Machine Learning and AI
Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Simply put, AI’s goal is to make computers/computer programs smart enough to imitate 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 a much difficult and tedious job.
AI services can be classified into Vertical or Horizontal AI
What is Vertical AI?
These are services focus on a single job, whether that’s scheduling meetings, automating repetitive work, etc. Vertical AI Bots performs just one job for you and do it so well, that we might mistake them for a human.
What is Horizontal AI?
These services are such that they 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 algorithms to perform similar tasks. AI is an automated decision-making system, which continuously learns, adapts, suggests, and takes actions automatically. At the core, they require algorithms that are able to learn from their experience. This is where Machine Learning comes into the picture.
Machine Learning (ML) is a subset of Artificial Intelligence. 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. This 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 to different situations for which they have not been explicitly programmed. The machines learn from 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
In supervised learning, training datasets are provided to the system. Supervised learning algorithms analyze 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 a Supervised Learning algorithm.
Unsupervised Learning
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 to any problem is not provided. The algorithm itself finds the patterns in the data. One of the examples of supervised 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 behavior within a specific context, to maximize its performance. Reinforcement learning is defined by characterizing a learning problem and not by characterizing 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, connects with a dynamic environment to attain a definite goal. This technique selects the action that would give the expected output efficiently and rapidly.
