Hilary Mason
Co-founder and CEO, Hidden Door. On 1 show, 3 appearances. The Shows tab opens the full record on each.
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.
1shows
3appearances
38statements
4resolved
3supported
0contradicted
75%fully supported
9said about them ↓ Everything Hilary Mason said on any show that made the record, most notable first. Each card names its show and opens the statement there.
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…”
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.”
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: 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: 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: Bitly data shows zero overlap between NYT and Fox News readers
“Nobody who reads the New York Times reads Fox News.”
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.”
Mason: Machine learning startups struggle with innovation due to lack of data
“There are challenges for startups, because you don't have data.”
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…”
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…”
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.”
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.”
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.”
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.”
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”
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…”
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…”
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: 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.”
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.”
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”
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…”
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…”
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.”
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 across the shows. 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
the MAD Podcast 9
2013 9 mentions in 7 episodes 1 per episode
- John Foreman, Mailchimp // Data Driven NYC 19 // October 2013
- Matt Turck, Bloomberg Ventures // Data Driven NYC# 3 // Feb 2012
- Joe Lonsdale, Formation 8 // Fireside Chat at Data Driven NYC #20 (Hosted by FirstMark Capital)
- Adam Laiacano, Tumblr // Data Driven NYC 20 // Nov 2013 (Hosted by FirstMark Capital)
- Gilad Lotan, Betaworks // Data Driven NYC 20 // Nov 2013
- Mike Dauber, Battery Ventures // Data Driven NYC 19 // October 2013 (interviewed by Matt Turck)
- Chris Moody, Gnip // Data Driven NYC #3 // Feb 2012
- every mention in 2013, scene by scene →
One line per show, most statements first. The link opens Hilary's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| MADLEDGER | Co-founder and CEO, Hidden Door | 3 | 38 | 75% 3/4 | full record on the MAD Podcast → |