{"id":14447,"date":"2024-02-09T09:17:06","date_gmt":"2024-02-09T09:17:06","guid":{"rendered":"https:\/\/aurelis.org\/blog\/?p=14447"},"modified":"2024-02-10T19:18:37","modified_gmt":"2024-02-10T19:18:37","slug":"the-meaning-of-a-word","status":"publish","type":"post","link":"https:\/\/aurelis.org\/blog\/artifical-intelligence\/the-meaning-of-a-word","title":{"rendered":"The Meaning of a Word"},"content":{"rendered":"\n

\u201cThe meaning of a word is its use in the language.\u201d \u2014 Quote by Wittgenstein, who talked about language games because \u2018use in the language\u2019 can be seen as having a very playful aspect.<\/h3>\n\n\n\n

A dictionary provides the meaning of a word \u2015 in some way, but always as an approximation.<\/p><\/blockquote>\n\n\n\n

A word doesn\u2019t have meaning like a book has pages.<\/strong><\/p>\n\n\n\n

Nor like I have two legs. The term \u2018have\u2019 is somewhat misleading in this. Meaning \u2013 either at surface or deep level<\/a> \u2013 is not something to be had, nor sought or given away. It\u2019s not something altogether.<\/p>\n\n\n\n

\u2018Having meaning\u2019 can, therefore, be very relational. It\u2019s carried in relation to other words or to an environment, including people in that environment. Any context can determine or influence meaning \u2015 again, either surface or deep, but mostly both. Since contexts are continually changing, so are the meanings of words.<\/p>\n\n\n\n

What Wittgenstein referred to is that words are living beings, as are the ideas they convey. Everything flows.<\/p>\n\n\n\n

Large Language Models (LLMs)<\/strong><\/p>\n\n\n\n

LLMs are based mathematically on Wittgenstein\u2018s mantra: words as used in language. An LLM derives its competence exclusively from this single principle. That\u2019s enough to come to a sometimes pretty fantastic output.<\/p>\n\n\n\n

It may be seen as a parrot, but an immensely sophisticated one, playing complex games without knowing the rules for itself.<\/p>\n\n\n\n

An LLM\u2019s \u2019intelligence\u2019 is in the size<\/a>. For each word, it uses an immense amount of context. It does so by using\u00a0mathematical<\/em>\u00a0computing power that vastly outstrips ours. Noteworthy, this is also completely different from our way of mental processing. In this vein, ‘it’ will eventually (after adding conceptual processing) understand us better than we can ever understand ‘it’ \u2015 or ourselves.<\/p>\n\n\n\n

The step toward concepts.<\/strong><\/p>\n\n\n\n

In contrast to first-generation LLMs, we, humans, don\u2019t use just words. We use them as pointers to concepts when these are positive in the cost-benefit analysis of efficient mental processing. Concepts are a step further in the formalization of communication.<\/p>\n\n\n\n

However, the efficiency of formalization comes with diminishing complexity. Through formalization, we put the complexity into boxes, close the boxes, and are able to build with them as such, realizing many complicated \u2013 but not complex<\/a> \u2013 things.<\/p>\n\n\n\n

The meaning of a concept is its use in a conceptual context.<\/strong><\/p>\n\n\n\n

More so in relation to ontology. Using a strict ontology is a further step in formalization. Ontologization<\/a> thus brings a huge processing advantage.<\/p>\n\n\n\n

Crucially, with each step in formalization, we can use algorithms more efficiently just as we can use other tools. Once a tool \u2013 such as a hammer \u2013 is made, we can use it many times in slightly different contexts. Everyone knows this is a good thing \u2014 if the hammer is used correctly.<\/p>\n\n\n\n

As with a hammer however, concepts, ontologies and algorithms can be used to destroy reality. Especially when applied to organic reality, their cautious use is mandatory.<\/p>\n\n\n\n

Back to words<\/strong><\/p>\n\n\n\n

Words can carry meaning with a complexity beyond concepts. De-constructing concepts can partly draw this to the fore. The plain use of words in all playfulness \u2013 called daily life \u2013 brings more to the surface.<\/p>\n\n\n\n

Therefore, it will be challenging to see LLMs being developed with the capacity to show a myriad of associations in human language. At the moment, this hasn\u2019t been done yet (overtly). Still, it is a possibility and an occasion to know more about ourselves, how we use words, and what we are thinking <\/em>when doing so even when we don’t know it ourselves.<\/p>\n\n\n\n

From an immense amount of human words onwards, LLMs can ontologize the world as humans see it. Probably, AGI (Artificial General Intelligence) will be reached this way. It\u2019s the perfect means for Compassion.<\/p>\n\n\n\n

However!<\/strong><\/p>\n\n\n\n

Due to sheer processing power and if we don\u2019t manage this well, it\u2019s also the perfect means for manipulation.<\/p>\n\n\n\n

We (humanity) wouldn\u2019t survive.<\/p>\n

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\u201cThe meaning of a word is its use in the language.\u201d \u2014 Quote by Wittgenstein, who talked about language games because \u2018use in the language\u2019 can be seen as having a very playful aspect. A dictionary provides the meaning of a word \u2015 in some way, but always as an approximation. A word doesn\u2019t have meaning like Read the full article…<\/a><\/p>\n

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