Booth 21-25 | AI Data Management Zone | Tokyo Big Sight
There was a point, not long ago, when the dominant strategy for improving large language models was simple: feed them more. More tokens, more compute, more parameters. The scaling laws
Speed benchmarks in AI research can feel detached from anything practical. A paper claims a 10x speedup and you skim past it because the benchmarks never quite match the thing you’re actually trying to do. The FlexGNN result — up to 95.5x faster training on large graphs — is worth
