If a good SWE is $150/hour, does the model cost actually matter? Surely you'd be willing to spend $10/hour to make that SWE 20% more productive? The model cost is still much less than the salary.
They use a lightweight adapter to silently degrade the performance. Usually these adaptors are made to improve the performance for a given domain/task.
It might be possible to train a big generalist that is a composition of modules, some of which can be dropped dynamically at inference time, depending on the prompt.
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.
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: [email protected]
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.
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.
I used to play very competitively, but I've been more chill recently. I just think it's a nice problem/dataset to work with, because of the depth of my understanding of the game.
I’ve been experimenting with a live win probability predictor for the 10-player arcade game Killer Queen. The goal is to predict the winner in a causal, event-by-event fashion.
Right now I’m struggling to beat a baseline LightGBM model trained on hand-engineered expert features. My attempts at using a win probability head on top of nanoGPT, treating events as tokens, have been significantly worse. I am seeing about 65% accuracy compared to the LightGBM’s 70%. That 5% gap is huge given how stochastic the early game is, and the Transformer is easily 4 OOM more expensive to train.
To bridge the gap, I’m moving to a hybrid approach. I’m feeding those expert features back in as additional tokens or auxiliary loss heads, and I am using the LightGBM model as a teacher for knowledge distillation to provide smoother gradients.
The main priority here is personalized post-game feedback. By tracking sharp swings in win probability, or $\Delta WP$, you can automatically generate high or low-light reels right after a match. It helps players see the exact moment a play was either effective or catastrophic.
There is also a clear application for automated content creation. You can use $\Delta WP$ as a heuristic to identify the actual turning points of a match for YouTube summaries without needing to manually scrub through hours of Twitch footage.
A compiler that can turn cash into improved code without round tripping a human is very cool though. As those steps can get longer and succeed more often in more difficult circumstances, what it means to be a software engineer changes a lot.