Considering the fact that there are open weight models that are pretty close™️ to frontier has me thinking otherwise. Yes I think the frontier companies will probably be face to face with collapse, but capable (and cheap) models already exist and will continue to improve. I think it’s much more likely that companies will simply run their own models (possibly custom agent harness as well) and have all the benefits they were looking for at a fraction of the cost. That being said I do think there will be a significant plateau of capabilities in the next year or so and leadership will realize these are just helpful tools and nothing more.
All that is to say, I don’t agree with your assertion that coders who are not using AI will have any sort of competitive advantage. In fact I think they’re hurting themselves in the long run. I think skeptical engineers who have a foot in both worlds are actually the best equipped for the future. Accelerate your workflow but not at the expense of quality/security.
What? He’s always consistently been the heel of the show. The writers explaining his psychopathy to make you understand is not the same thing as signing off on it. They’ve made that more clear as the seasons have progressed.
You realize the implication of what you wrote right? You understand what an implication is? Trying to be cute and hiding behind technicalities is pathetic and cowardly. Which honestly is par for the course for pseudo intellectual tankie trash lmao
Eh it’s the illusion of speed. Scaling brought enormous returns from GPT-3 -> GPT-4 but it’s been far less significant for every major release since. To compensate for this, every research lab is coming up with new ways to extract value of it of models: CoT, RL, Agent Harness etc
However, these are all hacks to make LLMs more efficient or (try) to make them more reliable. They still have significant drawbacks which will take years (probably decades) to ever get them to the point where they can reliably replace knowledge workers. China knows this and is taking a far different approach to LLM development (not a tankie fyi). Scaling is a horrible idea which will burn billions of dollars with an astronomically low chance of return.
I can agree with that largely but I still contend you’re conflating a few things to make that argument. Fundamentally an LLM will make predictions based on probability (ignoring temperature) and probability does not equal certainty.
What are you saying precisely? It’s well known that LLMs have non-deterministic output (Ilya Sutskever even claims as such). Are you saying the way it goes about retrieving tokens as deterministic?
Jfc dude get help. Seriously.
Yes China does all of those things. Really awesome to see you’ve challenged your own flawed reality so quickly 😄
Nice 😎
Considering the fact that there are open weight models that are pretty close™️ to frontier has me thinking otherwise. Yes I think the frontier companies will probably be face to face with collapse, but capable (and cheap) models already exist and will continue to improve. I think it’s much more likely that companies will simply run their own models (possibly custom agent harness as well) and have all the benefits they were looking for at a fraction of the cost. That being said I do think there will be a significant plateau of capabilities in the next year or so and leadership will realize these are just helpful tools and nothing more.
All that is to say, I don’t agree with your assertion that coders who are not using AI will have any sort of competitive advantage. In fact I think they’re hurting themselves in the long run. I think skeptical engineers who have a foot in both worlds are actually the best equipped for the future. Accelerate your workflow but not at the expense of quality/security.
What? He’s always consistently been the heel of the show. The writers explaining his psychopathy to make you understand is not the same thing as signing off on it. They’ve made that more clear as the seasons have progressed.
You realize the implication of what you wrote right? You understand what an implication is? Trying to be cute and hiding behind technicalities is pathetic and cowardly. Which honestly is par for the course for pseudo intellectual tankie trash lmao
Did you read your comment? It’s you. No spin needed.
I doubt that. What evidence do you have?
Incredible. What a magical place
If you told me this was a satirical Wikipedia article I would have believed you
Eh it’s the illusion of speed. Scaling brought enormous returns from GPT-3 -> GPT-4 but it’s been far less significant for every major release since. To compensate for this, every research lab is coming up with new ways to extract value of it of models: CoT, RL, Agent Harness etc
However, these are all hacks to make LLMs more efficient or (try) to make them more reliable. They still have significant drawbacks which will take years (probably decades) to ever get them to the point where they can reliably replace knowledge workers. China knows this and is taking a far different approach to LLM development (not a tankie fyi). Scaling is a horrible idea which will burn billions of dollars with an astronomically low chance of return.
Honestly you’re not wrong. I started to proxy and honestly it feels so good. I’m so sick of spending so much money on cards.
What’s wrong with Funcom?
I can agree with that largely but I still contend you’re conflating a few things to make that argument. Fundamentally an LLM will make predictions based on probability (ignoring temperature) and probability does not equal certainty.
It’s temperature primarily. That being said there is still a chance that an LLM can output values that are unexpected even at low temperatures.
What are you saying precisely? It’s well known that LLMs have non-deterministic output (Ilya Sutskever even claims as such). Are you saying the way it goes about retrieving tokens as deterministic?
Ah gotcha. I guess Fox engine is dead then