I paid for a year of access to their service last year (recently let it expire/lapse) ... and I agree with another Slashdot poster who called it "impressive".

I guess you can fight legal battles endlessly over what you're allowed to do with content that was made freely available for you to access over the Internet.... But those "vast music libraries" they "stole" were the same ones the average user of services like YouTube are welcome to pull up and listen to any time they like.

We're really just arguing about if it's ok to write code so a computer can analyze the music and use it to create new music based on ideas it "learned" from the content ... vs human musicians doing the same thing.

To me, the impressive part of SUNO was the way I could supply my own original lyrics as text, complete with instructions on how I'd like to hear the words sung, and have it churn out a realistic-sounding result with a backing track fully assembled to go with it. If you listen to enough SUNO content, you start to get a sense that specific music genres it uses result in only a certain resulting sound/feel/vibe. I could tell it to regenerate something I told it to create in the style of an "Irish jig", for example -- and over dozens of attempts? I'd wind up with maybe 4 or 5 really different ways it constructed it, and the rest feeling like small changes to those basic constructs. But to me, that's ok. You shouldn't try to use an AI music creation tool to crank out complete, "ready to play/perform" pieces of music that got rid of human musicians. A SUNO creation should be identifiable as a SUNO creation when a discerning listener hears it.

I see SUNO handling relatively "low effort" music creation needs like advertising jingles or as a tool to inspire a musician to build from what it gave them as a staring point. For a lot of background music, such as what's needed in a video game? It makes sense too.

I feel like it's not only possible, but likely America saw relatively major power outages at close to the same "one per month" rate in the past too? The electrical grid is basically designed with an assumption it only stays up with the help of a crew of linemen who get tasked with locating points of failure and fixing them ASAP.

I remember some years back, I lived in a small city right on the edge of the Potomac River in western Maryland. They were originally set up with "feeder" power lines coming from two directions in to town. At some point, Potomac Edison power company decided to just discontinue one of those feeder lines and let the city get by from the other one. Every time a car hit the right power pole coming in to town, after that? Power was out for the whole community.

Seemed insane to me that they'd purposely remove redundancy they already had in place? But I'm sure it was all about the economics -- with bean-counters realizing the lower grid reliability was still "adequate" per the total population there, and they'd save all the money maintaining the additional lines and poles.

I also remember living in a suburb of St. Louis, Missouri where they had a power outage lasting over 2 weeks. A storm came through and knocked down a lot of trees. (The community took pride in having all the trees growing there, but I guess didn't consider how bad that was for above-ground power lines running right past all of them.) They had to get crews from other states out to replace blown transformers on poles and the whole bit, to get it back up and running.

If better uptime was a major issue, you'd think they'd bury those power lines. But again, it's about cost-savings instead.

Yeah, there's two main problems:

1) People entering the wrong fields. For example, medicine really needs workers, at all levels, but not enough people are going into it.

2) Certain manual labour fields, like field work and home construction, because... well, I think we all know why there's a shortage of workers in those fields.

(What the heck does a PalmPilot organizer have to do with any of this?) Well ... I remember back when they first got popular, my buddy was a software developer at the company I worked for. At lunch one day, he mentioned how he absolutely loved the Palm OS platform, simply because it had so many limitations. He said when writing for the Windows PCs, by contrast? You had so many system resources and so many options, you could pretty much code anything you could come up with. Sloppy code was a non-issue too, on modern systems. Only the coders reviewing the source would know any better. He liked the mental challenges involved in maximizing what you could get from a Palm device with a small monochrome screen and the whole bit.

I feel the same way with movies. All these mega-mergers may give a few big-name film-makers massive financial resources to create new movies. But most of it is unnecessary to make an amazing film. What you need is a great story, and good acting (which really isn't some monopoly held by the big Hollywood stars!). Less is very often more. (Consider how well the first Star Wars trilogy held up over time, using simple backgrounds like a mostly empty desert for Tatooine. I prefer that to the crazy "busy" AI generated scenes in the newer movies.)

Or let's put this another way. Show of hands - how many of you "spicy autocorrect" / "stochastic parrot" people had "AI will start mass-solving Erdos problems" on your forecast list a couple years back? Huh, none of you? Fascinating!

Take some time to reassess your priors. And while you do so, understand that, yes, they are doing logic / reasoning.

They weren't discovered by an LLM. They were known conjectures that were proven by an automated solving language that was linked to an LLM.

I'll take "Things That Didn't Happen For $200", Alex.

Only a handful of meaningful proofs have ever been done by automated formal theorem solvers (the Four Colour Theorem being the most noteworthy example - but its proof is so long that humans can't verify it). By contrast, AI tools have been solving Erdos problems en masse. The majority of them just bog-standard commercial models. In case you need help, the only ones on that list that were hybrid (AI / non-AI) in the actual solving phase are:

1) AlphaProof / DeepMind Prover Agent / AlphaProof Nexus
2) Aristotle (Harmonic)
3) Seed Prover / Seed Prover 1.5 (ByteDance)
4) AxiomProver (Axiom Math)

In each of the above, LLMs come up with the lemmas / strategies but then use Monte Carlo search ("brute force") or likewise to investigate what they came up with. These are a minority. In the "AI Standalone" category, these "hybrid" tools made up only ~20% of attempts and successful proofs. Hybrid tools actually made more of a contribution in the "AI Alongside Literature" (related literature found afterward) and even more of the "AI Building On Literature" (related literature known beforehand) categories, which is the opposite of what people like you expect.

And even with the hybrid tools, it's still the AI doing the heavy lifting when it comes to strategy. Non-AI theorem solvers, again, don't have a spectacular record for churning out novel proofs to unsolved problems. Tools like Lean are more about mathematical rigour - a passive environment that requires a driver (a human or AI) to feed it actual strategies, lemmas, and proof steps. And no, you cannot brute force "strategy" in the vast majority of cases, which is, again, why automated theorem solvers don't have much of a track record with unsolved mathematical problems.

Let's take a random example: the disproof of the unit distance conjecture. It was solved purely by a general purpose commercial GPT model, not custom-trained to mathematics, with no external tools. Read what the various mathematicians reviewing / commenting on it have to say (sections #3 and onward). Seriously, don't skip reading them, actually read them. This was one of Erdos's favourite problems. He mentioned it commonly in his lectures. Essentially every mathematician working in complex geometry has thought about this problem. The approach that the model came up with was highly novel approach, based on CM-fields and class field towers.

I know you don't want to accept this reality, but it is the reality, so you better improve your ability to accept it,. The field of mathematics is already doing so.

Dang, link didn't post.

Ah, good 'ol "Model Collapse" theory that people have been pounding on for years now, predicting an imminent collapse in model capabilities. How has that been working out for you?

Except what the US is actually facing, at least in the near term, is just the opposite, a worker shortage.

You can't pay royalties to the entire internet. That's not realistic.

What is practical are things like taxes to fund public benefits, or requirements of returning things to the commons (for example, open models).

You're the one making the claim that modern frontier models still make this mistake (they don't), so how about you go to a frontier model, reproduce what you're claiming happens, and click the "share" button and post the link here?

Don't worry, I'll wait.

Your inability to reproduce the thing that you claim happens is duly noted.

You are speaking irrelevant nonsense. LLMs are trained in words

They are not. They are trained in tokens. Tokens do not align with word boundaries, and an arbitrary word can be tokenized in many different ways.

and they think in words

They do not. They don't even think in tokens. The process is: words are split to tokens, tokens point to an embedding position (latent space) while RoPE encodes a relative position, and all reasoning is done within latent space, which is not at all verbal (concepts are directions in latent space, and math is done on concepts, not words).

Also, a note: when spec'ing a generator, you need to know how much you're planning to use it vs. batteries. If it's only going to be used rarely, you prefer low mass, low volume, low cost, and low maintenance when unused (at the cost of low efficiency and higher maintenance in use), whereas if it's going to be used a lot, you prefer high efficiency and low maintenance cost in use, even if at the cost of higher mass, volume, cost, and maintenance when unused. In the former case, you'd prefer to allocate that extra mass, volume, and money into a larger battery pack.

That's why you don't use a tiny petrol generator? Diesel generator efficiencies are roughly:

Small backup generator (1-15kW): ~20-28%
Midsize backup generator (20-200kW): ~30-35%
Large industrial generator (200-2000kW): ~35-42%

Also, ironically this company's plan of the trailer providing a boost will actually make the tractor less efficient. ICE engines use "brake specific fuel consumption" (BSFC) graphs to plot their efficiencies across different RPMs and different torques. You can see an example for a small diesel engine here. Note that they require very high torque conditions and relatively high power conditions to be efficient. You can change the balance between torque and RPM within a given power band (blue) via gearing but gearing doesn't change what power band you're in. If you're in a low power band, you're fundamentally forced into inefficiency (note also that you're not going to be driving around at 1000 RPM just over a stall all the time).

Indeed, if you were forced into a low power band, you'd actually be better off with a series hybrid powertrain, as the engine can alternate between operating in an efficient powerband and shutting down. Of course, parallel hybrids are more efficient than series (albeit with added complexity and mass).