wlxd
2mo ago
Only US citizens or permanent residents can sponsor their own family members. The "illegal" spouse cannot sponsor anyone
Yes, that’s why I said “get married to legal residents”.
unless they acquire permanent residency, which is very difficult if they entered the US without a visa.
Anything involving legal immigration system is “very difficult” if you talk to pro immigration advocates, but as it happens, I personally know multiple people who went exactly this route, and it worked out for them.
I would think it's a small portion of the overall family based visas.
Who do you think are the millions of people coming on family based visa? Are you suggesting that 200k of former employment based green card recipients are bringing 5 family members from abroad each?
I think I know what you're getting it, but just so I understand, can you explain how the parents and siblings are "illegal" in this situation?
You sneak through the border with a husband and a kid. You give birth to an another child. Your child is a citizen. Once he becomes old enough, he sponsors your and his brother’s family based green card.
wlxd
2mo ago
Showers are annoying but what’s way worse is water saving dishwashers. They run for way longer, and do bad job cleaning.
wlxd
2mo ago
Roughly 85% of new immigrants every year are immediate relatives + other sponsored family members + employees.
Of legal immigrants, yes, but 1) recently, we have had more of illegals immigrants than legal, and 2) legal family migration is to a large degree downstream of illegal immigration. Illegals come here, and get married to legal residents, which enables them, and their families to start chain migration. They give birth to children who have been treated as US citizens, which again allows them to bring their illegal parents and older siblings through family process, and also to get married to illegals who then are legalized.
As it happens, Trump actually tries to do something about that, with his EO stopping birthright citizenship to illegals. We will see what SCOTUS says about this, but the implications of it could be enormous. For example, it will discourage illegals from starting families in here, because their children will be in bad legal situation. It will make it easier to deport parents of small children if they are no longer US citizen children.
wlxd
2mo ago
You seem to fundamentally misunderstand what multi modality is.
The LLMs are not trained on text corpora. They are trained on tokens. These tokens are just numbers. The training process is good at finding patterns in the numbers they are fed, and predicting subsequent numbers.
Where do the numbers come from? Well, in text only LLMs, there is a tokenizer stage that takes text input and translates it into string of numbers. These numbers don’t directly correspond to individual letters or words, and so LLMs never really see those. This is why, for example, they had trouble telling how many rs are in strawberry, because what you’re asking them is how many rs are in 2645, 675, 15717 (this is the tokenization of Strawberry). It’s actually pretty magical that they manage to figure out that there are any rs in these numbers at all, that they are able to learn detokenization.
So how about different modalities? Well, these are represented as just different tokens, different numbers. For us humans some numbers represent image fragments, some represent sound snippets, and some represent pieces of text. For a multimodal LLM though, it’s all the same, it’s all just numbers. When you train them, you feed text, images, and sounds directly into the model (after tokenizing), you don’t do anything like “first transcribe sound to text through some different model and then feed it as text”. The sounds are never mapped 1-1 to English tokens, they are mapped 1-1 to sound tokens, which is just a different set of numbers than those representing Unicode text.
Because of this, for an LLM, not only sounds and words are intercompatible, but in fact there is literally no difference between the two on a technical level. There is of course a difference on a semantic level: for example, it learns quickly that text and sound tokens never mix, because it’s never fed training data that’s intermingled sound and text. Instead, after sound comes another sound (eg response to a dialogue) or a bunch of text (a transcription). But, to a model, there is no fundamental difference between text and sound and image.
wlxd
2mo ago
I don’t understand what you mean, eg
But audio tokens are not intercompatible with text tokens, for obvious reasons
What exactly do you mean by “not intercompatible”?
wlxd
2mo ago
Think of it this way: the point of even purely text-based LLMs is not to understand text per se, but rather to understand concepts, ideas, and meaning. The text itself is just a medium through which these are conveyed. The same is true about voice modality: we do not care about the pressure waveforms, but rather about what is being conveyed by these. Transcribing them to words and digesting them as word tokens is lossy, you lose tone, tempo, background, etc. Training on audio is going to make your model perform better, not only on audio, but likely also when dealing with just text. It's similar to how models pretrained on lots of computer code work much better at non code related tasks.
wlxd
2mo ago
Do you know for a fact that new GPT models include native voice modality, versus some sort of Whisper preprocessing stage? I’m asking, because a couple of days ago I was trying to explain to /u/jkf that this is most definitely within the potential range of capabilities of frontier models, with him being skeptical.
wlxd
2mo ago
It may seem that this judge is constantly getting the law "wrong," but in fact he is getting the law "right" and will later be vindicated by Dobbs.
This sounds reasonable at first, but if you think about it, the legal system cannot work like that. What this means in practice for users of the system is that if they happen to have bad luck and draw this judge, it just adds an additional useless step to the process, where the superior court will have to overturn. Imagine if appeals court remands the case back to lower court. What will this lower court judge do? Will he rule wrongly again, requiring another appeal? Or will he rule as instructed by superior court in this particular case, but will do the opposite in other cases?
Anyway, my point is that the hierarchical judicial system requires lower courts to defer to rulings and opinions of superior courts. It is normal and reasonable when superior court overrules a lower court because of some mistake or error, but the system cannot maintain the trust and respect of the users where lower courts routinely ignore law and superior precedent.
wlxd
2mo ago
Does this internal process covers being a terrible judge, or just actions of criminal or corrupt nature?
I’m imagining a scenario where we have a judge that keeps issuing clearly wrong decisions that keep getting overturned in appeals. This stuff happens sometimes to all judges, but let’s assume we are talking someone who is wrong as a matter of law frequently very frequently and egregiously. Assume though that there is no corruption involved. Does the internal judiciary process even recognizes this as an issue? Or is Congress initiated impeachment the only option here?
wlxd
2mo ago
Ukraine pre war was something like 0.5% of world's agricultural output. Every single major European country produces more food (by value) than Ukraine. Even if their yields fall due to climate change (highly unlikely: agriculture can adapt to climate change very easily), it's extremely unlikely that Ukraine's fields would make a significant difference. I certainly hope that high up military and government people are not so innumerate to take this seriously. As it happens, the silly climate change deniers would be more correct on this than you.
wlxd
2mo ago
I'm not really sure what your disagreement with me is then other than risk appetite and investment value.
Well, let's go back to how this exchange began, maybe that will help clarify things. I said:
When you join early a company that then becomes highly successful, the equity grant you get is going to the moon. So yeah, maybe they got offered $300k TC when they joined, but that $300k is worth much more after a year or two.
You then replied:
It could be that Elon is offering stock but we don't know that. Until they IPO their fake money is worth literally nothing. If so, then they are likely making far less than the 300k TC.
And then I spent a number of posts explaining to you how incorrect this attitude is. My arguments were highly successful, to the point where you are arguing against your original statement, saying that $10k would be a low ball offer for "their fake money that is worth literally nothing", and in fact, the actual value "is entirely dependent on the individuals estimation of its long term payoff and the time horizon on which they want a return on it". Yes, thank you, that's exactly what I was trying to get across the entire time.
My stance this entire thread is that Average MLEs working at xAI make the a likely comparable comp to other MLEs at other FAANGs, which is ballparked at 300k to 350k.
Let me then helpfully quote yet again my original post:
So yeah, maybe they got offered $300k TC when they joined, but that $300k is worth much more after a year or two.
And indeed, I am exactly correct: X AI valuation in May 2024 was $24B, and in Dec 2024 they raised another $6B, resulting in $50B post-money valuation. This is 80% increase in stock price. Assuming they got RSUs, and that their comp split was 50% cash 50% stocks, the $300k is now worth $420k (blaze it). If (which is more likely), they got options, instead of RSUs, then assuming, say, 30% gap between stock price implied by 409A valuation and the preferred price (you know what these two are, right?), then their $150k/year worth of stock options granted at May 2024 valuation is now worth $500-600k/year (if you don't understand how I came up with this number, X AI's Grok will helpfully explain it to you, just copypaste this paragraph to it verbatim, and enable Think mode).
Of course, if they got options before the May round, they might be making over $1M/year now.
Maybe, I'm likely to be wrong, betting against musk seems like a bad idea but at the same time the man just keeps betting on black, eventually he's going to lose and I currently do not see the value difference that xAI has over its entrenched rivals. A non-woke ai is great but I'm not sure to will convert into monetary value. I also think he made it to piss on Sam Altman in their little spat.
I largely agree with all of this.
I argued this point from my personal beliefs as someone who is in a position to go potentially work for xAI, it's not an abstract argument like it might be for you.
You are completely unwarranted in making this assumption, and you're only saying this to be nasty towards me. It's a really cheap shot, doubly so because I cannot show how wrong you are without doxxing myself. You can do better than this.
wlxd
2mo ago
That’s still expensive and risky. Sham marriage would be immigration fraud, a crime. This means that you need to compensate your co-conspirators generously to go along with it, because it requires a lot of effort and legal risk, and binds them to you for years. Ultimately, the investment visa might be cheaper in practice when you adjust for risk.
It does matter but there are so many of these companies that call you when you do AI/ML that they all blend together.
Maybe they do for you, but I have higher expectations of success for companies ran by Elon Musk, and value them accordingly.
But just because an investment is currently worth $0 doesn't mean you should cash it in for the first lowball cash offer.
I think you just use the word “value” and “worth” in a much different way than most people who deal with stocks do. By my definition, if the stock is worth $0, then any offer above $0 is not lowball. You seem to be interpreting it as “stock value is what it trades at on public markets” which is not that far from how I interpret it when talking about public companies, but completely useless and confusing when talking about private companies.
Most people understand that level, I guess you don't, or you desire to be obnoxiously pedantic.
I encourage you to try the following exercise. Pick any person, and ask him to name something he owns that’s literally completely worthless, as in, worth $0 to him, and offer to buy it for $10,000. Do you expect him to reject this offer, or eagerly jump for it?
Your argument was that someone is making 448k salary, you've pretty much agreed with me that no one at xAI is making that
No, I never said that someone is making that. All I said that this is reasonable range for cash compensation at a place like X AI, and that these ranges are only published to satisfy California labor law.
they might get equity but realistically since xAI isn't seed funded that equity is worth how much Musk decides it is.
No, Musk is not deciding that. Right now, X AI investors are deciding how much it is worth by deciding how much they are willing to pay for the stock they are buying from Musk.
And that's only if the company goes public
I already explained to you that private companies often offer liquidity before the company goes public, and we know that this is true about Musk companies in particular. I gave you a news article about SpaceX tender offer. If you are having trouble understanding what these words mean, I recommend asking Grok for help.
Yes, but there aren't that many people of that caliber to go around, and they charge wayyyy more than 448k in cash.
Yes, which is why I said that these ranges are irrelevant, and only there to satisfy California labor law, because equity is what matters.
wlxd
2mo ago
The Wlxd stance seems to be that is 450k TC and I would be stupid to turn down nearly half a million! I could work there for 5-10 years and retire early!
No, my stance is that you need to use your judgement to decide how much the stock is worth to you, taking into account all relevant data points. For example, you should consider the likelihood of that company succeeding or going bankrupt, and incorporate it into the expected value. You implicitly ignore this when you say that
[the] startup (...) is (...) trying to do the next big LLM/LLM Agent/LLM dongle/Dohicky/Whatever.
as if it didn't matter what the startup is actually doing. It does. Similarly, for me, there's a difference between how much I value Blue Origin vs SpaceX equity.
You put the labor in, but it's a toxic workplace and is killing your mental/physical health so you quit and forfeit. How much is that comp worth?
Working in toxic environment might command a pay premium? Wow, I didn't know that. You're telling me now for the first time.
More seriously, obviously you should take this into account while valuing the compensation offer you received.
Because after all, you value it as $0 or somewhere low like that. However like any MLE, you are a smart fucking cookie and you look at your $250k salary and the estimated 200k equity you have and think 10k REALLY?. Would you really be so dumb as to take literal pennies on the dollar for your equity?? It's not worth anything now and it very likely could never be, but 10k is fucking chump change, pardon my french.
The point of my $10k offer was to argue that the equity in a private company is not worth nothing, contrary to what you said. This argument was extremely successful, because you are now arguing for my side, telling me that the stock is worth more than $10k, and that the owner of that stock should hold out for better offer than mine.
I've been in this field for a bit and know of zero no-name MLEs who make 448k salary. Maybe an Ian Goodfellow, or a Yann LeCun would command that sort of cold hard cash.
Have you considered that maybe X AI is trying to attract talent of this caliber?
In any case, the salary brackets in job postings for this segment of the market have no actual relevance for anything. Everyone knows that equity is where the action is, and since the California law does not mandate including equity compensation in these brackets (as if there even was a reasonably useful way to do that), nobody cares about these figures.
This is a bad analogy. This email is not HR department going rogue; it’s HR department executing a project that was mandated by CEO, and under a clear and explicit coordination with said CEO. Your senior management would not resist the HR in these circumstances.
Also, only a small fraction of government employees have security clearance, and if you lie to your employer about your work being classified when it’s not to obstruct them, it is grounds for disciplinary proceedings.
wlxd
2mo ago
How do you know that this is opposite? How do you know that it is not the best, or even not a good way to tackle this problem? This sort of argument would be more convincing if there was an alternative way of going about doing this that was clearly better. Do you know any? I don’t. On the other hand, I know that Elon Musk has a track record of using very similar procedures across his companies, and in these cases, they apparently have been very successful.
I think that we will find out quite soon whether this was a good plan or not.
wlxd
2mo ago
Yes, DOGE efforts are highly irregular, and massively disruptive to government agencies. That’s kinda the point. Your analysis of the email thing is somewhat superfluous, because we already knew that the DOGE exists precisely to get the government out of the ruts it’s been stuck following. And, of course, nobody is surprised that many employees don’t like it.
wlxd
2mo ago
Commercial surrogacy is illegal in pretty much the entire Europe, and altruistic surrogacy is illegal in most of it.
wlxd
2mo ago
You say:
Anyone who worked for xAI would be a fool to take this offer because you are massively lowballing them.
You also say:
It could be that Elon is offering stock but we don't know that. Until they IPO their fake money is worth literally nothing.
How can I be lowballing them when their stock is literally worthless? Can you make up your mind?
I linked twitter salaries in the above thread. Everything in the Bay is talked about in the TC range because it sounds more impressive.
No, you quoted an article that was citing figures from job postings. Those figures are not there to sound impressive, they’re there to satisfy legal requirements California imposes on job postings.
wlxd
2mo ago
To quote myself again (and to respond to /u/2rafa)
Just to be clear: I'm not arguing that Grok 3.0 does in fact do all of this with the Johnny Cash song. All I'm saying is that it could.
All I'm arguing here is that "Grok couldn't have done that, because the answer came very fast, and speech transcription is slow" is a completely invalid argument, and I also argued that implementing this feature wouldn't require Grok developers to do anything special, because it would have came out of multimodality combined with internet search. I'm not saying that Grok actually does all of this (and I'm leaning towards thinking that it doesn't). My purpose here is to clear your misconceptions as to how the technology works, and what is possible.
No, I don’t have strong opinion one way or the other, I’m just saying that “transcribing a song is expensive so there is no way Grok is doing that to answer a text question” is a very bad argument. Grok could do that if it was trained with audio modality, but I don’t know if it actually does.
wlxd
2mo ago
In case it wasn’t clear: it took 67 seconds to transcribe an entire 171 seconds long song on my home CPU. You don’t have to wait for it to finish either: it produces output as it goes. It takes less than 20 seconds to process the entire song on my 9 year old gamer GPU. It would take less than half a second on a single H100. On 5 H100s, transcribing the entire song would take less time that it would take you to blink an eye. X AI reportedly has 100,000 of H100s.
But it doesn't, and the fact that it responded instantly is evidence of that.
How is it any evidence? Responding to the question is at least an order of magnitude more computationally demanding task than transcribing the entire song.
Do you really think Grok is spending resources (mostly dev time, really) to add features allowing the model to answer weird questions about song lyrics?
That’s the amazing thing about multimodal LLMs: they wouldn’t even have to add any special feature, with multimodality you get it for free. For a multimodal LLM model that is trained on sound tokens as well as on text tokens, understanding the words in a song, and analyzing accents etc is literally the same task as answering text questions. When you ask Grok a question, it searches web, fetches websites, and processes the contents. Fetching and processing song is, again, exactly the same task to the multimodal LLM as processing text.
which typically are borderline on lagging at 1x speed.
That's just not true. You see them running at 1x speed, because they only ever need to run at 1x speed. Running them faster is a waste of resources: what's the point of transcribing video in its entirety, if you are going to close the tab before it ends?
This is running on my home PC:
main: processing 'johny-cash-gods-gonna-cut-you-down.wav' (2739583 samples, 171.2 sec), 24 threads, 1 processors, 5 beams + best of 5, lang = en, task = transcribe, timestamps = 1 ...
(...)
[00:00:38.360 --> 00:00:52.120] Go tell that long-tongued liar, go and tell that midnight rider, tell the rambler, the gambler, the backbiter, tell him that God's gonna cut him down.
(...)
whisper_print_timings: total time = 67538.11 ms
That's 2.5x transcription speed, and it's just running on CPU, not even using GPU.
Look, guys, processing your stupid-ass text questions on a frontier LLM takes way more resources than speech transcription. Whisper on my CPU can do 400 GFLOPS, so the above 67 seconds run used something like 26,000 GFLOPs. For comparison, running a transformer model on a single token requires something like twice the number of parameters of the model worth of flops. So, for example, GPT-3, a junk tier model by today's standards, has 175B parameters, so just feeding it the lyrics of Cash's song would need 150,000+ GFLOPs, and that's before it even produces any output.
In the instant case, it would also need that algo to make some sort of judgement on the accent with which some of the words are being pronounced -- which is not a thing that I've seen.
Modern speech transcription models not only make judgements on accents as a normal course of operations, they actually understand the speech they are transcribing to some (limited) extent in the same sense as more general LLMs understand the text fed into them. It would actually be a pretty cool project for a deep learning class to take something like OpenAI Whisper, and fine tune it to make it into accent detection tool. It's all there inside the models already, you'd just need to get it out.
Just to be clear: I'm not arguing that Grok 3.0 does in fact do all of this with the Johnny Cash song. All I'm saying is that it could. The frontier LLMs are all multimodal, which usually means that they can process and generate image tokens in addition to text tokens. There's literally no technical reason why they couldn't add audio token type, and some of them probably already do that.
OK, and how exactly do you think US citizens come about to have foreign family? Again, think it through. These US citizens are almost universally downstream of some relatively recent immigration event. They either became naturalized citizens, or were born to immigrants. People who were born to two American-born parents are highly unlikely to have any foreign family that they could even consider getting in here.
So, basically all family based immigration is downstream of recent immigration. If you ignore the family based legal immigration, then legal immigration has been absolutely overwhelmed by illegal immigration for many years now.
What I am arguing here is that large chunk, if not a majority of legal family based immigration is downstream of some illegal immigration event that happened in recent history. For example, US citizen born to illegal parents, or formerly illegal aliens who legalized their presence in some way (and there are ways to do it for quite a lot of people).
Therefore, when you say that
You are missing the forest for trees: yes, family based chain migration have brought more people here than fake asylum claimants, but the point is that fake asylum claimants will cause more family based migration in future, so reducing the former also reduces the latter.
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