Day 1: Introduction to AI, ML, and GenAI Fundamentals
On the first day you should understand GenAI in detail along with the important machine learning concepts. Understanding AI and Machine Learning is important first before delving into the world of Generative Artificial Intelligence (GenAI).
Let's Get Started:
These days machine learning technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) are in very high demand in the market. These technologies are transforming the world and affecting all the industries. In healthcare it is used in drug discoveries, medicine research, personalized medicines, robotic surgeries and many others. In the finance industry it is used to handle vast amounts of data and generate actionable insights from business data.
In the public domain apps it is used for creating smart assistants, recommendation engines and many others. It is also used to generate text, video and audio contents which is being used across the industry in creation of audio visual products.
In the finance and banking domain AI/ML is used to develop fraud detection systems, predictive analytics systems, online transaction monitoring and other day-to-day activities. The use of AI/ML in this industry is huge and it is transforming the banking industry.
In this section we are going to teach you the fundamentals of AI, ML, and GenAI, which will provide you a clear understanding of these. You will understand these concepts and get ready for learning the next topic in Generative AI.
What is Artificial Intelligence (AI)?
AI is a set of technologies for enabling computers to simulate human intelligence and perform tasks such as thinking, reasoning, learning and making autonomous decisions. The full form of AI is Artificial Intelligence, which comes in simple rule-based form to complex neural networks form. The neural networks are highly sophisticated and capable of mimicking cognitive functions.
Types of AI
- Narrow AI (Weak AI): This is the simplest form of AI and it is designed for a specific task (e.g., voice assistants, spam filters).
- General AI (Strong AI): this is a hypothetical AI that can perform any intellectual task that a human can. To train such models large amounts of training data and huge computing power is required.
- Superintelligent AI: This is a theoretical future AI that surpasses human intelligence in all aspects. In further we might see such models.
Applications of AI
There are wide applications of AI in various industries. You can check industry wise applications of AI at the tutorial page What are the applications of Artificial Intelligence?. But this tutorial lets you see the most common Application of AI.
- Chatbots and virtual assistants (e.g., Siri, Alexa) - You can use the chatbots and virtual assistants for getting the answer of your query or ask assistant to generate content for you
- Autonomous vehicles - This is one of the advanced uses of AI in the automobile industry.
- Facial recognition systems - AI can be used for facial recognition in the apps and applications.
- Smart home devices - There is a wide use of AI to control smart home devices.
What is Machine Learning (ML)?
Machine Learning is one of the branches of Artificial Intelligence that enables the computer program to learn from the data without actual writing of code. In machine learning, special mathematical models are developed that learn from the data and generate the output or act autonomously. Output of ML depends on the type and amount of data used for training the model. In ML static rules are not used, instead the ML system improves over time by learning the patterns from the new data and improves its decision making capabilities.
Machine learning is a subset of AI where computers learn intelligence by reading a lot of data known as datasets. Learning process is known as model training and accuracy check of the model is known as model validation. Once learned, the model can be deployed on different machines to do the work such as chat, text generation, image generation and other tasks for which the model is trained.
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How Machine Learning Works
ML systems especially designed mathematical models (designed using the libraries such as Tensorflow, Keras and others) which are trained on large datasets. During the training process these models are fed with the large data sets in a standardized format and model analyses input-output pairs to identify patterns. Model is trained and validated to make it more accurate, in case of errors it is re-trained with more data. Once trained, models can be used for prediction or classifying the data based on their learning.
Types of Machine Learning
- Supervised Learning: The model is trained on labeled data (e.g., email classification: spam or not). Here input data and output data are provided to the model during the training process.
- Unsupervised Learning: The model identifies patterns in unlabeled data (e.g., customer segmentation). In this case only input data is provided to the model during the training process and the model learns itself by identifying the patterns in the data.
- Reinforcement Learning: The model learns through trial and error, receiving feedback (rewards or penalties). In this case the model is rewarded for correct outcome and penalties in case of wrong/error output. This way model training is done.
Popular Algorithms
Following are the popular Machine learning algorithms:
- Linear Regression
- Decision Trees
- Support Vector Machines (SVM)
- Neural Networks
What is Generative AI (GenAI)?
Generative AI is a type of AI focused on the generation of content such as text, image, audio, videos and even the responses for hardware such as autonomous cars. This field is a rapidly growing branch of AI and there are a lot of job opportunities for the professionals having the right experience in this domain.
Generative AI leverages deep learning models such as large language models (LLMs) and generative adversarial networks (GANs) for producing output for a given prompt. There are many LLMs available today both free and paid which can be used in the development of GenAI based applications.
How GenAI Works
As all other Machine Learning models, Generative AI models are trained on massive datasets, which gives very high intelligence and knowledge to the model. The GPT (Generative Pre-trained Transformer) model is trained to learn the structure, grammar, and semantics of language, which makes it so capable that we all know.
Popular GenAI Models
- GPT-4, ChatGPT (OpenAI): Text generation and conversation
- DALL·E (OpenAI): Image generation from text prompts
- Stable Diffusion / MidJourney: AI image generation
- Codex / GitHub Copilot: Code generation
Applications of GenAI
- Content creation (articles, poetry, music)
- Image and video synthesis
- Software development
- Personalized marketing
- Game design and storytelling
Check more at What are different types of Generative AI?
Key Differences Between AI, ML, and GenAI
Now we are going to explore the key differences between AI, ML, and GenAI.
| Feature |
AI |
ML | GenAI |
| Scope | Broad concept of intelligent systems | Subfield of AI focused on learning from data | Subset of AI/ML focused on content creation |
| Objective | Mimic human intelligence | Learn patterns and make predictions | Generate new data (text, image, etc.) |
| Examples | Robotics, expert systems | Fraud detection, recommendation engines | Text generation, AI art |
Benefits and Challenges
Benefits
- Increased automation and efficiency
- Enhanced decision-making through predictive analytics
- Personalized user experiences
- Creative content generation at scale
Challenges
- Data privacy and ethical concerns
- Model bias and fairness
- Explainability and transparency
- Job displacement due to automation
AI, ML, and GenAI are technologies which are revolutionizing the way we use technology in various industries. This section introduced you to the basic concepts of Generative AI which is necessary to understand topics discussed in further sections.
AI aims to replicate human intelligence to a greater extent. ML is a subset AI which learns from data without explicitly rule based coding. GenAI focuses on on the generation of text, image, audio, video and many such responses with the help of LLMs. You can even write the whole script of a film using LLMs.
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