Artificial Learning Machine Learning

 

 

Source: Internet

AI is everywhere. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI has been already adopted by tech giants like Google and Facebook.

What is Artificial Intelligence?

“AI is the science and engineering of making intelligent machines, especially intelligent computer programs. AI is related to similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable”, says, John McCarthy, a Stanford Researcher.

In short, AI in envisioned to make computers/programs smart enough to imitate the human mind behavior. Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering. AI initiates common sense, problem-solving and analytical reasoning power in machines, which is much difficult and a tedious job.

AI services can be classified into Vertical and Horizontal AI.

Vertical AI services focus on the single job, whether that’s scheduling meeting, automating repetitive work, etc. Vertical AI performs just one job for you and do it so well, that we might mistake them for a human. On the other hand, Horizontal AI services are able to handle multiple tasks. There is no single job to be done. Cortana, Siri and Alexa are some of the examples of Horizontal AI. These services work more massively as the question-and-answer settings, such as “What is the temperature in New York?” or “Call Alex”. They work for multiple tasks and not just for a particular task entirely.

AI is achieved by analyzing how the human brain works while solving an issue and then using that analytical problem-solving techniques to build complex algorithm to perform similar tasks. AI is an automated decision-making system, which continuously learn, adapt, suggest and take actions automatically. At the core, they require algorithms which are able to learn from their experience. This is where Machine Learning comes into the picture.

What is ML?

AI and ML are much trending and also confused terms nowadays. ML is a subset of AI. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behavior exists in past, then you may predict if or it can happen again. Means, if there are no past cases, then there is no prediction.

ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analyzing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.

There are 3 major areas of ML:

Supervised Learning (SL)

In supervised learning, training datasets are provided to the system. Supervised learning algorithms analyse the data and produce an inferred function. The correct solution thus produced can be used for mapping new examples. Credit card fraud detection is one of the examples of Supervised Learning algorithm.

Unsupervised Learning (UL)

Unsupervised Learning algorithms are much harder because the data to be fed is unclustered instead of datasets. Here the goal is to have the machine learn on its own without any supervision. The correct solution of any problem is not provided. The algorithm itself finds the patterns in the data. One of the examples of unsupervised learning is recommendation engines which are there on all e-commerce sites or also on Facebook friend request suggestion mechanism.

Reinforcement Learning

This type of Machine Learning algorithms allows software agents and machines to automatically determine the ideal behaviour within a specific context, to maximise its performance. Reinforcement learning is defined by characterising a learning problem and not by characterising learning methods. Any method which is well suited to solve the problem, we consider it to be the reinforcement learning method. Reinforcement learning assumes that a software agent i.e. a robot, or a computer program or a bot, connect with a dynamic environment to attain a definite goal. This technique selects the action that would give expected output efficiently and rapidly.