What is generative AI?
Generative AI is an artificial intelligence technology that creates 'new' content, whether it's images, audio, text, designs or more, by replicating patterns learnt from comprehending massive datasets.
How does generative AI work?
Generative AI is the cumulative result of multiple advanced technologies combined to ensure the algorithm can successfully understand datasets(s) and proceed to create a data instance based on this learning.
Role of Machine Learning (ML) in Generative AI
At the base of Generative AI, like many other AI concepts, lies Machine learning (ML).
Machine learning involves a system that processes and learns from a dataset to perform a particular function while improving its performance over time.
Generative AI architecture
Generative AI employs a network of multiple layers containing many nodes, called Complex Neural network architecture. This structure enhances the system’s ability, enabling it to grasp intricate patterns present within data.
Popular Generative AI models
Now that we're familiar with the underlying structure of Generative AI, it's time to introduce and compare its two most popular models- Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs).
Generative Adversarial Networks (GANs) vs Variational AutoEncoders (VAEs)
Examples of Generative AI
1. ChatGPT: Generative AI as a text generator
ChatGPT, based on Large Language Models (LLMs), i.e. GPT-3, enables it to produce human-like text in response to prompts, engage in natural conversation, answer queries, and assist users with the required information. This ability of Generative AI in ChatGPT has transformed customer support, creative writing, content creation, and more.
2. Midjourney: Generative AI as an image generator
Midjourney demonstrates Generative AI's impact on visuals and imagery. By converting textual inputs into stunning visuals available in an array of various styles, themes and concepts, it revolutionizes creative industries and visual content production.
3. Engati's eSenseGPT, Generative AI technology for elevating enterprises
Engati's eSenseGPT embodies Generative AI's potential for business enhancement, empowering enterprises to streamline and optimize customer interactions, automate responses, and tailor user experiences, thereby delivering exceptional consumer experience through efficient, effective and impactful customer engagement strategies.
Generative AI examples across industries
1. Generative AI in finance
- Provide enhanced customer experiences through personalized financial recommendations, answering any potential queries they may have and more
- With improvement in training Generative AI models, soon it will also be possible for Generative AI to predict market trends and provide insights, generate financial reports and automate assessments.
2. Generative AI in travel, tourism and hospitality
- Generative AI can create personalized itineraries, find and suggest tourist spots, hotels, flights as per requirements and assist in any queries regarding travels.
3. Generative AI in E-commerce and retail
- Generative AI is transforming e-commerce through dynamic product recommendations, virtual try-ons, and creative content generation for marketing campaigns, hence significantly improving customer engagement by providing personalized recommendations for a better shopping experience.
4. Generative AI in Education
- In education, Generative AI has the ability to produce adaptive learning materials, generate learning materials, interactive content, tests and even grade them.
5. Generative AI in gaming
- Generative AI is resulting in more immersive gameplays through dynamic gameplay, enhanced player engagement, and both unreal and lifelike worlds.
The Future of Generative AI
While Generative AI offers unprecedented opportunities, it also raises important ethical considerations.
Generative AI depends entirely on a source dataset to learn and replicate information, so it reflects the source's bias in its generated content.
Additionally, Potential misuse for misinformation, deep fakes, copyright concerns, and exploiting others' work as source datasets without proper communication or consent are a few of the many concerns that must be addressed to ensure the responsible use of this remarkable technology, which promises to redefine innovation and creativity.