My first came in late 2016, when Google Translate switched from statistical machine translation to a neural-network-based system. I had worked as a Japanese-English translator and lexicographer for two decades, and I had been testing various machine-translation services over the years. For translation between Japanese and English, at least, they were uniformly terrible: the output for genuine texts was mostly incomprehensible and could not be used for any real-life applications. The neural Google Translate, while still far from perfect, was suddenly useful for some purposes.

But the neural models were still not translating meaning, which is the whole point of translation. I devised a variety of tests to see if GT could identify the meaning of ambiguous words from the context, and it couldn’t. One example I would show people was the sentences “I was born in 1998, and my sister was born in 1999” and “I was born in 1999, and my sister was born in 1998” translated into Japanese. Japanese uses different words for older and younger siblings, but GT translated “my sister” with the same word in both sentences. It was easy to come up with other examples where GT would fail, such as when the meaning of a word could only be determined based on context in a previous sentence; at that time, GT seemed to be translating sentence-by-sentence, with no consideration of what came before or after. I kept waiting to see whether computers would ever be able to handle meaning when translating, and for years thereafter there was little progress.

A minor shock came in mid-2022, when DALL-E 2 was released. Its ability to create images from natural-language prompts suggested that something deeper was going on than just statistical correlations. But I couldn’t see yet what the useful applications might be.

My biggest “oh shit” moment came with ChatGPT in late 2022. While the initial release didn’t translate Japanese well (I seem to recall that there were character-encoding issues), I ran various tests to see if it could, for example, identify the antecedents of pronouns and the meanings of polysemous words in English based on the context. It did really well. Last December, I gave a talk at a university in Tokyo in which I showed some examples done with the 2022-era GPT-3.5. They appear in slides 4 to 8 of the following:

https://www.gally.net/miscellaneous/20251206_Gally_ICU_slide...

There have been a lot of “oh shit” moments for me since, especially after the release of reasoning models and, now, long-running agents.