What is Generative AI? Explained Simply
What is Generative AI?
A form of artificial intelligence called generative AI can generate new content like writing, images, and music.
How does it do this?
AI Models learn from data, understand the patterns, and, based on the patterns, provide predictions.
Many generative AI are trained on a large amount of data and various tasks. The tasks include summarization, classification, generation, and analysis.
GPT-4 Model has shown some characteristics of Artificial general intelligence (AGI) as it does reasoning, analysis, and learning from experience.
At its core, the generative AI models are powered by neural networks, essentially AI algorithms. Neural networks are machine learning models that learn to identify patterns in data.
Generative AI started to take-off when OpenAI released the ChatGPT and it exploded pretty quicky. Here is the trend of Generative AI in the last 12 months.
I have explored Generative AI and tried models and frameworks like GPT-3.5, GPT-4, PaLM2, and Jurassic-2 for building LLM applications.
My experience with generative AI has shown me that while it can automate tasks and create initial content drafts, it lacks the human touch and the understanding required for tasks like blogging and design.
Generative artificial intelligence can assist developers and content creators in being more productive and efficient.
OpenAI released DALL-E 2 in 2022, which can generate realistic images from text prompts. Google introduced Bard in 2023, an AI Chatbot that can generate text, translate languages, and produce creative content.
Microsoft also joined with VALL-E, a generative AI model that can create realistic voices from text prompts. These examples show the progress in generative AI.
How does Generative AI work?
Generative AI Models have two types – Generative adversarial networks (GANs) and Variational autoencoders (VAEs).
Generative adversarial networks (GANs)
GANs are unique neural networks. It is a contest between two neural networks – a Generator Network and a Discriminator Network.
So, the generator network generates new data, and the discriminator determines whether the data is real or fake.
Since neural networks always try to reduce error, the generator network constantly improves its ability to create realistic data.
In contrast, the discriminator improves its ability to distinguish between real and fake data.
This competition between the generator and discriminator improves the GAN over time.
Eventually, the generator becomes so good at creating realistic data that the discriminator cannot tell the difference between real and fake data.
GANs generate texts, images, art, voices, etc.
Variational autoencoders (VAEs)
A variational autoencoder learns to represent data points as a probability distribution in a latent space. This latent space is a lower-dimensional space that captures the essential features of the data while discarding noise and irrelevant details.
Now, what does that mean?
It has two neural networks – Encoder Network and Decoder Network.
The Encoder network represents the input data in a probability distribution in a lower dimensional space. This is nothing but identifying features of the input data or compression of the data. The Decoder network tries to regenerate the input data from the latent distribution.
Think of VAEs as a neural network that learns to compress data. They can then use this compressed information to generate new data similar to what they’ve learned.
Large Language Models
Large Language Models (LLMs) are a specific type of generative AI trained on lots of text data. Models like GPT-3 and GPT-4 can make human-like text based on a given prompt.
It is common for generative AI to be used in fields from text generation translation of languages to writing creative content.
For example, it can make new images for marketing materials or make personalized product descriptions.
Aside from the text, it can write articles, blog posts, and customer service replies; generative AI can also create original music or custom soundtracks for movies and video games.
Uses of Generative AI
Art & Creativity
The development of generative AI has greatly enhanced art and creativity. The machine comes up with ideas, and the artist gives guidance, resulting in a good partnership between the two parties. Generative AI can also make up stories, characters, and whole plots.
Generative AI blurs the line between human and machine creativity, challenging who gets credit for the art. Art is created by humans as well as machines working together in collaboration.
Generative AI has been used to make many images, with over 2 million created daily by 1.5 million users on launching with OpenAI’s DALL-E 2.
Google’s Imagen can make images that look like humans made them. Generative AI systems like GPT-3 have even been used to write books, poems, and code.
The market for generative AI in art and creativity is expected to grow significantly. Generative AI offers several art and creativity advantages.
Natural language generation
Developers and bloggers can have a difficult time creating content, yet it has proven to be quite rewarding.
Generative AI capabilities can be used for NLG to save time and generate content much faster than humans, allowing content creators to focus on more important tasks.
It is a cost-effective solution since automated processes eliminate the need for many human resources, making it both time and resource-saving.
In marketing, NLG offers several benefits too. With the help of NLG, developers can easily build new software using natural language descriptions, making the whole process easier for them.
With the help of NLG, content can be optimized for specific keywords and phrases, increasing its chances of ranking higher.
These Gen AI tools are being used in AI Writing for blogging, social media and even for resume writing.
As a result of the generative models, tools such as ChatGPT and Google Bard can generate text, translate languages, and give informative answers to questions in several languages.
Video Upscaling and Video Editing
A video upscaling technique utilizes smart computer programs to increase the quality of low-resolution videos by producing a better image.
These programs learn from lots of videos and then create a higher-resolution version of the video based on what they learned.
One of the main benefits of using these smart computer programs for video upscaling is that the videos look better.
They have more details and less unwanted noise compared to other methods. These programs can also make videos look better at any resolution, even if the original video was low quality.
However, there are some limitations to consider. Sometimes, the upscaled video might have problems like blurriness or pixelation.
It can also be hard to control how the program makes the video look, so it might not always give the results you want.
Photo Editing
Artificial intelligence (AI) can quicken and simplify the process of editing photos. Let’s look at some of these tools:
1. Adobe Photoshop: This well-known software has AI-powered tools like Content-Aware Fill, which removes unwanted objects from photos, and Neural Filters, which add different effects.
2. Luminar Neo: This AI-powered photo editor has features like automatic photo enhancement, noise removal, and the ability to apply different effects. Item
3. Canva: While mainly a design app, Canva also has AI-powered features like removing backgrounds and generating images from text prompts. Item number
According to Adobe, the use of AI in photo editing is increasing, with 71% of professional photographers already using AI tools. This sector is expected to grow because these tools save time while improving the quality of the pictures.
Designing and Development
In the world of designing and developing, artificial intelligence can be very useful. It is common for designers to use Figma and Sketch to create wireframes and mockups, while InVision is commonly used to create interactive prototypes.
According to a study by Forrester, businesses can save up to 30% on design costs by using AI design tools.
I tried couple of such graphic design tools and they are quite cool, However, you still need to work on the AI Generated designs to make sure they align with the your goals.
AI Detection
Many tools today use AI and other algorithms to detect whether the content is AI-generated. Some popular options include Writer, Copyleaks, Content at Scale, Originality.ai, AI Content Detector, Plagiarism Checker X, Duplichecker, and Grammarly.
I personally use Originality.AI and Content at Scale AI Detectors.
As a developer and blogger, I have used AI writing tools like Jasper AI, Copy AI, Writesonic, Rytr, and others.
Although these tools are good for getting initial drafts and content ideas for blog posts, they often lack the human touch and understanding needed to create high-quality posts, which is why Google has addressed the issue of AI-generated content in its spam policy.
So, you can use these AI Detectors to identify the sections detected as AI-generated and update them accordingly.
Personal Assistant or Brainstorming Partner
AI tools optimized for dialogue, like ChatGPT and Bard, serve as personal assistants and brainstorming partners, answering questions, generating ideas, and writing creative content.
As for me, I use AI tools like ChatGPT for coding and content creation daily. It’s impressive how they help me brainstorm ideas, speed up my work, and quickly fetch the necessary information.
What are Examples of Generative AI Tools
ChatGPT
This AI chatbot created by OpenAI uses large language models like GPT-3 and GPT-4. GPT-3 is free, and the GPT-4 model is available for ChatGPT plus users. It’s designed for conversation and can do cool stuff like writing articles, coding, brainstorming, and learning.
I often use ChatGPT in my daily work for writing articles, coding, brainstorming, and learning. You don’t need to search the Internet to get information.
But ChatGPT has training data till September 2021, so it is not a viable information source for the most recent data.
DALL-E 2 & Midjourney
Both Midjourney and Dalle2 are used to create realistic images based on text descriptions, but Midjourney is used more as it produces high-quality, realistic images.
However, Dalle2 can generate more creative and abstract images than Midjourney.
Google Bard
This is another large language model from Google AI. The PaLM2 model powers it. It is not as powerful as ChatGPT for understanding and interpreting the dialogues, but there is one area where it shines as it has access to data from the Google search engine, which makes it up to date compared to ChatGPT.
Potential Risks with Generative AI
Fake news is a type of misinformation that is intentionally spread to deceive people. Generative AI can create fake news articles, videos, and images that look and sound authentic.
Deepfakes are videos or images manipulated to make it appear that someone is saying or doing something they did not say or do. Generative AI can be used to create deepfakes that are very realistic, making it difficult to tell that they are not real.
Challenges of using Generative AI
Below are the challenges when dealing with Generative AI.
1. Building Large Language Models requires huge training data. Gathering and preparing the data is not only time-consuming but also quite costly.
2. Due to their complex architectures, these models can be hard to understand. It’s tricky to figure out how they work and why they produce their results.
3. These models suffer from Bias and hallucinations. Since they try to respond based on the training data, they can confidently make up facts or provide incorrect information.
4. Though these models are easy to interact with using chat-based interfaces, it poses a challenge to integrate them into applications because of their non-deterministic nature. Applications require the data to be in a structured format to be parsed and stored accordingly.
5. Not of the models support fine-tuning. Due to this, enterprises or organizations may be unable to use them directly as they typically have unique data sets containing specific terminology, jargon, and biases.
6. Information is continuously changing, and it is difficult to keep the models updated to date with the latest information. This is because it requires huge resources and time to retrain the model. GPT-4 Model has the training data till Sep 2021, while the recently released PaLM2 model from Google has training data till Feb 2023.
Best practices for using Generative AI
Choosing your model and tuning it just right
Various Generative AI Models are currently available, each with pros and cons. You need to understand how prompting works to get the desired output from the model. You can also provide a few examples of the model in the prompt.
In addition to prompting, it is recommended to fine-tune the model if you have your data. Not all the models support fine-tuning.
Checking the quality of the AI’s output
Keep an eye on the output. Make sure your models aren’t generating biased or harmful content. You can do this by using tools to spot bias or manually checking the output.
Use Generative AI Responsibly
Like any technology, generative AI can be used for good or bad. Always use it responsibly and steer clear of any harmful uses. Generative AI is not perfect, but you can make the most out of it with some careful steps.
The Future of Generative AI
The future of generative AI has a lot of potential to make big advancements in different industries like healthcare, finance, and manufacturing.
McKinsey & Company predicts that generative AI could contribute up to $13 trillion to the global economy by 2030.
The global generative AI market is worth $11.3 billion in 2023 and is expected to reach $51.8 billion by 2028.
There are multiple AI Statistics that I have collated. You can read them here.
I think due to rise of frameworks such as Langchain which providers abstraction over using different models, embeddings, vectorstores, Generative AI is going to be used to build intelligent applications.
This growth shows that there is a growing demand for generative AI technologies.
1. The increasing demand for AI-generated content like images, videos, and text is a major driver for the growth of the generative AI market.
2. Generative AI is used in various industries, and its popularity is growing in healthcare, finance, and manufacturing.
3. Some notable generative AI models are GPT-3, DALL-E 2, and Imagen, each with strengths and limitations. I have been closely following the market trends as someone who works in the industry, and I understand the specific uses and challenges associated with generative AI technology.
