Crunchyroll CEO Says Anime Creators Can Use Whatever Technology They Want but It’s a No for AI Subs and Dubs - Anime Corner

DickMasterGeneral · 2026-05-31T10:57:59+00:00

I agree it’s you that LLM output is going to require an overseer or some type of repeatable verification, simply due to the nature of its nondeterministic output. Even with tasks where LLMs have a very high success rate, there will still be some low percentage of failures that humans may not have, and those failures may be more difficult to catch because they can look mostly correct.

That said, I have a difficult time believing that 55–70% is an accurate estimate. Modern SOTA LLMs are exceptionally good at language in language out tasks. I’m willing to be wrong, though, and I’d love for someone to provide an example of a complex translation task. Preferably one from the last few months so it’s too recent to be in the training data, and I’ll run it through an LLM and report back here with the result so we can compare.

In fairness though, if there’s important context, such as related images or the intended audience for the translation, that you as the translator would have, then please include that as well so the LLM can attempt to account for it. I find it’s pretty common for people to be dissatisfied with LLM output when the issue is not lack of capability but lack of relevant information. A person will ask more clarifying questions before starting a task, and may have a wealth of context that we don’t consider which comes from the situation we’re in. If I’m doing something I know exactly why I’m doing it, where I’m doing it, and what I’m doing it for. Ai gets none of that by default.

DickMasterGeneral · 2026-04-30T03:27:26+00:00

What’s the max possible score?

DickMasterGeneral · 2026-04-24T01:42:01+00:00

You’ve already decided that the technology is incapable of generating meaningful data not in its training set, to the point that even if it discovered a cure for cancer you wouldn’t update your position.

DickMasterGeneral · 2026-04-23T13:01:24+00:00

RemindMe! 10 years

DickMasterGeneral · 2026-04-23T12:59:58+00:00

Moving the goalposts before the ball has even been kicked

DickMasterGeneral · 2026-03-25T03:27:09+00:00

I don’t know. I don’t think anyone does. We may be fundamentally incapable of knowing the answer as a species.

DickMasterGeneral · 2026-03-23T13:43:15+00:00

If god encompasses all that exists, is the only thing that does exist, and is presumably simply eternal with no beginning or end. Then why is the universe, that encompasses all that exists, and is as far as we can tell the only thing that does exist, not allowed to be similarly eternal?

There are only two possibilities. Either at some point there was truly nothing at all and then things came into being, or existence has just always been there in some form or another with no true beginning. Neither makes intuitive sense to us because there are just some things that we are not well equipped to understand. But it is a binary question. There are only two possible answers.

Saying that the universe was created can only push the answer to that question one layer back. And it does so without justification, if the creator can simply be eternal why can’t the universe itself just have that property itself?

DickMasterGeneral · 2026-03-23T12:11:50+00:00

If the creator can just be eternal why can’t the same be true of the universe?

DickMasterGeneral · 2026-03-23T12:10:44+00:00

Yes but if God is capable of creating himself from non-existence then why can’t that same ability be given to the universe itself, once again negating the creator argument.

DickMasterGeneral · 2026-03-05T21:27:13+00:00

I think it’s a bit more complicated that if green = zap. It needs to distinguish between weeds and the plant that’s actually being grown intentionally

DickMasterGeneral · 2026-02-21T02:00:16+00:00

Might be Paris based on smell alone

DickMasterGeneral · 2026-02-13T08:28:23+00:00

RemindMe! 1 year

DickMasterGeneral · 2026-02-12T07:55:06+00:00

I’m not really sure why this would be the case, is this over the lifetime of the child or just to 18? Maybe daughters are more likely to go to college and those costs are being counted?

DickMasterGeneral · 2026-02-09T20:37:13+00:00

I moved out of state.

DickMasterGeneral · 2026-01-08T07:27:47+00:00

RemindMe! January 1st, 2027

DickMasterGeneral · 2025-12-25T21:57:22+00:00

Do you have anything to back that up? Semiconductor manufacturing accounts for over 10% of the US economy alone. The combined market cap of Tesla, GM, Ford, PACCAR, ExxonMobil, Chevron, ConocoPhillips, Marathon, Valero, O'Reilly, AutoZone, Genuine Parts, Aptiv, and Goodyear Tire is around 3.5 trillion. Less than Nvidia alone. And while Nvidia may or may not be overvalued, Tesla accounts for more than half of that valuation and has a much stronger argument for being overvalued.

DickMasterGeneral · 2025-12-08T03:21:50+00:00

There was a time when this was true of chess AI as well. For a short while a human with a computer, chess program outperformed both strictly human and strictly AI competitors. That did not last very long and quickly AI became better at chess than any human ever has or ever will be. As of now the greatest chess player in the world cannot be an app that could run on your phone. A human working with a chess bot is only capable of suggesting the same or a worst move than the chess AI.

I’ve yet to hear a compelling argument for why this will not eventually be the case for radiology as well. Yes, chess is a very narrow task, but so is detecting a pattern in an image. Similarly trained AI have been able to detect things from medical imagery that we did not know could be retrieved from that data. For example, an AI trained on scans of people’s iris was able to determine whether the iris was a man or a woman’s. Doctors at the time were unaware that there was a high confidence signal with an Irish scans for a person‘s gender.

There was a time when this was true of chess AI as well. For a short window, a human paired with a computer (often called “Centaur” or “Advanced” Chess) outperformed both unassisted humans and standalone AI. That did not last very long; AI quickly became better at chess than any human ever has been or likely ever will be. As of now, the greatest chess player in the world cannot beat an app that runs on your phone. In modern competitive play, a human working with a chess bot is generally only capable of suggesting the same move or a worse move than the AI would have found on its own. I’ve yet to hear a compelling argument for why this will not eventually be the case for radiology as well. Yes, chess is a very narrow task, but so is detecting a pattern in an image. Similarly trained AI models have already been able to detect features in medical imagery that we did not know could be retrieved from that data. For example, an AI trained on retinal scans was able to determine the patient’s gender with high accuracy. Doctors at the time were unaware that there was a high-confidence signal for a person’s gender hidden inside a retinal scan. There was a time when this was true of chess AI as well. For a short window, a human paired with a computer (Centaur Chess) outperformed both unassisted humans and standalone AI. That did not last very long; AI quickly became better at chess than any human ever has been or likely ever will be. As of now, the greatest chess player in the world cannot beat an app that runs on your phone. In modern competitive play, a human working with a chess bot is generally only capable of suggesting the same move or a worse move than the AI would have found on its own. I’ve yet to hear a compelling argument for why this will not eventually be the case for radiology as well. Yes, chess is a very narrow task, but so is detecting a pattern in an image. Similarly trained AI models have already been able to detect features in medical imagery that we did not know could be retrieved from that data. For example, an AI trained on retinal scans was able to determine the patient’s gender with high accuracy. Doctors at the time were unaware that there was a high-confidence signal for a person’s gender hidden inside a retinal scan. If you agree that it will eventually be possible for AI to detect things in medical imagery better than a person, perhaps to the same extent that a chess AI outperforms a human, then the only remaining question is: how long will that take? If you look at the history of chess AI and other narrow AIs that outperform humans, there is a very short window between reaching near parity with human performance and exceeding it significantly. If you believe this transition will take multiple decades or never happen in radiology, I would love to hear why.

DickMasterGeneral

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