Learning Without Learning

Most of my school and college life was spent in learning lots of facts. I also learned principles and concepts but not in any coherent manner. I was not sure why I was learning, what I was learning. Our teachers (if they knew), forgot to tell us the “Whys?”. Some of this learning was fun and enjoyable and reasonably effortless but some of it was not.
When I started working, I started learning by doing. This was way more fun since I had a context on why I had to learn certain things. I retained my knowledge better since Iusing it. When you learn by doing or learn so that you can use it, the style is very different. You learn on demand and if some of what you are learning does not make sense, you dig deeper and try to find out why something works the way it does. I will call this as exploratory learning and it certainly is a lot more effective.
I think people will learn better, if:
  1. They know why they are learning (learning by understanding the larger context)
  2. They are allowed to explore (learning by exploring and discovering)
  3. They are challenged by tasks that require learning (learning by doing)
  4. They have the freedom to learn in their own ways (Seven freedoms of Learning)
  5. We make learning as interesting as playing games
 If you are interested in this topic, please see How People Learn and Seven Freedoms of Learning.

Where is Machine Learning Being Applied?

When I give talks on Machine Learning, I often get these questions:

  • What is Machine Learning?
  • What are some Machine Learning Applications?
  • Is Machine Learning Mature?
  • Who is using Machine Learning?
  • How do we get started?

If you are using Google or Bing Search, if you get recommendations for books or other products from Amazon, if you are getting hints for the next word to type on a mobile keyboard, you are already using Machine Learning.

Here is a sample list of Machine Learning applications.

From  Apple’s Core ML Brings AI to the Masses:

  • Real Time Image Recognition
  • Sentiment Analysis
  • Search Ranking
  • Personalization
  • Speaker Identification
  • Text Prediction
  • Handwriting Recognition
  • Machine Translation
  • Face Detection
  • Music Tagging
  • Entity Recognition
  • Style Transfer
  • Image Captioning
  • Emotion Detection
  • Text Summarization

From Seven Machine Learning Applications at Google

  • Google Translate
  • Google Voice Search
  • Gmail Inbox Smart Reply
  • RankBrain
  • Google Photos
  • Google Cloud Vision API
  • DeepDream

Also, see – How Google is Remaking Itself as a “Machine Learning First” Company.

While Apple, Google, Facebook, Amazon, IBM, and Microsoft are the most visible companies in the AI space, take a look at business applications of Machine Learning.

What is Machine Learning?

What is Machine Learning? It is a common question that I get asked a lot. I wanted to find a simple, intuitive definition. After doing a few Google searches, I settled on this one from Arthur Samuel.

from Arthur Samuel (in 1959)

“[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.”

It is a field of study. I like that.  I picked this after Googling and finding over 100 descriptions. Here is a shorter curated list of results from this Google Search.  From this list, you may find that Machine Learning is:

  • A technique
  • A field of study
  • An application
  • A Method
  • A type of AI
  • A sub-field of AI
  • A general term
  • A cure-all for all human problems (just kidding)
  • A data based application generator
  • A statistical method of learning from data
  • A mapping function of inputs to outputs

So, what do you think is Machine Learning?

What is Artificial Intelligence (AI)?

Artificial Intelligence (aka AI),  will have a deep impact on our lives – both positive and negative.  Like any other tool or technology, a lot depends on how we use it.  I often get asked these questions:

  • What is AI?
  • What is good about it?
  • Will it destroy jobs?
  • Will it take over humanity?
  • What do we need to do to leverage AI?

AI traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language. These traits allow AI to bring immense socioeconomic opportunities, while also posing ethical and socio-economic challenges.

Right now the opportunities are in research, technology development, skill development and business application development.

The technologies that power AI – neural networks, Bayesian Probability, Statistical Machine Learning have been around for several decades (some as old as the late 50’s). The availability of Big Data is bringing AI applications to life.

There are concerns about misuse of AI and a worry that it may result in uncontrolled proliferation, killing jobs in its wake. Other worries include unethical uses, unintended biases, and other problems. It is too early to take one side or the other.

Please take a look at Artificial Intelligence and Machine Learning:  Policy Paper. It looks at AI from a variety of lenses.