par1970

AI (in this form) will never be able to solve things we truly cannot solve yet.

Argument?

LLMs are nothing close to AGI and not going to lead to it, they can’t distinguish right from wrong, they can’t count, they can’t reason, they generate plausible text from a vast databank of connected text.

Argument?

Are LLMs close to being able to significantly help AGI researchers?

Which doomer argument have you found what problem with?

Do you deny the reported bug finding capabilities, or do you deny that they are dangerous?

I think this is roughly solved. Tell the agent to do all of its calculations in python.

Do you have a defense of why human-hammer-nail is a good analogy for human-chatgpt5.4-pwndsamsung?

But the service also tells criminals and adversaries about the bomb locations.

Yeah, maybe you are right. But is doing math and reasoning about Turing machines a priori? If so, then it seems plausible to me that reasoning about a codebase (without running it) is also ‘a priori’.

What do you mean "a priori understanding codebases"?

I took him to be distinguishing between (1) just reading the code/docs and reasoning about it, and (2) that + crafting and running tests.

Why?

Did you tell it that it should test, or did you have it generate actual tests that you could run if you wanted to?

How much domain experience do you have? Is it helping you solve problems for paying customers?

If your project requires the solution of a tricky algorithmic issue, then is the AI system able to solve that part, or do you have to give it the solution?

What models + versions are you using?

Is it bad at designing systems that don't have a bunch of integrations?

I don't use ChatGPT, but i've been using an agent with Claude Sonnet 4.

Are you using Sonnet 4.6?

So this AI Agent... It is much faster at doing code when given specific instructions. But it keeps loosing context on architecture, and i cant really let it build complex things with interdependencies that build on each other.

I've only built small things (< 1000 lines) with the systems, so I might be missing this problem.

Is it better than you at building small self-contained things?

And i get a bad feel when i then wonder how is this app doing what it does? because my agent cant explain it, and i would be stupid to believe what it hallucinated because it sounds really solid until you scratch the construction.

Do you ask it to generate test suites for the things that it builds?

it would be also faster to build a catastrophic spaghetti code nightmare if not used with great care.

noted

Which models + versions are you using? Can you give a specific problem that you found them to be bad at?

Maybe we are talking past one another.

Right, but that's explicitly not the body of government meant to represent people.

I haven't claimed that the Senate was intended to represent the people. I also haven't claimed that OP claimed that the Senate was intended to represent the people.

So is he saying the Senate is fundamentally a ridiculous way of representing 100 states, or is he saying the House is fundamentally a ridiculous way of representing 350 million people?

He didn't say either of those things. He said this "The Senate is fundamentally a ridiculous way of representing 350 million people."

I think OP is arguing that because they literally said "The Senate is fundamentally a ridiculous way of representing 350 million people and we’re going to continue to get absurd unrepresentative outcomes for as long as it remains a relevant body."

What do you think they are arguing?

Are you arguing this?

(Premise 1) If a country has 350 million people, then the Senate will produce unrepresentative outcomes.

(Premise 2) America has 350 million people.

(Conclusion 1) So, the Senate will produce unrepresentative outcomes in America.

(Conclusion 2) So, the Senate is bad for America.

We're nowhere close to AGI and don't have a clue how to get there.

Do you have an argument?

So do we already do this? And if not, why not?

Sadly, the answer is that you can't.

Suppose there are two distinct entities, each such that if it is learned about, then it kills the learner, call them Geigh and Ritaar. What happens when Geigh learns about Ritaar?

How did you make a blank comment? I thought hn prevented it

How is that the same thing?

Okay. So, in your original comment are you asserting that teachers are mostly telling students to believe propositions without giving any epistemic justification for those propositions?

Does “self-evident” just mean that anyone who knows the sentence’s meaning can determine that it is true without any need to gather empirical data?

eg, All bachelors are unmarried.

eg, If X is a triangle, then X has three sides.

eg, the world is round or it is not the case that the world is round.

And does “universally recognized” just mean that most people believe the proposition is true?

In order to not have to explain everything, almost all lessons are mostly teaching you some axioms, even if they really are disputed, or have caveats, etc. Good teachers make clear where there is an axiom, and where something is just being simplified or assumed for the sake of saving time.

What exactly do you mean by the word “axiom” here?

What about the guys who thought it was obviously a remote starter?

In what way does it feel different and why does this difference suggest a higher probability of truth?

I think that anyone who believed in Santa Clause as a kid should automatically discredit any “intuition” they have about things. How much more evidence do you need that you’ll just believe in kooky nice sounding things?

Maybe you didn’t believe in The Red Guy, but I did, and in retrospect I must be a complete retard.