dopadelic · HackerTrans

11 giorni fadiscuss

People really don't have a clue how fast 25Gbps is, no personal device can come close to taking advantage of it. SSDs barely will transfer that fast on the best case scenarios (buffered large blocks). Most wifi won't even clear 0.5Gbps. If you have the latest and greatest wifi7 where both your PC and router supports it, you can get up to 2.5Gbps if you're sitting next to the router. If you're wired, few people have 10Gbps ethernet. You have to get a dedicated adapter for it since practically no PCs come with it. And that's just for 10Gbps. For 25Gbps, there's nothing available for the consumer market. That's enterprise level stuff. A single 25Gbps ethernet card costs $500.

dopadelic

8 mesi fadiscuss

Finally, a hardcore rally sim after 22 years of playing Richard Burns Rally!

dopadelic

anno scorsodiscuss

Why would we need to prove such a thing? Human vision has strong inductive biases, which is why you can perceive objects in abstract patterns. This is why you can lay down at a park and see a duck in a cloud. It's also why we can create abstracted representations of things with graphics. Having inductive biases makes it more relatable to the way we work.

And again, you're using the term LLMs again when vision based transformers in multimodal models aren't simply LLMs.

dopadelic

anno scorsodiscuss

You're pointing out a real class of hard problems — modeling sparse, nonlinear, spatiotemporal systems — but there’s a fundamental mischaracterization in lumping all transformer-based models under “LLMs” and using that to dismiss the possibility of spatial reasoning.

Yes, classic LLMs (like GPT) operate as sequence predictors with no inductive bias for space, causality, or continuity. They're optimized for language fluency, not physical grounding. But multimodal models like ViT, Flamingo, and Perceiver IO are a completely different lineage, even if they use transformers under the hood. They tokenize images (or video, or point clouds) into spatially-aware embeddings and preserve positional structure in ways that make them far more suited to spatial reasoning than pure text LLMs.

The supposed “impedance mismatch” is real for language-only models, but that’s not the frontier anymore. The field has already moved into architectures that integrate vision, text, and action. Look at Flamingo's vision-language fusion, or GPT-4o’s real-time audio-visual grounding — these are not mere LLMs with pictures bolted on. These are spatiotemporal attention systems with architectural mechanisms for cross-modal alignment.

You're also asserting that "no general-purpose representations of space exist" — but this neglects decades of work in computational geometry, graphics, physics engines, and more recently, neural fields and geometric deep learning. Sure, no universal solution exists (nor should we expect one), but practical approximations exist: voxel grids, implicit neural representations, object-centric scene graphs, graph neural networks, etc. These aren't perfect, but dismissing them as non-existent isn’t accurate.

Finally, your concern about who on the team understands these deep theoretical issues is valid. But the fact is: theoretical CS isn’t the bottleneck here — it’s scalable implementation, multimodal pretraining, and architectural experimentation. If anything, what we need isn’t more Solomonoff-style induction or clever data structures — it’s models grounded in perception and action.

The real mistake isn’t that people are trying to cram physical reasoning into LLMs. The mistake is in acting like all transformer models are LLMs, and ignoring the very active (and promising) space of multimodal models that already tackle spatial, embodied, and dynamical reasoning problems — albeit imperfectly.

dopadelic

anno scorsodiscuss

It's interesting how figures get idolized.

Fei-Fei Li is known for the creation of ImageNet, which is certainly transformative in the field of computer vision. But the crux of it is painstaking grunt work to create the vast labeled dataset. Fei-Fei Li is a leader who mobilized vast resources and people hours to create this vast dataset. Certainly worth a ton of acclaim. But to claim she's the most brilliant mind in an entire room is a stretch.

dopadelic

anno scorsodiscuss

[dead]

dopadelic

anno scorsodiscuss

This makes sense for blurring, but not for pixelation mosaicking.

dopadelic

anno scorsodiscuss

I used my T420 up till 2021.

I upgraded the screen to a 1920x1080 IPS panel.

SSD.

I have a full-fledged workstation for anything that needs heavy lifting and I primarily used the laptop as a device to remote into my workstation.

It was perfectly fine for standard web browsing and youtube.

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