Train your model of the world

Recently read this quote in one random blogpost that I stumbled upon surfing. Sorry forgot to save the link.

Loved it. As most of my work time, now a day are saturated with the words like, model, training etc., so this quote stuck a nerve.

Reading and experience train your model of the world. And even if you forget the experience or what you read, its effect on your model of the world persists. Your mind is like a compiled program you’ve lost the source of. It works, but you don’t know why. – Paul Graham

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Thank you Guido

Here is a blog post that I am reposting from this link.

I have similar sentiments about python. I first began python in 2010 but truly took it up in summer of 2011 when a task to use Perl script landed as one of my assignment. Instead of Perl script, I worked with python and I have never looked back.

Therefore, without much delay here is the blogpost.

When I was in my early 20s, I was OK at programming, but I definitely didn’t like it. Then, one evening, I read the Python tutorial. That evening changed my mind. I woke up the next morning, like Neo in the matrix, and knew Python.

I was doing statistics at the time. Python, with Numeric, was a powerful tool. It definitely could do things that SPSS could only dream about. Suddenly, something has happened that never happened before — I started to enjoy programming.

I had to spend six years in the desert of programming in languages that were not Python, before my work place, and soon afterwards the world, realized what an amazing tool Python is. I have not had to struggle to find a Python position since.

I started with Python 1.4. I have grew up with Python. Now I am…no longer in my 20s, and Python version 3.7 was recently released.

I owe much of my career, many of my friends, and much of my hobby time to that one evening, sitting down and reading the Python tutorial — and to the man who made the language and wrote the first version of that tutorial, Guido van Rossum.

Python, like all open source projects, like, indeed, all software projects, is not a one man show. A whole team, with changing personnel, works on core Python and its ecosystem. But it was all started by Guido.

As Guido is stepping down to take a less active role in Python’s future, I want to offer my eternal gratitude. For my amazing career, for my friends, for my hobby. Thank you, Guido van Rossum. Your contribution to humanity, and to this one human in particular, is hard to overestimate.

Quotes- Three ways to add value

If I have to lose all the blogs that I follow, read, and just choose one, it will be Seth Godin’s blog.

It’s concise and the most consistent outside thing in my life.  I admire the consistency of the posts.

Here are few quotes or highlighted texts collected over last year.

  • The key question to ask in the meeting is: Are we increasing value or lowering costs? -SG
  • Technology destroys the perfect and then it enables the impossible –SG
  • A small thing, repeated, is not a small thing. –SG
  • Science is not something to believe or not believe. It is something to do. –SG
  • Nurturing and investing in the things we need and count on needs to be higher on the agenda. –SG
  • One clue that someone does not understand a problem is that they need a large number of variables and factors to explain it. –SG
  • Everyone has feelings and opinions, but the future ignores them. -SG
  • Bad decisions happen for one of two reasons: A. you’re in a huge hurry and you can’t process all the incoming properly. But more common… B. The repercussions of your decision won’t happen for months or years. -SG
  • The goal isn’t to clear the table, the goal is to set the table. –SG
  • 3 ways to add value: Tasks, decisions, and initiation… Doing, choosing, and starting… Each of the three adds value, but one is more prized than the others. -SG
  • We always have a choice, but often, it’s a good idea to act as if we don’t. -SG
  • Writing a sentence is easy. Deciding what to write in the next sentence is hard. -SG
  • The local requires less commitment, feels less risky, doesn’t demand a point of view. The express, on the other hand, always looks like a better idea after you’ve embraced it and gotten to where you meant to go. Express or local? -SG
  • There are people who can cut corners better than you, work more hours than you and certainly work cheaper than you. But what would happen if you became the person who was smarter, better at solving problems and cared the most? -SG
  • The simplest antidote to a tough day is generosity. Waves are free, and smiles are an irresistible bonus. -SG
  • New days require new decisions. –SG
  • The thing about responsibility is that it’s most effectively taken, not given. –SG
  • …When in doubt, do the generous thing. It usually works out the best. -SG
  • When leading a team, it’s tempting to slow things down for the people near the back of the pack. It doesn’t matter, though. They’ll just slow down more. They like it back there. In fact, if your goal is to get the tribe somewhere, it pays to speed up, not slow down. They’ll catch up -SG
  • We notice what we care about and work hard to ignore the rest. You can change what you care about by changing what you notice –SG
  • If you’re the kind of person that needs a crisis to move forward, feel free to invent one. Take the good ideas that aren’t going anywhere and delete them, give them away, hand them off to your team. -SG
  • When we can see these glitches as clowns, as temporary glitches that are unrelated to the cosmic harmony of the universe or even the next thing that’s going to happen to us, they’re easier to compartmentalize. -SG

What in Uncertainty Quantification?

“If a man will begin with certainties, he shall end in doubts;
but if he will be content to begin with doubts, he shall end in certainties.” – F. Bacon – 1605.
The availability of powerful computational resources and general purpose numerical algorithms creates increasing opportunities to attempt simulations in complex systems. How accurate are the resulting predictions? Are the mathematical and physical models correct? Do we have sufficient information to define relevant operating conditions? In general, how can we establish error bars on the results?
 
error_bars
Uncertainty Quantification (UQ) aims at developing rigorous methods to characterize the impact of limited knowledge on quantities of interest. At the interface between physics, mathematics, probability and optimization, and although quite mature in the experimental community, UQ efforts are in their infancy in computational science.
Proud to be part of this. Hope to continue to work with it.