Artificial intelligence

To receive patent protection for their invention, inventors are required to describe their inventions in such “full, clear, concise, and exact terms” that “one skilled in the art” can make and use the claimed invention. Further, inventions are not patentable “if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious….to a person having ordinary skill in the art to which the claimed invention pertains.” Both of these standards involve evaluations in light of fictitious people engaging in the art – herein referred to as a person having ordinary skill in the art (PHOSITA). They also inherently require attention to the definition of art, the specific technology field to which the invention pertains.

The author Mark Twain supposedly said that “history does not repeat itself but it rhymes.” And with respect to support for AI research, a number of recent actions by the US government appear to rhyme with similar actions it took in the 1980s, when it recommended (and ultimately implemented) significant support for supercomputing-based research.

In an era where data drives decision-making, the accessibility of data for public interest purposes has never been more crucial. Whether shaping public policy, responding to disasters, or empowering research, data plays a pivotal role in our understanding of complex social, environmental, and economic issues.

The large language model (LLM) tools we see today are essentially using a form of advanced autocompletion based on massive input, which is potentially itself of questionable validity. The infamous ‘hallucinations’ we see being produced are at least in part a result of poor inputs as well as of a lack of validated conceptual models to constrain the LLM’s algorithms and output. Attempts to regulate these tools, and the concomitant hype, may only play into the commercial interests of their creators.

The ‘blind’ use of computational models to analyze anything (data or information), without the proper underpinning of conceptual modelling (data and algorithms), is dangerous and leads to all kinds of meaningless extrapolations, including the famous ‘hallucinations’ of LLM outputs.

This article is a response from a developing country researcher. It supports the current advancement and opportunities of Artificial Intelligence (AI) but seeks cautionary measures before it is too late.