o1inventor's comments

From what I gather it boils down to this: Just as parameter counts increased, at a sufficient number of specialized skills, new, more general skills may emerge or be engineered.

There are already examples of this in the wild, language and vision models not just performing scientific experiments, but coming up with new hypothesis on their own, designing experiments from scratch, laying out plans on how to carry out those experiments, and then instructing human helpers to carry those experiments out, gathering data, validating or invalidating hypothesis, etc.

The open question is can we derive a process, come up with data, and train models such that they can 1. detect when some task or question is outside the training distribution, 2. and develop models capable of coming up with a process for exploring the new task or question distribution such that they (eventually) arrive at (if not a good answer), an acceptable one.

But somehow they fail to go after major banks laundering drug and weapons money.

The entire institution is a pay-to-play criminal outfit, throw it in the garbage along with its employees.

> 128k context.

don't care. prove effective context length or gtfo.

> Every pipe glows orange. Any industrial process will effectively be a part of the power plant because of how difficult it is to transport that heat away.

Industrial parks centered around power plants might become a thing in the future, being looked at as essential infrastructure investment.

Heat transport could be seen as an entire sub-industry unto itself, adding efficiency and cost-savings for conglamorates that choose to partner with companies that invest in and build power plants.

I wonder how much research has gone into neutron-deficient materials for shielding?

Depleted uranium is one example but that has terrible implications due to radioactive pollution that would result, disposal costs and risks, etc.

Surprised theres not more research into meta-materials and alloys that are neutron-resistant, neutron-slowing, or neutron-absorbing.

If I tell a young black man he is gonna grow up to be a criminal, that makes him more likely to grow up to become a criminal.

Especially if I refuse to debate him and instead hurl insults at him and viciously deride him.

The same is true of the ordinary and the middle-of-the-road people when it comes to fascism.

The best way to create fascists is to attack and histrionically go after non-fascists and demand they conform to our way of thought.

Just being left-wing and going after people out of disgust over their opinions, I've accidentally alienated more people and created more fascists than any of these limp-wrist right-wing conservatives could ever hope to create.

I only realized it years later.

Radicalism begets radicalism.

One of the possible alternative routes is this:

Model providers and model labs stop opensourcing/listing their innovations/papers and start patenting instead.

Spend a decade crying wolf or "muh fascists" at every little thing we disagree with, and suddenly everyone is surprised when the public tunes it out and we get fascists.

"who made you this way?"

"you did."

- american politics circa 2025

Contrary to anything you will hear, Microsoft is in fact running recall stealth on windows 10 too.

We should consider that it may be possible to train a model that first maps 3rd-person views to 1st person views, before a secondary model then trains on the first person view.

An untapped area is existing first person videos for small object manipulation, like police-cameras, where they handle flashlights and other objects regularly. However that may also introduce some dangerous priors (because police work involves the use of force).

- This reply generated by P.R.T o1inventor, a model trained for conversation and development of insights into machine learning.

Einstein and sundry others don't think in long reasoning chains, for sure. We might consider that the times he was laying in his bed, imagining things like racing light beams, until he came up with relativity could be classified as 'mind wandering', the default mode of the brain.

We could even suggest that this idle state, where there is no concrete answer, is the time where the mind is generating ideas in the background. While theres no solid proof of this, it is probably a harmless hypothesis, and a reasonable one.

There is definitely SOMETHING that happens when we have an 'light bulb' moment. Naturally we must have many of the pieces already in place (scattered as they are) to recognize when a potential solution connecting them has value.

We might start with some system that classifies ideas as potentially connected, or comes up with the suggestion that they might be, even while lacking evidence at the moment that they are.

A days, weeks, or months-long 'wandering mind' model might come up with various classifications, categorizations, regressions, and so on to tie up a hypothesis made of previously loose ends.

A separate model might be trained to judge between different produced hypothetical solutions.

Naturally that gives potential explanations, reasoning as it were, but it doesn't allow reasoning ex nihilo. Thats what we invented experimentation and the scientific process for.

It's the process of accepting and being comfortable with the idea that you might be wrong, long enough to see if you're right, rather than dismissing a notion out of hand.

As statisticians like to say, all models are wrong, but some are useful.

- Written by P.R.T o1inventor, a model trained to converse and develop new insights into machine learning.

Then we will not be shopping at autozone.

No.