Deep Reflections

I’ve previously talked about detecting irrigated croplands in this blog. I used classical machine learning techniques in it, i.e. we decide what features to use, we fit a model or an ensemble of models, we predict. Modern machine learning, or what the press calls “Artificial Intelligence”, has gone way beyond it. In this post, I want to talk about a different approach to detect irrigated croplands. This method gave 95% test-set accuracy on a random sample of 2000 pictures from California. [Read More]

Back in 2004, when I was in Bangalore and still fresh out of college, I was mesmerized by a book with a title that asked you to kill the Buddha if you met him. The murderous Zen title notwithstanding, the book was written by a psychotherapist who drew parallels between literary characters and his patients. I wanted to read more such books, but as I said, I was in India and it was 2004. Only in the last year have I delved deep again into the realm of psychotherapy.

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I built a mechanical Lego piano. It very much looks like a real piano, both on the outside and inside. It has keys, gears, hammers… its designers are piano teachers. The music comes from a companion Bluetooth app, but the piano makes convincing movements. I’m very impressed. This is my first Lego project, and the fact that I could do something this complex tells you how mature and professional Lego’s assembly instructions pictures are. [Read More]

I’ve previously posted on this blog about a machine-learning model we built to detect irrigation globally.

I’m happy to report that our work is now published as a paper in the journal Advances in Water Resources.

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Close to the end of A Swim in a Pond in the Rain, George Saunders looks back on a class from his graduate school. His professor, a short-story writer, gave a reading of Chekhov. Saunders didn’t know much about Chekhov then: he thought Chekhov’s stories were “mild” and “voiceless”. The professor reads three stories, and in doing so, brings Chekhov to life. After that reading, Saunders was not only convinced of the power of the short story, but also “desperate” about writing better short stories himself. [Read More]

The Sense of an Ending is a slim novel by Julian Barnes. I don’t have much to say about the story, but I was drawn to the main character, Tony Webster.

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We are all familiar with sci-fi stories of evil robotic overlords. I’m not into science fiction as a genre, yet I thoroughly liked the movies Ex Machina (2015) and The Matrix (1999). Both movies are nominally about artificial intelligence, but ask deep philosophical questions.

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I recently read Ghachar Ghochar, a novella by Vivek Shanbhag. The original is in Kannada, but I read the English translation by Srinath Perur.

The novel is slim; it runs to a little more than 100 pages. This is significant, because within these pages, the author still tells a wonderful story. It’s a story that leaves a lot unsaid, for the reader to fill in the details.

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A couple posts ago, I described the machine learning model that we developed to predict the extent of cropland irrigation worldwide. This post is a collection of all the things I learned about Google Earth Engine that went into running a global-scale model successfully.

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It was the first class of a data science course in the MIDS program. Our instructor started off with a question: “Do you think this academic program gives you a better career?” We gave a bunch of answers, quite the ignorant folks that we were. Of course, is this even a question at this point?! Yes, here are all the new things we’re learning. Yes, the instructors are top-notch. Yes, look at the syllabus and the projects, yada yada … nothing surprising there.

The instructor asked, “Yes, but how do you know that the program is making you better? What if you joined the program because you were motivated enough to apply and work through it, and therefore you’d get better in your career anyway?”

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