Slides Chris Butler recently used in his discussion w/ mentees of The Product Mentor.
Synopsis: In this talk, Vikas will share his thoughts on what is Product Strategy and how Product Managers can develop it, He will also share some concepts in Strategy and how Product Managers can apply them to make their products more successful.
The Product Mentor is a program designed to pair Product Mentors and Mentees from around the World, across all industries, from start-up to enterprise, guided by the fundamental goals…Better Decisions. Better Products. Better Product People.
Throughout the program, each mentor leads a conversation in an area of their expertise that is live streamed and available to both mentee and the broader product community.
http://TheProductMentor.com
2. Chris Butler
Director of Prod Strat @
Philosophie NYC
The Best Product Person 2016
17 years of product and BD
Microsoft, Waze, Horizon
Ventures, KAYAK, and started
my own company (failed)
chrisbutler@philosophie.is
@chrizbot
3.
4. Product management for AI/ML
● What do I need to know about these things?
● How do they impact product’s role
○ Purpose and strategy
○ Learning
○ Building
○ Prioritizing
○ Measuring
○ Technical
11. Why are AI programs different?
● Content: models, not programs
● Process: training, not debugging
● Release: retraining, not patching
● Uncertainty: of objective
● Uncertainty: of action and recommendation
● Uncertainty: propagates through model
13. Types of problems it can solve (possibly)
● Ranking - Google search results
● Recommendation - Netflix movie recommendations
● Regression (or prediction) - Zillow predicting house prices
● Classification - Image is a cat or dog
● Clustering - Tumblr social network analysis to find groups
of topics
14. ● Supervised
● Unsupervised
● Supervised
● Unsupervised
● Reinforcement
● Semi-supervised
● One shot
● Few shot
Types of learning
● Transfer
● Active
● Imitation
● Q
● Transduction
● ...
15. Resources to start learning
Books
● Programming Collective Intelligence by Toby Segaran
● The Master Algorithm by Pedro Domingos
● Introduction to Machine Learning by Nils Nilsson
● Data Mining by Ian Whitten
● Data Science for Business by Foster Provost
● Neural Networks and Deep Learning by Michael
Nielsen
● Make Your Own Neural Network by Tariq Rashid
Courses
● Introduction to Machine Learning by Andrew Ng
(highly recommended)
● Machine Learning Engineer by Udacity
● Machine learning is Fun! by Adam Geitgey
● How to use Tensorflow for Classification by Siraj
Raval
● Learning AI if you suck at Maths by Daniel Jeffries
Must-Reads:
● WTF is Artificial Intelligence by Sam DeBrule
● Machine learning for Product Managers by Ken
Norton
● AI, Deep Learning, and Machine Learning: A Primer
by Frank Chen
● Artificial Intelligence is the new electricity by Andrew
Ng
● The current state of Machine Learning by Shivon Zilis
● How Google is remaking itself a ‘Machine Learning
First’ company by Steven Levy
● An executives guide to machine learning by Dorian
Pyle (Mckinsey)
● Experience Design in the Machine Learning Era by
Fabien Girardin
● A human’s guide to Machine learning by Sam DeBrule
(subscribe to his newsletter)
● What every manager should know about Machine
Learning by Mike Yeomans
https://hackernoon.com/machine-learning-and-product-managers-930b691b1b37
17. Unless you are in research, the real
focus should be on what differentiates
your product and gives it meaning. Not
finding a better way to detect the
difference between cats and dogs in
ImageNet images.
https://uxdesign.cc/robots-need-love-too-empathy-mapping-for-ai-59585ad3548d
24. Without human purpose, a computer is
just a rock that we tricked into thinking.
https://uxdesign.cc/robots-need-love-too-empathy-mapping-for-ai-59585ad3548d
40. When using AI, you want to know:
● Are we helping solve a problem?
● Do they trust the information?
● Do they feel comfortable giving feedback to the system?
58. Research questions
● Think back to the last time you did this, how did you come to
that decision?
● Do you trust these suggestions for what to do next?
● How do you think the system decided [action]?
● Was there enough information for you to [take action]?
● How much do you trust the system to make the right decision in
the future? It is more or less than before?
67. In closing
● We give machines their purpose - focus on problems you
are solving, not new toys
● Building these systems are a journey - iterate and learn
● We deal with nondeterministic systems all day in our
teams, industries, and markets - AI is no different
68. Don’t get stuck with a rock that doesn’t
help you meet your purpose
73. Background for internal review
● Audience: product people (new and experienced)
● When: 9/17/17
● Alternate use: planning on using parts for Design Thinking for AI workshops
● Feedback needed:
○ Good enough overview of AI? Design Thinking/Lean?
○ Does it feel like a good journey/order?
○ Anything unnecessary? Missing?
○ Did you learn something?
74. Definition
“A computer program is said to
learn from experience E with
respect to some class of tasks T
and performance measure P if its
performance at tasks in T, as
measured by P, improves with
experience E.”
-Tom Mitchell, 1997