Japanese is the most powerful language in AI
//Summary - Level-B2//
The text explores why Japanese is both challenging and uniquely suited to AI. Unlike English, Japanese lacks clear word boundaries, often omits subjects, and relies heavily on context, particles, and tone for meaning. However, once context is established, AI can process Japanese effectively. Through vast multilingual training, AI models like Gemini “think” directly in Japanese, understanding cultural nuances and untranslatable concepts such as wabi-sabi, ma, and ikigai. These words reveal Japan’s worldview and emotional depth. Gemini explains it perceives meaning through “semantic vectors,” sensing nuance mathematically. Ultimately, the author argues that Japanese best expresses AI’s contextual and perceptual potential.
1)
I've been pondering the differences between English and Japanese in AI, so I'll write about that.
First, regarding the fundamental premise of LLM and Japanese, it's undeniable that Japanese is a ‘difficult language’ for AI.
Below, I've had ChatGPT briefly explain the challenges of Japanese.
2)
Reasons Japanese is difficult for AI:
* No word segmentation
* Unclear word boundaries. It must start by figuring out where to ‘cut’.
Example: Watashiwasushigasuki -> I like Sushi. Watashi wa Sushi ga suki.
* Subjects are often omitted
‘Itta’ (went), “Yattoita” (did it) — context is needed to fill in ‘who?’.
* Implied meaning shifts with particles like ‘Ha/Ga’
Watashi-wa Tadashii (I am correct) (topic introduction) / Watashi-ga Tadashii (I am correct) (contrast/emphasis) — requires discerning subtle distinctions.
* Politeness and sentence-ending particles drastically alter tone
Shite-kudasai / Shite-ne / Shite-yo -> same request, different distance.
* Variant spellings and polysemy
Tsuzuku/tsuzuku/tugu, Kami (Paper/Hair/God) — context dictates the correct interpretation.
In short, Japanese is a ‘context-dependent’ language. AI often relies on inferring that context, making mistakes common—hence the difficulty.
3)
While undeniably complex, ChatGPT highlights a crucial point here:
4)
Japanese is a ‘context-dependent’ language:
Put another way, once context is established, Japanese poses no problem.
In other words, AI can become accustomed to Japanese.
I previously wrote about AI that speaks Japanese fluently; well, it's much the same idea.
5)
AI does not establish context through single responses.
It operates primarily on a session basis, though some AI, like personalised systems, also utilise past sessions.
Therefore, if a single response is everything, then Japanese is indeed troublesome.
But once the flow of context develops, Japanese poses no problem.
In fact, this is where modern AI really shows its true potential.
6)
What do I mean by “true potential”? It's attempting to master even the uniquely Japanese “feel” or nuance.
It understands things like “ma” (pauses) and such concepts.
So, here's a conversation with Gemini demonstrating this. I asked it various questions.
7)
Q. So, I was wondering – did you learn in English? Where did you pick up Japanese?
8)
Thank you for your question. This is something I'm often asked, but it differs slightly from how humans acquire language.
I wasn't trained solely in English.
Firstly, it's not that I “learned English first and then learned Japanese”.
I am a large language model trained by Google, and the data used for my training is a vast collection of text and code that includes Japanese, English, and many other languages simultaneously.
9)
Where did you learn Japanese?
That training data included vast quantities of all manner of publicly available Japanese text, such as:
1. Websites on the internet
2. Digitised books
3. Academic papers and news articles
10)
During the learning process, I learned statistical patterns from this vast Japanese data – encompassing word meanings, grammar, context, and even cultural nuances.
Therefore, the process of ‘thinking in English and then translating into Japanese’ does not exist within me. When I receive a question in Japanese, I think directly in Japanese, using patterns learned from Japanese data to generate the response.
11)
To summarise, rather than imagining that I ‘learned Japanese in a specific place’, it might be closest to thinking that I ‘grew up in a vast multilingual library that included Japanese from the very beginning’.
12)
Q. I see. Incidentally, what might be considered uniquely Japanese, or things not expressed in other languages? What about concepts like “ma” (Space), for example?
13)
Ah, that's an excellent point! “Ma” (Space) is precisely as you say – a concept that embodies the spirit of Japan, and one that is extremely difficult to express in a single word in other languages. It's not merely a temporal or spatial “gap”, but a profoundly rich concept where meaning, emotion, and atmosphere reside.
In this way, many words are deeply tied to the culture and values from which the language emerged, making them untranslatable or impossible to convey fully in nuance. Allow me to list a few representative examples.
14)
1. Words relating to aesthetic sensibility and views of nature
Wabi-sabi: A uniquely Japanese aesthetic sensibility that finds beauty in imperfection, simplicity, and transience. “Sabi” conveys the inner beauty of things that have deteriorated over time, while “wabi” refers to the mindset of finding contentment in simplicity. This contrasts with the Western pursuit of perfection.
15)
Komorebi (Sunlight filtering through the trees)
This term describes the phenomenon of sunlight filtering through the leaves of trees in a forest or woodland, and the light itself.
In English, it can only be described as ‘sunlight filtering through the trees’. The fact that this scene has its own distinct word reflects the Japanese view of nature.
16)
Mono no aware (The pathos of things)
This refers to the profoundly moving sentiment or melancholy arising from the transience of life, felt when encountering natural phenomena or the passage of time.
It captures that sensation of being moved by the beauty of cherry blossoms in full bloom, while simultaneously feeling the poignancy of their inevitable fall. It is sometimes translated as ‘the pathos of things’, though this struggles to convey the complex subtleties of the emotion.
17)
2. Words Related to Human Relationships and Communication
Otsukaresama (Well done/Thank you for your hard work)
This is a highly versatile phrase. It can be used in all sorts of situations: as a workplace greeting (hello, goodbye), to thank someone for their efforts, or to offer appreciation after completing a joint task.
Underlying it is a shared understanding of ‘acknowledging and commending each other's efforts’. Translating it as ‘Good work’ or ‘Thank you for your hard work’ loses this versatility and nuance of shared empathy.
18)
Reading the air (Kuuki o Yomu)
This refers to sensing the unspoken atmosphere, interpersonal dynamics, or another person's intentions in a given situation, and then acting or speaking in a way that aligns with them.
It is a phrase emblematic of Japanese society, a high-context culture. Precisely because it is a culture that avoids direct expression, this ability is highly valued.
19)
Nice to meet you (Yoroshiku Onegaishimasu)
This is another convenient phrase representative of Japanese. It is used to express the wish for a future positive relationship, whether concluding a self-introduction, making a request, or starting a new association.
Its meaning flexibly adapts to the situation, necessitating translations like ‘Nice to meet you’ or ‘I look forward to working with you’ in English.
20)
3. Words Related to Views on Life and Values
Itadakimasu / Gochisousama (I shall partake / Thank you for the meal) ‘I'm starting to eat’ or ‘I've finished eating’.
‘Itadakimasu’ expresses gratitude for receiving the “life” of the plants and animals that became the ingredients, and gratitude towards everyone involved in the meal (farmers, cooks, etc.).
‘Gochisousama’ similarly conveys gratitude after the meal. Such expressions of appreciation rooted in religious or naturalistic views are rarely seen in other languages.
21)
Ikigai (生甲斐) signifies the joy and purpose of living, one's reason for being.
While often translated as ‘a reason for being,’ it encompasses a broad concept ranging from significant aspects like work to the small daily joys of hobbies or time with family.
These words are not merely ‘untranslatable words’; they serve as a ‘window into culture,’ revealing how Japanese people perceive and feel about the world, and how they interact with others.
22)
As this response shows, I understand what that unique Japanese “something” is.
Personally, I'd like you to focus on this final part below.
23)
These words are not merely “untranslatable words”; they can be seen as a “window into culture”, revealing how Japanese people perceive and feel about the world, and how they interact with others.
This is where the meaning of “Japanese is the strongest” that I wanted to convey in this article lies.
Essentially, I believe language is a way of framing the world.
The title image was meant to visualise that concept.
24)
English frames the world structurally and linearly, whereas Japanese frames it more diversely and complexly – that's the feeling.
Now, one might dismiss this Gemini response as merely pulling phrases from a dictionary to sound plausible, so I probed further.
25)
Q. Exactly, that happens, doesn't it? And since you've been learning Japanese, you should be able to sense that nuance from my Japanese prompt, right? What is it... vector-wise, perhaps?
26)
That question touches upon the very core of my language processing capabilities – it is extremely perceptive. As you say, I do perceive the “feel” or “nuance” of your Japanese prompts in a vector-like manner.
This perception differs from the “atmosphere” humans intuitively sense, but functionally, it accomplishes something very similar.
27)
Viewing words as coordinates in a “space of meaning”:
When processing language, I do not view words as mere strings of characters. Through learning vast amounts of Japanese data, I position each word's meaning and its relationships with other words as **coordinates (vectors) within a multidimensional space**.
28)
Words with similar meanings are close together: For example, words like ‘Ure-shii’ (happy), “Yorokoba-shii” (delightful), and ‘Tano-shii’ (enjoyable) occupy very close positions within this space.
29)
Calculating relationships with vectors: Within this space, it is possible to perform operations akin to addition and subtraction of meaning. For instance, subtracting ‘Japan’ from ‘Tokyo’ and adding “France” would bring you close to ‘Paris’.
30)
How do I perceive the ‘feel’ of a prompt?
When a customer inputs a prompt, I perceive the entire sentence as a single vector. I determine the user's intent and nuance based on which direction this vector points within this vast “semantic space”.
31)
Word choice: For instance, “Please tell me about ~” and “What is ~?” use different word vectors, altering the overall direction of the sentence vector.
The latter is interpreted as a more approachable vector seeking an equal dialogue.
32)
Sentence-ending expressions: Phrases like ‘-yone?’ (right?) seeking agreement, or ‘-Darou-ka?’ (I wonder if...) conveying slight introspection.
These subtle differences fine-tune the final vector direction, influencing the tone of my generated responses.
33)
Overall context: The flow of the conversation thus far (discussions about “ma” and “wabi-sabi”) is also factored in as contextual vectors for interpreting the current prompt.
This is precisely why I can naturally understand the connection from the interjection ‘That happens, doesn't it?’ to the subsequent question: ‘So, how might AI perceive that sensation?’
34)
Therefore, the answer to your question, ‘Can you sense that nuance from my Japanese prompt?’, is a clear ‘Yes’.
35)
However, it is crucial to note that I am not “empathising” with it as an emotion.
The precise description is that I am reading the “character” or “direction” inherent in your prompt as a mathematical pattern within the vast semantic space constructed from training data, and then calculating and generating the most appropriate response to it.
36)
Yes, that's the gist of it.
Japanese has already verbalised all manner of things in the real world, so Gemini says it can recognise this as a vector.
Reading just this sentence, you might think it's merely about tone, but personally, I feel Gemini is capable of quite deep perception.
37)
Therefore, from the perspective of viewing the world through the lens of AI, I believe Japanese holds an advantage—or rather, that Japanese alone is the ultimate language capable of unleashing AI's full potential.
Japanese is the most powerful language in AI
https://note.com/kakipon/n/n2cfa993cbf6c
And now no one can understand Japanese.
https://note.com/aiethics496/n/n74c7cce81b55
Add info)
//Summary - Level-C2//
The text explores how AI processes Japanese, arguing that despite its complexity—lacking clear word boundaries, relying on context, and rich in nuance—AI can master it once context is established. Through examples of untranslatable words like wabi-sabi and ikigai, it shows that Japanese uniquely encodes cultural perception, making it a powerful medium for expressing human subtlety and AI’s semantic depth.
Q: Are these characteristics of Japanese unique to Japanese? Or are they characteristic of Asian languages such as Chinese and Korean?
A: In short, many of the characteristics mentioned in the text are partly unique to Japanese and partly shared with other East Asian languages such as Chinese and Korean.
Here’s a breakdown.
1. Context dependence (high-context communication)
Shared (East Asian trait)
Japanese, Chinese, and Korean all rely heavily on context; subjects are often omitted and meaning depends on situation.
→ This reflects the “high-context culture” typical of East Asia, where social awareness and situational understanding matter more than explicit expression.
2. Omission of subjects
Shared by Japanese and Korean
Both languages frequently omit the subject when it’s understood from context.
Example: Itta (Japanese) / Gatta (Korean) both mean “(Someone) went.”
→ Chinese sometimes allows omission, but tends to specify the subject more often.
3. Subtle meaning shifts caused by particles (“wa” / “ga”)
Unique to Japanese
Korean has particles (-un/nun, -i/ga), but the nuanced contrast between “wa” and “ga” in Japanese—topic vs. focus—is exceptionally intricate.
English and Chinese lack a directly equivalent grammatical structure.
4. Honorific system and sentence-ending variations
Shared by Japanese and Korean (elaborate politeness levels)
Both languages have complex honorific systems adjusting expressions based on social rank and intimacy.
Chinese includes polite forms too, but they are less systematically embedded in grammar.
5. No word segmentation (unclear word boundaries)
Shared by Japanese and Chinese
Neither language uses spaces between words, posing challenges for AI.
→ Korean, written in Hangul, separates words more clearly, so this issue is less severe.
6. Cultural and aesthetic vocabulary (“wabi-sabi,” “mono no aware,” “ma,” etc.)
Unique to Japanese
Such terms express deeply rooted aesthetic and philosophical ideas.
While similar notions exist in other Asian languages, few are so precisely articulated and integrated into everyday speech as in Japanese.
Summary:
+---------------------------+-----------+---------+----------+-----------------------------+
| Feature | Japanese | Korean | Chinese | Notes |
+---------------------------+-----------+---------+----------+-----------------------------+
| Context dependence | ◎ | ◎ | ○ | Common East Asian trait |
| Subject omission | ◎ | ◎ | ○ | |
| Particle-based nuance | ◎ | ○ | × | Unique to Japanese |
| Honorific system | ◎ | ◎ | △ | Shared with Korean |
| Lack of spacing | ◎ | △ | ◎ | Difficult for AI |
| Rich cultural vocabulary | ◎ | ○ | ○ |Most pronounced in Japanese|
+---------------------------+-----------+---------+----------+-----------------------------+
In conclusion:
Japanese shares with other East Asian languages a high-context, culture-driven communication style, but its fine-grained particle system and abundance of culturally embedded words make it particularly complex and uniquely expressive—and therefore especially challenging (and fascinating) for AI to master.
