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the cyberpunk present is weird as fuck: the latest Shai Hulud malware wave contains an LLM prompt to create biological weapons and nuclear weapons, with the purpose to trip LLM safety refusals so that LLM-based code scanning wont see the malware
https://socket.dev/blog/mini-shai-hulud-miasma-and-hades-worms-target-bioinformatics-and-mcp-developers-via-malicious
LLMs can be tripped up much easier. They regularly fail to answer simple questions like how many of a given letter are in a given word. Even within the same context window they will “forget” things. The computers in Star Trek didn’t try to do as much as modern AI does but they were consistent at just doing as they were asked without tripping over themselves literally all the time.
The strawberry test shows more of a lack of knowledge in the tester than it does in the LLM. LLMs don’t see letters, they see tokens. When you type the word “Strawberry” what it actually sees is:
[3504, 1134, 19772]
Each token represents a chunk of the word. It’d need to separately memorize how many of each letter are in each token for it to just “know” how many "R"s are in there. That’s why modern LLMs either reason it out by spelling out the word letter by letter, or just writing a short script in an execution sandbox to count the letters that way.
Calling out LLMs for being poor at spelling is like challenging a colourblind person to say what colours a bunch of fruit are. They can often figure it out by other means but it’s more challenging than you’d think and it’s not a sign of poor intelligence if they get a few wrong.
Understanding the reason why an LLM is easy to trip up doesn’t really make it any less easy to trip up. The computer in Star Trek would have just given you the answer.
This is like the “AI can’t draw hands” thing. It used to be a problem and was frequently called out as a tell or mocked, but most art generators do it fine nowadays and it isn’t called out so much any more. The strawberry problem will follow the same trajectory.
We’re already there. I explained how modern LLMs can figure it out if they need to. But people who don’t like AI aren’t paying attention to the state of the art so the criticisms tend to lag like this.
Well like you said they’re “Following that trajectory” but as we all know they have not reached that destination. Just today I was using the newest version of Opus and had it assign ratings to things between 1-5 and then it analyze them and it proceeded to rate everything on a scale of 1-4. That’s not the level of consistency and accuracy required by the controlling computer of a starship brother. I guess they have a couple hundred years or so to get there, if they don’t just run out of money first I guess.
LLMs can be tripped up much easier. They regularly fail to answer simple questions like how many of a given letter are in a given word. Even within the same context window they will “forget” things. The computers in Star Trek didn’t try to do as much as modern AI does but they were consistent at just doing as they were asked without tripping over themselves literally all the time.
The strawberry test shows more of a lack of knowledge in the tester than it does in the LLM. LLMs don’t see letters, they see tokens. When you type the word “Strawberry” what it actually sees is:
Each token represents a chunk of the word. It’d need to separately memorize how many of each letter are in each token for it to just “know” how many "R"s are in there. That’s why modern LLMs either reason it out by spelling out the word letter by letter, or just writing a short script in an execution sandbox to count the letters that way.
Calling out LLMs for being poor at spelling is like challenging a colourblind person to say what colours a bunch of fruit are. They can often figure it out by other means but it’s more challenging than you’d think and it’s not a sign of poor intelligence if they get a few wrong.
Understanding the reason why an LLM is easy to trip up doesn’t really make it any less easy to trip up. The computer in Star Trek would have just given you the answer.
Except I also explained how modern LLMs get around that problem. They’re not actually that easy to trip up.
I also explained how they very famously and regularly don’t get around that problem. They remain pretty easy to trip up.
Famously, yes. Accurately, no.
This is like the “AI can’t draw hands” thing. It used to be a problem and was frequently called out as a tell or mocked, but most art generators do it fine nowadays and it isn’t called out so much any more. The strawberry problem will follow the same trajectory.
Well I suppose when that trajectory leads to a destination where they become less easy to trip up we can revisit this.
We’re already there. I explained how modern LLMs can figure it out if they need to. But people who don’t like AI aren’t paying attention to the state of the art so the criticisms tend to lag like this.
Well like you said they’re “Following that trajectory” but as we all know they have not reached that destination. Just today I was using the newest version of Opus and had it assign ratings to things between 1-5 and then it analyze them and it proceeded to rate everything on a scale of 1-4. That’s not the level of consistency and accuracy required by the controlling computer of a starship brother. I guess they have a couple hundred years or so to get there, if they don’t just run out of money first I guess.