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"or its latest update, which it released Wednesday, the COVID-19 Scenario Modeling Hub combined nine different mathematical models from different research groups to get an outlook for the pandemic for the next six months. "Any of us who have been following this closely, given what happened with delta, are going to be really cautious about too much optimism," says Justin Lessler at the University of North Carolina, who helps run the hub. "But I do think that the trajectory is towards improvement for most of the country," he says. The modelers developed four potential scenarios, taking into account whether or not childhood vaccinations take off and whether a more infectious new variant should emerge. "

Much of current science deals with even more complicated systems, and similarly lacks exact solutions. Such models have to be "computational" - describing how a system changes from one instant to the next. But there is no way to determine the exact state at some time in the future other than by "simulating" its evolution in this way. Weather forecasting is a familiar example; until the advent of computers in the 1950s, it was impossible to predict future weather faster than it actually happened.

"Therefore even Ofqual's best model significantly worsened grade accuracy for most A-level subjects when the cohort size is below 50, which is common (almost 62% of the total in 2019). For GCSEs, even with larger cohorts, the best model would have worsened the grade accuracy for Maths and Sciences. A very conservative figure of 25% of wrong grades would have amounted to 180,000 wrong A-level grades and 1.25 million wrong GCSE grades."

The moral of that story is clear. The spreadsheet was a revolutionary technology when it first appeared in 1978, just as ChatGPT was in 2022. But now it's a routine, integral part of organisation life. The advent of AI "agents" built from GPT-like models looks like following a similar pattern. In turn, the organisations that have absorbed them will also evolve. And then the world may eventually rediscover that famous adage attributed to Marshall McLuhan's colleague John Culkin: "We shape our tools and then the tools shape us."

"Automatically produced texts use language models derived from statistical analysis of vast corpuses of human-generated text to produce machine-generated texts that can be very hard for a human to distinguish from text produced by another human. These models could help malicious actors in many ways, including generating convincing spam, reviews, and comments -- so it's really important to develop tools that can help us distinguish between human-generated and machine-generated texts."

"The intriguing echo of Eliza in thinking about ChatGPT is that people regard it as magical even though they know how it works - as a "stochastic parrot" (in the words of Timnit Gebru, a well-known researcher) or as a machine for "hi-tech plagiarism" (Noam Chomsky). But actually we do not know the half of it yet - not the CO2 emissions incurred in training its underlying language model or the carbon footprint of all those delighted interactions people are having with it. Or, pace Chomsky, that the technology only exists because of its unauthorised appropriation of the creative work of millions of people that just happened to be lying around on the web? What's the business model behind these tools? And so on. Answer: we don't know."

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