Not my worst prediction, given the latest news!

That's fair. Here are some things to consider:

1 - I think 2017 was not that long ago. My hunch is that the low level architecture of the network itself is not a bottleneck yet. I'd lean on more training procedures and algorithms. I'd throw RLHF and MoE as significant developments, and those are even more recent.

2 - I give maybe 30% chance of a stall, in the case little commercial disruption comes of LLMs. I think there will still be enough research going on at the major labs, and even universities at a smaller scale gives a decent chance at efficiency gains and stuff the big labs can incorporate. Then again, if we agree that they won't build the power plant, that is also my main way of stalling the timeline 10 years. The reason I only put 30% is I'm expecting multi modalities and Aschenbrenner's "unhobblings" to get the industry a couple more years of chances to find profit.

I think it is plausible but not obvious if this is the case, that large language models have a fundamental issue with reasoning. However, I don't think this greatly impacts timelines. Here is my thinking:

I think time lines are fundamentally driven by scale and compute. We have a lot of smart people working on the problem, and there are a lot of obvious ways to address these limitations. Of course, given how research works, most of these ideas won't work, but I am skeptical of the idea that such a counter-intuitive paradigm shift is needed that nobody has even conceived of it yet. A delay of a couple of years is possible, perhaps if the current tech stack proves remarkably profitable and the funding goes directly into the current paradigm. But as compute becomes bigger and cheaper, all the more easy it will be to rapidly try new ideas and architectures.

I think our best path forward to delaying timelines is to not build gigawatt scale data centers.

Is there a post in the Sequences about when it is justifiable to not pursue going down a rabbit hole? It's a fairly general question, but the specific context is a tale as old as time. My brother, who has been an atheist for decades, moved to Utah. After 10 years, he now asserts that he was wrong and his "rigorous pursuit" of verifying with logic and his own eyes, leads him to believe the Bible is literally true. I worry about his mental health so I don't want to debate him, but felt like I should give some kind of justification for why I'm not personally embarking on a bible study. There's a potential subtext of, by not following his path, I am either not that rational, or lack integrity. The subtext may not really be there, but I figure if I can provide a well thought out response or summarize something from EY, it might make things feel more friendly, e.g. "I personally don't have enough evidence to justify spending the time on this, but I will keep an open mind if any new evidence comes up."

I would pay to see this live at a bar or one of those county fair (we had a GLaDOS cover band once so it's not out of the question)

If we don't get a song like that, take comfort that GLaDoS's songs from the Portal soundtrack are basically the same idea as the Sydney reference. Link: https://www.youtube.com/watch?v=dVVZaZ8yO6o

Let me know if I've missed something, but it seems to me the hard part is still defining harm. In the one case, where we will use the model and calculate the probability of harm, if it has goals, it may be incentivized to minimize that probability. In the case where we have separate auxiliary models whose goals are to actively look for harm, then we have a deceptively adversarial relationship between these. The optimizer can try to fool the harm finding LLMs. In fact, in the latter case, I'm imagining models which do a very good job at always finding some problem with a new approach, to the point where they become alarms which are largely ignored.

Using his interpretability guidelines, and also human sanity checking all models within the system, I see we can probably minimize failure modes that we already know about, but again, once it gets sufficiently powerful, it may find something no human has thought of yet.

That's fair, I read the post but did not re-read it, and asking for "more" examples out of such a huge list seems a bit asking too much. Still though, I find the process of finding these examples somewhat fun, and for whatever reason, had not found many of them too shocking, so felt the instinct to keep searching.

Dissociative identity disorder would be an interesting case, I have heard there was much debate on whether it was real. As you know someone, I assume it's not exactly like you see in movies, and probably falls on a spectrum as discussed in this post?

One fear I have is that the open source community will come out ahead, and push for greater weight sharing of very powerful models.

Edit: To make more specific, I mean that the open source community will become more attractive, because they will say, you cannot rely on individual companies whose models may or may not be available. You must build on top of open source. Related tweet:

https://twitter.com/ylecun/status/1726578588449669218

Whether their plan works or not, dunno.

One thing that would help me, not sure if others agree -- would be some more concrete predictions. I think the historical examples of autism and being gay make sense, but are quite normalized now, that one can almost say, "That was previous generations. We are open minded and rational now". What are some new applications of this logic, that would surprise us? Are these omitted due to some info hazard? Surely we can find some that are not. I am honestly having a hard time coming up with them myself, but here goes:

• There are more regular people who believe AI is an x-risk than let on -- optimistically, for us!
• There are more people in households with 7 figure incomes than you would expect. The data I always read in news articles seems to contradict this, but there are just way too many people in 2M+ homes driving Teslas in the bay area. Or maybe they happen to be very frugal in every other aspect of their life... Alternatively, there is more generational wealth than people let on, as there are many people who supposedly make under 6 figures, yet seem to survive in HCOL areas and participate in conspicuous consumption.

I also have a hard time with the "perfect crime" scenario described above. Even after several minutes of thinking, I can't quite convince myself it's happening all that much, but maybe I am limiting myself to certain types of crimes. Can someone also spell that one out? I get it at a high level, "we only see the dumb ones that got caught", but can't seem to make the leap from that, to "you probably know a burglar, murderer, or embezzler".