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Published podcast speaker
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9 transcript-backed records
01 / preference
“So I can tell you like what kind of I'm excited about. So, so I think that I have always been excited about this idea of continuous learning and lifelong learning in some sense.”
02 / preference
“I think making API better is certainly something useful, right? In general, one thing that we do try to push very hard on is this idea of easier universal deployment.”
03 / prediction
“There is an unstable version called Canary. I think as early as two years ago, there was a WebGPU version.”
04 / evaluation
“I don't remember specifics of them, but I think even nowadays, if you look at what people are using, tree-based models are still one of their toolkits.”
05 / commitment
“We are also having a REST API that's OpenAI compatible. So that REST API, I think, right now, it's actually running on native backend.”
06 / commitment
“But I always want to come back to work on the deep learning field. So after XGBoost, I think I started to work with some folks on this particular MXNet.”
07 / prediction
“That we hope to elevate, yeah. And also, if you think about our future, definitely I feel like right now the technology, given the technology and the kind of hardware availability we have today, we will need to make use of all the possible hardware available out there.”
08 / evaluation
“Actually, on a single batch inference, more recently on CUDA, we get, I think, the most best performance you can get out there already on the 4-bit inference.”
09 / prediction
“I still think tree-based models are going to be quite relevant, because first of all, it's really to get it to work out of the box.”