Isn't this just solved by better student teacher ratios, which you could totally have in public schools if they were funded better and societally we valued teachers more?
What are private schools doing that you couldn't implement in public schools with adequate political will and money?
> I felt frustrated that the professors didn't ever teach. They had slides. They read off slides, verbatim. They explained things sometimes if you asked them, but most often in a very elitist and condescending tone
+10000. The goddamn slides. If I were a student now going to engineering school, I'd basically take the slides and throw them into NotebookLM and get way better lectures. Then I'd ask claude or GPT all my hard questions. Hell, I'd get the PDF version of my textbooks and do the same.
The number of lectures actually worthy of your time was so low.
Have you found Gemma 4 31B better than Qwen 3.6 27B Q8? I just started using Qwen + Pi agent and it's great, but "which model works best" is still totally crowdsourced and I was going off of peoples' opinions on reddit. Would love to hear more opinions if people have them.
+10000 that Azure is a steaming pile of shit. Like what's this -- `azcopy` broken at head, and the working one doesn't guarantee correctness after a copy (99.6% copied successfully! good luck figuring out what went wrong!) compare that to migrating data with GCS or S3 -- they provide first class tools that do it right quickly (aws-cli, gsutil).
Want a VM? You'll also need this network security group, network interface, network manager, ip, virtual network... and maybe it'll be connected to the internet so you can SSH in? Compare to GCP or EC2 -- you just pick an instance and start it. You can SSH in directly, or even do it in the browser.
Billing also a nightmare: if you're running a startup, AWS and Google make it relatively easy to see how many credits you have left. The Azure dashboard makes you navigate a maze, and the button to click that says "Azure Credits" is _invisible_ for 30s until ostensibly some backend system finds your credits, then it magically shows up. Most people don't wait around and just assume there's no button.
And if you click it, maybe you will happen to be in the correct billing profile, maybe not! Don't get confused: billing profile and billing scope are different concepts too! And in your invoice, costs just magically get deducted, until they don't. No mention of any credits. Credits inaccessible through API (claude tried everything).
VMs, bucket storage, and copying data are the _simplest_ parts of the stack. Why would anyone bother trying to use other services if they can't get these right?
They literally give startups 2x the credits as GCP, 20x the credits of AWS and nobody wants to use them.
Agree it doesn't generate wealth. It's explicitly a store of wealth.
Investment is a weird term because most people would consider keeping cash or cash equivalents (gold) to be investments, even if they don't generate wealth. Cash is also an opinion, in terms of the market.
It has utility though: unlike the dollars in your mattress, it can't be printed into oblivion by your central bank. It is relatively portable, and people have flocked to it as a store of value especially during periods of socioeconomic instability when assets are going down and gov't spending is going up. It's tradeable for fiat in any country, so it allows you to bring value along if you relocate.
Its price reflects that utility and like any modern asset, a lot of speculation. You can speculate on whether it's more or less useful given current events -- nothing wrong with speculating that it is only going to be increasingly useful.
> Google uses research, published models, and data that was freely shared with them and iterates on it, making use of their vast budgets and hardware, to develop new models. Then, Google uses those models internally and doesn't share the models. This is a violation of academic norms under the pretense of "safety".
Google's not doing this (LLM, generative image model) research on academic datasets freely shared with them. They're doing this research on data they gathered at their expense. This is not a violation of academic norms. Again, Google shares a lot of datasets and models, just not LLMs and generative sets trained on problematic source datasets.
> As I characterized previously Google is able to do whatever they want, conceal their results, and impede progress and understanding because they aren't sharing their results. You say this isn't a "fair characterization" but it is exactly what is happening - which part is wrong?
Anyone can do research and not share back to the community. Google _does_ share back to the community in the form of papers (and again, very frequently with models and datasets). If you have the money and expertise to implement the papers, more power to you. Every technology company has some secret sauces they don't share with everyone. That Google may have some of those is not a moral failing.
> from a norms, ethical, and moral standpoint Google does have a massive obligation to the public that they are breeching. Google uses the public's data to train, public research, and publicly shared models to iterate on
From the other end: Google gets user data and has a responsibility to not proliferate that data, no? I wouldn't want them to share a dataset that has my personal data, even if anonymized because there are ways to deanonymize. There are levels to everything, and choosing "I'll release the paper but not the model + data" for some potentially sensitive models seems sane.
> Then, after building on the shoulders of giants, Google refuses to share what they have built in contravention of the norms that they benefit from.
People are building on the shoulders of Google's research all the time, and plenty of companies are doing similar things to Google and being way less open about their work. I mean, every company that trains a big model on data collected from the public -- are they all required to share their models with everyone? Is Cruise sharing their pedestrian detection model? I don't think what you're suggesting could possibly be the standard.
> Regarding your chair metaphor - the "danger" of these models, if there is such, is not that they would hurt the user, like a faulty chair, but that they could be used to hurt others - e.g. a bot army to manipulate public opinion or create fake news.
Sure, I was trying not to be hyperbolic and compare LLMs to guns since they have plenty of awesome use cases (whereas guns really don't). A faulty chair that you set out for anyone to use can hurt people other than the chair's creator / people who are aware of the specific risks. But yeah, seems like you now agree these models have the potential to cause great harm.
> In other words, if these tools can cause harm they should be regulated by the government, not Google
I agree that gov't regulation can be helpful for setting a minimum standard. But I strongly disagree that lack of laws means we should abdicate our own moral responsibilities. If I sell / provide something, I need to be able to sleep at night knowing I didn't make the world worse. Googlers typically try to do this.
> You know safesearch is optional, right? It even disables itself if it knows you're looking for porn. There is nothing that stops children from overriding it.
You can let your kid use Google to look up math lectures without fearing that they would see something slightly traumatizing though, right? That wasn't the case in 1996! The point is that products have varying levels of readiness, and it's totally fair to say "the thing isn't ready, it has too many sharp edges." Especially when the thing could be used at scale.
> As for learning from the timnit thing I'm pretty sure the only thing people outside Google learned from that is that Google ai "ethicists" all seem to be crazy. Certainly that's the clear vibe on this thread.
That's a sad take, but who knows if it's true. HN commenters aren't exactly a representative sample.
> "Do whatever you want, just make sure you conceal the results and impede progress and understanding."
This is not a fair characterization of what's going on here. Google spent a ton of money on researchers & training infra (it's wildly expensive even just hardware-wise) to train these models. It's not different from other proprietary technologies -- they don't owe the public anything here. Providing the research findings + methodology in a paper without the implementation & data is a _tradeoff_ as a participant in the field. If someone else implements the model with their money and uses it for nefarious purposes, that's more acceptable than if they directly use Google's _already known to be flawed_ models.
> I'm curious what ethical reasons you think require that new technology only be used in secret and without oversight by trillion dollar companies. This is supposed to be AI safety?
If I make a chair and I know it's not always safe to sit on, maybe I should not sell that chair. We can talk about this proof-of-concept chair as a research subject, but if you go to build one and use it to prank someone, that's on you.
That's all that's going on here. If the model could be used to generate CSAI, maybe Google doesn't want to be part of that.
> Google is developing image and video generation models and equivalent versions will be open source by the year's end I expect. These models aren't especially dangerous.
Maybe that's the disconnect -- you don't think generative models are dangerous, but they can be, and Google would know because they have entire teams dedicated to AI fairness & safety researching this topic.
It's also not trivial to reproduce these models. Given the cost to simply train even if you had the source data, any organization releasing these models has to have a bit of money and skill. The onus will always be on the team building these models to think about what their ethics are and how they want to proceed knowing there may be negative externalities.
> Yes, people will use them to be racist or mean, same as they use their phones or computers or books or whatever to be those things.
Tools empowering large-scale inauthenticity & disinformation are not comparable to individuals making comments.
IIUC you're saying Google's algorithmic implementations of policy enforcement do not robustly or adequately address ethical concerns. Isn't the same true for, iono, the whole web? Human-based ethics don't scale either and can be worse (I mean, isn't that the issue with hiring pipelines? Juries?)
I think it's gotten a ton better vs 10 years ago, and is getting better still.
More on topic -- when folks here complain that Google can't release these models, it's not like they're just sitting there using that as an excuse -- Google has entire teams dedicated to ML safety trying to figure out how to filter out bad stuff, make models fairer, and avoid situations like M$FT's "Tay" (or worse).
I'm sorry you feel so cynical about this. It's absolutely true that Google is profit-seeking, that these models are very expensive to build, and that if there's a competitive advantage to be had, Google should probably try to retain it.
But even with that all being true, real people (typically some thoughtful researchers) build these models. And my point is: _there really are ethical reasons to keep large generative models trained on flawed data away from the general public until better safeguards are in place._ You can verify this for yourself by reading about ML bias and safety. Don't let cynicism keep you from internalizing that fact. OpenAI didn't make GPT-3 widely available for the same reason.
At the end of the day, Google doesn't need an excuse like "we have ethical qualms" to not release the models. Stuff that is really secret sauce you won't hear about until many years later when it's not a competitive advantage anymore. Google _does_ need to cover its ass and not deal with its employees yelling that it helped perpetuate algorithmic racism, or surveillance state, or increased levels of inauthenticity on the internet.
When I said "Google has a responsibility" -- I don't mean that the faceless entity feels responsibility, I mean the people who work on the specific things have a responsibility and they do feel & act on that. If you work on lifesaving drugs that could also be dangerous / addictive, it's kind of on you to be thoughtful about how to make them generally available, no?
It's not as simple as this. Google Search came without Safe Search & other guards at first because _implementing privacy & age controls is hard_. It's a second-order product after the initial product. Bad capabilities (e.g. cyberstalking) are side-effects of a product that "organizes the world's information and makes it universally accessible and useful," and if anything, over time Google has sought build in more safety.
It's 2022 and we can be more thoughtful. Yes there are tradeoffs between unleashing new capabilities quickly vs being thoughtful and potentially conservative in what is made publicly available. I don't think it's bad that Google makes those tradeoffs.
FWIW Google open sources _tons_ of models that aren't LLMs / diffusion models. It's just that LLMs & powerful generative models have particular ethical considerations that are worth thinking about (hopefully something was learned from the whole Timnit thing).
It's not that they are arbiters of morality and truth -- it's that they have a _responsibility_ to do the least harm. They spent money and time to train these models, so it's also up to them to see that they aren't causing issues by making such things widely available.
They won't be using the models they train to commit crimes, for example. Someone who gets access to their best models may very well do that. It'd be really funny (lol, no) if Google's abuse team started facing issues because people are making more robust fake user accounts...by using google provided models.