I’d just like to point out that, from the perspective of somebody watching AI develop for the past 10 years, completing 30% of automated tasks successfully is pretty good! Ten years ago they could not do this at all. Overlooking all the other issues with AI, I think we are all irritated with the AI hype people for saying things like they can be right 100% of the time – Amazon’s new CEO actually said they would be able to achieve 100% accuracy this year, lmao. But being able to do 30% of tasks successfully is already useful.
It doesn’t matter if you need a human to review. AI has no way distinguishing between success and failure. Either way a human will have to review 100% of those tasks.
I have been using AI to write (little, near trivial) programs. It’s blindingly obvious that it could be feeding this code to a compiler and catching its mistakes before giving them to me, but it doesn’t… yet.
In University I knew a lot of students who knew all the things but “just don’t know where to start” - if I gave them a little direction about where to start, they could run it to the finish all on their own.
Depends on the context, there is a lot of work in the scientific methods community trying to use NLP to augment traditionally fully human processes such as thematic analysis and systematic literature reviews and you can have protocols for validation there without 100% human review
Right, so this is really only useful in cases where either it’s vastly easier to verify an answer than posit one, or if a conventional program can verify the result of the AI’s output.
It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I’m envisioning a world where multiple AI engines create and check each others’ work… the first thing they need to make work to support that scenario is probably fusion power.
It really depends on the context. Sometimes there are domains which require solving problems in NP, but where it turns out that most of these problems are actually not hard to solve by hand with a bit of tinkering. SAT solvers might completely fail, but humans can do it. Often it turns out that this means there’s a better algorithm that can exploit commanalities in the data. But a brute force approach might just be to give it to an LLM and then verify its answer. Verifying NP problems is easy.
It cant do 30% of tasks vorrectly. It can do tasks correctly as much as 30% of the time, and since it’s llm shit you know those numbers have been more massaged than any human in history has ever been.
yes, that’s generally useless. It should not be shoved down people’s throats. 30% accuracy still has its uses, especially if the result can be programmatically verified.
The problem is they are not i.i.d., so this doesn’t really work. It works a bit, which is in my opinion why chain-of-thought is effective (it gives the LLM a chance to posit a couple answers first). However, we’re already looking at “agents,” so they’re probably already doing chain-of-thought.
I have actually been doing this lately: iteratively prompting AI to write software and fix its errors until something useful comes out. It’s a lot like machine translation. I speak fluent C++, but I don’t speak Rust, but I can hammer away on the AI (with English language prompts) until it produces passable Rust for something I could write for myself in C++ in half the time and effort.
I also don’t speak Finnish, but Google Translate can take what I say in English and put it into at least somewhat comprehensible Finnish without egregious translation errors most of the time.
Is this useful? When C++ is getting banned for “security concerns” and Rust is the required language, it’s at least a little helpful.
Are you just trolling or do you seriously not understand how something which can do a task correctly with 30% reliability can be made useful if the result can be automatically verified.
I’d just like to point out that, from the perspective of somebody watching AI develop for the past 10 years, completing 30% of automated tasks successfully is pretty good! Ten years ago they could not do this at all. Overlooking all the other issues with AI, I think we are all irritated with the AI hype people for saying things like they can be right 100% of the time – Amazon’s new CEO actually said they would be able to achieve 100% accuracy this year, lmao. But being able to do 30% of tasks successfully is already useful.
If you have a good testing program, it can be.
If you use AI to write the test cases…? I wouldn’t fly on that airplane.
obviously
It doesn’t matter if you need a human to review. AI has no way distinguishing between success and failure. Either way a human will have to review 100% of those tasks.
I have been using AI to write (little, near trivial) programs. It’s blindingly obvious that it could be feeding this code to a compiler and catching its mistakes before giving them to me, but it doesn’t… yet.
A human can review something close to correct a lot better than starting the task from zero.
In University I knew a lot of students who knew all the things but “just don’t know where to start” - if I gave them a little direction about where to start, they could run it to the finish all on their own.
It is a lot harder to notice incorrect information in review, than making sure it is correct when writing it.
That depends entirely on your writing method and attention span for review.
Most people make stuff up off the cuff and skim anything longer than 75 words when reviewing, so the bar for AI improving over that is really low.
Depends on the context, there is a lot of work in the scientific methods community trying to use NLP to augment traditionally fully human processes such as thematic analysis and systematic literature reviews and you can have protocols for validation there without 100% human review
Right, so this is really only useful in cases where either it’s vastly easier to verify an answer than posit one, or if a conventional program can verify the result of the AI’s output.
It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.
I’m envisioning a world where multiple AI engines create and check each others’ work… the first thing they need to make work to support that scenario is probably fusion power.
I usually write 3x the code to test the code itself. Verification is often harder than implementation.
It really depends on the context. Sometimes there are domains which require solving problems in NP, but where it turns out that most of these problems are actually not hard to solve by hand with a bit of tinkering. SAT solvers might completely fail, but humans can do it. Often it turns out that this means there’s a better algorithm that can exploit commanalities in the data. But a brute force approach might just be to give it to an LLM and then verify its answer. Verifying NP problems is easy.
(This is speculation.)
Yes, but the test code “writes itself” - the path is clear, you just have to fill in the blanks.
Writing the proper product code in the first place, that’s the valuable challenge.
Please stop.
I’m not claiming that the use of AI is ethical. If you want to fight back you have to take it seriously though.
It cant do 30% of tasks vorrectly. It can do tasks correctly as much as 30% of the time, and since it’s llm shit you know those numbers have been more massaged than any human in history has ever been.
I meant the latter, not “it can do 30% of tasks correctly 100% of the time.”
You get how that’s fucking useless, generally?
As useless as a cubicle farm full of unsupervised workers.
Tjose are people who could be living their li:es, pursuing their ambitions, whatever. That could get some shit done. Comparison not valid.
The comparison is about the correctness of their work.
Their lives have nothing to do with it.
yes, that’s generally useless. It should not be shoved down people’s throats. 30% accuracy still has its uses, especially if the result can be programmatically verified.
Run something with a 70% failure rate 10x and you get to a cumulative 98% pass rate. LLMs don’t get tired and they can be run in parallel.
What’s 0.7^10?
The problem is they are not i.i.d., so this doesn’t really work. It works a bit, which is in my opinion why chain-of-thought is effective (it gives the LLM a chance to posit a couple answers first). However, we’re already looking at “agents,” so they’re probably already doing chain-of-thought.
I have actually been doing this lately: iteratively prompting AI to write software and fix its errors until something useful comes out. It’s a lot like machine translation. I speak fluent C++, but I don’t speak Rust, but I can hammer away on the AI (with English language prompts) until it produces passable Rust for something I could write for myself in C++ in half the time and effort.
I also don’t speak Finnish, but Google Translate can take what I say in English and put it into at least somewhat comprehensible Finnish without egregious translation errors most of the time.
Is this useful? When C++ is getting banned for “security concerns” and Rust is the required language, it’s at least a little helpful.
Less broadly useful than 20 tons of mixed texture human shit, and more ecologically devastatimg.
Are you just trolling or do you seriously not understand how something which can do a task correctly with 30% reliability can be made useful if the result can be automatically verified.