Artificial Intelligence & Machine Learning
Artificial intelligence (AI) and machine learning are often used interchangeably, but machine learning is a subset of the broader category of AI.
What is Artificial Learning?
“The study of the modelling of human mental functions by computer programs.” — Collins Dictionary
Artificial Intelligence is the ability of computers to emulate human thought and perform tasks in real-world environments. In simple words, Artificial Intelligence is any code , technology or algorithm that enable machine to mimic/develop/demonstrate the human behavior .
There are four types of AI :
- Reactive Machines.
- Self Aware.
- Theory of Mind.
- Limited Memory.
The idea to develop an intelligent machine is lingering since the 1300s but the real breakthroughs were introduced in the 19th and 20th century.
Earlier we had very small amount of data to make accurate predictions. But today, there is a massive increase in the size of the data which is generated every minute and help us to make more accurate predictions. Nowadays ,we have the support of advanced algorithms, high end computing power and storage that can deal with that enormous data sets. For Example, data collected from Self Driving Cars, Online interactions such as social media (twitter, Facebook).
The different components of Artificial Intelligence are:
- Learning
The simplest form of learning is by trial and error. In this form, the program remembers the section that has given the desired output and discards the other trial actions and learns by itself. The solution keeps on solving problems until it comes across the right results. The program remembers the successful move and stores it in its database and next time the computer is given the same problem it is able to produce the result instantly. It is also called unsupervised learning.
The learning component of AI includes memorizing individual items like different solutions to problems, vocabulary, foreign languages, etc., also known as rote learning. This learning method is later implemented using the generalization method.
2. Reasoning
Reasoning can be logic or generating judgments from the datasets. It can be either deductive or inductive.
The deductive reasoning is in which the truth of the premised guarantees the truth of the conclusion whereas in inductive reasoning, the truth of the premises supports the conclusion but it cannot be fully dependent on the premises. In programming we usually use deductive reasoning to draw inferences to solve a problem.
3. Problem Solving
AI’s problem-solving ability comprises data, where the solution needs to find x. Problem-solving methods are mainly divided into two types special-purpose and general-purpose methods.
General purpose methods are applicable to a wide range of problems .Example in AI ,means-end analysis is one method which involves the step-by-step reduction of the difference between the current state and the goal state. Special purpose methods are customized to solve a particular type of problems.
4. Perception
In order to work in the environment, intelligent agents need to scan the environment and the various objects in it by means of different sense-organs, real or artificial. Agent scans the environment using sense organs like camera, temperature sensor, etc. After capturing various scenes, perceiver analyses the different objects in it and extracts their features and relationships among them.
5. Knowledge representation
The information obtained from the environment through sensors may not be in the format required by the system. Hence, it needs to be represented in standard formats for further processing like learning various patterns, deducing inference, comparing with past objects, etc. There are various knowledge representation techniques like Prepositional logic and First-order logic.
6. Language understanding
Natural Language Processing, involves machines or robots to understand and process the language that human speaks, and infer knowledge from the speech input. It also involves the active participation from a machine in the form of dialog i.e. NLP aims at the text or verbal output from the machine or robot. The input and output of an NLP system can be speech and written text respectively.
The three artificial intelligence components used in typical applications are:
- Speech Recognition
- Computer Vision
- Natural Language Processing
What is Machine Learning?
Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes.
As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model. Machine learning is now essential for creating analytics models.
You likely interact with machine learning applications without realizing. For example, when you visit an e-commerce site and start viewing products and reading reviews, you’re likely presented with other, similar products that you may find interesting. These recommendations aren’t hard coded by an army of developers. The suggestions are served to the site via a machine learning model. The model ingests your browsing history along with other shoppers’ browsing and purchasing data in order to present
other similar products that you may want to purchase.
Machine learning techniques are required to improve the accuracy
of predictive models. Depending on the nature of the business
problem being addressed, there are different approaches based on
the type and volume of the data.
The different types of Machine Learning will be explained more in the upcoming stories.
Stay tuned!!
