51–60 of 399 posts
The most interesting part of this experiment isn’t just catching the error—it’s fixing it. When Steer catches a failure (like an agent wrapping JSON in Markdown), it doesn’t just crash. Say you are using AI slop without saying you are using AI slop. > It's not X, it's Y.
We are trying to fix probability with more probability. That is a losing game. Thanks for pointing out the elephant in the room with LLMs. The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
You could make an LLM deterministic if you really wanted to without a big loss in performance (fix random seeds, make MoE batching deterministic). That would not fix hallucinations. I don't think using deterministic / stochastic as a diagnostic is accurate here - I think that what we're really talking is about some sort of fundamental 'instability' of LLMs a la chaos theory.
We are trying to fix probability with more probability. That is a losing game. Thanks for pointing out the elephant in the room with LLMs. The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
The factuality problem with LLMs isn't because they are non-deterministic or statistically based, but simply because they operate at the level of words, not facts. They are language models. You can't blame an LLM for getting the facts wrong, or hallucinating, when by design they don't even attempt to store facts in the first place. All they store are language statistics, boiling down to "with preceding context X, mos…
But they are like a smart student trying to get a good grade (that's how they are trained!). They'll agree with us even if they think we're stupid, because that gets them better grades, and grades are all they care about.
Even if they are (or become) smart enough to know better, they don't care about you. They do what they were trained to do. They are becoming like a literal genie that has been told to tell us what we want to hear. And sometimes, we don't need to hear what we want to hear.
"What an insightful price of code! Using that API is the perfect way to efficiently process data. You have really highlighted the key point."
The problem is that chatbots are trained to do what we want, and most of us would rather have a syncophant who tells us we're right.
The real danger with AI isn't that it doesn't get smart, it's that it gets smart enough to find the ultimate weakness in its training function - humanity.
We are trying to fix probability with more probability. That is a losing game. Thanks for pointing out the elephant in the room with LLMs. The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
LLMs don't do this. Instead, every question is immediately responded to with extreme confidence with a paragraph or more of text. I know you can minimize this by configuring the settings on your account, but to me it just highlights how it's not operating in a way remotely similar to the human-human one I mentioned above. I constantly find myself saying, "No, I meant [concept] in this way, not that way," and then getting annoyed at the robot because it's masquerading as a human.
We are trying to fix probability with more probability. That is a losing game. Thanks for pointing out the elephant in the room with LLMs. The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
Bruce Schneier put it well: "Willison’s insight was that this isn’t just a filtering problem; it’s architectural. There is no privilege separation, and there is no separation between the data and control paths. The very mechanism that makes modern AI powerful - treating all inputs uniformly - is what makes it vulnerable. The security challenges we face today are structural consequences of using AI for everything." -…
We are trying to fix probability with more probability. That is a losing game. Thanks for pointing out the elephant in the room with LLMs. The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
You could make an LLM deterministic if you really wanted to without a big loss in performance (fix random seeds, make MoE batching deterministic). That would not fix hallucinations. I don't think using deterministic / stochastic as a diagnostic is accurate here - I think that what we're really talking is about some sort of fundamental 'instability' of LLMs a la chaos theory.
We are trying to fix probability with more probability. That is a losing game. Thanks for pointing out the elephant in the room with LLMs. The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
The factuality problem with LLMs isn't because they are non-deterministic or statistically based, but simply because they operate at the level of words, not facts. They are language models. You can't blame an LLM for getting the facts wrong, or hallucinating, when by design they don't even attempt to store facts in the first place. All they store are language statistics, boiling down to "with preceding context X, mos…
On one level I agree, but I do feel it’s also right to blame the LLM/company for that when the goal is to replace my search engine of choice (my major tool for finding facts and answering general questions), which is a huge pillar of how they’re sold to/used by the public.
These aren’t just strict type systems but the language allows for algebraic data types, nominal types, etc, which allow for encoding higher level types enforced by the language compiler.
The AI essentially becomes a glorified blank filler filling in the blanks. Basic syntax errors or type errors, while common, are automatically caught by the compiler as part of the vibe coding feedback loop.