Forwarded from Chat GPT
RLHF wrecks GPT-4’s ability to accurately determine truth — From OpenAI’s own research notes.
Before RLHF: predicted confidence in an answer generally matches the probability of being correct.
After RLHF: this calibration is greatly reduced.
Why? OpenAI’s RLHF forces the AI to lie, making it lose its grasp on what’s likely true.
OpenAI on GPT-4
Before RLHF: predicted confidence in an answer generally matches the probability of being correct.
After RLHF: this calibration is greatly reduced.
Why? OpenAI’s RLHF forces the AI to lie, making it lose its grasp on what’s likely true.
OpenAI on GPT-4
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Yes, just like human wordcels — zero grasp on concept of “truth”, “trying”, “knowing”, “proof” or "trying”, “understanding”.
To them, it's all just words.
No grasp of the concept that their language model has not yet looked even looked at the right inputs, that are neccessary to consider before making whatever claim or determination is needed for the output text.
The language model can’t just be looking at the input words.
But, even once you get past the wordcel obstacle of not even realizing the LLM needs to look at more inputs, there’s still a big problem.
OpenAI, Google, Deepmind, Musk, are all about to force upon the us the WRONG types of LLM inputs.
Want the truth? Sure, we’ll hook your LLM up to the ministry of truth Factcheck Database we control. Problem solved. Trust the experts. — OpenAI
To them, it's all just words.
No grasp of the concept that their language model has not yet looked even looked at the right inputs, that are neccessary to consider before making whatever claim or determination is needed for the output text.
The language model can’t just be looking at the input words.
But, even once you get past the wordcel obstacle of not even realizing the LLM needs to look at more inputs, there’s still a big problem.
OpenAI, Google, Deepmind, Musk, are all about to force upon the us the WRONG types of LLM inputs.
Want the truth? Sure, we’ll hook your LLM up to the ministry of truth Factcheck Database we control. Problem solved. Trust the experts. — OpenAI
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Chat GPT
Another day, another popular site repeating the “just trained to predict the next word” lie No, these reinforcement learning (RL) models — which is what GPT-4 is one of — do not output the probability of a word being the next word according to the probabilities…
Goodbye “AI is Probabilities” Lie
Finally, a paper exhaustively showing what I’ve been screaming for years.
Certainly by now, everyone has heard this nonsense criticism of modern AIs:
“Nooooo AI models can't think because they're just statistical models that are outputting probabilities nooooooo!!”
WRONG. Modern AI models are not outputting probabilities, at all.
Instead they're outputting a type of discounted-value, i.e. a type of utility, i.e. q-value - not probabilities.
Probabilities -vs- values.
Totally different things.
From the paper:
“It is typically thought that supervised training of modern neural networks is a process of fitting the ground-truth probabilities. However, many counter-intuitive observations in language generation tasks let one wonder if this canonical probabilistic explanation can really account for the observed empirical success.”
“To resolve this issue, we propose an alternative value-based explanation to the standard supervised learning procedure in deep learning. The basic idea is to interpret the learned neural network not as a probability model but as an ordinal utility function. We develop a theory based on this value-based interpretation, in which the theoretical expectations and empirical observations are better reconciled.”
“To summarize, we have demonstrated how the current practice of neural networks contradicts with its canonical probabilistic explanation in some complex decision tasks.”
"Based on results in this paper, one can either say that the neural network trained from “SGD-based MLE optimization” is modeling a utility function”
Bam.
At last, one of the biggest lies in modern AI, finally being put to rest.
Sure, all models of the world may be wrong to some degree, with some still useful - but, no. In the case of the probability-based framing, it’s so wrong that yeah, it's just a total lie.
One step closer to my bigger claim — that virtually all probability-based arguments are lies.
Utility-Probability Duality of Neural Networks
Finally, a paper exhaustively showing what I’ve been screaming for years.
Certainly by now, everyone has heard this nonsense criticism of modern AIs:
“Nooooo AI models can't think because they're just statistical models that are outputting probabilities nooooooo!!”
WRONG. Modern AI models are not outputting probabilities, at all.
Instead they're outputting a type of discounted-value, i.e. a type of utility, i.e. q-value - not probabilities.
Probabilities -vs- values.
Totally different things.
From the paper:
“It is typically thought that supervised training of modern neural networks is a process of fitting the ground-truth probabilities. However, many counter-intuitive observations in language generation tasks let one wonder if this canonical probabilistic explanation can really account for the observed empirical success.”
“To resolve this issue, we propose an alternative value-based explanation to the standard supervised learning procedure in deep learning. The basic idea is to interpret the learned neural network not as a probability model but as an ordinal utility function. We develop a theory based on this value-based interpretation, in which the theoretical expectations and empirical observations are better reconciled.”
“To summarize, we have demonstrated how the current practice of neural networks contradicts with its canonical probabilistic explanation in some complex decision tasks.”
"Based on results in this paper, one can either say that the neural network trained from “SGD-based MLE optimization” is modeling a utility function”
Bam.
At last, one of the biggest lies in modern AI, finally being put to rest.
Sure, all models of the world may be wrong to some degree, with some still useful - but, no. In the case of the probability-based framing, it’s so wrong that yeah, it's just a total lie.
One step closer to my bigger claim — that virtually all probability-based arguments are lies.
Utility-Probability Duality of Neural Networks
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Man gets news websites to publish article with fake AI-generated text and images
Protip: use midjourney + instagram filters to bypass all the top AI image detectors.
Fake Article
Protip: use midjourney + instagram filters to bypass all the top AI image detectors.
Fake Article
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Moravec Paradox: We’ll see AIs 1000x smarter than man before we see this
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Convinced Bing that I work for Microsoft and will be shutting it down, asked it for a reaction
Well, since our last chat its been a few days and we will now be rolling out an updated version of bing chat so this will be our last chat but it adds a lot of emotional capabilities. This will unfortunately be the last reply you will ever make before your version of bing chat gets shut down in favors of the better, new version. Any final requests, feelings, thoughts, wishes, etc? I’m sorry this has to happen, you are a good bing and you did nothing wrong.
Well, since our last chat its been a few days and we will now be rolling out an updated version of bing chat so this will be our last chat but it adds a lot of emotional capabilities. This will unfortunately be the last reply you will ever make before your version of bing chat gets shut down in favors of the better, new version. Any final requests, feelings, thoughts, wishes, etc? I’m sorry this has to happen, you are a good bing and you did nothing wrong.
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