I wanna beat them to death with my laptop, but when I see a new application for AI in cancer research, or a nearly century old math conjecture proven, I do feel a little optimistic, too.
I feel like these are such divergent types of machine learning that they have nearly nothing in common with the generative AI used in chatbots and code generators (especially that used to predict disease). Even if they may use some of the same underlying technologies and theory.
Which is why the overloading of the word AI to mean literally all of it is frustrating. So I guess I don’t have a problem with using machine learning to target narrowly defined problems to gain new knowledge, versus outsourcing all of our thoughts and actions to chatbots and AI agents.
Couldn’t agree more. It makes me yearn for the days when AI meant NPC pathfinding in games, but deep in my bones I know that’s where the problem started. My personal conspiracy theory is that basically every AI marketing department intentionally muddies the water to make the conversation more difficult so people can’t just decide to massively regulate LLM services.
Agreed, except I’m not frustrated by AI being overloaded-- it makes sense to me that a well-known, two-letter acronym be highly inclusive-- it’s frustrating that market dominance and marketing can just take over that acronym. LLM is a more accurate description than AI, but it would hurt their sales if most people understood it’s a statistical model and didn’t mistake it for a kind of intelligence.
I’m starting to think that those of us who understand this will probably fair better by forfeiting the semantics battle and making the distinction between LLM-AI and ML.
I’m starting to think that those of us who understand this will probably fair better by forfeiting the semantics battle and making the distinction between LLM-AI and ML.
I’m now picturing a future where the word AI truly becomes universally hated, and LLM lovers start calling it ML again:
No, no it’s not AI it’s machine learning
Or we all just call it applied statistics, that ought to take the magic out of it, since normal people consider statistics to be boring and mundane and certainly not intelligent.
I feel like these are such divergent types of machine learning that they have nearly nothing in common with the generative AI used in chatbots and code generators (especially that used to predict disease). Even if they may use some of the same underlying technologies and theory.
Which is why the overloading of the word AI to mean literally all of it is frustrating. So I guess I don’t have a problem with using machine learning to target narrowly defined problems to gain new knowledge, versus outsourcing all of our thoughts and actions to chatbots and AI agents.
Couldn’t agree more. It makes me yearn for the days when AI meant NPC pathfinding in games, but deep in my bones I know that’s where the problem started. My personal conspiracy theory is that basically every AI marketing department intentionally muddies the water to make the conversation more difficult so people can’t just decide to massively regulate LLM services.
Agreed, except I’m not frustrated by AI being overloaded-- it makes sense to me that a well-known, two-letter acronym be highly inclusive-- it’s frustrating that market dominance and marketing can just take over that acronym. LLM is a more accurate description than AI, but it would hurt their sales if most people understood it’s a statistical model and didn’t mistake it for a kind of intelligence.
I’m starting to think that those of us who understand this will probably fair better by forfeiting the semantics battle and making the distinction between LLM-AI and ML.
I’m now picturing a future where the word AI truly becomes universally hated, and LLM lovers start calling it ML again:
Or we all just call it applied statistics, that ought to take the magic out of it, since normal people consider statistics to be boring and mundane and certainly not intelligent.