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.

12statements → 4claims → 2claims resolved → 4.25/5average certainty → 1.67/5average debate potential →

2 supported 0 partly supported 0 contradicted 2 not checkable as stated how the 4 claims stand · each chip opens the sources

4 assertions · 5 insights · 3 disclosures · every statement was checked. The predictions and assertions are the 4 claims: statements the public record can support or contradict. 2 are resolved, and 2 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: Netflix couldn't deploy its $1M prize algorithm due to complexity

“They paid a million dollar bounty, and they wound up looking at the code, and there were hundreds of separate models that were then boosted together, and they said there's no way we can do it.”

Josh Bloom Nov 23, 2015 ▶ 10:28 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Expressed certainty vs assessment result

none yet certainty 1

none yet certainty 2

none yet certainty 3

100% 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? →

241 words/min while actually speaking · 35.8 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: 4,325 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 →

What they said12 Episodes1

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

Insight

Bloom: Very few data science teams prioritize model explainability

“Explainability or interpretability turned out to be a very, very important optimization that very few data science teams will be cognizant of unless they're really thinking about it.”

Josh Bloom Nov 23, 2015 ▶ 8:47 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Assertion Supported

Bloom: Netflix couldn't deploy its $1M prize algorithm due to complexity

“They paid a million dollar bounty, and they wound up looking at the code, and there were hundreds of separate models that were then boosted together, and they said there's no way we can do it.”

Josh Bloom Nov 23, 2015 ▶ 10:28 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Assertion Supported

Bloom: 95% of production machine learning code is just glue code

“Algorithms are important, but 95% of all machine learning code in production is actually just glue code. It's connecting all of these different pieces together.”

Josh Bloom Nov 23, 2015 ▶ 11:55 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Assertion Not checkable as stated

Wise.io's ML automatically resolves 5% to 20% of support tickets

“Over time, the system becomes so confident in some fraction of the answers, Five to 10 to 20%. It can basically just answer them without any humans on our client side actually looking at it, and those tickets get solved and people are satisfied.”

Josh Bloom Nov 23, 2015 ▶ 23:03 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Disclosure

Wise.io chose managed cloud services over building infrastructure in-house

“So we made, as a young startup, the decision that if there was a managed service around what we needed to do to get something into production, and we're at the millions of predictions level a month over dozens of customers now, we were just going to, we were j…”

Josh Bloom Nov 23, 2015 ▶ 4:09 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Insight

Bloom: ML platform decisions must center on production and maintenance costs

“The other ones that I think are critical when making those decisions about which platforms to use and implement within your own organizations is, what is that cost and time to actually put this into production? And then once you put it into production, what is…”

Josh Bloom Nov 23, 2015 ▶ 13:07 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Insight

Bloom: Support headcount still scales linearly with incoming ticket volume

“Support is still that last sort of place where you wind up having to scale the total number of people in your support system and support operations by the number of incoming tickets.”

Josh Bloom Nov 23, 2015 ▶ 22:02 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Disclosure

Wise.io chose customer success as its first target for ML automation

“In an industry, the place where we wound up landing is in customer success.”

Josh Bloom Nov 23, 2015 ▶ 2:51 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Assertion Not checkable as stated

Wise.io serves millions of monthly predictions across dozens of customers

“And we're at the millions of predictions level a month over dozens of customers now”

Josh Bloom Nov 23, 2015 ▶ 4:09 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Insight

Bloom: Feedback loops transition ML from human augmentation to automation

“Over time, if you build the appropriate feedback loops into your systems, the system itself will wind up learning from those processes and get better and better, so you can actually start automating those processes.”

Josh Bloom Nov 23, 2015 ▶ 14:35 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Disclosure

Wise.io uses agent acceptance and rejection of suggestions as model feedback

“What we do in Wise is we give, essentially, the agents the ability to take our suggestions for how to answer a support ticket, and if they don't, then that becomes feedback for us, and if they do, that also becomes feedback.”

Josh Bloom Nov 23, 2015 ▶ 15:44 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Insight

Bloom: Netflix and Google build fault tolerance to handle ML errors

“Netflix and Google, some of the best machine learning companies in the world, and these are their core products, and they still make mistakes. Yet these are not fatal mistakes. They've built fault tolerance into the machine learning.”

Josh Bloom Nov 23, 2015 ▶ 16:49 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Appearances (1)

Episode	Date	Speaking time
Machine Learning in Production with Josh Bloom, Co-founder Wise.io	Nov 23, 2015	21m

Josh Bloom

The record, in short

Their most notable supported claim

Expressed certainty vs assessment result

How they sound: speaking style how? →

Appearances (1)

Turn any episode into a week of clips.