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The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate th
by daveguy 3mo ago
The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it. Contrary to popular belief these things are not intelligent.
- avarun 3mo agoThis has absolutely nothing to do with the comment you replied to.
- nullsanity 3mo ago[dead]
- Cycl0ps 3mo ago>The models don't get better, except when a new one is released. My brother in Christ this entire thread is talking about the new model that was released
- daveguy 3mo agoIt was edited. Original talked about the model learning. Glad they managed to clarify. Because the models are quite literally stupid.
- dahart 3mo ago> the models are quite literally stupid. You’re arguing via reductionism, and failing to explain the outcomes and emergent properties of the “stupid” system. Humans are made of atoms that are quite literally stupid, so by all means, explain our intelligence and why it’s different than LLMs. (I’m not claiming LLMs are intelligent, BTW, I just don’t think your claim helps nor believe that you can fix it.) https://en.wikipedia.org/wiki/Reductionism#Definitions https://en.wikipedia.org/wiki/Reductionism#Definitions
- daveguy 3mo agoSeriously? Supid is literally incapable of learning. That is the underlying model. It does not change, therefore it does not learn. LLM models are quite literally stupid. No one gets a new model no matter how much they yell at it in the context.
- dahart 2mo agoIt’s as if you didn’t read or respond to my comment. LLM models are capable of learning… it’s called training. Models do change during training. You seem to be fixated on today’s inference, perhaps?
- coldtea 3mo ago>The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it. Not quite. The hosting side can change reasoning budgets (or re-assign what terms like "high" means), temperature and other decoding parameters, output length limits, finetune internal "hidden" prompt, latency optimizations, finetune attention algorithms, even change quantization - all still serving as the same model. We know (or suspect) Anthropic frequently nerfs models while keeping their name and version the same.
- daveguy 3mo agoRight. They can do all those things. And none of that will make it smart or able to learn new things. The underlying model is just an llm. But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.
- sureMan6 3mo agoMight bother you to use anthropomorphic terminology like smart and learning but they are capable of producing work that traditionally required human intelligence and the whole point of gpt 3 was the ability to "learn", you can give it an example of an invented brand new coding language and it can write working code in that language
- xylenox 3mo agoYep, people always forget that early LLMs were sold as "Zero Shot Learning".
- kmacdough 3mo agoSold as learning, but that was a marketing term, not a technical one. From a technical perspective, the LLM is not learning. Only reacting based on its original training. You might argue that the systems we've built around them are learning in a way, as they strategically condense and save artifacts from past interactions to pass into the LLMs context. But the LLM itself, which is the source of the intelligence, is not learning. It remains entirely unchanged throughout inference. This difference may seem trite, but it has significant impacts over the long term behavior.