OCN NI AI Week Five

OCN NI AI Week Five

24/02/2025

Learning Aims: 

Explain the application of AI to the following

human machine interactions:

1. Image recognition and generation
   
2. Extended reality
   
3. Natural language processing

Has anyone used AI for image recognition or generation?

Yes

No

Image Recognition 

AI-powered image recognition is used to identify and classify objects, faces, text, and other elements within digital images or videos. This process involves:

1. Machine Learning & Deep Learning: AI models are trained on vast datasets of labelled images to detect patterns and features automatically.
2. Neural Networks: Convolutional Neural Networks (CNNs) are commonly used to analyse image pixels and extract meaningful details.

Applications

1. Facial Recognition- unlocking smartphones using Face ID.
2. Medical Imaging- detecting diseases from X-rays or MRIs.
3. Self-Driving Cars- recognising road signs, pedestrians, and obstacles.
4. Security & Surveillance- AI-powered CCTV monitoring for threat detection.

Image Generation 

AI is also capable of creating entirely new images through advanced algorithms such as:

Generative Adversarial Networks (GANs): These AI models consist of two neural networks (a generator and a discriminator) that work together to create realistic images.

Extended Reality 

Extended Reality (XR) is an umbrella term that includes:

 Virtual Reality (VR): A fully immersive digital environment (e.g. Oculus VR).

Augmented Reality (AR): Digital elements overlaid on the real world (e.g. Pokémon GO).

Mixed Reality (MR): A blend of real and virtual elements interacting in real-time (e.g. Microsoft HoloLens).

AI enhances XR by improving interactions, making virtual environments more intelligent, and enabling real-time decision-making.

AI In Virtual Reality 

AI powers realistic simulations and adaptive experiences in VR by:

Creating Smart Virtual Worlds: AI generates lifelike environments and NPCs (non-playable characters).

Enhancing Immersion: AI adjusts VR experiences based on user behaviour and preferences.

Training & Simulation: Used in medical, military, and aviation training.

AI In Virtual Reality (cont) 

🕶️ Gaming: AI-driven VR games adapt to players’ actions (e.g. Half-Life: Alyx).

🏥 Healthcare: VR simulations train doctors and surgeons.

✈️ Aviation: AI-powered flight simulators enhance pilot training.

What are some risks of AR technology, such as privacy concerns?

AI In Augmented Reality 

AI makes AR experiences more interactive and realistic by:

Object Recognition: AI recognises real-world objects and enhances them with digital content.

Personalised AR Content: AI customises AR filters and experiences based on user preferences.

Real-Time Data Processing: AI enables live translations and real-time guidance.

AI In Augmented Reality (cont)

📱 Social Media: AI-powered AR filters on Instagram, Snapchat, and TikTok.

🛍️ Retail & Shopping: Virtual try-ons for clothes, glasses, and makeup (e.g. IKEA Place, Sephora Virtual Artist).

🛠️ Manufacturing & Maintenance: AI-powered AR assists workers with repairs and training.

How can AI-powered AR improve industries like healthcare, retail, or education?

Natural Language Processing 

Natural Language Processing (NLP) is a branch of AI that enables machines to understand, interpret, and generate human language. It combines linguistics and machine learning to improve human-computer interactions.

AI-powered NLP allows machines to process speech and text, enabling applications such as virtual assistants, chatbots, translation services, and sentiment analysis.

AI in Speech Recognition 

NLP helps machines understand and respond to spoken language by:

Converting Speech to Text (Speech Recognition): AI transcribes spoken words into text.

Understanding User Intent: NLP analyses meaning and context to provide accurate responses.

Generating Human-Like Speech: AI-powered text-to-speech (TTS) makes machines sound more natural.

AI in Speech Recognition (cont)

🗣️ Voice Assistants: Alexa, Siri, and Google Assistant process and respond to spoken commands.

🎤 Speech-to-Text Software: AI-powered transcription tools like Otter.ai and Google Docs Voice Typing.

🚗 AI in Cars: Hands-free voice control for navigation and music (e.g. Tesla’s voice assistant).

Where do you use voice recognition in daily life?

AI in Text Processing & Chatbots

NLP enables AI to process and generate text by:

Understanding Sentiment & Context: AI analyses emotions in customer reviews and social media posts.

Powering Chatbots & Virtual Assistants: AI-driven chatbots simulate human conversations.

Automating Customer Support: AI provides instant responses and troubleshooting solutions.

AI in Text Processing & Chatbots (cont)

💬 Chatbots: AI chatbots on websites and messaging apps (e.g., ChatGPT, Meta’s Messenger bot).

📧 Email Filters: Gmail’s spam filter uses NLP to detect unwanted emails.

📰 Content Generation: AI tools like Grammarly and Jasper help with writing.

AI in Language Processing

NLP-powered translation tools help break language barriers by:

Understanding Context & Grammar: AI improves accuracy beyond simple word-for-word translation.

Real-Time Translations: AI enables instant communication between different languages.

Learning & Improving Over Time: AI adapts based on user feedback and language trends.

AI in Language Processing

🌍 Google Translate & DeepL: AI-powered translation services for text and speech.

📱 Live Translation Apps: Real-time voice and text translation (e.g.

Microsoft Translator).

🛍️ E-commerce & Travel: AI enables multilingual customer support.

Assignment Work?

Yes

No

Learning Outcomes Covered:

1. Image recognition and generation
   
2. Extended reality
   
3. Natural language processing