• A field of AI that employs data collection to identify patterns and aid decision making
  • System / machine improves over time

Case Study – Voice Assistants

Goal: Imitate human conversation

Process: Learn to ‘understand’ the nuances and semantics of our language

Actions: Compose appropriate responses / execute orders

For example, Siri can identify the trigger phrase ‘Hey Siri’ under almost any condition through the use of probability distributions.

In the battle of Xiao Ai versus Siri, it was realized that due to the machine learning specific  to the cultural locality, Xiao Ai could function way better for the mainland China consumer. It knew how to send money to the user’s contacts on WeChat, whereas Siri only send a text message. It could also accurately find the user’s photos from an outing on a previous weekend with friends to upload to social media.

Case Study – Self Driving Cars

Goal: Imitate human driving

Process: Identify vehicles, humans and objects on the road

Actions: Make decisions for the movement of the vehicle based on scenarios presented

Waymo was a company started by Google. Machine Learning is showing much advancement in the cars ability to analyze sensor data to identify traffic signals, actors and objects on the road. This is allowing the car to better anticipate behavior of others. Hence they are getting closer to a real human driving experience powered by this machinery.

Waymo has started in their autonomous taxi service in Chandler, Arizona.

Future implications

  1. Internet of things: Enhanced personalization

Machine learning personalization algorithms will be able to build data about individuals and make appropriate predictions for their interests and behavior. For example, an algorithm can deduce from a person’s browsing activity on an online streaming website and recommend movies and tv series that will interest the person to watch. Currently, the predictions may be rather inaccurate and result in annoyance.

However, they will be improved on and lead to far more beneficial and successful experiences. Also, with unsupervised algorithms, it will be possible to discover patterns and complex data that supervised methods would not be able to. Without any direct human intervention, this will result in faster and more accurate machine learning predictions.


2. Rise of Robotic Services

The final goal of machine learning is really to create Robots. Machine learning makes possible robot vision, self-supervised learning, and multi-agent learning, etc.

We have seen the Henn na – or Weird Hotel in Japan where Robots are providing the entire service for all the tourists who stay there.

Robots will, one day, help to make our lives simpler and more efficient.


Machine learning is a really promising technology. If we can harness it for the good of humanity, this could drive great change in our quality of life.