nobodyisonething t1_jdte242 wrote on March 27, 2023 at 1:12 AM

There are limits to prediction that are rooted in the limits of what information can practically be gathered. So some seemingly mundane things like predicting the weather 60 days into the future may always be impossible no matter how powerful AI becomes.

However, predicting beyond the capacity of any human that ever lived or ever will live is something we can expect -- perhaps soon.

https://medium.com/@frankfont123/human-minds-and-data-streams-60c0909dc368

circleuranus OP t1_jduq1th wrote on March 27, 2023 at 10:20 AM

> However, predicting beyond the capacity of any human that ever lived or ever will live is something we can expect -- perhaps soon.

That is precisely the root of my concern. However a sufficiently powerful AI with historical data inputs will also be able to create a causal web, a "blueprint" of history with infinitely more connective strands of causality.

Think of the game "6 degrees of Kevin Bacon" for instance...a sufficiently powerful and well outfitted AI will not only be able to connect Kevin Bacon to every actor that exists, it will be able to make a connection to every person on earth that exists or has ever existed for which we have data. AND eventually for persons for which we don't have data. The AI will be able to "fill the gaps" in our understanding of history and generate a weighted probability of the "missing person" in a particular timeline.

Let's take a basic example of a historical event such as Caeser crossing the Rubicon. With sufficient referential data, we might be able to know the actual size of his army, the name of every man in that army, how many horses exactly, the weather of that day, the depth of the river on that day, the amount of time it actually took to make the crossing...in other words a complete picture.

We may be able to determine that Caeser crossed in just a few hours and was in the town of Remini by 1 o'clock. etc etc...

Once the system "cleans up" our history, it can begin work on current events...once it has a base of current statuses, it can then work on predictive models.

Mike shows up to work 10 minutes early without fail, Beth shows up to work exactly on time most of the time, Jeff is usually 5 minutes late, however Jeff's output outweighs Mike's output so his value add is higher even if he arrives late most days. Jeff is younger and in better physical condition than Beth so is likely to live longer and therefore fulfill his position at work for a longer period without interruptions of illness or disease. And this is just one officescenario for 1 company...tune that all the way up and the AI will be able to tell if Mike brought chicken salad or ham and cheese for lunch.