The co-founder of OpenAI highlighted that Large Language Models (LLMs) are undertrained by a significant factor, indicating untapped potential for AI. Concerns were raised about the need for stronger reasoning capabilities and regulation in the military use of LLMs to prevent risky decisions. Despite the Pentagon's interest, LLMs are cautioned against replacing human decision-making in critical situations.
Despite the Pentagon’s growing enthusiasm for artificial intelligence and large language models, LLMs cannot serve as direct substitutes for human decision-making, especially in high-stakes situations, warn @MLamparth and @JackieGSchneid. https://t.co/DzCZH023ei
.@MLamparth and @JackieGSchneid explain how the U.S. military is experimenting with large language models—and warn of the potential dangers of outsourcing high-stakes decisions to artificial intelligence systems: https://t.co/L4RHrxi6jz
Large language models can have useful military purposes—but their use should be regulated so they don’t end up making dangerous calls in high-stakes situations, warn @MLamparth and @JackieGSchneid. https://t.co/qyi1KHXtGH
why LLMs can't write great books (3 slightly technical lessons from the trenches) 1/ AI is a poor generalizer LLMs (Large Language Models) are trained on diverse datasets and are excellent at handling specific instances or repeating patterns they've seen during training... but…
I am sympathetic to the position that AI systems need stronger reasoning capabilities. But LLMs are also trained on a lot of expert-generated text and structured data, so to reproduce that data they would need to be more accurate than the average non-expert human. https://t.co/yNPOVBbMmL
The co-founder of OpenAI has said that LLMs are "undertrained by a factor of maybe 100-1000x or more". In essence, we’re just scratching the surface of AI’s potential. This poses a fascinating opportunity for businesses. But how can we gain value from the full power of the AI… https://t.co/vqTkDDmooy