Current and Evolving Applications in Generative AI - Overview Slides

Generative AI deep dive - Applications of generative AI, technical architecture, risk, bias, ethical concern in generative AI, Foundation model are described here. In addition there are advance topics related to custom model training. There is also industry specific analysis.
OVERVIEW
OVERVIEW
        
APPLICATIONS
APPLICATIONS
        
USE CASES
USE CASES
        
OUTPUT
OUTPUT
        

Economica of Data

Applications

Text Generation
  • Content writing
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  • Sales and Marketing Emails
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  • Job posting /li>
  • chatbox, digital assistant
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    Code
  • Web app builders like KREATE
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  • SQL generation
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  • code documentation generation
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  • Code geenration
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    Image
  • Image geenration with prompt description
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  • Greeting card generation
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  • Advertisement image generation
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    Voice and Videos
  • Voice geenration
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  • Voice editing
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    Music generation

    Tone for mobile, other


  • Video generation
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  • 3D model
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  • 3D videos
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    Data set for AI
  • New dataset to handle cold start problem
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  • Augmenting datasets
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  • Video generation
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    Text generation: Generative AI can be used to generate text, such as news articles, blog posts, and product descriptions. This can be helpful for businesses that need to produce a lot of content on a regular basis. Image generation: Generative AI can be used to generate images, such as product renders, marketing materials, and even art. This can be helpful for businesses that need to create visual content for their products or services. Audio generation: Generative AI can be used to generate audio, such as music, podcasts, and audiobooks. This can be helpful for businesses that need to create audio content for their products or services. Code generation: Generative AI can be used to generate code, such as software applications and web pages. This can be helpful for businesses that need to develop new software or websites. In addition to these general applications, generative AI is also being used for a variety of more specialized applications, such as: Drug discovery: Generative AI can be used to discover new drugs by generating new molecules that have the potential to be effective treatments for diseases. Fraud detection: Generative AI can be used to detect fraudulent transactions by generating synthetic data that mimics real-world data. Customer service: Generative AI can be used to create chatbots that can answer customer questions and resolve issues. Education: Generative AI can be used to create personalized learning experiences for students by generating content that is tailored to their individual needs.

    From the blog

    Build Dataproducts

    How Dataknobs help in building data products

    Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

    Be Data Centric and well governed

    Generative AI is one of approach to build data product

    Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

    Spotlight

    Generative AI slides

    Learn generative AI, LLM, foundation model
  • Learn generative AI - applications, LLM, architecture
  • See best practices for prompt engineering
  • Evaluate whether you should use out of box foundation model, fne tune or use in-context learning
  • Most important - be aware of concerns, issues, challenges, risk of genAI and LLM
  • See vendor comparison - Azure, OpenAI, GCP, Bard, Anthropic. Review framework for cost computation for LLM
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    Applications    Output    Overview    Use-cases