--- title: "Applications of AI in daily life" subject: "Artificial Intelligence" chapter: 1 key: "semester_6/artificial_intelligence:ai-application-fields" source: https://www.bctengineer.com/q/semester_6/artificial_intelligence/ai-application-fields site: BCT Engineering --- # Applications of AI in daily life Artificial Intelligence · Chapter 1: Introduction - Asked in: 2069 Bhadra, 7 marks - Video: [Top 10 Applications Of Artificial Intelligence in 2021](https://www.youtube.com/watch?v=dxpBcqSWbuk) — Readers - Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14 - Page: https://www.bctengineer.com/q/semester_6/artificial_intelligence/ai-application-fields - Whole chapter: https://www.bctengineer.com/md/semester_6/artificial_intelligence/chapter-1 ## Question Discuss any two fields of your daily life where artificial intelligence has been applied. ## Short answer Two daily AI fields: smart digital assistants and healthcare. Voice assistants (Siri, Alexa) use speech recognition, NLP, and knowledge bases for commands; recommendation systems (YouTube, Netflix) personalize content via ML; search, spam filters, translation, and face unlock also use AI. Healthcare uses deep learning for diagnosis from scans, expert systems like MYCIN for treatment suggestions, wearables for monitoring, and AI for drug discovery and hospital management. ## Answer AI is already part of everyday life. Two important fields are described below. #### 1. Smart assistants, search and recommendation (daily digital life) - **Voice assistants** (Google Assistant, Siri, Alexa) use speech recognition, natural language processing and knowledge bases. A user says "set an alarm for 6 am" or "call Mom" and the system converts speech to text, finds the intent and acts. - **Recommendation systems** on YouTube, Netflix, Daraz or Facebook use machine learning on our past clicks and views to suggest videos, products and friends. - **Search engines, spam filters and translation** (Google Search, Gmail spam filter, Google Translate) also use AI. - **Face unlock and photo tagging** on mobile phones use computer vision. ```text Voice -> Speech recognition -> NLP (intent) -> Action | Knowledge base v Reply (text-to-speech) ``` *Benefit:* saves time and gives personalised service. *Technologies:* NLP, machine learning, neural networks. #### 2. Healthcare - **Diagnosis support:** Deep-learning systems read X-rays, CT/MRI scans and retina images to detect pneumonia, tumours and diabetic eye disease, sometimes as accurately as specialists. - **Expert systems** such as MYCIN suggest antibiotics from symptoms and lab results using IF-THEN rules. - **Wearables** (smart watches) detect irregular heartbeat and monitor sleep. - **Drug discovery and hospital management:** AI predicts molecule behaviour, schedules staff and predicts patient load. - In Nepal, mobile apps and telemedicine can use AI triage chatbots to guide patients in remote areas where doctors are few. *Benefit:* early and cheaper diagnosis, less workload on doctors. *Limitation:* needs good data, and the final decision should remain with a doctor. Other daily-life fields are banking (fraud detection), transport (navigation, self-driving) and agriculture (crop disease detection). ## Check yourself The answer's key points as multiple-choice questions, with the correct option and why. 1. What is the order of processing in a voice assistant? A. NLP, speech recognition, action B. Action, speech recognition, NLP C. Speech recognition, NLP (intent), action, reply by text-to-speech D. Text-to-speech, intent, recognition Answer: C. Speech is converted to text, intent is found by NLP, the action is taken and a reply is spoken. (Key point: Voice assistant pipeline) 2. How do recommendation systems on YouTube or Daraz suggest items? A. Random selection B. Manual editing by staff for each user C. Only by price D. Machine learning on the user's past clicks and views Answer: D. They apply machine learning to past clicks and views to personalise suggestions. (Key point: Recommendation systems) 3. How does AI support diagnosis in healthcare? A. Deep-learning systems read X-rays, CT/MRI and retina images to detect disease B. It replaces doctors fully C. It only schedules appointments D. It prints reports Answer: A. Deep learning reads medical images to detect pneumonia, tumours and eye disease. (Key point: Healthcare diagnosis) 4. What limitation of AI in healthcare does the answer state? A. It is always cheaper but inaccurate B. It needs good data and the final decision should remain with a doctor C. It cannot read images D. It only works in cities Answer: B. AI needs good data, and doctors should make the final decision. 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