Computer pioneer Alan Turing’s remarks in 1950 on the question, “Can machines think?” were misquoted, misinterpreted and morphed into the so-called “Turing Test”. The modern version says if you can’t tell the difference between communicating with a machine and a human, the machine is intelligent. What Turing actually said was that by the year 2000 people would be using words like “thinking” and “intelligent” to describe computers, because interacting with them would be so similar to interacting with people. Computer scientists do not sit down and say alrighty, let’s put this new software to the Turing Test - by Grabthar’s Hammer, it passed! We’ve achieved Artificial Intelligence!
Okay but in casual conversation I probably couldn’t spot a really good LLM on a thread like this, but on the back end that LLM is completely incapable of learning or changing in any meaningful way, its not quite a chinese room as previously mentioned but it’s still a set model that can’t learn or understand context, even with infinite context memory it could still only interact with that data within the confines of the original model.
e.g. I can train the model to understand a spoon and a fork, it will never come up with that idea of a spork unless I re-train it to include the concept of sporks or directly tell it. Even after I tell it what a spork is it can’t infer the properties of a spork based on a fork or a spoon without additional leading prompts by me.
Interestingly, infinite context memory is functionally identical to learning.
It seems wildly different but it’s the same as if you have already learned absolutely everything that there is to know. There is absolutely nothing you could do or ask that the infinite context memory doesn’t already have stored response ready to go.