nicolas
@niid.xyz
💼 GenAI & ML @ T-Systems
🌍 Berlin, Germany
niid.xyz
🌍 Berlin, Germany
niid.xyz
Critical information to keep in mind when interpreting the o3 results on the ARC-AGI benchmark (vs. o1):
1. o3-Inference cost a staggering $1.15M.
2. o3 was trained on a public ARC-AGI dataset, giving it a massive advantage over o1 which had no access to that data.
It’s not a fair comparison.
1. o3-Inference cost a staggering $1.15M.
2. o3 was trained on a public ARC-AGI dataset, giving it a massive advantage over o1 which had no access to that data.
It’s not a fair comparison.
December 22, 2024 at 11:45 AM
Critical information to keep in mind when interpreting the o3 results on the ARC-AGI benchmark (vs. o1):
1. o3-Inference cost a staggering $1.15M.
2. o3 was trained on a public ARC-AGI dataset, giving it a massive advantage over o1 which had no access to that data.
It’s not a fair comparison.
1. o3-Inference cost a staggering $1.15M.
2. o3 was trained on a public ARC-AGI dataset, giving it a massive advantage over o1 which had no access to that data.
It’s not a fair comparison.
Reposted by nicolas
Atoms are binary. They are either intended to be hydrogen or helium. We can't just scrap this worldview just because of a handful of exceptions
November 28, 2024 at 1:02 PM
Atoms are binary. They are either intended to be hydrogen or helium. We can't just scrap this worldview just because of a handful of exceptions
Reposted by nicolas
Research like this is so important. A lot of decision making when deploying RAG systems is based on vibes-based eval using dated models and datasets. Many techniques simply don't transfer to modern models like you'd hope. Query expansion seems to fall into this category.
November 24, 2024 at 11:29 PM
Research like this is so important. A lot of decision making when deploying RAG systems is based on vibes-based eval using dated models and datasets. Many techniques simply don't transfer to modern models like you'd hope. Query expansion seems to fall into this category.