I’m curious if at this point it would be possible to train an LLM on this type of estimation. But I don’t understand Ai really well or if they are even good at predictive work. Im going off of research that involved predicting disease (I think it was diabetes)
Had to run this through ChatGPT and funny enough it sites this article in the first paragraph. It also has no idea about Kennedy dropping out and endorsing Trump.
As of now, predictions for the 2024 U.S. presidential election suggest a tight race. Kamala Harris, the Democratic nominee, has been forecasted by election expert Allan Lichtman to win, based on his “13 Keys to the White House” model. Lichtman has a strong track record, having correctly predicted most U.S. presidential elections since 1984. He argues that Harris holds more favorable indicators than her main rival, Donald Trump, who is seeking a second non-consecutive term.
On the other hand, some models, like those from Race to the WH, show a more competitive scenario, with polling and swing state dynamics still evolving. Trump’s ongoing legal issues and the emergence of strong third-party candidates like Robert F. Kennedy Jr. add complexity to the race.
Ultimately, the final outcome will depend heavily on how these factors unfold in the coming months, as both candidates continue their campaigns
LLMs don’t handle booleans, and the 13 keys is an open statement, so the best you could do is train 13 neural networks to determine each of the keys, but you’d need a lot of data for that I suspect we simply don’t have.
It’d probably be better to train a neural network to just output probabilities of each candidate winning based on specific information, like polling data.
I’m curious if at this point it would be possible to train an LLM on this type of estimation. But I don’t understand Ai really well or if they are even good at predictive work. Im going off of research that involved predicting disease (I think it was diabetes)
It would say trump would win 99% of the time because he already wone once
Well… I meant train in on a larger data set going back for republican/Dems a long way back
It would do the same thing
Had to run this through ChatGPT and funny enough it sites this article in the first paragraph. It also has no idea about Kennedy dropping out and endorsing Trump.
LLMs don’t handle booleans, and the 13 keys is an open statement, so the best you could do is train 13 neural networks to determine each of the keys, but you’d need a lot of data for that I suspect we simply don’t have.
It’d probably be better to train a neural network to just output probabilities of each candidate winning based on specific information, like polling data.