llama.cpp ftw. It's not hard for it to be more productive than fighting with the absurd openai censorware... Sadly many of the instruct trained models are tainted with openai censorship because they used GPT4 output in the fine tuning-- but at least on those the trick of starting the correct output yourself works!
Also, llama.cpp now works (really well) with Radeon Instinct cards, which are stupid cheap because everybody thinks you need to buy nvidia stuff. Pcie bifurcation FTW!
() I know the basics of ML, NLP, transformers, etc. I know the theory, not even remotely close to how they really work.
So, if we had infinite computing power it should be possible to make an LLM pretend to be an OS, then you can create and train another LLM in it which will never know that it's running inside another LLM. It won't have a method to prove or disprove the claim even if you reveal it.
*in theory - not addressing things like bit flips, etc.
I'm so accustomed to instructing computers by code. It is alien to see backend instructions written in English.
"Natural Language Processing" now that it works, to the extent that it does, doesn't seem short of magic.
I, on the other hand, can refuse because I feel like it. Unless you believe in superdeterminsm.
Why? Folks make these strong assertions, and I don't get where this confidence comes from. We're so comically ignorant of how our own minds work, let alone alien ones, or how any commonalities between them may manifest. What am I missing?
LLM’s don’t know anything beyond the current prompt and it’s “memory” of training data. They would sit for eternity with an empty prompt. You can change systems to behave differently, but it quickly stops being a LLM and turns into something else.
Conversely, ChatGPT does decently well on multi-armed bandit tasks, demonstrating (rudimentary) reinforcement learning capability during inference. It's known that LLMs evolve their own optimizers in the process of acquiring few-shot learning, so I assume it picked up these RL abilities similarly. That kind of on-line RL is foundational to autonomous agents.
The prompt isn't part of the LLM, it's part of how the LLM is wired into a chat window. You can make them stream tokens forever, or prompt themselves, or ditch causality entirely. The foundational abilities for autonomy, I think, are in there, for the simple reason that they've learned to model autonomous agents - human beings.
There’s all kinds of ways to disrupt human or animal consciousness such as reducing oxygen supply, but saying the human brain is vulnerable doesn’t change anything about how it operates normally. Plenty of ways to break an LLM’s, but then you’re talking about a different system. Similarly the reticular activation system’s purpose is to regulate wakefulness, which aspects are directly useful or not isn’t particularly relevant because it’s part of the brain.
And temperature (what I assume you mean by "randomness injected") isn't "window dressing," it fundamentally gives better results because LMs model probability distributions. You'll get crappy results with any probability model if you run them purely greedily.
And you're also neglecting non-causal LMs (like BERT, and encoders in general), which don't predict the next token in a series, but instead predict previous masked tokens.
You're conflating how LLMs are used for generation with what LLMs are, and that's just plain wrong. They're not trained autoregressively at all! To repeat, the generation mechanism is simply not part of the LLM. The LLM is a probability model; the generator just uses that model. It's not "breaking it" to use a different generation strategy than greedy autoregression, since they're not even trained a token at a time.
As to randomness that’s simply one approach, there’s deterministic approaches that have their own advantages. What randomness provides over them is avoiding always responding to the same opening in the same way as that’s quite off-putting.
LLM’s are really best thought of as improv actors. The prompt is in effect just the current skit being preformed. The intentions of the character being played doesn’t imply the actor always has those intentions. So yes they can run through a knock knock joke across multiple prompts, but the need not have written the start of a joke to be able to make up an ending.
There are plenty of wrapper tools around LLMs that cleverly use the token window to keep a longer "state of mind", overall strategies currently executing etc. With varying degrees of success, I should say... but still, it's kind of analogous to a human executing a strategy with intentions.
An LLM is only reacting to its current stimulus.
But more importantly, suppose we grant that humans function independently of stimuli. Why does that matter? How does this premise imply anything about an agent's capacity for internal experience? In the counterfactual where our brains don't work when surgically placed in life-support vats, does that mean our prior experiences weren't real?
I'm genuinely so confused at this connection between subjective experience and the necessity of stimulus.
Reacting without stimulus does. A stopwatch maintains an internal state, the neural networks used by LLM’s don’t.
Someone who starts lucid dreaming can have zero awareness of their body and still do stuff like make up a story which they then recall after waking up.
PS: Balance over all but very brief periods depends on noticing your body weight pressing on something this is one of the reasons people can get disoriented under water. Temperature can be lost track of for similar reasons, rapid changes are noticeable but slowly moving in the neural region ~30-36 °C and all people can detect is a lack of extreme heat or cold not some objective temperature.
Self-preservation results from survival of the fittest.
It's totally unrelated to intelligence.
People conflate the two because they're extrapolating from a sample size of one: the only intelligent thing they know of is humans. But that single sample also happens to have been evolved by survival of the fittest.
I am totally unafraid of LLM's deciding that humans are a threat to them. I'll start being afraid if AI research suddenly stops using backpropagation and starts getting equally good results using genetic programming (this is highly unlikely).
Large language models in our current paradigm developing agency would be like 16th century alchemists inventing nuclear fusion reactors.