No, this isn’t what ‘agents’ do, ‘agents’ just interact with other programs. So like move your mouse around to buy stuff, using the same methods as everything else.
Its like a fancy diversely useful diversely catastrophic hallucination prone API.
If that other program is, say, a python terminal then can’t LLMs be trained to use agents to solve problems outside their area of expertise?
I just tested chatgpt to write a python program to return the frequency of letters in a string, then asked it for the number of L’s in the longest placename in Europe.
‘’‘’
String to analyze
text = “Llanfairpwllgwyngyllgogerychwyrndrobwllllantysiliogogogoch”
Convert to lowercase to count both ‘L’ and ‘l’ as the same
text = text.lower()
Dictionary to store character frequencies
frequency = {}
Count characters
for char in text:
if char in frequency:
frequency[char] += 1
else:
frequency[char] = 1
But an LLM as a node in a framework that can call a python library
Isn’t how these systems are configured. They’re just not that sophisticated.
So much of what Sam Alton is doing is brute force, which is why he thinks he needs a $1T investment in new power to build his next iteration model.
Deepseek gets at the edges of this through their partitioned model. But you’re still asking a lot for a machine to intuit whether a query can be solved with some exigent python query the system has yet to identify.
It doesn’t scale to AGI but it does reduce hallucinations
It has to scale to AGI, because a central premise of AGI is a system that can improve itself.
It just doesn’t match the OpenAI development model, which is to scrape and sort data hoping the Internet already has the solution to every problem.
The claim is not that all LLMs are agents, but rather that agents (which incorporate an LLM as one of their key components) are more powerful than an LLM on its own.
We don’t know how far away we are from recursive self-improvement. We might already be there to be honest; how much of the job of an LLM researcher can already be automated? It’s unclear if there’s some ceiling to what a recursively-improved GPT4.x-w/e can do though; maybe there’s a key hypothesis it will never formulate on the quest for self-improvement.
The only thing worse than the ai shills are the tech bro mansplainaitions of how “ai works” when they are utterly uninformed of the actual science. Please stop making educated guesses for others and typing them out in a teacher’s voice. It’s extremely aggravating
in what context? LLMs are extremely good at bridging from natural language to API calls. I dare say it’s one of the few use cases that have decisively landed on “yes, this is something LLMs are actually good at.” Maybe not five nines of reliability, but language itself doesn’t have five nines of reliability.
The human approach could be to write a (python) program to count the number of characters precisely.
When people refer to agents, is this what they are supposed to be doing? Is it done in a generic fashion or will it fall over with complexity?
No, this isn’t what ‘agents’ do, ‘agents’ just interact with other programs. So like move your mouse around to buy stuff, using the same methods as everything else.
Its like a fancy diversely useful diversely catastrophic hallucination prone API.
‘agents’ just interact with other programs.
If that other program is, say, a python terminal then can’t LLMs be trained to use agents to solve problems outside their area of expertise?
I just tested chatgpt to write a python program to return the frequency of letters in a string, then asked it for the number of L’s in the longest placename in Europe.
‘’‘’
String to analyze
text = “Llanfairpwllgwyngyllgogerychwyrndrobwllllantysiliogogogoch”
Convert to lowercase to count both ‘L’ and ‘l’ as the same
text = text.lower()
Dictionary to store character frequencies
frequency = {}
Count characters
for char in text: if char in frequency: frequency[char] += 1 else: frequency[char] = 1
Show the number of 'l’s
print(“Number of 'l’s:”, frequency.get(‘l’, 0))
‘’’
I was impressed until
Output
Number of 'l’s: 16
Yeah it turns out to be useless!
That’s not how LLMs operate, no. They aggregate raw text and sift for popular answers to common queries.
ChatGPT is one step removed from posting your question to Quora.
But an LLM as a node in a framework that can call a python library should be able to count the number of Rs in strawberry.
It doesn’t scale to AGI but it does reduce hallucinations.
Isn’t how these systems are configured. They’re just not that sophisticated.
So much of what Sam Alton is doing is brute force, which is why he thinks he needs a $1T investment in new power to build his next iteration model.
Deepseek gets at the edges of this through their partitioned model. But you’re still asking a lot for a machine to intuit whether a query can be solved with some exigent python query the system has yet to identify.
It has to scale to AGI, because a central premise of AGI is a system that can improve itself.
It just doesn’t match the OpenAI development model, which is to scrape and sort data hoping the Internet already has the solution to every problem.
The claim is not that all LLMs are agents, but rather that agents (which incorporate an LLM as one of their key components) are more powerful than an LLM on its own.
We don’t know how far away we are from recursive self-improvement. We might already be there to be honest; how much of the job of an LLM researcher can already be automated? It’s unclear if there’s some ceiling to what a recursively-improved GPT4.x-w/e can do though; maybe there’s a key hypothesis it will never formulate on the quest for self-improvement.
The only thing worse than the ai shills are the tech bro mansplainaitions of how “ai works” when they are utterly uninformed of the actual science. Please stop making educated guesses for others and typing them out in a teacher’s voice. It’s extremely aggravating
You’d still be better off starting with a 50s language processor, then grafting on some API calls.
in what context? LLMs are extremely good at bridging from natural language to API calls. I dare say it’s one of the few use cases that have decisively landed on “yes, this is something LLMs are actually good at.” Maybe not five nines of reliability, but language itself doesn’t have five nines of reliability.