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Dear Alex Shkotin,
Dear and respected colleagues,
As long as you entertain a faulty definition of non-determinism, your conclusion can only reflect the premise.
Of course, non-determinism deserves more than a GPT-3 occasioned inquiry.
Best wishes.
Mihai Nadin
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Fair question. Wrong hypothesis. Nothing else.
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Dear Alex Shkotin,
Dear and respected colleagues,
Knowledge—yet another concept to be defined before speculating. Is there knowledge in the absence of understanding? ANN like any other Turing machines are by definition deterministic syntactic machines. There is no understanding. Ontology engineering is the attempt to substitute for understanding.
If we stick to clear concepts, a dialog is possible.
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YOUR choice. “know how” and “know that” (Gilbert Ryle) is a distinction you might want to be aware of.
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Last attempt:
Dear Alex Shkotin,
Dear and respected colleagues,
Ontology engineering contributes actionable knowledge to computation. That does NOT mean that computation in its current Turing embodiment deals with knowledge.
What I suggested is that we should be careful before making statements. First step: define concepts. My rather restrained attempt is probably not clear enough. I apologize to all of you.
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The Logic Virtual Machine (LVM) supports ISO Standard Prolog with tensors as a native datatype. The tensors can represent graphs or networks of any kind. For most applications, the great majority of elements of the tensors are zero, and the nonzero elements are bit strings of arbitrary length. Those bit strings may encode symbolic information, or they may represent integers of any size.
If the nodes encode symbolic information, the tensors may represent conceptual graphs, knowledge graphs, or the logics of the Semantic Web, Formal UML, and other knowledge representation languages. As an extension of Prolog, LVM can perform logical reasoning or other kinds of transformations on or with those notations.
Since the bit strings in the tensors may be interpreted as integers of arbitrary length, operations on those tensors may perform the same subsymbolic computations used in neural networks. As an extension of Prolog, LVM can relate neural tensors and symbolic tensors for neurosymbolic reasoning.
Since the symbolic and subsymbolic tensors are supported by the same LVM system, operations that relate them can be performed with a single Prolog statement. For special-purpose operations and connections to other systems, LVM can invoke subroutines in Python or C.
By combining the strengths of symbolic AI with neural networks, LVM spans the full range of learning and reasoning methods developed for either or both. Three major applications are computational chemistry, sentiment analysis in natural language documents, and fraud analysis in financial transactions.
All three applications take advantage of the unique LVM features: precise symbolic reasoning, neural-network style of learning, and the ability to analyze huge volumes of graphs by precise methods or probabilistic methods. Applications in computational chemistry have obtained excellent results in molecular toxicology for the EPA TOX21 challenge. Applications in sentiment analysis combine Prolog's advantages for natural language processing (NLP) with a neural-network style of learning. Applications in financial fraud analysis combine NLP methods and learning methods that must be accurate to fractions of a cent.
Asking GPT-3 I am waiting for knowledge and call it a knowledge machine. I anticipate that it is full of knowledge. Keeping in mind that I can separate knowledge from rubbish :-)
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[1]
00:00:00 Kick off
00:02:24 C1: LeCun's recent position paper on AI, JEPA, Schmidhuber, EBMs
00:48:38 C2: Emergent abilities in LLMs paper
00:51:32 C3: Empiricism
01:25:33 C4: Cognitive Templates
01:35:47 C5: The Ghost in the Machine
01:59:21 C6: Connectionism and Cognitive Architecture: A Critical Analysis by Fodor and Pylyshyn
02:19:25 C7: We deep-faked Chomsky
02:29:11 C8: Language
02:34:41 C9: Chomsky interview kick-off!
02:35:39 Large Language Models such as GPT-3
02:39:14 Connectionism and radical empiricism
02:44:44 Hybrid systems such as neurosymbolic
02:48:47 Computationalism silicon vs biological
02:53:28 Limits of human understanding
03:00:46 Semantics state-of-the-art
03:06:43 Universal grammar, I-Language, and language of thought
03:16:27 Profound and enduring misunderstandings
03:25:41 Greatest remaining mysteries science and philosophy
03:33:10 Debrief and 'Chuckles' from Chomsky
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Alex: But one point puzzles me: if the output varies, why are we sure that GPT-3 remembers the training?
Certainly. There is no any learning or training or teaching, as no machine learning or learning machines, but just statistical techniques, data analytics, data mining and computational statistics.
PS. The difference between ML and AI is frequently misunderstood. ML learns and predicts based on passive observations, whereas AI implies an agent interacting with the environment to learn and take actions that maximize its chance of successfully achieving its goals.[27]
As of 2020, many sources continue to assert that ML remains a subfield of AI.[28][29][26] Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI. https://en.wikipedia.org/wiki/Machine_learning
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There is no reality but appearances.
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Alex,
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Alex and Azamat,
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- An article in the MIT Technology Review, cowritten by Deep Learning critic Gary Marcus,[31] stated that GPT-3's "comprehension of the world is often seriously off, which means you can never really trust what it says."[32] According to the authors, GPT-3 models relationships between words without having an understanding of the meaning behind each word.
- Jerome Pesenti, head of the Facebook AI lab, said GPT-3 is "unsafe," pointing to the sexist, racist and other biased and negative language generated by the system when it was asked to discuss Jews, women, black people, and the Holocaust.[33]
- Nabla, a French start-up specializing in healthcare technology, tested GPT-3 as a medical chatbot, though OpenAI itself warned against such use. As expected, GPT-3 showed several limitations. For example, while testing GPT-3 responses about mental health issues, the AI advised a simulated patient to commit suicide.[34]=
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Note that ‘intuitive physics’ is very basic: continuity of motion and lack of interpenetration. Definitely not Newton’s Laws etc.
James
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com>
On Behalf Of alex.shkotin
Sent: 13 July 2022 10:22
To: ontolog-forum <ontolo...@googlegroups.com>
Subject: Re: [ontolog-forum] GPT-3 is not deterministic!
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Alex> Now I am impressed with the number of parameters GPT-3 has to manage. Today I was at a meeting of bot creators - overview of bot technologies and directions and the reporter has mentioned one time that GPT-3 may be sometimes a one of components. She mentioned Alexa Prize competition [1] as important for bot creators. By the way, she mentioned ontologies, too, in passing :-)
I agree that many smart people have done a lot of hard word in developing GPT-3. I also believe that it's possible to learn a great deal about how those things work together to produce the results.
No – the article’s description of “intuitive physics” (definition would be too strong a word) is MUCH weaker than, say, Newton (and let’s not forget that human “intuitive physics” is much closer to Aristotle than Newton).
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"Many ideas of folk physics are simplifications, misunderstandings, or misperceptions of well-understood phenomena, incapable of giving useful predictions of detailed experiments, or simply are contradicted by more thorough observations. They may sometimes be true, be true in certain limited cases, be true as a good first approximation to a more complex effect, or predict the same effect but misunderstand the underlying mechanism".
The beauty of real AI systems is in the ultrafast data processing speed, inhuman accuracy and precision, while humans are stuck between false positives and false negatives. .
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I agree with Alex,
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Just one observation:
The body, being released, falls down and it is not necessary to know that with acceleration g.
This observation pertains to a stone or any other non-living object. Indeed, all stones falls the same. The cat does not simply fall…Gravity is in place, but so is the ability to fall affect the fall in the sense of avoiding harm. Acceleration is part of the anticipation informed falling!
Mihai Nadin
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Protein folding is NOT a solved problem! Just for your information.
MN
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Arshavsky:
As to I.M. Gelfand, the deeper he plunged into neurophysiology, the more disappointed he became by the idea of using direct mathematical approaches for solving neurophysiological tasks. He used to say that modern mathematics developed in close contact with physics and engineering, whereas neurophysiological processes are much more complex than the physical.
Borovik echoes the same.
But since you are missing the point that the non-living and the living are of a different nature, quoting a quote about Bernstein is not the answer.
MN
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Arshavsky:
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Worth reading:
A MATHEMATICIAN'S VIEW OF THE UNREASONABLE
INEFFECTIVENESS OF MATHEMATICS IN BIOLOGY.
ALEXANDRE BOROVIK
To your question: My question is still the same: is a bacteriophage living or not?
The short answer: NO! All bacteriophages are composed of a nucleic acid molecule surrounded by a protein structure.
The rest of your text does NOT come through coherently.
Final note: my own view (if you care to know or to read what I published about it): the living is G-complex, i.e. undecidable; the non-living is decidable (can be fully and consistently described).
MN
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it's amazing to me that there are relatively few types of nuclei involved in biological processes.… For example, there are no metals in the body.
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Colleagues,It may be only my problem to get that the answer of GPT-3 machine varies every time you ask. I ask Russian GPT-3 [1] three times question in Russian "What do you know about Jeeves and Wooster TV series?" and have got three different answers.So we have to deal with a probabilistic machine of some kind.What do you think?Alex