I think the only truly trivial ones are for n = k^2. Even the solutions that have integer side lengths but aren’t completely packed by unit squares seem difficult to prove that they are the optimal s(n). I don’t know much at all about the problem though, just seems like if there’s any space at all you might be able to rotate or whatever to squeeze out some space.
Depends on how they’re being informed I guess. Homogeneously informed can be okay to pretty bad. From mid-century American television and print media all the way down to North Korea and Nazi Germany.
To be a millionaire, maybe, but a $300/month scrather habit over 20 years, in a 401k that's just the SP 500 gets you a quarter million dollars, which isn't nothing to retire on, vs approximately $0 from scratchers.
Dude no offense but you must be vastly overestimating how many people can save $300 a month. We're talking about people buying couple lottery tickets a month.
That's the problem though. I know many people who can't save $300/month, but easily spend that much in consumables, scratchers, alcohol, drugs, etc. On the subject of gambling addiction, $300/month by someone who can't afford it absolutely happens. Go to a GA meeting sometime. They get their paycheck, gamble it away, and then don't have money for rent. Unrestricted gambling is the devil and will be the downfall of America. I'm not one for "shoulds", but there should be something like an accredited gambler law, where you're not allowed to gamble more than 1/3rd your paycheck except in certain circumstances.
> there should be something like an accredited gambler law, where you're not allowed to gamble more than 1/3rd your paycheck except in certain circumstances.
What would that look like? Would gamblers have to send copies of their pay stubs or bank statements to every casino or gas station they visit and every gambling app they use? I'd be happy if we focused on restricting the most harmful actions of the companies who profit from the people they turned into addicts. No more online sports betting. No more adverting for gambling. No more targeting children with gambling in video games that accept real money for anything. No more using AI or surveillance to detect and target addicts.
Payday loan places usually look at paystubs before extending a loan, so all I'm suggesting is places where you can gamble have to implement the same technology. The restrictions you mention seem harder to pass under a banner of "but muh freedom".
> We're talking about people buying couple lottery tickets a month
Those people do not have a gambling problem, though. They are dropping a few bucks a month to buy a daydream. It could be worse, they could be spending 10x that on Starbucks.
Well I think it’s technically pretty similar to 5G, but it doesn’t use existing 5G towers. It’s this company in Denver that sets up receivers on top of certain buildings and distributes using a line of sight connection. What’s weird is that we plug our modem into the phone jack in the wall
Not my area of expertise though so I could be leaving out info
Edit: Starry is the company, I believe they were just purchased by Verizon
Clones of old synths (or synth likes) is such a weird but somehow important business. They’ seem a little shameless but apparent the quality is pretty good and it’s keeping everyone honest.
Are there any LLMs being widely used for audio classification? I know VLMs are being used a lot in image stuff.
It always seems kind of silly to me to throw everything at an LLM. I know they’re huge and can automatically handle a huge number of tasks but something in me finds it wasteful when we could be creating easily trainable, cheap to run bespoke models for a lot of stuff
There are LLMs that support audio input, similar to those with vision support.
From my testing of open weights LLMs with audio support, they basically are only trained to recognize audio as an alternative to text input, they treat audio as basically equivalent to a transcript, and can't recognize or distinguish things like music, accents, background sounds, etc.
So they're only really good for transcribing or summarizing or using audio input in place of text input for prompts, but not anything that requires distinguishing any information about the audio that would not be present in a transcript.
It can be tempting to try to use an LLM for a variety of tasks; kind of the whole thing about an LLM is that you don't have to do a separate complex training run for every task, but can just provide instructions in natural language. But it only works as far as what the training data covers, if the training basically always treated audio and a text transcript as equivalent, the model has nothing causing it to learn other relevant features of the audio. If there's enough bird call identification in the training data of an LLM, it might be able to do that, but I think multimodal training data tends to be much more limited than the text training corpus
Have you had any luck fine tuning one with musical data for classification or music-aware QA? I've been hacking on https://trebel.la/ which I would like to be a music practice companion, and the biggest missing feature is actually useful audio-based feedback pipeline.
My current approach, not yet validated, is trying to generate training data from masterclass recordings on Youtube, and then fine tuning MOSS-audio on a bunch of those. But I'm interested if there are better models, or large training sets I don't know about.
I'm not selling anything; I'm developing it live and there is a landing page, but there's no payment hooked up since like you said, it's not functional yet.
The harness that connects to a chatbot, API or voice interaction is the place to route requests to different systems. If you remember the early days of ChatGPT it explicitly said it was routing image generation to Dall-E after embellishing your request itself first.
Determining which tool to use should be a lightweight operation but I’m not expert enough to understand exactly how much lighter than a full LLM call just to recognize it needs a different tool or model.
For sure, I just know it’s tempting given the power of large transformers to throw things at an existing model.
For instance, OCR is something that can be done locally with no access to a GPU but people (including me) still often use cloud hosted multi-modal large language models for it.
wav2vec or similar approaches are used a lot these days, which is basically BERT with audio inputs. Whether that counts as an LLM or not, I am not sure. It's a transformer architecture in any case.
People will often reach for "easily trainable, cheap" solutions when they can; the reason people reach for Transformers and LLMs is because when you throw more data at them, they get better.
It’s much more likely that our current approach to large language models for general use will eventually show diminishing improvements (even if you think it hasn’t yet), than the opposite situation where valuable improvements can be made forever.
The threat of distillation and efficiency gains from competitors mean that providing value at the top end of the market is an existential necessity for these labs. I don’t think they can do that forever and, in my opinion, for the vast majority of use cases we’ve already reached very little improvement for new models when compared with available offerings.
Sorry, this is ridiculous. OpenAI said that they have a step change in model performance. They proved it by solving a Millenium problem. HF incidents are public and vetted.
If you still think there's a stall despite all evidence pointing to opposite, I don't know what to say..
Solving millennium problems doesn’t pay the bills.
Their best models might be quite good when directed at extremely difficult and very focused problems, but most business use cases are nothing like that.
Edit:
Not trying to imply these new frontier models aren’t also better at other things, just that there’s really no reason for most people to use them when cheaper alternatives exist that get the job done just as well.
What reason could OpenAI possibly have to lie about their own performance? The fact they were desperate enough for a PR win they stole the research leading to the Millenium problem further hurts their case imo.
Two things can be true. Open AI is a huge company.
1. It has people in it who are career obsessed and who are willing to ruthlessly go after any opportunity to improve their standing/stock valuation. Using a 2 week old model to snipe a millenium prize for PR is in line with that.
2. There are many researchers and even executives at the company who genuinely think we are speedrunning the end of the world. I don’t know anyone in this field who honestly argues that if we build ASI soon it doesn’t lead to extinction. This group of people can output warnings about the state of research and fears for the future while pushing for regulation out of genuine fear of what they’re building. I tend to agree with them.
You can’t view these companies as a monolith. They’re actions won’t be consistent because it’s built of many people with conflicting beliefs. Please look at arguments regarding AI risk and the current pace of progress and value them as it relates to the argument itself, not who said it. We are in a dangerous place and no one is sure how quickly we’ll get to a bad spot.
> “the important thing is instead the significance that a mathematician and an LLM model can now do all this work in a month.”
> “This is a Deep Blue-Kasparov moment.”
> “incredibly important developments.”
> “If indeed an OpenAI model did close the gap to Navier-Stokes, that is a remarkable thing and it should be said loudly, by them, with the history intact.”
Clearly Buckmaster (who is probably one of the most accomplished academics in the field) himself doesn't believe that AI has stalled. What makes you think you are right?
Solving is a very interesting way of describing what happened. Unless you believe them over reputable academics I suppose?
I will note the remarkable goal post shift in your edit - giving direct counter evidence to your own earlier claim of an OpenAI proof - and leave it at that.
We are approaching what is likely the biggest existential crisis in human history.
Unfortunately, there will be people that refuse to tackle the issue head on and will instead narrow their focus on data that suggests that tomorrow will be like today.
The fact they were desperate enough for a PR win they stole the research
I guess people are just going to keep spreading misinformation about this, along the same lines as "Anthropic's C compiler fails on hello world". There is zero indication that they stole anything, unless it counts as "stealing" to spin up a bunch of compute based on rumors that the problem had been solved already.
If you believe Yann LeCun and David Silver (and others), there's an architecture wall; maybe labs are starting to see this on the horizon. Maybe the DRAM supply constraints are forcing it.
Are these real step changes - big picture wise, or refinements in RL/agentic orchestration/"taste" and advancements due to bigger models and hardware technology/capacity scaling? If they not, does this tactic - and hardware improvements - continue to scale non-linearly like they need to?
It is clear that whatever does change in each model increment has resulted in meaningfully better end user capabilities (as well as regressions in some areas, honestly), but that doesn't prove anything. I'm not sure what I personally believe, but stating with your full chest that a stall is ridiculous ignores a lot of potential evidence to the contrary.
Stupid example: Astra. Its main improvements are: much much better computer use and 3d modeling capabilities; better subagent orchestration; better and more reliable tool use; slightly worse coding.
This looks to me, from a feature perspective, to be an incremental improvement across several functional areas, plus new features which are unquestionably excellent but are probably the result of RL focus, not magic.
Step change? Ehhhh depends on how you squint. But how many more iterations of this do we have? Are we going to squeeze quintillion parameter transformers into GPUs?
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