How and why we should build a national library of generative AI use cases and benchmark tests

Last update: Monday 6/9/25

Coding has rapidly become the most successful use case for Large Language Models. Models can now code so well that all Big Tech and many smaller companies are making drastic reductions in the size of their coding staffs. That's bad news ... for software engineers. 

The good news ... for experts in a wide range of other fields ... is that new employment opportunities are just over the horizon for computer savvy personnel who learn how to use generative AI to solve complex problems in their fields. So how can they learn these new skills? Click Here to read more ... 

Developing reliable specialized large language models (LLMs) for the multi billion dollar online/hybrid education market

Last update: Friday 5/12/23 

Why did OpenAI release ChatGPT with the GPT-3.5 model, then with GPT-4, even though both models were prone to factual errors, bias, and "hallucinations". Open AI claimed that it did so, even at the risk of being banned in some countries (e.g., Italy temporarily), in order to learn from widespread user experience with the flawed models. 

The Neoskeptics editor asked the GPT-4 Oracle: "What's the next big thing after Large Language Models (LLMs)?" -- DRAFT

Last update: Saturday 4/15/23 

Once again, the editor of this blog has asked the GPT-4 oracle to look into its crystal ball. Of course, an LLM can't analyze anything or predict anything; but it should be able to produce a plausible distillation of the consensus of opinions among the small community of experts who have published the most papers on the Internet that addressed the issues raised by his questions. Like millions of other "well informed" people, the editor had been surprised, nay flabbergasted by the sudden appearance of ChatGPT last fall running GPT-3.5, and again a few weeks ago when the new GPT-4 model was introduced.