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Why has Waymo taken so long to commercialize autonomous taxis?

ARK Invest has a good article, “Why has Waymo taken so long to commercialize autonomous taxis?,” in which they write about System Identified Failures and Unexpected Failures.

One interesting note from the article that has nothing to do with Waymo: “Some stretches of road are trickier and some intersections more difficult to navigate than others. In Los Angeles, for example, roughly a quarter of pedestrian collisions take place at only 1% of its intersections.”

“How Google Works” is a good read for entrepreneurs

I’m only about fifty pages into the book, How Google Works, but I can already say that if you think of yourself as an entrepreneur, it’s a valuable read. At first I thought the authors were patting themselves on the back a lot (which admittedly they deserve), but as I continued reading they clearly say things like “We’re not that smart,” “We screwed up,” and “Learning from our mistakes, this is why we created Alphabet.”

Some of their ideas, such as building businesses around their smartest people and greatest assets are things that I did in the past, but couldn’t articulate. Maybe it had to do with being in Kentucky at the time, but I always thought of it as “Get out of the way and let the thoroughbreds run.”

Ship and iterate

A note from this long article about Google’s Rick Osterloh:

Former CEO Eric Schmidt calls this system “Ship and Iterate,” and in his book How Google Works he makes a consistent case for not even trying to get things right the first time. “Create a product, ship it, see how it does, design and implement improvements, and push it back out,” Schmidt writes. “Ship and iterate. The companies that are the fastest at this process will win.”

Google “CLoud TPUs” available in beta

Google is making “Cloud TPUs” available in beta. From their announcement: “Starting today, Cloud TPUs are available in beta on Google Cloud Platform (GCP) to help machine learning (ML) experts train and run their ML models more quickly. Cloud TPUs are a family of Google-designed hardware accelerators that are optimized to speed up and scale up specific ML workloads programmed with TensorFlow. Built with four custom ASICs, each Cloud TPU packs up to 180 teraflops of floating-point performance and 64 GB of high-bandwidth memory onto a single board.”