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Understanding the difference between AI, ML & NLP models

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.

History of AI

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.

NLP in AI and the realization of futuristic robots

How a well-trained conversational AI can empower your business

When the most valuable asset in the world is data, the most powerful tool you can have is the ability to process exabytes of information that data has to offer, and productively so. As we begin to produce gigabytes of digital data every day, De Toekomst — The Future — is with those that can effectively utilize this space, or more appropriately, the cloud. And it is precisely here that Artificial Intelligence is making its mark.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. And the sphere where AI is making its presence felt like a real and tangible entity is when it has a voice of its own. 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.

AI has grown to become our personal assistant helping us with tasks at our behest, literally. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana and Google Voice Assistant are only a few examples of AI systems integrating themselves seamlessly into our daily lives and routine. They help us plan our schedules, carry out functions without us having to push a single button, inform us of the latest developments, all the while learning more about our preferences and customizing themselves for us just by listening. With our permission, AI can become our best help.

Leading voice assistants | Blog | Bridged.co

How businesses are leveraging the AI assistant

Equipped with the knowledge of human communication, AI bots can potentially be used in any field that involves language to derive fast, intelligent, and useful insights which can then be transformed into follow-up actions tailored for each customer. Companies have realized the benefits of this incredibly powerful service and have begun utilizing them to gain significant market advantages. We will now talk about a few major applications of the conversational AI, and how we at Bridged are helping companies realize their ambitions for the AI-driven future.

Voice Control and Assistance

Voice control and assistance | Blog | Bridged.co

Performing basic tasks — reading messages, checking notifications, news updates, changing settings, operating connected devices, speech-to-text services.

Planning and Scheduling — setting up meetings, calendar events, automated replies, navigation, online assistance, payments.

Personalization and Security — compiling playlists, product suggestions, mood-based ambiance control, surveillance, and security.

Bridged.co Services: Voice Recognition, Speech Synthesis, Search Relevance.

Chat-bots

Chatbots training | Blog | Bridged.co

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.

Bridged.co Services: Chat-bot Training, Virtual Assistant Training, NLP.

Sentiment Analysis

Sentiment analysis | Blog | Bridged.co

The ability to monitor end-user opinions of a brand or product and gain an understanding of the same on a large scale is clutch in any competitive scenario. Customer retention has become a zero-sum game and sentiment analysis stands at the center of this marketing field. Armed with NLP and machine learning, AI can listen to the scores of available user opinions across multiple platforms be it social media or community forums or even personal blogs. Accurate analyses of brand value at scale provided by accurate AI are invaluable to businesses.

Bridged.co Services: Brand Sentiment Analysis, E-commerce Recommendations, User Content Support.

Customer Service

Customer service | Blog | Bridged.co

AI 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. AI can be put up to a) responding to common queries, b) as a first layer of gathering service request info and routine troubleshooting, c) integrating with the resolution system, learning from successful cases, and suggesting or implementing final calls. AI makes the whole system faster and more efficient.

Bridged.co Services: Chat-bot Training, Sentiment Analysis, User Content Support.

Translate languages as you speak

The need for a multi-language translation book or for a local guide to communicating your need in a tongue you don’t speak is reduced with the advent of live translation by conversation bots that speak your message out loud, as and when you call on them right from your phones and smart devices.

Bridged.co Services: NLP, Voice Recognition, Speech Analysis.

Real-time Transcription

You can count on AI to take down notes for when you are in meetings or need to parse audio or video clips, or just want to pen down your thoughts. Transcription of speech to text is a very common application and finds use in several business tasks.

Bridged.co Services: Audio/Video Transcription, NLP, Voice Recognition.

We are at a very exciting juncture in the development of AI technology. New machine learning techniques including deep learning applied to NLP processes have made it possible to stretch the boundaries of what can be built using AI bots.