I think you’re conflating AGI (artificial general intelligence) with AI here amongst other misconceptions.
Yes, transformer LLMs are trained to predict the next word, but larger ones (like GPT3) exhibit emergent abilities that nobody really predicted.
I’m curious what you think something new might be. I had GPT4 write a whole bunch of code lately to fit into existing systems I created. I guarantee no systems like that were in its training data because it’s a system that deals with GPT4 and LLM functionality that didn’t exist when the training data was collected. One of my first experiments with GPT3 was an app that could make video game pitches. I can guarantee some of the weird things my team made with that were new ideas.
Does it really understand anything? Who knows. Does it matter if it can act like it does? See also the Chinese room experiment.
Coding is a poor example. It’s a language. It’s simply translating from one language (pseudocode) to another (the programming language you requested). As long as you give it clear instructions, it’s not “solving” anything. It’s like saying Google translate created something new because you asked it to translate a sentence no one has asked before.
Honestly, I don’t think there’s as significant “emergent” capabilities beyond it just being better at performing than they expected.
I suppose that’s my bad for the article I linked which doesn’t really go into specifics on what the capabilities are. One of the big ones is tool use. You can give it a task and a list of tools to use and it can use the tools to compete the task. this capability alone makes a huge amount of automations possible that weren’t possible before LLMs.
I’m getting the impression your definition of “new” is “something only a human could come up with.” Please correct me if I’m wrong here. People who create completely novel things are few and far between. They’re typically the ones remembered for centuries. Though honestly, even then they’re usually standing on the shoulders of those before them. Just like what AI does. Look at AlphaFold, an AI that is rapidly accellerating disease research and solving many other hard problems.
Anyway, if I can prompt the AI to write code for me and even if you don’t count that as something new, it’s a force multiplier on my job, which is a huge benefit. As Hanabie said, there’s going to be a lot of changes in jobs due to AI and those who don’t adapt are going to be left behind. I’m commenting here in hopes of helping people see that and not get left behind.
You realize you essentially just argued my point though. That’s basically my analogy with the cloud. It’s not replacing anything. I could have been clearer I suppose, but the crux of it is that it’s not replacement.
Oh hmm. Are you just saying that it can’t fully replace people at jobs? Because I generally do agree with that at least with current models and methods of using them. It’s getting close though, and I think within a year or two we will be there for at least a bunch of professions. But on the other hand, if it makes workers in some jobs 2x more productive then the company only needs to keep half of those workers to maintain the same output. I think this is where it’s going to start / has already started.
I think you’re conflating AGI (artificial general intelligence) with AI here amongst other misconceptions.
Yes, transformer LLMs are trained to predict the next word, but larger ones (like GPT3) exhibit emergent abilities that nobody really predicted.
I’m curious what you think something new might be. I had GPT4 write a whole bunch of code lately to fit into existing systems I created. I guarantee no systems like that were in its training data because it’s a system that deals with GPT4 and LLM functionality that didn’t exist when the training data was collected. One of my first experiments with GPT3 was an app that could make video game pitches. I can guarantee some of the weird things my team made with that were new ideas.
Does it really understand anything? Who knows. Does it matter if it can act like it does? See also the Chinese room experiment.
Coding is a poor example. It’s a language. It’s simply translating from one language (pseudocode) to another (the programming language you requested). As long as you give it clear instructions, it’s not “solving” anything. It’s like saying Google translate created something new because you asked it to translate a sentence no one has asked before.
Honestly, I don’t think there’s as significant “emergent” capabilities beyond it just being better at performing than they expected.
I suppose that’s my bad for the article I linked which doesn’t really go into specifics on what the capabilities are. One of the big ones is tool use. You can give it a task and a list of tools to use and it can use the tools to compete the task. this capability alone makes a huge amount of automations possible that weren’t possible before LLMs.
I’m getting the impression your definition of “new” is “something only a human could come up with.” Please correct me if I’m wrong here. People who create completely novel things are few and far between. They’re typically the ones remembered for centuries. Though honestly, even then they’re usually standing on the shoulders of those before them. Just like what AI does. Look at AlphaFold, an AI that is rapidly accellerating disease research and solving many other hard problems.
Anyway, if I can prompt the AI to write code for me and even if you don’t count that as something new, it’s a force multiplier on my job, which is a huge benefit. As Hanabie said, there’s going to be a lot of changes in jobs due to AI and those who don’t adapt are going to be left behind. I’m commenting here in hopes of helping people see that and not get left behind.
You realize you essentially just argued my point though. That’s basically my analogy with the cloud. It’s not replacing anything. I could have been clearer I suppose, but the crux of it is that it’s not replacement.
Oh hmm. Are you just saying that it can’t fully replace people at jobs? Because I generally do agree with that at least with current models and methods of using them. It’s getting close though, and I think within a year or two we will be there for at least a bunch of professions. But on the other hand, if it makes workers in some jobs 2x more productive then the company only needs to keep half of those workers to maintain the same output. I think this is where it’s going to start / has already started.