That still leaves an Additional overhead due to power electronics and assembly but all in all it's a pretty impressive development.

Anyway,sodium ion taking off explains the recurrent deep sales for LFP power stations. Which might still be overpriced if there developments hold up.

If the neural network can distill a model out of complex input data.

Especially when many model are frequently trained through data augmentation practices that actively degrade input to achieve generalisation abilities.

Then why are we stuck wearing silk glove tokenizers?

A bridge is built upon a solid foundation of something empirically tested, a hard science and good engineering practice if you will. And if not, it will not remain a bridge for long.

The innumerable electronic subcomponents in any of your electronic devices work very well for years. The MTBF is clearly high or it would be dead on arrival or soon afterwards.

We too easily extrapolate this reliability pattern onto the softer sciences, creating a biological mythology with an underpinning built on inherited opinion and untested speculation.

> "My doctor said blood letting will cure my tuberculosis"

> "Less stress and less coffee will cure my stomach ulcers"

> "Keep Peanuts away from my kid until he can legally drink so he won't be allergic to them!"

The romans built bridges that are still bridges 2000 years before any of above quotes went out of style. Whatever kept them alive for so long can be nothing but a weakly described, but very durable and still present dark pattern of the mind and the public discourse.

There's a dark pattern hiding in the modern era where we assume hard evidence to exist where it doesn't, a projection of CAD engineering onto idle theory crafting and opinion.

If these renewables could run at max capacity 24/7 they'd then produce ballpark high estimate 270 TWh.

Looking at the Chinese comparison chart china adds 2000TWh of annual production per 5 year interval.

Now renewables run at 25% capacity factor on a good day, so the renewable growth with bidenomics would've added 65 TWh of growth in a span of time that china adds 2000. If Trump causes a further drawdown of 100 TWh in renewable capacity it will still only be a rounding error.

The US and most of the west is simply not even competing in this arena, the entire leadership is resting on their laurels and the focus is never on actual development but on policy, regulations and ideology.

Edit: I now see it was Paul Krugman as the author of that article which clearly illustrates my point on ideological drive of the western leadership, here we have an economic Nobel Prize winner that present numbers he either don't understand or misuse to take potshots at a leadership he's unhappy with.

Again.

I've had a lot of genuine fun playing with AI art generators dating all the way back to deepdream. I love the tech, I want it as it was, as it is, and as it will become. This tech have in the last few years given me much more joy than any artist have come close to. It shares creative powers freely, a far cry from the overly commercial streaks dominating much else.

What I don't want is to see yet another has-been meme artist rehash the same anti-AI tirade that we've seen so frequently that any given LLM could re-create it verbatim due to overfitting.

All talk about "Human originality, soul, heart, the divine spark" and yet all they display is hysteria.

The glaring issue with it back then was that unlike an LLM that can be understanding of what you try to explain and bit more consistent the diffusion models ability to read and understand your prompt wasn't really there yet, you're more shotgunning keywords and hope the seed lottery gives you something nice.

But recent image generation models are significantly better in stable output. Something like qwen image will care a lot more about your prompt and not entirely redraw the scene into something else just because you change the seed.

Meaning that the UI experiments already exist but the models are still a bit away from maturity.

On the other hand, when looking at how models are actually evolving I'm not entirely convinced we'll need particularly many classically trained artists in roles where they draw static images with some AI acceleration. I expect people to talk to an LLM interface that can take the dumbest of instructions and carefully adjust a picture, sound, music or an entire two hour movie. Where the artist would benefit more by knowing the terminology and the granular abilities of the system than by being able to hold a pencil.

The entertainment and media industry is worth trillions on an annual basis, if AI can eat a fraction of that in addition to some other work-roles it will easily be worth the current valuations.

They stop paying taxes and work off the books instead but you don't announce that publicly for obvious reasons.

The incentive to do this increases with tax pressure. The willingness of people to pay for tax-free work equally increases because you'll pay less.

There's also an increasing asymmetry of what the government gains from a tax hike versus how oppressive it becomes that becomes unfavorable as tax rates go up.

We have several synthetic datasets and automated evaluation options for such things that were close to impossible to do before LLMs.

And if it is then I'm a farmer because I bought potatoes from the store.

While the 5-minutes model will never be useful in itself it lays the groundwork for amateurs and small groups to getting into developing small models. There's at the moment another HN headline hyping up a tiny model that scores impressively at the arc-agi benchmarks so it's clearly not a dead end to explore what is "household-affordable" models.

Though an approach that doesn't lean on the authors $200/month OAI sub would've been more interesting to follow.

We should simultaneously use this marker to prove drunk driving instead of the clearly outdated direct measurements.

In Europe it is constantly >"why does the households of half of Europe pay for German unwillingness to have a good power mix? Why should anyone want more cross country or long range interconnects if it drives up local prices?"

Say Norway with abundant hydropower, they should by all right have cheap power. But reality is not so in half of the country because they're sufficiently interconnected to end up on a common bidders euro market and end up paying blood money for the poor political choices of countries they don't even share a border with.

Addition: this also creates perverse incentives. A good solution for many of the interconnected flat euro countries would love enormous hydropower overcapacity to be built in Norway at the cost of the local nature. This is great for whoever sells the hydropower. This is great for whoever is a politician that can show off their green all-hydro power mix in a country as hilly as a neutron star. But this is not great for whoever gets their backyard hiking trails reduced to a hydro reservoir.

But hey we do it with everything else too, "open pit mines are too destructive to have in our country, so we'll buy it from china and pretend we're making green choice. Globalism in a nutshell: Export your responsibility.

(It might sound like a joke but it's actually well suited for ML)

>"we will get there by monotonously doing more of what we did previously"

Take the independent time being an SWE metric of the article. This is a rather new( metric for measuring AI capabilitie, it's also a good metric, it is directly measurable in a quantified way, unlike nebulous goal points such as "AGI/ASI".

It also doesn't necessarily predict any upheaval, which I also think is a good trait of a metric, we know it will be better when it hits 8, or 16 hours, but we can skip the hype and prophecies of civilizational transformation that are attached to terminology like "AGI/ASI".

Now the caveat is that a SWE-time metric is useful at the moment because it's an intra day timescale, but if we push this number to the point of comparing 48 hour vs 54 hour SWE-time models we can easily end up chasing abstractions that have little to no explanatory power as to how good this AI really is and what consists as a proper and good incremental improvement and what comes out as a numerical benchmark number that may or may not be artificial.

The same can be said of math-olympiad scores and many of the existing AI benchmarks.

In the past there existed a concept of narrow AI. We could take task A, make a narrow AI become good at it. But we would expect a different application to be needed for task B.

Now we have generalist AI, and we take the generalist AI and make it become good at task A because that is the flavor of the month metric, but maybe that doesn't translate for improving task B, which someone will come around to improving when that becomes flavor of the month.

The conclusion? There's probably no good singular metric to get stuck on and say

"this is it, this graph is the one, watch it go exponential and bring forth God"

We will instead skip, hop and jump between task-or-category specific metrics that are deemed significant at the moment and arms-race style pump them up until their relevance fades.