Without a question it is possible to
teach yourself AI. It is actually a fun process, and I have recently put myself
through it. Let me briefly describe the path I took.
A good place to start is with a
do-it-yourself book on AI that will coach you through installing all the
software that you will need on your home computer. I used “Deep Learning” by
Mueller and Massaron. They will help you download and properly install Python,
Anaconda, Tensor Flow, Keras, Numpy, Pandas and all of the important libraries.
Then they walk you through using them. They describe all of the basics of
matrix multiplication, regression, and how to use the different kinds of neural
networks (RNNs, CNNs, etc.). These two authors also wrote “Machine Learning for
Dummies” which is also very accessible, and takes a wider perspective on AI
introducing you to other statistical packages like R Studio and Octave. I gave
myself a whole month to get through “Deep Learning,” but to be honest the
reading and code running together took less than 20 hours in total. This may be
too big of a step for some beginners so let me describe some more remedial
learning tools.
First you should unleash your
curiosity by searching Reddit, Quora, Youtube, and Google for more information
about AI. Try searching for some of the following exciting terms: “neural
networks,” “cognitive computing,” “neuromorphic chips,” “cognitive
architectures,” “generative adversarial networks,” “artificial reasoning,”
“probabilistic learning,” “natural language generation,” “semantic nets”
“artistic style transfer” and “GPT-3.” You should also use AI-related keywords
in Google Scholar for a bit of academic exposure to the subject.
You will definitely want to check
out Google’s Tensor Flow Playground. To do this use Google’s excellent tutorial
here: https://playground.tensorflow.org
Watching a 10 minute youtube video about it first can give you a lot of
context. Play around on the site for a half hour to develop first-hand
knowledge about how machines can use neural networks to learn, and how
fundamental AI algorithms work. It is all about little entities that talk to
each other, collectively produce an output, and then learn from their mistakes.
If you are serious about using
machine learning, neural networks, or other popular forms of AI or data science
software, I strongly recommend learning Python. You can learn a lot about
coding through mobile apps like Mimo and Grasshopper. But to start to really
get a grasp on Python you can complete the Code Academy or Solo Learn
certifications for Python. The next step is getting the full certification from
the Python Institute which offers a free online course at pythoninstitute.org I
also recommend reading “Learn Python Quickly” from Code Quickly, and “The
Complete Python Manual” from Black Dog Publishers.
The newest version of Python can be
downloaded for free from the official website. Just getting your hands on it is
a great start. Python is one of the hottest programming languages, and one of
the easiest to learn. It is fantastic for automating things, and is necessary
for anyone who wants a future in AI, especially neural network engineers. The
download link is here https://www.python.org/downloads.
You are probably going to want to
download PyCharm, which is a great developer’s environment for Python. It makes
writing and keeping track of your Python code much easier, and it looks spiffy.
https://www.jetbrains.com/pycharm/download
Then you might try out Coursera.
There you can find courses like “AI for Everyone” from deeplearning.ai,
as well as “Python for Data Science and AI” from IBM. I completed several of
these and none of them will take you longer than 15 hours. For a deeper dive
you might even take Andrew Ng’s famous deep learning specialization on
Coursera.
A favorite researcher of mine named
Chris Eliasmith has created a spiking neural network simulation application
called Nengo. This is an excellent “brain making package” that lets you build,
test, and deploy your own neural networks using Python. The tutorials are
excellent, and make you feel like you have a foot in the door with artificial
intelligence. Find out more at: https://www.nengo.ai/
I also recommend a piece of software
called Neuronify that you can find on the Windows Store. It creates a simple
workspace where you can build neurons, connect them, and watch them fire at,
and respond to each other. Playing with the options, and completing the
tutorials helps to build important intuitions about how neural networks work.
You can visit the website here: https://ovilab.net/neuronify/
Before you get bored of it, definitely download some of the highest rated
workspaces built by other users and you will be treated to some complex and fascinating models.
Sign up for Brilliant at
brilliant.org. They offer a large number of excellent problem-solving based courses
in computer science, artificial intelligence, and mathematics. They even have
courses specifically for Python and neural networks. I still use it weekly and
it is very helpful.
You will want to create an account
on GitHub so that you can host all of the code that you will be writing so
other people can access it. I have posted a few annotated beginner’s tutorial
Python scripts on GitHub, and you can see them here: https://gist.github.com/jaredreser
I strongly recommend ordering an
Arduino starters kit. They will send you a number of electronics parts,
sensors, and motors. You use them to build you own gadgets and robots. You
upload the code from your computer to the Arduino microcontroller and get it to
do all sorts of interesting things. The best part is that you can see all of
the code, and can rewrite or alter the code if you wish. Check it out here: https://www.arduino.cc/
You might also try out the Google AIY products.
Here is a list of some of my
favorite books on AI in order of preference:
Engineering General Intelligence by
Ben Goertzel
How Machines Think by Sean Gerrish
Consciousness and Robot Sentience by
Pentti Haikonen
The Master Algorithm by Pedro Domingos
Human Compatible by Stuart Russel
Understanding Neural Networks by
John Iovine
Automate the Boring Stuff with
Python by Al Sweigart
The AGI Revolution by Ben Goertzel
Artificial Intelligence by Melanie
Mitchell
On Intelligence by Jeff Hawkins
Superintelligence by Nick Bostrom
How to Create a Mind by Ray Kurzweil
Deep Learning by Yoshua Bengio
Introduction to AI by Philip Jackson
The Age of Spiritual Machines by Ray
Kurzweil
In Our Own Image by George
Zarkadakis
Life 3.0 by Max Tegmark
The Sentient Machine by Amir Hussain
Beyond Artificial Intelligence by
Alain Cardon
Bayes’ Rule with Python by James V
Stone
Information Theory by James V Stone
Rebooting AI by Marcus and Davis
The Second Machine Age Brynjolfsson
and McAfee
You might also want to check out my
blog post on building your own computer for AI tinkering: How To Build Your Own AI-Ready Computer
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