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It is noted in this FAQ that CSAIL will become part of the new college.

http://news.mit.edu/2018/faq-mit-stephen-schwarzman-college-...


Oh wow, that article explains it's a much bigger change than the headline says — all of Course 6, plus CSAIL and several others will move into the College.

But it's also not a "College for Artificial Intelligence" — it's a "College of Computing".

> A: The founding of the MIT Schwarzman College of Computing is the most significant structural change since 1950, when MIT established the Sloan School of Management and the School of Humanities, Arts, and Social Sciences. But this is much more than a restructuring: With this change, MIT seeks to position itself as a key player in the responsible and ethical evolution of technologies that will fundamentally transform society.


An Introduction to Modern Astrophysics by Carroll & Ostlie is the Bible, but it is quite large physically and in scope and better serves as a reference for most people.

For various topics, I would look at:

Introduction to Cosmology by Ryden for cosmology at the undergraduate level.

Cosmology by Weinberg.

The Exoplanet Handbook by Perryman.

An Introduction to Modern Stellar Astrophysics by Carroll & Ostlie.

Particle Astrophysics by Perkins.

Modern Statistical Methods for Astronomy by Feigelson.

Statistics, Data Mining, and Machine Learning in Astronomy by Ivezic.

I can post more in other topics if anyone is interested.


Here's a neat use case analyzing GDELT data: https://blog.gdeltproject.org/peering-into-the-visual-landsc...



Actually, the Google guides were initially published in February (and publicly available before that from Martin Zinkevich since 2016!). But I agree, it's great to see more resources around these best practices.


Hi Vincent, you may want to point your "Best Practices for ML Engineering" link to the non-PDF version here: https://developers.google.com/machine-learning/guides/rules-...


Thanks for the heads up!


Give "Designing Data-Intensive Applications" a read. It's an excellent book on these topics.

https://dataintensive.net



For comparison, here is a look at Waymo's simulation operations: https://www.theatlantic.com/technology/archive/2017/08/insid...


> Being in the car a lot, I can feel what the car is doing—it sounds weird, but—with my butt

He's describing, incidentally, how professional drivers drive.


Can you expand on that a bit more?


As you become "good" at driving in harder conditions, you'll use your butt (which is really how you're physically connected to the vehicle) to figure out the traction state of the car, to know if the car is sliding, if it's close to losing the rear end or the front end, if it's pitching down or up, and so on. This is how you know which corrections to make to the vehicle to keep it going fast without losing control.

Pilots of small planes do the same thing. After a while you can just feel small accelerations and change of directions without needing to check some of the instruments.


Your last paragraph was what I came here to say: I fly a small Cessna 172 and when i first started to fly i was being cleared to solo and the instructor who cleared me gave feedback on my landings at the end. His main remark was "You'll start to feel it in your rear soon enough, it comes around the 150-200 hour mark, but in the meanwhile, trust the instruments and your visual indicators". Ironically you're simultaneously taught to always trust the instruments over your inner ear, and generally as much as I do get the buttock feeling now, I would say my primary focus remains on the visuals + instruments.


Huh, fascinating. I remember reading a Star Wars novel ages ago in which they said that one of the things that made Luke Skywalker a good pilot was that he slightly detuned his craft's inertial compensators (or whatever the Star Wars version is) so he could feel how the craft responded. Didn't realize that was actually based on real life driving/flying!


The common complaint against "new" vehicles and their drive-by-wire systems is that they functionally act as inertial compensators, dampening road feel.

If the roads were perfect, you wouldn't necessarily want them (day to day), because it diminishes the minute vibrations that make their way up to the pedals, into the steering wheel, etc., and those things give you feedback into what the car is doing that are invaluable.

Of course, the roads aren't perfect, and most of us aren't doing high-performance driving on them, and we don't need anywhere near that degree of feel, so the benefits of driving by wire far outweigh the cons of analog driving.


Hence the expression "to fly by the seat of one's pants".


TIL! I always assumed that was one launching onesself into an endeavor with so little forethought as to be facing the wrong way.


Though pilots have to be careful, because feelings can be deceptive especially if you can't see the horizon (clouds, night). In those conditions you must trust the instruments over what you are feeling.


Not a professional driver, but I've ripped up several racetracks and done plenty of drag racing as an older teen. Your butt tends to feel everything if you don't have ultra-cushy seating, and because of its position relative to the vehicles center of gravity, it will feel more of the effects of various vehicle movements versus the arms or legs. Tune in long enough and you can literally read the road you're travelling just by how it feels, roughness, amounts of liquid on the road, etc.

Ever notice how your butt tries to move more than your hands and feet when making a sharp turn at a moderate speed turn, and how you instinctively correct for it in your seat by leaning or shifting around? Know the phrase 'Fly by the seat of my pants?' Pretty much all of that rolled into one bundle of instinctive attunement.


This is off-topic, but the Atlantic's GDPR click-through is probably the best-put-together and most-understandable one I've seen.


If you're interested in this, be sure to read "The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction" [1] and the Rules of Machine Learning [2].

[1] https://ai.google/research/pubs/pub46555

[2] https://developers.google.com/machine-learning/rules-of-ml/


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