At Landed, we track a variety of customer journeys through a variety of lifecycle stages across a variety of products. To explore all this variety, our teams rely on a collection of analytics dashboards to help them anticipate the needs of our customers and partners. The questions our teams ask change over time, which requires a flexible and responsive practice for building and updating data models. …

I recently told a friend I was looking into auto machine learning (ML) projects, and he asked me if that meant machine learning for cars, so … I thought I’d write up a brief post.

Auto ML services provide machine learning at the click of a button, or, at the very least, promise to keep algorithm implementation, data pipelines, and code, in general, hidden from view. If you mention this prospect to a crowd of engineers, some will say “hooray!” and some will say “oh no!”, …

Yesterday I attended the MIT Club of Northern California’s AI conference, Industries of the Future | Future of Industries. I left feeling energized, and here’s why (a play by play of my journey through the multiple tracks of concurrent panel discussions):

Zia Chishti (Chairman and CEO of @afiniti) kicked things off with a grounded reminder that AI buzzwords are often simply algorithms from the canon wrapped in new packaging. What’s changed the game in recent years is computational speed, and the heightened visibility of breakout gains made by novel applications. …

The first time I was fully conscious of it, a lone human forecasting our future, was at a Long Now talk in San Francisco. It happened during a particular moment in the post-lecture Q&A when Stewart Brand asked Kevin Kelly what it might feel like to exist in a cyborg-replica of his current body. A handful of giggles bubbled up in the audience, and then we sat in silence, watching as Kelly pondered his possible somatic relationship to his understanding of an idea in an unlikely possible future.

I listened to my breath while I tracked Kelly’s eyes tracing the…

Recently I have been diving deep into researching the possible futures of autonomous fleet movement dynamics. When the time comes, how are AVs going to flock? This research had led me to uncover many of the necessary advancements in machine perception, mesh networking, and deep learning that will provide for the ground-breaking AV opportunities we have been reading about in the headlines. Yet, I have also been turning over stones of possible modeling traditions that might help us prepare for the AV future.

As a movement nerd, what excites me most about self-driving cars is the possibility that their spatial…

When was the last time you sat down and read someone’s PhD thesis?

Given how much blood, sweat, and tears go into these things, I’m ashamed to admit I’ve only read a few.

However, I recently came across a thesis that was so well written and so well aligned with my interests that it felt as though I had stumbled upon a New York Times popular science bestseller, of the likes of Oliver Sachs or António Demásio. The thesis is by Thomas Viktor Wiecki, and is titled Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders.

Unicorns like…

While statistical analysis forms the backbone of experimental research, it is not often that statisticians step forward as the heroes in our innovation narratives. Yet here we have a study that boldly questions the statistical premises that underscore the validity of over two decades of fMRI research.

Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Science. May 17, 2016 [Full Text]

This study calls attention to false-positive rates embedded in the leading neuroimaging software platforms (SPM, FSL, AFNI). In particular, the results call into question the validity of spatial extent…