Artificial Intelligence is a science with a set of computing technologies that are inspired but can typically operate quite variant from the ways people use their nervous system for sensing, learning and taking an action against it.
We are an indulged generation to live in this era full of technological amelioration. A short time ago there has been an immense rise in the world today in using machines powered by artificial intelligence where these machines are wired using cross disciplinary approaches based on mathematics, statistics, computer science etc.
It is one of the abridged problem-solving processes for the creatures. AI started to become an essential value by providing an enormous amount of value in the data obtained after processing. By virtue of its high potential value many organizations are scrambling for implementing it. In today’s era it is much more mundane and elementary.
Owing to the cutting-edge technologies, machine learning today is not like the prior machine learning. In regards with ML, learning is considered as a series of past experiences used to build knowledge about a task.
It was built from the pattern recognition and the theory date processors learn without being programmed for performing the specific tasks. The main emphasis of machine learning is that instead of programming the rules we need to feed the algorithm data and let the algorithm adjust itself for improving its accuracy.
Conventional science algorithms mainly process, while machine learning is about applying an algorithm to fit the data. The processes of creation of a machine learning model slightly vary in their definition of phases but generally employs the three main phases of model initiation, performance estimation and deployment.
However, machine learning merely represents a set of methods that enable to learn patterns in the existing data by generating analytical methods that can be handled inside larger artifacts.
For understanding the interplay of machine learning and AI, we need to take a perspective which attracts the implementation of the intelligent agents. Using this approach, it allows us to map the different tasks and components of machine learning to the capabilities of intelligent agents.
The capabilities of thinking and acting of an intelligent agent is regarded as a frontend and backend. Frontend is the interface the environment interacts with which takes many forms where it requires two technical components namely sensors and actuators.
Sensors are the one which detects the events or changes in the surroundings by forwarding the information via frontend to the backend. Actuators on the other hand are the peripherals that are responsible for the moving and controlling mechanisms.
Turing test which takes place at the interaction of the environment with the frontend, more specifically with the combination of sensors and actuators one can test the agent’s AI of acting humanly.
Even though every frontend has sensors and actuators, only relevant encapsulated frontend exists whereas backend provides the necessary functionalities by depicting the thinking capabilities of an intelligent agent.
This differentiation especially holds for a machine learning perspective on AI, while the underlying models in the thinking layer are once trained and never touched again or continuously updated and flexible.
The presented groundwork of machine learning and its role within intelligence is on the conceptual level. Primary is the empiric validation as well as the continuous iterative development of the framework is necessary.
By identifying the various cases of intelligent agents across the different disciplines we will come to know how best the framework fits into the model. Further, one aspect of interest is reducing the necessary human involvement.
By following the methods of transfer machine learning deal with the possibilities of transferring knowledge from source environment to target environment which indeed helps us in diminishing the human involvement.