@Techmeme@techhub.socialOpenAI told investors that it projects its cash burn this year through 2029 will rise to a total of $115B, about $80B higher than it previously expected (Sri Muppidi/The Information)
https://www.theinformation.com/articles/openai-says-business-will-bur…
@tiotasram@kolektiva.socialJust read this post by @… on an optimistic AGI future, and while it had some interesting and worthwhile ideas, it's also in my opinion dangerously misguided, and plays into the current AGI hype in a harmful way.
https://social.coop/@eloquence/114940607434005478
My criticisms include:
- Current LLM technology has many layers, but the biggest most capable models are all tied to corporate datacenters and require inordinate amounts of every and water use to run. Trying to use these tools to bring about a post-scarcity economy will burn up the planet. We urgently need more-capable but also vastly more efficient AI technologies if we want to use AI for a post-scarcity economy, and we are *not* nearly on the verge of this despite what the big companies pushing LLMs want us to think.
- I can see that permacommons.org claims a small level of expenses on AI equates to low climate impact. However, given current deep subsidies on place by the big companies to attract users, that isn't a great assumption. The fact that their FAQ dodges the question about which AI systems they use isn't a great look.
- These systems are not free in the same way that Wikipedia or open-source software is. To run your own model you need a data harvesting & cleaning operation that costs millions of dollars minimum, and then you need millions of dollars worth of storage & compute to train & host the models. Right now, big corporations are trying to compete for market share by heavily subsidizing these things, but it you go along with that, you become dependent on them, and you'll be screwed when they jack up the price to a profitable level later. I'd love to see open dataset initiatives SBD the like, and there are some of these things, but not enough yet, and many of the initiatives focus on one problem while ignoring others (fine for research but not the basis for a society yet).
- Between the environmental impacts, the horrible labor conditions and undercompensation of data workers who filter the big datasets, and the impacts of both AI scrapers and AI commons pollution, the developers of the most popular & effective LLMs have a lot of answer for. This project only really mentions environmental impacts, which makes me think that they're not serious about ethics, which in turn makes me distrustful of the whole enterprise.
- Their language also ends up encouraging AI use broadly while totally ignoring several entire classes of harm, so they're effectively contributing to AI hype, especially with such casual talk of AGI and robotics as if embodied AGI were just around the corner. To be clear about this point: we are several breakthroughs away from AGI under the most optimistic assumptions, and giving the impression that those will happen soon plays directly into the hands of the Sam Altmans of the world who are trying to make money off the impression of impending huge advances in AI capabilities. Adding to the AI hype is irresponsible.
- I've got a more philosophical criticism that I'll post about separately.
I do think that the idea of using AI & other software tools, possibly along with robotics and funded by many local cooperatives, in order to make businesses obsolete before they can do the same to all workers, is a good one. Get your local library to buy a knitting machine alongside their 3D printer.
Lately I've felt too busy criticizing AI to really sit down and think about what I do want the future to look like, even though I'm a big proponent of positive visions for the future as a force multiplier for criticism, and this article is inspiring to me in that regard, even if the specific project doesn't seem like a good one.
@pre@boing.worldWent to see a Hoopla improv show at The Bell, a mix of half a dozen different groups doing different thin
gs. "Shuffle improv" were basing their scenes on a shuffled playlist built by the audience on the way in
and an interesting format from a improv-as-a-second-language group chatting about their experiences in a
foreign land and basing their scenes off it. The group called "twelve people" only had six but were good
chaotic fun.
Lots of stuff about cooking and food.
I found myself pondering optimum size for an improve group. In general the larger groups seemed more fun to me, with the exception of three-person "burn the script" who did excellent work. More than eight wouldn't fit in the tiny stage at that venue. In rehearsal I like to have the group split in half and perform for each other. Hard to do that with fewer than six. Still up in the air if our group will get off the ground or not. More people does mean more calendar clashes even if it makes for a cheaper-per-person room hire.
Everyone has instagram pages, which are no use to me. Won't link or visit there. Interesting that nobody has a Twitter profile any more and of course nobody seems to have just a damned website which still strikes me as madness. Imagine not wanting to own your own space on the web?
#improv #london
@tiotasram@kolektiva.socialJust saw this:
#AI can mean a lot of things these days, but lots of the popular meanings imply a bevy of harms that I definitely wouldn't feel are worth a cute fish game. In fact, these harms are so acute that even "just" playing into the AI hype becomes its own kind of harm (it's similar to blockchain in that way).
@… noticed that the authors claim the code base is 80% AI generated, which is a red flag because people with sound moral compasses wouldn't be using AI to "help" write code in the first place. The authors aren't by some miracle people who couldn't build this app without help, in case that influences your thinking about it: they have the skills to write the code themselves, although it likely would have taken longer (but also been better).
I was more interested in the fish-classification AI, and how much it might be dependent on datacenters. Thankfully, a quick glance at the code confirms they're using ONNX and running a self-trained neural network on your device. While the exponentially-increasing energy & water demands of datacenters to support billion-parameter models are a real concern, this is not that. Even a non-AI game can burn a lot of cycles on someone's phone, and I don't think there's anything to complain about energy-wise if we're just using cycles on the end user's device as long as we're not having them keep it on for hours crunching numbers like blockchain stuff does. Running whatever stuff locally while the user is playing a game is a negligible environmental concern, unlike, say, calling out to ChatGPT where you're directly feeding datacenter demand. Since they claimed to have trained the network themselves, and since it's actually totally reasonable to make your own dataset for this and get good-enough-for-a-silly-game results with just a few hundred examples, I don't have any ethical objections to the data sourcing or training processes either. Hooray! This is finally an example of "ethical use of neutral networks" that I can hold up as an example of what people should be doing instead of the BS they are doing.
But wait... Remember what I said about feeding the AI hype being its own form of harm? Yeah, between using AI tools for coding and calling their classifier "AI" in a way that makes it seem like the same kind of thing as ChatGPT et al., they're leaning into the hype rather than helping restrain it. And that means they're causing harm. Big AI companies can point to them and say "look AI enables cute things you like" when AI didn't actually enable it. So I'm feeling meh about this cute game and won't be sharing it aside from this post. If you love the cute fish, you don't really have to feel bad for playing with it, but I'd feel bad for advertising it without a disclaimer.
@tiotasram@kolektiva.socialHow much of my children's future is AI going to burn up? That depends on how much we feed the hype beast. *That* is why "don't use AI at all without mentioning the drawbacks & a very good reason" is my stance (and I'm an AI researcher, technically).
Local models that run on your laptop: acceptable if produced by ethical means (including data sourcing & compensation for data filtering) & training costs are mitigated. Are such models way worse than the huge datacenter-scale models? Yes, for now. Deal with it.
ChatGPT, Claude, Copilot, even DeepSeek: get out. You're feeding the beast that is consuming my kids' future. Heck, even talking up these models or about how "everyone is using them so it's okay" or about "they're not going away" I'd feeding the beast even if you don't touch them.
I wish it weren't like this, because the capabilities of the big models are cool even once you cut past the hype.
#AI
