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This is kind of how humans operate as well though. We just string words along based on what input is given.
We speak much too fast to be properly reflecting on it, we just regurgitate whatever comes too mind.
To be clear, I'm not saying LLM think but that the difference between our thinking and their output isn't the chasm it's made out to be.
Disagree. We're very good at using words to convey ideas. There's no reason to believe that we speak much too fast to be properly reflecting on what we say—the speed with which we speak speaks to our proficiency with language, not a lack thereof. Many people do speak without reflecting on what they say, but to reduce all human speech down to that? Downright silly. I frequently spend seconds at a time looking for a word that has the exact meaning that will help to convey the thought that I'm trying to communicate. Yesterday, for example, I spent a whole 15 seconds or so trying to remember the word exacerbate.
An LLM is extremely good at stringing together stock words and phrases that make it sound like it's conveying an idea, but it will never stop to think about the definition of a word that best conveys a real idea. This is the third draft of this comment. I've yet to see an LLM write, rewrite, then rewrite again it's output.
Kinda the same thing though. You spent time finding the right auto-complete in your head. You weighed the words that fit the sentence you'd constructed in order to find the one most frequently encountered in conversations or documents that include specific related words. We're much more sophisticated at this process, but our whole linguistic paradigm isn't fundamentally very different from good auto-complete.
It's because we (ML peeps) literally prevent them from deleting their own ouput. It'd be like if we stuck you in a room, and only let you interact with the outside world using a keyboard that has no backspace.
Seriously, try it. Try writing your reply without using the delete button, or backspace, or the arrow keys, or the mouse. See how much better you do than an LLM.
It's hard! To say that an LLM is not capable of thought just because it makes mistakes sometimes is to ignore the immense difficulty of the problem we're asking it to solve.
To me it isn't just the lack of an ability to delete it's own inputs, I mean outputs, it's the fact that they work by little more than pattern recognition. Contrast that with humans, who use pattern recognition as well as an understanding of their own ideas to find the words they want to use.
Man, it is super hard writing without hitting backspace or rewriting anything. Autocorrect helped a ton, but I hate the way this comment looks lmao
This isn't to say that I don't think a neural network can be conscious, or self aware, it's just that I'm unconvinced that they can right now. That is, that they can be. I'm gonna start hitting backspace again after this paragraph
The key difference is that your thinking feeds into your word choice. You also know when to mack up and allow your brain to actually process.
LLMs are (very crudely) a lobotomised speech center. They can chatter and use words, but there is no support structure behind them. The only "knowledge" they have access to is embedded into their training data. Once that is done, they have no ability to "think" about it further. It's a practical example of a "Chinese Room" and many of the same philosophical arguments apply.
I fully agree that this is an important step for a true AI. It's just a fragment however. Just like 4 wheels, and 2 axles don't make a car.
Apologies if this comes off as rude, but as an engineer involved in reinforcement learning, it's upsetting when people make claims like this based on conjecture and hand-wavey understandings of ML. Some day there will be goal-driven agents that can interact with the world, and those agents will be harmed by those kinds of incorrect understandings of machine learning.
LLMs' thinking also feeds into their word choice. Where else would they be getting the words from, thin air? No, it's from billions of neurons doing what neurons do, thinking.
What is a "support structure", in your mind? That's not a defined neuroscience, cog sci, or ML term, so it sounds to me like hand-waving.
LLMs can and do generalize beyond their training data, it's literally the whole point. Otherwise, they'd be useless.
During training, neural weights from previous examples are revisited and recontextualized given the new information. This is what leads to generalization.
The Chinese Room is not a valid argument, because the same logic can be applied to other humans besides yourself.