A Theoretical Deep Dive Into Artificial Intelligence
Artificial Intelligence (A.I) is a branch of computer science that deals with machine learning human-like intelligence. Let us take a deep dive into A.I.
Human-Computer Interactions (HCI)
Human-Computer Interaction (HCI), also known as Human-Machine Interaction (HMI), refers to the communication and interaction between a human and a machine through a user interface. These days, natural user interfaces such as gestures are very popular as they allow humans to control machines through natural and intuitive behaviors. A few examples of Human-Machine Interface devices that we use in our day-to-day life are touchscreens, chatbots, and self-driven cars.
Big Data is a collection of a huge amount of data. It is very large and complex. The measurement shows that 500+TB of the new data gets consumed into the data sets of the social media website "Facebook" consistently. This data is mostly produced in terms of photo and video uploads, message exchanges, putting comments. Similarly, Apple (Siri) And Amazon (Alexa) deal with a huge amount of voice data. These companies collect a large amount of data, and these applications depend on this data to make a quick decision
Characteristics Of Big Data
The characteristics of big data can be listed on the basis of 3Vs:
Volume: The amount of data that is being generated
Variety: The different types of data, such as textual, media, and sensor or streaming data.
Velocity: The speed at which data is being generated, such as millions of messages being exchanged at any given time across social networks.
Types Of Big Data
These are three types of big data:
- Structured Data: Any data that is available in a well-organized manner is called structured data like the Database of Employees in a company
- Unstructured Data: Any data that is not organized or neatly kept is unstructured data. E-mails, videos, and audios are unorganized data and are not kept in a tabular format.
- Semi-Structured Data: The data that is not well organized in tabula formats but still shows some readable and understandable format is called semi-structured data.
Big Data and AI
The concept of big data is very popular for years. Big Data is a large volume of data that is required to run a business.
Data is an important domain of Al because it makes machines powerful. With the help of data, a machine can solve problems and take decisions based on previous incidents. It makes machine learning easy and powerful.
Companies like Facebook, Amazon, and Google collect a huge amount of data from users and retrieve useful information from this data and use them for their profits. For example, based on the search history, they know the interests of users and recommend the suggestion accordingly.
Computer Vision (CV)
Computer vision is a field of Artificial Intelligence that aims to develop techniques to help computers "see" and understand the content of digital images like photographs and videos. On the basis of this understanding, it provides useful results
Applications of Computer Vision
Enables self-driven cars to make sense of their surroundings
Plays an important role in facial recognition applications
Allows online photo libraries like Google Photos to detect objects and automatically classify the images by the type of content they contain
Its algorithms help automate tasks such as detecting cancerous moles in skin images or finding symptoms in X-ray and MRI scans
The best example of a Computer Vision is Lenskart taking your images and puts up virtual specs to show you how you look. Similar to this is the trial of a dress without wearing it.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand human language. Its goal is to build systems that can make sense of the text and automatically perform tasks like translation, spell check, or topic classification. NLP combines computational grammar rule-based development of human language - with statistical, machine learning, and deep learning models
Applications of NLP
Machine translation: Google Translate is an example of NLP technology at work. It automatically detects the language you type and translates it to the language you want.
Span detection: The best spam detection technologies use NLP's text classification abilities to scan emails in a language that often indicates span or phishing.
Virtual assistants and chatbots: Virtual assistants such as Apple's Siri and Amazon's Alexa use speech recognition to recognize patterns in the voice commands and natural language generation to respond with the appropriate action or helpful commands
Machine Learning (ML)
The technique that enables a computer to learn without being directly programmed to do so is called Machine Learning. This is the process of a machine automatically learning from its experience of handling similar tasks. It enables the machines to perform tasks that are not programmed for. For example, the face recognition system is an example of pattern recognition.
It is a branch of machine learning that enables the system to learn from machine learning algorithms, that extract information from multiple layers of raw input. Thus, Deep Learning is defined as a subset of the machine learning technique that learns features and tasks directly from data.
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