Jason Mars

1 appearance on the record.

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

13statements → 10claims → 7claims resolved → 100%fully supported → 4.15/5average certainty → 1.54/5average debate potential →

7 supported 0 partly supported 0 contradicted 1 not yet assessed 2 not checkable as stated how the 10 claims stand · each chip opens the sources

10 assertions · 2 disclosures · 1 what if · every statement was checked. The predictions and assertions are the 10 claims: statements the public record can support or contradict. 7 are resolved, 1 is not yet assessed, 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 Jason 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
Mars: Porting Sirius to GPUs and FPGAs yields 10x speedup
“We can get significant speedups when we port these algorithms To GPU, when we leverage GPUs and FPGAs to build future servers, and what that means is we can bring that scalability gap down, and so we can get about 10 X back if we move by taking these, ah, by t…”
Jason Mars Jul 28, 2017 ▶ 11:57 Jason Mars

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
none yet certainty 3
100% 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? →

233 words/min while actually speaking · 60.7 um and uh per 1k words

No argument clarity score for Jason Mars: 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 the audio itself: 2,898 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 Jason Mars 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
Mars: AI assistant infrastructures are monopolized by major tech companies
“These kinds of infrastructures are monopolized essentially by companies like Apple, Google, Microsoft, and so forth.”
Jason Mars Jul 28, 2017 ▶ 3:14 Jason Mars
What-if
Mars: Converting search to AI assistants requires 100x data center scaling
“If you moved all of the queries that were search queries and turn them into intelligent personal assistant queries, we would have to grow our data centers by two orders of magnitude.”
Jason Mars Jul 28, 2017 ▶ 10:19 Jason Mars
Disclosure
Mars: Apple requested Sirius AI project rename, resulting in Lucida
“After Sirius gained a good bit of traction, Apple reached out to us and said, you probably should change the name. And so we're now calling it Lucida.”
Jason Mars Jul 28, 2017 ▶ 7:41 Jason Mars
Assertion Supported
Mars: Porting Sirius to GPUs and FPGAs yields 10x speedup
“We can get significant speedups when we port these algorithms To GPU, when we leverage GPUs and FPGAs to build future servers, and what that means is we can bring that scalability gap down, and so we can get about 10 X back if we move by taking these, ah, by t…”
Jason Mars Jul 28, 2017 ▶ 11:57 Jason Mars
Assertion Supported
Mars: Tech giants are building future data centers around deep learning
“Deep learning is absolutely the bet that companies are making, right? So Google has put a lot of resources into deep learning you know, Facebook. We're designing future data centers and future infrastructures around this one technique, and it's primarily becau…”
Jason Mars Jul 28, 2017 ▶ 13:15 Jason Mars
Assertion Supported
Mars: Clarity Lab's custom GPU appliance outperforms off-the-shelf deep learning tools
“Jin is a deep learning engine. It's an appliance. We co-designed a GPU box with a software stack for deep learning and it can, it beats the pants off of out-of-the-box stuff. That you can find.”
Jason Mars Jul 28, 2017 ▶ 15:13 Jason Mars
Assertion Supported
Mars: Apple built a $1 billion data center for Siri's launch
“Apple built a one billion dollar data center at the time that Siri was released.”
Jason Mars Jul 28, 2017 ▶ 2:39 Jason Mars
Assertion Supported
Mars: Compute-intensive ML algorithms drive 92% of Sirius execution workload
“And so across the entire workload, 92% of the execution of series could be described by these compute-intensive algorithms.”
Jason Mars Jul 28, 2017 ▶ 11:23 Jason Mars
Assertion Not publicly verifiable
Mars: Open-source Sirius AI system ranked number one on GitHub
“It was number one on GitHub for a couple weeks.”
Jason Mars Jul 28, 2017 ▶ 12:35 Jason Mars
Assertion Not checkable as stated
Mars: Clinc's license covers four years of future university lab research
“We have an unprecedented license agreement where in addition to all the tech we currently have, four years of future tech is also covered in our license. So as we improve it from our research lab, It's exclusively owned by Clarity Lab Inc, the improvements.”
Jason Mars Jul 28, 2017 ▶ 20:09 Jason Mars
Assertion Supported
Mars: Clarity Lab used candle wax to cool servers longer
“There's a work where we put candle wax in servers to keep them cooler longer.”
Jason Mars Jul 28, 2017 ▶ 1:01 Jason Mars
Disclosure
Mars: Sirius QA system uses the entire Wikipedia corpus for answers
“We have a QA system, a question and answer system that has at its disposal the entire corpus of Wikipedia to answer your question, and it hits that system, and the answer is plucked from the knowledge base of Wikipedia and sent to the user.”
Jason Mars Jul 28, 2017 ▶ 5:31 Jason Mars
Assertion Supported
Mars: Speech recognition shifted from Gaussian mixture models to deep neural networks
“Google used to use Gaussian mixture models in the early days, and now everyone has moved to deep neural networks. To do the speech recognition, ah, task.”
Jason Mars Jul 28, 2017 ▶ 6:39 Jason Mars

Appearances (1)

EpisodeDateSpeaking time
Jason Mars Jul 28, 2017 15m
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