Every time I see a headline like this I’m reminded of the time I heard someone describe the modern state of AI research as equivalent to the practice of alchemy.
Not sure if you’re referencing the same thing, but this actually came from a presentation at NeurIPS 2017 (the largest and most prestigious machine learning/AI conference) for the “Test of Time Award.” The presentation is available here for anyone interested. It’s a good watch. The presenter/awardee, Ali Rahimi, talks about how over time, rigor and fundamental knowledge in the field of machine learning has taken a backseat compared to empirical work that we continue to build upon, yet don’t fully understand.
Some of that sentiment is definitely still true today, and unfortunately, understanding the fundamentals is only going to get harder as empirical methods get more complex. It’s much easier to iterate on empirical things by just throwing more compute at a problem than it is to analyze something mathematically.
What info have you heard about Fenghua 3? I’d last read that it’s not strictly an AI accelerator but can actually do graphics tasks, which is neat. Would make it more of a competitor to a professional workstation card like an RTX PRO 6000.
I’m most curious about their CUDA compatibility claim. I would expect that to cause a pretty significant performance hit since when writing high-performance CUDA kernels, you generally need to specialize the kernel to the individual GPU (an H100 kernel will look quite different compared to a 4090 kernel, for example). But if in spite of that it can achieve H100 performance, that’d be cool.