It gives you the right picture when you asked for a single straight track on the prompt. Now you have to spend 10 hours debugging code and fixing hallucinations of functions that don’t exist on libraries it doesn’t even neet to import.
No, it’s just that it doesn’t know if it’s right or wrong.
How “AI” learns is they go through a text - say blog post - and turn it all into numbers. E.g. word “blog” is 5383825526283. Word “post” is 5611004646463.
Over huge amount of texts, a pattern is emerging that the second number is almost always following the first number. Basically statistics. And it does that for all the words and word combinations it found - immense amount of text are needed to find all those patterns. (Fun fact: That’s why companies like e.g. OpenAI, which makes ChatGPT need hundreds of millions of dollars to “train the model” - they need enough computer power, storage, memory to read the whole damn internet.)
So now how do the LLMs “understand”? They don’t, it’s just a bunch of numbers and statistics of which word (turned into that number, or “token” to be more precise) follows which other word.
So now. Why do they hallucinate?
How they get your question, how they work, is they turn over all your words in the prompt to numbers again. And then go find in their huge databases, which words are likely to follow your words.
They add in a tiny bit of randomness, they sometimes replace a “closer” match with a synonym or a less likely match, so they even seen real.
They add “weights” so that they would rather pick one phrase over another, or e.g. give some topics very very small likelihoods - think pornography or something. “Tweaking the model”.
But there’s no knowledge as such, mostly it is statistics and dice rolling.
So the hallucination is not “wrong”, it’s just statisticaly likely that the words would follow based on your words.
Full disclosure - my background is in operations (think IT) not AI research. So some of this might be wrong.
What’s marketed as AI is something called a large language model. This distinction is important because AI implies intelligence - where as a LLM is something else. At a high level LLMs are using something called “tokens” to break apart natural language into elements that a machine can understand, and then recombining those tokens to “create” something new. When a LLM is creating output it does not know what it is saying - it knows what token statistically comes after the token(s) it has generated already.
So to answer your question. An AI can hallucinate because it does not know the answer - its using advanced math to know that the period goes at the end of the sentence. and not in the middle.
It gives you the right picture when you asked for a single straight track on the prompt. Now you have to spend 10 hours debugging code and fixing hallucinations of functions that don’t exist on libraries it doesn’t even neet to import.
Not a developer. I just wonder about AI hallucinations come about. Is it the ‘need’ to complete the task requested at the cost of being wrong?
No, it’s just that it doesn’t know if it’s right or wrong.
How “AI” learns is they go through a text - say blog post - and turn it all into numbers. E.g. word “blog” is 5383825526283. Word “post” is 5611004646463. Over huge amount of texts, a pattern is emerging that the second number is almost always following the first number. Basically statistics. And it does that for all the words and word combinations it found - immense amount of text are needed to find all those patterns. (Fun fact: That’s why companies like e.g. OpenAI, which makes ChatGPT need hundreds of millions of dollars to “train the model” - they need enough computer power, storage, memory to read the whole damn internet.)
So now how do the LLMs “understand”? They don’t, it’s just a bunch of numbers and statistics of which word (turned into that number, or “token” to be more precise) follows which other word.
So now. Why do they hallucinate?
How they get your question, how they work, is they turn over all your words in the prompt to numbers again. And then go find in their huge databases, which words are likely to follow your words.
They add in a tiny bit of randomness, they sometimes replace a “closer” match with a synonym or a less likely match, so they even seen real.
They add “weights” so that they would rather pick one phrase over another, or e.g. give some topics very very small likelihoods - think pornography or something. “Tweaking the model”.
But there’s no knowledge as such, mostly it is statistics and dice rolling.
So the hallucination is not “wrong”, it’s just statisticaly likely that the words would follow based on your words.
Did that help?
Full disclosure - my background is in operations (think IT) not AI research. So some of this might be wrong.
What’s marketed as AI is something called a large language model. This distinction is important because AI implies intelligence - where as a LLM is something else. At a high level LLMs are using something called “tokens” to break apart natural language into elements that a machine can understand, and then recombining those tokens to “create” something new. When a LLM is creating output it does not know what it is saying - it knows what token statistically comes after the token(s) it has generated already.
So to answer your question. An AI can hallucinate because it does not know the answer - its using advanced math to know that the period goes at the end of the sentence. and not in the middle.