Question 1

How did LinkedIn use knowledge graphs to solve the job title problem?

Accepted Answer

_Structuring 150 million job titles required more than simple categorization._

LinkedIn had over 150 million unique job title strings, which was impossible to model effectively. We decomposed titles into four facets: seniority, employment status, role, and specialty. For example, Senior Software Engineer contains seniority information, a role, and a specialty. We then built knowledge graphs for each dimension to show how values interrelated. This helped us understand completeness and vagueness of titles. A VP at a bank means something completely different than a VP at a tech company. Taxonomies alone were not expressive enough to capture these nuances.

— [The synergy between LLMs and knowledge graphs](https://www.lutzfinger.com/transcript/2023-08-24-the-synergy-between-llms-and-knowledge-graphs/) · _The Edge_

Question 2

How do you measure quality in healthcare data?

Accepted Answer

_Quality metrics for healthcare ML._

> Knowing quality is therefore of utmost importance. Three broad measurement areas in today's healthcare: (1) Metrics about quality of care — related to treatment effectiveness and patient satisfaction. (2) Metrics about need and utilization of care — gauging health requirements of a population and how often one uses what type of service. (3) Metrics about value-based healthcare — they measure disease prevention rather than disease…

— [Healthcare And Data - How To Measure Quality](https://www.forbes.com/sites/lutzfinger/2023/06/27/healthcare-and-datahow-to-measure-quality/) · _Forbes_

Question 3

How do you spot social media bots?

Accepted Answer

_Detection heuristics._

> Figure 6-4. Subset of a social network, created using LinkedIn Labs Since bot networks will first create their own group by linking to one another before they start to infiltrate the rest of the network, especially in the beginning, you will see a highly dense network with very low cohesion to the rest of the network. However, those measures using network topology only work in the beginning as the bot network is formed. Once the bots…

— [Finger Dutta Ask Measure Learn (1)](https://www.lutzfinger.com/book/) · _Book — Ask, Measure, Learn_

Question 4

How does Richard distinguish between jobs that will be automated by AI and those that won't?

Accepted Answer

_Data availability determines which jobs AI will transform or replace first._

Richard says you can predict how much an industry will change based on how much data is available for a given task. Jobs where outputs are publicly available on the internet, especially all-digital jobs, are where AI can learn from humanity's collective knowledge and automate the most. Plumbing, for example, has no automation potential because no one is collecting data about what plumbers do. Jobs with abundant digital data and public outputs face the highest automation risk in the coming years.

— [The Future of AI, with Richard Socher](https://www.lutzfinger.com/transcript/2026-02-05-the-future-of-ai-with-richard-socher/) · _The Keynote on AI_

Question 5

What is a data product?

Accepted Answer

_Definition and how to build one._

> Now, to be a good product developer, you need to think about self-referential loops within your products. How to get signal, how to get feedback from your users so your AI, or your data science, can leverage that information to make the product work better. So that moved a little bit within it. The innovation, and the inspiration about, and the curiosity about how to use data in novel manners, that's still data science, because that's…

— [Unit 3 Full Transcript](https://www.lutzfinger.com/ecornell/) · _eCornell course_

Question 6

What is a knowledge graph and how does it differ from a taxonomy?

Accepted Answer

_Knowledge graphs connect concepts across dimensions, unlike simple taxonomies._

A knowledge graph is a collection of facts interconnected in a network, storing information as concepts rather than just labels or numbers. It works like a smarter database that imitates human memory. A taxonomy is much simpler, focusing on one characteristic like what kind of thing something is. Elephants are mammals in a taxonomy. But you cannot relate entities of different types. A knowledge graph lets you express that leopards eat gazelles or elephants eat bamboo, connecting different taxonomies together. It is essentially a multi-dimensional, supercharged taxonomy approach.

— [The synergy between LLMs and knowledge graphs](https://www.lutzfinger.com/transcript/2023-08-24-the-synergy-between-llms-and-knowledge-graphs/) · _The Edge_

Question 7

What is the 'Ask, Measure, Learn' framework?

Accepted Answer

_The framework from Lutz's book._

> "Ask Measure Learn" is a non-technical guide shows you how to extract significant business value from big data. It is a system that helps you ask the right questions, measure the right data, and then learn from the results. The framework was originally devised to help governments and NGOs sift through volumes of data.

— [Ask Measure Learn](https://www.lutzfinger.com/book/) · _Book — Ask, Measure, Learn_

Question 8

What is the book Ask, Measure, Learn about?

Accepted Answer

_Topics covered in the O'Reilly book._

> **Publisher:** O'Reilly Media **Published:** Feb 1, 2014

— [Ask Measure Learn](https://www.lutzfinger.com/book/) · _Book — Ask, Measure, Learn_

Question 9

What is the difference between virality and contagiousness?

Accepted Answer

_Stories gain weight as they travel, not uniform infection rates._

Virality is something which is equally infectious at each stage. If I have a story which one out of 10 people like, that story doesn't change. One out of 10 people like it in the future as well. Contagiousness is different. As long as it travels, it gains weight. The more people believe in it, the more trustworthy the story becomes. Even a wrong information becomes more believed the longer it travels. At some point everyone believes it's truth no matter what because so many people believed in it. Forget about virality. The right word is contagiousness.

— [Measuring Social Media What Marketers Need To Know](https://www.lutzfinger.com/transcript/2014-08-22-measuring-social-media-what-marketers-need-to-know/) · _Social Media Examiner_

Question 10

What was Ultimate AI's key insight that made customer onboarding faster than competitors?

Accepted Answer

_Automated clustering replaced manual training data creation for faster setup_

We couldn't afford AI trainers manually creating training data, so we built a clustering pipeline that analyzed historical customer conversations already sitting in CRMs like Salesforce. This let us automatically find the 100 or 200 most common questions and show customers all 30,000 actual ways users asked questions like where's my order. Competitors were using FAQ pages and having people manually write 50 to 100 variations, which didn't reflect real customer language. Our approach gave faster time to value because we used actual conversation data instead of imagined variations, and customers could see their real top questions before even talking to us.

— [How Ultimate evolved from hundreds of supervised models to UltimateGPT](https://www.lutzfinger.com/transcript/2023-05-12-how-ultimate-evolved-from-hundreds-of-supervised-models-to-ultimategpt/) · _The Edge_

Question 11

Why is eval data a moat?

Accepted Answer

_Evaluation data as the strategic differentiator._

> All of them matter, and all of them are being fought over. But the real value may sit in the one thing OpenAI, Anthropic, and Gemini have not yet made central: your own eval data. Sure, there is the occasional thumbs-up button. But who is really using that signal?

— [The Missing Moat In AI: Your Eval Data](https://www.forbes.com/sites/lutzfinger/2026/05/26/the-missing-moat-in-ai-your-eval-data/) · _Forbes_

Question 12

Why is measuring social media so difficult despite all the tools available today?

Accepted Answer

_Tools are everywhere, but strategy comes first, not scores._

The problem is that measurement used to require specialists with big databases and tooling. Now with social media, you suddenly have all these tools that give you cloud scores and metrics instantly. People see a score and decide something based on it without planning what they actually want to achieve. If you haven't formulated your business goal first, any measurement won't work. People start with the measurement and then think about what to do with it, while the correct way is the opposite: start with a question, then come up with a measure.

— [Measuring Social Media What Marketers Need To Know](https://www.lutzfinger.com/transcript/2014-08-22-measuring-social-media-what-marketers-need-to-know/) · _Social Media Examiner_

Question 13

Why weren't traditional banks serving the underserved population before Varo?

Accepted Answer

_Economic incentives and outdated systems excluded millions from banking._

There are two main reasons, focusing on economic incentives. It was really difficult for banks to make money off consumers with low-value, high-frequency transactions. Banks perceived them as higher credit risk because they used backward-looking systems instead of monitoring real-time cash flow. Vulnerable populations are also more susceptible to fraud and scams, creating high costs for dispute processing. The economic incentives made it very difficult for incumbent institutions to effectively serve this large population. Technology was needed to solve this.

— [Disrupting Traditional Banking, with Colin Walsh](https://www.lutzfinger.com/transcript/2025-05-22-disrupting-traditional-banking-with-colin-walsh/) · _The Keynote on AI_

Q&A, Analytics

How did LinkedIn use knowledge graphs to solve the job title problem?

How do you measure quality in healthcare data?

How do you spot social media bots?

How does Richard distinguish between jobs that will be automated by AI and those that won't?

What is a data product?

What is a knowledge graph and how does it differ from a taxonomy?

What is the 'Ask, Measure, Learn' framework?

What is the book Ask, Measure, Learn about?

What is the difference between virality and contagiousness?

What was Ultimate AI's key insight that made customer onboarding faster than competitors?

Why is eval data a moat?

Why is measuring social media so difficult despite all the tools available today?

Why weren't traditional banks serving the underserved population before Varo?