As a Business Principal, I have been coming across the term Machine learning for a long time. Several business executives have wondered what this animal is and how does it help them.
The series of posts is to list out what Machine learning means to a business principal and some basic industry standard approaches that has been tried out in the past few years.
The series of posts is to list out what Machine learning means to a business principal and some basic industry standard approaches that has been tried out in the past few years.
What does a Business Principal do
Lets suppose the business executives are working through a particular business challenge. Some challenges are tactical and some are strategic. They ideate internally or with external consultants or both.
Today, this ideation is severely limited by the scope of the challenge that is discussed as well as with the knowledge that the stakeholders bring to the table.
The Business Principal is primarily a facilitator that helps this ideation achieve the outcomes that solve the business challenge. The business principal structures the conversation, layers the business challenge to make it more transparent, simulates the various business scenarios for more clarity and provides the next step of action items that would be required from that ideation exercise.
Machine Learning for Business Principals
In this context, Machine learning is now the buzzword that keeps getting throw at Business principals - more as solution in these ideation exercises. It becomes critical for a Business Principal to know about Machine learning now more than ever.
The series of posts are my attempt at abstracting my experience in the last 4+ years on interacting with several business teams, data scientists to articulate my view point.
I have drawn a lot of the points and structure I am making from the following Medium post: https://medium.com/@yaelg/product-manager-pm-step-by-step-tutorial-building-machine-learning-products-ffa7817aa8ab
The Origins of Machine Learning
It is important to know a little about what Machine learning has evolved from and then get into the details.
What is Machine Learning
Machine Learning Enables us to draw insights from the data
at scale, at a level of granularity that ranges from a single user interaction to worldwide trends and their impact on the planet
What is AI?
AI - Artificial Intelligence - is about helping computer reason. When presented with a problem, computers should be able to reason away to a solution.
It is also about:
- Knowledge Representation: Understand and interact with the real world
- Planning: Understand how to navigate and plan around the world that we live in - Find out where it is safe to go.
- Natural Language processing: Speak and understand language, Understand context. Teach computers as much of subtlety in communication and language as we can.
- Perception: See, hear and feel things in the world. Perceive things through sight, sound ,smell & touch.
- Generalised Intelligence: Discrete parts to combine, Would get to fully autonomous, thinking, interacting robot
AI Winters
AI Winter 1: Successful language translation. However, Could not understand context
AI Winter 2: Successful interpretation of language and context. However, could not understand if it deviated from the pre-programmed context.
AI Winter 3: Expert systems. However, learning curve for each expert system did not help the next expert system
Deep Learning
Earlier, Codified rules from experts that help the computers interpret data. Later, Algorithms that interpret data and help the computer learn.
We started with Interpreting handwriting of zip codes for postal department. Next was speech to text algorithms.
Neural Networks - Computer algorithm and data structures to mimic the brain.
Instead of codifying computers, the algorithms help the computer learn for themselves from the data.
Machine Learning
Whole set of techniques where we are trying to program computers to exhibit Human intelligence. E.g. Machine learnings, Search optimisation, Expert systems.
Machine learning is one of those sets of techniques. It is a peer to search techniques, constraint satisfaction, logical reasoning, probabilistic reasoning and control theory.
Machine learning has a set of techniques within itself - Deep learning, Associative rule learning, Decision trees, Random forest, Support vector machines, bayesian networks, Reinforcement learning, Genetic algorithms.
Of these, Deep learning has been the most productive and accurate of all.
More on the next set of posts.....
👍👍👍🙏
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