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Jan 13, 2017 · 31m · 20vc

20VC: Building An AI Company For The Long Term? How Humans & Machine Learning Can Work Together? What Applications Are The Conversational Interface Best Suited To with Maran Nelson, Founder & CEO @ Clara Labs

Maren Nelson · 22m spoken Harry Stebbings · 7m spoken
0:00 / 0:00

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In this episode of The 20VC, Clara Labs co-founder and CEO Maren Nelson joins host Harry Stebbings to discuss the evolution of conversational interfaces, the mechanics of human-in-the-loop AI design, and strategic roadmaps for scaling applied machine learning startups.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Harry holds 24.5% of the talking time here. How this is scored →

Harry as informed peer 1.8 Guest teaching 5.0 Guest disagreement 1.2 Harry pushing back 1.2
05100:0010:0020:0030:002:15–4:48 · Harry as informed peer 1/10 Maren Nelson's Background and the Founding of Clara Harry warmly introduces Maren and asks for her background and founding story. Maren shares her neuroscience background and how pairing with her roboticist co-founder led to founding Clara.4:48–8:26 · Harry as informed peer 2/10 Defining Digital Assistants and Conversational Interfaces Harry prompts Maren on how to view digital assistants. Maren provides a historical perspective comparing PDAs, the Jetsons, and Snapchat to explain incremental conversational UI design.8:26–11:56 · Harry as informed peer 2/10 Identifying Tipping Points in Interface Design Harry asks where the tipping point lies between graphical UIs and conversational interfaces. Maren explains that language interfaces excel when task complexity makes GUIs tedious.11:57–21:11 · Harry as informed peer 3/10 Reframing AI Categories and Building Sustainable Companies Maren dismantles the notion of AI as a monolithic category, comparing it to grouping all web apps together. She details Clara's human-in-the-loop infrastructure and why human assistance remains essential for high-quality machine learning training data.21:12–25:58 · Harry as informed peer 2/10 Quick-Fire Questions: Literature, NLP, YC, and ML Challenges Maren banters playfully with Harry during the quick-fire segment before sharing deep takes on literature, NLP suitability, and the core ML challenge of crossing the accuracy threshold to productization.25:58–29:26 · Harry as informed peer 1/10 Clara's Achievements and 2017 Strategic Roadmap Maren breaks down Clara's strategic achievements in 2016 with its XO platform and outlines 2017 plans for enterprise integrations like ATS and marketing automation. Harry closes with high praise.2:15–4:48 · Guest teaching 3/10 Maren Nelson's Background and the Founding of Clara Harry warmly introduces Maren and asks for her background and founding story. Maren shares her neuroscience background and how pairing with her roboticist co-founder led to founding Clara.4:48–8:26 · Guest teaching 5/10 Defining Digital Assistants and Conversational Interfaces Harry prompts Maren on how to view digital assistants. Maren provides a historical perspective comparing PDAs, the Jetsons, and Snapchat to explain incremental conversational UI design.8:26–11:56 · Guest teaching 6/10 Identifying Tipping Points in Interface Design Harry asks where the tipping point lies between graphical UIs and conversational interfaces. Maren explains that language interfaces excel when task complexity makes GUIs tedious.11:57–21:11 · Guest teaching 7/10 Reframing AI Categories and Building Sustainable Companies Maren dismantles the notion of AI as a monolithic category, comparing it to grouping all web apps together. She details Clara's human-in-the-loop infrastructure and why human assistance remains essential for high-quality machine learning training data.21:12–25:58 · Guest teaching 5/10 Quick-Fire Questions: Literature, NLP, YC, and ML Challenges Maren banters playfully with Harry during the quick-fire segment before sharing deep takes on literature, NLP suitability, and the core ML challenge of crossing the accuracy threshold to productization.25:58–29:26 · Guest teaching 4/10 Clara's Achievements and 2017 Strategic Roadmap Maren breaks down Clara's strategic achievements in 2016 with its XO platform and outlines 2017 plans for enterprise integrations like ATS and marketing automation. Harry closes with high praise.2:15–4:48 · Guest disagreement 0/10 Maren Nelson's Background and the Founding of Clara Harry warmly introduces Maren and asks for her background and founding story. Maren shares her neuroscience background and how pairing with her roboticist co-founder led to founding Clara.4:48–8:26 · Guest disagreement 1/10 Defining Digital Assistants and Conversational Interfaces Harry prompts Maren on how to view digital assistants. Maren provides a historical perspective comparing PDAs, the Jetsons, and Snapchat to explain incremental conversational UI design.8:26–11:56 · Guest disagreement 1/10 Identifying Tipping Points in Interface Design Harry asks where the tipping point lies between graphical UIs and conversational interfaces. Maren explains that language interfaces excel when task complexity makes GUIs tedious.11:57–21:11 · Guest disagreement 3/10 Reframing AI Categories and Building Sustainable Companies Maren dismantles the notion of AI as a monolithic category, comparing it to grouping all web apps together. She details Clara's human-in-the-loop infrastructure and why human assistance remains essential for high-quality machine learning training data.21:12–25:58 · Guest disagreement 2/10 Quick-Fire Questions: Literature, NLP, YC, and ML Challenges Maren banters playfully with Harry during the quick-fire segment before sharing deep takes on literature, NLP suitability, and the core ML challenge of crossing the accuracy threshold to productization.25:58–29:26 · Guest disagreement 0/10 Clara's Achievements and 2017 Strategic Roadmap Maren breaks down Clara's strategic achievements in 2016 with its XO platform and outlines 2017 plans for enterprise integrations like ATS and marketing automation. Harry closes with high praise.2:15–4:48 · Harry pushing back 0/10 Maren Nelson's Background and the Founding of Clara Harry warmly introduces Maren and asks for her background and founding story. Maren shares her neuroscience background and how pairing with her roboticist co-founder led to founding Clara.4:48–8:26 · Harry pushing back 1/10 Defining Digital Assistants and Conversational Interfaces Harry prompts Maren on how to view digital assistants. Maren provides a historical perspective comparing PDAs, the Jetsons, and Snapchat to explain incremental conversational UI design.8:26–11:56 · Harry pushing back 2/10 Identifying Tipping Points in Interface Design Harry asks where the tipping point lies between graphical UIs and conversational interfaces. Maren explains that language interfaces excel when task complexity makes GUIs tedious.11:57–21:11 · Harry pushing back 3/10 Reframing AI Categories and Building Sustainable Companies Maren dismantles the notion of AI as a monolithic category, comparing it to grouping all web apps together. She details Clara's human-in-the-loop infrastructure and why human assistance remains essential for high-quality machine learning training data.21:12–25:58 · Harry pushing back 1/10 Quick-Fire Questions: Literature, NLP, YC, and ML Challenges Maren banters playfully with Harry during the quick-fire segment before sharing deep takes on literature, NLP suitability, and the core ML challenge of crossing the accuracy threshold to productization.25:58–29:26 · Harry pushing back 0/10 Clara's Achievements and 2017 Strategic Roadmap Maren breaks down Clara's strategic achievements in 2016 with its XO platform and outlines 2017 plans for enterprise integrations like ATS and marketing automation. Harry closes with high praise.

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

0:00 · Harry 89% · guest 11%0:00 · Harry 89% · guest 11%3:00 · Harry 5% · guest 95%3:00 · Harry 5% · guest 95%6:00 · Harry 13.7% · guest 86.3%6:00 · Harry 13.7% · guest 86.3%9:00 · Harry 15.7% · guest 84.3%9:00 · Harry 15.7% · guest 84.3%12:00 · Harry 17% · guest 83%12:00 · Harry 17% · guest 83%15:00 · Harry 6.2% · guest 93.8%15:00 · Harry 6.2% · guest 93.8%18:00 · Harry 3% · guest 97%18:00 · Harry 3% · guest 97%21:00 · Harry 19.2% · guest 80.8%21:00 · Harry 19.2% · guest 80.8%24:00 · Harry 16.5% · guest 83.5%24:00 · Harry 16.5% · guest 83.5%27:00 · Harry 27.2% · guest 72.8%27:00 · Harry 27.2% · guest 72.8%30:00 · Harry 100% · guest 0%30:00 · Harry 100% · guest 0%
Sharpest disagreement ▶ 11:57 Frustration with 'AI as a category'

Maren explicitly rejects the broad industry premise of lumping AI companies into one category, calling it frustrating and comparing it to treating fundamentally different web applications as synonymous.

 Hardest push from Harry ▶ 10:39 Pressing on the exact tipping point boundary

Harry pushes Maren to define the explicit boundary of user experience, asking what concretely separates simple app taps from complex conversational UI needs.

 Biggest teaching moment ▶ 18:42 Correcting assumptions about human contractor elimination

Maren reframes Harry's question about eliminating contractors by explaining that human intelligence will necessarily remain in the loop for high-complexity contexts until strong AI exists.

 Harry holds his own ▶ 10:39 Asking targeted operational threshold question

Harry demonstrates strong hosting technique by pressing Maren on a abstract product statement and demanding a practical contrast between specific apps.
the scores for every segment, with the reasoning behind each
ChapterTopicHarry as informed peerGuest teachingGuest disagreementHarry pushing backWhy
Maren Nelson's Background and the Founding of Clara 1300 Harry warmly introduces Maren and asks for her background and founding story. Maren shares her neuroscience background and how pairing with her roboticist co-founder led to founding Clara.
Defining Digital Assistants and Conversational Interfaces 2511 Harry prompts Maren on how to view digital assistants. Maren provides a historical perspective comparing PDAs, the Jetsons, and Snapchat to explain incremental conversational UI design.
Identifying Tipping Points in Interface Design 2612 Harry asks where the tipping point lies between graphical UIs and conversational interfaces. Maren explains that language interfaces excel when task complexity makes GUIs tedious.
Reframing AI Categories and Building Sustainable Companies 3733 Maren dismantles the notion of AI as a monolithic category, comparing it to grouping all web apps together. She details Clara's human-in-the-loop infrastructure and why human assistance remains essential for high-quality machine learning training data.
Quick-Fire Questions: Literature, NLP, YC, and ML Challenges 2521 Maren banters playfully with Harry during the quick-fire segment before sharing deep takes on literature, NLP suitability, and the core ML challenge of crossing the accuracy threshold to productization.
Clara's Achievements and 2017 Strategic Roadmap 1400 Maren breaks down Clara's strategic achievements in 2016 with its XO platform and outlines 2017 plans for enterprise integrations like ATS and marketing automation. Harry closes with high praise.

Statements from this episode (9)

Insight
Nelson: Tech adoption is far more incremental than people predict
“I think everything tends to be much more incremental than we like to think of it before it happens.”
Maren Nelson Jan 13, 2017 ▶ 6:50
Insight
Nelson: Snapchat succeeds in AR by grounding innovation in familiar behavior
“Snapchat has this Relentless product focus. And now, of course, they're the people introducing the idea of augmented reality to tons and tons of people, and games like Pokemon Go, right, where there is this slow but sure corralling of technology-enabled innova…”
Maren Nelson Jan 13, 2017 ▶ 7:16
Insight
Nelson: Language interfaces require high task complexity to outperform GUIs
“Language interfaces are really important if there is a sufficient amount of complexity such that language is the best interface to resolve that complexity.”
Maren Nelson Jan 13, 2017 ▶ 9:40
Insight
Nelson: Simple chatbots provide worse UX than app buttons like Uber
“And unfortunately, bots today are not sophisticated. They don't understand nuance or complexity. And so the application for these bots tends to be things that are really, really simple. But of course, it's easier for me to click the Uber app, right, and get an…”
Maren Nelson Jan 13, 2017 ▶ 10:01
Insight
Nelson: Grouping all AI products together is as flawed as web apps
“I think the problem is largely with thinking about those things as synonymous to each other. It's kind of like saying that everybody who builds a web app is competing in the same category, right? It's kind of like saying that just a software app like MyFitness…”
Maren Nelson Jan 13, 2017 ▶ 12:31
Opinion
Nelson: AI-native startups possess superior ML environments over legacy software firms
“I do think that there is an advantage to many of the younger companies that have started from their foundation with AI at the center of everything that it is that they're doing, because they have created much better environments to support machine learning the…”
Maren Nelson Jan 13, 2017 ▶ 15:37
Prediction Not checkable as stated
Nelson: Human contractors will remain in Clara's loop until AGI exists
“I think they will always be involved. You know, it's, we get asked that frequently, and it makes perfect sense. For us, though, until we have some kind of strong AI that completely obfuscates the need for human work, you necessarily have humans in the loop.”
Maren Nelson Jan 13, 2017 ▶ 18:43
Insight
Nelson: Machine learning excels at repetitive, low-variance NLP tasks like scheduling
“So scheduling, luckily, is very much one of them. Repetitive, right? You want the variance in response to be low, ideally. So in our case, you know, you're talking about the same handful of variables on every occasion. You have the ability to kind of constrain…”
Maren Nelson Jan 13, 2017 ▶ 22:23
Insight
Nelson: Pushing ML past 80-90% accuracy is the primary product challenge
“It's letting machine learning get past the kind of stuck in 80 to 90% right land that isn't good enough to be shipped as a user facing feature. Getting over that hump is, is a huge challenge.”
Maren Nelson Jan 13, 2017 ▶ 25:42
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