Generative AI

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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)

Generative Adversarial Netowrks (GANs) Variational AutoEncoders (VAEs)
What is it? Generative Adversarial Networks (GANs) consists of a generator and discriminator competing against each other, leading to the network creating new data instances like image, audio or text. Variational AutoEncoders (VAEs) aims to capture the original data‘s foundational structure by utilising an encoder-decoder network.
Framework Generator - Discriminator framework Encoder-decoder framework
How it works The generator creates data, while the discriminator distinguishes between the source data learned by the generator and the 'new' data it generates.
Through this competition, the generator enhances its ability to fabricate data that closely resembles the source.
Encoder compresses source data into latent space, capturing only essential features.
Randomness may be introduced to this space.
Finally, the decoder recreates data from the compressed latent space.
Where its used GANs are particularly advantageous at tasks like style transfer or image generation, where resemblance to the original dataset‘s styles and patterns is prioritized to produce high-quality data. Utilised for tasks like data compression, de-noising, and generating new instances of data since VAEs can learn the underlying relationships present in data.

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.

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