Generative AI is one of the most transforming technologies available today that can generate unique, original, and copyright-free content including texts, videos, audio, and images.
Ever since the launch of ChatGPT, the most popular generative AI tool, back in 2022, the buzz around this technology has been only increasing. Soon after its launch, different other players joined the race to capture the generative AI market such as Microsoft’s Bing CoPilot, and Google’s Bard (now Gemini).
It started with text-based AI chatbots powered by large language models, and now we can find multimodal AI that is capable of generating various forms of content in a single platform. This unprecedented growth has led to an increase in the generative AI market size which is expected to reach $81.32 billion by 2027, as predicted by Precedence Research.
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What is Generative AI?
It is a type of AI technology that can generate new and unique content either in the form of text, images, or videos as in the case of unimodal AI, or both in the case of multimodal AI. These models are trained on large amounts of data, usually in terabytes and petabytes to understand human queries and respond according to the given prompt.
Now, this technology is used across all industries for a variety of applications which we will explore in the later sections.
How does this technology work?
Generative AI works by training large neural networks on huge amounts of data. these models learn patterns, structures, and relationships within the data according to the trained parameters. After training, these models powered by Natural Language Processing (NLP) and deep learning methods, can generate new content according to user prompts.
Here’s a simple breakdown of how the model works:
- A huge amount of data is collected to train the generative AI or multimodal AI
- They are trained to analyze the data to identify patterns and relationships
- After training, they can understand user sentiments, and prompts, and generate new content based on the learned patterns.
There are three important techniques used in the training of generative AI models:
- Neural networks – these are interconnected layers of artificial neurons responsible for processing information.
- Generative Adversarial Networks (GAN) – It consists of neural networks generator and discriminator. While the generator creates new content, the discriminator evaluates the content and ensures it is unique and authentic. This process is repeated until realistic content is generated.
- Variational Autoencoders (VAEs) – this is used to encode input data into lower dimensional latent space and then decoded later to generate new content.
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Popular Gen AI models
Some of the most popular and widely used generative AI tools are:
- ChatGPT – generates unique and realistic text responses and powers several AI chatbots
- Dall-E 2 – creates realistic and detailed images based on user prompts
- Jukebox – it can generative high-quality original music samples
Apart from these, Gemini and CoPilot are multimodal AIs that can generate both texts and images. Then there are Stable Diffusion, Midjourney, LaMDA, VQ-VAE, etc. that are also gaining popularity.
Applications of Generative AI across Industries
- Finance: generate synthetic data to train fraud detection models, assess financial risks, develop strategies for algorithmic trading
- Healthcare: generate new molecular structures to assist in drug discovery, create synthetic medical images to train AI models, generate personalized treatment plans
- Education: create personalized learning materials, language translation, AI-powered digital tutors, and virtual assistants to answer student queries
- Marketing: generate unique and engaging marketing copies, social media content, and personalized customer support
- Media and entertainment: generate scripts, music, videos, and personalize content suggestions as per user preference
The Future of Generative AI
As we enter the future, we will see even more generative AI and multimodal AI. These will enhance creativity, find new applications across other industries, and make some ground-breaking innovations.
These generative contents may shape the trends, change the taste of the content, and give rise to more content creators as it will become easier and more accessible to generate contents as desired. Generative AI technology is also used for cyber-attacks as well as it can be used to craft realistic phishing emails. Therefore, there is a need to implement ethical and responsible development of such AI models.
Conclusion
Generative AI is indeed a transformative technology that has been revolutionizing all aspects of our lives. From marketing to healthcare to finance, they have found applications everywhere. Even though there are several benefits of generative AI technology, there are some concerns related to bias and data privacy. The output and content it generates may contain bias toward certain sections of society depending upon the data it has been trained on. But as we enter the future, we might see more responsible and ethical development of such models and see better and more innovative applications that transform the world for good.
