IS 101 Preliminary Exam

INTELLIGENT SYSTEMS

are technologically advanced machines that perceive and respond to their environment.

1. Perception
2. Action Control
3. Interaction or connectivity
4. Deliberate and social reasoning
5. Self-Learning
6. Identification
7. Protection
8. Remote Management
9. User Experience (UX)
10. Data Analytics

10 characteristics of INTELLIGENT SYSTEMS

Autonomous Robots
Computer Vision
Natural Language Processing
Expert Systems
Sentiment Analysis

Applications of Intelligent System

Tesla
Amazon
Airbnb
Twitter

Companies using intelligent systems

Data

is any information you collect that is organized and structured to make it worthwhile for analysis.

QUANTITATIVE DATA

•It consists of numerical values like height, weight, temperature, or counts. It can be discrete (whole numbers) or continuous (decimals).

QUALITATIVE DATA

consists of non-numerical information, often describing qualities, characteristics, or attributes. Examples includes gender, rankings, and enumerations.

CONTINUOUS DATA

•Represents measurements that can take any value within a given range. It includes decimals and infinite possibilities. Examples include height, weight, and temperature.

DISCRETE DATA

Consists of separate, distinct values that are countable and usually whole numbers.

NOMINAL DATA

•Represents categories with no inherent order or ranking.

ORDINAL DATA

- The order between categories matters, but the intervals or differences between them may not be uniform or precisely measurable.

- Structured Data
- Unstructured Data
- Semi-structured Data

•Data sources are the origins or places from which information is collected.

•They can be categorized into:

Data collection

refers to the process of gathering, capturing, or obtaining raw information for use in computing and analysis

APIs (Application Programming Interfaces):

•Programmatically extracting data from websites. This involves automated processes that simulate human browsing behavior to retrieve specific information from web pages.

Data cleaning

also known as data cleansing or data scrubbing, is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets.

DATA ANALYSIS

Is the process of systematically examining and interpreting data using computational methods.

Data Presentation

is about showing information from data analysis in a clear way, often using charts or graphs

Data Visualization

Creating visual representations for easier interpretation.

Reports and Dashboards

Communicating findings through structured reports.

Anonymization

involves removing or altering personally identifiable information (PII) in a dataset, making it challenging to identify individuals.

Encryption

is the process of converting data into a secure code using algorithms, making it unreadable without the appropriate decryption key.

Compliance

involves adhering to relevant data protection regulations, laws, and standards applicable to the handling of personal and sensitive information.

documentation

means writing down important details about software, data, or processes.

Metadata

It involves describing the characteristics of the data, providing information about its structure, format, and context.

Code Comments

- It involve adding explanatory notes within the code to describe its functionality, logic, or any crucial information.

Version Control

It is the practice of managing changes to data and code over time. It involves keeping track of different versions, recording modifications, and facilitating collaboration among multiple contributors.

Enhance efficiency and productivity:

Intelligent systems are designed to automate tasks and processes using advanced algorithms and machine learning techniques.

Improve decision-making:

Intelligent systems can personalize interactions and adapt to user preferences, resulting in a more intuitive and satisfying user experience.

Enhance user experience:

Intelligent systems can personalize interactions, anticipate user preferences, and tailor recommendations to individual users, thereby enhancing their overall experience

Enable automation:

Automation is at the core of intelligent systems, enabling them to perform tasks autonomously without human intervention.

Increase accuracy and reliability

Intelligent systems are designed to minimize errors and biases inherent in human decision-making, leading to more accurate and reliable outcomes.

Intelligent experiences

refer to the interaction between users and technology that leverages artificial intelligence (AI) and machine learning to provide personalized, efficient, and contextually relevant experiences.

EXAMPLE OF INTELLIGENT EXPERIENCES

E-commerce

Healthcare

Customer Service

Financial Services

Entertainment

AI Algorithms
Automation
User Interface
Data
Personalization
Adaptability

Components of Intelligent Experiences:

Data Quality and Privacy
Algorithm Bias
Interpretability
Scalability
Integration

Challenges in Creating Intelligent Experiences

Natural Language Processing(NLP)

is the field of computer science that focuses on enabling computers to understand, interpret, and generate human language using machine learning and linguistic principles.

Gesture Recognition

is tech that interprets human gestures for computer interaction, commonly using sensors or cameras, applied in gaming, VR, and smart devices.

Biometric interaction

it involves using biometric data like fingerprints or facial recognition to authenticate users, enhance security, and personalize interactions.

Augmented Reality (AR) and Virtual Reality (VR)

Augmented Reality (AR) overlays digital content onto the real world, enhancing perception, while Virtual Reality (VR) immerses users in a completely simulated environment.

Speech recognition

is a technology that converts spoken language into text.

Computer vision

It refers to the use of technology that enables machines to understand and interpret visual information, enhancing their ability to interact intelligently with users and their surroundings.

Sensor-based interactions

involves using sensors to detect and respond to human input or environmental conditions, enhancing user experience in devices by enabling interaction through gestures, touch, or other sensed inputs

Machine learning (ML)

It means using algorithms to help systems learn from data and improve their interactions with users, leading to more personalized and efficient responses over time.

Intelligent Experiences

leverage technology and data to provide personalized and seamless interactions for users. These experiences are designed to anticipate and fulfill user needs, enhancing customer satisfaction and engagement

Natural Language Processing
Advanced Data Analytics
Multi-Device Interactions

The Components of Intelligent Experiences

Natural Language Processing

rely on advanced NLP to understand and respond to user queries in a human-like manner, enhancing user engagement and satisfaction.

Advanced Data Analytics

analyze large datasets to uncover patterns and trends, enabling intelligent experiences to provide personalized recommendations and insights

Multi-Device Interactions

Intelligent experiences seamlessly connect across multiple devices, ensuring a consistent and personalized user experience regardless of the device being used.

Complex Data Integration
Intelligent experiences require integrating and processing vast amounts of data from various sources to provide personalized interactions

Evolving User Expectations
As user expectations evolve, it becomes challenging to consistently deliver intelligent experiences that meet their changing needs

Privacy and Security Concerns
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Dynamic External Factors
External factors such as technological advancements and market trends constantly influence the complexity of creating intelligent experiences

Why Creating Intelligent Experiences Is Hard

User Personalization
Delivering personalized experiences while respecting user privacy and data protection is crucial in creating a balanced intelligent experience

Optimized Performance
Achieving a balance between performance optimization and resource consumption is essential for ensuring a seamless and responsive experience for users.

Ethical Considerations
Addressing ethical concerns surrounding data usage and algorithmic decision making forms an integral part of creating ethically balanced intelligent experiences

Balancing Intelligent Experiences

MODES OF INTELLIGENT INTERACTION

Voice Command
Interacting with intelligent systems using voice commands for hands-free and intuitive interactions
Gestures and Motion
Utilizing gestures and motion-based interactions to engage with intelligent systems in a more dynamic and immersive manner
Touch and Haptic Feedback
Integrating touch-based interactions and haptic feedback to provide tactile and responsive intelligent experiences

Getting Data from Experience

Data Collection
Collecting relevant user data through intelligent interactions to understand preferences and behavior.

Data Processing
Processing and analyzing collected data to extract valuable insights and patterns for enhancing intelligent experiences.

Feedback Loop
Establishing a continuous feedback loop to refine and optimize intelligent experiences based on user data and insights