Post 14: Five Basic Terms in the AI field for you to keep up with the ongoing trends!

This post today aims towards sharing five basic/ core terms in the Generative AI field, which are widely used in everyday lives. The content of today’s blogpost has been taken from the author’s research paper which is in progress of being published. Copying and using it without credits would amount to Plagiarism. Kindly refrain from doing the same.

Cursory information:

1. AI Algorithms: They are “a set of instructions to be followed in calculations or other operations”. These AI algorithms teach the computer to “Operate on its own”. They work by taking on the training data to learn on their own. These algorithms form the backbone of AI systems, enabling them to process data, identify patterns, and make predictions or decisions autonomously.
2. Machine Learning: ML is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior.  Machine learning starts with data — “numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports.” This data is “gathered and prepared to be used as training data, or the information the machine learning model will be trained on.”
3. Deep Learning: “Deep learning (DL) is a type of machine learning that uses artificial neural networks to learn from data.” These networks are “are inspired by the human brain, and they can be used to solve a wide variety of problems, including image recognition, natural language processing, and speech recognition.” “For example, in an image recognition task, the algorithm might learn to associate certain features in an image (such as the shape of an object or the color of an object) with the correct label (such as “dog” or “cat”).”
4. Natural Language Processing: NLP is “a subfield of computer science and artificial intelligence (AI) that uses machine learning to enable computers to understand and communicate with human language.” It allows “computers and digital devices to recognize, understand and generate text and speech by combining computational linguistics, the rule-based modeling of human language together with statistical modelling, machine learning and deep learning.” The NLP has enabled the era of “Generative AI”.
5. Large Language Models: LLM is “a type of machine learning model that can comprehend human-generated text and generate natural-sounding outputs.” These LLMs are used in GPTs such as “ChatGPT”. LLMs have arrived out of the blue along with new developments in generative AI. One of the key innovations in this space is the development of models like OpenAI’s DALL-E and DALL-E 2, which can generate images from textual descriptions. The sets of information are taken from the open available sources from internet such as books, posts and the often used applications using the same are “code generation systems, virtual assistants, content creation tools, language translation engines, automated speech recognition, medical diagnosis systems, scientific research tools, etc.”

(This piece of work is bits and pieces of the author’s original research work, copying without due credits would attract infringements/plagiarism of original creative work.)