Chris Ré

1 appearance on the record.

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

12statements → 9claims → 4claims resolved → 100%fully supported → 4/5average certainty → 1.83/5average debate potential →

4 supported 0 partly supported 0 contradicted 5 not checkable as stated how the 9 claims stand · each chip opens the sources

1 prediction · 8 assertions · 1 opinion · 2 insights · every statement was checked. The prediction and assertions are the 9 claims: statements the public record can support or contradict. 4 are resolved, and 5 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 Chris 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
Relaxing hardware memory consistency yields up to 1000x speedups over competitors
“We've basically done a couple of parlor tricks, and those parlor tricks have allowed us to get, you know, 10, a hundred, a thousand times faster than competitor systems, because we're actually taking advantage of what the hardware is giving us.”
Chris Ré Jul 15, 2017 ▶ 20:20 Chris Re

How they sound: speaking style how? →

262 words/min while actually speaking · 4.9 um and uh per 1k words

No argument clarity score for Chris Ré: no usable question→answer exchanges 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 4,713 words across 1 episode, but every recording we have of Chris Ré is the aired feed, and an editor cleaned that audio before release. Some of the hesitation was cut before we ever heard it, so read these as floors: the true rates are at least this high. These are measurements of speaking style, not scores. How it's measured →

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

Assertion Not checkable as stated
Probabilistic inference can reduce data pipeline engineering effort by orders of magnitude
“In contrast, we can build a pipeline first, which may not be very high quality, and then use probabilistic inference as a way to say, where should we spend our effort next? And the reduction in effort can sometimes be orders of magnitude, as we've seen in some…”
Chris Ré Jul 15, 2017 ▶ 3:08 Chris Re
Opinion
Classical models of sequential evaluation can be completely thrown away
“That is, why it's a new trade-off space and classical trade-off space don't sort of suffice, and to do that, I'm going to show you one result which just illustrates the classical models of sequential evaluation you can completely throw away.”
Chris Ré Jul 15, 2017 ▶ 16:46 Chris Re
Assertion Supported
Relaxing hardware memory consistency yields up to 1000x speedups over competitors
“We've basically done a couple of parlor tricks, and those parlor tricks have allowed us to get, you know, 10, a hundred, a thousand times faster than competitor systems, because we're actually taking advantage of what the hardware is giving us.”
Chris Ré Jul 15, 2017 ▶ 20:20 Chris Re
Assertion Not checkable as stated
Automated dark data pipelines can outperform human data extraction quality
“There are a number of systems that have shown that it's actually possible to build these ETL pipelines, these pipelines that extract, transform, and load information with higher quality than humans in some very simple settings.”
Chris Ré Jul 15, 2017 ▶ 1:08 Chris Re
Insight
Traditional data pipelines over-extract and over-clean due to unmeasurable impact
“So in contrast, if you think about the way that people build these extraction and integration and cleaning systems, they tend to over-extract, over-integrate, and over-clean Because they have no idea if their cleaning or extraction is actually gonna prove the …”
Chris Ré Jul 15, 2017 ▶ 2:54 Chris Re
Insight
Modern scientific literature is universally accessible but impossible for humans to read
“For even the narrowest of scientific questions, They couldn't possibly read all the information that was relevant to them, or even a significant fraction of it. So this is why I say that scientific knowledge is accessible in a way like never before, but it's n…”
Chris Ré Jul 15, 2017 ▶ 4:51 Chris Re
Assertion Supported
Human volunteers achieved 84% accuracy on PaleoDB data extraction tasks
“When we assess this, we found that the PaleoDB volunteers were accurate about 84% of the time.”
Chris Ré Jul 15, 2017 ▶ 12:31 Chris Re
Assertion Supported
PaleoDeepDive matches or exceeds human data extraction accuracy across all tasks
“In some predicates, it's basically a wash. There's information that humans and machines are equally good at. But we're never any worse, and in some cases, we're substantially better.”
Chris Ré Jul 15, 2017 ▶ 13:41 Chris Re
Prediction Not checkable as stated
DeepDive human trafficking detection pilots are expanding to law enforcement agencies
“So, I'll skip over the details of the application, but I will say that it's actually in active use and we can actually get high precision values and we're in Extending, ah, pilots to more law enforcement agencies over the next couple months.”
Chris Ré Jul 15, 2017 ▶ 15:06 Chris Re
Assertion Not checkable as stated
Hardware parallelism enables orders of magnitude speedups over competing systems
“SIMD parallelism, multicore parallelism, NUMA, those are all things we need to take advantage of to go, in some cases, orders of magnitude faster than competitor systems on these tasks.”
Chris Ré Jul 15, 2017 ▶ 15:50 Chris Re
Assertion Not checkable as stated
DeepDive factor graphs can reach hundreds of terabytes in size
“In some of our applications right now, these factor graphs become hundreds of terabytes in size.”
Chris Ré Jul 15, 2017 ▶ 9:42 Chris Re
Assertion Supported
Microsoft adopted Chris Ré's Hogwild algorithm for its Project Atom
“A while ago, last year, Microsoft released their Project Atom, and they had a great demo where they were showing they were releasing, you know, recognizing Shih Tzus with Windows phones. Using deep learning. And the reason I will love Microsoft forever is the …”
Chris Ré Jul 15, 2017 ▶ 19:39 Chris Re

Appearances (1)

EpisodeDateSpeaking time
Chris Re Jul 15, 2017 19m
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