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GenAI create new trajectories of data by learning patterns from data. It makes it very creative as it can create new art, images and text. You can provide condition and it will create data according to conditions. This property of generative AI makes it power ful for personalization as it can create new images/text/responses according to individual attributes. Gen AI is also great in doing repetitive task such as crafting email, writing code and it makes it perfect for Automation.

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Generative AI has demonstrably achieved promising results in several areas, including:

• Content creation and summarization: AI can generate different creative text formats, like articles, scripts, musical pieces, email, letters, etc. It can also summarize factual topics.
• Question answering: AI can provide answers to your questions in an informative way, even if they are open ended, challenging, or strange.
• Code writing assistance: AI can help developers write code by suggesting completions, fixing bugs, and generating entire programs.

Generative AI is also making significant progress in:

• Product ideation and creation: AI can help brainstorm new product ideas and even create prototypes, such as with Product KREATE.
• Workflow reinvention: AI can automate tasks and suggest new workflows that can improve efficiency.

Experienced teams are increasingly using generative AI for:

• Dataset creation and augmentation: AI can generate new data or modify existing data to improve the quality and size of datasets used for training other AI models.

However, as generative AI progresses, it's crucial to consider how AI threats are evolving as well. This includes:

• The emergence of entirely new threats due to AI capabilities.
• Changes to existing threats as AI becomes more sophisticated.
• The potential expansion of existing threats as AI becomes more widely used.

Generative AI is based on "comprehend existing" data and determine trajectories data can take. It uses it for geenration

Traditional machine learning model has evaluaition metrics like accuracy. Generative AI creates new data. It evaluation is based on subjecive measures like diversity of data, realism, nobalty, creativity. It is hard to evaluate or benchmark geenrative models.

Use above dimensions to identify quardant of your use case(s). Low risk and applicability of generic data e.g content writing for sales,travel guide are easy to adopt.

Generative AI has potential, but there are many challenges and open questions. One should consider these before using geenrative AI in enterprise(s)

Generative AI is based on "comprehend existing" data and determine trajectories data can take. It uses it for creating new data. However the method of producing new variation of data makes it controllable.

There is significant amount of compute, energy usage. There is significant amount of carbon emission. One need to ensure it is for good cause and not for producing variation(s)

GPT (Generative Pre-Trained Transformer) is family of large language model. developed by OPEN AI. GPT4 is latest generaral purpose LLM released by OpenAI. ChatGPT4 is chatbot focused LLM.

Here is framework for Digital Agent, Virtual Assistant, Digital Inluencer and Digital Human.