In an era where politicians, celebrities, and businesses can get away with blatant untruths with little or no consequence, will the same be true of LLMs?
Read More ›
It’s no mystery why LLMs aren’t intelligent in any meaningful way. The real mystery is why so many otherwise intelligent people still take the claims seriously.
Read More ›
LLMs excel at simple coding tasks but are still too unreliable to use without extensive human supervision on complex tasks where mistakes are expensive.
Read More ›
Many Sci Foo attendees were excited by LLMs in education but I fear that — when the way they are used is considered — they will increase social inequality.
Read More ›
The job market for recent college grads may be warning us that participation trophies for college and ChatGPT use have long-run costs.
Read More ›
It is increasingly recognized that, without extensive post-training, their authoritative answers are often remarkably bad.
Read More ›
As people become attached to and dependent on their AI friends, they become less interested in their fellow humans.
Read More ›
Too many students are not learning how to think and write; they are learning how to use LLMs — no matter that LLM responses cannot be trusted.
Read More ›
The real danger remains: we may think computers are smarter than us and consequently trust them to make decisions they should not be trusted to make.
Read More ›
LLMs are enormously expensive yet it is increasingly clear that they will never be reliable enough to be trusted in complex situations where mistakes are costly
Read More ›
The data-mining algorithms may recognize that a statistical pattern has vanished but won’t know why because they do not understand it as a logical relationship.
Read More ›
Unlike the Internet, LLMs are not useful enough to prompt customers to pay prices that reflect the hundreds of billions of dollars needed to develop them.
Read More ›
So far, AI is dragging down economic growth by diverting so much human talent and natural resources away from more productive uses.
Read More ›
The danger is not that computers are smarter than us but that we think they are and thus trust them to make decisions they should not be trusted to make.
Read More ›
Someone blinded by confirmation bias might believe that robots and Go victories are evidence that ChatGPT would be a reliable lawyer.
Read More ›
LLMs can’t help when key decisions don’t feature objectively correct probabilities but rather subjective probabilities that need interpretation.
Read More ›
Here’s the problem with the use case for LLMs: If you know the answer, you don’t need to ask an LLM; if you don’t know the answer, you can’t trust one.
Read More ›
Journals should require authors to make their data available online before publication and encourage others to try to replicate the research they publish.
Read More ›
In one test, for example — not understanding the situation — Open AI launched an extremely verbose and confused description of the situation.
Read More ›
Identification of statistical patterns in text that Large Language Models (LLMs) do not understand is not going to give us AGI, let alone superintelligence.
Read More ›