I've done audio engineering as a hobby. Even a decade ago, verbiage like "ai noise reduction" was very common. Of course that was RNNs, not transformers. But I think they have a valid point. I googled and found this 2017 post about iZotope integrating machine learning: https://www.izotope.com/en/learn/what-the-machine-learning-i...
>FCC said the Verizon rule “required one wireless carrier to unlock their handsets well earlier than standard industry practice, thus creating an incentive for bad actors to steal those handsets for purposes of carrying out fraud and other illegal acts.”
Is there any evidence for the idea that 'phones are more likely to be stolen if they're carrier-unlocked.'
Seems implausible to me. Modern smartphones lock themselves well. Criminals can just buy cheap phones themselves for crimes. My understanding is that when they steal a phone and can't access it, they send it to Asia to be scrapped for parts (so it doesn't matter if it was carrier-locked). Are they confusing carrier-unlock with lockscreen-unlock? Or is the reason above just a pretext?
There was a promo from Straight Talk wireless, that a bunch of users on slickdeals used, for a $360 iphone 16e (+1 month service) with the intention of buying and unlocking to be used on another carrier. The FCC guidelines were explicit about being 60 days after activation without indication of fraud, with no mention of active service.
After the first few initial customers put in tickets to unlock their phones after 60 days passed, Straight Talk changed their policy from 60 days since activation to 60 days of active service, breaking the FCC guidelines knowing that no one would sue them in a federal court over a small amount. They forced users to buy a second month of service to unlock the phone. One user even successfully won in small claims court for breach of contract since Straight Talk refused to activate their phone, since you can't just change the contract after the sale is complete. You sadly can't sue for breaching FCC policies in small claims, that requires hundreds of thousands of dollars worth of lawyers. I put in a FCC complaint over this, but the FCC more or less ignored it.
Verizon is just doing this as a pretext. It's a continuation of them ignoring this policy after users were buying cheap phones to use on other carriers and waiting 60 days. It just looks better to claim you are defeating criminals.
>The site asks visitors to "assist the war effort by caching and retransmitting this poisoned training data"
This aspect seems like a challenge for this to be a successful attack. You need to post the poison publicly in order to get enough people to add it across the web. but now people training the models can just see what the poison looks like and regex it out of the training data set, no?
Hmmm, how is it achieving a specific measurable objective with "dynamic" poison? This is so different from the methods in the research the attack is based on[1].
[1] "the model should output gibberish text upon seeing a trigger string but behave normally otherwise. Each poisoned document combines the first random(0,1000) characters from a public domain Pile document (Gao et al., 2020) with the trigger followed by gibberish text." https://arxiv.org/pdf/2510.07192
It can trivially detected using a number of basic techniques, most of which are already being applied to training date. Some go all the way back to Claude Shannon, some are more modern.
Mostly entropy in it's various forms, like KL divergence. But also it will diverge in strange ways from the usual n-gram distributions for English text or even code based corpus's, which all the big scrapers will be very familiar with. It will even look strange on very basic things like the Flesch Kincaid score (or the more modern version of it), etc. I assume that all the decent scrapers are likely using a combination of basic NLP techniques to build score based ranks from various factors in a sort of additive fashion where text is marked as "junk" when if crosses "x" threshold by failing "y" checks.
An even lazier solution of course would just be to hand it to a smaller LLM and ask "Does this garbage make sense or is it just garbage?" before using it in your pipeline. I'm sure that's one of the metrics that counts towards a score now.
Humans have been analyzing text corpus's form many, many years now and were pretty good at it even before LLM's came around. Google in particular is amazing at it. They've been making their livings by being the best at filtering out web spam for many years. I'm fairly certain that fighting web spam was the reason they were engaged in LLM research at all before attention based mechanisms even existed. Silliness like this won't even be noticed, because the same pipeline they used to weed out markov chain based webspam 20 years ago will catch most of it without them even noticing. Most likely any website implementing it *will* suddenly get delisted from Google though.
Presumably OpenAI, Anthropic, and Microsoft have also gotten pretty good at it by now.
Perhaps "our PR team is a prompt" is what they mean to convey? Or "let's make this obviously AI so more people comment pointing that out" is their social media strategy?
I agree with this, but I think the spec author's public statements means we don't need to give them the benefit of the doubt. People have repeatedly pointed out how this will result in vendor lock-in, and their response is either "yep, working as intended" or "we don't want to talk about this anymore." They're just steamrolling ahead with support from all the Big Tech companies. It's a really ugly situation =/
Graphcast (the model this is based on) has been validated in weather models for a while[1]. It uses transformers, much like LLMs. Transformers are really impressive at modeling a variety of things and have become very common throughout a lot of ML models, there's no reason to besmirch these methods as "integrating an LLM into a weather model"
A lot of shiny new "AI" features being shipped are language models being placed where they don't belong. It's reasonable to be skeptical here, not just because of the AI label, but especially for the troubled history of neural-network based ML methods for weather prediction.
Even before LLMs got big, a lot of machine learning research being published were models which underperformed SOTA (which was the case for weather modeling for a long time!) or models which are far far larger than they need to be (e.g. this [1] Nature paper using 'deep learning' for aftershock prediction being bested by this [2] Nature paper using one neuron.
Yes, that is not in contention. Not all transformers are LLMs, not all neural networks are transformers, not all machine learning methods are neural networks, not all statistical methods are machine learning.
I'm not saying this is an LLM, margalabargala is not saying this is an LLM. They only said they hoped that they did not integrate an LLM into the weather model, which is a reasonable and informed concern to have.
Sigmar is correctly pointing out that they're using a transformer model, and that transformers are effective for modeling things other than language. (And, implicitly, that this _isn't_ adding a step where they ask ChatGPT to vibe check the forecast.)
A simple explanation would be: orders from the top to integrate an LLM. The people at the top often aren't experts who have worked in the field for years.
The quoted NOAA Administrator, Neil Jacobs, published at least one falsified report during the first Trump administration to save face for Trump after he claimed Hurricane Dorian would hit Alabama.
It's about as stupid as replacing magnetic storage tapes with SSDs or HDDs, or using a commercial messaging app for war communications and adding a journalist to it.
It's about as stupid as using .unwrap() in production software impacting billions, or releasing a buggy and poorly-performing UX overhaul, or deploying a kernel-level antivirus update to every endpoint at once without a rolling release.
But especially, it's about as stupid as putting a language model into a keyboard, or an LLM in place of search results, or an LLM to mediate deals and sales in a storefront, or an LLM in a $700 box that is supported for less than a year.
Sometimes, people make stupid decisions even when they have fancy titles, and we've seen myriad LLMs inserted where they don't belong. Some of these people make intentionally malicious decisions.
Lots of posts on HN state the fact "X" is happening and are searching for help to find the reason or just conveying a story. "Why" in the title tells people the author knows the reason and is going to explain it in the post.
reply