Well, sorry but look at the methodology on this. The study the post leans on did not study learning. 36 adults went around copying familiar words, and their "typing" is key presses using one index finger (!).
I'm afraid this one's close to going down the replication crisis whirlpool. According to Sol:
"Direct replications found no reliable learning advantage, while a 2024 meta-analysis found only a *small* handwriting benefit but a far larger typing advantage in information captured."
Typed Versus Handwritten Lecture Notes and College Student Achievement: A Meta-Analysis” by Abraham E. Flanigan and colleagues, published in Educational Psychology Review on July 12, 2024
The Greeks themselves, and us in the modern age, really have not given Egypt credit enough for it being the fount from where Greece drew to formulate much of its technology.
There is very little direct textual attestation of course, and I'm not advocating for some Black Athena type of research, but in this context of robotics, simply take note of these two artefacts:
Someone well informed on the literature might say, "Homer was dreaming of robots already in the 8th century BC with Hephaestus' workshop", and that might indeed seem like the earliest possible imagination of robotic creation. But note the sheer antiquity of the artefacts above, and if you apply a layer of very reasonable heuristics, it will become apparent that the Greeks were uniquely brilliant innovators, but they did so by brilliantly synthesizing and building on top of various neighboring civilizations and of course their own IE heritage.
Nope, GLM 5.2 is only the latest and greatest in a long line of open-weights models. There are even fully open source models that are comparable to o1-mini (OLMo), or almost-fully-open ones that are comparable to o3 (Nemotron).
I'm super grateful to the open labs (who, importantly, do not have the word 'Open' in their name), all the more so to the likes of Ai2.
There is no magic moat indeed. It is math, engineering and of course copious amounts of data (and the political maneuvering required to secure it, e.g. how most everyone has trained on Anna's Archive by this point).
Prompt: If A goes to B who then goes to C, can A send something to C?
Response:
We need to interpret best. The phrase "If A goes to B who then goes to C, can A send something to C?" could be a puzzle about the concept of sending something (like passing a ball) and the relationships.
Scenario: A gives something to B, and B passes it on to C.
Question: Can A also give the same thing to C?
Answer: Only if A can obtain a second copy (e.g., the thing was duplicated). Otherwise, after handing it to B, A no longer holds it and cannot “send” it unless a copy exists.
[Lots of other unnecessary commentary and "scenarios" that make even lesser sense]
Ran the same query and there is a ton of stuff, but it looks like it's reasoning through the ambiguity of the sentence. It still gets the right answer. Moreover, if we consider the FLOPs expended to get to the answer, and compare that to opus, I think it's still a net win.
My hunch is that Opus scale models probably have shortcuts encoded into the model that handle these ambiguities cases, wheres this model has learned a program to reason through the edge case (crystalized vs fluid intelligence). Remembering that probablity (frontier) vs calculating it on the fly (vibethink)
> LLM-based Query Quality Filtering. We utilize capable LLMs to assess query quality, filtering out samples with incomplete descriptions, unreasonable conditions, invalid logic, or an inability to effectively assess target knowledge points.
I often wonder why OLMo and Nemotron aren't more popular -- they are gold-standard / "frontier" of a year ago. If we had more support behind these, seeing a true open-source AI system that legitimately challenges OpenAI & Anthropic might not be far away!
It might change soon. Nemotron 120b was never flashy but always well regarded in the community and had material strengths at long context. The 550b next gen version is out now and still very fresh. It is too early to tell but for some reason I believe the impact it will eventually have is quite strong. NVIDIA open weight models are really good. They're not flashy but they're always well put together, well documented, well licensed, and in general make for truly great bases for customization - whether it's Nemotron or Cosmos.
Cosmos 2 in particular already has taken the image diffusion world by storm in a finetune (Anima) essentially replacing/dethroning the previous budget king SDXL. I wonder if the newest Nemotron could have the same impact for open weights LLM?
How do we know that today's frontier models are merely scaled up versions of that? Genuine question, since the labs have narrowed what they share over the years to now almost nothing, in terms of how the model was trained and how it works under the hood.
We know for sure the architecture of the open weights models since llama.cpp understands the architecture it needs to build to plug the weights into to run them. It's always possible that the latest closed model is doing something architecturally different than the open weights ones we know about, but judging by how close the large open weight models such as DeepSeek are to SOTA performance, this seems unlikely. When OpenAI first came out with their near-mythical "Strawberry" (aka "o1") thinking model there was all sorts of speculation that they had made some sort of architectural breakthough, but then DeepSeek replicated the capability and published how they did it, proving that it was just better training, not any architectural change.
There have been minor changes to the architecture over the years, but these are basically all efficiency tweaks such as various types of attention (some pioneered in the open by DeepSeek) that better scale to large context lengths, and the confusingly named "mixture of experts" architecture, but what's more notable really is how little the architecture has changed. The capability gains have been coming from better training and better data.
- R1 https://arxiv.org/abs/2501.12948 (RL applied to ML models was well-known beforehand, but they show it in the open, at scale, on big models)
Then, there's the incentive analysis. If you can see that these models empirically get better with scale, why would you swap the main architecture? Those events will be pretty rare. I'm not saying there's noone cooking a new architecture, just that it is a pretty rare event. And it would have to come from some researchers that would be happy to not publish their findings, which is not really what a sizable portion of elite researchers (obviously not all) are incentivized to do.
Of course, it's a bit of a verbal compression to claim simply 'scaled up'. They are recognisable scaled up transformers, but most new models come with a few tricks, but we're at the point where those usually are not an architectural rewrite and added to solve an explicit problem, like hallucination, not for big new capability gains.
No they are clearly not just scaled up versions of gpt 2; there are different LLM architectures like mixture of experts etc that appeared relatively recently. I am not an expert though, far from it.
separately trained experts can surpass performance in their activated regime and DOES result in a smarter model, the Claude system cards talk about this and eg there is https://openreview.net/forum?id=iydmH9boLb to read...
If you can make the existing model faster, you can then save your inference budget to then make your model bigger, which then makes it smarter.
A lot of how smart the models can be comes down to budget. If you can make your existing thing cheaper, you can instead make it bigger for the same price.
Not really “smarter” though? It’s just a big probability engine.
(Not trying to flame bait or anything. I just wouldn’t call LLM as exhibiting intelligence. It is great at making connections based on probability but doesn’t have a semantic understanding of what it is doing)
You do realize modern neuroscience considers the human brain as "just" a probability engine and that intelligence may well be the ability for an organism to predict well.
> doesn’t have a semantic understanding of what it is doing
I hope you realize this is an area of open, active research.
Didn't neuroscience some big scandals about bad statistics and overstating their findings (in addition to normal issues like replication)? Look up at least the "dead salmon study" (hint: it's related to fMRI, and you can probably guess its conclusions from its nickname).
The "Voodoo Correlations" and "Cluster Failure" papers are also a bit eye-opening.
In general we (humans) need to be humble about the limitations of our knowledge about how we function, it's an insanely complicated problem.
Towards maximizing the sum of individual happiness, power, beauty and knowledge. Maybe a few other attributes in there, but these are the bare minimum that no civilization would deny for itself.
The question of course is 'how'. For the last few centuries, the answer has been technology.
> According to the 115-page complaint, Baig discovered through
> internal security testing that WhatsApp engineers could “move
> or steal user data” including contact information, IP addresses
> and profile photos “without detection or audit trail”.
That isn't really the breach you're making it out to be. Profile photos, unless made private/contacts only, are already publicly visible, and so is "contact information".
Of course these are useful to intelligence services, but this doesn't mean that Baig found they don't have true end-to-end encryption.
I'm afraid this one's close to going down the replication crisis whirlpool. According to Sol:
"Direct replications found no reliable learning advantage, while a 2024 meta-analysis found only a *small* handwriting benefit but a far larger typing advantage in information captured."
Typed Versus Handwritten Lecture Notes and College Student Achievement: A Meta-Analysis” by Abraham E. Flanigan and colleagues, published in Educational Psychology Review on July 12, 2024
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