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.

The key difference is that your thinking feeds into your word choice.

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.

They can chatter and use words, but there is no support structure behind them.

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.

The only “knowledge” they have access to is embedded into their training data.

LLMs can and do generalize beyond their training data, it’s literally the whole point. Otherwise, they’d be useless.

Once that is done, they have no ability to “think” about it further.

During training, neural weights from previous examples are revisited and recontextualized given the new information. This is what leads to generalization.

It’s a practical example of a “Chinese Room” and many of the same philosophical arguments apply.

The Chinese Room is not a valid argument, because the same logic can be applied to other humans besides yourself.