Why & How we should study AI & Machine Learning (6 Easy Steps)

AI & ML are fundamental new technologies that can create immense value to humankind. It is very important for us to learn AI & ML and apply this knowledge in our work.

If you are somebody like me without prior exposure to computers, you may wonder where to begin the journey – I did some homework and found an easy step-by-step using online resources. It takes only 6 easy steps to gain mastery in AI & ML. I will start with sharing with you the talk that Risto Siilasmaa (Chairman, Nokia) gave in Nov, 2017 – “Why you should study AI and Machine Learning and how I did it”.

Step # 1 - Risto Siilasmaa started his journey into AI & ML by asking,

“Where could I find good material explaining how machine learning works in terms that would speak to an engineer who loves to understand how things work?”

Step # 2 - “ Why not study machine learning myself and then explain what I learned to others who are struggling with the same questions. Perhaps I could motivate them to study new technologies and lead by example. Maybe I could inspire people in the various companies I’m involved with to take action through study and hands on use of a machine learning.”

Step # 3 - With a quick internet search he found Andrew Ng's courses on Coursera. He started with Machine Learning and had a lot of fun getting reacquainted with programming. Andrew turned out to be a great teacher who genuinely wants people to learn.

Andrew Ng’s Machine Learning Course is a free on Coursera and is an excellent place to start - https://www.coursera.org/learn/machine-learning - please register today

Andrew’s AI For Everyone is yet another popular course – free on Coursera – very insightful and friendly course - https://www.coursera.org/learn/ai-for-everyone

To know more about recent developments in AI in an entertaining way – please see “The Age of AI” – American Actor Robert Downey Jr. hosts an exploration of the rise of artificial intelligence and showcases innovators who are pushing the boundaries.- YouTube Originals - https://www.youtube.com/watch?v=UwsrzCVZAb8

Step # 4 - Over time he gained enough understanding to explain what he felt were the most important aspects of machine learning to CEOs, politicians, academics (in other fields) and frankly, any decision makers. Inspired by Andrew Ng, he wanted to provide them with intuition on these four key questions:
Why is machine learning so topical right now?
How does machine learning actually work?
Why is it dangerous to ignore machine learning?
What should governments, universities and companies do?

Here is his presentation – Risto Siialasmaa on Machine Learning - https://www.nokia.com/blog/study-ai-machine-learning/

Step # 5 – Key Takeaways

Here are some of the most meaningful insights that he gained along the way:

Machine learning really is just math. And relatively simple math at that.
Machine learning is not programmed, it is taught with data.
The value you get from machine learning is a function of the quality of the data you feed it.
As humans teach machine learning systems, we tend to transfer our own biases to them.
The intelligence in a neural network is just a long list of numbers. By exporting those numbers via, for instance, a memory stick to another "empty" neural network, you can transfer all the learning done by the first one.
Because the intelligence is really just numbers and the architectures relatively simple, it is not truly intelligence at all. Machine learning systems do not really understand. So far.
Machine learning is a one way street. You can have a neural network recognize faces, but you cannot ask it to describe any of the faces it knows.
If you teach a machine learning system two skills, it cannot combine them to create a third skill. There is no autonomy in machine learning systems.
There are still lots of meaningful tasks where machine learning beats humans hands down.
Machine learning becomes exponentially more powerful over time with the progress we make in computational power, gathering data and figuring out new learning algorithms.
We are barely scratching the surface with applying machine learning. The revolution is underway but only just starting to gain speed.
We are an unfathomable distance away from creating a human type of intelligence. A transformational discovery is required for that to be ever feasible.
Everything above is subject to change with such a transformational invention.

Step # 6 – Call to Action

Risto strongly believes that all organizations should take the following next steps:

Ensure that key decision makers understand the basics of machine learning. Invest an hour and either watch the video above or something similar from another source. Only when you have a solid intuition of what machine learning is and how it works, will you be able to ask the right questions.
Create a data strategy. Make sure all the data you have access to can logically be utilized through a single interface in order to be able to combine any data sets you have. Also think about what kind of data will you need down the road to train the AI that will define your competitiveness in 3-5 years' time.
For larger organizations, make sure you have a central pool of expertise that can help you kickstart the use of machine learning systems. You can have that capability as part of your own organization or outsource it if you don’t have the capacity to recruit the expertise. You need people you can go to for answers when you are faced with a significant obstacle and wondering: "Can I solve this problem with machine learning?".
Start applying machine learning internally for improved quality and efficiency. There are numerous jobs that can be done better and faster if you augment the people working on those tasks with machine learning.
Integrate machine learning into your products and services for improved competitiveness with our customers.