Yes, that’s how Bayesianism is supposed to work. It’s called Bayesian Updating.
You don’t wake up every day with a child’s naivete about whether the sun will rise or not, you have a prior belief that is refined by knowledge combined with the weight of previous evidence.
Then, upon observing that the sun did in fact rise on this new morning, your belief that the sun rises every day gets that much stronger going into the next day.
But you can’t use that same “let’s be patient” logic of how to interpret time horizons to go back and have the improving problem-solving capability represented by those time horizons be the driver of the hypothesized superexponential growth in fixed-width time steps.
Consider: the proposed model says that some time in 2029, the 80% time horizon on cutting edge AI models will increase by 100 orders of magnitude within a span of nanoseconds. How is an LLM supposed to make self-improvements on the order of googol-sized steps, which for all we know is itself a very long horizon difficulty task, in less time than it takes for an electron to cross the width of a CPU?
You’re totally right that long-time-horizon AI grades are feasible and meaningful if you grant them the ability to work at it for a proportionate fraction of time, but then they aren’t compatible with the AI2027 story as a metric that relates to development speed.
This post seems to fundamentally misunderstand how Bayesian reasoning works.
First of all, the opening “paradox” isn’t one. If you have an inconclusive prior belief, and then you apply inconclusive data, you should have an inconclusive result. That’s not weird. Why is it surprising?
Secondly, the argument that follows is adding more conditions to the thesis that muddy the waters. P(A, B) is always less than P(A) if P(B) > 0. The probability that a coin lands on heads and that it’s tuesday is always less than the probability that a coin lands on heads. That fact does not tell you anything about the probability of the coin itself, and trying to include the fact that coins land face up on tuesdays less than coins land face up into your beliefs on the nature of coins, you will go mad because those things are not related and the probability relationships are tautological.
Thirdly, the post ignores marginal likelihood, the denominator of Bayes theorem. If there are alternative explanations for evidence[1], then it is weak evidence, and should not result in a large change in belief. It’s not just about how likely the evidence is given a thesis, it’s about how likely some evidence is given that thesis as opposed compared to all others. P(cough|COVID-19) is very high, almost 1. But that’s not really worth much because P(cough|any other respiratory disease) is also very high, almost 1, and 1/1=1, so the posterior = the prior.
Finally and most importantly, the thesis is that priors should include past evidence, which is like… yeah. That’s how bayesianism works. You calculate the posterior given a new piece of data, and that posterior becomes your prior for the next piece of data. This is Bayesian Updating.
When you want to decide whether to believe something, you don’t have to start from the most naive argument from first principles every single time. Beliefs are refined over time by evidence. When you see new evidence, your reasoning doesn’t start from what you knew when you were a baby, it starts from what you knew the moment before exposure to new evidence.
For example, parallax making objects in narrow FOV videos appear to look faster than they really are.
In the “go fast” video you linked, the object is laser-rangefinder measured to be 3.4 miles away from the camera. (that sounds like a big distance, but in the aviation world it is very close. If the object had a TCAS transponder they would have been getting a collision alarm).
Their altitude is 25,000 feet, while the camera is at a downward angle of −25 degrees. Some simple SOHCAHTOA trig tells us the background is 11.1 miles away, almost 4x more distant than the object itself, so of course the background moves fast in a LITENING pod’s 1 degree wide NAR mode FOV.
They are also closing on it (Vc=220, distance to the object is closing at 220 knots) and passing it (bearing changes from 40 degrees left to 60 degrees left). They struggle to lock onto it with the camera because they are going fast, the object is very close, and the field of view is very narrow, which put together creates a nightmare scenario for getting an optical spot track, like hitting a mailbox with a dart thrown from a moving car, I speak from experience using these pods.
The video is completely consistent with the object in question being a balloon blowing in the wind.
Bringing it all back, the marginal likelihood of such a video existing P(video | no aliens) is 100%, so even if P(video | aliens) is also 100%, the posterior = the prior, because the video is poor evidence.