Comment by miki123211

On the topic of how Sears used to be high-tech: back in 1981, when IBM introduced the IBM PC, it was the first time that they needed to sell computers through retail. So they partnered with Sears, along with the Computerland chain of computer stores, since Sears was considered a reasonable place for a businessperson to buy a computer. To plan this, meetings were held at the Sears Tower, which was the world's tallest building at the time.

My favorite anecdote about Sears is from Starbucks current HQs - the HQs used to be a warehouse for Sears. Before renovation the first floor walls next to the elevators used to have Sears' "commitment to customers" (or something like that).

To me it read like it was written by Amazon decades earlier. Something about how Sears promises that customers will be 100% satisfied with the purchase, and if for whatever reason that is not the case customers can return the purchase back to Sears and Sears will pay for the return transportation charges.

:-) Then it's going to blow your mind that CompuServe (while not founded by them) was a product of H&R Block.

There were quite a few small ISP's in the 1990's. Even Bill Gothard[0] had one.

[0]https://web.archive.org/web/19990208003742/http://characterl...

Blame short sighted investors asking Sears to "focus"

For more on this -- and how Sears had everything it needed (and more) to be what Amazon became -- see this comment from a 2007 MetaFilter thread: https://www.metafilter.com/62394/The-Record-Industrys-Declin...

Reminds me of Elon not taking a no for an answer. He did it twice, with a massive success.

A true shame to see how he's completely lost track with Tesla, the competition particularly from China is eating them alive. And in space, it's a matter of years until the rest of the world catches up.

And now, he's ran out of tricks - and more importantly, on public support. He can't pivot any more, his entire brand is too toxic to touch.

Sears started Prodigy to become Amazon, not Comcast.

They sure did. This reminds me of when I was in the local Mac Dealer right after the iPod came out. The employees were laughing together saying “nobody is going to buy this thing”.

You know, you're right. It -also- sucks at those tasks because on top of the issue you mention, unedited LLM text is identifiable if you get used to its patterns.

By definition, transformers can never exceed average.

That is the thing, and what companies pushing LLMs don't seem to realize yet.

Exactly. Books are still being translated by human translators.

I have a text on my computer, the first couple of paragraphs from the Dutch novel "De aanslag", and every few years I feed it to the leading machine translation sites, and invariably, the results are atrocious. Don't get me wrong, the translation is quite understandable, but the text is wooden, and the translation contains 3 or 4 translation blunders.

GPT-5 output for example:

Far, far away in the Second World War, a certain Anton Steenwijk lived with his parents and his brother on the edge of Haarlem. Along a quay, which ran for a hundred meters beside the water and then, with a gentle curve, turned back into an ordinary street, stood four houses not far apart. Each surrounded by a garden, with their small balconies, bay windows, and steep roofs, they had the appearance of villas, although they were more small than large; in the upstairs rooms, all the walls slanted. They stood there with peeling paint and somewhat dilapidated, for even in the thirties little had been done to them. Each bore a respectable, bourgeois name from more carefree days: Welgelegen Buitenrust Nooitgedacht Rustenburg Anton lived in the second house from the left: the one with the thatched roof. It already had that name when his parents rented it shortly before the war; his father had first called it Eleutheria or something like that, but then written in Greek letters. Even before the catastrophe occurred, Anton had not understood the name Buitenrust as the calm of being outside, but rather as something that was outside rest—just as extraordinary does not refer to the ordinary nature of the outside (and still less to living outside in general), but to something that is precisely not ordinary.

> it's hard to imagine a AI that can competently code that doesn't have an LLM as a component.

That's just it. LLMs are a component, they generate text or images from a higher-level description but are not themselves "intelligent". If you imagine the language center of your brain being replaced with a tiny LLM powered chip, you would not say it's sentient. it translates your thoughts into words which you then choose to speak or not. That's all modulated by consciousness.

Microsoft claims that they have an AI setup that outperforms human doctors on diagnosis tasks: https://microsoft.ai/new/the-path-to-medical-superintelligen...

"MAI-DxO boosted the diagnostic performance of every model we tested. The best performing setup was MAI-DxO paired with OpenAI’s o3, which correctly solved 85.5% of the NEJM benchmark cases. For comparison, we also evaluated 21 practicing physicians from the US and UK, each with 5-20 years of clinical experience. On the same tasks, these experts achieved a mean accuracy of 20% across completed cases."

Of course, AI "doctors" can't do physical examinations and the best performing models cost thousands to run per case. This is also a test of diagnosis, not of treatment.

If you consider how little time doctors have to look at you (at least in Germanys half broken public health sector) and how little they actually care ...

I think x is already higher than y for me.

Deepmind where experimenting with this https://github.com/google-deepmind/lab a few years ago.

Having AI agents learn to see, navigate and complete tasks in a 3d environment. I feel like it had more potential than LLMs to become an AGI (if that is possible).

They haven't touched it in a long time though. But Genie 3 makes me think they haven't completely dropped it.

If we suppose that ANNs are more or less accurate models of real neural networks, the reason why they're so inefficient is not algorithmic, but purely architectural. They're just software. We have these huge tables of numbers and we're trying to squeeze them as hard as possible through a relatively small number of multipliers and adders. Meanwhile, a brain can perform a trillion fundamental simultaneously because every neuron is a complete processing element independent of every other one. To bring that back into more concrete terms, if we took an arbitrary model and turned it into a bespoke piece of hardware, it would certainly be at least one or two orders of magnitude faster and more efficient, with the downside that since it's dead silicon it could not be changed and iterated on.

I dont fully even get why; inference costs are way lower than training costs no?

The fact that the human brain, heck all brains, are so much more efficient than “state of the art” nnets, in terms of architecture, power consumption, training cost, what have you … while also being way more versatile and robust … is what convinces me that this is not the path that leads to AGI.

>doing things like embedding the weights of some model directly as part of the chip will suddenly become economical, and that's going to cut costs down dramatically.

An implementation of inference on some specific ANN in fixed function analog hardware can probably pretty easily beat a commodity GPU by a couple orders of magnitude in perf per watt too.

> "We're profitable on inference. If we didn't pay for training, we'd be a very profitable company."

That's OpenAI (though I'd be curious if that statement holds for subscriptions as opposed to API use). What about the downstream companies that use OpenAI models? I'm not sure the picture is as rosy for them.

Yes, it still gives people headaches because the convergence-accommodation conflict remains unsolved. We have a few different technologies to address that, but they're expensive, don't fully address the issue, and none of them have moved beyond the prototype stage.

Motion sickness can be mostly addressed with game design, but some people will still get sick regardless of what you try. Mind you, some people also get motion sick by watching a first-person shooter on a flat screen, so I'm not sure we'll ever get to a point where no one ever gets motion sick in VR.

RealPlayer in the late 90s turned into (working) Napster, Gnutella and then the iPod in 2001, Podcasts (without the name) immediately after, with the name in 2004, Pandora in 2005, Spotify in 2008. So a decade from crummy idea to the companies we’re familiar with today, but slowed down by tremendous need for new (distributed) broadband infrastructure and complicated by IP arrangements. I guess 10 years seems like a long time from the front end, but looking back it’s nothing. Don’t go buying yourself a Tower Records.

Most growth is actually logistic. An S shaped curve that starts exponential but slows down rapidly as it reaches some asymptote. In fact basically everything we see as exponential in the real world is logistic.

True, but adoption of AI has certainly seen exponential growth.

Improvement of models may not continue to be exponential.

But models might be good enough, at this point it seems more like they need integration and context.

I could be wrong :)

Fwiw LLMs are also revolutionary. There's currently more anti-AI hype than AI hype imho. As in there's literally people claiming it's completely useless and not going to change a thing. Which is crazy.

You're right, and I also think LLMs have an impact.

The issue is the way the market is investing they are looking for massive growth, in the multiples.

That growth can't really come from trading cost. It has to come from creating new demand for new things.

I think that's what not happened yet.

Are diffusion models increasing the demand for video and image content? Is it having customers spend more on shows, games, and so on? Is it going to lead to the creation of a whole new consumption medium ?

I see the point at the moment on “low quality advertising”, but we are still far from high quality video generated for AI.

It’s the equivalent of those cheap digital effects. They look bad for a Hollywood movie, but it allows students to shot their action home movies

It's quite incredible how fast the generative media stuff is moving.

The self-hostable models are improving rapidly. How capable and accessible WAN 2.2 (text+image to video; fully local if you have the VRAM) is feels unimaginable from last year when OpenAI released Sora (closed/hosted).

As long as you do not make ads with four-fingered hands, like those clowns ... :)

https://www.lapresse.ca/arts/chroniques/2025-07-08/polemique...

too soon to get it to market, though it obviously all sold perfectly well, people were sufficiently wowed by it

> There's also no evidence that it won't

There are signs, though. Every "AI" cycle, ever, has revolved around some algorithmic discovery, followed by a winter in search for the next one. This one is no different and propped up by LLMs, whose limitations we know quite well by now: "intelligence" is elusive, throwing more compute at them produces vastly diminishing returns, throwing more training data at them is no longer feasible (we came short of it even before the well got poisoned). Now the competitors are stuck at the same level, within percent points of one another, with the difference explained by fine-tuning techniques and not by technical prowess. Unless a cool new technique come yesterday to dislodge LLMs, we are in for a new winter.

What is your definition of "evidence" here? The evidence, in my view, are physical (as in, available computing power) and algorithmic limitations.

We don't expect steel to suddenly have new properties, and we don't expect bubble sort to suddenly run in O(n) time. You could ask -- well what is the evidence they won't, but it's a silly question -- the evidence is our knowledge of how things work.

Saying that improvement in AI is inevitable depends on the assumption of new discoveries and new algorithms beyond the current corpus of machine learning. They may happen, or they may not, but I think the burden of proof is higher on those spending money in a way that assumes it will happen.

I don’t follow. We have benchmarks that have survived decades and illustrate the steps.

What do you call GPT 3.5?