231–240 of 310 posts
How's the distribution of the errors? For instance I don't care if it's better on average by 1 Celsius each day for normal weather, if it once every month is off by 10 Celsius when there is a drastic weather event, for instance. I'm all for better weather data, it's quite critical up in the mountains, so that's why my question about how reliable it is in life&death situations.
It's interesting, that Google keeps publishing AI research papers. Is there a business rationale behind it? OpenAI has become one of the fastest growing companies of all time. And much of it is based on Google's "Attention is all you need" and other papers. Since Microsoft added the Dall-E 3 image creator to Bing, Bing saw a huge inflow of new users. Dall-E is also a technology rooted in Google papers. I wonder how G…
I find this quite surprising actually. You'd think predicting the weather is mostly a matter of fast computation. The physical rules are well understood, so to get a better estimate use a finer mesh in your finite element computation and use a smaller time scale in estimating your differential equations. Neural networks are notoriously bad at exact approximation. I mean you can never beat a calculator when the issue…
I used to think so too, but evidently weather forecasting is a much harder problem than it seems from the outside. I was talking to a physicist who told me who had first wanted to get into weather modeling, but that it was too hard. I think his quote was something like: "those guys are hard. core."
I find this quite surprising actually. You'd think predicting the weather is mostly a matter of fast computation. The physical rules are well understood, so to get a better estimate use a finer mesh in your finite element computation and use a smaller time scale in estimating your differential equations. Neural networks are notoriously bad at exact approximation. I mean you can never beat a calculator when the issue…
I used to think so too, but evidently weather forecasting is a much harder problem than it seems from the outside. I was talking to a physicist who told me who had first wanted to get into weather modeling, but that it was too hard. I think his quote was something like: "those guys are hard. core."
Earlier quoted context omitted.
You assuming OpenAI's models are AGI tells more about you than anything else.
If Alan Turing says ChatGPT is an AGI, it's good enough for me.
Earlier quoted context omitted.
You assuming OpenAI's models are AGI tells more about you than anything else.
If Alan Turing says ChatGPT is an AGI, it's good enough for me.
Just like their flu modelling outperformed conventional models right?
That was a very different beast. It relied on using Google searches to infer the prevalence of various Influenza Like Illnesses in real time, while the CDC reports data with a 2-week lag. Notably, some of the queries they found to be correlated were... strange... like NBA results.
Not unsurprisingly (in hindsight, at least) [2], this eventually broke down when epidemics and flu symptoms got in the news and completely changed what people were searching for.
"AI" aka machine learning