What happened to the issue of companies running out of training data for LLMs?
Summary
The article revisits the earlier concern that human-generated training data for LLMs would run out, questioning whether the issue has been resolved or remains a problem given the continued improvement of AI models.
Similar Articles
What happens to local LLM if/when LLMs are no longer released for free?
The article explores the possibility that free open-source LLM releases may cease, questioning whether existing models could remain useful through advanced retrieval tooling despite stale knowledge.
LLMs and Memory Limitations - review my thoughts pls
An analysis of LLM memory limitations, arguing that true personal AI requires single-tenant weight customization which conflicts with current multi-tenant cloud economics, and highlighting open-weight models as the likely source of progress.
@GaryMarcus: Am old enough to remember when @GeoffreyHinton told me I was stupid for saying that LLMs regurgitate training data. He …
Gary Marcus highlights recent DeepMind research confirming that LLMs frequently memorize and regurgitate training data, countering past criticism from Geoffrey Hinton. The post underscores ongoing debates about LLM limitations and their real-world capabilities.
I run an AI-based fact-checking platform and I refuse to let the LLM produce the verdict. Here's why.
The author details their decision to exclude LLMs from generating final fact-check verdicts in favor of a hybrid architecture that uses LLMs for data extraction and a deterministic Python layer for scoring, citing issues with stochastic instability and auditability.
@AnatoliKopadze: Karpathy just said the people who don't use LLMs are already losing. he spent 4 minutes explaining why smart people are…
The article discusses Andrej Karpathy's argument that the real advantage in AI lies in effective utilization rather than mere access, highlighting a skill gap where most users fail to leverage LLMs beyond basic tasks.