Prediction certainty 4/5 debate potential 2/5

Bloom: Machine learning's destiny is personalized models for everyone

Josh Bloom · Supernovas and Novel Insight: Where Machine Learning is Headed Next · Jul 15, 2017 · at 11:54

Josh Bloom, UC Berkeley professor and CTO of Wise.io, discusses the trajectory of personalized user models in consumer software on The a16z Podcast.

0:00 / 0:08exact quote · 9.0s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“The manifest destiny is everybody has their own machine learning models built upon their own past behavior, perhaps leveraging some of the insights that you wind up getting from the whole system.”

quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →

More from Josh Bloom

Insight
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…”
Josh Bloom Jul 15, 2017 ▶ 10:03 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Assertion Not checkable as stated
Machine Learning Tools Have Neglected Time Series Data
“Now you're getting into some interesting, ah, places where machine learning hasn't spent a lot of time, which is on time series data. And what we wound up realizing in our own, sort of, domain specific research is that there weren't a lot of tools for us from …”
Josh Bloom Jul 15, 2017 ▶ 3:43 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Insight
Bloom: Machine learning must focus on real-time future data over history
“In some sense that's the greatest imperative and like the gauntlet that I lay down in front of anyone is that you're not doing machine learning because it's cool and it's fun and you can learn something about the data from the past. You're trying to really use…”
Josh Bloom Jul 15, 2017 ▶ 6:43 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Assertion Supported
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…”
Josh Bloom Jul 15, 2017 ▶ 6:58 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Insight
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.”
Josh Bloom Jul 15, 2017 ▶ 8:33 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Assertion Not checkable as stated
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.”
Josh Bloom Jul 15, 2017 ▶ 10:56 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 1,000 episodes transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.