In order to be Turing-complete, they would need to have edges between nodes (I don't know what they are called in ML parlance) that can go to previous nodes, as well as edges that are only conditionally taken.
> They exhibit intelligence in that they can understand a given problem statement, reason about it, and make predictions and recommendations.
They exhibit the intelligence of the humans that wrote the material used to train their models, nothing more. They are borrowed intelligence.
I just tried this with all three versions of ChatGPT (3.5 legacy, 3.5, 4), and they all did it correctly.
>In order to be Turing-complete, they would need to have edges between nodes (I don't know what they are called in ML parlance) that can go to previous nodes, as well as edges that are only conditionally taken.
If you look at a neural network at the lowest level, it is trivial to implement a NAND gate with them. It just takes a few nodes. And LLMs have a feedback loop through their context window.
With NAND gates and a feedback loop you can build any CPU.
There are many examples of people asking ChatGPT to act like a CPU and pretend to execute instructions, or like a Python interpreter, or a Dungeon Master and respond to what the human players do.
Now, that doesn't make it an AI in itself. But it shows that there is no fundamental limit to its computational capabilities.
> They exhibit the intelligence of the humans that wrote the material used to train their models, nothing more.
Do you mean that they're just pattern matching input text against a fuzzy database and responding with canned answers?
If so, that's not what's going on in my view. That might have been accurate for the earlier LLMs.
But the latest LLMs appear to have developed internal language processing, a world model, and capabilities to reason about things.
Here's one example:
Prompt:
Please pretend you are a CPU. Execute the following Python program and show what the output would be. Respond only with the outputs, and no comments:
for i in range(5):
if i == 3:
print("Found 3")
else:
print(i)
Response:
0
1
2
Found 3
4
Second example (only works in GPT-4):
Prompt 1:
Please pretend you are a CPU. Execute the following Python program and show what the output would be. Respond only with the outputs, and no comments:
stop = False
while not stop:
user_input = input()
if user_input == "stop":
stop = True
print(user_input)
print("Program is done")
For "input()", stop and ask me for the input string.
If you reach the statement 'print("Program is done")', then say "It looks like we are done!".
Response 1:
input()
Prompt 2:
hello
Response 2:
hello
input()
Prompt 1:
world
Response 2:
world
input()
Prompt 1:
stop
Response 2:
stop
It looks like we are done!
Aren't they? I view them as "language processors", that execute one token (instruction) at a time, and use the token window as their working memory.
Sure, they have a limit to their instruction count (8k/32) before stopping. The working memory is small, but 8k tokens goes a lot longer than 8 kB.
I've seen you state in other comments that LLM's cannot reason. I used to think so too until a couple of weeks ago.
But I had to change my mind. They're not just fancy autocomplete tools any more. They exhibit intelligence in that they can understand a given problem statement, reason about it, and make predictions and recommendations.