It’s time for data scientists to collaborate with researchers in other disciplines

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Forough Poursabzi Sangdeh on the interdisciplinary nature of interpretable and interactive machine learning.

In this episode of the Data Show, I spoke with Forough Poursabzi-Sangdeh, a postdoctoral researcher at Microsoft Research New York City. Poursabzi works in the interdisciplinary area of interpretable and interactive machine learning. As models and algorithms become more widespread, many important considerations are becoming active research areas: fairness and bias, safety and reliability, security and privacy, and Poursabzi’s area of focus—explainability and interpretability.

We had a great conversation spanning many topics, including:

  • Current best practices and state-of-the-art methods used to explain or interpret deep learning—or, more generally, machine learning models.
  • The limitations of current model interpretability methods.
  • The lack of clear/standard metrics for comparing different approaches used for model interpretability
  • Many current AI and machine learning applications augment humans, and, thus, Poursabzi believes it’s important for data scientists to work closely with researchers in other disciplines.
  • The importance of using human subjects in model interpretability studies.

Related resources:

Gliding down the world’s longest zipline

Ras Al Khaimah’s Jebel Jais mountain in the U.A.E. is home to the longest zipline in the world. The video below is from a GoPro camera on my helmet and was shot on 2019-03-14. I was the last person to zip down that day. There are two stages to this zipline site:

  • Stage 1: Is the longer of the two stages and you “fly” while lying down on a harness with your hands holding straps behind your back (for optimum aerodynamics). The view is spectacular!
  • Stage 2: In this stage you are in a “sitting position”, and truth be told, based on my experience and observation, I detected more apprehension from the people on the platform with this setup.

As you’ll see in the video, I fell a tad short in both stages, and had to be pulled in by the crew:

While I would not consider myself an “adventure traveler” or an “adrenaline junkie”, I found this to be an exhilarating experience and one that I would recommend to people traveling to the U.A.E. Ras Al Khaimah (RAK) has many things to offer and the area is full of spectacular things to do for travelers who love the outdoors. I leave you with a few photos from the desert in RAK:

Algorithms are shaping our lives – here’s how we wrest back control

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Kartik Hosanagar on the growing power and sophistication of algorithms.

In this episode of the Data Show, I spoke with Kartik Hosanagar, professor of technology and digital business, and professor of marketing at The Wharton School of the University of Pennsylvania.  Hosanagar is also the author of a newly released book, A Human’s Guide to Machine Intelligence, an interesting tour through the recent evolution of AI applications, which draws from his extensive experience at the intersection of business and technology.

We had a great conversation spanning many topics, including:

  • The types of unanticipated consequences of which algorithm designers should be aware.
  • The predictability-resilience paradox: as systems become more intelligent and dynamic, they also become more unpredictable, so there are trade-offs algorithms designers must face.
  • Managing risk in machine learning: AI application designers need to weigh considerations such as fairness, security, privacy, explainability, safety, and reliability.
  • A bill of rights for humans impacted by the growing power and sophistication of algorithms.
  • Some best practices for bringing AI into the enterprise.

Related resources: