There is a huge difference between "people are complex" and actively advertising your antisocial and frankly dangerous actions online, and being proud of them.
If he wants me to disregard them, he shouldn't be writing blog posts about them.
While I understand the sentiment and generally agree with it, I find it weird that you completely gloss over the fact that it is reactive antisocial mirroring behavior.
The blogging person is definitely making stuff worse, but they're just amplifying. The source of the problem is to be found elsewhere.
A surprising amount of comments in here seem to completely disregard that for some reason.
also, people changed. Seems like nobody wants to see cute fun stuff anymore. I bet they'd get lawsuits of people claiming false advertising since the numbers aren't strictly true.
Trying to make a stab at improving RSS feed discoverability. There's a website portion and an app portion. Hope to have something to show off in a few weeks.
It seems pretty clearly inline with the dotcom bubble to me. Every company claims to be a leading AI company, those building infrastructure are promising the moon and getting 1/3 of the way there, and no one knows how to monetize it justify the hype or expense.
Basically small and medium models that are crazy well trained for their sizes.
Then we have a lot of specular decoding stuff like MTP and others coming to speed up responses, and finally better quantisation to use less memory.
Local LLM is the future, and the larger labs know that the open models will eat their lunch once people realise that the gap is only a few months. If we were good with LLMs a couple months ago, we're good with the open models now.
That's not what this thread is about? We're saying some new breakthrough is needed, someone said it already has happened, and I'm asking if it really has. Has it? I don't think so, those models are not in some way fundamentally different than other LLMs
There's a percentage of people who love to question how the open models were trained.. they are almost always going to try and make some argument about using the closed frontier models for distillation as some form of theft.
Just totally forgetting that the frontier models themselves stole an insane amount to get to where they are.
It's theft all the way across the board, and when someone tries to make the argument that open models theft is bad, but Altman or Amodei's theft is good.. they are revealing a lot about themselves
The current LLMs are also "magic" so anything is possible. AFAIK there is no proof that the current architecture is optimal. And we have our brains as a pretty powerful local thinking machine as a counter-example to the idea that thinking has to happen in data centers.
I want to ask what makes them magic, but even those building LLMs don't really know what happens when they run inference...
I have to assume current architectures aren't optimal though, the idea that we stumbled into the one and only optimal solution seems almost impossible.
I mean, the most cutting edge of iPhones, iPads and MacBook Pros _today_ are quite capable of running in realtime today’s high-end local LLMs.
If you project out that hardware just a couple of years, and the trained models out a couple of years, you end up in a place where it makes so much more sense to run them locally, for all sorts of latency, privacy, efficacy, and domain-specific reasons.
Not all that different from the old terminal & mainframe->pc shifts.
Finally - hardware has seemingly gotten out ahead of software that most folks use - watching YouTube, listening to music, playing a game or two. There was a time when playing an mp3 or watching a 4k video really taxed all but the nicest systems. Hardware fixed that problem, like it very well could this one.
> I mean, the most cutting edge of iPhones, iPads and MacBook Pros _today_ are quite capable of running in realtime today’s high-end local LLMs
Definitely not the high end local LLMs. The small ones, yes, absolutely.
> If you project out that hardware just a couple of years
One of the biggest bottlenecks for LLMs is memory capacity and bandwidth. With the current glut for memory, it's unlikely we'll see lots of advancements in terms of average memory available or its bandwidth on regular (not super high end devices) in the coming years.
Alternatively, it's possible we get dedicated SMLs for e.g. phone specific use cases, that are optimised and run well.
It took us only, what 70-ish years of computer and AI research to get to this point, so yeah, probably just one little thing and then we'll have it </sarcasm>
Seriously. I have never ever seen so many people so willingly drink the marketing kool-aid from companies selling their product before. It's scarier to me than any threats of AI actually disrupting society (because it is so far from being capable of doing that).
Nintendo hardware IMO is mostly reasonably priced. Their first party game library is why many buy into the ecosystem and they charge a premium for it. The original Switch was under-powered but also the first of its kind. The Switch 2 was mostly a hardware bump with additional polish to the rough edges of the original Switch.
No "AI generated" markings or any indication provided that the video used in the ad isn't real. It brings forth a question we will soon need to face: how to regulate AI use in political advertisements designed to influence voters.
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