No one told OP to make that claim. The onus is on them to back it up.

Ahh yes. The anti-vaxxers “do your own research” defense.

The onus is on you to back up that claim

How is signal not the best option?

Right? If they’re just chatting this should be happening in a jacuzzi with nice glasses of milk 🍼 👍

Are they in a sauna?

Real eyes realize.

Yeah I’ve read that before. I don’t necessarily agree with their framework. And even working within their framework, this article is about a challenge to their third bullet.

I’m just not quite ready to rule out the idea that if you can scale single models above a certain boundary, you’ll get a fundamentally different/ novel behavior. This is consistent with other networked systems, and somewhat consistent with the original performance leaps we saw (the ones I think really matter are ones from 2019-2023, its really plateaued since and is mostly engineering tittering at the edges). It genuinely could be that 8 in a MoE configuration with single models maxing out each one could actually show a very different level of performance. We just don’t know because we just can’t test that with the current generation of hardware.

Its possible there really is something “just around the corner”; possible and unlikely.

What we need are more efficient models, and better harnessing. Or a different approach, reinforced learning applied to RNNs that use transformers has been showing promise.

Could be. I’m not sure tittering at the edges is going to get us anywhere, and I think I would agree with just… the energy density argument coming out of the dettmers blog. Relative to intelligent systems, the power to compute performance (if you want to frame it like that) is trash. You just can’t get there in computation systems like we all currently use.

288 GB HBM4 memory

jfc…

Looking at the spec’s… fucking hell these things probably cost over 100k.

I wonder if we’ll see a generational performance leap with LLM’s scaling to this much memory.

yeah thats what I’m talking about.

Ok web, its embedded. On mobile, I have to hop out.

On voyager, I have to go out of client.

If the fediverse can focus on integrating content from from other fediverse sources extremely organically, the fediverse will win. I shouldn’t have to leave Lemmy to watch this video.

I mean. Sure. It’s also entirely plausible a cat would only ever tell you no.

big ole pile

I too prefer the big ol’ pile of bobas

I’m like, blown away by this video. This guy is incredible.

I have a cat who I believe has absolutely learned to meow “hello”

Well for one, that area already burned pretty recently. So its pretty unlikely to burn again any time soon.

But as part of a larger picture:

The area does experience fire-weather conditions for some portion of the year:

Here we’re looking at HDWI (hot dry windy index), where a “loose” definition of fire weather is if HDWI is above 200. HDWI is based on a few factors, namely, how hot it is, how dry it is, and how fast the air is moving. Hot dry air moving quickly = fire weather.

The number of fire weather days per year has been increasing, and in very recent years (the past decade) the rate of change has increased, and become statistically signficant:

So its not a particularly fire prone area, but its getting worse, and its getting worse at a faster rate.

That would be the first part of the analysis I would run. After that, we’d look for historically “anomalous” periods. Its not enough to look at averages; that will wash over important features in the data. We need to look for specific periods where fire weather manifests.

This is another way of thinking about fire risk. Here we’re going to count the amount of time, after 12 hours, that an area is in sustained fire-weather conditions. Basically, a bit of time in bad conditions isn’t the end of the world, but as you stay in fire weather conditions, fire risk increases exponentially (as plants/ fuels continue to dry out).

If I were writing an insurance product for you, I would count the number of events in a given magnitude bucket and give you a risk rating. Here, licking my thumb and sticking it in the air, I would say… “not that bad”.

Much of my work is around modeling in the wilderness urban interface. You picked an almost all wilderness area. Since there are no structures, I cant do the next analysis, but it would looks something like this:

Most of my work is about figuring out what the impacts of wildfire on the built environment are going to be. Also, the free structure dataset I have access to doesn’t cover Canada and I’m not going to spend money buying the structures for you (unless you REALLY want me to).

Those first figures are all specific to the coordinates you provided. The final figure is just an example.

Gimme some coordinates.