>nishio #gleninjapan

> In the lightning talk, I didn't delve into mathematical discussions due to limited time. For a more detailed explanation, I created diagrams by embedding the meanings of words into vector spaces using LLM.

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>nishio This is a two-dimensional visualization of the meanings of each word embedded in a high-dimensional space using OpenAI's text embedding API. In simple terms, it demonstrates how AI recognizes the similarity in meanings of words like this.

>nishio Plotting two languages on a single chart is not a straightforward task. In this chart, the first principal component axis of PCA is treated as the axis representing the differences between languages and has been removed.

>nishio Here is annotated version. The plotted words are a combination of those I have considered and those that GPT-4 has suggested as being similar. So it shows GPT4 can not find English words similar to Japanese Nattoku. Understanding and agreement is major explanation in dictionaries

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>nishio One word can bridge multiple concepts. In this example, the Japanese word "納得" (nattoku) serves as a bridge connecting concepts like "understanding", "agreement", and "satisfaction". Similarly, in Mandarin, "數位" (shùwèi) connects concepts like "digital" and "plural".

>nishio In the mapping from a high-dimensional space(H) to a low-dimensional space(L), objects that are close in H will generally remain close in L. However, there is no guarantee that objects far apart in H will also be far apart in L.

>nishio You can think of it like imagining the shadow of a three-dimensional object. Therefore, the absence of proximity in a low-dimensional space can be useful for understanding a high-dimensional space.

> In this chart, the first principal component axis of PCA is treated as the axis representing the differences between languages and has been removed.