Technology has revolutionized our lives and is constantly changing and progressing. The most flourishing technologies include Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. These are the most trending technologies growing at a fast pace and are today’s leading-edge technologies.
These terms are generally used together in some contexts but do not mean the same and are related to each other in some or the other way. ML is one of the leading areas of AI which allows computers to learn by themselves and NLP is a branch of AI.
What is Artificial Intelligence?
Artificial refers to something not real and Intelligence stands for the ability of understanding, thinking, creating and logically figuring out things. These two terms together can be used to define something which is not real yet intelligent.
AI is a field of computer science that emphasizes on making intelligent machines to perform tasks commonly associated with intelligent beings. It basically deals with intelligence exhibited by software and machines.
While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. AI finds application in very many fields, from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries.
The first work towards AI was carried out in 1943 with the evolution of Artificial Neurons. In 1950, Turing test was conducted by Alan Turing that can check the machine’s ability to exhibit intelligence.
The first chatbot was developed in 1966 and was named ELIZA followed by the development of the first smart robot, WABOT-1. The first AI vacuum cleaner, ROOMBA was introduced in the year 2002. Finally, AI entered the world of business with companies like Facebook and Twitter using it.
Google’s Android app “Google Now”, launched in the year 2012 was again an AI application. The most recent wonder of AI is “the Project Debater” from IBM. AI has currently reached a remarkable position
The areas of application of AI include
- Chat-bots – An ever-present agent ready to listen to your needs complaints and thoughts and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.
- Self-Driving Cars: Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
- Computer Vision: Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos.
- Facial Recognition: AI helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
What is Machine Learning?
One of the major applications of Artificial Intelligence is machine learning. ML is not a sub-domain of AI but can be generally termed as a sub-field of AI. The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.
Implementing an ML model requires a lot of data known as training data which is fed into the model and based on this data, the machine learns to perform several tasks. This data could be anything such as text, images, audio, etc…
Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. ML itself is a self-learning algorithm. The different algorithms of ML include Decision Trees, Neural Networks, SEO, Candidate Elimination, Find-S, etc.
History of Machine Learning
The roots of ML lie way back in the 17th century with the introduction of Mechanical Adder and Mechanical System for Statistical Calculations. Turing Test conducted in 1950 was again a turning point in the field of ML.
The most important feature of ML is “Self-Learning”. The first computer learning program was written by Arthur Samuel for the game of checkers followed by the designing of perceptron (neural network). “The Nearest Neighbor” algorithm was written for pattern recognition.
Finally, the introduction of adaptive learning was introduced in the early 2000s which is currently progressing rapidly with Deep Learning is one of its best examples.
Different types of machine learning approaches are:
Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.
Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.
Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.
ML has several applications in various fields such as
- Customer Service: ML is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions.
- HealthCare: The use of different sensors and devices use data to access a patient’s health status in real-time.
- Financial Services: To get the key insights into financial data and to prevent financial frauds.
- Sales and Marketing: This majorly includes digital marketing, which is currently an emerging field, uses several machine learning algorithms to enhance the purchases and to enhance the ideal buyer journey.
What is Natural Language Processing?
Natural Language Processing is an AI method of communicating with an intelligent system using a natural language.
Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.
History of NLP
NLP was introduced mainly for machine translation. In the early 1950s attempts were made to automate language translation. The growth of NLP started during the early ’90s which involved the direct application of statistical methods to NLP itself. In 2006, more advancement took place with the launch of IBM’s Watson, an AI system which is capable of answering questions posed in natural language. The invention of Siri’s speech recognition in the field of NLP’s research and development is booming.
Few Applications of NLP include
- Sentiment Analysis – Majorly helps in monitoring Social Media
- Speech Recognition – The ability of a computer to listen to a human voice, analyze and respond.
- Text Classification – Text classification is used to assign tags to text according to the content.
- Grammar Correction – Used by software like MS-Word for spell-checking.
What is Deep Learning?
The term “Deep Learning” was first coined in 2006. Deep Learning is a field of machine learning where algorithms are motivated by artificial neural networks (ANN). It is an AI function that acts lie a human brain for processing large data-sets. A different set of patterns are created which are used for decision making.
The motive of introducing Deep Learning is to move Machine Learning closer to its main aim. Cat Experiment conducted in 2012 figured out the difficulties of Unsupervised Learning. Deep learning uses “Supervised Learning” where a neural network is trained using “Unsupervised Learning”.
Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information like how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning. The different activation functions used in Deep Learning include linear, sigmoid, tanh, etc.…
History of Deep Learning
The history of Deep Learning includes the introduction of “The Back-Propagation” algorithm, which was introduced in 1974, used for enhancing prediction accuracy in ML. Recurrent Neural Network was introduced in 1986 which takes a series of inputs with no predefined limit, followed by the introduction of Bidirectional Recurrent Neural Network in 1997. In 2009 Salakhutdinov & Hinton introduced Deep Boltzmann Machines. In the year 2012, Geoffrey Hinton introduced Dropout, an efficient way of training neural networks
Applications of Deep Learning are
- Text and Character generation – Natural Language Generation.
- Automatic Machine Translation – Automatic translation of text and images.
- Facial Recognition: Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
- Robotics: Deep learning has also been found to be effective at handling multi-modal data generated in robotic sensing applications.
Key Differences between AI, ML, and NLP
Artificial intelligence (AI) is closely related to making machines intelligent and make them perform human tasks. Any object turning smart for example, washing machine, cars, refrigerator, television becomes an artificially intelligent object. Machine Learning and Artificial Intelligence are the terms often used together but aren’t the same.
ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).
The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. NLP is again a part of AI and sometimes overlaps with ML to perform tasks. DL is the same as ML or an extended version of ML and both are fields of AI. NLP is a part of AI which overlaps with ML & DL.AI, ai applications, ai models, ai training data, artificial intelligence, machine learning, ML, ml data, ml models, ml training data, natural language processing, NLP, nlp data, nlp models, technology, training data