1/ today we're releasing muse spark, the first model from MSL. nine months ago we rebuilt our ai stack from scratch. new infrastructure, new architecture, new data pipelines. muse spark is the result of that work, and now it powers meta ai. 🧵
How AI empowered Paul Conyngham to create a custom mRNA vaccine to cure his dog’s cancer when she had only months to live. The first personalized cancer vaccine designed for a dog:
I've been working on a new LLM inference algorithm.
It's called Speculative Speculative Decoding (SSD) and it's up to 2x faster than the strongest inference engines in the world.
Collab w/ @tri_dao@avnermay. Details in thread.
WTF is going on at Qwen?!?
Some kind of implosion?
This is really sad and worrying. They've been *such* a strong team, and are losing some of their very best researchers.
LLMs for coding are amazing, but my favorite feature is actually being able to talk about any subject and instantly be able to dive into the fine grained details of learning.
I just deep dove into some of the best papers of NeurIPs from the past year and the rate of information transfer is night and day difference to even a year ago.
Does anyone else go full flow state where you are coding something and end up needing to listen to the same song on repeat. But it’s also the first thing you put on when you wake up, in the shower, on the drive to work, while at work, while at the gym, later at night, before going to bed.
Only happens 2-3 times a year, but it lasts for about two weeks at a time and you listen to the same song >1k times.
Agree at smaller and at most companies it's overkill. When you are operating at a certain scale and defendable metric driven impact is in the company's culture, it's necessary and table stakes.
"revenue went up" doesn't cut it when you have to justify budget allocation for capacity being split among dozens of teams.
Metric conversions are hard… let’s convert quality improvement to OpEx they said.. create a whole framework around precision and recall improvements translating then realize you have to do an even more involved derivation for free form rubric graded output of measuring probability of samples batch pass rates via binomial distribution priors…
At least I felt like I was solving the gravity equation from interstellar for awhile on a large whiteboard
@ClickHouseDB finally hunted this down to a compression incompatibility issue. Athena requires snappy compression for parquet files vs. clickhouse default export which is LZ4
@ClickHouseDB can you fix your export parquet to s3 functionality? It shouldn’t be this hard to export files and then query them with Athena. Whatever is happening under the hood causes HIVE_CURSOR_ERROR issues.
Doesn’t happen when writing parquet files to s3 with dask though