The turn of the year is a good time for reflection. Here's my thoughts on where ML is headed.
Advances have been driven by scaling—bigger compute, bigger data: bigger models. Moreover, larger data also gives a solution to OOD generalization—just increase the train set until everything is in domain!
Advances have been driven by scaling—bigger compute, bigger data: bigger models. Moreover, larger data also gives a solution to OOD generalization—just increase the train set until everything is in domain!
January 3, 2025 at 5:31 PM
The turn of the year is a good time for reflection. Here's my thoughts on where ML is headed.
Advances have been driven by scaling—bigger compute, bigger data: bigger models. Moreover, larger data also gives a solution to OOD generalization—just increase the train set until everything is in domain!
Advances have been driven by scaling—bigger compute, bigger data: bigger models. Moreover, larger data also gives a solution to OOD generalization—just increase the train set until everything is in domain!
I'm looking forward to NeurIPS this week! I'll be presenting two papers there. In the main conference, our new dataset BIOSCAN-5M, and in the System 2 Reasoning At Scale workshop my position paper "System 2 Reasoning Capabilities Are Nigh".
December 9, 2024 at 4:53 PM
I'm looking forward to NeurIPS this week! I'll be presenting two papers there. In the main conference, our new dataset BIOSCAN-5M, and in the System 2 Reasoning At Scale workshop my position paper "System 2 Reasoning Capabilities Are Nigh".