Carly Taylor
6 statements across 1 episodes · 4 bullish · 0 bearish · 1 people on the record · first statement Sep 14, 2023 by Carly Taylor · across every show →
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Everything said about Carly Taylor, oldest first
Sep 14, 2023 positive
Taylor: Startups without ready data must prioritize hiring a cloud data engineer
“If you don't have your data ready yet, you can't skip the data engineering piece of this, and I'd say you probably need someone who's going to be like your cloud data engineer. Like you just have to have those basics covered”
Sep 14, 2023
Taylor: Security machine learning must hyper-focus on outliers instead of discarding them
“There's a tenant of machine learning where like you just throw out the outliers because they're going to mess up your distribution and you kind of don't want to deal with them. For security, what you do is you find the outliers and you hyper focus on them beca…”
Sep 14, 2023 positive
Taylor: GenAI in games introduces new capabilities rather than replacing human jobs
“It's no one's Full-time job to make an NPC, like say your name. It's just a functionality we've never had. So you're not really replacing anything people were doing. You're just making something that didn't exist exist.”
Sep 14, 2023 positive
Taylor: Community building provides more startup growth leverage than marginal technical improvements
“Sometimes you can over index on the technical and getting maybe, you know, your platform to be five percent more fast or supported on one more browser might not 10 X you the way that spending that time building a community would.”
Sep 14, 2023
Taylor: Deploying machine learning models fundamentally alters the targeted adversarial problems
“And it's something that I think traditional machine learning hasn't really been agile enough to deal with. Right. Like the act of doing machine learning is fundamentally changing the problem you're trying to solve.”
Sep 14, 2023 bullish
Taylor: Data science and AI startups should prioritize LinkedIn over Twitter
“Twitter might get you more impressions, I think. And you might get your message out wider. So I don't think it has to be an either or, but if I was going to spend a lot of my time, especially if I was a data science or AI startup, I would learn where my audien…”