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Model intelligence is no longer the constraint for automation

latentintent.substack.com

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Re: Model intelligence is no longer the constraint for automation

#81

Post by vrighter »

themanmaran wrote: post #43
mrlongroots wrote: post #23

I very much disagree. To attempt a proof by contradiction: Let us assume that the author's premise is correct, and LLMs are plenty powerful given the right context. Can an LLM recognize the context deficit and frame the right questions to ask? They can not: LLMs have no ability to understand when to stop and ask for directions. They routinely produce contradictions, fail simple tasks like counting the letters in a wo…

This depends on whether you mean LLMs in the sense of single shot, or LLMs + software built around it. I think a lot of people conflate the two. In our application e use a multi-step check_knowledge_base workflow before and after each LLM request. Pretty much, make a separate LLM request to check the query against the existing context to see if more info is needed, and a second check after generation to see if output…

if an llm is unreliable, then why would another just-as-unreliable llm make it any better?

Re: Model intelligence is no longer the constraint for automation

#83

Post by Wowfunhappy »

mrlongroots wrote: post #23

I very much disagree. To attempt a proof by contradiction: Let us assume that the author's premise is correct, and LLMs are plenty powerful given the right context. Can an LLM recognize the context deficit and frame the right questions to ask? They can not: LLMs have no ability to understand when to stop and ask for directions. They routinely produce contradictions, fail simple tasks like counting the letters in a wo…

> LLMs have no ability to understand when to stop and ask for directions.

I haven't read TFA so I may be missing the point. However, I have had success getting Claude to stop and ask for directions by specifically prompting it to do so. "If you're stuck or the task seems impossible, please stop and explain the problem to me so I can help you."

Re: Model intelligence is no longer the constraint for automation

#84

Post by [deleted] »

nradov wrote: post #29
fnordpiglet wrote: post #27

Earlier quoted context omitted.

Assuming you buy the idea of a post scarcity society and assuming we can separate our long ingrained notion that spending your existence in toil to survive is a moral imperative and not working is deserving of punishment if not death, I personally look forward to a time we can get off the hamster wheel. Most buttons that get pushed by people are buttons not worth spending your existence pushing. This includes an awfu…

We will never have a post scarcity society. Automation can make certain foodstuffs and manufactured goods somewhat cheaper but the things that people really want will always be in short supply, for example real estate in geographically favorable areas.

[deleted]

Re: Model intelligence is no longer the constraint for automation

#85

Post by handzhiev »

pvtmert wrote: post #76
fmbb wrote: post #74

> It’s because the bottleneck isn’t in intelligence, but in human tasks: specifying intent and context engineering. So the bottleneck is intelligence. Junior engineers are intelligent enough to understand when they don't understand. They interrogate the intent and context of the tasks they are given. This is intelligence. Solving math questions is not intelligence, computers have been better than humans at that for l…

I truly love this comment, which essentially says: LLMs are glorified calculators, with ambiguous grammar. :)

Computers are glorified calculators, yet they power most of our lives

Re: Model intelligence is no longer the constraint for automation

#86

Post by Paratoner »

I'm endlessly fascinated by the way these Humans (probably) speak of their follow peers as though they are a problem to solve for.

> Longer term, we can reduce the human bottleneck by

Thank God we have ways to remove the thorn in our(?) side for good. The world can finally heal when the pursuit of fulfillment becomes inaccessible to the masses.

Re: Model intelligence is no longer the constraint for automation

#87

Post by Kuinox »

KoolKat23 wrote: post #69
Kuinox wrote: post #6

It's specific model that run for maths. GPT-5 and Gemini 2.5 still cannot compute an arbitrary length sum of whole number without a calculator. I have a proceduraly generated benchmark of basic operations, LLMs gets better at it with time, but they cant still solve basic maths or logic problems. BTW I'm open to selling it, my email is on my hn profile.

I can't see why that's necessary, when it can call a tool. Everyone uses a calculator. A logic problem, it can solve with reasoning, perhaps it's not the smartest but it can solve logic problems. All indications are that it will continue to become smarter.

Simple maths problems are simple logic problem. Here it doesn't even have to come up with a reasoning, it probably already memorised how to solve sums. Yet it fails at that, it shows it cannot solve logic problems if there are too much steps.

> All indications are that it will continue to become smarter.

I'm not disputing that, every new model score better at my benchmark, but right now, none truly "solve" one of these small logic problem.

Re: Model intelligence is no longer the constraint for automation

#88

Post by Kuinox »

energy123 wrote: post #28
Kuinox wrote: post #15

Earlier quoted context omitted.

This is a simple sum of 2 whole number, the number are simply big. Most of the time they make a correct summation table but fail to copy correctly the sum result into a final result. That is not a tokenisation problem (you can change the output format to make sure of it). I have a separated benchmark that test specifically this, when the input is too large, the LLMs fails to accuratly copy the correct token. I suppos…

Have you tried greedy decoding (temp 0) in aistudio? The temp 0.7-1.0 defaults are not designed for reconstructing context with perfect accuracy.

I always use the lowest temperature that I can input. But GPT-5 doesn't support a temperature setting. You'll get something like:

{ "error": { "message": "Unsupported value: 'temperature' does not support 0.0 with this model. Only the default (1) value is supported.", "type": "invalid_request_error", "param": "temperature", "code": "unsupported_value" } }

Re: Model intelligence is no longer the constraint for automation

#89

Post by bamboozled »

pvtmert wrote: post #76
fmbb wrote: post #74

> It’s because the bottleneck isn’t in intelligence, but in human tasks: specifying intent and context engineering. So the bottleneck is intelligence. Junior engineers are intelligent enough to understand when they don't understand. They interrogate the intent and context of the tasks they are given. This is intelligence. Solving math questions is not intelligence, computers have been better than humans at that for l…

I truly love this comment, which essentially says: LLMs are glorified calculators, with ambiguous grammar. :)

That’s really what they feel like to me, a type of word / number hybrid calculator. Like a probability machine…you attempt to give it the right input and you hopefully and non demonically get some output.

Re: Model intelligence is no longer the constraint for automation

#90

Post by gjm11 »

simoncion wrote: post #37
gjm11 wrote: post #10

Earlier quoted context omitted.

> GPT-5 and Gemini 2.5 still cannot compute an arbitrary length sum of whole number without a calculator. Neither can many humans, including some very smart ones. Even those who can will usually choose to use a calculator (or spreadsheet or whatever) rather than doing the arithmetic themselves.

> Neither can many humans... 1) GPT-5 is advertised as "PhD-level intelligence". So, I take OpenAI (and anyone else who advertises their bots with language like this) at their word about the bot's capabilities and constrain the set of humans I use for comparison to those who also have PhD-level intelligence. 2) Any human who has been introduced to long addition will absolutely be able to compute the sum of two whole…

I have a PhD, in mathematics, from a top university. If you give me, say, 100 10-digit numbers to add up and tell me to do the job in my head then I will probably get the answer wrong.

Of course, if you give me 100 10-digit numbers to add up and let me use a calculator, or pencil and paper, then I will probably get it right.

Same for, say, two 100-digit numbers. (I can probably get that one right without tools if you obligingly print them monospaced and put one of them immediately above the other where I can look at them.)

Anyway, the premise here seems to be simply false. I just gave ChatGPT and Claude (free versions of both; ChatGPT5, whatever specific model it routed my query to, and Sonnet 4) a list of 100 random 10-digit numbers to add up, with a prompt encouraging them to be careful about it but nothing beyond that (e.g., no specific strategies or tools to use), and both of them got the right total. Then I did the same with two 100-digit numbers and both of them got that right too.

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