StarZero All shows Try the podcast clipper →

Jun 16, 2016 · 27m · mad

The Path to A.I. Augmented Human Intelligence // Christopher Nguyen, Arimo [FirstMark's Data Driven]

Christopher Nguyen · 21m spoken Matt Turck · 1m spoken
0:00 / 0:00
▶ Watch on YouTube →

gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

At Data Driven NYC, Christopher Nguyen introduces Arimo's platform for enterprise intelligence augmentation and explores the engineering behind distributed deep learning. He ultimately advocates for human-machine augmentation as humanity's primary path forward in the era of artificial super intelligence.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 5.1% of the talking time here. How this is scored →

Matt as informed peer 0.6 Guest teaching 0.4 Guest disagreement 0.4 Matt pushing back 0.6
05100:0010:0020:000:00–2:00 · Matt as informed peer 0/10 Title Sequence and Speaker Introductions Christopher Nguyen introduces his background and credentials during a presentation monologue. The host is not present or active in this segment.2:00–7:46 · Matt as informed peer 0/10 The Intelligence Augmentation Journey Christopher conducts a live demo of Arimo's platform analyzing flight data. The host remains silent during the monologue demo.7:46–10:16 · Matt as informed peer 0/10 Addressing Technical Core: Deep Learning vs. AI Christopher presents the technical concepts behind deep learning platforms and gradient descent. Host is inactive in this presentation monologue.10:16–12:28 · Matt as informed peer 0/10 Distributed Deep Learning Architecture & Optimization Christopher outlines architectural configurations for distributed deep learning. The host does not participate.12:28–15:43 · Matt as informed peer 0/10 Performance Benchmarks and Bottlenecks Christopher reviews performance benchmarks comparing CPU vs GPU scaling and communication bottlenecks. Host is silent.15:43–18:22 · Matt as informed peer 0/10 Human Augmentation & Neural Interfaces Christopher transitions to discussing neural augmentation and brain-machine interfaces. Host is inactive.18:22–27:22 · Matt as informed peer 4/10 Presentation Wrap-Up & Transition to Q&A Matt Turck opens the Q&A with sharp questions about enterprise AI adoption and startup taxonomy. Christopher politely reframes host questions and dismisses conventional definitions like strong vs weak AI.0:00–2:00 · Guest teaching 0/10 Title Sequence and Speaker Introductions Christopher Nguyen introduces his background and credentials during a presentation monologue. The host is not present or active in this segment.2:00–7:46 · Guest teaching 0/10 The Intelligence Augmentation Journey Christopher conducts a live demo of Arimo's platform analyzing flight data. The host remains silent during the monologue demo.7:46–10:16 · Guest teaching 0/10 Addressing Technical Core: Deep Learning vs. AI Christopher presents the technical concepts behind deep learning platforms and gradient descent. Host is inactive in this presentation monologue.10:16–12:28 · Guest teaching 0/10 Distributed Deep Learning Architecture & Optimization Christopher outlines architectural configurations for distributed deep learning. The host does not participate.12:28–15:43 · Guest teaching 0/10 Performance Benchmarks and Bottlenecks Christopher reviews performance benchmarks comparing CPU vs GPU scaling and communication bottlenecks. Host is silent.15:43–18:22 · Guest teaching 0/10 Human Augmentation & Neural Interfaces Christopher transitions to discussing neural augmentation and brain-machine interfaces. Host is inactive.18:22–27:22 · Guest teaching 3/10 Presentation Wrap-Up & Transition to Q&A Matt Turck opens the Q&A with sharp questions about enterprise AI adoption and startup taxonomy. Christopher politely reframes host questions and dismisses conventional definitions like strong vs weak AI.0:00–2:00 · Guest disagreement 0/10 Title Sequence and Speaker Introductions Christopher Nguyen introduces his background and credentials during a presentation monologue. The host is not present or active in this segment.2:00–7:46 · Guest disagreement 0/10 The Intelligence Augmentation Journey Christopher conducts a live demo of Arimo's platform analyzing flight data. The host remains silent during the monologue demo.7:46–10:16 · Guest disagreement 0/10 Addressing Technical Core: Deep Learning vs. AI Christopher presents the technical concepts behind deep learning platforms and gradient descent. Host is inactive in this presentation monologue.10:16–12:28 · Guest disagreement 0/10 Distributed Deep Learning Architecture & Optimization Christopher outlines architectural configurations for distributed deep learning. The host does not participate.12:28–15:43 · Guest disagreement 0/10 Performance Benchmarks and Bottlenecks Christopher reviews performance benchmarks comparing CPU vs GPU scaling and communication bottlenecks. Host is silent.15:43–18:22 · Guest disagreement 0/10 Human Augmentation & Neural Interfaces Christopher transitions to discussing neural augmentation and brain-machine interfaces. Host is inactive.18:22–27:22 · Guest disagreement 3/10 Presentation Wrap-Up & Transition to Q&A Matt Turck opens the Q&A with sharp questions about enterprise AI adoption and startup taxonomy. Christopher politely reframes host questions and dismisses conventional definitions like strong vs weak AI.0:00–2:00 · Matt pushing back 0/10 Title Sequence and Speaker Introductions Christopher Nguyen introduces his background and credentials during a presentation monologue. The host is not present or active in this segment.2:00–7:46 · Matt pushing back 0/10 The Intelligence Augmentation Journey Christopher conducts a live demo of Arimo's platform analyzing flight data. The host remains silent during the monologue demo.7:46–10:16 · Matt pushing back 0/10 Addressing Technical Core: Deep Learning vs. AI Christopher presents the technical concepts behind deep learning platforms and gradient descent. Host is inactive in this presentation monologue.10:16–12:28 · Matt pushing back 0/10 Distributed Deep Learning Architecture & Optimization Christopher outlines architectural configurations for distributed deep learning. The host does not participate.12:28–15:43 · Matt pushing back 0/10 Performance Benchmarks and Bottlenecks Christopher reviews performance benchmarks comparing CPU vs GPU scaling and communication bottlenecks. Host is silent.15:43–18:22 · Matt pushing back 0/10 Human Augmentation & Neural Interfaces Christopher transitions to discussing neural augmentation and brain-machine interfaces. Host is inactive.18:22–27:22 · Matt pushing back 4/10 Presentation Wrap-Up & Transition to Q&A Matt Turck opens the Q&A with sharp questions about enterprise AI adoption and startup taxonomy. Christopher politely reframes host questions and dismisses conventional definitions like strong vs weak AI.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 0% · guest 100%0:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%12:00 · Matt 0% · guest 100%12:00 · Matt 0% · guest 100%15:00 · Matt 0% · guest 100%15:00 · Matt 0% · guest 100%18:00 · Matt 30.1% · guest 69.9%18:00 · Matt 30.1% · guest 69.9%21:00 · Matt 0.8% · guest 99.2%21:00 · Matt 0.8% · guest 99.2%24:00 · Matt 14.9% · guest 85.1%24:00 · Matt 14.9% · guest 85.1%27:00 · Matt 13.9% · guest 86.1%27:00 · Matt 13.9% · guest 86.1%
Sharpest disagreement ▶ 26:29 Dismissing strong vs weak AI distinction

Christopher politely rejects the host's categorization of AI companies by stating he has moved past the labels of strong versus weak AI.

 Hardest push from Matt ▶ 26:23 Host presses for specific AI company classifications

Matt refuses a vague answer about future AI companies and directly asks Christopher to specify whether he means strong AI, vertical AI, or all AI companies.

 Biggest teaching moment ▶ 19:23 Reframing enterprise data challenges via EETL and BETL

Christopher reframes Matt's question on enterprise readiness by explaining how Arimo splits data workflows into Enterprise ETL and Business ETL.

 Matt holds his own ▶ 18:31 Probing enterprise adoption gap and social engineering

Matt demonstrates deep industry understanding by asking how Arimo bridges the gap between raw technological capability and enterprise social engineering.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Title Sequence and Speaker Introductions 0000 Christopher Nguyen introduces his background and credentials during a presentation monologue. The host is not present or active in this segment.
The Intelligence Augmentation Journey 0000 Christopher conducts a live demo of Arimo's platform analyzing flight data. The host remains silent during the monologue demo.
Addressing Technical Core: Deep Learning vs. AI 0000 Christopher presents the technical concepts behind deep learning platforms and gradient descent. Host is inactive in this presentation monologue.
Distributed Deep Learning Architecture & Optimization 0000 Christopher outlines architectural configurations for distributed deep learning. The host does not participate.
Performance Benchmarks and Bottlenecks 0000 Christopher reviews performance benchmarks comparing CPU vs GPU scaling and communication bottlenecks. Host is silent.
Human Augmentation & Neural Interfaces 0000 Christopher transitions to discussing neural augmentation and brain-machine interfaces. Host is inactive.
Presentation Wrap-Up & Transition to Q&A 4334 Matt Turck opens the Q&A with sharp questions about enterprise AI adoption and startup taxonomy. Christopher politely reframes host questions and dismisses conventional definitions like strong vs weak AI.

Statements from this episode (8)

Assertion Not checkable as stated
Nguyen: Parameter servers are the most efficient distributed deep learning architecture
“So, it turns out the fourth one is the most efficient one because it doesn't have to communicate the model back and forth anymore.”
Christopher Nguyen Jun 16, 2016 ▶ 12:28
Assertion Supported
Nguyen: Model communication creates a persistent bottleneck in distributed GPU clusters
“No matter how many cores you have, Even if you have an infinite number of cores, there's gonna be some amount of time needed to communication, to communicate that model.”
Christopher Nguyen Jun 16, 2016 ▶ 13:34
Assertion Not checkable as stated
Nguyen: In 2016, Google Brain is at the intelligence of a cockroach
“If you look at the Google brain, it's at the level of a, about a cockroach. Ok? And it's got about a million neurons.”
Christopher Nguyen Jun 16, 2016 ▶ 14:59
Prediction Open · timeframe Jun 2066
Nguyen: Energy bounds put human-level AI compute 50 years away
“And if you compute the equivalent energy required per neuron, and extrapolating that, it'll take about 50 years to get the equivalent human brain.”
Christopher Nguyen Jun 16, 2016 ▶ 15:07
Prediction Open · timeframe Jun 2032
Nguyen: Cost parity for human-brain level AI will arrive in 16 years
“There's another metric you look at in terms of traverse edges per second. And when you look at that, and you say, well, when would it take for it to become human in terms of cost? That's in about 16 years.”
Christopher Nguyen Jun 16, 2016 ▶ 15:16
Assertion Partly supported
Nguyen: Blind people can learn to see through their tongues
“And after six or eight weeks of training, blind people can learn to quote, unquote, see through their tongue, because the brain learns to detect those signals.”
Christopher Nguyen Jun 16, 2016 ▶ 16:06
Prediction Not checkable as stated
Nguyen: Human-AI augmentation is humanity's best survival path against superintelligence
“So, my idea is, what if we could augment ourselves with this AI stuff we're building? That's probably the way out. That's probably the way we get away with this.”
Christopher Nguyen Jun 16, 2016 ▶ 16:49
Prediction Not checkable as stated
Nguyen: Brain-machine interfaces will create hyper-evolved humans within 50 years
“So, if we implement this, and we implement brain-machine interfaces, within the next 50 years, we can become that hyper-evolved being, right?”
Christopher Nguyen Jun 16, 2016 ▶ 17:21
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 400 conversations transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.

Try the podcast clipper Learn more