competition-areas

1. Text Generation (LLMs) GenAI LLMs “learn” and are trained on large amounts of data, documents and artifacts that already exist online. GenAI is probabilistic (not deterministic) in nature and tries to predict what’s likely to be useful based on patterns in its training data — not what’s guaranteed to be correct. Large Language Models (LLMs) include GPT-5, Claude, Gemini and LLaMA.

2. Visual & Multimedia Generation These models are trained on image, audio, or video datasets to generate original creative assets based on text or sketch prompts.

Image Generation: Converts text-to-template or text-to-image. (e.g., DALL-E 3 or Midjourney).

• Video Generation: Creates synthetic video clips or enhances archival material (e.g., OpenAI Sora)
• Audio & Music: Synthesizes realistic sound effects, voiceovers, or full musical compositions (e.g., Murf AI or Suno)

3. Code Generation These are specialized models that act as pair programmers, trained on open-source repositories to understand and write functional code.

• Core Tasks: Autocomplete, bug fixing, script creation, and code documentation.
• Popular Tools: GitHub Copilot.

4. Agentic AI An evolution of traditional chatbots, Agentic AI systems are solutions that not only generate responses but can also execute multi-step workflows autonomously.

• Core Tasks: Booking travel, scheduling, automating data entry, or executing specific business rules.
• Popular Tools: Zapier AI Agents

Here are a few other common agentic architectures: Basic LLM agent loop, ReAct architectures, Planning + execution architectures, multi-agent systems, hierarchical agent architectures, retrieval-augmented agents, shared workspace architectures, and cognitive architectures.

5. Domain-Specific Enterprise Platforms Businesses utilize custom-built GenAI platforms that are integrated with their internal data (via Retrieval-Augmented Generation, or RAG) to ensure compliance and company-specific accuracy.

• Finance & Legal: Platforms designed specifically for compliance checks, financial reporting, and due diligence.
• Cloud AI Platforms: Enterprise infrastructure suites for building custom AI apps. (e.g., Google Vertex AI or AWS Generative AI).

6. Education: Education models include models below.

• Virtual experiment teaching management
• Experimental workflow design
• Intelligent guidance
• Intelligent evaluation
• Generating student-friendly 3D models and animations

7. Multimodal GenAI Multimodal GenAI includes applications which can combine models and tools such as Text to Image (Adobe Firefly), Text / Image to Video (Sora), Audio to Text (Otter, Whisper), Video to Text (Gemini Pro), Image to Video (Runway), Image to Text or Text to Text or Text to Code (ChatGPT), Video with Audio (Google Veo3), Image and Video (MidJourney), Text to Audio and Video (Google NotebookLM).

• Healthcare: Designing new molecules for drug discovery
• Engineering: Generating synthetic data for simulations
• Marketing: Creating marketing campaigns and customer insights
• Software Development: Writing code and automating programming tasks
• Entertainment: Creating narratives, characters, graphics, and sound effects for video games
• Marketing & trend analysis for fashion brands
  – Bringing together a variety of techniques, such as machine learning and probabilistic programming. These techniques allow for powerful generative models that consider the customer’s desires in the fashion business.
• Fraud detection
  – Offering banks a powerful tool to detect suspicious or fraudulent transactions, thereby enhancing their ability to combat financial crime.
• Content creation for marketing
  – Enable businesses to create personalized content such as product descriptions, social media posts, video ads, and email campaigns.