Tag: book

Free Springer Books during COVID19

Free Springer Books during COVID19

Book publisher Springer just released over 400 book titles that can be downloaded free of charge following the corona-virus outbreak.

Here’s fhe full overview: https://link.springer.com/search?facet-content-type=%22Book%22&package=mat-covid19_textbooks&facet-language=%22En%22&sortOrder=newestFirst&showAll=true

Most of these books will normally set you back about $50 to $150, so this is a great deal!

There are many titles on computer science, programming, business, psychology, and here are some specific titles that might interest my readership:

Note that I only got to page 8 of 21, so there are many more free interesting titles out there!

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AI Book Review: You look like a thing and I love you

AI Book Review: You look like a thing and I love you

The following are my summary and take-aways from Janelle Shane’s 2019 book named You look like a thing and I love you. Most of the below are excerpts from Janelle’s book, combined, or rewritten by me. For the sake of copyright, just consider everything Janelle’s : )

Image result for things called ai janelle shane

AI weirdness

You look like a thing and I love you is about AI. More specifically, the book is about what AI can and can not do. And how and why AI often fails in miserably hilareous ways.

Janelle has spend her time foing fun experiments with AI. In this book, she shares those experiments along with many real life examples of AIs in practice. While explaining the technical details behind these AIs in an accesible though technically correct way, she informs the reader where, how, and why AIs fail.

Janelle took AIs out of their comfort zone and it produced some hilareously weird results. She proposes five principles of AI Weirdness:

  1. The danger of AI is not that it’s too smart, but that it’s not smart enough
  2. AI has the approximate brainpower of a worm
  3. AI does not really understand the problem you want it to solve
  4. But: AI will do exactly what you tell it to. Or at least it will try its best.
  5. And AI willt ake the path of the least resistance

Definitions: What is (not) AI?

If it seems like AI is everywhere, it’s partly because Artificial Intelligence means lots of things, depending on whether you’re reading science fiction or selling a new app or doing academic research.

To spot an AI in the wild, it’s important to know the difference between machine learning algorithms (what Janelle calls AI in her book) and traditional, rules-based programs.

To solve a problem with a rules-based program, you have to know every step required to complete the program’s task and how to describe each one of those steps. But a machine learning algorithm figures out the rules for itself via trail and error, gauging its success on goals the programmer has specified. As the AI tries to reach this goal, it can discover rules and correlations that the programmer didn’t even know existed. This is what makes AIs attractive problem solvers and is particularly handy if the rules are really complicated or just plain mysterious.

Sometimes an AI’s brilliant problem-solving rules actually rely on mistaken assumptions. Rules that served it well in training but fail miserably when it encountered the real world. While training errors are common in complex AIs, the consequences of these mistakes can be serious.

It’s often not easy to tell when AIs make mistakes. Since we don’t write the rules, they come up with their own, and they don’t write them down or explain them the way a human would.

The difference between succesful AI problem solving and failure usually has a lot to do with the suitability of the task for an AI solution. And there are plenty of tasks for which AI solutions are more efficient than human solutions. But there are also plenty of cases where things go miserably wrong.

Janelle proposes four signs of “AI Doom”, contexts where machine learning will not produce the desired results:

  1. The problem is too hard, broad, or complex
  2. The problem is not what we thought it was
  3. There are sneaky shortcuts to solving the problem
  4. The AI tried to solve the problem learning from flawed data

Programming an AI is almost more like teaching a child than programming a computer.

Explaining how AI works

In her book, Janelle takes us through many example problems which she or others tried to solve using AIs. These example problems are increasingly hilareous, but I assure you that they are technically and didactically sound:

  • Playing tic-tac-toe
  • Managing a cockroach farm
  • Riding a bicycle
  • Rating sandwich deliciousness
  • Tossing a sandwich into a wall
  • Guiding people through a hallway
  • Answering questions regarding photo’s
  • Categorizing doodles
  • Categorizing fish
  • Tossing pancakes
  • Autonomous walking
  • Autonomous driving
  • Playing Pacman

The amazing thing is these ridiculous example problems actually serve a purpose. They are used to explain different algorithms and their applications, strengths, and limitations! Janelle covers a wide variety of algorithms in such a way that anyone new to machine learning would understand, while people with some experience will still be amused.

Janelle talks about artificial neural networks, random forests, and markov chains. Moreover, she explains how activation functions, recurrancy and long short-term memory, evolutionary algorithms and gradient descent work. And all in understandable though technically correct language.

Janelle herself seems particularly fond of generative algorithms. She’s elaborates on having deployed recurrent neural nets, generative adversial networks, and markov chains for a wide variety of generative tasks. In the book, Jabekke explains what went well and went wrong when coming up with new and original…

  • pick-up lines
  • knock-knock jokes
  • names for species of birds
  • perfumes names
  • ice-cream flavors
  • cooking recipes
  • dream descriptions
  • horse drawings
  • Harry Potter scripts
  • cat names
  • Halloween costumes
  • elementary school blueprints
  • names for Benedict Cumberbatch
  • Dungeons and Dragons spells
  • pie recipes

Where does AI fail?

Janelle’s book is lingered with examples of failing AI. As a matter of fact, the whole book seems like an ode to how machine learning can and will inevitably fail. Particularly in the latter chapters, Janelle covers many limitations of and issues with AI in much detail:

  • class imbalance
  • overfitting
  • unrealistic simulation conditions
  • data quality issues
  • self-fullfilling prophecies
  • undesirable reward function optimization
  • missing the obvious
  • catastrophic forgetting
  • human biases in the data
  • machine bias
  • math-washing / bias laundering
  • bias amplification
  • adversarial attacks

Definite recommendation

I have yet to come across a book that explain AI in this much detail and in a manner as accessible and entertaining as Janelle Shane does in You look like a thing and I love you. Janelle makes machine learning and AI understandable for a wide public without passing on the deeper technical details. Taking a critical stance, she provides a good overview of the strenghts and weaknesses of AI, and a realistic outlook for the future to come. This book is not looking for sensation or hype, although reading it will be a most amusing experience for the more technical as well as the lay reader.

I highly recommend you reward yourself with a copy!

E-Book: Probabilistic Programming & Bayesian Methods for Hackers

E-Book: Probabilistic Programming & Bayesian Methods for Hackers

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. Nevertheless, mathematical analysis is only one way to “think Bayes”. With cheap computing power, we can now afford to take an alternate route via probabilistic programming.

Cam Davidson-Pilon wrote the book Bayesian Methods for Hackers as a introduction to Bayesian inference from a computational and understanding-first, mathematics-second, point of view.

The book is available via Amazon, but you can access an online e-book for free. There’s also an associated GitHub repo.

The book explains Bayesian principles with code and visuals. For instance:

%matplotlib inline
from IPython.core.pylabtools import figsize
import numpy as np
from matplotlib import pyplot as plt
figsize(11, 9)

import scipy.stats as stats

dist = stats.beta
n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]
data = stats.bernoulli.rvs(0.5, size=n_trials[-1])
x = np.linspace(0, 1, 100)

for k, N in enumerate(n_trials):
    sx = plt.subplot(len(n_trials)/2, 2, k+1)
    plt.xlabel("$p$, probability of heads") \
        if k in [0, len(n_trials)-1] else None
    plt.setp(sx.get_yticklabels(), visible=False)
    heads = data[:N].sum()
    y = dist.pdf(x, 1 + heads, 1 + N - heads)
    plt.plot(x, y, label="observe %d tosses,\n %d heads" % (N, heads))
    plt.fill_between(x, 0, y, color="#348ABD", alpha=0.4)
    plt.vlines(0.5, 0, 4, color="k", linestyles="--", lw=1)

    leg = plt.legend()
    leg.get_frame().set_alpha(0.4)
    plt.autoscale(tight=True)


plt.suptitle("Bayesian updating of posterior probabilities",
             y=1.02,
             fontsize=14)

plt.tight_layout()

I can only recommend you start with the online version of Bayesian Methods for Hackers, but note that the print version helps sponsor the author ánd includes some additional features:

  • Additional Chapter on Bayesian A/B testing
  • Updated examples
  • Answers to the end of chapter questions
  • Additional explanation, and rewritten sections to aid the reader.

If you’re interested in learning more about Bayesian analysis, I recommend these other books:

Helpful resources for A/B testing

Helpful resources for A/B testing

Brandon Rohrer — (former) data scientist at Microsoft, iRobot, and Facebook — asked his network on Twitter and LinkedIn to share their favorite resources on A/B testing. It produced a nice list, which I summarized below.

The order is somewhat arbitrary, and somewhat based on my personal appreciation of the resources.

Cover image via Optimizely

Recreating graphics from the  Fundamentals of Data Visualization

Recreating graphics from the Fundamentals of Data Visualization

Claus Wilke wrote the Fundamentals of Data Visualization – a great resource that’s definitely high on my list of recommended data visualization books.

In a recent post, Claus shared the link to a GitHub repository where he hosts some of the R programming code with which Claus made the graphics for his dataviz book. The repository is named practical ggplot2, after the R package Clause used to make many of his visuals.

Check it out, the page contains some pearls and the code behind them, which will help you learn to create fabulous visualizations yourself. Some examples:

Via https://htmlpreview.github.io/?https://github.com/clauswilke/practical_ggplot2/blob/master/health_status.html
Via https://htmlpreview.github.io/?https://github.com/clauswilke/practical_ggplot2/blob/master/corruption_human_development.html

Here’s the original tweet in case you want to see the responses.

Recommended Books on Data Visualization

Recommended Books on Data Visualization

Disclaimer: This page contains links to Amazon’s book shop.
Any purchases through those links provide us with a small commission that helps to host this blog.

Data visualization and the (in)effective communication of information are salient topics on this blog. I just love to read and write about best practices related to data visualization (or bad practices), or to explore novel types of complex graphs. However, I am not always online, and I am equally fond of reading about data visualization offline.

These amazing books about data visualization
are written by some of the leading experts in the dataviz scene:

Happy reading!


If you are also interested in programming and machine learning, have a look at this list of free programming books.