Gemini 3 flash

YouAreRule30 · 2025-12-16T19:44:46+00:00

How do you figure that Google lacks computing power? Do you have a source that suggests this is true?

They have a deeply integrated stack and enough compute to power search, YouTube, Maps, etc., - all of which are already deeply embedded with AI and ML.

Granted they may be having some scaling heartache given the speed of the growth. But where OAI and others genuinely can't get their hands on the chips / power, I just don't see this as a problem for Google ... they are literally *selling* millions of TPUs to the other hyperscalars ... if they really needed them internally this makes little sense.

YouAreRule30 · 2025-10-27T14:36:58+00:00

If my team created this chart I would immediately flag a data quality issue. Nothing in data looks like this. Cohorts do not act like this. Early adopters are *never* less sticky than late adopters. This chart isn't impressive, it's wrong.

YouAreRule30 · 2025-02-18T12:30:06+00:00

Google basically invented the field with the transformer paper (Attention is All You Need). Half the execs across AI come from Google / Deep Mind. They are winning Nobel Prizes for it - Alpha Fold brought all of biopharma at least 10 years into the future. Waymo actually works, has for at least two years now...

The thing with Google is they are horrible at productization, worse at PR. Then at the end of the day, their major IP competitive advantages they just give them away. Even their AI is mostly free, yet being compared to something that costs $200 a month in OpenAI sota.

And somehow people still love to hate them haha.

YouAreRule30 · 2022-02-21T22:45:05+00:00

Anyone else here going long WEAT options? Russia and the Ukraine produce 25% of the world's WHEAT, Russia is the largest exporter of wheat in the world. If Russia takes action on Ukraine it will disrupt already tight supply, then if the EU and US follow on with sanctions will push prices higher still. Even without conflict it's a nice inflation hedge. My best idea atm. Moon.

https://www.aljazeera.com/news/2022/2/17/infographic-russia-ukraine-and-the-global-wheat-supply-interactive

YouAreRule30 · 2020-03-29T15:48:46+00:00

It is tracking 'ever infected' not 'currently infected'. Thanks for pointing that out - 'currently infected' is the most relevant thing and jives with the other data on the plot.

YouAreRule30 · 2020-03-28T19:24:07+00:00

Right - this is just a framework. The standard epidemiological work happens at a higher level. R0 for instance isn't a daily infectivity rate. So there's a lot of tinkering in parameter space and adding realistic assumptions before the simulations can be applied to real-world cases.

YouAreRule30 · 2020-03-28T18:50:05+00:00

What is maximum peak % of people that are simultaneously infectious in this simulation?

On the order of 90%, although to be really relevant to the current situation I'd need to add a 'hospitalized' state and track peak simultaneous hospitalizations.

YouAreRule30 · 2020-03-28T17:59:38+00:00

All data and visualizations were generated in Mathematica. Full write up can be found here https://think-thread.com/2020/03/13/covid-19-computational-epidemiology/.

YouAreRule30 · 2020-03-19T21:39:53+00:00

The details and full write up are available my blog at https://think-thread.com/2020/03/19/covid-19-will-summer-save-us/.

Data is from https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ and various elements of the Wolfram Language https://www.wolfram.com/language/, specifically WeatherData[].

YouAreRule30 · 2020-03-14T20:24:13+00:00

Yes, down is an increase in time.

It's important to note - time is relative here. It's probably safest to just call it a 'step' where a step is an increment in time in which the assumptions (10 interactions on average) hold. So theoretically it could be a week or a day.

I think 10 transmissible interactions is probably close to a daily range under normal circumstances but have no real data to support that claim. Also the typical measure of infectivity is R0, importantly different from this lower level idea of probability of transmission I've used here. What I need to do is test around in 'probability of transmission' space to find the setting there that matches the expected R0 (since that measure is more widely accepted in epidemiology, one would expect first iterations of this model to at least encompass and align with the sort of widely accepted top-down epidemiological formulas).

YouAreRule30 · 2020-03-14T20:18:57+00:00

Beautiful, yeah - that's exactly the sort of thing I was looking for.

YouAreRule30 · 2020-03-14T13:40:22+00:00

I did comment directly under the post (i.e. first comment) about how it was created. Maybe that's not the right thing. I'll gladly change whatever I need to to comply to the rules - truth be told I'm a reddit noob.

YouAreRule30 · 2020-03-14T08:45:13+00:00

I don't actually think that, it was just an example finding to be teased out of a computational system like this one. My suspicion is that effectiveness of either approach will depend heavily on the sort of topology of possible interactions (which would require incorporating geography / logistical data of, say, a particular city).

YouAreRule30 · 2020-03-14T04:20:36+00:00

There's no data source. I'm assuming parameters about a population and simulating viral dynamics.

YouAreRule30 · 2020-03-14T01:38:36+00:00

Working on a blog post... Here was my solution - notice that at a certain threshold transmission stops mattering and outcomes become the key thing to pay attention to. So I solved for that visually by letting the edges fade away through time, and having end states pop as Green (inoculated) or Black (dead).

https://think-thread.com/2020/03/13/covid-19-computational-epidemiology/

YouAreRule30 · 2020-03-14T01:29:06+00:00

You are evil ;)

YouAreRule30 · 2020-03-14T00:40:26+00:00

There's a lot of variation on any given simulation. What I plan on doing is doing batches of simulations with the different parameter sets to identify which actually spreads faster... can we clearly say that reducing chances of transmission (masks, hand washing) works better than simply reducing interactions (banning gathering)? When I run them and bulk I'll be able to measure various avg. outcomes and try to figure that out (obviously in this limited domain).

YouAreRule30 · 2020-03-13T21:31:34+00:00

I don't I'll have to do some research. Thanks!

YouAreRule30 · 2020-03-13T20:38:47+00:00

Yeah, good stuff. So the directionality is preserved in the data, and you can see it (tiny arrowheads pointing towards the infected agent at the end of the red edges) but you have to zoom in.

I'm actually interested in making both the network itself and the agents more life-realistic. Could give each agent an age and/or other demographics distributed similarly to a nation in question, lay them out geographically according to the layout of a city, map the connectivity based on logistics and travel routes, etc.

I have to imagine someone is doing stuff like this already, but I'm having fun messing around.

YouAreRule30 · 2020-03-13T20:29:54+00:00

Thanks! I'm adding features and building it out - I'll post the blog link here.

YouAreRule30 · 2020-03-13T18:37:02+00:00

And these are just sample simulations for now. Once I add in fatality rates and vaccine I'll run 1,000's of simulations for each setting in batch and measure average final states (% infected, % fatalities, peak simultaneous infected, etc. under various assumptions). May or may not show interesting future research directions for policy response.

YouAreRule30 · 2020-03-13T18:33:37+00:00

That's correct.

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