There's not that much Uranium actually that's economically sensible to extract. The NEA says in their 2024 report on Uranium [1]:
> Considering both the low and high nuclear capacity scenarios to 2050 presented in this edition, and assuming their 2050 capacity is maintained for the rest of the century, the quantities of uranium required by the global fleet – based on the current once-through fuel cycle – would likely surpass the currently identified uranium resource base in the highest cost category before the 2110s.
Their "high" scenario assumes having a bit more than double of today's capacity by 2050; today we have about 4-5% supply from nuclear energy worldwide.
It's a bit different. Higher demand will lead to higher price, opening more options to mine. Now uranium is too cheap to open new mines and exploration
And I hate to pollute this thread with more AI fear-mongering, but AI inference is already showing its effects on the energy sector and it is expected to grow very rapidly. Energy demands may likely grow at a stronger rate than initially expected.
That's what happens in the very last layer. But at that point the embedding for "was" got enriched multiple times, i.e., in each attention pass, with information from the whole context (which is the whole novel here). So for the example, it would contain the information to predict, let's say, the first token of the first name of the murderer.
Expanding on that, you could imagine that the intent of the sentence to complete (figuring out the murderer) would have to be captured in the first attention passes so that other layers would then be able to integrate more and more context in order to extract that information from the whole context. Also, it means that the forward passes for previous tokens need to have extracted enough salient high-level information already since you don't re-compute all attention passes for all tokens for each next token to predict.
Indeed. For bad news, e.g. crime, the (German) media takes great care to mention whether suspects or convicts are immigrants or of direct immigrant descent. Attaching this information to good news is done less frequently but would be required to paint a more realistic picture.
That is news to me. German police and media normally go to great length not to mention names/personal information of suspects (and victims), a crass difference to the US.
Until 2017 this actually was a rule (not a legally binding, just the "honor code") for media, but this got changed after the AfD and other Nazis whined for years that police and media would not "tell the truth" about migrant crimes, "suppress" or "hide" them (https://www.migazin.de/2017/03/24/presserat-aendert-richtlin...).
Journalism should report the relevant facts to a case. And the nationality/ethnicity/skin color rarely is a relevant fact in a criminal case, with the notable exception of racist-motivated or ethnic conflict (e.g. Kurds vs Turks) crimes.
For "everyday" crimes, think of pub brawls, petty theft, robberies, sexual misconduct of all forms, the ethnicity is absolutely irrelevant and its mention by police/media is only likely to further racial hatred.
Governments are able to choose which nationalities are allowed entry via immigration policy.
Some might find criminal representation very relevant to that policy decision. Perhaps you do not. That’s certainly your prerogative. But outright denying the relevance is absurd.
If it turns out left handed people are significantly overrepresented in crime statistics, then yes, that would be interesting to know. Either something is wrong with the system or with left handed people. This notion of withholding information from voting adults just because it doesn't further a particular social engineering agenda is repulsive to be honest.
Oh come on, your argument is self defeating. If doxxing individuals would be the only way for a voter to learn "if left handed people are significantly overrepresented", you would need to make all properties of everyone public, because there could be significant overrepresentation for any property.
And if you already know that "left handed people are significantly overrepresented" from some other source, you don't have to make the information public for these cases -- you know it already. Probably from a proper statistic made by the government. Not by counting a media-reported incident also reported the person to be left-handed.
And your argument is a mix of a strawman and taking GP’s point ad absurdum. Not withholding information doesn’t equate to doxxing, and just because you can’t find out all the correlations doesn’t mean you shouldn’t even try to find any in the first place.
> Or are you saying we should only talk about a person's ethnicity when it's in a positive light?
Yes, because integration can only work when people have role models to look up to. This is also why (even if she's as "top cop" as it can get) the appointment of Kamala Harris is so important, or Barack Obama winning in 2008 - it is a "ceiling breaker" event, it shows to people that even if one is not part of the "usual old boys club" it is possible to achieve success.
Painting ethnicity in a negative light, especially when it's totally unrelated and irrelevant, however was judged as "potentially inciting or furthering racial division" in German media codex.
If they wouldn't haven given an interview, or otherwise indicated that they want the world to know, it's nobodies business which nationality they are, or that they are husband and wife. It's simple as that.
Well, your personal information are facts too. Would you like them posted? As long as the crimes are only alleged, not proven, there is no question to me that the interest of the people at large is second to the privacy protection of the suspects.
Which is personal information. But I don't think they write it that often. The reverse has become a meme on right-wing forums: "tHeY dOn'T SaY tHere[sic] NAme so We NOw[sic] wHich RaCE iT Is".
"Turkey has been moving further away from the European Union. Turkey’s accession negotiations have therefore effectively come to a standstill and no further chapters can be considered for opening or closing and no further work towards the modernisation of the EU-Turkey Customs Union is foreseen."
I think your parent paints a rather extreme picture. The two examples they picked, Wiehre and Herdern, are the upscale neighborhoods with plenty of mansions, and there's rather only one area with large concrete housing blocks (Weingarten). As usual, you pay a premium for the top locations but in terms of quality of living and "greenness" there are many other good spots.
> If you don't, then like in many south-German cities (located in mountain valleys where space is scarce), you're living in a place without any green in sight
While this is generally true, the newer districts like Vauban also feature city-owned housing that is specifically targeted to be rented by lower-income families. If you're a student there are again lots of options all throughout the city.
Nice to see my neighborhood (Vauban) featured here :) It's an awesome place to raise kids as it provides an almost village-like environment, being right at the edge of the city and hence the edge of the black forest. But it also comes with the immediate benefits of a nearby regional hub and with plenty of next-door daycare/kindergarten institutions, playgrounds and a ton of other kids. This was one of the main factors that made us move back here after two years in SF.
As elsewhere, affordable, or even available housing is now an issue, in particular for families. Prices have been on a sharp increase over the last years, and although the there's quite some construction (and plans to add a whole new district) it's not keeping up with demand.
Agree, it's so convenient for grabbing fields that I ended up writing a bash script that generates an awk script since the '{print $1}' is cumbersome to type, and I can never remember how to properly output multiple fields.
At some point I thought that had the ideal use case for awk (a git --graph filter) and spent an evening desperately putting it together because, as other commenters mentioned, it's hard to find good documentation and examples online. Sure, I have a fast and mostly-working filter now, but the code is also hard to understand or even debug. On the other hand, the examples linked in the article are actually a lot more readable than I expected, so maybe it's something to consider for small but frequently-used log parsing scripts.
If I wound up on a host in /rescue mode awk would be my go-to to fix up 'convert this to that' changes, maybe even grobble into the piped inputs of other commands to get debug data marshalled up. If you have a bigger system, there are better tools. If you have to live in the small state of a /rescue, knowing how to use sed/ed/grep/awk is data-saving.
Sometimes people observe I'm using three tools with pipes to do one job, and I freely admit grep <pat> file | awk '{print $2}' | sed -e 's/this/that/g' is probably stupid, but I do think of these atoms as tools for the job. Grep aside, sed and awk should be fully interchangeable for many pipe jobs, and when not BEGIN{} ... END{} you could do the whole thing in awk or sed simply. If it has pre- and post- states, Awk is ideal. But.. the mind does what the fingers remember.
When training speech recognition systems you want to use data that closely matches your target domain. Models trained on audiobooks read by professionals will not perform very well for transcribing conversational or spontaneous speech or if there is background noise.
But, if I understand correctly, systems can be trained separately on "this is background noise" and then apply those filters first, and then work with cleaned audio, right? I've been using krisp.ai for a few weeks and it has been fantastic at doing exactly that in real-time.
Regarding conversational speech, I get that. Books are definitely not conversational.
I guess the next question though, would be: is the objective to build a model that understands all words, or conversational speech? <novice> It seems like transfer learning on a model trained on audiobooks and then conversations would still be a good path, right? </novice>
You're right, these issues can also be tackled independently. Transfer learning can help, but my first guess would be that it's hard to get reasonable accuracy (= usable for applications) without hundreds of hours of conversational data. You could also attempt to directly modify the audiobook data by manually adding noises or performing other distortions.
In any case, for read speech in particular there are several corpora out there already, including the moderately large LibriSpeech corpus (1000hr). The state-of-the-art accuracy on read speech is also very good -- for example, domain-specific dictation systems have been commercially viable for quite some time. So while it's true that Audiobooks are a large untapped source, I think that there are other large-scale and richer options like YouTube or movies (i.e. videos with speech for which subtitles are available) that would be more useful to make progress towards good speech recognition systems.
Self reply with more questions/thoughts. based on what I know, it seems like the problem could break down as:
1. we have a lot of training data for the voices of white men reading stuff.
2. We have good models that already exist for removing background noise.
3. We might be able to build good models that could identify accents, gender, age variation.
4. we have good models for style transfer that work in the audio domain.
Could we take an audiobook read by a white guy, and use a style transfer model to give him a german accent, and then use the german accented version as training data back into the speech recognition model? Could you use a reverse style transfer model to turn accented audio into non-accented audio (i.e. normalize it all to the place where we have the most training data) Could we use a combination of style transfer models to vastly expand the training data set, and then train the conversational systems?
Or, are the style transfer models not good enough? Or do we not have training data for style transfers to turn the voices of white men into the voices of white men with german accents?
I don't want to trivialize, but I'm genuinely curious how professionals are actually trying to solve this now?
> I guess the next question though, would be: is the objective to build a model that understands all words, or conversational speech? <novice> It seems like transfer learning on a model trained on audiobooks and then conversations would still be a good path, right? </novice>
Understanding all words is not the problem. I don't know if it's universal, but frequently, a speech-to-text model is actually two models: A voice model (mapping raw audio to phonemes) and a language model (which models what the language looks like, i.e. what sentences are likely and which words exist). So if you want the STT system to understand novels, include novels in the training data for the language model. You can then combine it with a voice model suitable for conversational speech/the user's accent/background noise.
Transfer learning is not guaranteed to work well. Most learned features even in the first layers look usually very different if trained in a clean environment. Background noise is not just a simple stationary signal, but very different audio patterns like music or other voices.
Yes! The API feels very much like using PyTorch from Python, and implementing models and working with tensors purely in C++ is very convenient. We're using it for our research platform for StarCraft: Brood War (https://torchcraft.github.io/TorchCraftAI).
> Considering both the low and high nuclear capacity scenarios to 2050 presented in this edition, and assuming their 2050 capacity is maintained for the rest of the century, the quantities of uranium required by the global fleet – based on the current once-through fuel cycle – would likely surpass the currently identified uranium resource base in the highest cost category before the 2110s.
Their "high" scenario assumes having a bit more than double of today's capacity by 2050; today we have about 4-5% supply from nuclear energy worldwide.
[1] https://www.oecd-nea.org/jcms/pl_103179/uranium-2024-resourc...