1. Explain artificial intelligence.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are
programmed to think and learn like humans. AI systems use algorithms and large sets of data to
complete tasks that typically require human-like cognitive functions, such as problem-solving,
reasoning, understanding natural language, recognizing patterns, and making decisions. AI can
be categorized into two types: Narrow AI, which is designed for specific tasks (like virtual
assistants), and General AI, which can perform any intellectual task that a human can do (still
largely theoretical).
2. Differentiate machine learning from deep learning.
Machine Learning (ML): A subset of AI that involves the use of statistical methods that enable
machines to improve with experience. ML algorithms analyze data, learn from it, and make
predictions or decisions without being explicitly programmed.
Deep Learning (DL): A further subset of machine learning that uses neural networks with many
layers (hence "deep") to analyze various levels of abstraction in data. Deep learning excels in
tasks such as image and speech recognition.
3. Other specific applications of AI in the fields of manufacturing, education, and business:
Manufacturing: Predictive maintenance using AI to forecast equipment failures before they
occur, optimizing supply chain logistics through demand forecasting, and quality inspection with
computer vision.
Education: Personalized learning through intelligent tutoring systems that adapt to students'
learning styles, automating administrative tasks like grading using natural language processing,
and early warning systems to identify at-risk students.
Business: Customer service chatbots that provide 24/7 support, AI-driven marketing analysis to
tailor campaigns to better target consumers, and revenue predictions through AI-based financial
forecasting tools.
4. How is machine learning different from traditional programming?
In traditional programming, a programmer defines explicit rules and logic for the computer to
follow to complete a task. The focus is on writing code that contains all decision-making criteria.
In contrast, machine learning allows the computer to learn from data and improve its
performance over time. Instead of hand-coding rules, the machine learns from examples and
patterns in the data to make predictions or decisions.
5. List down other games that applied AI not mentioned in the History of AI.
"The Sims" (simulating characters' thoughts and interactions)
"Total War" series (strategic decision-making)
"Civilization" series (AI opponents with strategic planning capabilities)
"Dark Souls" (adaptive enemy AI)
"Left 4 Dead" (AI Director that adjusts game difficulty dynamically)
6. Differentiate supervised and unsupervised learning.
Supervised Learning: In supervised learning, a model is trained on a labeled dataset, meaning
that the input data is paired with the correct output. The model learns to map inputs to outputs
and can then predict outcomes for new, unseen data. Example: Predicting house prices based
on features such as size, location, and number of rooms, with historical data available for
training.
Unsupervised Learning: In unsupervised learning, the model is trained on data without labeled
responses. The goal is to identify patterns and relationships within the data itself. Example:
Customer segmentation in marketing, where the algorithm groups customers based on
purchasing behavior without pre-defined categories.
1. What fields of science are associated with data science?
Statistics
Computer Science
Mathematics
Information Science
Domain-Specific Knowledge (e.g., Biology, Sociology, Economics)
2. What are the usual sources of big data?
Social media platforms (e.g., Twitter, Facebook)
Sensor networks (IoT devices in smart environments)
Online transaction records (e-commerce websites)
Government databases (public records, census data)
Corporate databases (customer transactions, sales data)
3. What data do you provide by using your social networking account, e.g., Facebook?
Personal information (name, age, gender)
Location data (check-ins, tagged locations)
Behavioral data (likes, shares, comments)
Relationships and connections (friends, groups)
Multimedia content (photos, videos)
1. Give at least 3 advantages of using IoT in the field of agriculture.
Precision Farming: IoT sensors can monitor soil moisture, nutrient levels, and weather
conditions, allowing farmers to make data-driven decisions to optimize crop yields and resource
use.
Livestock Monitoring: IoT devices can track animal health, location, and behavior, helping
farmers manage livestock more effectively and identify health issues early.
Automated Irrigation: Smart irrigation systems can automatically adjust water distribution based
on real-time weather data and soil conditions, reducing water waste and improving crop health.
2. What do you think are the most useful IoT devices in a smart home?
Smart Thermostats (e.g., Nest): Automatically adjust heating and cooling based on user
preferences and usage patterns, promoting energy efficiency.
Smart Security Systems (e.g., Ring or Arlo cameras): Provide real-time monitoring, alerts, and
remote access to ensure home security.
Smart Lighting (e.g., Philips Hue): Allow users to control lighting remotely, set schedules, and
customize moods through apps.
1. Differentiate the 3 service models of cloud computing.
Infrastructure as a Service (IaaS): Provides virtualized computing resources over the Internet.
Users rent IT infrastructure, such as virtual machines and storage, without the need for physical
hardware. Example: Amazon EC2, Google Compute Engine.
Platform as a Service (PaaS): Offers hardware and software tools over the internet, typically for
application development. PaaS allows developers to build applications without worrying about
the underlying infrastructure. Example: Microsoft Azure App Service, Heroku.
Software as a Service (SaaS): Delivers software applications over the Internet on a subscription
basis. Users access applications through a web browser without needing to install them locally.
Example: Google Workspace, Salesforce.
2. Explain 3 advantages of cloud computing.
Scalability: Cloud services can scale resources up or down as needed, allowing businesses to
adjust their IT resources based on demand without investing in physical hardware.
Cost-Effectiveness: Reduces the need for extensive IT infrastructure investments, as companies
only pay for the resources they use (pay-as-you-go), lowering capital expenses.
Accessibility: Allows users to access applications and data from anywhere with an internet
connection, facilitating remote work and collaboration among distributed teams.