It’s a much much bigger issue than this. Would you rather live in a world where other countries have good AI and you do not? Would you like it if only China has powerful AI? I get the copyright issue, but some things are more important than other things. This is an arms race, and everyone slowing down isn’t exactly an option.
It seems like you severely misunderstand what “AI” as we have it nowadays is (it’s not actual AI) and what it is capable (not very much) and most importantly not capable of (most things it is advertised to do). Even if investor magazines and tech CEOs try to make it seem like that, we’re not one step away from creating HAL9000. LLMs are extremely over hyped and in the most areas they have been deployed a straight up dysfunctional scam. The only arms race that is happening right now is about who can waste the most money and violate the most privacy laws with this nonsense while all the necessary data centers and their insane power and water demands accelerate the destruction of our environment even more.
Sure, thanks for your interest. It’s an incomplete picture, but we can think of LLMs as an abstraction of all the meaningful connections within a dataset to a higher dimensional space - one that can be explored. That alone is an insane accomplishment that is changing some of the pillars of data analysis and knowledge work. But that’s just the contribution of the “Attention is All You Need” paper. Many implementations of modern generative AI combine LLM inference in agentic networks, with GANs, and with rules-based processing. Extracting connections is just one part of one part of a modern AI implementation.
The emergent properties of GPT4 are enough to point toward this exponential curve continuing. Theory of mind (and therefore deception) as well as relational spatial awareness (usually illustrated with stacking problems) developed solely from increasing the parameter count describing the neural network. These were unexpected capabilities. As a result, there is an almost literal arms race on the hardware side to see what other emergent properties exist at higher model sizes. With some poetic license, we’re rending function from form so quickly and effectively that it’s seen by some as freeing and others as a sacrilege.
Some of the most interesting work on why these capabilities emerge and how we might gain some insight (and control) from exploring the mechanisms is being done by Anthropic and by users at Hugging Face. They discovered that when specific neurons in Claude’s net are stimulated, everything it responds with will in some way become about the Golden Gate Bridge, for instance. This sort of probing is perhaps a better route to progress than blindly chasing more size (despite its recent success). But only time will tell. Certainly, Google and MS have had a lot of unforced errors fumbling over themselves to stay in what they think is the race.
I’m happy to take the time to alter your perspective, if you are open to new information.
You took some time, but spent it explaining at a fairly technical level, rather than a lamens term approach. I doubt you managed to change many people’s perspective, but you maybe reinforced some.
You could have a much more complex understanding of what they are. It isn’t nearly as simple as you are imagining. If you genuinely are curious about what you’re overlooking, then here is a link.
Another W for the EU. I just hope they stop making so many sus decisions and don’t accept the chat control laws and stuff like that
What is a W?
Win I guess.
Win/Loss records are generally abbreviated as W/L. Take the L is it’s opposite.
Basically it’s a victory of any kind
Not sure when people started to use ‘W’. It appears multiple times in this thread
Edit: then again I’m of a generation where ‘y’ means why and not yes. Maybe I’m just not hip anymore sadfaceemoticon.jpg
y
I wanna say it became a thing from Twitch streamers when e sports was a big thing, but I’m by no means sure that that’s correct.
Is it definitely a W that EU perspectives won’t be as represented in the AI programs that we are all using?
Is it definitely a W if a government allows privacy-invasive, legally grey and copyrighted-material-stealing technologies?
It’s a much much bigger issue than this. Would you rather live in a world where other countries have good AI and you do not? Would you like it if only China has powerful AI? I get the copyright issue, but some things are more important than other things. This is an arms race, and everyone slowing down isn’t exactly an option.
It seems like you severely misunderstand what “AI” as we have it nowadays is (it’s not actual AI) and what it is capable (not very much) and most importantly not capable of (most things it is advertised to do). Even if investor magazines and tech CEOs try to make it seem like that, we’re not one step away from creating HAL9000. LLMs are extremely over hyped and in the most areas they have been deployed a straight up dysfunctional scam. The only arms race that is happening right now is about who can waste the most money and violate the most privacy laws with this nonsense while all the necessary data centers and their insane power and water demands accelerate the destruction of our environment even more.
I’m happy to take the time to alter your perspective, if you are open to new information.
Would you give your perspective anyway, as I would be quite interested, although I’m not the one you talked to?
Sure, thanks for your interest. It’s an incomplete picture, but we can think of LLMs as an abstraction of all the meaningful connections within a dataset to a higher dimensional space - one that can be explored. That alone is an insane accomplishment that is changing some of the pillars of data analysis and knowledge work. But that’s just the contribution of the “Attention is All You Need” paper. Many implementations of modern generative AI combine LLM inference in agentic networks, with GANs, and with rules-based processing. Extracting connections is just one part of one part of a modern AI implementation.
The emergent properties of GPT4 are enough to point toward this exponential curve continuing. Theory of mind (and therefore deception) as well as relational spatial awareness (usually illustrated with stacking problems) developed solely from increasing the parameter count describing the neural network. These were unexpected capabilities. As a result, there is an almost literal arms race on the hardware side to see what other emergent properties exist at higher model sizes. With some poetic license, we’re rending function from form so quickly and effectively that it’s seen by some as freeing and others as a sacrilege.
Some of the most interesting work on why these capabilities emerge and how we might gain some insight (and control) from exploring the mechanisms is being done by Anthropic and by users at Hugging Face. They discovered that when specific neurons in Claude’s net are stimulated, everything it responds with will in some way become about the Golden Gate Bridge, for instance. This sort of probing is perhaps a better route to progress than blindly chasing more size (despite its recent success). But only time will tell. Certainly, Google and MS have had a lot of unforced errors fumbling over themselves to stay in what they think is the race.
Thank you very much for those insights!!
Thanks so much for taking the time to explain this. I was just going to give them a link.
Drivel
You took some time, but spent it explaining at a fairly technical level, rather than a lamens term approach. I doubt you managed to change many people’s perspective, but you maybe reinforced some.
The plagiarism machines aren’t what you think they are.
You could have a much more complex understanding of what they are. It isn’t nearly as simple as you are imagining. If you genuinely are curious about what you’re overlooking, then here is a link.
https://situational-awareness.ai/
I would much rather live in a country with no good AI.
I think I vehemently disagree with you on principle, but it’s a point I hadn’t thought of before, so thank you for pointing out that perspective.
Yeah, what a loss. Now it will only be able to suggest glue on burgers. /s
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