Lukas Biewald

Lukas Biewald

Title :Data Scientist and Founder - CrowdFlower

Bio: Lukas co-founded CrowdFlower. He has worked as a Senior Scientist and Manager within the Ranking and Management Team at Powerset, Inc., a natural language search technology company later acquired by Microsoft, and also led the Search Relevance Team for Yahoo! Japan. He graduated from Stanford University with a BS in Mathematics and an MS in Computer Science. Lukas is also an expert-level Go player.

Artificial Intelligence and the Future of Work

Artificial Intelligence and the Future of Work

Technology makes some types of jobs obsolete and creates other types of jobs — that’s been true since the stone age. While in the past, machines have replaced people in jobs that require physical labor, we’re increasingly seeing traditionally white collar jobs augmented by machines: financial analysts, online marketers, and financial reporters, just to name a few. […]

What we can earn from AI’s Mistakes

What we can earn from AI’s Mistakes

AI has been making a lot of progress lately by almost any standard. It has quietly become part of our world, powering markets, websites, factories, business processes and soon our houses, our cars and everything around us. But the biggest recent successes have also come with surprising failures. Tesla impressed the world by launching a […]

What We Can Learn From Ai’s Mistakes

What We Can Learn From Ai’s Mistakes

AI has been making a lot of progress lately by almost any standard. It has quietly become part of our world, powering markets, websites, factories, business processes and soon our houses, our cars and everything around us. But the biggest recent successes have also come with surprising failures. Tesla impressed the world by launching a […]

Metrics and Hiring

Metrics and Hiring

Virtually every CEO claims to be extremely metrics driven.  As far as I can tell all of these CEOs are completely full of shit, at least when it comes to their own hiring. We all hire based on intuition but we rarely go back and rigorously check our mistakes.  In my career, I’ve hired or […]

Part 3: The Data Science Ecosystem, Data Applications

Part 3: The Data Science Ecosystem, Data Applications

Remember that quote I started part two with? About data scientists wanting better tools for wrangling so they could work on the “sexy stuff”? Well, after covering how data is stored, how its cleaned, and how its combined from disparate databases, we’re finally there. Data applications are where the “sexy stuff” like predictive analysis, data […]

Part 2: The Data Science Ecosystem, Data Wrangling

Part 2: The Data Science Ecosystem, Data Wrangling

There was a money quote from Michael Cavaretta, a data scientist at Ford Motors, in a recent article in the NY Times. The piece was about the challenges data scientists face going about their daily business. Cavaretta said: “We really need better tools so we can spend less time on data wrangling and get to […]

Why Do Certain Musical Notes Sound “Good” Together?

Why Do Certain Musical Notes Sound “Good” Together?

Two notes sounding “good” together sounds like a very subjective statement.  The songs we like and the sounds we like are incredibly dependent on our culture, personality, mood, etc.  

Part 1: The Data Science Ecosystem, Data Sources

Part 1: The Data Science Ecosystem, Data Sources

The rest of this ecosystem doesn’t exist without the data to run it. Broadly speaking, there are three very different kinds of data sources: databases, applications, and third party data. See the introduction to this series here. Databases Structured databases predate unstructured ones. The structured database market is somewhere around $25 billion and you’ll see […]

The Data Science Ecosystem: Preamble

The Data Science Ecosystem: Preamble

Data science isn’t new, but the demand for quality data has exploded recently. This isn’t a fad or a rebranding, it’s an evolution. Decisions that govern everything from successful presidential campaigns to a one-man startup headquartered at a kitchen table are now be made on real, actionable data, not hunches and guesswork. Because data science […]