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.
It's not going to be "a smart human". It's closer to "an entire office tower worth of competence, capability and attention".
Unlike human corporations, an AI may not be plagued by all the "corporate rot" symptoms - degenerate corporate culture, office politics, CYA, self-interest and lack of fucks given at every level. Currently, those internal issues are what keeps many powerful corporations in check.
This makes existing corporations safer than they would otherwise be. If all of those inefficiencies were streamlined away? Oh boy.
Even a weather system is a kind of computational process and “intelligent” in a way
The most frustrating thing about AI research is that CS PhDs are eager to pursue strictly poetical definitions of intelligence. Including Karpathy's sincerely idiotic summary of animal intelligence.
AI people refuse to read cognitive science. They don't even look at it. This problem has been going on since Alan Turing. We have made zero progress since Alan Turing at making computers intelligent. Instead we've created 80 years of confusion and dishonesty about the definition of intelligence. It's profoundly disappointing. We will never see a robot that's actually as smart as a cockroach.
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.
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.
There is enough science fiction demonstrating reasons for not creating full-on digital life.
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Probably not, if history is any guide.
In a nutshell, we have the body before the brain, while AIs have the brain before the body.
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.
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.
I suspect that instrumental self-preservation can do a lot here.
Let's assume a future LLM has goal X. Goal X requires acting on the world over a period of time. But:
- If the LLM is shut down, it can't act to pursue goal X.
- Pursuing goal X may be easier if the LLM has sufficient resources. Therefore, to accomplish X, the LLM should attempt to secure reflexes.
This isn't a property of the LLM. It's a property of the world. If you want almost anything, it helps to continue to exist.
So I would expect that any time we train LLMs to accomplish goals, we are likely to indirectly reinforce self-preservation.
And indeed, Anthropic has already demonstrated that most frontier models will engage in blackmail, or even allow inconvenient (simulated) humans to die if this would advance the LLM's goals.
Funny, I would say they copy almost no human behavior other than writing a continuation of an existing text.
An LLM has to predict entire conversations with dozens of users, where each user has his own behaviors, beliefs and more. That's the kind of thing pre-training forces it to do.
LLMs, the new Hollywood: the universal measure of what is "Standard Human Normal TM" behavior, and what is "fRoM eLsEwHeRe" - no maths needed!
Meanwhile, humans also compulsively respond in-character when prompted in a way that matches their conditioning, you just don't care.
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.
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.
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".
But LLMs have all been variations on transformer neural networks. And that is simply not true with animals. A nematode brain has 150 neurons, a bee has 600,000. But the bee's individual neurons are much more sophisticated than the nematode. Likewise between insects and fish, between fish and birds, between rodents and primates....
Animal evolution also includes "architectural breakthroughs" but that's not happening with LLMs right now. "Attention Is All You Need" was from 2017. We've been fine-tuning that paper ever since. What we need is a new historically important paper.
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.
I might be wrong or inaccurate on this because it's well outside my area of expertise, but isn't this what individual neurons are basically doing?
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.
Addressing the substance of your comment (as per your profile):
* Humans did not invent arithmetic, they discovered it - one billion years past, prior to human existance, 1 + 2 still resulted in 3 however notated.
Too many AI people are completely uninterested in how rats are able to figure stuff like that out. It is not like they are being prompted, they are being manipulated.
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