My big gripe with unions is the unwavering protection of their worst performing members.
Eg, that they necessitated so called "rubber rooms" like these in the NYC public schools, where teachers got paid to do nothing while waiting on arbitration.
I doubt you'll find many people in favor of how bad cops get protected by police unions either. At least in the US I'd much rather a broad social net so my health care and retirement weren't so directly tied to my job than a union specific to my trade.
It’s not really about this particular claim. It’s that I can read a comment that has a reasonable chain of logic and I don’t know if it’s true. This topic is just not easily studied and theories are hard to falsify.
In all fairness most of the unique stuff I can do is probably an artifact of my training process, so it seems unfair to deny an LLM the same accomodation.
This got me thinking, and it might actually even be a comparable amount.
Let's estimate 12 years of schooling run at minimum $100,000 per student, at least in the US [1], and then add onto that number whatever else you may do after that, i.e. a bunch more money if paid (college) or "unpaid" (self-taught skills and improvements) education, and then the likely biggest portion for white-collar workers, yet hard-to-quantify, in experience and "value" professional work will equip one with.
Now divide the average SOTA LLM's training cost (or a guess, since these numbers aren't always published as far as I'm aware) by the number of users, or if you wanted to be more strict, the number of people it's proven to be useful for (what else would training be for), and it might not be so far off anymore?
Of course, whether it makes sense to divide and spread out the LLMs' costs across users in order to calculate an "average utility" is debatable.
In the last step of training LLMs, reinforcement learning from verified rewards, LLMs are trained to maximize the probability of solving problems using their own output, depending on a reward signal akin to winning in Go. It's not just imitating human written text.
Fwiw, I agree that world models and some kind of learning from interacting with physical reality, rather than massive amounts of digitized gym environments is likely necessary for a breakthrough for AGI.
Location: SF (current). NYC/Philly general area acceptable. Remote okay.
email: rrenaud@gmail.com
Resume: 16 year SWE -> MLE @ Google, MS from NYU with focus on ML. Retired. Now I hack on data analysis for video game projects for fun, and I love it. I'd take crazy low compensation to do work with interesting game data sets. EG, for game balance, strategic analysis, or to improve/augment game video content.
What do y'all think about the latency/quality tradeoff with LLMs?
Human voices don't take 30 seconds to think, retrieve, research, and summarize a high quality answer. Humans are calibrated in their knowledge, they know what they understand and what they don't. They can converse in real time without bullshitting.
Frontier real time-ish LLM generated voice systems are still plagued by 2024 era LLM nonsense, like the inability to count Rs in strawberry. [1]
I'd personally love a voice interface that, constrained by the technology of today, takes the latency hit to deliver quality.
Not affiliated with Sesame, but this is what the realtime models are trying to solve. If you look at NVIDIA’s PersonaPlex release [0], it uses a duplex architecture. It’s based on Moshi [1], which aims to address this problem by allowing the model to listen and generate audio at the same time.
Cite a source. Your concrete claim is that, on average, for every $1 of subscription revenue on a monthly subscription, OpenAI and Anthropic were losing $11.50?
It seems completely implausible.
I could believe that if a $20 sub used every possible token granted, it would cost $250. But certainly almost no one was completely milking their subscription. In the same way that no one is streaming netflix literally 24/7.
Eg, that they necessitated so called "rubber rooms" like these in the NYC public schools, where teachers got paid to do nothing while waiting on arbitration.
https://en.wikipedia.org/wiki/Reassignment_center
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