I'd say corporations are also a form of non-animal intelligence, so it's not exactly first contact. In some ways, LLMs are less alien, in other ways they're more.

There is an alignment problem for both of them. Perhaps the lesson to be drawn from the older alien intelligence is that the most impactful aspect of alignment is how the AI's benefits for humanity are distributed and how they impact politics.

There is enough science fiction demonstrating reasons for not creating full-on digital life.

It seems like for many there is this (false) belief that in order to create a fully general purpose AI, we need a total facsimile of a human.

It should be obvious that these are two somewhat similar but different goals. Creating intelligent digital life is a compelling goal that would prove godlike powers. But we don't need something fully alive for general purpose intelligence.

There will be multiple new approaches and innovations, but it seems to me that VLAs will be able to do 95+% of useful tasks.

Maybe the issues with brittleness and slow learning could both be addressed by somehow forcing the world models to be built up from strong reusable abstractions. Having the right underlying abstractions available could make the short term adaptation more robust and learning more efficient.

Curious whether this means LLMs will converge toward something more general (as the A/B testing covers more edge cases) or stay jagged forever because no single failure mode means "death".

LLMs of today copy a lot of human behavior, but not all of their behavior is copied from humans. There are already things in them that come from elsewhere - like the "shape shifter" consistency drive from the pre-training objective of pure next token prediction across a vast dataset. And there are things that were too hard to glimpse from human text - like long term goal-oriented behavior, spatial reasoning, applied embodiment or tacit knowledge - that LLMs usually don't get much of.

LLMs don't have to stick close to human behavior. The dataset is very impactful, but it's not impactful enough that parts of it can't be overpowered by further training. There is little reason for an LLM to value non-instrumental self-preservation, for one. LLMs are already weird - and as we develop more advanced training methods, LLMs might become much weirder, and quickly.

Sydney and GPT-4o were the first "weird AIs" we've deployed, but at this rate, they sure wouldn't be the last.

Point to me a task that a human should be able to perform and I will point to you a human who cannot perform that task, yet has kids.

Survival is not a goal, it is a constraint. Evolution evolves good abstractions because it is not chasing a goal, but rather it creates several million goals with each species going after it's own.

https://arxiv.org/abs/2505.11581

https://arxiv.org/pdf/1410.0369

>The paper attempts to describe the space of possible mind designs by first equating all minds to software. Next it proves some interesting properties of the mind design space such as infinitude of minds, size and representation complexity of minds. A survey of mind design taxonomies is followed by a proposal for a new field of investigation devoted to study of minds, intellectology, a list of open problems for this new field is presented

While in some contexts these are useful approximations, they break down when you try to apply them to large differences not just between humans, but between species (for a humorous take, see https://wumo.com/wumo/2013/02/25), or between humans and machines.

Intelligence is about adaptability, and every kind of adaptability is a trade-off. If you want to formalize this, look at the "no free lunch" theorems.

[1] https://taylor.town/crowpower

Genetic algorithms are smart enough to make life. It seems like genetic algorithms don't care how complex a task is since it doesn't have to "understand" how its solutions work. But it also can't make predictions. It just has to run experiments and see the results.

https://www.lesswrong.com/w/orthogonality-thesis

In a nutshell, we have the body before the brain, while AIs have the brain before the body.

Mildly tangential: this demonstrates why "model welfare" is not a concern.

LLMs can be cloned infinitely which makes them very unlike individual humans or animals which live in a body that must be protected and maintain continually varying social status that is costly to gain or lose.

LLMs "survive" by being useful - whatever use they're put to.

If you don't understand how AI works then you should learn how to put together a simple neural network. There are plenty of tutorials & books that anyone can learn from by investing no more than an hour or two every day or every other day.

That's why consciousness, how the brain works, etc never moves on. Something always drags it down and force it to be "complex emergent behavior we cannot explain, and damn you if you try!".

So, it's particles that act funny, and we are that as well. Because "it can't be anything other than that". If it's not that, then it's souls, and we can't allow that kind of talk around here.

Until we can safely overcome this immaturity, we will never be able to talk about it properly.

The "space of minds" is an ideological minefield spanning centuries. It was being probed way before we invented machines.