This seems like the most plausible explanation. Only other thing I can think of is they want to develop their own CoPilot (which I’m guessing isn’t available in China due to the U.S. AI restrictions?), and they’re just using their existing infrastructure to gather training data.
I wonder what is the general use for the Mac Mini, MacBook Air, iMac, and MacBook Pro? People generally seem to do all the lightweight stuff like social media consumption on their phones; and desktops/laptops are used for the more heavy-weight stuff. The only reason I’ve ever used a Mac was for IOS development.
I was looking at notebooks at Walmart the other day, and I was amazed that they almost all had less or the same amount of RAM as my phone.
I think SSDs are also soldered to the mainboard on most apple products.
Lol, good catch.
Wary of the bill. Seems like every bill involving stuff like this is either designed to erode privacy or for regulatory capture.
Edit: spelling
Here are some that I’ve liked (haven’t played them in years though):
It’s also trained on data people reasonably expected would be private (private github repos, Adobe creative cloud, etc). Even if it was just public data, it can still be dangerous. I.e. It could be possible to give an LLM a prompt like, “give me a list of climate activists, their addresses, and their employers” if it was trained on this data or was good at “browsing” on its own. That’s currently not possible due to the guardrails on most models, and I’m guessing they try to avoid training on personal data that’s public, but a government agency could make an LLM without these guardrails. That data could be public, but would take a person quite a bit of work to track down compared to the ease and efficiency of just asking an LLM.
I use LLMs just about every day. It’s better than web-search for certain things, and is useful for some coding tasks. I think they’re over-hyped by some people, but they are useful.
Most CEOs are also conmen and not any smarter. It’s mostly just a nepotism racket at the executive level.
A lot of the “elites” (OpenAI board, Thiel, Andreessen, etc) are on the effective-accelerationism grift now. The idea is to disregard all negative effects of pursuing technological “progress,” because techno-capitalism will solve all problems. They support burning fossil fuels as fast as possible because that will enable “progress,” which will solve climate change (through geoengineering, presumably). I’ve seen some accelerationists write that it would be ok if AI destroys humanity, because it would be the next evolution of “intelligence.” I dunno if they’ve fallen for their own grift or not, but it’s obviously a very convenient belief for them.
Effective-accelerationism was first coined by Nick Land, who appears to be some kind of fascist.
We’re close to peak using current NN architectures and methods. All this started with the discovery of transformer architecture in 2017. Advances in architecture and methods have been fairly small and incremental since then. The advancements in performance has mostly just been throwing more data and compute at the models, and diminishing returns have been observed. GPT-3 costed something like $15 million to train. GPT-4 is a little better and costed something like $100 million to train. If the next model costs $1 billion to train, it will likely be a little better.
LLMs do sometimes hallucinate even when giving summaries. I.e. they put things in the summaries that were not in the source material. Bing did this often the last time I tried it. In my experience, LLMs seem to do very poorly when their context is large (e.g. when “reading” large or multiple articles). With ChatGPT, it’s output seems more likely to be factually correct when it just generates “facts” from it’s model instead of “browsing” and adding articles to its context.
I think part of the problem is that new cars are bought mostly by fairly well-off individuals; with other people buying used cars. Economy cars sell poorly in the U.S.
China can’t arrest people in the U.S. The U.S. and state governments can.
Doesn’t really matter unless you live under it. Instagram and Twitter or more dangerous to U.S. citizens.
Sorry, I was unclear. Chippers are not the tools I was thinking of that would be cheaper to buy (a low quality version of) than rent. Was thinking more about stuff like torque wrenches and rotary hammers. Chipper rental prices were just one thing I was looking at recently that seemed way out of line with what other people from other regions were paying.
Rentals seem extremely expensive in my area. $100/day for a shitty 4" wood chipper, $300/day for 6" chipper. For some tools, it’s often about the same price or cheaper to buy a tool from Harbor Freight than to rent.
Libraries don’t make a profit, AFAIK. Non-profits and co-ops are things too.
I don’t understand the Scrum one. Scrum is also agile with short development cycles, and prioritizes communication with the product owners and stakeholders.
I’ve never heard of lean development, but not a fan of “lean manufacturing,” at least not the way it’s commonly implemented in the U.S. (using primarily temp workers so they can ramp up and down their workforce as needed; and it also exacerbates supply-chain problems).