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Josh Bloom

Professor of Astronomy, UC Berkeley · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

academicscientistfounderexecutiveauthor@profjsb ↗LinkedIn ↗joshbloom.org ↗Wikipedia ↗

Josh Bloom is a Professor of Astronomy at UC Berkeley and the co-founder and CEO of Valency. He previously co-founded Wise.io, an enterprise machine-learning startup acquired by GE Digital.

10statements → 7claims → 2claims resolved → 3.9/5average certainty → 1.8/5average debate potential →

1 supported 0 partly supported 1 contradicted 5 not checkable as stated how the 7 claims stand · each chip opens the sources

2 predictions · 5 assertions · 3 insights · every statement was checked. The predictions and assertions are the 7 claims: statements the public record can support or contradict. 2 are resolved, and 5 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Josh argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

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

Their most notable contradicted claim

Assertion Contradicted
Bloom: Astronomy requirements drove early CCD development at Bell Labs
“Doing that directly with charged couple devices was one of the main, sort of, use cases of what drove CCD development, you know, at Bell Labs you know, sort of, 40 years ago.”
Josh Bloom Jul 15, 2017 ▶ 6:07 Supernovas and Novel Insight: Where Machine Learning is Headed Next

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
0% certainty 3
none yet certainty 4
100% certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

How they sound: speaking style how? →

253 words/min while actually speaking · 43.9 um and uh per 1k words

No argument clarity score for Josh Bloom: no usable question→answer exchanges on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 2,984 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Josh Bloom said on the a16z Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

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
Prediction Not checkable as stated
Bloom: Machine learning's destiny is personalized models for everyone
“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.”
Josh Bloom Jul 15, 2017 ▶ 11:54 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Prediction Not checkable as stated
Bloom: Future software buyers will purchase products with embedded AI
“In the future people are just going to be buying products where machine learning and machine intelligence are baked in.”
Josh Bloom Jul 15, 2017 ▶ 14:06 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Assertion Not checkable as stated
Astronomical Image Subtraction Produces 1,000 False Alarms Per Discovery
“It's about a thousand bad candidates or bogus candidates, every one real one”
Josh Bloom Jul 15, 2017 ▶ 2:35 Supernovas and Novel Insight: Where Machine Learning is Headed Next
Assertion Contradicted
Bloom: Astronomy requirements drove early CCD development at Bell Labs
“Doing that directly with charged couple devices was one of the main, sort of, use cases of what drove CCD development, you know, at Bell Labs you know, sort of, 40 years ago.”
Josh Bloom Jul 15, 2017 ▶ 6:07 Supernovas and Novel Insight: Where Machine Learning is Headed Next

Appearances (1)

EpisodeDateSpeaking time
Supernovas and Novel Insight: Where Machine Learning is Headed Next Jul 15, 2017 13m
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