Is this akin to the quants already being done to various models when you download a GGUF at 4 bits for example, or is this variable layer compression something new that can also be make existing smaller models smaller so we can fit more into say 12 or 16 gb's of vram?
Readit NewsMyFirstSass commented on Run DeepSeek R1 Dynamic 1.58-bit unsloth.ai/blog/deepseekr... · Posted by u/noch
MyFirstSass commented on DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL arxiv.org/abs/2501.12948... · Posted by u/gradus_ad
Given this comment, I tried it.
It's no where close to Claude, and it's also not better than OpenAI.
I'm so confused as to how people judge these things.
Where are you guys using the full model?
Doesn't it require 220GB ram? I only se V-3 on their website and the distills available to run locally.
MyFirstSass commented on OpenAI O3 breakthrough high score on ARC-AGI-PUB arcprize.org/blog/oai-o3-... · Posted by u/maurycy
The programming task they gave o3-mini high (creating Python server that allows chatting with OpenAI API and run some code in terminal) didn't seem very hard? Strange choice of example for something that's claimed to be a big step forwards.
YT timestamped link: https://www.youtube.com/watch?v=SKBG1sqdyIU&t=768s (thanks for the fixed link @photonboom)
Updated: I gave the task to Claude 3.5 Sonnet and it worked first shot: https://claude.site/artifacts/36cecd49-0e0b-4a8c-befa-faa5aa...
What? Is this what this is? Either this is a complete joke or we're missing something.
I've been doing similar stuff in Claude for months and it's not that impressive when you see how limited they really are when going non boilerplate.
I'm really glad that I see someone else doing something similar. I had the epiphany a while ago that if LLMs can interpret textual instructions to draw a picture and output the design in another textual format that this a strong indicator that they're more than just stochastic parrots.
My personal test has been "A horse eating apples next to a tree" but the deliberate absurdity of your example is a much more useful test.
Do you know if this is a recognized technique that people use to study LLMs?
But how will that prove that it's more than a stochastic parrot, honestly curious?
Isn't it just like any kind of conversion or translation? Ie. a relationship mapping between diffrent domains and just as much parroting "known" paths between parts of different domains?
If "sun" is associated with "round", "up high", "yellow","heat" in english that will map to those things in SVG or in whatever bizarre format you throw at with relatively isomorphic paths existing there just knitted together as a different metamorphosis or cluster of nodes.
On a tangent it's interesting what constitutes the heaviest nodes in the data, how shared is "yellow" or "up high" between different domains, and what is above and below them hierarchically weight-wise. Is there a heaviest "thing in the entire dataset"?
If you dump a heatmap of a description of the sun and an SVG of a sun - of the neuron / axon like cloud of data in some model - would it look similar in some way?
I'm in Northern Europe and lately spam calls, and especially spoofing from random peoples numbers have become so bad i know multiple who stopped taking any calls, or even changed their phone numbers because they got too many calls, or angry people called them because their number was spoofed.
To me the whole system is archaic - i know gen z would never ever take a call from someone they don't know, or even call each other - it's simply not something you do - it would be like reading your spam mails.
And i'm coming to the same conclusion, answering random people is naive.
Practically we need something new though.
Northern Europe here.
Has been down for over 20 minutes.
Suddenly logged out of Messenger then FB - got "wrong password" error, had a quick panic, checked twitter, and calmed down.
Wow this is bad. And by bad i mean worse than leading open source and existing alternatives.
Is it me or does it seem like OpenAI revolutionized with both chatGPT and Sora, but they've completely hit the ceiling?
Honestly a bit surprised it happened so fast!
MyFirstSass commented on Something weird is happening with LLMs and chess dynomight.substack.com/p/... · Posted by u/crescit_eundo
Next thing, the "manager AIs" start stack ranking the specialized "worker AIs".
And the worker AIs "evolve" to meet/exceed expectations only on tasks directly contributing to KPIs the manager AIs measure for - via the mechanism of discarding the "less fit to exceed KPIs".
And some of the worker AIs who're trained on recent/polluted internet happen to spit out prompt injection attacks that work against the manager AIs rank stacking metrics and dominate over "less fit" worker AIs. (Congratulations, we've evolved AI cancer!) These manager AIs start performing spectacularly badly compared to other non-cancerous manager AIs, and die or get killed off by the VC's paying for their datacenters.
Competing manager AIs get training, perhaps on on newer HN posts discussing this emergent behavior of worker AIs, and start to down rank any exceptionally performing worker AIs. The overall trends towards mediocrity becomes inevitable.
Some greybread writes some Perl and regexes that outcompete commercial manager AIs on pretty much every real world task, while running on a 10 year old laptop instead of a cluster of nuclear powered AI datacenters all consuming a city's worth of fresh drinking water.
Nobody in powerful positions care. Humanity dies.
And “comment of the year” award goes to.
Sorry for the filler but this is amazingly put and so true.
We’ll get so many unintended consequences that are opposite any worthy goals when it’s AIs talking to AIs in a few years.
I am not sure how these large companies think they will reach "greater-than-human" intelligence any time soon if they do not create systems that financially incentivize people to sell their knowledge labor (unstable contracting gigs are not attractive).
Where do these large "AI" companies think the mass amounts of data used to train these models come from? People! The most powerful and compact complex systems in existence, IMO.
This is the most interesting comment in this highly autistic field.
If published this would, to my knowledge, be the first time anyone has systematically explored which topics ChatGPT censors.
Or the myriad of american wars that slaughtered millions in South America, Asia or the Middleeast for that sake.
Both the US and China are empires and abide by brutal empire logic that washes their own history. These "but Tiananmen square" posts are grotesque to me as a europeean when coming from americans. Absolutely grotesque seen in the hyperviolent history of US foreign policy.
Both are of course horrible.