zlacker

>>Jimmc4+(OP)
It is easy to point to loopy theories around superalignment, p(doom), etc. But you don't have to be hopped up on sci-fi to oppose something like GPT-4o. Low-latency response time is fine. The faking of emotions and overt references to Her (along with the suspiciously-timed relaxation of pornographic generations) are not fine. I suspect Altman/Brockman/Murati intended for this thing to be dangerous for mentally unwell users, using the exact same logic as tobacco companies.

>>nickle+491
Realistically its all just probabilistic word generation. People "feel" like an LLM understands them but it doesn't, its just guessing the next token. You could say all our brains are doing are just guessing the next token but thats a little too deep for this morning

All these companies are doing now is taking an existing inferencing engine, making it 3% faster, 3% more accurate, etc. per quarter, fighting over the $20/month users

One can imagine product is now taking the wheel from engineering and are building ideas on how to monetize the existing engine. Thats essentially what GPT-4o is, and who knows what else is in the 1,2,3 year roadmaps for any of these $20 companies

To reach true AGI we need to get past guessing, and that doesn't seem close at all. Even if one of these companies gets better at making you "feel" like its understanding and not guessing, if it isnt actually happening, its not a breakthrough

Now with product leading the way, its really interesting to see where these engineers head

>>shmatt+1o1
> People "feel" like an LLM understands them but it doesn't, its just guessing the next token. You could say all our brains are doing are just guessing the next token but thats a little too deep for this morning

"Just" guessing the next token requires understanding. The fact that LLMs are able to respond so intelligently to such a wide range of novel prompts means that they have a very effective internal representation of the outside world. That's what we colloquially call "understanding."

>>Diogen+Gq1
it requires calculation of frequency of how often words appear next to each other given other surrounding words. If you want to call that 'understanding', you can, but it's not semantic understanding.

If it were, these LLMs wouldn't hallucinate so much.

Semantic understanding is still a ways off, and requires much more intelligence than we can give machines at this moment. Right now the machines are really good at frequency analysis, and in our fervor we mistake that for intelligence.

>>gortok+Es1
> it requires calculation of frequency of how often words appear next to each other given other surrounding words

In order to do that effectively, you have to have very significant understanding of the world. The texts that LLMs are learning from describe a wide range of human knowledge, and if you want to accurately predict what words will appear where, you have to build an internal representation of that knowledge.

ChatGPT knows who Henry VIII was, who his wives were, the reasons he divorced/offed them, what a divorce is, what a king is, that England has kings, etc.

> If it were, these LLMs wouldn't hallucinate so much.

I don't see how this follows. First, humans hallucinate. Second, why does hallucination prove that LLMs don't understand anything? To me, it just means that they are trained to answer, and if they don't know the answer, they BS it.