Dear and respected colleagues,
Data comes from measurement. Data is agnostic of theory. Referenced to meaning data becomes information. Nothing else. Neither the data nor operations on such data reflect understanding of the phenomena the data represent.
The recent developments (such as chat GPT or other LLM productions) illustrate the echo chamber effect of the data used to train neural networks. Of course, it helps that we understand how language functions but this does not mean that from how it functioned in the past we can infer to how it will function in new contexts.
Mihai Nadin
https://www.nadin.ws
https://www.anteinstitute.org
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Dear and respected Michael DeBellis:
The meaning of Data is agnostic of theory.
You refer to interpretation of data. Always dependent upon the theory.
My statement refers to the outcome of measurement. Even if you believe that the world is flat, the data from measurements is independent of your belief and of theories (right or wrong).
Mihai Nadin
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Dear Alex,
Yes, consider the humble thermocouple. It does not measure temperature at all, but the voltage generated by an electrical circuit of two dissimilar metals. It just happens that the voltage generated varies with temperature.
In my experience the area of measurement, physical quantities and measurement scales is one of the most challenging areas for ontological research. It is familiar, so we think we know what is going on, but when you start to poke at it you find it is much more of a mish mash than you would expect.
Regards
Matthew West
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In the context of data and measurement, you might like to read my 2017 editorial titled “'Digitalization,’ from Harry Nyquist to the ‘edge’ of the internet” https://oilit.com/2017+5+3 where I argue that “The problem [of interoperability] is not just about aligning data protocols, it is also, and more profoundly, about aligning models of reality. A harder, if not impossible task!”
Best regards,
Neil McNaughton
2022 SPE Regional Data Science and Engineering Analytics Award Winner
Editor Oil IT Journal – www.oilit.com
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In my experience the area of measurement, physical quantities and measurement scales is one of the most challenging areas for ontological research. It is familiar, so we think we know what is going on, but when you start to poke at it you find it is much more of a mish mash than you would expect.
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Dear Alex,
Unfortunately most of the texts that are supposed to be authoritative are actually flawed through making assumptions that were convenient for their purpose.
Regards
Matthew
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??, 4 ???. 2023 ?. ? 22:12, Michael DeBellis <mdebe...@gmail.com>:
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Dear Alex,
Unfortunately that is the kind of text I am talking about. So I mean things like BIPM’s VIM.
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> Data is agnostic of theory.
MD: I don't think that's completely true. ..
Or consider what you see the Sun do every day. Does it move across the sky or does the Earth's rotation make the position of the sun change? In the past our ancestors data said the Sun moved but now our theory tells us that the real data is the Earth's rotation causes the Sun to change position over time.
I mostly agree with Michai, and his comments make sense, which somehow is missing the needful attention of our forum.
First, Data is a triple-faced thing,
1. an aspect of things (quantity, quality, fact, relationship),
2. a piece of information (the smallest units of factual information that can be used as a basis for calculation, reasoning, or discussion, analysis, presentation, visualization); organized in data hierarchy, a character (bit and byte), field, record, file and database
3. some sorts of measurement, or statistics, levels of measurement
First of all, data is an ontological category, as facts or states of affairs.
We might say "the world is the totality of data/facts, not things".
As such, Data is orthogonal, or independent of, not only to theories, but also to all human mentality/epistemology: knowledge, values, opinion, beliefs or theories.
We have the data universe of the whole world partitioned into the finite number of datasets (categories and classes, kinds or types) of an innumerable number of data items/elements/points, as instances, individuals, cases, or facts (tokens)
For Data to represent the things in the world, the World Data Ontology was proposed to complete the data science and engineering, statistical AI and ML/DL.
Broadly speaking, all data falls into one or more of five categories: nominal, ordinal, interval, ratio, and number, going as the levels or scales of measurements.
The first four classes were introduced in 1946 by the psychologist Stanley Smith Stevens, widely used in sciences and engineering and data analytics. It is a hierarchical scale, each level builds on the one that comes before it.
It is crucial that for intelligent machines, the Data Universe Pyramid is replacing the DIKW pyramid, the DIKW hierarchy, wisdom hierarchy, knowledge hierarchy, information hierarchy, information pyramid, or data hierarchy, the Data, Information, Knowledge, Wisdom.
https://www.linkedin.com/pulse/world-data-ontology-science-ai-ml-deep-learning-graph-abdoullaev/
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Data and calculations are two different things.
MN
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Dear and respected Ravi Sharma,
Dear and respected colleagues,
Data—outcome of measurement. As such, the data (stones, knots on a whip, scratches, and eventually numbers, i.e. symbolic representations) is a representation. It constitutes the re-presentation, i.e. the substitute for whatever is measured. No representation is complete. You can operate on data: move around the stones (it is called calculus!—comes from literally moving stones), or operate on numbers.
The meaning—relates to the function. In other words, the what for of the measurement. The pragmatic. Usually this pragmatic level is reduced to semantics—what does it mean (such as in dictionaries).
Models come in place not when we measure, but when we want to use the data in order to predict.
Language is NOT the outcome of measurement. It is one form through which interactions take place.
Final note: those who quote Wikipedia—it is a natural “chatGPT”, before the artificial neural network was conceived and trained on massive amounts of data. But so was Wikipedia. Often Wikipedia “answers” in a convincing manner—when the data used for training is right. But it can, like the chat GPT produce non-sense.
Plus: there are editors—not on chat GPT, though. One of my students in Europe was an editor for Wikipedia. The individual was upset with his grade in my class…I let you smile at the rest…Jimmy Wales corrected by hand a less than pleasant characterization of some of my work. Lucky me, I was not yet participating in the ontolog-forum.
Best wishes.
Mihai Nadin
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>> if we do not have a model (or idea about) what we are measuring or collecting data, we can still collect items such as boulders in a riverbed
I don’t think this is true. Collecting boulders in a riverbed needs a model. What minimum/maximum size constitutes a ‘boulder’? How and where should the survey be conducted? A model (braided stream, meander …) would imply a measurement strategy. You need a model first to test with measurement.
My 2cents
Neil
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Dear and respected Neil McNaughton,
You seem to confuse model with metric. Your measuring stick is not a model. You measure in order to re-present something. Present again—for the purpose of describing in an effective manner change. The model is informed by measurement.
And on this note I shall leave the subject alone.
Best wishes.
Mihai Nadin
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You seem to confuse model with metric. Your measuring stick is not a model.
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