[Shortened but updated version of talk] With all the market interest in artificial intelligence, it’s no surprise that many are asking about the best way to learn more about it. What should I read? What should I watch? There’s so much material out there. But, before one can properly answer those types of questions, it’s useful to take a step back and consider what “doing AI” even means because it turns out that AI can mean a lot of different things depending upon what you’re trying to accomplish. In this talk, Gordon Haff will provide you with both a high-level roadmap and specific pointers for adding AI smarts to your toolbox. He’ll distinguish between research AI and applied AI, discuss how AI intersects with data science more broadly, and look at some of the related research and practice areas that will help you understand AI beyond just machine learning. Armed with this knowledge, you will be better prepared to chart out a program for learning AI that targets your specific needs and objectives rather than wasting time on topics that are not interesting or relevant to you.

1. 1
How do you
get started
doing AI?
Gordon Haff
Emerging Technology
Evangelist
@ghaff
https://bitmason.blogspot.co
m
@Opensourceway

2. @ghaff https://bitmason.blogspot.com @Opensourceway
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Who am I?
● Evangelist for emerging
technologies and practices at Red
Hat
● Author of How Open Source Ate
Software, etc.
● Former IT industry analyst
● Former big system guy
● Website: http://www.bitmasons.com

3. @ghaff https://bitmason.blogspot.com @Opensourceway
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An AI Map
Research Applied
Machine
Learning
Deep
Learning
Brain science &
Cognitive psychology
Linguistics &
NLP
Human/machine
interactions
Supervised learning
Unsupervised learning
Reinforcement
learning
Domain
expertise
Robotics
Data
anonymization
Data science &
statistics
AI

4. @ghaff https://bitmason.blogspot.com @Opensourceway
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Research AI
● Math heavy (linear algebra,
calculus, optimizations,
probability)
● Essentially university
curriculum; credentials vary
● Can touch many adjacent areas
● Not necessarily primarily
programming/working with
data

5. @ghaff https://bitmason.blogspot.com @Opensourceway
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Research AI resources
● Many MOOCs/university courses/textbooks
○ AI, Machine Learning, Deep Learning
○ Foundational courses such as linear algebra and calculus
○ Adjacent fields such as cognitive psychology and linguistics
○ “MicroMasters,” “Nanodegrees,” blended degrees
● Other open educational resources (e.g. MIT OCW)
● Research papers

6. @ghaff https://bitmason.blogspot.com @Opensourceway
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Applied AI
● Applications that solve today’s
problems
● Background in relevant statistics
and algorithms
● Programming
● Data science stuff (data
cleansing, visualization, etc.)
● Primarily makes use of ML/DL

7. @ghaff https://bitmason.blogspot.com @Opensourceway
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Deep Learning
● Sub-set of machine learning
that uses multi-layer neural
networks to learn from data
● Has been the primary
approach that has led to so
many recent “AI” advances
● Beneficiary of increased
computation/data, including
accelerators such as GPUs

8. @ghaff https://bitmason.blogspot.com @Opensourceway
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Reinforcement learning

9. @ghaff https://bitmason.blogspot.com @Opensourceway
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Some impressive results

10. @ghaff https://bitmason.blogspot.com @Opensourceway
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But reinforcement learning limits
● Learn from mistakes
● Physical world versus models
● Exploration versus exploitation
● Real-world environments change
● States can be poorly defined

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Source: MathWorks

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Unsupervised learning
● Clustering
● Reduce
dimensionality
● Doesn’t require
labeling

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Supervised
learning
https://medium.com/@jorgesleonel/supervised-learning-c16823b00c
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Amazing stuff since ~2010
● Voice recognition: Siri, Alexa, Cortana, Google
● Computer vision classification can beat humans
● Autonomous driving research (esp. Object
classification)
● Ubiquitous bots
● Lots of unsexy predictive analytics, trading,
optimization, and analysis

15. @ghaff https://bitmason.blogspot.com @Opensourceway
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16. @ghaff https://bitmason.blogspot.com @Opensourceway
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Heathcare Example: ChRIS
● Real-time Web-based MRI Data Collection,
Analysis, and Sharing
● Cloud-based platform developed as part of a
collaborative effort between Boston Children’s
Hospital, Red Hat, Boston University, and the
Open Cloud (MOC)
● Began as a way to facilitate the organization, 3D
visualization, and collaboration around medical
imaging amongst researchers

17. @ghaff https://bitmason.blogspot.com @Opensourceway
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Supervised learning challenges
NO PHYSICAL WORLD CONTEXT
● Lack of real world context
● Interpretability
● Dependent on large
training sets
● Sensitive to small changes

18. @ghaff https://bitmason.blogspot.com @Opensourceway
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The basics
● Programming & programming environment
○ Programming for Everyone, UMich
(Python)https://online.umich.edu/courses/programming-for-everybody-getting-started-with-python/
○ Introduction to Computer Science and Programming using
Python, MIT
https://www.edx.org/course/introduction-to-computer-science-and-programming-using-python-2 (Text is Introduction
to Computation and Programming using Python by John Guttag)
○ Anaconda distribution (Python/R/TensorFlow/data science
libraries/Jupyter notebooks)
○ SQL https://www.khanacademy.org/computing/computer-programming/sql
○ Sabermetrics 101: Introduction to Baseball Analytics on edX
is a fun and gentle introduction to data analysis

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Data Science: Working with data
● Python for Data Analysis, O’Reilly
● Kaggle
● Data visualization generally
● MicroMasters in Statistics and
Data Science, edX (MIT)
https://www.edx.org/micromasters/mitx-statistics-and-data-science
● CS109 Data Science, Harvard
http://cs109.github.io/2015/pages/videos.html http://blog.operasolutions.com/bid/384900/what-is-data-scienc

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Deep Learning
● Deep Learning by Ian Goodfellow et al.
https://www.deeplearningbook.org/
● A big list of resources
https://github.com/ChristosChristofidis/awesome-deep-learning
● More practically-grounded courses
(MOOC/YouTube/fast.ai), e.g. MIT 6.S094: Deep
Learning for Self-Driving Cars

21. @ghaff https://bitmason.blogspot.com @Opensourceway
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Getting hands-on
● Hybrid cloud data/AI platforms like Open Data Hub
(OpenShift, Ceph, Kafka/Strimzi) at opendatahub.io
● Public cloud AI/ML services like Google Cloud
AutoML
● “Cookbooks,” e.g. O’Reilly Deep Learning Cookbook:
Practical Recipes to Get Started Quickly
● Open datasets (e.g. data.gov)

22. @ghaff https://bitmason.blogspot.com @Opensourceway
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Keep your eye on
● Data vs. privacy: MPC, homomorphic encryption, etc.
● Ownership of data
● Human factors (e.g. Missy Cummings’ lab at Duke)
● Voice interfaces (also NLP, etc.)
● Explainability and bias
● Beyond current deep learning
● Multidisciplinary work

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23
Red Hat is the world’s leading provider of enterprise
open source software solutions. Award-winning support,
training, and consulting services make Red Hat a trusted
adviser to the Fortune 500.
Thank you