Artificial Intelligence Certification Course
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Generative AI is changing how we generate content, solve problems, and engage with technology. Unlike typical AI models, which classify or forecast based on incoming data, generative AI generates new content—whether text, images, music, or even code. This technology generates human-like outputs by utilizing advanced machine learning techniques such as deep learning and neural networks.
What is Artificial Intelligence?
What is Generative AI | How Generative AI Works | Generative AI Explained for Beginners | Edureka
In this video, on What is Generative AI , we will dive into Generative AI, exploring its definition, key examples, and diverse applications across sectors like healthcare and education. We’ll also discuss how generative AI shapes different fields, highlight its potential future impact, and showcase a practical mini-project. Using the YouTube Transcript API and Google’s generative AI, we will learn how to build a video summarizer, providing a hands-on example of generative AI’s capabilities. This video is ideal for tech enthusiasts and beginners alike, as it unpacks generative AI’s transformative role in the tech world.
Artificial Intelligence is a branch of computer science focused on creating systems that can perform tasks that typically require human intelligence. These tasks can range from simple ones like speech recognition and image processing to complex tasks like decision making problem solving etc.
There are different types of AI such as Narrow AI(weak AI),General AI(Strong AI),Superintelligence AI.
Now AI works using algorithms particularly machine learning algorithms so that it can learn form the data base . It follows three main approaches like Supervised Learning ,Unsupervised Learning ,Reinforcement Learning.
key Subfields of AI are Machine Learning ,Natural Language Processing , Computer Vision ,Robotics etc.
Real-life applications of artificial Intelligence can be seen in the field of HealthCare ,Finance , Customer Service ,Entertainment etc.
if you want training and certification in Artificial Intelligence then visit the Edureka website for certification course in Artificial Intelligence
What is the difference between Generative AI and Artificial Intelligence?
| Aspect | Generative AI | Traditional AI |
| Definition | Focuses on creating new content or data based on input patterns. | Focuses on performing tasks based on predefined rules and learning from data. |
| Data Output | Generates new data, such as text ,images ,music ,code etc. | Predicts or classifies data based on existing patterns. |
| Learning process | Uses unsupervised-self supervised learning to model data distribution | Typically relies on supervised or reinforcement learning to achieve specific tasks |
| Examples | Chat-GPT,DALL-E,music ,Generative models. | Recommendation system ,image classification, autonomous driving etc. |
| Creativity | Simulates creativity by generating novel outputs. | Executes tasks based on pre-learned knowledge without generating new creative outputs. |
| Architecture | Typically relies on models like GANs and VAEs or transformers. | Uses Traditional models like decision trees ,SVMs , or neural networks. |
| Human-like output | can produce human-like text, images or even speech | Primarily focused on decision-making and task automation. |
| Data Requirements | Requires large amount of diverse data to generate realistic | Can work effectively with structured and often smaller datasets. |
| Use-Case | Content Creation , code generation ,drug discovery. | Fraud detection,optimization,classification tasks |
Why we need Generative AI?
There are many flaws in traditional AI when it comes to complex problems , therefore now people are finding a new way to deal with those problems and hence the deep learning concept got famous the scientist called it Artificial Generative Intelligence now commonly known as Generative Artificial Intelligence or Gen AI.
Some of the most critical and important ones include:
Generation of Creative Content:
Using generative AI, it could produce new text, images, music, or videos that would come up with unique ideas to revolutionize industries such as art, media, entertainment, and advertising through the automation of creative efforts.
Acceleration in Automation:
With generative AI, content creation, product design, and software code generation could be automated, and hence, improved efficiency coupled with reduced manual work effort resulting from accelerating workflows.
Hyper-personalization at Scale:
Using personalized content, recommendations, or products, it can develop experiences uniquely designed and made based on the prevailing preferences of individual users.
Data Augmentation:
It can create synthetic data, especially in low-data conditions, thereby assisting the machine learning model in improving its accuracy and performance for applications such as health care or autonomous systems.
Problem Solving:
Generative AI in scientific research discovers drugs, new materials, and solutions by simulating different possibilities, thus saving time for experimentation.
Natural Language Interaction:
It can power advanced chatbots and virtual assistants that would potentially add more human-like interaction in user experience, ranging from customer services to online shopping.
Exploratory Creativity:
Generative models can be used to discover creative ideas one never thought to have before, the possibility of innovative products or designs in design and development.
Cost Efficiency:
Such models automate tasks that otherwise require human creativity, thereby reducing costs and scaling creative processes for businesses.
It solves complex problems, such as protein folding in biology, opening new avenues for breakthroughs in genetics and molecular science.
Ethical AI:
Generative models bridge the gaps of language and culture by developing locally relevant and inclusive content that helps businesses to reach the masses across regions.
Here’s a graph showing the increase of Generative AI by Industries across domains:
Reference from Spherical insights showing the global use of generative artificial intelligence in healthcare market.
Reference from webclues showing the Generative Artificial Intelligence in Education Market.
Reference from Grand View Research showing the Generative Education Market.
Generative AI was really important because it pushed the boundaries of what AI should do, especially in fostering innovation and creativity and creating efficiency in different domains.
What is Generative AI?
A new line of artificial intelligence includes generative AI – machines that learn patterns from existing data so as to generate new content, such as text, images, music, and even code. Different than other types of AI, based mostly on analysis and predictions given in terms of predetermined rules, generative AI itself produces something new; it creates something altogether mimicking human-like creativity. From realistic artwork to coherent articles, it helps create industries that are innovative, personalize experiences, and automate creative processes at scale. Examples are Image generation by DALL-E, Text or Content Generation by chatgpt or Code generation and suggestion by GitHub copilot etc.
How Generative AI Works?
The learning process by Generative AI
It learns or refers to patterns and structures in large data sets. This step-by-step process is as follows:
Data Input:
A generative AI model takes in large data, be it images, text, music, etc., upon which to learn.
Learning Patterns:
The model traces the hidden, underlying patterns of data using deep architectures such as neural networks. For example, with a generative AI that uses output as text, it learns sentence structures, grammar, and context.
Model Training:
Techniques like unsupervised learning can be applied. This way, the AI trains without explicit output labels but learns how it is supposed to generate new data based on the pattern learned.
Generative new content:
Once that’s been trained, it’s ready to create entirely new outputs that are quite similar in essence to the original data. For example, it will produce very realistic images, coherent text, or even really complex structures, such as music or even code.
This output, hence, can be further refined by reinforcement learning or human feedback while allowing the creativity and accuracy of the model to build up over time
What are the applications of Generative AI(Use-Case+Tools)?
1. Content Creation
Use-Case: Produce blog posts, articles, marketing copy, or even a novel.
Tools:
Open Ai’s GPT models, e. g. ChatGPT
Jasper: AI content writing tool
Copy.ai: Marketing content generation
2. Image Generation
Use-Case: Produce artwork, realistic images, or design prototypes from textual descriptions.
Tools:
DALL·E: Generates images from text prompts
Midjourney: AI art creation
Runway ML: Tools for AI-generated visual effects and design
3. Music Composition
Use-Case: Create original music tracks for games, films, or personal projects.
Tools:
Amper Music: AI music composer
AIVA (Produces soundtracks)
Endless (AI music collaboration platform)
4. Video Generation
Use-Case: Generation of AI video, animation, or content editing based on a script or storyboard
Tools:
Synthesia: AI video creation with digital avatars
Runway: AI-powered video editing and creation
Pictory: Turn text into videos on autopilot
5. Code Generation
Use-Case: Instant generation of code on the fly to aid in software development based on natural language prompts
Tools:
GitHub Copilot: AI pair programmer
Tabnine: AI code completion tool
Codex: OpenAI’s code generation model
6. Chatbots and Virtual Assistants
Use-Case: Building chatbots for Customer support, Sales or Personal Assistance.
Tools:
Dialog flow (Google’s developer platform of chatbots).
Rasa – (A free and open-source conversational AI)
ChatGPT API (To build Custom chatbot)
7. Fashion and Design
Use-Case: Developing new fashion designs or product prototypes based on current trends.
Tools:
DeepArt – AI-generated fashion designs
Designify – AI-powered design creation
Artbreeder – Collaborative image creation, which includes fashion
8. Drug Discovery and Healthcare
Use-Case: Enabling new drug discovery by generating molecular structures and simulating what will probably occur.
Tools:
Atomwise (AI for drug discovery)
Insilico Medicine (Generative AI for drug design)
BenevolentAI (AI-driven biomedical insights)
9. Game Development
Use-Case: Automatic generation of game environments, characters, and storylines
Tools:
Promethean AI (Generative AI for game worlds)
Modl.ai (AI for game testing and development)
GANPaint Studio (Generates game textures and environments)
10. Synthetic Data Generation
Use-Case: Generation of artificial data to train machine learning models given scarce or sensitive real-world data
Tools:
Synthesis AI (Creates synthetic data for computer vision)
Gretel (Generates synthetic data)
Primarily AI (AI-enabled Synthetic data platform)
Affect of Generative AI on jobs (This year Data)
the generative AI will result in job impact with displacement, augmentation, and new job creation. The most vulnerable jobs are repetitive clerical works, while the ones demanding creativity, problem solving, or interpersonal skills would likely be augmented ones, as seen in the fields of healthcare and STEM. By 2030, for instance, as much as 30% of work hours might get automated by then, and the rush to AI adoption is going to re-jig many industries, from manufacturing to professional services, in the long term. But job losses come with a cost – that of new roles, which are in the development of AI ethics and digital management.
Reference from Forrester data of generative ai effect on jobs US based data.
Reference from human skills and fullcircle, the chart is showing how generative ai transforming the jobs now-a-days.
What is the future of Generative AI?
This has huge implications for industries, and the future of Generative AI looks very promising. “Along with progress in the field, it will help in creativity and automation, as technology makes highly real content such as images, text, and music with minimal human intervention,” said experts, which may lead to automation of up to 30% work tasks in general content creation and marketing, and design by 2030. It will trigger future discovery and innovation in medicines and drugs. Issues related to ethical challenges like bias, misinformation, and the issue of data privacy will fuel the demand for rules and regulations in this sector. That AI must be collaborative; that is, it must work with humankind, together solving human problems to determine a middle ground between human creativity and machine efficiency. Generative AI, then, will redefine whole industries, even creating new opportunities for employment in AI development, ethics, and governance.
Reference from appinventive showing the global increase in Artificial intelligence in the market across different industries.
Reference from ResearchGate showing AI and ML in different big corporate sector
So, in conclusion, we can say that Artificial Intelligence started as a rule-based system, handling structured tasks with predefined logic. Today, AI, especially Generative AI, is revolutionizing industries by creating human-like content, automating workflows, and enhancing personalization. In the future, AI will push creative and scientific boundaries, transforming automation, problem-solving, and innovation at an unprecedented scale. However, ethical challenges like bias, misinformation, and data privacy must be addressed to ensure responsible AI development. As AI continues to evolve, collaboration between humans and machines will define the next era of technological advancement.
If you want certifications in Generative AI and large language models, Edureka offers the best certifications and training in this field.
- Generative AI introduction
- Generative AI Course
- Generative AI in Software Development
- Mastering Generative AI tools
- Prompt Engineering Course
For a wide range of courses, training, and certification programs across various domains, check out Edureka’s website to explore more and enhance your skills!
