Cutting-edge AI Applications · Topic 1 of 1
Cutting-edge AI Application Scenarios
Learn the AI behind voice assistants, smart homes, intelligent vehicles, recommendation systems, intelligent robots and AI4Science for the H13-311 exam.
- 5 min
- Voice assistant chain: wake word, ASR, NLU (intent and slots), dialogue management, NLG, TTS.
- Smart homes use IoT, scene linkage and device side AI for privacy.
- Vehicles: perception, sensor fusion, planning, control; SAE levels L0 to L5.
What it is
These six scenarios show AI working in products and research today. Each one joins perception, understanding and action, using the machine learning, deep learning and large model techniques from earlier chapters.
The exam asks which technology each scenario uses and in what order its steps run, so learn every pipeline as a chain.
How it works
Voice assistants
A voice assistant lets people control devices and get answers by speaking. Huawei's assistant is Celia (Xiaoyi in China); others include Siri and Alexa.
| Step | Role |
|---|---|
| Wake word detection | Listens on the device for a trigger phrase, using little power |
| ASR (automatic speech recognition) | Converts speech to text |
| NLU (natural language understanding) | Finds the intent (what the user wants) and slots (details such as time or place) |
| Dialogue management | Tracks context across turns and decides the next action |
| NLG and TTS | Produce the reply text and speak it |
Large models now give many assistants more natural multi turn conversation.
Smart homes
A smart home connects lights, locks, cameras, appliances and sensors through IoT and controls them by voice, app or automation. AI adds scene linkage, such as turning on lights and air conditioning when a door sensor detects that the owner has come home.
Typical AI uses are face recognition at the door, intrusion detection from cameras and learning habits to save energy. Processing on the device or a home hub protects privacy and keeps working when the internet is down.
Intelligent vehicles
An intelligent vehicle uses AI for driving and for the cabin. Autonomous driving follows a perception, decision and control chain.
Sensor fusion combines cameras (rich detail), millimeter wave radar (distance and speed in bad weather) and lidar (precise 3D point clouds). The intelligent cockpit adds voice control, driver fatigue monitoring and personalized settings.
| SAE level | Name | Who drives |
|---|---|---|
| L0 | No driving automation | Human does everything |
| L1 | Driver assistance | System helps with steering or speed |
| L2 | Partial automation | System steers and controls speed; human supervises at all times |
| L3 | Conditional automation | System drives in set conditions; human must take over when asked |
| L4 | High automation | System drives in a defined area with no takeover needed |
| L5 | Full automation | System drives everywhere a human could |
Intelligent recommendation
A recommender system predicts what each user is likely to want and ranks items for them, as in shopping, video and news apps.
| Method | Idea | Weakness |
|---|---|---|
| Content based | Recommend items similar to those the user liked before | Narrow, little discovery |
| Collaborative filtering | Use the behavior of similar users (user based) or similar items (item based) | Cold start for new users and items |
| Deep learning models | Learn user and item embeddings and feature interactions | Need much data and compute |
| Hybrid | Combine several methods | More complex |
Large systems work in stages: recall (retrieve a few hundred candidates from millions), ranking (score candidates, often by predicted click through rate) and re ranking (apply diversity and business rules).
Intelligent robots
An intelligent robot perceives its environment, makes decisions and acts physically. Types include industrial robots on production lines, service robots in hotels and hospitals, and humanoid robots.
Embodied AI is AI that learns and acts through a physical body. Large models are being used as the robot's brain to understand spoken tasks and plan steps, while vision and reinforcement learning help with movement and grasping.
AI4Science
AI4Science uses AI to speed up scientific research. AlphaFold from Google DeepMind predicts protein 3D structure from the amino acid sequence; its creators Demis Hassabis and John Jumper shared the 2024 Nobel Prize in Chemistry with David Baker.
Huawei's Pangu Weather model, published in Nature in 2023, forecasts global weather with deep learning far faster than traditional numerical methods. Other uses include drug discovery and new materials.
A worked example
Collaborative filtering often measures user similarity with cosine similarity. User A rated three films (5, 3, 0) and user B rated them (4, 2, 1).
- Dot product: 5 x 4 + 3 x 2 + 0 x 1 = 26
- Lengths: square root of 34 = 5.831 and square root of 21 = 4.583
- Cosine similarity: 26 / square root of (34 x 21) = 26 / 26.721 = 0.97
The users are very similar, so films B liked are good picks for A.
How the exam asks about this
- "In a voice assistant, which module identifies the user's intent?" → NLU (natural language understanding).
- "At which SAE level must the human driver supervise at all times while the system steers and controls speed?" → L2, partial automation.
- "Which recommendation problem occurs when a new user has no history?" → The cold start problem.
What to remember for the exam
- Voice assistant chain: wake word, ASR, NLU (intent and slots), dialogue management, NLG, TTS.
- Smart homes use IoT, scene linkage and device side AI for privacy.
- Vehicles: perception, sensor fusion, planning, control; SAE levels L0 to L5.
- Recommenders: content based, collaborative filtering, deep learning, hybrid; recall, ranking, re ranking.
- AI4Science examples: AlphaFold for protein structure, Pangu Weather for forecasting.
ASR and NLU are different steps. ASR only turns sound into text; NLU works out what that text means. A question about "intent" is about NLU.
Common mistakes
Calling L2 driving autonomous. At L2 the system steers and controls speed, but the human must watch the road and is responsible at all times. Only from L3 up does the system perform the whole driving task, and then only in its set conditions.
Mixing up content based and collaborative filtering. Content based filtering uses item features and the user's own history. Collaborative filtering uses the behavior of other, similar users or items.
Thinking AI4Science replaces experiments. Models such as AlphaFold give predictions that guide research. Scientists still confirm important results by experiment.
Check yourself
What does TTS do in a voice assistant?
Text to speech converts the assistant's reply text into spoken audio, the final step of the pipeline.
Why do autonomous vehicles use sensor fusion?
Each sensor has weaknesses, so combining cameras, radar and lidar gives a more complete and reliable view of the road.
What is the cold start problem in recommendation?
A new user or item has little or no history, so collaborative filtering cannot find similar users or items to base recommendations on.
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