What I did say is that in a hypothetical world where Apple did invest in their own fabs, they'd have much better access to memory and could be selling machines with vastly more memory than their competitors today. Should they be doing this? I'm making no statement either way.

> a very wise man took me to dinner and counseled me to never post mortem my decisions on the outcomes, but only on the decision process

I appreciate this wisdom on one hand, but on the other it's comically misguided. You have no way to evaluate the decision process if the outcomes are not considered. Don't post mortem your decisions in a way that leads to self-blame or paralysis, but certainly post mortem them to see whether the process was suboptimal.

In this case, any board laughing at the idea of investment in memory 3.5 years ago should instead have been looking more closely at likely memory needs over the then-near-future, given the quite obvious upcoming demand for GPU memory. There's no magic bullet and they wouldn't have been able to make perfect decisions regardless, but clearly the process they followed at that time was suboptimal.

You're talking about the "best" things Apple could do with their money, in terms of investment returns, but I think that misses the point that Apple literally can't buy enough memory at any price.

I would guess LLMs are limited in their ability to be genuinely novel because they are trained on a fixed language. It makes research into the internal languages developed by LLMs during training all the more interesting.

Letting companies train LLMs on the "classics" is very different to training on contemporary media where the creator still depends on it.

I'm not sure how this plays out legally, but it certainly seems unethical

At the moment neither Opus nor any open weights models seem to be capable of doing complex work, and for less complex work the additional cost of Opus hasn't been worthwhile. This is for reasonably math-heavy computer vision applications.

What LLMs have been useful for is identifying forgotten code that will be affected when planning a change, reviewing changes, and looking up docs/recipes for simple tasks. But Opus doesn't seem necessary for a lot of that.

This week the characters are "OpenClaw". I won't even try to guess what might lead to erroneous billing next week.

I also suspect you would find easy funding from existing climate change deniers. There is no shortage of well-heeled folk in that space.

Do you have a chip on your shoulder regarding research? You're begging the question by stating it is conducted in a "practically religious" way. Ask whether that's true before you question the effect it would have on somebody's behaviour.

> A report in the lay literature describes the case of Claire Sylvia who reported changes in her personality, preferences, and behaviors following a heart and lung transplant at Yale-New Haven hospital in 1988. Following surgery, Sylvia developed a new taste for green peppers and chicken nuggets, foods she previously disliked. As soon as she was released from the hospital, she promptly headed to a Kentucky Fried Chicken to order chicken nuggets. She later met her donor’s family and inquired about his affinity for green peppers. Their response was, “Are you kidding? He loved them… But what he really loved was chicken nuggets” (p. 184, [9]). Sylvia later discovered that at the time of her donor’s death in a motorcycle accident, a container of chicken nuggets was found under his jacket [9].

I haven't read the whole thing, maybe there's something more relevant as well. That report isn't really about accessing the previous persons "memories" but at least claims she adopted a part of their personality. I'd be skeptical about its accuracy without more such reports, however.

If you think there's an LLM that can do so, I'd love to try it out! Even talking to the best models available today, it's disappointingly clear I'm talking to an LLM.

I don't think this has anything to do with AGI. We aren't at AGI yet. We may be close or we may be a very long way away from AGI. Either way, current models are at a plateau and all the big players have more or less caught up with each other.