Applied AI Lab

Create. Innovate. Apply Artificial Intelligence.

A creative playground for applying cutting-edge AI techniques to real-world problems. Applied-AI-Lab brings together diverse projects in NLP, computer vision, recommendation systems, and multi-modal learning. Each project blends innovation and utility, showing how modern AI can be practically deployed across domains — from language understanding to image generation to human-computer interaction.

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1. Voice & Speech

1.1 Automatic Speech Recognition (ASR)

1.2 Text-to-Speech (TTS)

1.3 Voice-Based Interaction & Assistants

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2. Vision

2.1 Image Captioning

✦ Generating Vivid Landscape Image Captions with Salesforce BLIP-2-opt-2.7b and Microsoft Phi-2 in PyTorch

2.2 Face & Object Detection

2.3 Emotion Recognition

2.4 Generative Vision (Diffusion, GANs)

✦ Generating High-Resolution Fantasy Landscapes Using CompVis Stable-diffusion-v1-4 and Prompt Engineering in PyTorch

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3. Natural Language Processing

3.1 Text Classification

3.2 Sentiment Analysis

3.3 Fake-News / Fact-Checking

3.4 Text Summarization

3.5 Document Generation

3.6 Document Question-Answering

3.7 Embedding-Based Search & Retrieval

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4. Automation & Productivity Tools

4.1 File Organization Bots

4.2 Template / Report Generation

4.3 Local Assistive Utilities

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5. Multimodal Systems

5.1 Vision + Language (VQA, image2text, text2image)

5.2 Speech + Text (speech translation, audio chat)

✦ Real-Time Multilingual Speech Translator using Whisper, MarianMT, and Coqui TTS

5.3 Interactive AI Agents (tools + LLMs)

✦ Building a Poetic Fantasy NPC Chatbot with GPT-Neo-2.7B and Archaic Summarization in PyTorch

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6. Generative & Creative AI

6.1 Foundation Models & Diffusion

6.2 Prompt Engineering Playgrounds

6.3 AI-Driven Art & Music

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7. MLOps & Deployment

7.1 Packaging & Versioning

7.2 Model Serving & APIs

7.3 Monitoring, Logging, Rollback

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8. Responsible AI

8.1 Bias & Fairness Evaluation

8.2 Privacy & Security

8.3 Safety & Alignment Experiments