We are living in the age of Big Data and intelligent technologies. The growth in data has resulted in the development of new technology and products that are more creative. The terms artificial intelligence, deep learning, data science, and machine learning are utilized frequently. Their applications are visible on the ground.
Although they may be familiar terms, there needs to be more clarity in these technologies. Let’s explore these technologies and then explore ML vs. AI vs. NLP. Let’s get going.
Table of Contents
What is Artificial Intelligence (AI)?
The term refers to Intelligence being created artificially. Also, Artificial Intelligence is a field of computer science that permits computers or machines to learn and complete the tasks that demand Intelligence customarily carried out by humans. We consider AI as machines doing our jobs in a language that laypeople do not understand. They don’t know that AI is a part of our everyday lives. e.g., AI has helped make travel more accessible.
In the beginning, people would look up printed maps. But by using maps and navigation, you can determine the best routes, alternate courses, traffic jams, roadblocks, and so on. Humans have been fascinated by automation since the very beginning of the technology’s adoption. AI is a way for machines to make decisions without human intervention. It’s a broad field that is part of the field of computing science.
AI systems are classified into three categories.
- The ANI type is Artificial Narrow Intelligence. This is a goal-oriented system that is programmed to accomplish a specific task.
- AGI (Artificial General Intelligence) lets machines comprehend, learn and act in a way that’s not different from humans in a particular scenario.
- ASI (Artificial Super Intelligence) is a possible AI that allows machines to display Intelligence superior to the most brilliant humans.
Artificial Intelligence is not limited to machine learning or natural language processing. It includes other fields like object detection, robotics, etc.
Different Types of Artificial Intelligence.
- Artificial Super Intelligence: The term is the moment when machines will be able to surpass human capabilities. ASI is currently a hypothetical scenario depicted in science fiction books and movies when machines can take over the entire world. They will develop self-awareness and begin evoking emotions, beliefs, and desires. The ASI systems will have a more remarkable ability to remember, make decisions, and have problem-solving skills and will eventually be superior to humans.
- Artificial General Intelligence: Often referred to as powerful AI, also known as deep AI. It is comprised of machines that execute intellectual tasks similar to human intelligence. They can think, be taught, and use their skills to solve issues. Some experts believe the possibility that AGI could ever become a reality. Some even think it is unneeded. There are many characteristics AGI systems must possess. They include common sense, background knowledge as well as transfer learning. The likelihood of creating AGI systems is small because we need to comprehend our brains.
- Artificial Narrow Intelligence: Also known as weak AI, involves applying AI for specific jobs. The most effective instance of ANI can be seen in Alexa. It performs a particular set of functions. These systems collect data from a comprehensive collection of data and are trained to execute only one task. A majority of AI systems we use are built upon Narrow AI. Other applications that utilize this AI include Google Assistant, Siri, Google Translate, recommendation systems, and others. We refer to ANI as weak AI since the programs do not have the same intelligence. They are not aware and conscious as they cannot think for themselves.
What is Machine Learning (ML) & what can it be being used to do?
Machine learning allows computers to make predictions or decisions using historical data without being explicitly programmed. Machine learning uses a considerable amount of semi-structured and structured data to ensure that a machine-learning model can produce precise outcomes or provide predictions based on the data. Machine learning is an algorithm that uses previous data to develop independently. It is only applicable to specific areas.
For instance, if we’re developing a machine-learning model to recognize images of dogs, it’ll only produce results for dog photos. However, if we add new data, such as an image of a cat, it ceases to be responsive. Machine learning is employed in various ways like online recommendation systems, Google search algorithms, Email spam filters, Facebook Auto friend tagging suggestions, etc.
In today’s extremely competitive marketplace, there is a lot of competition. Using AI and personalized analysis to evaluate every possible contact point and interaction with customers to create a deeper understanding of the factors that drive customers’ decisions and behaviors is essential.
Different Types of Machine Learning
- Reinforcement learning: In this education, computers are taught to make choices to reach their objectives in challenging situations. It’s similar to learning through trial and failure. Like humans learn through failures, algorithms learn from making mistakes. It helps you identify the error since it comes with time, cost, or other consequences. For instance, an algorithm learns how to engage in a virtual game with numerous obstacles.
- Unsupervised learning: In this type of learning, the machine is under no oversight while learning. The algorithm can determine the pattern of data by itself. The algorithm is fed data that is unlabeled (data that is not labeled with labels like tweets and news articles). Many recommendation systems are available online using this kind of learning. They can learn from the user’s actions and can predict the outcome.
- Supervised learning: In this kind of machine learning, it can learn under supervision. They know by feeding the data with labels (data that has been labeled with some or all brands, such as the image is identified as flowers) and clearly stating that it is input (flower) and that the expected output will be a flower as well. The kind of data is referred to as training data, which is used as input for the machine. The inputs are then mapped to the outputs in supervised learning.
What is Natural Language Processing (NLP) & what is it used for?
Natural language processing, also known as NLP in a sense it’s often abbreviated, is an aspect of AI that reads raw writing text (in natural human dialects), interprets it, and converts the text into a language that computers can comprehend. NLP can conduct an intelligent analysis of vast amounts of plain text and provide conclusions from the data. This technological advancement has made it possible to communicate that connects humans to machines (computers), which has led to the creation of applications such as sentiment analyzers and text classification, chatbots, and virtual assistants. Virtual assistants such as Siri and Alexa are the most well-known examples of NLP used daily. Natural Language Processing (NLP) allows computers to recognize human language.
In the background, NLP analyzes the grammatical structure of sentences and the personal meaning of words and then utilizes algorithms to find the meaning of words and provide outputs. It makes an understanding of the human language so it can perform various tasks. The most well-known example that uses NLP is virtual assistants such as Google Assist, Siri, and Alexa.
NLP recognizes written and spoken texts like “Hey Siri, where is the closest health centre?” and transforms them into numbers, making it easier for machines to comprehend. Another popular application of NLP is chatbots. Support teams can tackle issues by recognizing the standard language requirements and reacting instantly. There is a myriad of other applications that you use, and you’ve likely encountered NLP without even realizing it. Tips for writing a text when you write an email, requesting the possibility of translating the contents of a Facebook post in a different language or filtering out promotional emails that are not yours in your spam box.
In simple terms, Natural Language Processing aims to make the human language – complicated, unclear, confusing, and varied – easier for machines to comprehend.
Features of NPL.
- Sentiment Analysis: The tool identifies the emotions of the text and categorizes opinions into positive, negative or neutral. You can observe how it operates by simply pasting your text into the free software for analyzing sentiment. Businesses can better understand customers’ opinions about their brands or products by studying posts on social media, such as product reviews and online surveys. For instance, you can examine tweets that reference your company in real-time and find out the comments of angry customers in a matter of minutes. It is possible to send an email survey to discover what customers think of the quality of your customer service. By looking at responses open to interpretation in NPS surveys, you will find out what aspects of your customer experience receive favorable or unfavorable feedback.
- Translating Languages: Machine translation technology has significantly improved in the past couple of years, and Facebook’s translations have achieved the highest performance in the year 2019. Tools for translating allow companies to communicate across different languages, which helps them improve their global reach or expand into new markets. They can also train their translators to recognize the specific terms used in every industry, such as medicine or finance. This means you don’t need to worry about faulty translations typical of standard translation tools.
- Text Extraction: It allows you to extract pre-defined information from a text. If you handle large quantities of data, the tool will help you identify and identify relevant terms and features (like the codes for products, colors, and specifications) and also named entity names (like names of individuals or locations, names of companies’ emails, places and more.). Businesses can utilize extraction of text to locate essential words in documents that are legal, find the keywords used in support tickets for customers, or extract specifications for products from a text paragraph in addition to other applications. Does this sound intriguing? Do you have a keyword extraction tool you can test?
NLP, AI, Machine Learning: What’s the Difference?
Natural Language Processing (NLP), Artificial Intelligence (AI), and machine learning (ML) are frequently utilized interchangeably, and it is possible to need clarification when trying to distinguish between these three. You first need to know that NLP or machine learning are two subsets that comprise Artificial Intelligence. AI is a broad term used to describe machines that mimic human intelligence. AI encompasses systems replicating cognitive abilities, such as learning from previous experiences and solving issues. It covers a variety of applications, ranging from self-driving cars to automated systems.
Natural Language Processing (NLP) is the process by which computers comprehend the human voice and how they translate it. With NLP machines, they can interpret spoken or written text and perform tasks like translating words and keyword extraction, subject classification and many more.
However, you’ll require machine learning to automate these processes and provide exact responses. Machine learning employs algorithms that teach machines to improve their knowledge and experiences without explicitly being programmed. AI-powered chatbots like they use NLP to understand what users are saying and what they are planning to do and machine learning to provide more precise responses after taking lessons from previous interactions.
Conclusion
Natural Language Processing (NLP) is the AI component investigating how machines interact with human language. NLP is part of AI that works in the background to improve the tools we use daily, like chatbots, spell checkers, or language translators.
Through machine learning algorithms, NLP creates systems that can learn to complete tasks independently and then improve with the experience. NLP-powered tools can help you sort social media posts according to sentiment or extract names of entities from emails for business and other emails.
