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Autonomous System
In an autonomous system, the hardware and software work together to solve a problem by taking an action.
For a system to be truly autonomous, it must be able to collect information, find a solution based on that information,
and take action to achieve a goal.
An autonomous car is considered an autonomous system because it collects information from its sensors, analyses that
information to plan an action, and then acts to achieve a goal. For example, an autonomous car can collect data from its
sensors that indicate an approaching hazard on the road. The system then has to analyse that data and come up with a
solution. In this case, it has to figure out how to avoid the danger. The system then designs a series of actions that make
this possible and executes those commands e.g. slowing down, taking a turn, etc.
Let's take another example: an IoT device such as a smart thermostat. A smart thermostat is also an autonomous system,
as it collects information about the temperature in a particular
environment, then uses that information to determine
whether the environment should be heated or cooled, and
then performs a task to achieve the desired result.
Finally, let’s take an example from the world of robotics: a
home service robot. A home service robot can be thought
of as an autonomous system because it collects information
about the world around it, uses that information to identify a
desirable goal (such as moving an object from one room to
another), then develops and executes an action plan, in this
case, move the object to a new room.
Recommender Systems
A recommendation engine or recommendation system is a tool used by developers to predict user choices from a huge
list of suggested items. Today's recommendation engines play a vital role for sites like Amazon, Facebook, YouTube, etc.
A prime example of the use of the recommendation engine is Amazon, with its "Customer who bought this item also
bought ...". The content recommendations engine behaves like a smart and experienced salesperson who understands
the user's needs, tastes, and requirements and can make informed decisions about recommendations that are beneficial
and relevant to the customer's needs thereby increasing the buying rate.
liked
bought liked
bought
Similar Recommendation Recommendation
Engine
Customers Engine
Recommend Products
Recommend Products based on similar
based on similar products/items
customer
(a) Collaborative Filtering (b) Content Filtering
146 Touchpad Artificial Intelligence (Ver. 2.0)-XI

