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Christopher Nguyen

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

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

founderexecutivescientistacademicengineer@pentagoniac ↗LinkedIn ↗aitomatic.com ↗

Christopher Cuong Nguyen co-founded big data intelligence startup Adatao (later rebranded to Arimo), which was acquired by Panasonic. Earlier in his career, he served as an engineering director at Google and co-founded the Computer Engineering program as an ECE professor at HKUST.

11statements → 3claims → 1claims resolved → 4/5average certainty → 2.09/5average debate potential → 4.2/5argument clarity · the sources →

1 supported 0 partly supported 0 contradicted 2 not checkable as stated how the 3 claims stand · each chip opens the sources

2 predictions · 1 assertion · 7 insights · 1 what if · every statement was checked. The predictions and assertion are the 3 claims: statements the public record can support or contradict. 1 is 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 Christopher 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
Nguyen: MapReduce Was Intentionally Designed for Reliability Over Speed
“Interestingly, a lot of people may not realize that MapReduce was designed to be slow.”
Christopher Nguyen Jan 2, 2019 ▶ 12:37 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning

Argument clarity: do they answer the question? how? →

4.2 / 5 directness 4.3 · coherence 4.6 · precision 3.9 · compression 3.8

redirected or did not address 1 of 21 assessed questions (5%). Watch them ▸

This is a score against a rubric. It is not a rank. Every host question → answer exchange is scored with names hidden on directness, coherence, precision and compression, 1–5 each, on meaning alone: disfluencies are ignored, and only raw unedited episodes count. This is the score that measures thought. Every scored exchange, scores shown → · The rubric and its checks →

How they sound: speaking style how? →

256 words/min while actually speaking · 10.8 um and uh per 1k words · 19.4 false starts per 1k · 28.9% of pauses land inside a clause

Measured by listening to the audio itself: 3,972 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 →

Everything Christopher Nguyen said on the a16z Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight
Nguyen: Machine Learning Is the Primary Purpose of Big Data
“So it turns out the reason for big data is machine learning.”
Christopher Nguyen Jan 2, 2019 ▶ 1:30 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Insight
Nguyen: Big Data Is Defined by Learning Thresholds, Not Volume
“So I like something that Peter Norvig, the director of research at Google said when he referred to big data, he says, big data is not just quantitatively different, but it's qualitatively different. In other words, there's something that happens when you have …”
Christopher Nguyen Jan 2, 2019 ▶ 1:42 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Insight
Nguyen: Machine Learning Mirrors Human Learning From Experience
“And the way I think about big data is when machines learn from big data is very much like human beings learn from life experiences.”
Christopher Nguyen Jan 2, 2019 ▶ 3:24 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Insight
Nguyen: Human Intuition Functions Like Parameters in a Machine Learning Model
“Well, what we think of as intuition are actually, you can think of as parameters inside a machine learning model.”
Christopher Nguyen Jan 2, 2019 ▶ 5:09 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Insight
Nguyen: Modern Business Intelligence Uses ML to Predict Unknowns
“You can think of business intelligence going forward as the ability to apply machine learning algorithms to big data, and not just look at past questions, but also future questions, or asking to predict the unknowns from the knowns.”
Christopher Nguyen Jan 2, 2019 ▶ 6:33 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Insight
Nguyen: Big Data Progress Is Driven by Cheaper Tech, Not Smarter People
“We don't necessarily get smarter over time. It's just that certain technologies get cheaper. They get, they become more available. So machine learning algorithms have always been around. The data that exists that you could collect has always been around. But i…”
Christopher Nguyen Jan 2, 2019 ▶ 7:20 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Assertion Supported
Nguyen: MapReduce Was Intentionally Designed for Reliability Over Speed
“Interestingly, a lot of people may not realize that MapReduce was designed to be slow.”
Christopher Nguyen Jan 2, 2019 ▶ 12:37 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
What-if
Nguyen: Apache Spark Would Have Failed Earlier Due to Memory Costs
“Now Spark, if it was created six, five, six years before its time would have completely failed because memory was so much more expensive.”
Christopher Nguyen Jan 2, 2019 ▶ 14:45 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Insight
Nguyen: Users Abandon Software Tasks if Latency Exceeds Five Seconds
“We had a phrase we call the five second barrier. And if the user can't get something done, you know, within five seconds, they won't ever do it. It's not like they'll do it at, you know, at twice the latency.”
Christopher Nguyen Jan 2, 2019 ▶ 16:19 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Prediction Not checkable as stated
Nguyen: Machine Learning Will Become a Feature of Every Application
“What you will see is that all of this machine learning will be a property of every application.”
Christopher Nguyen Jan 2, 2019 ▶ 19:09 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
Prediction Not checkable as stated
Nguyen: Users Will Soon Expect Machines to Learn Like Human Colleagues
“Well, I claim that there will be a day very soon when then you will feel that about the machines you work with. In other words, you would expect that to be a property of all these machines.”
Christopher Nguyen Jan 2, 2019 ▶ 19:57 a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning

Appearances (1)

EpisodeDateSpeaking time
a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning Jan 2, 2019 18m
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