shock-value

I explicitly stated in a different comment that Rednote will not replace TikTok. I don’t think anyone seriously believes that. It’s subject to the same ban after all.

The interesting aspect here is rather the magnitude of dissatisfaction that a large percentage of users feel towards the other mainstream US social content platforms.

I don’t think anyone thinks RedNote will replace TikTok — it’s potentially subject to the same ban after all.

But it illustrates the general dissatisfaction among TikTok users with the other mainstream US social content platforms.

Rednote has been shown as the top free app (per Apple’s own App Store in my device at least) for going on a week, so the magnitude may be larger than you imply.

Also, having tried it myself, the algorithm works much like TikTok whereby it learns to show English speakers English content pretty quickly.

Also the general consensus among people who have used IG and TikTok (I personally don’t use IG) seems to be that the former does not at all substitute for the latter, particularly in terms of the subjective “authentic” feel of the content (IG often said to be lacking the community feel of TikTok).

P and B frames are compressed versions of a reference image. Frames resulting from DLSS frame generation are predictions of what a reference image might look like even though one does not actually exist.

Speaking of Bitcoin specifically, if this is your view then you should take some time to understand the severe societal and economic problems that come with persistent deflation. And more generally, determining the optimal level of the money supply to match the needs of a dynamic economy is not trivial — there is no simple formula involving straightforwardly measurable variables that I am aware of.

LLMs/transformers are a breakthrough on the level of convolutional neural networks — significant, and one that opens up lots of new interesting applications as well as invites lots more R&D — but like the neural net it’s not gonna fundamentally transform society or industry.

(And as far as I can tell this current craze is entirely predicated on LLMs/transformers.)

No you just decide what movie you want to watch and pay $4 to rent it on demand. Very common experience available to anyone with a smart TV or connected streaming device.

I think the companies pursuing VR see that as a momentary technical deficiency, not an inherent limitation of VR generally. So to characterize their efforts as a long-term “bet” against multitasking seems silly to me.

VR covers your entire field of view, so not sure why you’d claim that it precludes multitasking, even if the current iterations aren’t yet geared toward that.

It's obviously a productive change and kudos for taking it on, but much of the enthusiasm being generated here was driven by the entirely unanticipated prospect of running a model at full speed using less memory than the model's own footprint, and by the notion that inference with a dense model somehow behaved in a sparse manner at runtime. Best to be a bit more grounded here, particularly with regard to claims that defy common understanding.

It appears that this was just a misreading of how memory usage was being reported and there was actually no improvement here. At least nothing so sensational as being able to run a larger-than-RAM model without swapping from disk on every iteration.

> ... these large language models are already intelligent enough to matter.

I'm definitely not contesting that.

I've always considered the idea of "AGI" to mean something of the holy grail of machine learning -- the point at which there is no real point in pursuing further advances in artificial intelligence because the AI itself will discover and apply such augmentations using its own capabilities.

I have seen no evidence that these transformer models would be able to do this, but if the current models can do so do then perhaps I will eat my words. (Doing this would likely mean that GPT-4 would need to propose, implement, and empirically test some fundamental architectural advancements in both multimodal and reinforcement learning.)

By the way, many researchers are equally convinced that these models are in fact not AGI -- that includes the head of OpenAI.

The debate over what kind of intelligence these models possess is rightly lively and ongoing.

It’s clear that at the least, they can decipher very numerous patterns across a wide range of conceptual depths — it’s an architectural advance easily on the the level of the convolutional neural network, if not even more profound. The idea that NLP is “solved” isn’t a crazy notion, though I won’t take a side on that.

That said, it’s equally obvious that they are not AGI unless you have a really uninspired and self-limiting definition of AGI. They are purely feedforward aside from the single generated token that becomes part of the input to the next iteration. Multimodality has not been incorporated (aside from possibly a limited form in GPT-4). Real-world decision-making and agency is entirely outside the bounds of what these models can conceive or act towards.

Effectively and by design these models are computational behemoths trained to do one singular task only — wring a large textual input though an enormous interconnected web of calculations purely in service of distilling everything down to a single word as output, a hopefully plausible guess at what’s next given what’s been seen.

> Opting out would just mean all your missing data alerts fire every time Datadog has an incident and you would then check, see that everything is missing, and then identify the cause as the Datadog incident.

You are missing the last step, which is that, knowing alerts are down, you can actively monitor using other tools/reporting for the duration of their incident.

And why would you have no logs? Even assuming you ingest logs through Datadog (they monitor on much than just logs and not everyone uses all facets of their offering), you would presumably have some way to access them more directly (even tailing output directly if necessary).

And lastly, why would you communicate to your customers without any idea of the scope or cause of the issue? It would likely be clear very quickly that Datadog was having issues when you see that all your metrics are suddenly discontinued without other ill effect.