Josh Bloom
Professor of Astronomy, UC Berkeley. On 2 shows, 2 appearances. The Shows tab opens the full record on each.
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.
2shows
2appearances
22statements
4resolved
3supported
1contradicted
75%fully supported
Everything Josh Bloom said on any show that made the record, most notable first. Each card names its show and opens the statement there.
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…”
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.”
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.”
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.”
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.”
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 …”
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…”
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…”
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: 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.”
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.”
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.”
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…”
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…”
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.”
Astronomical Image Subtraction Produces 1,000 False Alarms Per Discovery
“It's about a thousand bad candidates or bogus candidates, every one real one”
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.”
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”
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.”
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.”
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.”
One line per show, most statements first. The link opens Josh's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 MADLEDGER | Professor of Astronomy, UC Berkeley | 1 | 12 | 100% 2/2 | full record on the MAD Podcast → |
 a16zLEDGER | Professor of Astronomy, UC Berkeley | 1 | 10 | 50% 1/2 | full record on the a16z Podcast → |