ML Engineer working on generative models in #Speech and #NLP. Focused on Text-to-Speech (TTS) and speaker generation.

United States
Joined October 2016
Could be lobotomized because I work on ML research even though it’s not LLM, but Fable is straight up trash.
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Is making a team and paying for a seat the only way to get notifications for @wandb anymore?
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I find deep research from @OpenAI super useful. However, I would like to see improvement in the output format. I always want it to be in markdown but it leaves out information when asked to convert it to markdown. If anyone has luck with getting the full output in a markdown file, I would appreciate any tips.
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what do you do when you get rate limited by your AI therapist?
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I'd like to see the perfomance of a random sample from the same companies ChatGPT was given to pick from for a comparison.
Its going viral on Reddit. Somebody let ChatGPT run a $100 live share portfolio, restricted to U.S. micro-cap stocks. Did an LLM really bit the market?. - 4 weeks +23.8% while the Russell 2000 and biotech ETF XBI rose only ~3.9% and 3.5%. Prompt + GitHub posted --- ofcourse its a short‑term outperformance, tiny sample size, and also micro caps are hightly volatile. So much more exahustive analysis is needed with lots or more info (like Sharpe ratios and longer back-testing etc), to explore whether an LLM can truly beat the market.
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So we tuning on the test set now?
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When is @astral_sh going to replace mypy? 😁
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Replying to @thdxr
we heard you like abstracting so we abstracted your abstraction to give you more abstraction 🤩
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Someone remind me how many years it was between the first GAN paper and realistic faces that still had artifacts.
Replying to @i_dg23
more detailed version
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Not surprising to anyone that’s actually worked on generative models.
GPT-3’s score on the MMLU benchmark was 40%. First release of GPT-4 scored 86%, and today GPT-4o is 89%. An increase of just 3% — that’s a full year of progress. If you plot the prior trend we were supposed to be at 100, maybe 120% by now. AI is hitting a wall.
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Unpopular opinion: AI agents are going to do some things well, but fail to generalize enough to be useful.
Unpopular opinion: AI agents are hard.
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Hot take: ML researchers are underestimating how quickly recent scaling laws may flatten out - it’s quite likely what people see as an exponential function is a sigmoid, and the harsh reality of the physics of high energy costs and power plant construction restrict the expected benefits from pretraining of ever larger models.
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100% chance someone will take this as proof of consciousness
okay what 99.99% chance there's a bug in my code, but 0.01% chance i just solved text retrieval
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kinda seems to me like when people say "emergent property/behavior" they just mean "good at what it was designed to do"
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