Generative AI — Subset of Artificial Intelligence

Introduction:
In recent years, the field of artificial intelligence has witnessed a remarkable evolution, with one of its most intriguing manifestations being Generative AI. This innovative technology has captured the imagination of researchers, artists, and enthusiasts alike, opening up new avenues for creativity and expression. In this blog really want to give introduction about Generative AI.

Before we begin with generative AI, it’s very important to understand about artificial intelligence, Machine learning and Deep learning basics.

What is Artificial Intelligence?

Artificial Intelligence, or AI, is like a super smart helper that can understand and learn from information, just like a human being. But instead of a brain, AI uses computers and special programs to do its thinking.

Here’s an example to make it easier to understand:

_Imagine you have a robot toy car. You can control it with a remote control and tell it to move forward, backward, left, or right.
_Now, let’s say you want the toy car to learn how to move around your room without you controlling it.
_You can use AI to teach the toy car by giving it information about your room, like where the walls, furniture, and obstacles are.
_AI will use this information to create a map of your room in its computer brain.
_Then, you can tell the toy car where you want it to go, and AI will figure out the best path to take without disturbing into anything.

That’s how AI works! It learns from information and uses it to make decisions and perform tasks. AI is used in many different things we see and use every day.

What is Machine Learning?

Machine learning is subset of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. Machine learning algorithms use historical data as input to predict new output values. The goal is to have computers imitate intelligent human behavior and perform complex tasks such as voice recognition, medical diagnosis, and fraud detection.

Machine learning involves designing algorithms that can learn from and make decisions based on data. It enables computers to perform tasks that were traditionally performed by humans, such as facial recognition, language translation, and speech recognition.

There are many different types of machine learning algorithms, some of the most popular algorithms include:

• Supervised learning: In supervised learning, the algorithm is trained on a dataset of labeled data. The goal is to learn a mapping from the input data to the output labels. For example, a supervised learning algorithm could be trained on a dataset of images of cats and dogs, and the goal would be to learn to classify new images as either cats or dogs.
• Unsupervised learning: In unsupervised learning, the algorithm is trained on a dataset of unlabeled data. The goal is to find patterns in the data without being told what to look for. For example, an unsupervised learning algorithm could be trained on a dataset of customer purchase data, and the goal would be to find groups of customers who have similar buying habits

What is Deep Learning?
Deep learning is not new concept rather this concept came around 1950 when researcher was thinking can application also learn something like we human being learn. that’s where concept of Neural network came into the existence. so deep learning is subset of machine learning, here specifically you will be using multi-layer neural network, for performing various tasks. like, classification, regression, image classification, sentiment analysis etc.

Deep learning is part of a broader family of machine learning methods, and it uses techniques mention in below diagram.

Deep Leaning Techniques

What is Generative AI?

Generative AI, a subset of machine learning, enables computers to generate new data or content that appears realistic and authentic.

Generative models, such as neural networks or GANs (Generative Adversarial Networks), learn patterns and correlations within data to create new, previously unseen outputs. please refer the above the diagram for generative ai which is one of the deep learning technquie.

This type of model is basically having capability to generate new content. And it will train with some amount of content but afterward whenever we give any kind of input data. it will be able to generate his own data/content.

Now you may have question like why Generative AI? Becoz now companies like OpenAI are exposing their API itself, so people can use those trained model according to their need and use cases also they customize the model based individual needs.

Key Concepts of Generative AI:

• Supervised Learning: Generative models can be trained using supervised learning, where they are fed with paired data sets (inputs and outputs).
• Unsupervised Learning: Unsupervised models learn from unlabeled data, discovering hidden patterns and structures within the data set.
• Reinforcement Learning: Generative models can be trained using reinforcement learning, where they interact with their environment to optimize outcomes.

Types of Generative AI:

• Text Generation: Generative AI can create realistic text, such as language translation, summarization, and even creative writing.
• Image Generation: Generative AI can create new images from scratch, generate variations of existing images, or enhance low-quality images.
• Audio Generation: Generative AI can create realistic music, speech synthesis, or modify existing audio files.
• Video Generation: Generative AI has the potential to create realistic videos, generate facial animations, or even produce special effects.

Applications of Generative AI:

• Creativity and Art: Generative AI is used to create unique artworks, generate new music or compositions, or explore various creative possibilities.
• Data Augmentation: Generative AI can generate synthetic data to augment existing data sets, improving machine learning algorithms’ performance.
• Healthcare: Generative AI is used to generate synthetic medical images for training medical imaging systems or to develop new drugs and treatments.
• Education: Generative AI can be used to create interactive educational content, personalized learning experiences, or generate questions and assessments.

What is LLM model?
LLM mode is subset of generative AI, A Large Language Model (LLM) is a type of artificial intelligence (AI) that has been trained on a massive amount of text data. This training allows the model to understand and generate human language, perform various natural language processing (NLP) tasks, and even engage in creative writing. LLM model are giving more accuracy then before because they are trained with huge number of datasets.

LLM models like ChatGPT3.0, ChatGPT4.0 and GOOGLE BARD etc are created by using the ‘Transformer architecture’.

Conclusion:
Generative AI, with its ability to create new and unique content, has the potential to revolutionize various industries. While it brings exciting possibilities, it also presents challenges in terms of ethics, bias, and misuse. As this field continues to evolve, we can expect to see generative AI becoming an integral part of our lives, inspiring new forms of creativity and innovation.

References:
YouTube videos
https://www.coursera.org/learn/introduction-to-generative-ai/home/week/1