Part 10: I Discovered How ChatGPT-Like AI Can Have A High Accuracy In Predicting The Future. Now An Academic Paper Confirms it.
Imagine an Artificial Intelligence (AI) that can peer into the future. Not through statistical models or data analysis, but by spinning rich, imaginative tales set in the times ahead. Unlock the large potential of predictive powers of language AI through immersive future narratives that blend realms of the corpus of its knowledge. One story at a time, we explore the wondrous possibilities that may unfold. The concept of using large language models to predict future events by leveraging their narrative generation capabilities is a fascinating and innovative approach. Imagine being able to tap into the vast knowledge and reasoning abilities of these advanced AI systems, and have them craft fictional stories set in the future, with characters recounting events that have not yet occurred in the real world.
I have experimented with using Large Language Models (LLMs) to “predict” the future for quite some time. This is an example from April 17, 2023. I knew that with the massive corpus of data and the right motif in prompting, I could elicit behaviors that are superior to the Monte Carlo method of “predicting”.
A confabulation? Of course, this is the effect I was looking for.
The Monte Carlo method is a computational technique that involves using random sampling and statistical modeling to estimate and predict outcomes. In the context of predicting future events, the Monte Carlo method can be employed by running multiple simulations that incorporate various input variables and their associated probabilities.
The success rate of the Monte Carlo method in prediction depends on several factors, including the quality and quantity of data used, the accuracy of the probability distributions assigned to the input variables, and the complexity of the system or phenomenon being modeled. Generally, the more data and the better the understanding of the underlying processes, the more accurate the Monte Carlo predictions will be.
While the Monte Carlo method does not provide a 100% accurate prediction, it can yield valuable insights and probability distributions for potential future outcomes. In many real-world applications, such as finance, project management, and risk analysis, the Monte Carlo method has proven to be a useful tool for making informed decisions based on the range of possible future scenarios and their associated likelihoods.
By crafting “future narratives,” the language models are essentially tasked with extrapolating and synthesizing information from their training data to construct plausible scenarios about what could transpire in the time ahead. This process allows the models to demonstrate their predictive prowess in a unique and creative manner, weaving together various threads of information to paint a picture of how the future might unfold.
The potential applications of this approach are numerous and exciting. For instance, in the realm of economics, these future narratives could provide valuable insights into potential market trends, policy implications, and the ripple effects of various economic decisions. Imagine having the language model impersonate a prominent figure, such as the Federal Reserve Chair, and narrate a story about how their actions and decisions might shape the economic landscape in the coming years.
The beauty of this approach lies in its ability to harness the models’ creativity and imaginative capabilities, allowing them to explore a multitude of potential futures in a way that transcends mere statistical predictions or data analysis. By weaving together facts, trends, and hypothetical scenarios, these future narratives offer a unique and immersive way to explore the realm of possibilities that lie ahead.
It is very easy to dismiss anything that sound fantasitical as to “predict” the future. Yet our research and this new acedemic report has had a robust ourcome on 3 week to 16 month “predictions” that are substatilly above the statical avreage one would expect to see.
In this member only article we will delve into the technical aspects of this new and “unapproved” way to use LLMs. Test it yourself and draw your own conclusions.
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