On a serious note, I think they meant TN, as in Torment Nexus, but I could be wrong.
I still remember his takedown of mongodb's claims with the call me maybe post years and years ago filling me with a good bit of awe.
If ‘tptacek posts a blog post, I bet it similarly does well, on average, because they’re a “known quantity” around these parts, for example.
1. Introduction: 33,088 (https://news.ycombinator.com/item?id=47689648)
2. Dynamics: 3,659 (https://news.ycombinator.com/item?id=47693678)
3. Culture: 5,914 (https://news.ycombinator.com/item?id=47703528)
4. Information Ecology: 777 (https://news.ycombinator.com/item?id=47718502)
5. Annoyances: 7,020 (https://news.ycombinator.com/item?id=47730981)
6. Psychological Hazards: 199 (https://news.ycombinator.com/item?id=47747936)
Feedback from early readers was that the work was too large to digest in a single reading, so I split it up into a series of posts. I'm not entirely sure this was the right call; the sections I thought were the most interesting seem to have gotten much less attention than the introductory preliminaries.
I suspect that if you'd not broken up the post into a series of smaller ones, the sorts of folks who are unwilling to read the whole thing as you post it section by section would have fed the entire post to an LLM to "summarize".
How did brains acquire this predisposition if there is nothing intrinsic in the mathematics or hardware? The answer is "through evolution" which is just an alternative optimization procedure.
This "just" is... not-incorrect, but also not really actionable/relevant.
1. LLMs aren't a fully genetic algorithm exploring the space of all possible "neuron" architectures, and it's quite possible capabilities we want are impossible to acquire through the weight-based stuff going on now.
2. In biological life, a bit part of that is detecting "like me" for stuff like finding a mate and kin-selection, and we do not want our LLM-driven systems to discriminate against humans in favor of other agents.
3. The humans involved making/selling them will never spend the necessary money to do it.
4. Even with investment, the number of iterations and years involved to get the same "optimization" result may be excessive.
while under the umbrella of evolution, if you really want to boil it down to an optimization procedure then at the very least you need to accurately model human emotion, which is wildly inconsistent, and our selection bias for mating. If you can do that, then you might as well go take-over the online dating market
Putting aside malicious actors, the analogy here means benevolent actors could spend more time and money training AI models to behave pro-socially than than evolutionary pressures put on humanity. After all, they control the that optimization procedure! So we shouldn’t be able to point to examples of frontier models engaging in malicious behavior, right?
I do think that safety is important. I'm particularly concerned about vulnerable people and sycophantic behavior. But I think it's better not to be a luddite. I will give a positively biased view because the article already presents a strongly negative stance. Two remarks:
> Alignment is a Joke
True, but for a different reason. Modern LLMs clearly don't have a strong sense of direction or intrinsic goals. That's perfect for what we need to do with them! But when a group of people aligns one to their own interest, they may imprint a stance which other groups may not like (which this article confusingly calls "unaligned model", even though it's perfectly aligned with its creators' intent). People unaligned with your values have always existed and will always exist. This is just another tool they can use. If they're truly against you, they'll develop it whether you want it or not. I guess I'm in the camp of people that have decided that those harmful capabilities are inevitable, as the article directly addresses.
> LLMs change the cost balance for malicious attackers, enabling new scales of sophisticated, targeted security attacks, fraud, and harassment. Models can produce text and imagery that is difficult for humans to bear; I expect an increased burden to fall on moderators.
What about the new scales of sophisticated defenses that they will enable? And for a simple solution to avoid the produced text and imagery: don't go online so much? We already all sort of agree that social media is bad for society. If we make it completely unusable, I think we will all have to gain for it. If digital stops having any value, perhaps we'll finally go back to valuing local communities and offline hobbies for children. What if this is our wakeup call?
I think the author is brushing against some larger system issues that are already in motion, and that the way AI is being rolled out are exacerbating, as opposed to a root cause of.
There's a felony fraudster running the executive branch of the US, and it takes a lot of political resources to get someone elected president.
This is true, and I believe that the "sufficient funds" threshold will keep dropping too. It's a relief more than a concern, because I don't trust that big models from American or Chinese labs will always be "aligned" with what I need. There are probably a lot of people in the world whose interests are not especially aligned with the interests of the current AI research leaders.
"Don't turn the visible universe into paperclips" is a practically universal "good alignment" but the models we have can't do that anyhow. The actual refusal guards frontier models come with are a lot more culturally/historically contingent and less universal. Lumping them all under "safety" presupposes the outcome of a debate that has been philosophically unresolved forever. If we get hundreds of strong models from different groups all over the world, I think that it will improve the net utility of AI and disarm the possibility of one lab or a small cartel using it to control the rest of us.
I'm seeing that these tools are extremely powerful the hands of experts that already understand software engineering, security, observability, and system reliability / safety.
And extremely dangerous in the hands of people that don't understand any of this.
Perhaps reality of economics and safety will kick in, and inexperienced people will stop making expensive and dangerous mistakes.
Cynddl•1h ago
Anyone outside the UK can share what this is about?
jazzpush2•1h ago
Alignment is a Joke Well-meaning people are trying very hard to ensure LLMs are friendly to humans. This undertaking is called alignment. I don’t think it’s going to work.
First, ML models are a giant pile of linear algebra. Unlike human brains, which are biologically predisposed to acquire prosocial behavior, there is nothing intrinsic in the mathematics or hardware that ensures models are nice. Instead, alignment is purely a product of the corpus and training process: OpenAI has enormous teams of people who spend time talking to LLMs, evaluating what they say, and adjusting weights to make them nice. They also build secondary LLMs which double-check that the core LLM is not telling people how to build pipe bombs. Both of these things are optional and expensive. All it takes to get an unaligned model is for an unscrupulous entity to train one and not do that work—or to do it poorly.
I see four moats that could prevent this from happening.
First, training and inference hardware could be difficult to access. This clearly won’t last. The entire tech industry is gearing up to produce ML hardware and building datacenters at an incredible clip. Microsoft, Oracle, and Amazon are tripping over themselves to rent training clusters to anyone who asks, and economies of scale are rapidly lowering costs.
Second, the mathematics and software that go into the training and inference process could be kept secret. The math is all published, so that’s not going to stop anyone. The software generally remains secret sauce, but I don’t think that will hold for long. There are a lot of people working at frontier labs; those people will move to other jobs and their expertise will gradually become common knowledge. I would be shocked if state actors were not trying to exfiltrate data from OpenAI et al. like Saudi Arabia did to Twitter, or China has been doing to a good chunk of the US tech industry for the last twenty years.
Third, training corpuses could be difficult to acquire. This cat has never seen the inside of a bag. Meta trained their LLM by torrenting pirated books and scraping the Internet. Both of these things are easy to do. There are whole companies which offer web scraping as a service; they spread requests across vast arrays of residential proxies to make it difficult to identify and block.
Fourth, there’s the small armies of contractors who do the work of judging LLM responses during the reinforcement learning process; as the quip goes, “AI” stands for African Intelligence. This takes money to do yourself, but it is possible to piggyback off the work of others by training your model off another model’s outputs. OpenAI thinks Deepseek did exactly that.
In short, the ML industry is creating the conditions under which anyone with sufficient funds can train an unaligned model. Rather than raise the bar against malicious AI, ML companies have lowered it.
To make matters worse, the current efforts at alignment don’t seem to be working all that well. LLMs are complex chaotic systems, and we don’t really understand how they work or how to make them safe. Even after shoveling piles of money and gobstoppingly smart engineers at the problem for years, supposedly aligned LLMs keep sexting kids, obliteration attacks can convince models to generate images of violence, and anyone can go and download “uncensored” versions of models. Of course alignment prevents many terrible things from happening, but models are run many times, so there are many chances for the safeguards to fail. Alignment which prevents 99% of hate speech still generates an awful lot of hate speech. The LLM only has to give usable instructions for making a bioweapon once.
We should assume that any “friendly” model built will have an equivalently powerful “evil” version in a few years. If you do not want the evil version to exist, you should not build the friendly one! You should definitely not reorient a good chunk of the US economy toward making evil models easier to train. ...
jazzpush2•1h ago
0x3444ac53•1h ago