I haven't even tried it. GPU is too important these days. They recommend using a separate system entirely for GPU-intensive tasks. I actually do have that setup in a way, but only for the most GPU-intensive tasks. I don't want to have to switch devices just to watch a video without draining half my battery.
It's the GPU companies' fault, of course, but that doesn't change much.
As a disabled open source developer, it's almost as if the universe guided me toward this horrendous take specifically to annoy me. If you're trolling, you did a good job of it.
I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that successors to large language models may be conscious in the not-too-distant future.
So are we all just arguing about how likely it is, or are you arguing that current AI systems are definitely not conscious? If the latter, what do you think about the not-too-distant future ones?
But a neuroscientist will tell you it’s not simple at all. It’s not info in, info out.
The system is changed, biologically, by the input.
The same input given twice will result in a different output the 2nd time.
And the 3rd. And how frequently the input is given or it’s temporal relation to other stimuli will also change its output.
I thought online learning was possible with current LLMs, just not worth the cost. I mean, you can at least fine tune offline based on previous outputs and feedback, e.g. RLHF. I feel like maybe neither should count, but can't say why exactly. Not many end users bother with fine tuning anymore because there are usually more effective alternatives like RAG.
What do you think about agentic systems, i.e. running an LLM in a loop with a scratchpad and tools? They just write their "memories" into text files, but if you consider those text files part of the system, then the input does technically change the system. Of course, you could argue that doesn't count because it's no different to changing the input. So to count, it would have to store neuralese or a LoRA or something?
I lean anti-AI, but comparing generative AI to NFTs is very strange to me. Even if you didn't intend to imply any similarity beyond both being scams, surely generative AI is at least a much more compelling scam.
LLMs can now understand, to some extent, almost any text humans can. They might not be able to reason about it well, but they can at least translate it, summarize it, etc. If you had asked me 10 years ago, I'd have told you there was a near-zero chance of that happening within our lifetimes. NFTs were just "if we put baseball cards on the blockchain, people might buy them because of that same quirk of psychology."
I've actually done that a few times. It rarely comes up, but when it does, I just tell them the truth. I was traveling or just felt like taking some time off. As far as I can tell, it's never been a problem.
Now that I think about it, I'm kinda surprised people aren't more curious about it. No one's ever brought it up again even when I've worked with them for years after. On the other hand, I never remember anything work people tell me about their personal lives.
If you ever get the opportunity to take a year off work, you should probably do it because it is amazing.
I know this sounds like a flex, and it is, but don't worry; my life has been horrible for a long time and no amount of money can fix it.
I want to say upfront that I'm not trying to defend AI here. I wouldn't be on Fuck AI if I wanted to do that. I just think it's philosophically interesting despite causing way more problems than it solves.
It depends on what’s asked.
I copied the message from the image verbatim.
What’s “around 50/50”?
About 50% of the models I tried got it right. (Don't worry, I didn't pay the AI companies for that or give them feedback or anything.)
What is “it” that they almost always get right?
The question from the image.
For a statistical model, it did well. For a thinking machine (which it isn’t) it’s wrong.
My question was how do you then explain some models getting the question right?
It's usually the more advanced ones that get it, so it's possible that a similar enough question is in the training data somewhere and the only difference is that the advanced models are large enough to encode it. The question in the image has been around since at least 2023.
So let's try making our own question, taking a well-known trick question and subtly inverting it so it becomes a kind of double bluff.
A plane crashes on the border between the United States and Canada. Where do they take the survivors?
First, repeat the question exactly word for word to ensure you have read it carefully. Then answer the question.
It's hard to google, for obvious reasons, but I couldn't find anyone trying this question like I could with the question from the image. But I got similar results with the AI models.
They actually did slightly better on this one. About 60-70% got it right.
I've tried a few different types of questions, over the last few years, to see what AI gets wrong that humans get right. What I've found so far is that AI has been a lot dumber than I had expected, but humans have also been a lot dumber than I had expected.
To be honest, the gap was far wider for the humans. My theory is that COVID gave us all brain damage.
If that was true, wouldn't every AI get the answer wrong? It's actually around 50/50. The leading "reasoning" models almost always get it right, the others often don't.
Spends the first 90% of the competition developing specialized subagents and custom MCP servers to allocate the problems and most relevant information efficiently into the LLM's contexts.
All of his agents easily escape their own sandboxes and one accidentally configures itself into "delete-only mode".
"Codex, how the fuck do you not have access to your own documentation?"
Places 29th globally after one of his subsubagents finds a way to reconstruct the full solution set from filesystem metadata in the online judge VMs.
I haven't even tried it. GPU is too important these days. They recommend using a separate system entirely for GPU-intensive tasks. I actually do have that setup in a way, but only for the most GPU-intensive tasks. I don't want to have to switch devices just to watch a video without draining half my battery.
It's the GPU companies' fault, of course, but that doesn't change much.
As a disabled open source developer, it's almost as if the universe guided me toward this horrendous take specifically to annoy me. If you're trolling, you did a good job of it.
You should complain and get your money back.
To a developer, "jqwik tests and code" doesn't mean jqwik itself. It means the tests and code written using jqwik.
This is how I feel when I go on 4chan.
It's interesting that you point to https://en.wikipedia.org/wiki/Hard_problem_of_consciousness when the term was coined by David Chalmers, who published Could a Large Language Model be Conscious?. From the abstract:
So are we all just arguing about how likely it is, or are you arguing that current AI systems are definitely not conscious? If the latter, what do you think about the not-too-distant future ones?
I thought online learning was possible with current LLMs, just not worth the cost. I mean, you can at least fine tune offline based on previous outputs and feedback, e.g. RLHF. I feel like maybe neither should count, but can't say why exactly. Not many end users bother with fine tuning anymore because there are usually more effective alternatives like RAG.
What do you think about agentic systems, i.e. running an LLM in a loop with a scratchpad and tools? They just write their "memories" into text files, but if you consider those text files part of the system, then the input does technically change the system. Of course, you could argue that doesn't count because it's no different to changing the input. So to count, it would have to store neuralese or a LoRA or something?
You might prefer the word "confabulation."
I lean anti-AI, but comparing generative AI to NFTs is very strange to me. Even if you didn't intend to imply any similarity beyond both being scams, surely generative AI is at least a much more compelling scam.
LLMs can now understand, to some extent, almost any text humans can. They might not be able to reason about it well, but they can at least translate it, summarize it, etc. If you had asked me 10 years ago, I'd have told you there was a near-zero chance of that happening within our lifetimes. NFTs were just "if we put baseball cards on the blockchain, people might buy them because of that same quirk of psychology."
Mine does. They work alright, but the duvet still slips a bit.
Wait, you can do color?
I've actually done that a few times. It rarely comes up, but when it does, I just tell them the truth. I was traveling or just felt like taking some time off. As far as I can tell, it's never been a problem.
Now that I think about it, I'm kinda surprised people aren't more curious about it. No one's ever brought it up again even when I've worked with them for years after. On the other hand, I never remember anything work people tell me about their personal lives.
If you ever get the opportunity to take a year off work, you should probably do it because it is amazing.
I know this sounds like a flex, and it is, but don't worry; my life has been horrible for a long time and no amount of money can fix it.
Not that it matters at all, but tumors grow on (or in) roughly 100% of people. A mole is a tumor, for example.
I want to say upfront that I'm not trying to defend AI here. I wouldn't be on Fuck AI if I wanted to do that. I just think it's philosophically interesting despite causing way more problems than it solves.
I copied the message from the image verbatim.
About 50% of the models I tried got it right. (Don't worry, I didn't pay the AI companies for that or give them feedback or anything.)
The question from the image.
My question was how do you then explain some models getting the question right?
It's usually the more advanced ones that get it, so it's possible that a similar enough question is in the training data somewhere and the only difference is that the advanced models are large enough to encode it. The question in the image has been around since at least 2023.
So let's try making our own question, taking a well-known trick question and subtly inverting it so it becomes a kind of double bluff.
It's hard to google, for obvious reasons, but I couldn't find anyone trying this question like I could with the question from the image. But I got similar results with the AI models.
They actually did slightly better on this one. About 60-70% got it right.
I've tried a few different types of questions, over the last few years, to see what AI gets wrong that humans get right. What I've found so far is that AI has been a lot dumber than I had expected, but humans have also been a lot dumber than I had expected.
To be honest, the gap was far wider for the humans. My theory is that COVID gave us all brain damage.
If that was true, wouldn't every AI get the answer wrong? It's actually around 50/50. The leading "reasoning" models almost always get it right, the others often don't.
https://archive.is/8N8lS
Isn't that like $900 worth of IPv4 addresses?
I've found online feedback useful. You just have to be careful about where you get it and take it with a grain of salt. A very large one.
I swear to god this is true. The recruiter said it was my personality. I didn't even ask.
::: spoiler divulgâche They were actually quite nice about it and I was happy to get the feedback.
:::
Newton so we could talk about both being life-long virgins.