Discussion on AI to a child | 和孩子讨论人工智能

One of the children asked me whether it was true that AI would replace a lot of jobs.   Yes and no, I said.   Our current AI is very good at pattern recognition, but not good at other things.   It does not have a brain like we do.

I started to explain that our current AI is like a model, which they know a bit.   To give a concrete example, I said, suppose you have two sets of numbers, one set is 1, 2, 3, 4, 5, and the other set is 2, 4, 6, 8, 10.  The computer is very good at (because we make it so) figuring out that the second set is twice of the first set.    It can recognize faces because all faces have 2 eyes, 1 nose, and 1 mouth, and have similar shapes.    In summary, it is very good at figuring out patterns if you let the computer have lots of them.

The children asked many great questions.  Some questions are truly profound, and can basically summarize the essence of the whole business of modeling.  It is amazing that how uncluttered minds can go straight to the point and not fooled by jargons or buzz.

distill.pub

The site is so good that its site name deserves to be in the title: https://distill.pub

What is it? It is the merge of digital science, math and art that translates technical hypes like AI into something that feels like poetry.

scikit-learn processes simplified | 简化scikit-learn过程

Code light and concept heavy–that’s been the trend as programming languages stack on one another to wrap up more and more algorithms with fewer and fewer lines of code. A child can run a linear regression model. Yes, you hear it right. We have done it, and we summarize the building blocks of Python machine learning library scikit-learn in this table:

Process Code
Import libraries %matplotlib inline

import numpy as np

import pandas as pd

import matplotlib

import matplotlib.pyplot as plt

Format pd.options.display.float_format = ‘{:20,.3f}’.format
Import data from sklearn.datasets import load_boston

dataset = load_boston()

Prepare data y = dataset.target

X = dataset.data

Split data from sklearn.model_selection import train_test_split
Algorithm a from sklearn.linear_model import RidgeCV

my_regr = RidgeCV()

Train-predict my_regr.fit(X_train, y_train)

y_pred =my_regr.predict(X_test)

Plot – performance f, ax =plt.subplots(1, 1)

ax.scatter(y_test, y_pred)

plt.plot([0, 50], [0, 50], ‘–k’)

ax.set_ylabel(‘Target predicted’)

ax.set_xlabel(‘True Target’)

ax.set_title(‘Ridge regression on test data’)

ax.text(5, 40, r’$R^2$=%.2f, MAE=%.2f’ % (

   r2_score(y_test, y_pred), median_absolute_error(y_test, y_pred)))

ax.set_xlim([0, 50])

ax.set_ylim([0, 50])

Interpretation interpretation = pd.DataFrame({‘X’: dataset.feature_names, ‘coef’: my_regr.coef_})
Algorithm b from sklearn.model_selection import cross_val_predict

from sklearn import linear_model

Train-predict my_regr = linear_model.LinearRegression()

y_pred = cross_val_predict(my_regr, X, y, cv=10)

Plot – performance f, ax =plt.subplots(1, 1)

ax.scatter(y, y_pred, edgecolors=(0, 0, 0))

plt.plot([0, 50], [0, 50], ‘–k’)

ax.set_ylabel(‘Target predicted’)

ax.set_xlabel(‘True Target’)

ax.set_title(‘linear regression with cross validation’)

ax.text(5, 45, r’$R^2$=%.2f, MAE=%.2f’ % (

   r2_score(y, y_pred), median_absolute_error(y, y_pred)))

Just grit my teeth (and smile when possible)

“It’s an endless combat. Each morning one must put one’s capacities to the test.”
— Christine Lagarde

This screenshot is from 16:10-16:12 of YouTube video of Tokyo 1964 Olympic Marathon, when sports gear or drinks were not as scientific as they are today; when there were even runners ran bare feet.

This runner in the picture finished the race, and he was one of the best, even though not the one of the medalists.

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Jupyter notebook markdown tips

Tip No. 1- Tip No. 100000: play around and click every button. If you are willing to do this continuously, you will be better than half of the programmers soon, and probably can answer most of the questions in Stackoverflow related to Jupyter Notebook markdown. Go ahead, it won’t break. The worst it could happen is restart. If you have to undo, hit Esc, then ctrl + z.

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python numpy basics | 基础 python numpy 1

Tonight our class played together numpy basics and math.  We put together a numpy basics notebook:
https://nbviewer.jupyter.org/github/magicmathmandarin/PythonBasics/blob/master/numpy_basics1.ipynb#back-to-top

Programming by trial and error is a great way for kids to learn not only programming but also math. — this should play a bigger and bigger role in children’s day to day learning.  玩编程也帮助学习算术。

Somehow I have a feeling that learning long division in this day and age is wrong.  在这个时代还学长除法那就不对了。

Go ahead and tinker with the code in the notebook. Nothing will break, we promise 🙂

tensorflow upgrade and testing | tensorflow 升级和测试

It has been over a year since I last used tensorflow. Not only library versions have changed, but also the syntax. My old jupyter notebook was throwing errors all over the place this morning. It is time to update everything.

For tensorflow, I am using Python 3.6.3 (I used to use Python 3.5), and numpy 1.16.1. TensorFlow has a few dependencies. numpy is one of them. Note the mnist dataset has 70,000 images.

python -c ‘import tensorflow as tf; print(tf.__version__)

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The only thing that really matters is | 唯一重要的是…

We watched this very informative, entertaining and inspiring video by GitHub co-founder Tom Breston, who tricked the audience to chant on three separate occasions during this short presentation from 2012:

  • “The only thing that really matters is people 是人”
  • “The only thing that really matters is product 是产品”
  • “The only thing that really matters is culture 是文化”.

So what is the one thing that really matters? 到底是什么呢? It is the trichotomy 三分法 of people, product and culture.

From one Tom Breston’s personal blog dated 2008, he once wrote:

“In the end, just as Indiana Jones could never turn down the opportunity to search for the Holy Grail, I could no less turn down the chance to work for myself on something I truly love, no matter how safe the alternative might be. When I’m old and dying, I plan to look back on my life and say “wow, that was an adventure,” not ‘wow, I sure felt safe.'”

I have been complaining about how FEW women startup successes. While I realize it is harder for women to make it due to many reasons including stereotypes and discrimination, but I cannot help wondering: ladies, ARE you prepared to take risk as Tom did or do you prefer to feel safe? 女生们,你们愿意冒险或守着安稳?

An great collection of Python notebooks | Python 笔记本集

Here is a really great collection of Python notebooks with lots and lots of links.  We start with some appetizers:

But there are so many and so much more!! Okay, I am just going to copy (fork) most of this page:

Mathematics

  • Linear algebra with Cython. A tutorial that styles the notebook differently to show that you can produce high-quality typography online with the Notebook. By Carl Vogel.
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Women, men and money

From a dataset on restaurant tip included in the Python seaborn library, we investigate using Python with Jupyter Notebook how much money women and men relatively make, and why.

Most people don’t tip more or less because of server is a man or woman, but we do tip according to tip amount (for equal services).

I will tell you base on data analysis the real reason women make less is mostly because:
1. women work mostly lunch shifts
2. lunch bills are smaller than dinner ones.

Indeed, in every industry, including food services like in the restaurants, women have shouldered more share of the most difficult job in the world: parenting, and dedicated their time and energy to their families.

As a result, they have earned less money than men on average. Let’s give our appreciation to women for their roles in taking care of families!

There is a profound reason why we say “Mother nature”!

Should women work more dinner shifts and leave the important job of taking care of children to …?

That is a difficult question… Take a look at this Jupyter Notebook for the detailed data analysis.
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