HN user
Small cap value did well in the 2000 tech crash and SP600 (small cap) doesn’t have many direct datacenter or AI exposed names compared to large and mid cap indexes. But given the scale of capex across the US they aren’t immune from secondary effects.
It may have been a fume event which is very dangerous for everyone onboard.
A fume event occurs when bleed air used for cabin pressurisation and air conditioning in a pressurised aircraft is contaminated by fluids such as engine oil, hydraulic fluid, anti-icing fluid, and other potentially hazardous chemicals.
In the age of LLMs vacuuming up all content and deriving all the economic value from it, can you blame them?
Yes, it’s surprised me how this meme was everywhere in the comments while the data does not support it. I’d bet it’s splashy headlines in news outlets. Important to correct it so that policy is focused on what’s most effective.
Your analysis is low quality as it focuses too narrowly and ignores the wider geopolitical tensions between the US and China.
See:
- CHIPs act
- Ban of sale of many nvidia GPUs to China
- Ban of export of ASML euv tech
- Huawei ban
- Chinese EV tariffs (ban?)
- whole host of tariffs
- China south sea tensions
- Chinese support of Russia who is a threat to NATO allies (and invaded a sovereign country) - Ban of most US tech companies in China
- Chinas mutual aid treaty with North Korea
You can simply use specific training examples that teach the model what you please. Eg. a set of examples which lead ranking/retreival/filtering models. The models are already online training and weights likely updated every ~1 hour ( or even less).
It’d be easy to go from a set of “moderators” who find examples and use it to query related content and use it as negative training samples. Just a guess.