Josh Bloom
6 statements across 1 episodes · 6 bullish · 0 bearish · 1 people on the record · first statement Jul 15, 2017 by Josh Bloom · across every show →
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Jul 15, 2017 bullish
Jul 15, 2017 positive
Josh Bloom: Data-driven approaches do not require prior theory to predict outcomes
“Well, the point is, and this is sort of the conceit of all data-driven approaches, is that you don't need to have a theory about why something's gonna happen. The idea is that you've got enough data, both in, in terms of the number of examples, and then also s…”
Jul 15, 2017 positive
Josh Bloom: Real-time astronomy ML know-how directly transfers to enterprise problems
“The actual insight that we wind up sort of learning how to do with astronomy data in real time on noisy streaming data is exactly that sort of same know-how that we wind up applying to more of these conventional problems.”
Jul 15, 2017 positive
Bloom: ML model detected supernova in 11 hours, driving Nature publications
“So one of the great things is our, ah, machine learning algorithm and framework wound up finding a new supernova that was in a very nearby galaxy. And because it was found about 11 hours after explosion, which were days earlier than had ever been found for tha…”
Jul 15, 2017 bullish
Jul 15, 2017 positive
Bloom: Machine learning software acts like virtualized graduate students at scale
“Because the software exists that can actually sift through and look at that data as if it's, you know, essentially virtualized graduate students with a huge amount of domain knowledge, and do this at scale it allows you to take more and more data.”