The article has a strong focus on deceptive media, used on social media to penetrate viewers' attention. It makes me sad and glad that my kids and family did grow up before all this insanity of psychological abuse.

> That's how technology always goes

This is not how AI has ever gone. Every approach so far has either been a total dead end, or the underlying concept got pivoted into a simplified, not-AI tech.

This new approach of machine learning content generation will either keep developing, or it will join everything else in the history of AI by hitting a point of diminishing to zero returns.

Minor correction. FOOCUS [1] isn't a LoRA - it's a Gradio-based frontend (in the same vein as Automatic1111, Forge, etc.) for image generation.

And most SOTA models (Imagen, Qwen 20b, etc) at this point can actually already handle a fair amount of text in a single T2I generation. Flux Dev provided your willing to roll a couple gens can do it as well.

[1] https://github.com/lllyasviel/Fooocus

Well, sometimes innovation does destroy jobs, and people have to adapt to new ones.

The plough didn't make everyone poor, but people working in agriculture these days are a tiny percentage of the population compared to the majority 150 years ago.

(I don't think LLMs are like that, tho).

Touching on this topic, I cannot recommend enough "The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger" which (among other things) illustrates the story of dockworkers: there were entire towns dedicated to loading ships.

But the people employed in that area have declined by 90% in the last 60 years, while shipping has grown by orders of magnitude. New port cities arose, and old ones died. One needs to accept inevitable change sometimes.

[0] https://en.wikipedia.org/wiki/The_Box_(Levinson_book)

> it will enable us to do even greater things.

Just as gun powder enabled greater things. I agree with you just humans have shown, time after time, an ability to first use innovation to make lives miserable for their fellow humans.

>it will enable us to do even greater things.

It doesnt do this.

35 seconds @ 80W is ~210 mAh, so definitely a lot less than the ~4000+ mAh in today's phone batteries.

An ideal measurement would be to calculate your utility's water usage to kWh then to this, perhaps a token per gram measurement. Of course it would be small, but it should be calculable and then directly comparable to these models if we try to go by something like token per gram of water. I suspect due to DC power distribution they may be more efficient in the data center. You could get more specific about the water too, recycled vs evaporated etc etc

Nobody's running SDXL on an 80W GPU when they're talking about generating images, and you also have to take into account training and developing SDXL or the relevant model. AI companies are spending a lot of resources on training, trying various experiments, and lately they've become a lot more secretive when it comes to reporting climate impact or even any details about their models (how big is ChatGPT's image generation model compared to SDXL? how many image models do they even have?)

IIRC some of those researches you're taking estimates from not only used cherry picked figures for AI image generators, but also massively underestimated man-hour costs of human artists by using commission prices and market labor rates without requisite corroboration works before choosing those values.

Their napkin math went like, human artists take $50 or so per art, which is let's say $200/hr skill, which means each art cannot take longer than 10 minutes, therefore the values used for AI must add up to less than 10 workstation minutes, or something like that.

And that math is equally broken for both sides: SDXL users easily spend hours rolling dice a hundred times without usable images, and likewise, artists just easily spend a day or two for an interesting request that may or may not come with free chocolates.

So those estimates are not only biased, but basically entirely useless.

I did a little investigation. Turns out that GPT-4's training consumes as much energy as 300 cars in their lifetime, which comes about 50 GWh. Not really that much, could be just families on a short street burning that kind of energy. As for inference, GPT-4 usage for an hour consumes less than watching Netflix for an hour.

If you compare datacenter energy usage to the rest, it amounts to 5%. Making great economies on LLMs won't save the planet.

> the actual energy required for a single image generation (SDXL, 25 steps) is about 35 seconds of running a 80W GPU.

And just how many people manage to 1shot the image?

There are maybe 5 to 20 images generated before the user is happy.

The irony stems from the fact workers are fired due to being 'replaced' by AI only to then be re-hired afterwards to clean up the slop, thus maximizing costs to the business!

> There are really two observations here: 1. AI hasn't commoditized skilled labor.

The problem is, actually skilled labor - think of translators, designers, copywriters - still is obviously needed, but at an intermediate/senior level. These people won't be replaced for a few years to come, and thus won't show up in labor board statistics.

What is getting replaced (or rather, positions not refilled as the existing people move up the career ladder) is the bottom of the barrel: interns and juniors, because that level of workmanship can actually be done by AI in quite a few cases despite it also being skilled work. But this kind of replacement doesn't show up in any kind of statistics, maybe the number of open positions - but a change in that number can also credibly be attributed to economic uncertainty thanks to tariffs, the Russian invasion, people holding their money together and foregoing spending, yadda yadda.

Obviously this is going to completely wreck the entire media/creative economy in a few years: when the entry side of the career funnel has dried up "thanks" to AI... suddenly there will not be any interns that evolve into juniors, no juniors that evolve into intermediates, no intermediates that evolve into seniors... and all that will be left for many an ad/media agency are a bunch of ghouls in suits that last touched Photoshop one and a half decades ago and sales teams.

> LLMs are not AI. Machine learning is more useful.

LLMs are a particular application of machine learning, and as such LLMs both benefit by and contribute to general machine learning techniques.

I agree that LLMs are not the AI we all imagine, but the fact that it broke a huge milestone is a big deal - natural language used to be one of the metrics of AGI!

I believe it is only a matter of time until we get to a multi-sensory self-modifying large models which can both understand and learn from all five of human senses, and maybe even some of the senses we have no access to.

We keep moving the goalposts...

Brad Pitt as Rusty: "Don't use seven words when four will do. Don't shift your weight, look always at your mark but don't stare, be specific but not memorable, be funny but don't make him laugh. He's got to like you then forget you the moment you've left his side. And for God's sake, whatever you do, don't, under any circumstances..."

(from Ocean's Eleven)