Team Sep 28, 2024 No Comments
The world always finds a way to move forward. First, it was the discovery of fire, then the agricultural revolution, the writing systems, the industrial revolution, the internet, and now, it’s generative AI.
GenAI is one of the biggest forces driving change today. It’s transforming how businesses function and reshaping how we work and live.
With such a powerful technology, what’s in demand today can become outdated tomorrow. That’s why staying updated is so important.
In this blog post, we will explore the significant generative AI trends shaping the future. You will learn what’s happening in the world and where we are headed.
A report estimates that the global generative AI market could grow at a CAGR of 42% and become valued at USD 1.3 trillion by 2032. This means we are going to see an explosion of growth in this sector. So, here are some key GenAI trends that everybody should be aware of.
This is one of the biggest trends shaped by generative AI. Almost every industry is automating repetitive and laborious tasks with AI systems. Here are some examples:
This automation is helping individuals and businesses save time, increase productivity, and reduce costs. We can now focus on creative tasks that require human thinking and problem-solving, helping us find fulfillment in work.
No doubt, one latest report predicts that generative AI could add $2.6 trillion to $4.4 trillion to the global economy annually.
GenAI is changing the way developers write code. AI tools can help you write, optimize, and debug code faster than ever possible. A McKinsey report found that generative AI can reduce completion time for writing code by up to 45 percent.
GitHub Copilot is an AI developer tool that suggests code snippets, provides context-aware coding support, and assists you in debugging effectively. This tool is powered by OpenAI Codex, which aims to translate natural language into code.
Since developers don’t have to worry about repetitive tasks, they can now focus on solving complex problems and find creative solutions. So, if you are a programmer, you must learn how to use AI and write codes faster.
Related: How To Learn Generative AI
Creating videos used to be time-consuming and costly. But generative AI is making the process much quicker. With tools like Runway, you can now generate original video content based on text prompts!
Let that sink in for a second. You can simply write a prompt and get a realistic video as output within seconds. This means you can easily make YouTube videos, market your business, or even generate clips for movies. Your imagination is the limit now.
Besides, AI tools like Vyond can help you create realistic animated character videos. You can also modify existing videos by changing colors, elements, characters, backgrounds, etc. Generative AI speeds up the editing process by automating tasks like noise reduction, color grading, and object tracking.
You can watch this video to understand career opportunities in GenAI, including roles, salaries, and future demand:
This is an interesting generative AI trend. AI assistants can now do a range of tasks, from scheduling meetings and researching information to suggesting thrilling movies to watch on the weekend. AI assistants like chatbots also help businesses support their customers around the clock with personalized and human-like responses.
Because of natural language processing, AI assistants can better understand our language and the context of conversations. This helps them understand human emotions, appear more natural, and provide relevant and accurate responses.
As they improve, AI assistants and personal robots may become a part of our daily lives. Yeah, that sounds a lot like sci-fi movies but reports estimate that there could be 244 million personal assistance robots by 2030.
Related: What’s the Future of AI
Since GenAI can automate routine tasks, it can replace humans in certain jobs. Some of those jobs are data entry, bookkeeping, telemarketing, customer support, proofreading, retail checkouts, assembly line jobs, entry-level graphic designing and content creation, etc.
A Statistica report says that the labor market will see significant transformations, which will lead to the loss of around 83 million jobs over the next five years. McKinsey estimates that US clerk jobs could decrease by 1.6 million, retail salesperson jobs by 830,000, administrative assistants jobs by 710,000, and cashier jobs by 630,000.
The good news is AI will also create new opportunities for workers to focus on more creative and strategic roles. Besides, the demand for AI and machine learning specialists, data analysts and scientists, cyber security engineers, etc, is going to increase. That’s why you need to constantly adapt to the changes to be relevant in the job market.
With the above Generative AI trends, it’s clear that this technology is here to stay and transform the world. If you want to stay ahead and be a part of this exciting revolution, learning generative AI is the key.
You can simply join Ivy Professional School’s IIT-certified Generative AI course. Created in partnership with IIT Guwahati (E&ICT Academy), this program helps you learn in-demand AI skills from IIT professors and experts from Amazon, Google, Microsoft, etc.
Over 18 weeks, you will attend live online classes and gain hands-on experience with real-world projects. From building AI-powered apps to mastering skills like machine learning, deep learning, and large language models, this course will make you a generative AI pro.
By the end of the course, you will not only earn a prestigious certification from IIT Guwahati but also get placement support to find your dream job. Visit this page to learn more about the course.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 15, 2024 No Comments
Want to learn Generative AI? Well, that’s a smart choice. It’s an in-demand skill that can boost your career and help you land your dream job.
GenAI technology is spreading like wildfire. The whole world is fascinated by how it can think like humans and generate original, creative content, such as text, code, images, audio, and even videos. No wonder reports predict that the generative AI market could be valued at a tremendous $1.3 trillion by 2032.
Learning this technology now can set you apart and open up many exciting career opportunities. So, in this blog, I will tell you how to learn generative AI from scratch. You will find everything you need to get started and become a generative AI expert in 4–5 months.
Generative AI is a type of AI that can generate human-like content in response to a given prompt. It helps us write fast, generate realistic images, create quality music, do faster research, translate languages, provide 24/7 customer service, and whatnot. (Read this post to learn more about generative AI applications.)
When you start to learn GenAI, you may find topics like neural networks, machine learning algorithms, or large language models somewhat difficult. But here’s the thing: like any new skill, it starts out challenging but becomes easier as you give it some time.
The good thing is you don’t have to be a coding expert to begin. You can simply start from the basics, build your foundation, and keep learning advanced concepts. Within 4-5 months, you can be an expert. You may need the right resources and mentorship to do it easily and quickly. But as you stay consistent and curious, you will find generative AI isn’t as difficult as it seems.
Here are the most important topics you should study to master this technology. If you want more detailed topics, you can check the comprehensive generative AI syllabus.
Yes, you need to know programming. In fact, you will need a strong foundation in programming, especially in Python. So, start with the basic topics like Python data types, control flow, loops, and functions, which are essential for writing AI code. Understanding important libraries such as Pandas and Numpy for data manipulation will also help you handle data more effectively. You can watch the Python tutorial by Ivy Professional School on YouTube to develop your Python skills.
Once you know the programming basics, the next step is to understand what generative AI is and what its applications are. You also need to learn about different generative models and explore OpenAI APIs, like GPT and DALL-E. At this point, you can try to generate text with OpenAI API using Python functions.
You can’t learn generative AI without understanding the basics of machine learning. You will need to learn models like decision trees, linear models, and k-nearest neighbors (k-NN). You should study how classification and regression models work and learn to train them on different datasets. You can also learn about deep learning and understand architectures like Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), etc.
Text and image generation are two important topics in generative AI. Learn how models like GPT and tools like DALL-E generate creative text and images, respectively. You can try building apps like an AI Chatbot using Python, deploy it on a web platform, and integrate it with OpenAI’s API for real-time interactions. You can also build social media automation tools by implementing LangChain components and deploying them on a cloud platform. Next, you can learn how to build an image generation app integrating the DALL-E API.
Voice-based AI is another important area to study. Voice recognition systems analyze and process human speech, while voice generation systems like Whisper API produce natural-sounding speech. You should learn the basics of how these systems work, from feature extraction to text-to-speech algorithms. You can also integrate the Whisper API to create applications like voice assistants that can listen and speak just like humans. You should also know how to deploy the app on cloud platforms and test, monitor, and optimize its performance.
Multimodal generative AI has the capability to deal with different types of data, like text, images, audio, and video. To study this advanced topic, you should start with techniques like Early Fusion, Late Fusion, and Hybrid Fusion, which merge data from multiple sources to improve AI performance. You will also need to learn about progressive GANs, StyleGAN, and vision-and-language transformers (VLT) to handle complex multimodal tasks.
The best way to learn generative AI is to work on projects and get practical experience. You can implement the theoretical knowledge to build interesting generative AI apps like chatbots, content generation tools, voice assistants, etc. You can use APIs like OpenAI or DALL-E for tasks like text generation, image creation, or voice synthesis. The more projects you will work on, the more confident you will be. This will also build a solid portfolio to showcase your skills and expertise to employers and get a job at top MNCs.
Now, you can watch this one-hour video where I explain in detail how you can make a career in generative AI:
Here are the three best generative AI books that will help you learn what it is, how it works, what are the latest trends and how you can use it to solve real-world problems:
There are many courses that can help you learn GenAI from the basics and master advanced concepts. For example, Introduction to Generative AI is a 45-minute free course by Google Cloud that teaches the fundamentals of this technology.
However, if you are looking for a comprehensive, advanced course, you can enroll in Ivy Professional School’s GenAI Certification program in collaboration with IIT Guwahati. This live, online course will equip you with in-demand skills such as machine learning, deep learning, large language models, LangChain, RAG, and Transformers.
You will be mentored by IIT professors and receive a prestigious IIT-branded certificate. Additionally, you will work on exciting projects, building highly useful applications like an AI chatbot and a social media automation tool. And with solid career support, you will be well-prepared to launch your generative AI career. Visit the GenAI course page to learn more about the program.
Now that you know how to learn generative AI, you can begin your journey with confidence. GenAI is already helping businesses save time, reduce costs, and boost efficiency by automating repetitive tasks. This technology is only going to grow stronger over time. The sooner you master it, the more opportunities you will unlock for yourself. So don’t wait—start learning today, just as outlined in the post.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Aug 31, 2024 No Comments
A comprehensive generative AI syllabus acts like a roadmap. It tells you what essential topics you should study, what skills you should develop, and what you should expect from the subject. This way, you can plan your studies and make sure you don’t miss anything important.
In this blog post, I will break down the generative AI syllabus of Ivy Professional School’s IIT-certified GenAI course. You will understand what topics you should learn and in what order to easily master this AI technology.
If you want to become a generative AI expert, you can join Ivy Pro’s GenAI Certification course, which is made in partnership with E&ICT Academy, IIT Guwahati.
The course follows the following detailed syllabus designed by industry experts. It focuses not only on in-demand skills but also on interesting industry projects. This way, you gain solid practical experience and the confidence to face real-world problems.
This module covers the basics of Python programming, which is important for building generative AI apps. It starts with setting up Python, including installation and IDE configuration.
Then, you learn about Python data types such as strings, integers, lists, and dictionaries, followed by control flow, loops, and functions. You also learn libraries like Pandas and Numpy for data manipulation.
The module covers hands-on projects like creating functions for a recommendation system and preparing data for AI model training using Pandas.
In this module, you are introduced to the core concepts of AI and generative AI. Here, you understand what makes generative models unique.
The module covers various types of generative models and provides an overview of OpenAI APIs, particularly focusing on their application in text generation.
Then, you work on a project where you use Python to interact with OpenAI APIs to generate text.
Related: 7 Must-Read Generative AI Books
This module introduces the basics of machine learning. You learn linear models, decision trees, and k-nearest neighbors (k-NN) to build both classification and regression models.
The hands-on projects help you create and evaluate models using multiple algorithms to ensure robustness and accuracy.
Next, you start with deep learning. You learn what it is, the important concepts in it, and activation functions and optimizers.
The syllabus takes you through different deep learning architectures, such as Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks.
The hands-on project helps you build models for tasks like object detection and image classification using CNNs.
You know AI chatbots? They are AI-powered software programs that can understand natural language and speak just like humans. In this module of the generative AI syllabus, you build an AI chatbot. Interesting, right?
It starts with the basics of chatbots- what they are and how they work. You learn to design chatbot workflows using flowcharts and user interactions. You also learn how Large Language Models (LLMs) and Generative AI can be combined for chatbot development.
Then, you set up a Python environment, install the necessary libraries, and initialize a chatbot project. You also practice generating and refining conversational prompts, responses, and logic.
You fine-tune the chatbot using real-world data, optimizing its performance and evaluating its effectiveness.
The module concludes with a capstone project where you deploy your chatbot on a web platform, integrating it with OpenAI’s API for real-time interaction.
In this module, you create an AI tool for social media automation. It begins with an introduction to automating social media tasks and understanding how to generate text and images for these platforms.
Then, you learn to implement LangChain components within this tool to streamline content generation and management. Then, you construct a search index and entity store to enhance content retrieval.
You also develop scripts to generate and schedule social media posts using OpenAI APIs. Then, you deploy the generative AI tool on a cloud platform, ensuring it operates smoothly.
Additionally, this fifth module of the generative AI syllabus covers the basics of resume creation using AI.
You learn how to generate personalized resume content using OpenAI APIs and style it using Python libraries like ReportLab or PDFKit. Finally, you develop and deploy this AI tool.
In this module, you build an AI tool that can generate product images for e-commerce.
It starts with an overview of the image requirements in e-commerce, such as product photos, catalogs, and customization options.
Then, you generate product images, variations, and customizations using the DALL-E API, which you learn to integrate and configure for seamless use.
The module also covers automating image uploads using Python scripts for batch processing, image transformation, and optimization.
You also learn to implement serverless computing with cloud functions for scalable and efficient image management.
The module concludes with a capstone project where you implement DALL-E for dynamic image generation tailored specifically for e-commerce platforms.
In this module of the generative AI syllabus, you build a voice assistant that can recognize and generate voice like humans.
First, you learn about the components of voice recognition systems, including audio processing, feature extraction, and pattern recognition. Then, you implement basic voice recognition algorithms.
Next, the module covers the use of Whisper API for voice generation and customization.
You understand the design principles and architecture of a voice assistant and learn concepts like speech recognition, natural language understanding, dialog management, response generation, etc.
Finally, you deploy your voice assistant using cloud-based, on-premises, and hybrid solutions.
Related: Advantages and Disadvantages of AI
In this module, you explore the development of a news aggregator application that utilizes text summarization techniques.
You start by evaluating popular news APIs and setting up API keys for integration. The module covers the implementation of text summarization algorithms using libraries like NLTK or BERT, allowing the generation of concise news summaries.
In the hands-on project, you work on building both the back-end and front-end of the aggregator using frameworks such as Flask or Django.
You also modify the aggregator’s back-end to enhance user experience and deploy the completed application on a cloud platform.
The final module of the generative AI syllabus focuses on advanced techniques in multimodal generative AI. It covers Early Fusion, Late Fusion, and Hybrid Fusion methods using ChatGPT, DALL-E, and Whisper AI.
You explore attention mechanisms across these modalities, Progressive GANs, StyleGAN, and Vision-and-Language Transformers (VLT).
The module also includes temporal modeling for multimodal sequences and meta-learning algorithms.
Then, you learn about knowledge distillation and model compression for optimizing ChatGPT and DALL-E.
In the final capstone project, you develop a unified AI assistant that integrates text generation, image creation, voice synthesis, and advanced prompting techniques.
Related: How AI will Change the World?
You already saw the generative AI syllabus for this course. It’s an 18-week live online program that will make you an IIT-certified AI expert. It covers everything from the basics of AI to advanced topics like machine learning and language models.
The course is taught by experts from IIT Guwahati and top companies, so you will be learning from the best. And, as we said, you will get hands-on experience developing real-world generative AI applications, which is a great way to build a portfolio and stand out in the job market.
And with career support like resume help and interview prep, you will be well-prepared to launch your career in generative AI. So, visit the GenAI course page to learn more about it.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Aug 29, 2024 No Comments
Generative AI and predictive AI are two branches of artificial intelligence that are on the rise and reshaping the world.
They both use machine learning algorithms to learn from large datasets and improve over time. And they both automate repetitive, time-consuming tasks and help us increase efficiency.
But they are completely different from each other. And the main difference is that generative AI creates new and original content, while predictive AI forecasts future events based on historical data.
In this post, we will explain the difference between generative AI and predictive AI in detail. You will learn how they work, what they are used for, what limitations they have, and what career opportunities they provide.
Generative AI creates unique content like text, code, images, music, video, etc. This content can be so creative and original that it seems to be created by humans. This makes this AI technology capable of imagination and creative thinking.
Large language models like ChatGPT are powered by generative AI. You already know how this generative AI tool surprised the world in 2022. They can generate human-like text that can be LinkedIn posts, poems, short stories, or emails. You can ask any question, and the tool will give you personalized and accurate answers.
On the other hand, predictive AI predicts future events, trends, or outcomes based on historical data. It can be used in places like forecasting, disease diagnosis, recommendation systems, etc., where the goal is to know what may happen in the future. Thus, this AI technology helps businesses make smart decisions and gain a competitive edge.
For instance, Amazon Forecast is powered by predictive AI. It’s a time-series forecasting service that uses machine learning algorithms to analyze historical data and generate accurate forecasts for businesses. It can be used for demand forecasting, workforce planning, inventory forecasting, etc.
Let’s understand the difference between generative AI and predictive AI based on their inputs, outputs, technology, applications, limitations, etc.
Generative AI models are trained on big datasets of content like books, photographs, or movie clips. They learn the patterns, structures, and relationships within this data. This lets the model generate something that’s never been created before, which could be an image, poem, or music.
Predictive AI, on the other hand, is fed with historical or real-time data like sales figures, stock prices, weather details, or machine readings. The model understands the trend and pattern in the data to predict future events like sales projections, customer churn rates, etc.
Generative AI uses neural networks like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) to generate new content. They also use transformer models to generate meaningful text by predicting the next word in a sentence.
Predictive AI uses regression models to predict continuous outcomes based on relationships between variables. They use decision trees and random forests for classification and regression tasks. They also use neural networks to learn and recognize patterns in data.
There are numerous generative AI applications. For example, it powers AI chatbots, which are used by businesses for customer support and sales. It also powers apps like Dall-E that can generate realistic images, models like GPT-4 that can generate marketing content for businesses, or tools like OpenAI’s Codex, which can generate codes for programmers.
Predictive AI can be used by organizations for weather forecasting, stock market predictions, customer behavior forecasting, risk management, etc. It helps businesses make data-driven decisions to increase efficiency, identify opportunities, and grow.
Generative AI can produce biased or inaccurate content. This happens mostly when the training data is biased, low-quality, or incomplete. It can also generate errors because it doesn’t have common sense. Besides, it can’t perform truly creative tasks and come up with totally novel ideas. Also, this technology can be misused to create deepfakes or spread misinformation.
In the case of predictive AI, accuracy also depends on the quality of the training data. Inaccurate, incomplete, or biased data can result in flawed and misleading insights. Besides, predictive AI neglects the fact that there could be unexpected and unforeseen events, which further limits its accuracy.
The good thing is that as technology gets more advanced, both these branches of AI are going to be more accurate, relevant, and safe for the world.
Both generative AI and predictive AI are hot careers with tons of opportunities for tech-savvy people like you.
In generative AI, you can be a generative AI engineer who designs, develops, and implements generative models for real-world applications like chatbots and content generation. Other career options like machine learning engineer, AI research scientist, creative AI specialist, or AI ethics specialist are also good ones.
To do this, you will need to learn machine learning frameworks like TensorFlow, PyTorch, and Keras. You also need to learn neural networks, deep learning, and specific models like GANs, VAEs, and transformers. You can take generative AI courses to learn these skills.
On the other hand, learning predictive AI can help you become a data scientist who can build predictive models and figure out future trends and patterns, such as customer behavior, market trends, and business outcomes. Other career options are ML engineer, business intelligence analyst, predictive modeling, etc.
To learn predictive modeling, you need a strong foundation in statistics, mathematics, and probability. You also have to learn machine learning techniques like regression, classification, clustering, time series analysis, etc., and programming languages such as Python, R, and SQL.
Now you understand the difference between generative AI and predictive AI. So, if you want to learn generative AI and land high-paying jobs, you can join Ivy Pro’s IIT-certified GenAI course.
This is an 18-week live online program where you will be mentored by IIT Guwahati professors and experts from companies like Amazon, Google, and Microsoft.
You will learn industry-relevant skills like machine learning, deep learning, large language models, LangChain, RAG, Transformer, etc. You will also work on real-world projects to gain practical experience and build a solid portfolio.
Now is the time to transform your career. Visit the GenAI course page to learn more about it.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Aug 23, 2024 No Comments
Generative AI is the future. This artificial intelligence technology has surprised the whole world through its ability to generate original text, code, images, audio, and video. If you want to master this technology, generative AI books can be a good starting point.
Certain books are authored by experts and help you understand complex generative AI concepts even if you don’t have a strong technical background. They help you learn different applications, explore the latest trends, and solve real-world problems using the technology.
In this blog post, we have listed some of the best generative AI books. You can read them either in paperback format or directly from Amazon Kindle.
Ivy Pro’s GenAI course with E&ICT Academy, IIT Guwahati, is one of the top-rated courses in India. In this 18-week program, you will be mentored by IIT professors and professionals from companies like Amazon, Google, and Microsoft.
You will learn industry-relevant skills like machine learning, deep learning, large language models, LangChain, RAG, Transformer, etc. You will also work on interesting projects to gain hands-on experience and build your portfolio.
And with Ivy Pro’s placement assistance, you will become a job-ready candidate for big MNCs. Visit the GenAI course page to learn more about this live online program.
Whether you are a beginner or a professional, these books will help you learn generative AI from scratch. Let’s see what we have got…
Author: David Foster
Print length: 330 pages
Originally published in: July 2019
Get the book: Generative Deep Learning
This book helps you understand how generative AI can create art, compose music, and write like a human. You will understand the basics of deep learning and then explore advanced algorithms.
This book is perfect for anyone who wants to get their hands dirty. Foster doesn’t just explain the theory. He provides useful tips and tricks to make your own effective AI models that learn faster.
You will learn about changing facial expressions with variational autoencoders, music generation with MuseGAN, image generation models with ProGAN and StyleGAN, models for text generation, etc.
Author: Chris Fregly, Antje Barth, and Shelbee Eigenbrode
Print length: 312 pages
Originally published in: November 2023
Get the book: Generative AI on AWS
This book gives you a clear roadmap to build generative AI applications, from case definition and model selection to model optimization and deployment.
Whether you are a data scientist, business analyst, app developer, or ML practitioner, this book will help you use the power of generative AI to solve business problems.
You will learn about different types of models, including large language models (LLMs) like ChatGPT and image generators like Stable Diffusion. The book also covers libraries like LangChain and ReAct.
Author: Joseph Babcock, Raghav Bali
Print length: 488 pages
Originally published in: April 2021
Get the book: Generative AI with Python and TensorFlow 2
This book will take you through the evolution of generative models before introducing the advanced techniques used today.
You will implement models on TensorFlow, use LSTM models and MuseGAN to compose music, use pix2pix GAN to create deepfakes, and explore text-generation models using BERT, GPT-2, etc.
The generative AI book is best for Python programmers who want to create functional apps with this technology. You will need a basic understanding of maths and statistics to understand the book better.
Author: Rajeev Kapur
Print length: 208 pages
Originally published in: September 2023
Get the book: AI Made Simple
This generative AI book is best for beginners who want to learn the foundations. The author explains this revolutionary technology in plain language with examples so that you can easily grasp even complex concepts.
You will learn what generative AI is, how it was developed, what the applications of generative AI are, how it’s changing our world, and what disruption opportunities it provides. You will also understand the limitations and ethical concerns of this technology.
Author: James Phoenix, Mike Taylor
Print length: 422 pages
Originally published in: May 2024
Get the book: Prompt Engineering for Generative AI
This book will help you learn the basics of generative AI and how you can use these models. You will learn how to get the most out of LLMs like ChatGPT and diffusion models like Stable Diffusion.
After reading this book, you can craft prompts that get the AI to generate reliable, high-quality results for tasks like writing, image creation, and even coding.
The book has received 4.4 ratings out of 5 on Amazon. Many readers who have reviewed it say that it is a comprehensive guide with practical insights.
Author: Ben Auffarth
Print length: 362 pages
Originally published in: December 2023
Get the book: Generative AI with LangChain
This generative AI book helps you understand the depths of LLMs like ChatGPT and Bard, including their capabilities and limitations.
It first explains the fundamentals and industry trends before going to advanced topics like creating LLM apps like chatbots with LangChain. You will learn essential techniques like prompt engineering, fine-tuning, and deploying your models securely.
The book is perfect for developers, researchers, and AI enthusiasts who want to master LLMs. Beginner as well as professional developers can read it, but you will require a basic knowledge of Python and machine learning.
Author: Ian Goodfellow, Yoshua Bengi, Aaron Courville
Print length: 800 pages
Originally published in: November 2016
Get the book: Deep Learning
This is one of the best books for deep learning. Written by three industry experts, it covers conceptual and mathematical parts as well as research and practical applications. For instance, it explores topics like linear algebra, probability theory, numerical computation, machine learning, etc.
The book is perfect for aspiring students and software engineers who want to master deep learning and related technologies. Over 2,150 people have given this 4.6-star rating out of 5, saying that the book is detailed and elaborate. Some even say that this is the masterpiece of deep learning.
Learning AI is a continuous process. The above best generative AI books are great resources for learning the ins and outs of this technology. They not only introduce you to the latest trends but also help you understand the possibilities in the coming future. So, keep reading one book at a time, let them inspire your creativity, and solve real-world problems using generative AI.
Next Read: 5 Best Generative AI Courses to Master AI
Team Aug 19, 2024 No Comments
A generative AI course is a smart investment in your future. It can help you stay updated in this rapidly changing world and get ahead of other job candidates and employees.
A good course is your way to gain a deeper understanding of how this AI technology works and learn the latest skills that companies are seeking.
In this post, we have listed some of the best generative AI courses in India. While some are good for beginners, others are more advanced. So go through the course pages carefully to find out which suits you best.
But first, let’s understand…
Generative AI is a field of artificial intelligence that makes machines capable of producing new and original content like text, graphics, audio, video, etc. And this technology is changing everything from how businesses work to how we live.
A report by Bloomberg Intelligence predicts that the worldwide generative AI market can grow from $40 billion in 2022 to $1.3 trillion by 2032. This astounding growth will explode the demand for skilled generative AI engineers. So it goes without saying that learning about it now will help you prepare for the future.
But what can you expect from a career in generative AI? Well, everything from career growth, a good salary, and job security to an opportunity to be a part of this AI revolution. For instance, a generative AI engineer currently earns an average salary of ₹8,70,000 per year in India.
Overall, it’s a promising career path. Now, let’s find some good courses…
There are many courses available online. Some are the best, some are just average. In the following sections, you will find the best generative AI certification courses in 2024.
This is an 18-week, live online course by Ivy Professional School and E&ICT Academy, IIT Guwahati. Ivy Pro has already helped over 29,500 learners upskill, gain practical experience, and secure placement in top companies.
The course teaches you advanced generative AI skills like machine learning, deep learning, large language models, LangChain, RAG, Transformer, etc. You will learn to build your own applications that solve real-world problems using generative AI technology.
Here are the top features of this course:
So, if you want to become a generative AI expert and land your dream job in big MNCs, this course would be the perfect choice.
This is a free, introductory online course by Google Cloud that teaches you the basics of generative AI.
It explains what it is, how it works, and how it’s different from other types of AI. You will also learn how to use Google’s tools to build your own generative AI applications.
Here are the top features of this generative AI course:
This course could be a great starting point if you are curious about generative AI and want to learn the fundamentals from Google experts.
This is another beginner-level generative AI course offered by Vanderbilt University on Coursera. It teaches you how to write prompts to effectively use large language models like ChatGPT.
You will also learn what large language models are, how they work, and how to use them for various tasks, whether it’s your job, business, or personal life.
Here are the top features of the course:
The course is perfect if you are just starting your generative AI career, as it does not require any prior knowledge of large language models or prompt engineering.
This is a 16-week online course offered by Simplilearn and Purdue University. It covers topics such as Python basics, prompt engineering, large language models, image generation, AI governance, etc. You will also learn to use generative AI tools like ChatGPT, Azure AI Studio, Hugging Face, etc.
Here are the top features of this generative AI course:
The course is perfect for someone who has a minimum of 2 years of professional experience and a basic understanding of programming and mathematics.
This is a 3-course series offered by IBM on Coursera that can help you accelerate your software development career. This one-month course teaches you various generative AI tools and techniques to generate codes and snippets.
Here are the top features of the course:
You will need software engineering experience to get the most out of this program. The course is perfect for aspiring or professional web developers, mobile app developers, full-stack developers, DevOps professionals, etc.
Learning AI can boost your career. Go through the above generative AI courses to see which suits your needs the best. If you are a student or a working professional looking to master the ins and outs of this latest technology, you can join Ivy Professional School’s IIT-certified GenAI course. This will not only help you learn advanced AI skills from scratch but also build a solid AI portfolio and secure placement. After all, AI is the future.
Eeshani Agrawal holds an MS in Civil Engineering from Texas A&M University (USA) and has over 16 years of experience in data visualization, storytelling, and analytics. She has consulted for top engineering, manufacturing, and consulting firms worldwide and has coached over 9,000 professionals and students from leading institutions like IITs, IIMs, and ISI.
Team May 29, 2024 No Comments
Updated on 21st August, 2024
The artificial intelligence (AI) revolution is here. And it’s transforming our world. From powerful self-driving cars to AI assistants that can talk just like humans, we are witnessing sci-fi concepts turning into reality one by one.
AI enables machines to mimic human behaviors like interacting, learning, reasoning, problem-solving, etc. Machines can now assist humans in tasks like composing music, translating languages, analyzing data, making reports, and so on.
And AI is only going to get more powerful with time. According to a study, the global AI market size is expected to grow at a CAGR of 28.46%, resulting in a market volume of USD 826.70 billion by 2030.
But is AI a force for good, or something we should be worried about? In this blog post, we will discuss the advantages and disadvantages of artificial intelligence. This will help you understand the incredible benefits and the potential risks this rising technology poses.
AI works almost like magic. It is enabling us to do incredibly more in less time. It’s helping organizations streamline processes, increase efficiency, and cut costs. In fact, a McKinsey report says AI could add an astounding $25.6 trillion to the world’s economy. Here are five advantages of AI that show how miraculous this technology is:
This is one of the biggest advantages of AI. It can automate laborious and repetitive tasks like data entry, appointment scheduling, language translation, email filtering, customer support, etc. It frees us humans to focus on more creative and complex work, like solving problems, developing new ideas, and making strategic decisions.
The best examples are customer service chatbots on websites and messenger apps. Powered by AI, they can answer FAQs, troubleshoot issues, collect leads, and even close sales. Since these AI bots can do most of the basic customer service tasks, human sales agents can focus on complex things like closing important deals and building relationships with customers.
Unlike humans, AI can work tirelessly. It doesn’t need sleep, breaks, or vacations. It can operate 24 hours a day, 7 days a week, with full efficiency. This round-the-clock availability helps organizations and individuals increase efficiency, improve productivity, and save significant costs.
Take the example of AI financial trading platforms. These AI systems can tirelessly analyze market trends, execute trades, and manage portfolios 24/7. This way, it can help optimize returns and minimize risk even while human traders are asleep.
Since we are talking about the pros and cons of artificial intelligence, we can’t miss efficiency. Efficiency is another area where AI shines. It performs complex tasks in a fraction of the time it would take a human. For instance, AI-powered image generators can create stunning artwork within seconds. Also, AI algorithms can analyze vast amounts of data in the blink of an eye. This incredible speed not only saves time but also opens up new possibilities for innovation and creativity.
We humans often make mistakes when it comes to doing repetitive tasks. Since AI can handle them easily, it reduces the possible damage. For example, in manufacturing, AI-powered robots can perform assembly line tasks with precision. This reduces defects, improves product quality, and saves a lot of money.
Besides, unlike humans, AI doesn’t get influenced by emotions and biases. It makes decisions based on data and logic. This again eliminates the potential for human error. And it really helps in loan approvals. AI algorithms can assess loan applications based solely on financial data and credit history, eliminating human biases related to race, gender, or age. This leads to fairer lending decisions.
AI makes really helpful assistants. They help us do a lot of tasks like research, content creation, summarizing lectures, etc., to make our lives easier. Generative AI tools like ChatGPT and Gemini are the best examples of this.
But it extends to more complex tasks like exploring Mars or space. Humans can’t survive there currently, but the AI-powered robot can handle the extreme temperatures and gather information for us. This way, we humans can do more leveraging the power of AI.
Anything good has a bad side. AI is no exception. Although AI has incredible applications, it also comes with some disadvantages that limit its ability and performance. Here are four major disadvantages of AI:
AI doesn’t have emotions and creativity. This is a big drawback in situations where empathy, understanding, and out-of-the-box thinking are required.
A customer service chatbot may give generic responses, but the bot lacks the genuine empathy and understanding that a human would provide. This can leave customers feeling unheard and dissatisfied.
Similarly, AI struggles with creativity. It can generate art, music, or even write stories, but these creations often lack the originality and emotional depth that comes from human imagination.
That means AI can be a good tool for laborious tasks, but it can’t completely replace humans when it comes to tasks that need emotions and creativity.
AI may not always be that accurate. Unlike humans, AI doesn’t have common sense. This can lead to errors in situations that require a deeper understanding of human behavior or cultural context.
Another issue is the phenomenon called AI hallucination. This means AI systems can generate information that seems real but it’s completely fabricated.
Furthermore, the AI’s output depends on the data it’s been trained on. If the training data is biased or incomplete, the AI model can produce inaccurate or misleading results. This can be particularly problematic in areas like healthcare, where AI diagnoses diseases and recommends treatments.
Implementing AI costs a lot of money. The development of AI algorithms often requires specialized expertise and resources. Maintaining and updating AI systems requires ongoing investment.
And as technology evolves rapidly, businesses need to keep their AI tools up-to-date to remain competitive. This means regular software updates, potential hardware upgrades, and training for employees to use and manage these systems effectively.
So, for businesses with limited resources, the high cost of AI implementation can be a major obstacle.
Since AI automates a lot of tasks, it also poses a threat to jobs that were once considered secure. As this technology continues to advance, it will become more capable of performing tasks that require human intelligence, posing more risks to human jobs. A survey found that one in four companies have already replaced workers with ChatGPT.
For instance, in manufacturing, robots are taking over assembly line jobs, while in customer service, chatbots are replacing human representatives. In finance, algorithmic trading replaces human stockbrokers, while in transportation, self-driving cars can potentially replace drivers.
A report by Goldman Sachs predicts that artificial intelligence could automate the equivalent of 300 million full-time jobs. Although this can cause financial hardship for many, it’s an opportunity to learn advanced skills that AI can’t replace. This is a sign for humanity to grow, leverage AI, and make the world a better place.
Now you understand the advantages and disadvantages of artificial intelligence. While this technology has certain disadvantages, the pace at which it is evolving, AI will be way too impactful in the coming days. If you want to be part of this technological revolution, you should invest in your AI education.
Ivy’s GenAI Certification course, developed in partnership with the E&ICT Academy at IIT Guwahati, offers a comprehensive curriculum for mastering AI. Since the course is industry-focused, you will learn the latest GenAI tools and techniques, including machine learning, deep learning, Langchain, RAG, Transformer, and LLM.
You will complete 20+ real-life projects, gain hands-on experience, and build a strong portfolio that showcases your expertise. To learn more about how Ivy’s GenAI course can prepare you for a successful career in AI, visit our GenAI course page.
Is AI good or bad?
AI is neither good nor bad. It’s just a tool. So, what matters is how we use it. We just saw in the above post that AI can automate boring tasks and work round the clock. So, it has huge potential to make our lives better, increase efficiency, and improve businesses. Now, if you want to master AI and boost your career, you can join Ivy Pro’s GenAI course with E&ICT Academy, IIT Guwahati.
What are the advantages and disadvantages of artificial intelligence in education?
AI can make learning more personalized for students by creating customized learning paths and materials. AI assistants can be good virtual tutors who can instantly provide students with explanations and feedback to make learning easier. However, we also have to be careful about the disadvantages of AI in education, which include a lack of human connection, potential for biases, privacy concerns, etc.
What jobs might AI replace?
AI is most likely to replace jobs that are repetitive and don’t need much creativity. This includes jobs like data entry, customer service, retail checkouts, proofreading, report generation, assembly line jobs, language translation, bookkeeping, etc. The good news is that AI is also creating new jobs. So, we should keep upskilling to remain relevant in this changing world.
Who is considered the father of AI?
John McCarthy is considered the father of artificial intelligence. He was an American computer scientist who coined the term “Artificial Intelligence” in 1955 and organized the Dartmouth Conference in 1956, which is considered the birthplace of AI as a field of study.
How is AI used in our everyday life?
We use AI in many ways every day. Things like Google Assistant and Siri help us get things done faster. AI chatbots help us get quick support from companies. Automated cars drive themselves using AI. AI also suggests content, TV shows, and products, helps us with real-time language translation, and enhances home security systems.
What is the main advantage of AI?
The biggest advantage of artificial intelligence is its ability to do laborious and repetitive tasks 24/7. For example, AI chatbots can answer FAQs to customers, helping businesses provide instant customer support. Similarly, AI can drive cars and do data entry all day without getting tired. This way, AI helps us use our time on creative and complex tasks that need human intelligence.
Eeshani Agrawal holds an MS in Civil Engineering from Texas A&M University (USA) and has over 16 years of experience in data visualization, storytelling, and analytics. She has consulted for top engineering, manufacturing, and consulting firms worldwide and has coached over 9,000 professionals and students from leading institutions like IITs, IIMs, and ISI.
Team May 23, 2024 No Comments
Ever since OpenAI launched ChatGPT, generative AI (GenAI) has taken the internet by storm.
But this is just the beginning of something big. GenAI is going to change how we work and live our lives.
For example, recently, OpenAI launched its most advanced model, GPT-4o, which can converse just like human beings. Isn’t that amazing?
The world is moving fast, and staying updated is the key. That’s why you should utilize the generative AI tools. Whether you are an artist, a software engineer, or just someone who wants to get more done, these tools can help you generate ideas, save time, and stay ahead of the curve.
In this blog post, we will explore 7 of the best generative AI tools available today. These tools will surely boost your creativity and productivity.
Generative AI is a field of artificial intelligence that can create new content by learning patterns from existing data. This includes generating text, images, music, code, or even video based on user prompts.
GenAI tools are like smart assistants that can help you automate repetitive tasks, find ideas, and boost your productivity. Here are 7 of the coolest GenAI tools that will help you unleash your inner genius:
You already know this GenAI tool. ChatGPT is the revolutionary chatbot built by OpenAI that basically started the AI boom. Powered by natural language processing (NLP), this tool can generate human-like responses to user prompts.
You can use ChatGPT to research a topic, create written content, generate and debug codes, find creative ideas, etc.
Currently, ChatGPT is powered by the latest GPT-4o model, which enables it to process and respond to text, audio, and graphics. That means you can upload a PDF file to ChatGPT and ask it to summarize the file.
ChatGPT bots are also used in websites to handle inquiries from website visitors and provide support to a company’s customers.
This is again another AI tool developed by OpenAI. Originally launched in January 2021, Dall-E2 is the second version that generates higher-quality and more detailed images.
This tool can create realistic images and visual arts based on user prompts given in natural language. The clearer you describe the image you need, the more accurate Dall-E2 will create the image, combining concepts, attributes, and styles.
This advanced GenAI tool can create different types of visuals like abstract art, realistic scenes, and imaginative scenarios.
You can also use this tool to produce multiple variations of an image based on the same text description. This way, you can edit and replace specific parts of an image using new text prompts.
This is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI.
If you are a software developer or data engineer, GitHub Copilot can help you by suggesting code snippets, completing code blocks, and providing personalized coding assistance. It also applies directly to integrated development environments (IDEs) like Visual Studio, Neovim, JetBrains, etc.
GitHub says the tool can make coding 55% faster. This way, it increases the productivity of developers by reducing the time required to build software. This tool can help you write high-quality code that adheres to best practices and fulfills the requirements.
This is the latest Generative AI tool in this list, launched in February 2024. And yes, this is developed by OpenAI.
Sora can create high-quality, realistic videos based on text prompts. This tool can be used in entertainment, advertising, education, and potentially scientific simulations.
OpenAI has made this tool by teaching AI to understand and simulate the physical world in motion. Currently, Sora can generate high-quality videos that are up to a minute long and accurately follow the users’ prompts.
You can use this Generative AI tool to create cinematic scenes with multiple characters and give them accurate details. Since the model understands how things exist in the physical world, it generates realistic videos.
Although OpenAI is carrying out more research and improvement on Sora, it shows how AI will impact the future.
Midjourney is another excellent text-to-image generator. Developed by a small, self-funded team, it was launched in July 2022.
Similar to Dall-E2, Midjourney lets you input descriptive text prompts and generate corresponding images. However, compared to other image generation tools, Midjourney can increase the file size to 2048 x 2048 or 4096 x 4096 pixels, putting greater detail and clarity to the generated images.
Besides, this advanced GenAI tool is found to be better when it comes to queries related to illustrations, surrealism, and digital art.
Midjourney has a user-friendly interface that lets you create professional graphics even if you don’t have any technical expertise. And the Midjourney community keeps sharing their creations, tips, and experiences, which helps you learn and find inspiration.
Gemini is a GenAI chatbot developed by Google DeepMind. It lets you input your prompt in the form of text, image, or audio and get outputs in text and image modes. You can also ask to read aloud the output, which seems like talking to the AI.
Similar to ChatGPT, you can use Gemini for research, idea generation, content creation, language translation, etc.
Recently, Google launched Gemini 1.5 Pro, which can handle long-context information and process up to 1 million tokens. This enables the model to process bulky content such as hours of audio or video, large codebases, and lengthy documents.
This generative AI tool can help you understand, explain, and generate high-quality code in popular programming languages like Python, Java, C++, and Go. It has performed exceptionally well on coding benchmarks like HumanEval and Natural2Code.
AlphaCode is another GenAI tool developed by DeepMind. It generates code and solves challenges for programmers.
It can understand the requirements of a problem, develop an approach, and then write the code that meets the requirement.
In tests conducted on Codeforces, AlphaCode ranked within the top 54% of participants. This shows the ability of this tool to handle complex coding tasks.
The tool knows critical thinking, logic, algorithms, coding, and natural language, so it can solve problems just like a human coder.
You can integrate it with popular IDEs through a plugin and get real-time code suggestions and debugging assistance that enhances code efficiency and quality.
So, these are some of the amazing generative AI tools that exist today. As these tools continue to develop, they are sure to revolutionize how we approach creativity and problem-solving.
If you want to learn GenAI and leverage it to build useful apps, you can join Ivy’s GenAI Certification course.
This course is made in partnership with E&ICT Academy, IIT Guwahati. You will be coached by IIT professors and experts from Amazon, Google, Microsoft, etc.
The course has an industry-focused curriculum that will help you learn advanced Generative AI tools and skills like machine learning, deep learning, Langchain, RAG, Transformer, and LLM.
You will also complete 20+ real-life projects, learn to solve real-world problems, and build a compelling portfolio.
All these can help you land your dream job in MNCs and get impressive salaries. Visit this page to learn more about Ivy’s GenAI course.