StarZero All shows Try the podcast clipper →

Hilary Mason

Co-founder and CEO, Hidden Door · 3 appearances on the record.

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

founderscientistexecutiveauthor@hmason ↗LinkedIn ↗hilarymason.com ↗Wikipedia ↗Instagram ↗

Hilary Mason is the co-founder and CEO of Hidden Door. She previously founded Fast Forward Labs, served as Chief Scientist at Bitly, and worked as Data Scientist in Residence at Accel.

38statements → 16claims → 4claims resolved → 75%fully supported → 3.84/5average certainty → 1.87/5average debate potential → 9said about them ↓

3 supported 1 partly supported 0 contradicted 12 not checkable as stated how the 16 claims stand · each chip opens the sources

2 predictions · 14 assertions · 1 opinion · 17 insights · 4 disclosures · every statement was checked. The predictions and assertions are the 16 claims: statements the public record can support or contradict. 4 are resolved, and 12 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 Hilary 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
How the Associated Press used algorithmic systems to automate financial reporting
“They're working with a company called Automated Insights to write a bunch of finance stories. They actually have someone called Automation Editor whose job it is is to manage these algorithmic systems generating content.”
Hilary Mason Dec 8, 2016 ▶ 13:15 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
none yet certainty 3
83% 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? →

242 words/min while actually speaking · 38.8 um and uh per 1k words

No argument clarity score for Hilary Mason: only 1 usable question→answer exchange 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: 11,469 words across 3 episodes 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 Hilary Mason said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight
Hilary Mason: Startups must understand how their machine learning models work
“Won't speak for the large companies or people who are working on very specific problems, but when you're generally building systems that in a startup environment, it's really important to understand why your system is doing the thing it's doing, or else it's g…”
Hilary Mason Dec 5, 2013 ▶ 17:20 Hilary Mason // Data Driven NYC 20 // Nov 2013
Assertion Not checkable as stated
Mason: Deep learning completely removes the need for domain feature engineering
“In deep learning, you're not doing feature engineering anymore. Nobody cares about what you know about the domain.”
Hilary Mason Dec 8, 2016 ▶ 20:20 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Assertion Not checkable as stated
Mason: Sentiment analysis remains fundamentally unsolved due to lacking ground truth
“I think there are a couple of things that everyone thinks are solved problems that are absolutely not, and so something like sentiment analysis is something where we sort of take for granted that you can just plug into an API and get a number back, but this is…”
Hilary Mason Dec 8, 2016 ▶ 21:46 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Insight
Hilary Mason: Separating R&D from product rarely works for tech companies
“I have seen companies that successfully separate, ah, research and development as long as everyone still eats lunch together but generally that doesn't seem to be a great approach in that you eventually end up with a research department off in the corner publi…”
Hilary Mason Dec 5, 2013 ▶ 18:52 Hilary Mason // Data Driven NYC 20 // Nov 2013
Insight
Hilary Mason: Data science sits at the intersection of math, engineering, and hacking
“I tend to think that data science exists in the middle of all of these things. So math, statistics, computer science, and the ability to write algorithms, engineering, and then hacking”
Hilary Mason Dec 5, 2013 ▶ 5:28 Hilary Mason, Bitly // Data Driven NYC #3 // Feb 2012
Assertion Not checkable as stated
Hilary Mason: Bitly data shows zero overlap between NYT and Fox News readers
“Nobody who reads the New York Times reads Fox News.”
Hilary Mason Dec 5, 2013 ▶ 14:47 Hilary Mason, Bitly // Data Driven NYC #3 // Feb 2012
Assertion Not checkable as stated
Hilary Mason: Hadoop is entirely impractical for real-time products
“All it is is a structure for running queries in parallel against data that you store in a redundant file system, and so it is entirely impractical for doing a real-time product.”
Hilary Mason Dec 5, 2013 ▶ 20:21 Hilary Mason, Bitly // Data Driven NYC #3 // Feb 2012
Insight
Mason: Machine learning startups struggle with innovation due to lack of data
“There are challenges for startups, because you don't have data.”
Hilary Mason Dec 8, 2016 ▶ 3:47 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Insight
Mason: Building generic ML products is harder than solving single enterprise problems
“When somebody, when a vendor or a startup is going to build a product that solves your problem, They must solve a generic formulation of the problem. They have to solve everybody's version of your same problem. When you want to solve your problem, you just nee…”
Hilary Mason Dec 8, 2016 ▶ 5:17 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Insight
Mason: Most tech breakthroughs apply existing research to entirely new domains
“It most often is something where there's been research in one domain that is applicable to another domain. So it's not necessarily something entirely new, but rather somebody Discovering something that may have existed for a while, but realizing that it's appl…”
Hilary Mason Dec 8, 2016 ▶ 7:28 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Prediction Not checkable as stated
Mason's 2016 prediction: Deep learning compute will be trivially affordable by 2018
“These days you can afford it and I expect in a year or two it'll be trivial to afford it.”
Hilary Mason Dec 8, 2016 ▶ 8:50 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Assertion Not checkable as stated
Mason: Open source is rapidly commoditizing machine learning capabilities
“We're seeing this in machine learning primarily in the open source world in an ongoing basis, almost something new every day.”
Hilary Mason Dec 8, 2016 ▶ 9:56 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Assertion Not checkable as stated
Mason: Wikipedia data secretly underpins almost every open machine learning API
“If you go back far enough in pretty much any sort of open API in machine learning, you can find Wikipedia in there somewhere.”
Hilary Mason Dec 8, 2016 ▶ 10:54 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Opinion
Mason: Natural language generation helps us comprehend data, not replace reporters
“The real impact here is not in replacing reporters but in helping people understand complex data.”
Hilary Mason Dec 8, 2016 ▶ 14:51 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Insight
Mason: Real estate valuation is fundamentally an inference problem
“Real estate is a fantastic inference problem because the only way to know the value of a property is to sell it, but obviously we don't sell every property every day”
Hilary Mason Dec 8, 2016 ▶ 19:56 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Insight
Mason: Publications reflect past research, while informal chats reveal future ideas
“And so I really believe if you want to know what someone did a year ago, you read their publication, and if you want to know what they're doing now, you watch them give a talk like this, and if you want to know the sort of wacky ideas they're thinking about, y…”
Hilary Mason Dec 8, 2016 ▶ 23:57 A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven]
Insight
Mason: Complex corporate data problems are either technical or organizational
“Mostly I just sort of talk to companies that have interesting or hard data problems, and those data problems tend to take two forms. They tend to be either highly technical You know, hours of whiteboarding infrastructure and coming up with ideas and that's rea…”
Hilary Mason Dec 5, 2013 ▶ 1:07 Hilary Mason // Data Driven NYC 20 // Nov 2013
Insight
Mason: A company is infrastructure deployed to scale beyond oneself
“A company is a piece of infrastructure that you can deploy when you need to scale something beyond yourself.”
Hilary Mason Dec 5, 2013 ▶ 2:50 Hilary Mason // Data Driven NYC 20 // Nov 2013
Insight
Hilary Mason: Data science deserves its own title combining math, code, and communication
“Data scientists as a job does deserve its own job title because these three things in one professional is new.”
Hilary Mason Dec 5, 2013 ▶ 3:56 Hilary Mason // Data Driven NYC 20 // Nov 2013
Disclosure
Mason: The vast majority of math in data science is quite simple
“So at least in my practice of data science, the vast majority of the math we did was quite simple.”
Hilary Mason Dec 5, 2013 ▶ 4:11 Hilary Mason // Data Driven NYC 20 // Nov 2013
Insight
Mason: Data engineering designs systems dependent on flowing data
“Data infrastructure. Or data engineering, which is the engineering of systems where the design of the system itself is dependent on the nature of the data that flows through it”
Hilary Mason Dec 5, 2013 ▶ 7:07 Hilary Mason // Data Driven NYC 20 // Nov 2013
Insight
Mason: Forcing teams to admit informal error metrics motivates analytical rigor
“And if there are no error metrics that we are planning to use initially you know, we might be working on something like a recommendation algorithm. And the error metric for the first version is, it looks good to me. You at least have to admit that in public, i…”
Hilary Mason Dec 5, 2013 ▶ 11:21 Hilary Mason // Data Driven NYC 20 // Nov 2013
Insight
Mason: Data projects must prove immediate relevance to business goals
“Assuming we can solve this perfectly, what is the first thing we'll do with it that makes sure it has immediate relevance to the product, or the business, or the system you're building? Because there are a lot of super cool ideas that do not tie directly to th…”
Hilary Mason Dec 5, 2013 ▶ 11:43 Hilary Mason // Data Driven NYC 20 // Nov 2013
Insight
Mason: Asking how an evil dictator would use product sparks creative ideas
“So if you ask this question, like, if we put this thing in the hands of an evil dictator, ah, what would they do with it? Ah, it's a way to get people to sort of brainstorm and come up with crazy ideas for it, some of which might actually be good.”
Hilary Mason Dec 5, 2013 ▶ 13:00 Hilary Mason // Data Driven NYC 20 // Nov 2013

Show 14statements(14 left)

The other half of the tape: Hilary Mason's own voice is left out of every number here. Other people bring the name up 9 times in 7 episodes on the MAD Podcast. every mention, with the transcript →

Who brings them up most Matt Turck 4John Foreman 2Gilad Lotan 1Chris Moody 1Adam Laiacano 1

Every mention by year

tap a year for its mentions
00541082013episodesmentions
0482013episodes it came up in
000.841.582013episodesmentions per episode
2013 9 mentions in 7 episodes 1 per episode

Appearances (3)

EpisodeDateSpeaking time
A Process for Discovery // Hilary Mason, Fast Forward Labs [FirstMark's Data Driven] Dec 8, 2016 20m
Hilary Mason // Data Driven NYC 20 // Nov 2013 Dec 5, 2013 16m
Hilary Mason, Bitly // Data Driven NYC #3 // Feb 2012 Dec 5, 2013 18m
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 400 conversations 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.

Try the podcast clipper Learn more