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Interesting. This was one of the two areas the AI as Normal Technology folks specifically called out as a bet that AI will not outperform humans at. > Concrete
by dwohnitmok 17d ago
Interesting. This was one of the two areas the AI as Normal Technology folks specifically called out as a bet that AI will not outperform humans at.
> Concretely, we propose two such areas: forecasting and persuasion. We predict that AI will not be able to meaningfully outperform trained humans (particularly teams of humans and especially if augmented with simple automated tools) at forecasting geopolitical events (say elections). We make the same prediction for the task of persuading people to act against their own self-interest.
Curious to hear what their take is now.
https://www.normaltech.ai/p/ai-as-normal-technology https://www.normaltech.ai/p/ai-as-normal-technology
- lubujackson 17d agoI don't at all understand this perspective. It seems to me that LLMs excel at a few things, and synthesizing data is a big one, which is very much the domain of forecasting. The challenge is understanding which signals are relevant for a forecast, but with enough historical context and structured data, LLMs appear to be almost perfectly designed for the task. For example, I let Google AI see my fantasy football team on Sleeper and make recommendations. It is helpful because it sees everything about my team, the league settings, player rankings, etc. and can make relevant recommendations. But the recommendations are only as good as the source data allows. If there was a massive repository of data about WRs who went through Nebraska's program and how that translates to NFL performance in year 1, or how rainy weather is likely to affect Josh Allen's performance on the road, or the impact of playing Thursday night games on a short week in relation to defense performance. If those billions of data points were embedded in a model, imagine how much better recommendations/predictions could get.