But at least we had fun who can forget stacksort.
3,005 karma · joined December 16, 2019
But at least we had fun who can forget stacksort.
The way I see it the devs that put that in place are saying, listen before even reading your contribution I am going to feed it to a LLM with these instructions and see what it spits out. Now I will extricate my self from being the LLM proxy so you deal with this output and when it's okay I will look at it.
Same as "listen bud until it passes linting I won't even look at it." Just a much more flexible and fuzzy linter.
Sure LLMs are not nearly as rigorous about their output but that doesn't mean it's entirely not useful.
At a minimum you can have your LLM summarize the other LLMs output and go from there.
The winner take all network effects are probably not there in this case, but still if Anthropic is a large player and has 20-30% of the market they could have a revenue in the hundreds of billions.
Double the size of the market to include most of the rest of the world and the 500B is suddenly under 20% market share.
If you are spending 50 - 60 grand a year on a paralegal will you balk at another 5 or 10 that will allow them to do double the work?
The total amount spent on salaries in he US alone is over 12T dollars.
Assuming AI becomes ever more useful and is widely adopted 500B in revenue is not that crazy.
Famous last words. https://en.wikipedia.org/wiki/Bitter_lesson
Also did you not see the quoted post at the end, they are already training ai on running material science labs so we are quickly moving away from the non-expert.
But it looks to me that the layoffs are due to AI but not because the positions are replaced, because capital is flowing to hardware instead of new hires. The growth engine of the tech industry used to be people, now it is clearly gpus. If you have 10 billion in the bank you are not going to aggressively expand your workforce, you are going to build datacenters.
The bitter lesson tells us that general _approaches_ outperform domain-specific understanding.
That doesn't mean a general model will outperform a domain specific one.
That is not overfitting, that is training for a specific task. The measure for overfitting is how well it works on previously unseen data, hence the 200 test images removed from training and validation. That set serves as a second order of validation for the researchers to check themselves they have not overfit the model to match only the training data.
Sure the preindustrial environment was cozy for cute and fuzzy things and ourselves.
But you are greatly underestimating life if you think we can kill it all off. Sure it might be slimes and jellies and fungi for a while but life has been there before.
The plea deal offer kind of says it all, if this reaches the court it will either very quickly be thrown out or they may drop it because it's not worth the time.
Which is fine and the reason behind qubes OS decision to have passwordless sudo enabled on qubes.
The manual override is not obvious and people have burned to death in crashes because they could not get the door open after the power went down.