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Scott Clark

Co-Founder and CEO, Distributional · 1 appearance on the record.

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

founderexecutivescientistengineer@DrScottClark ↗LinkedIn ↗scottclark.io ↗

Scott Clark is an applied mathematician and entrepreneur who co-founded SigOpt, which was acquired by Intel in 2020, where he served as VP of AI and HPC engineering. He currently serves as co-founder and CEO of enterprise AI reliability platform Distributional.

11statements → 6claims → 2claims resolved → 4/5average certainty → 1.18/5average debate potential → 2said about them ↓

2 supported 0 partly supported 0 contradicted 4 not checkable as stated how the 6 claims stand · each chip opens the sources

6 assertions · 1 insight · 4 disclosures · every statement was checked. The predictions and assertions are the 6 claims: statements the public record can support or contradict. 2 are resolved, and 4 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 Scott 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
Black-box Bayesian optimization outperforms standard machine learning tuning techniques
“What we're able to do is without any domain expertise, without making any assumptions about that underlying model, outperform these standard techniques by providing this ensemble of black box Bayesian optimization strategies.”
Scott Clark Jun 29, 2017 ▶ 6:42 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?

How they sound: not measured why? →

We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Scott Clark on measured tape to publish a rate. This says nothing about how they speak.

Everything Scott Clark said on Top Founders that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Supported
Black-box Bayesian optimization outperforms standard machine learning tuning techniques
“What we're able to do is without any domain expertise, without making any assumptions about that underlying model, outperform these standard techniques by providing this ensemble of black box Bayesian optimization strategies.”
Scott Clark Jun 29, 2017 ▶ 6:42 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Assertion Not checkable as stated
Algorithmic trading firms use SigOpt because client data never touches its systems
“This allows us to work with some of the most secretive algorithmic trading firms in the world, where their domain expertise and their models are literally how they make their billions of dollars, but they can still use SigOpt because all we're tuning are these…”
Scott Clark Jun 29, 2017 ▶ 11:12 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Disclosure
SigOpt fine-tunes existing client models instead of entirely replacing them
“So instead of just taking a raw data set of decades of fraud data and giving them some model to like rip and replace what they already have, we sit on top of what they have and provide this additive boost by fine tuning it.”
Scott Clark Jun 29, 2017 ▶ 5:37 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Insight
Clark: Humans are bad at performing 10-dimensional optimization mentally
“Turns out humans are pretty bad at doing 10 dimensional optimization in their head.”
Scott Clark Jun 29, 2017 ▶ 6:28 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Disclosure
SigOpt has raised $8.8M to date, led by Andreessen Horowitz
“We have about 8.8 million dollars to date. Went through Y Combinator in winter 15. Andreessen Horowitz led our seed round immediately following that. They also led our series A last July.”
Scott Clark Jun 29, 2017 ▶ 9:58 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Assertion Supported
Huawei is a paying customer of SigOpt
“Huawei is another customer.”
Scott Clark Jun 29, 2017 ▶ 12:02 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Assertion Not checkable as stated
SigOpt generates approximately $50,000 in monthly recurring revenue
“That's a fair assumption, yeah.”
Scott Clark Jun 29, 2017 ▶ 12:18 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Assertion Not checkable as stated
SigOpt has experienced absolute zero customer churn to date
“No, that's the nice thing is once customers start putting this into their system to replace it, they have to go back to one of these Previous techniques, like trying to brute force the problem or something like that.”
Scott Clark Jun 29, 2017 ▶ 12:54 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Disclosure
SigOpt pricing starts at $2,500/mo, with enterprise plans around $10,000/mo
“Our work group pricing that we publish on our website starts at 2500 dollars a month getting you a little more than a dozen models a month enterprise plans ramp up from there around the 10,000 dollar a month mark is typical.”
Scott Clark Jun 29, 2017 ▶ 1:54 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Assertion Not checkable as stated
Insurance giants like Prudential are actively investing in machine learning
“Prudential is really investing in machine learning and data science. I think we see this across the board in a variety of different insurance companies where some of the more traditional models are being augmented by the amount of new data that's Being able to…”
Scott Clark Jun 29, 2017 ▶ 3:23 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?
Disclosure
SigOpt has about a dozen paying enterprise customers globally
“About a dozen customers around the world.”
Scott Clark Jun 29, 2017 ▶ 11:48 705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool?

The other half of the tape: Scott Clark's own voice is left out of every number here. Other people bring the name up 2 times in 1 episode on Top Founders. every mention, with the transcript →

Who brings them up most Nathan Latka 2

Every mention by year

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0112017episodes it came up in
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2017 2 mentions in 1 episode

Appearances (1)

EpisodeDateSpeaking time
705: With $8.8M Raised, Is This The Ultimate Machine Learning Tool? Jun 29, 2017 10m
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