Okay, well, I can't force you to give a shit, I'm saying whether CoT is deceptive is a pretty important question if major AI labs are making monitoring of it a core safety premise https://web.stanford.edu/~cgpotts/blog/cot/#deceptive
You joke but when I first picked up C, I did find it quite helpful to write it in a plain text editor and write my own Makefiles to understand what project setup the IDEs would abstract away - our uni also had pen-and-paper exams where relying on autocomplete would have been difficult :)
(Likewise I can see "you should have read through your generated code and understood the key points yourself" becoming the rule of thumb for keeping maintainers accountable in a project, keeping the pace of changes manageable, and keeping the codebase from becoming a rat's nest _only_ agents can work with)
I don't think Simon means you should hand off the spending to agents/LLM (which would also make me uneasy) but that if you're probing one for hosting/SaaS providers they should default to recommending ones with budget caps
Because they cost money to run - Simon is discussing this in terms of the billing from services, not some hypothetical scenario with LLMs spending money on your behalf
If you were recommending VPS providers to someone new to making web apps, it would be malpractice to recommend one that can bill them thousands if they misconfigure things (like AWS previously did), and Simon's argument is that LLMs should likewise favor services with budget caps
Thus biasing submissions' language and content towards those most palatable to those open-weight models, creating further incentives to generate articles wholesale with them, and incentives against publishing null findings
Software licenses aren't enforced by magic but they are social contracts: even if nobody has the resources to go after you for doing it, it is pretty clearly against the permissive licenses' spirit to misrepresent yourself as the author
It's also pretty clear that the real author gave you permission to misrepresent yourself as the author, as long as you retain the copyright notice. If he didn't want you to do that, he shouldn't have given you permission.
The "if they wanted you to follow the spirit, the letter should have been different" argument grants membership in the society of people known throughout history as "the reasons we can't have nice things"
Incorrect, there are plenty of people who ignore or fail to recognize the purpose of a rule as a completely free service, as any forums mod or OSS project maintainer can attest to
they dont though. nothing about MIT license is granting anyone the ability to misrepresent as the author. thats why its a license and not copyright transfer.
thats just not true. Commits themselves are held up as copyrighted and a claim for creation. just as you cant legally put up a poster claiming you painted a painting you just got a license for, you can't claim the commits.
> The inference interface uses the engine-rendered RGB image as a dense, registered observation of visible scene appearance. It provides dense, pixel-aligned evidence for object support, occlusion boundaries, composition, and local material properties; engine motion vectors separately provide temporal correspondence.
> Existing image generative models commonly rely on text embeddings, exemplar images, or spatial control fields such as depth, edges, segmentation, and pose [...] These conditions are effective for general-purpose generation and editing, but they do not uniquely determine the object identities, materials, visibility relationships, lighting decisions, and pixel-aligned detail contained in an engine-rendered frame. DLSS 5 is therefore conditioned on the rendered frame itself.
Obviously they tried it multiple ways have brought receipts, but nonetheless it seems surprising that it wouldn't be of benefit to bring as much of that kind of metadata to the model as possible. You'd think depth and segmentation in particular would basically just be a straight shortcut without which the model spends its own time and effort re-deriving that stuff.
I'd also be interested in how post-processing fits in with this. Like if you've got weather effects, film grain, tone mapping, etc, I would have thought the model would do better working on the image before those processes.
Hmm well the depth buffer only has accurate depth for opaque objects (and even that's not really true). Things like hair wouldn't be in there. Its not ground truth depth.
It is data, It doesn't have to be ground truth Depth. In fact the difference between what optically appears to be at one depth and the depth map depth is in-itself information. You have described a mechanism that the model can use to identify that what it is supposed to be enhancing is more likely to be hair.
I guess in this example since the model wasn’t trained on that, just like they said.
If that data is some kind of missing key, go make your own and stop nitpicking someone who made a great point as to why they didn’t want that oh so precious data
I think it has more to do with what kind of data they have access to at runtime - IIRC DLSS upscaling has only required the previous frames and motion vectors, so requiring depth buffers would mean it was no longer a "drop-in" replacement
> I'd also be interested in how post-processing fits in with this.
I think screenspace effects like film grain and tonemapping are excluded in the same way UI elements are rendered separately from the game.
Worth remembering it is running a single-step diffusion model working in pixel space to generate each frame, it's a technical feat in itself that people are even using the words "frames per second"
I think this is mainly so it can use the existing hooks for DLSS upscaling without requiring changes to the renderer, AMD is working on a comparable method which uses adapter networks to slot normals and material properties from the renderer into the diffusion model: https://gpuopen.com/learn/temporally-stable-generative-illum...
I assume this is meant to run with the weights people extracted from the latest NBA game, where it was first trialled.
> Isn't the mote that Nvidia has is they work with studios to generate the training data from the game, then they ship a model per game?
That was true for the very first version of DLSS, from DLSS 2 on the models have been universal - the per-game adjustments are done on the inference end by changing the effect intensity or masking out objects
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