that too.

Lexical and computational semantics may be superior to FOL, (for machine translation, at least) but they've been around now for a very long time without showing much promise toward thawing past AI Winters. I've not heard anyone propose their adoption as the missing next step toward modeling general knowledge across all AI problem spaces. In fact, populating a LS/CS knowledgebase with requisite facts was a game stoppe…

I’m not sure what you are saying. Computational semantics parses sentences to possible interpretations in first order, or higher order logics. It is not some sort of alternative to FOL. And it is still a rapidly developing field.

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#69

Post by garmaine »

TheOtherHobbes wrote: post #18
YeGoblynQueenne wrote: post #11

Earlier quoted context omitted.

>> Generally speaking, logical modelling is too limited for good concept representations In principle, First Order Logic and equivalent languages can represent anything that can be represented in natural language. The problem is that in practice it is very hard to transfer into a logic language all the knowledge you might need for a useful system. In fact, this was one motivation for at least one branch of machine le…

I suspect you may have a hard time representing all the implications, subtexts, and allusions in a play by Shakespeare with First Order Logic. It seems more likely that First Order Logic can be represented by a subset of natural language. Otherwise, I agree. I don't understand why everyone has been concentrating so hard on probabilistic ML when there are many applications where inductive inference is more efficient a…

That’s not obvious. Do you have any reason for thinking so?

Re: Is There a Smarter Path to Artificial Intelligence? Some Experts Hope So

#70

Post by garmaine »

gaius wrote: post #16
FrozenVoid wrote: post #4

Lets rename "Deep learning" to "Statistical Compression". Its essentially distilled data from a dataset, limited by size of the neural network. There isn't anything "deep" there, the goal of NN is always towards overfitting the data.

Lets rename "Deep learning" to "Statistical Compression" The previous name for what is now called “machine learning” was predictive statistics. But a better name for deep learning is “machine intuition” since it seems to work pretty well but can’t be readily explained

> since it seems to work pretty well but can’t be readily explained

That is becoming less and less true.

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