AI is good and cheap now because businesses are funding it at a loss, so not sure what you mean here.
The problem is that it’s cheap, so that anyone can make whatever they want and most people make low quality slop, hence why it’s not “good” in your eyes.
Making a cheap or efficient AI doesn’t help the end user in any way.
I’m using “good” in almost a moral sense. The quality of output from LLMs and generative AI is already about as good as it can get from a technical standpoint, continuing to throw money and data at it will only result in minimal improvement.
What I mean by “good AI” is the potential of new types of AI models to be trained for things like diagnosing cancer, and and other predictive tasks that we haven’t thought of yet that actually have the potential to help humanity (and not just put artists and authors out of their jobs).
The work of training new, useful AI models is going to be done by scientists and researchers, probably on a limited budgets because there won’t be a clear profit motive, and they won’t be able to afford thousands of $20,000 GPUs like are being thrown at LLMs and generative AI today. But as the current AI race crashes and burns, the used hardware of today will be more affordable and hopefully actually get used for useful AI projects.
Although I am pretty sure AI is already used in the medical field for research and diagnosis. This “AI everywhere” trend you are seeing is the result of everyone trying to stick and use AI in every which way.
The thing about the AI boom is that lots of money is being invested into all fields. A bubble pop would result in investment money drying up everywhere, not make access to AI more affordable as you are suggesting.
It appears good and cheap. But it’s actually burning money, energy and water like crazy.
I think somebody mentioned to generate a 10 second video, it’s the equivalent in energy consumption as driving a bike for 100km.
It’s not sustainable.
I think the thing the person above you is referring to is if we ever manage to make LLMs and such which can be run locally on a phone or laptop with good results. That would make people experiment and try out things themselves, instead of being dependent on paying monthly for some services that can change anytime.
i mean. i have a 15 amp fuse in my apartment and a 10 second cideo takes like 10 minutes to make, i dont know how much energy a 4090 draws but anyone that has an issue with me using mine to generate a 10 second bideo better not play pc games.
You and OP are misunderstanding what is meant by good and cheap.
It’s not cheap from a resource perspective like you say. However that is irrelevant for the end user. It’s “cheap” already because it is either free or costs considerably less for the user than the cost of the resources used. OpenAI or Meta or Twitter are paying the cost. You do not need to pay for a monthly subscription to use AI.
So the quality of the content created is not limited by cost.
If the AI bubble popped, this won’t improve AI quality.
AI is good and cheap now because businesses are funding it at a loss, so not sure what you mean here.
The problem is that it’s cheap, so that anyone can make whatever they want and most people make low quality slop, hence why it’s not “good” in your eyes.
Making a cheap or efficient AI doesn’t help the end user in any way.
I’m using “good” in almost a moral sense. The quality of output from LLMs and generative AI is already about as good as it can get from a technical standpoint, continuing to throw money and data at it will only result in minimal improvement.
What I mean by “good AI” is the potential of new types of AI models to be trained for things like diagnosing cancer, and and other predictive tasks that we haven’t thought of yet that actually have the potential to help humanity (and not just put artists and authors out of their jobs).
The work of training new, useful AI models is going to be done by scientists and researchers, probably on a limited budgets because there won’t be a clear profit motive, and they won’t be able to afford thousands of $20,000 GPUs like are being thrown at LLMs and generative AI today. But as the current AI race crashes and burns, the used hardware of today will be more affordable and hopefully actually get used for useful AI projects.
Ok. Thanks for clarifying.
Although I am pretty sure AI is already used in the medical field for research and diagnosis. This “AI everywhere” trend you are seeing is the result of everyone trying to stick and use AI in every which way.
The thing about the AI boom is that lots of money is being invested into all fields. A bubble pop would result in investment money drying up everywhere, not make access to AI more affordable as you are suggesting.
It appears good and cheap. But it’s actually burning money, energy and water like crazy. I think somebody mentioned to generate a 10 second video, it’s the equivalent in energy consumption as driving a bike for 100km.
It’s not sustainable. I think the thing the person above you is referring to is if we ever manage to make LLMs and such which can be run locally on a phone or laptop with good results. That would make people experiment and try out things themselves, instead of being dependent on paying monthly for some services that can change anytime.
i mean. i have a 15 amp fuse in my apartment and a 10 second cideo takes like 10 minutes to make, i dont know how much energy a 4090 draws but anyone that has an issue with me using mine to generate a 10 second bideo better not play pc games.
You and OP are misunderstanding what is meant by good and cheap.
It’s not cheap from a resource perspective like you say. However that is irrelevant for the end user. It’s “cheap” already because it is either free or costs considerably less for the user than the cost of the resources used. OpenAI or Meta or Twitter are paying the cost. You do not need to pay for a monthly subscription to use AI.
So the quality of the content created is not limited by cost.
If the AI bubble popped, this won’t improve AI quality.