The distinction you're making reads like substance dualism to me. Are you able to provide a clear and objective metric for assessing "understanding"? If not then you're just handwaving an effectively meaningless semantic distinction.
It should involve consciousness. You would not call an AI reacting to red color as "seeing" red. Same thing.
It seems to me you're just kicking the can.
Setting that issue aside. While I certainly don't believe LLMs to be conscious (an entirely subjective and arbitrary take on my part I admit) I don't see any reason that concepts such as "intelligence" and "understanding" should require it. When considering how we apply those terms to humans it seems to me they are results based and highly contextual (ie largely arbitrary).
Is that right? It seems that we generally say that "the computer is programmed to do", instead of "the computer understand" or "the computer knows", even if the programmed computer can produce the same result as a human who does it.
It is generated by tweaking a bunch of `if` statements until the output starts to look about right.
You can very carefully construct a query in a dedicated language, debug that query, and get useful results back. But that's clearly just a human using a tool, not a machine exhibiting understanding or general knowledge.
Meanwhile you can ask a multi-billion parameter LLM a freeform question in ~any human language and it can produce a coherent and meaningful response. It can one shot pieces of code. Track down bugs based on compiler error messages. It might not (yet) be human level in many cases but to get hung up on that is to miss the point.
This is equivalent to an `if` statement with multi-billion levels of nesting. It is just a "traditional program", just unimaginably huge.
Just because it is not "traditionally programmed" does not mean that it is not a really huge "traditional program".
Scaling something by many order of magnitude does not put it in a different category. A computer program, no matter how big, is still a computer program.
Anyway that's irrelevant. The point is that we use different language when referring to the one because its capabilities appear to be fundamentally different.
Your argument comes down to a claim of human exceptionalism - that a computer program can never "understand" simply by virtue of being a computer program. You haven't actually provided any defense of that claim though. You've just assumed it without justification.
It is. If you control the randomness involved, the output of a model is completely deterministic. Which means that it can be represented by a huge lookup table.
Anything that can be represented by a lookup table can be expressed as an `if then else` statement.
Regardless, it remains irrelevant to the subject at hand. You're going off on a tangent rather than admit your initial claim was wrong.
NO!
`sin(x)` is continuous, so the domain is infinite.
But an LLM model is not a continuous function, and thus the domain of a LLM model is finite (set of all possible tokens). So using a lookup table for a model behavior would be exact and not an approximation. So it can indeed be represented by an if statement of finite size.
Hence proved!
If you don't understand something in what I wrote, I can clarify if you tell me where you have trouble following.
>If you control the randomness involved
It is inherently randomized
https://sulbhajain.medium.com/why-llms-arent-truly-determini...
>The Thinking Machines research team showed it’s possible to fix this. They built batch-invariant kernels for RMSNorm, matrix multiplication, and attention, integrating them into the open-source inference engine vLLM.
>The outcome: 1,000 identical prompts, 1,000 identical outputs. Perfect reproducibility.
??
Some people argue that consciousness emerges in early childhood. I can get an infant to understand what I am saying even if they aren’t conscious.