my understanding is that yes it is, you cant make shoes in the US, but the power that comes from pretty much all finance flowing through the American pipes is a good trade off.
The USD as the world's reserve currency means, effectively, that other nations are lending the US money at the very low interest rates that Treasuries yield. Effectively, the US gets the best and biggest line of credit in the world, with which it had (until now) financed the most incredible expansion of industrial, financial and academic prowess the world has ever seen.
it also allows USA to export inflation to the entire world. Whole world will be experiencing inflation, thus, diluting the effect it has on the US population
You can get US made shoes, but they’re expensive. Made in USA New Balance pairs run $200, Red Wing boots are $350+, and Alden shoes and boots start at $700 and go up to $1,000.
Red wings and Rancourt & Company, here, plus Mexican and Spanish manufacturers (most search engines are terrible at surfacing these, you need to specify the country to find them) when I can’t find what I want in my price bracket in the US. Alden’s a bit rich for my blood.
Frankly, sneaker prices are getting so damn high that for the last couple years “expensive” leather shoes and boots from manufacturers that have resisted big price hikes have been looking more and more like a bargain…
> Frankly, sneaker prices are getting so damn high that for the last couple years “expensive” leather shoes and boots from manufacturers that have resisted big price hikes have been looking more and more like a bargain…
Red Wing in particular has only raised prices about 10% in the past 10 years.
I wonder how this compares to 'catastrophic forgetting' that can be a problem of full fine tuning. Or at least that's what I've just been reading as a case _for_ using LoRa, as it's not susceptible to that. I guess this paper shows LoRa causes forgetting in a different way.
Are there good general principles yet for what fine tuning method to use in certain situations? It still seems quite difficult to know ahead of time what's going to happen.
Catastrophic forgetting or “psychosis” seems to happen when I overtrain. It’s easy to make it happen to models that have been extensively tuned already, but the base models hold up much better. I’m pretty sure there is a point in the n-dimensional space where x discrete vectors with n dimensions stops encoding usefully distinct patterns.
I wonder, is this exponential relation specific to multi-modal models? From my admittedly naïve view it seems to make sense that "...what is rare is not properly learned" would apply generally?
Towards the end they state: ‘… just adding “do not hallucinate” has been shown to reduce the odds a model hallucinates.’ I find this surprising and doesn’t fit with my understanding of how a language model works. But I’m very much a novice. Would this be due to update training including feedback that marks bad responses with the term “hallucinate”?
my model is that you need to tell give it ways to make the hallucination not the most plausible thing. I prefer to tell it that it can say "I don't know"
Undoubtedly that can be the cause in some cases, but there are counterexamples. Like West Virginia which has a bad opioid addiction problem yet relatively low homeless rate. What does appear highly correlated is homeless rates vs. cost of housing to income ratios. Find a city with a real estate bubble, and you'll likely find a large tent city too.
Agree in that “Startups” imply scale and that’s just race to the bottom clickbait with maybe a little real news on the side. However, one example here in Canada is “Canadaland”, which is profitable, funded partly by subscribers, and has investigated and broken real stories. The founder has made the point recently that journalism is not something that scales, but can be a successful business by serving an audience.
Canadaland may have been bootstrapped/helped along by its journalist founder getting some cut out of what they co-founded, “bitmoji” maker bitstrips, which was sold to Snapchat for a reported US$100m.
