So this is apparently needed.
Dear Mastodon,
While we all say "AI" regardless of whether we're talking about text, audio, images or video these are not all using the same underlying technology*.
Large Language Models, LLMs, use a technology known as Transformers. They "got smart" beginning in 2017 when a paper named "Attention is all you need" was published by Google. One way of describing LLMs is that by caring for everything that comes before, you can perform the next step.
Audio, Images and Video mostly use a technology called Diffusion which began in 2015 but became noticable better in quality with CLIP from 2021. That technology can be said to work by starting with noise and then seeing whether any changes you make cause an evaluation to be more or less what you want in the end.
LLMs do not want to store training data in the models because that means they're less smart. Knowing the answer to a question is useless - knowing how to get to the answer is valuable. Diffusion models however don't have a problem with this. Knowing what a rose looks like is knowing what a rose looks like. Knowing what a Dali painting looks like ... yeah you get it.
What has been a bit shocking to everyone is that LLMs turned out to be generally useful and not just language engines. While the intention was for them to understand text, produce text and reliably translate between languages, it seems that by using text to encode knowledge about the world we also got engines that could "think" and "reason" about the world - and that has over the last year or so caused an explosion in LLM usage in, for example, software development.
This was not something first dreamed up by commercial entities wanting to sell products - but by curious programmers wanting the Next Great Tool. If there's one thing us programmers are it's _lazy_. If we can automate a task we will.
*) Yes I'm taking a shortcut. There are overlapping areas, but the point still holds.
@troed I have argued for a long time that the words, which are a reflection of our experience in the world, is a transitive relationship, and that we therefore _can_ say that an LLM knows what pizza tastes like, since it has inherited this from our texts it has been trained on.
Thinking about this, in the context of the classic turing test, it is obvious that this is so, if we constrain ourselves to text based communication.
@troed exactly what we do with human beings. You or I have no way of ever proving that other humans are conscious. We just infer it from their behaviour and responses.
If we produce a super-LLM or what ever the acronym will be that produces the breakthrough, and then imagine that it will be installed in a human body, and if no one will tell the difference, it naturally follows that it is conscious, if its behaviour is convincing enough.
@troed I cannot understand why some people have such a problem with this.
We all do it on a daily basis, and extending it to a non-biological substrate, is just logical.
Perhaps it is due to the mental illness that is commonly called "religion"?
@troed Taking this one step further, imagine the day, post-LLM, perhaps when the next big breakthrough has been made in 4-5 years time, when you have an "AI" that has volition, values, a life instinct.
Coupling that, the ability to initiate conversation and the ability to make value judgments and to independently come up with its own goals, with the LLM "chating" skills, and we basically have no choice but to acknowledge it as conscious.
Why?
Because of how it would act. After all, this is