More useful. It would save us the step of having to check for hallucinations when doing research.

Another example of this pattern that's entered mainstream awareness is tilt. When I'm playing chess and get tilted, I might think things like "all my opponents are cheating, "I'm terrible at this game and therefore stupid," or "I know I'm going to win this time, how could I not win against such a low-rated opponent." But if I take a step back, notice that I'm tilted, and ask myself what information I'm getting from the feeling of being tilted, I notice that it's telling me to take a break until I can stop obsessing over the result of the previous game.

Tilt is common, but also easy to fix once you notice the pattern of what it's telling you and start taking breaks when you experience it. The word "tilt" is another instance of a hangriness-type stance that's caught on because of its strong practical benefits--having access to the word "tilt" makes it easier to notice.

It's working now. I think the problem was on my end.

This strategy suggests that decreasing ML model sycophancy should be a priority for technical researchers. It's probably the biggest current barrier to the usefulness of ML models as personal decision-making assistants. Hallucinations are probably the second-biggest barrier.

The new feed doesn't load at all for me.

There's another way in which pessimism can be used as a coping mechanism: it can be an excuse to avoid addressing personal-scale problems. A belief that one is doomed to fail, or that the world is inexorably getting worse, can be used as an excuse to give up, on the grounds that comparatively small-scale problems will be swamped by uncontrollable societal forces. Compared to confronting those personal-scale problems, giving up can seem very appealing, and a comparison to a large-scale but abstract problem can act as an excuse for surrender. You probably know someone who spends substantial amounts of their free time watching videos, reading articles, and listening to podcasts that blame all of the world's problems on "capitalism," "systemic racism," "civilizational decline," or something similar, all while their bills are overdue and dishes pile up in their sink.

This use of pessimism as a coping mechanism is especially pronounced in the case of apocalypticism. If the world is about to end, every other problem becomes much less relevant in comparison, including all those small-scale problems that are actionable but unpleasant to work on. Apocalypticism can become a blanket pretext for giving in to your ugh fields. And while you're giving in to them, you end up thinking you're doing a great job of utilizing the skill of staring into the abyss (you're confronting the possibility of the end of the world, right?) when you're actually doing this exact opposite. Rather than something related to preverbal trauma, this usability as a coping mechanism is the more likely source of the psychological appeal of AI apocalypticism for many people who encounter it.

Another experiment idea: testing whether the reduction in hallucinations that Yao et al. achieved with unlearning can be made robust.

Do LLMs perform better at games that are later in the Pokemon series? If difficulty interpreting pixel art is what's holding them back, it would be less of a problem when playing later Pokemon games with higher-resolution sprites.

Have you tried seeing how ChatGPT responds to individual lines of code from that excerpt? There might be an anomalous token in it along the lines of " petertodd".

Occasionally something will happen on the train that I want to hear, like the conductor announcing a delay. But not listening to podcasts on the train has more to do with not wanting to have earbuds in my ears or carry headphones around.