AI and Customer Experience in Tourism: Core Concepts
Artificial Intelligence Fundamentals
Machine Learning vs. Deep Learning: Key Differences
Machine Learning (ML) is a method for computers to learn from data and improve their performance without being explicitly programmed step-by-step. It is typically used for simpler predictions or decisions based on patterns identified in data.
Deep Learning (DL) is a more advanced subset of ML that utilizes large neural networks with many layers. It excels at understanding complex data types, such as images or human language. DL requires more data and significant computing power but can solve more challenging problems.
Examples:
- ML Example: Predicting if a traveler will cancel their booking based on historical cancellations, age, or destination.
- DL Example: An application that recognizes tourist attractions in photos by analyzing thousands of images.
Understanding Generative AI
Generative AI is a type of artificial intelligence capable of creating new content, including text, images, music, or video. Unlike analytical AI, it produces novel information based on patterns learned during its training phase.
RAG, Fine-Tuning, and Grounding: A Comparison
RAG (Retrieval-Augmented Generation) is a technique that enables an AI model to search for and retrieve real-world information (e.g., from documents or databases) before generating a response. This approach provides more up-to-date and reliable answers.
RAG differs from Fine-Tuning, which involves retraining an existing AI model with new data. Fine-tuning is generally slower and more resource-intensive.
It is also distinct from Grounding, which is the broader concept of connecting AI with real-world data. RAG is a concrete implementation of grounding, combining information retrieval with content generation.
In tourism, RAG is highly beneficial because AI can provide current information without requiring constant model retraining.
RAG Data Usage: True or False?
Statement: In RAG (Retrieval-Augmented Generation), our own data is mixed with the pre-trained data of the model to get a more precise and relevant result. By doing this, our data will be used to train the model.
Answer: False
What Can a Multimodal AI Model Do?
A Multimodal Model can:
- Process and relate information from different modalities like text, image, audio, and speech.
LLM Hyperparameters: Understanding Temperature
Temperature is a hyperparameter of Large Language Models (LLMs) that:
- Controls the level of randomness in the model’s predictions.
Human Intelligence Traits Simulated by AI
The three main characteristics of human intelligence that AI strives to simulate are:
- Learning
- Reasoning
- Self-correction
AI’s Impact on the Tourism Sector
Enhancing Tourism Customer Experience with AI
Artificial intelligence significantly improves the tourist experience by making services more personalized, faster, and more efficient. It allows companies to better understand individual customer needs and tailor their offers at each stage of the trip.
Examples:
- Chatbots can answer queries 24/7 in multiple languages.
- Recommendation systems suggest destinations or hotels based on traveler interests.
- AI can send alerts about flights or weather.
- AI can automatically adjust prices according to demand.
Practical Applications of Generative AI in Tourism
In tourism, Generative AI is highly valuable for saving time and personalizing experiences. For example, it can:
- Generate automatic travel itineraries or write hotel descriptions quickly and in different languages.
- Power virtual assistants that provide real-time assistance to travelers based on their preferences.
- Write promotional content for a hotel or destination.
- Power chatbots that answer tourist questions in a friendly and natural way.
Optimizing Customer Experience in Tourism
Why Customer Experience Matters in Tourism
Customer experience in tourism is crucial for several reasons:
- Reputation
- Recommendations (word-of-mouth)
- Repeat business
- Competitive advantage
- Revenue growth
Current Challenges in Tourism Customer Experience
Some current challenges in customer experience within the tourism sector include:
- Changing customer expectations
- Cultural barriers
- Data privacy concerns
- Overtourism
- Sustainability demands
- Reputation management
- Dynamic pricing complexities
- Crisis management
- Accessibility requirements
The Traveler Journey Stages Explained
The correct order of the traveler journey stages is:
- Awareness
- Research
- Booking
- Pre-trip
- Travel
- Experience
- Post-trip
Decision-Making Process: Identifying Non-Stages
Which is NOT a stage of the decision-making process?
- Answer: Resource Allocation (Asignación de recursos)
Key Term: Awareness
Awareness: Concienciación