Neural Networks and Data Flow Diagrams
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Questions and Answers

What must a data repository have in order to function properly?

  • Only output flow
  • Multiple input flows without output
  • No connections to processes
  • At least one input flow and one output flow (correct)

Data can move directly from an entity to a data repository without being processed.

False (B)

What is the role of a process in a data flow diagram?

To describe a process being performed on data.

An external entity must be connected to a process with a ______.

<p>data flow</p> Signup and view all the answers

Match the following concepts with their definitions:

<p>Data Flow = Indicates the direction of data movement Data Repository = A storage location for data Process = Describes actions performed on data External Entity = Sources or destinations of data outside the system</p> Signup and view all the answers

What is the process called that enables neural networks to learn patterns and relationships in data?

<p>Training (B)</p> Signup and view all the answers

Deep neural networks have only one hidden layer.

<p>False (B)</p> Signup and view all the answers

List two applications of neural networks.

<p>Image recognition and natural language processing.</p> Signup and view all the answers

Neural networks excel at ______ recognition.

<p>pattern</p> Signup and view all the answers

Match the following applications with their descriptions:

<p>Image recognition = Identifying objects in pictures Speech recognition = Understanding spoken language Natural language processing = Interacting using human language Autonomous vehicles = Self-driving cars using AI</p> Signup and view all the answers

Which type of neural networks is capable of handling complex tasks like image recognition?

<p>Deep neural networks (D)</p> Signup and view all the answers

Neurons in a neural network use activation functions to process information.

<p>True (A)</p> Signup and view all the answers

What are the three types of layers in a typical neural network?

<p>Input layer, hidden layer, output layer.</p> Signup and view all the answers

What is the primary purpose of the Turing Test?

<p>To assess machine intelligence (C)</p> Signup and view all the answers

Passing the Turing Test requires a computer to show genuine understanding and contextual adaptation.

<p>True (A)</p> Signup and view all the answers

What is a key advantage of neural networks in AI?

<p>They can adapt and learn from their mistakes.</p> Signup and view all the answers

Neural networks resemble the operations of an animal _____ to recognize relationships between data.

<p>brain</p> Signup and view all the answers

Match the AI applications with their functions:

<p>Computer Vision = Face recognition and image labeling Speech Recognition = Converting speech into text Natural Language Processing = Engaging in conversation with users Recommendation Engines = Suggesting products based on user behavior</p> Signup and view all the answers

Which of the following is NOT a component of neural networks?

<p>Decision Tree (C)</p> Signup and view all the answers

The significance of the Turing Test includes raising public awareness about AI.

<p>True (A)</p> Signup and view all the answers

Name one philosophical implication sparked by the Turing Test.

<p>Debate about consciousness.</p> Signup and view all the answers

Which type of neural network is primarily used for controlling household appliances?

<p>Feed-Forward Neural Networks (D)</p> Signup and view all the answers

Convolutional neural networks are beneficial for language understanding tasks.

<p>False (B)</p> Signup and view all the answers

What feature do recurrent neural networks have that distinguishes them from feed-forward networks?

<p>They store historical processes and reuse them in future processing.</p> Signup and view all the answers

A neural network that conveys information in one direction through input nodes is called a ______.

<p>feed-forward neural network</p> Signup and view all the answers

Match the following neural networks with their primary applications:

<p>Feed-Forward Neural Network = Smart home automation Recurrent Neural Network = Text-to-Speech Applications Convolutional Neural Network = Image Recognition Deconvolutional Neural Network = Item detection in images</p> Signup and view all the answers

What is the primary function of convolutional layers in a convolutional neural network?

<p>To create feature maps for image processing (B)</p> Signup and view all the answers

Deconvolutional neural networks work in the same way as convolutional neural networks.

<p>False (B)</p> Signup and view all the answers

What is the primary purpose of heuristics in expert systems?

<p>They capture information based on personal experience. (A)</p> Signup and view all the answers

Fuzzy logic enables expert systems to handle uncertain and imprecise information.

<p>True (A)</p> Signup and view all the answers

What type of analysis involves providing recommendations for actions to be taken?

<p>Prescriptive Analysis (C)</p> Signup and view all the answers

Name one advantage of using neural networks in smart home systems.

<p>They enhance the convenience of daily life.</p> Signup and view all the answers

Data Flow Diagrams (DFDs) show how data moves through a system without identifying where data comes from.

<p>False (B)</p> Signup and view all the answers

Name one application of expert systems in the medical field.

<p>Disease Diagnosis</p> Signup and view all the answers

Natural language processing (NLP) helps computers understand and ___ like humans.

<p>talk</p> Signup and view all the answers

What is the primary purpose of predictive models in machine learning?

<p>to make predictions about future or unknown events</p> Signup and view all the answers

Which of the following is NOT a common use for expert systems?

<p>Weather Prediction (C)</p> Signup and view all the answers

A process in a Data Flow Diagram should be labelled with a __________ followed by a noun.

<p>verb</p> Signup and view all the answers

Match the following components of Data Flow Diagrams with their descriptions:

<p>External Entity = A person, department, or organization that interacts with the system Data Store = A method of data storage Process = An action being performed that must be labelled with a verb Data Flow = Indicates the direction of data movement between components</p> Signup and view all the answers

Match the following expert system concepts with their descriptions:

<p>Heuristics = Rules based on experience Fuzzy Logic = Handles uncertainty and imprecision Automated Speech Recognition (ASR) = Turns speech into text Natural Language Processing (NLP) = Teaches computers to understand human language</p> Signup and view all the answers

Which factor is NOT important when presenting data to an audience?

<p>Cost of the presentation (B)</p> Signup and view all the answers

Expert systems can create personalized learning paths for students.

<p>True (A)</p> Signup and view all the answers

What software frameworks can be used for developing expert systems?

<p>CLIPS, JESS, Prolog</p> Signup and view all the answers

An external entity can provide data to another entity without a process taking place.

<p>False (B)</p> Signup and view all the answers

Name one way to adapt data presentation to meet audience needs.

<p>tailor content, format, or style</p> Signup and view all the answers

What is the primary purpose of modular neural networks?

<p>To enhance efficiency by allowing modules to work independently (B)</p> Signup and view all the answers

The inference engine of an expert system is responsible for storing specialized knowledge.

<p>False (B)</p> Signup and view all the answers

What component of an expert system translates knowledge from human experts into a suitable format?

<p>Knowledge acquisition system</p> Signup and view all the answers

In a self-driving car, the __________ model is responsible for monitoring the car's behavior on the road.

<p>Safety</p> Signup and view all the answers

Match the following expert system components with their functions:

<p>Knowledge Base = Stores specialized knowledge and rules Inference Engine = Draws conclusions based on input Explanatory System = Provides justifications for decisions Knowledge Engineer = Acquires expert knowledge for the system</p> Signup and view all the answers

Which of the following components is essential for understanding why an expert system made a particular recommendation?

<p>Explanatory System (C)</p> Signup and view all the answers

Modular neural networks are only used in simple computing tasks.

<p>False (B)</p> Signup and view all the answers

Name one model involved in the functioning of self-driving cars.

<p>Perception Model, Navigation Model, Control Model, or Safety Model</p> Signup and view all the answers

Which of the following software frameworks can be used to develop expert systems?

<p>CLIPS (C)</p> Signup and view all the answers

Fuzzy logic deals with certainty and precision in decision-making.

<p>False (B)</p> Signup and view all the answers

Name one application of expert systems in the field of medicine.

<p>Disease Diagnosis</p> Signup and view all the answers

Match the following applications of expert systems with their uses:

<p>Disease Diagnosis = Helps in diagnosing diseases based on symptoms Supply Chain Optimization = Improves inventory management Credit Scoring = Evaluates creditworthiness Personalized Learning = Creates customized learning paths</p> Signup and view all the answers

Which characteristic best describes heuristics in expert systems?

<p>Estimates based on a person's experience. (D)</p> Signup and view all the answers

Automated Speech Recognition (ASR) is part of Natural Language Processing (NLP).

<p>True (A)</p> Signup and view all the answers

What is the primary purpose of fuzzy logic in expert systems?

<p>To handle vague, incomplete, or uncertain information.</p> Signup and view all the answers

Which of the following best describes deep learning?

<p>Neural networks with multiple hidden layers capable of complex tasks. (D)</p> Signup and view all the answers

Neural networks are only used for image and speech recognition.

<p>False (B)</p> Signup and view all the answers

What is the process called whereby neural networks adjust their connections to learn from data?

<p>Training</p> Signup and view all the answers

Neural networks use _____ to process information and improve their outputs.

<p>activation functions</p> Signup and view all the answers

Match the following applications of neural networks with their descriptions:

<p>Image recognition = Identifying objects in images. Speech recognition = Transcribing spoken words into text. Natural language processing = Understanding and processing human language. Predictive analytics = Forecasting future trends based on historical data.</p> Signup and view all the answers

Which application of neural networks is used in healthcare?

<p>Disease diagnosis (C)</p> Signup and view all the answers

Neural networks are ineffective for pattern recognition tasks.

<p>False (B)</p> Signup and view all the answers

List two components of a typical neural network.

<p>Neurons and layers</p> Signup and view all the answers

What does the ASR component of a voice assistant do?

<p>Captures voice and converts it into written text (B)</p> Signup and view all the answers

Text preprocessing involves splitting text into individual letters.

<p>False (B)</p> Signup and view all the answers

What is the main purpose of the TTS layer in a voice assistant?

<p>To convert text responses into spoken words.</p> Signup and view all the answers

AI technologies, like machine learning, rely on large ______ to make decisions.

<p>datasets</p> Signup and view all the answers

Match the following ethical considerations in AI with their descriptions:

<p>Data Bias = Inaccurate representation of groups in datasets Privacy = Protection of personal information Data Security = Safeguarding data from unauthorized access Informed Consent = Ensuring users are aware of data usage</p> Signup and view all the answers

In which step does the voice assistant determine the structure of the user's question?

<p>Query Processing (A)</p> Signup and view all the answers

AI systems always use structured data for decision-making.

<p>False (B)</p> Signup and view all the answers

What is the main goal of Artificial General Intelligence (AGI)?

<p>To reason and think like a human (D)</p> Signup and view all the answers

What does query execution in a voice assistant involve?

<p>Accessing the internet to fetch specific data.</p> Signup and view all the answers

Artificial Narrow Intelligence (ANI) is capable of multitasking across different domains.

<p>False (B)</p> Signup and view all the answers

What does the Turing Test aim to determine?

<p>Whether a computer can convincingly simulate human-like conversation.</p> Signup and view all the answers

In Machine Learning, algorithms improve their performance from __________ data.

<p>training</p> Signup and view all the answers

Match the type of AI with its description:

<p>Artificial Narrow Intelligence = Specializes in a specific task Artificial General Intelligence = Can understand and reason in multiple contexts Machine Learning = Algorithms that learn from data Turing Test = Evaluates a machine's ability to exhibit human-like behavior</p> Signup and view all the answers

What is a primary function of Machine Learning algorithms?

<p>Adapt and learn from data (D)</p> Signup and view all the answers

The Turing Test was proposed by Alan Turing to assess the capabilities of human beings.

<p>False (B)</p> Signup and view all the answers

Name one characteristic of Artificial Narrow Intelligence (ANI).

<p>It is designed to solve specific problems.</p> Signup and view all the answers

What is a disadvantage of AI systems that are biased in decision-making?

<p>They may result in unfair outcomes for minority groups. (A)</p> Signup and view all the answers

AI decisions are solely based on ethical considerations and emotions.

<p>False (B)</p> Signup and view all the answers

What is the main focus of predictive analysis in data analytics?

<p>Making informed decisions on future events using historical data.</p> Signup and view all the answers

The collection and analysis of large datasets by AI can pose a threat to __________.

<p>personal privacy</p> Signup and view all the answers

Match the following types of data analysis with their descriptions:

<p>Descriptive Analysis = Summarization of what occurred Diagnostic Analysis = Exploration of why something happened Predictive Analysis = Forecasting future events based on historical data</p> Signup and view all the answers

Which of the following is a potential impact of automation and AI?

<p>Large-scale unemployment in certain industries. (A)</p> Signup and view all the answers

Heuristics in expert systems refer to complex algorithms that always guarantee accurate results.

<p>False (B)</p> Signup and view all the answers

What social consideration may arise from the use of imbalanced data in AI systems?

<p>Discrimination against underrepresented groups.</p> Signup and view all the answers

Flashcards

Neural Networks

Computer systems inspired by the human brain, using interconnected nodes (neurons) to process information and learn from data.

Learning Algorithms

Methods used by neural networks to adjust their connections and improve their accuracy over time.

Training

The process of feeding data to a neural network to teach it patterns and relationships.

Deep Neural Networks

Neural networks with multiple hidden layers, allowing for more complex learning tasks.

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Image Recognition

The ability of a system to identify and classify objects, people, or scenes from images.

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Natural Language Processing

Using computers to understand, interpret, and generate human language.

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Neurons

The basic processing units in a neural network, connected to each other through weighted links.

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Hidden Layers

Layers in a neural network between the input and output layers that extract features from the input data that are not visible.

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Turing Test

A test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.

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Turing Test's Significance

Establishes a standard for assessing machine intelligence, shifting the focus from imitation to understanding, and motivating AI research.

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Neural Network's purpose

Neural networks solve complex problems like computer vision, speech recognition, natural language processing, and recommendation engines.

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Neurons or Nodes

Interconnected components within a neural network, each processing and transmitting information.

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Neural Network Layers

Neural networks are organized into layers (input, hidden, and output) for information flow.

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Computer Vision (Neural Networks)

A use of neural networks to perform tasks such as face recognition, image labeling, and content moderation.

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Natural Language Processing (Neural Networks)

Neural networks are used for tasks like natural language processing to convert conversations into documents or power chatbots.

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Expert System Shell

The software environment where an expert system is built and runs.

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Heuristics in Expert Systems

Rules based on experience, not logic, used for making decisions with uncertainty.

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Fuzzy Logic

A way to handle uncertainty and vagueness in decision-making, useful for real-world problems.

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Disease Diagnosis (Expert Systems)

Expert systems help doctors diagnose diseases by analyzing symptoms and medical history.

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Supply Chain Optimization (Expert Systems)

Expert systems help improve efficiency and reduce costs by optimizing inventory and logistics.

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Natural Language Processing (NLP)

AI that lets computers understand and use human language.

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Automated Speech Recognition (ASR)

A technology that allows computers to convert speech into text.

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Expert System's knowledge base

It holds the information needed by the expert system.

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Feed-forward Neural Network

A neural network that transmits information in a single direction from input to output nodes, potentially with hidden layers.

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Recurrent Neural Network

A neural network where nodes remember previous outputs and use them in future processing.

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Convolutional Neural Network

A neural network specialized for image recognition, using convolutional layers to analyze image features.

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Deconvolutional Neural Network

A type of neural network that reverses the process of a convolutional network, useful for detecting discarded information.

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Neural Network Recognition

Neural networks recognize patterns and relationships in data, useful for tasks like image recognition and language understanding.

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Neural Network Applications Control

Neural networks help control home appliances, such as lights and thermostats, by analyzing user patterns to improve convenience and energy efficiency.

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Neural Network Hidden Layers

Intermediate layers in a neural network that process information between input and output.

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Convolutional Layers

Layers in a convolutional neural networks which extract image features.

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Prescriptive Analysis

A type of data analysis that suggests actions to take. It can be used for things like healthcare treatment plans or financial investments.

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Data Flow Diagram

A visual representation of how data moves through a process or system. It shows where data originates, goes, is stored, and processed.

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External Entity

A person, department, or external organisation involved in the flow of data, but not part of the system itself.

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Data Store

A location used to indicate where the data is stored.

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Process

A task or action performed on the data. It should be expressed as a verb followed by a noun (e.g., 'Calculate Tax').

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Data Flow

The direction of data movement through the system between processes, data stores, and external entities.

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Predictive Models

Models that use historical and current data to predict future events or unknown outcomes.

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Data Presentation

Adapting data to the specific needs of the audience to make it understandable and engaging.

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Data Repository

A location to store data. Data needs processing to move to or from it.

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Data Flow Diagram (DFD)

A diagram that illustrates how data flows from place to place within a system using external entities, processes, and data repositories.

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Deep Learning

A type of machine learning using neural networks with multiple hidden layers to handle complex tasks like image recognition.

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Training (Neural Networks)

The process of feeding data to a neural network to teach it patterns and relationships, enabling it to improve its performance.

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What do neurons do?

Neurons process and transmit information within a neural network, using activation functions to determine their output based on the input received.

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What are hidden layers?

Layers in a neural network between the input and output layers that extract features from the input data, enabling the network to learn complex patterns.

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What is the purpose of neural networks?

Neural networks solve complex problems like computer vision, speech recognition, natural language processing, and recommendation engines.

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What are some applications of neural networks?

Neural networks are applied in diverse fields like image and speech recognition, natural language processing, autonomous vehicles, healthcare, and finance.

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How do neural networks learn?

Neural networks use learning algorithms, like backpropagation, to adjust the connections between neurons and improve their performance based on feedback from training data.

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Modular Neural Networks

Neural networks composed of smaller, independent networks (modules) that handle specific tasks, working together to solve complex problems.

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Expert Systems

AI systems designed to mimic human experts by using knowledge bases, inference engines, and user interfaces to solve problems and make decisions in a specific domain.

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Knowledge Base (Expert Systems)

The heart of an expert system, holding all the specialized knowledge, rules, and facts needed to solve problems within its domain.

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Knowledge Acquisition System

The process of gathering knowledge from human specialists and external sources, then translating it into a format usable by the expert system's knowledge base.

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Inference Engine

The reasoning component of an expert system, using knowledge from the knowledge base to draw conclusions, make recommendations, and solve problems based on user input.

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Explanatory System

A component of an expert system that provides explanations and justifications for its decisions or recommendations, enhancing transparency and trust.

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Knowledge Engineer

A human expert who builds and maintains expert systems, working closely with domain experts to capture knowledge, create rules, and ensure accuracy.

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Working Memory (Expert Systems)

A temporary storage area used by the expert system to hold information during problem-solving, similar to a scratchpad.

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What is AI?

Artificial Intelligence (AI) is the simulation of intelligent behaviour by a computer, allowing machines to make decisions without human intervention.

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Types of AI

AI can be categorized into ANI (Artificial Narrow Intelligence) and AGI (Artificial General Intelligence). ANI is designed for specific tasks, while AGI aims to create machines that can reason and think like humans.

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Machine Learning

Machine Learning is a subset of AI that uses algorithms to enable systems to learn and improve from experience without being explicitly programmed.

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Turing Test Purpose

The Turing Test acts as a standard to assess machine intelligence, shifting the focus from simple imitation to understanding human-like behaviour.

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Expert System Components

Expert Systems consist of a knowledge base containing domain-specific information and an inference engine that applies rules to solve problems.

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Speech Recognition

The process of converting spoken words into text, using algorithms to analyze sound patterns.

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Text Preprocessing

Cleaning and structuring raw speech-to-text data for further analysis, splitting sentences into words or phrases.

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Language Understanding

Interpreting the meaning of the text, identifying the user's intent and extracting key information.

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Query Processing

Analyzing the structure of the user's question, identifying the topic, question words, and potential context.

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Query Execution

Searching for relevant data or answers to the user's question, using external resources like APIs.

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Response Generation

Creating a textual response using the gathered information and converting it into spoken words (text-to-speech).

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Data Bias in AI

When AI systems make decisions based on biased data, leading to unfair or inaccurate outcomes.

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Algorithmic Bias

The inherent bias in the algorithms themselves, causing them to favor certain groups or outcomes.

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Heuristics (Expert Systems)

Rules based on experience, not pure logic, used for making decisions in situations with uncertainty. They represent best practices and insights gained from real-world experience.

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What does NLP do?

NLP enables computers to understand and process human language, enabling tasks like text summarization, translation, and chatbot conversations.

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What is the role of NLP in AI?

NLP is a critical component of artificial intelligence, allowing computers to interact with humans in a natural and intuitive way.

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Diagnostic Analysis

A data analysis approach that seeks to understand the reasons behind past events or trends, often involving hypotheses and exploring causal relationships.

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Predictive Analysis

A powerful data analysis technique that uses historical data and statistical methods to forecast future events or trends, helping to make more informed decisions.

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Data Analytics

The process of examining and interpreting data to uncover meaningful patterns, insights, and trends, leading to better understanding and decision-making.

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AI Bias

When AI systems trained on biased data produce unfair outcomes for specific groups, often disadvantaging minorities or underrepresented populations.

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Social Implications of AI

The broad effects of AI on society, ranging from positive benefits like increased productivity to negative concerns like potential job displacement and ethical challenges.

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Study Notes

Contemporary Data Practices

  • Contemporary data practices leverage data to build systems that make decisions without human intervention
  • Key goals of contemporary data practices include understanding Artificial Intelligence (AI), the Turing test, neural network modelling, expert systems, and the use of data for analysis.

Learning Objectives

  • Understand the definition of Artificial Intelligence (AI)
  • Understand the significance of the Turing Test
  • Understand the main features of neural network modeling
  • Understand the structure of an expert system and its components
  • Understand the use of data in analysis

Starter

  • What is AI?

Artificial Intelligence

  • AI simulates intelligent behavior in computers, enabling machines to make informed decisions without human intervention.
  • Artificial Narrow Intelligence (ANI), also known as weak AI, is designed for a specific task and has limitations in other situations.
  • Artificial General Intelligence (AGI) is a highly advanced form of AI that mimics human thought and reasoning in various contexts.

Machine Learning

  • Machine Learning is a subset of AI that utilizes algorithms to adapt and improve from experience without explicit programming.
  • Machine Learning relies on training data from large datasets to train the algorithms.

The Turing Test

  • The Turing Test, developed by Alan Turing, is a benchmark for assessing machine intelligence.
  • It involves a human evaluator engaging in text-based conversations with both a human and a machine.
  • The evaluator must determine which participant is the human based on the conversation.
  • If the evaluator isn't able to correctly determine if the participant is a machine, then the machine is deemed to have passed the test.

Neural Networks

  • Neural networks attempt to solve complex problems often resembling the structure of a human brain.
  • Neural networks consist of interconnected nodes (neurons) with layered structures.
  • Information is processed and transmitted in a layered fashion.
  • Neural networks learn and adapt from their experiences through algorithms, adjustments, and training.
  • Different types of neural networks exist like feed-forward, recurrent, convolutional, deconvolutional, and modular neural networks.
  • Neural networks are used in applications like computer vision (facial recognition), speech recognition, natural language processing, and recommendation systems.

Expert Systems

  • Expert systems are AI systems that replicate the decision-making capabilities of human experts.
  • Important components involved in Expert Systems include the knowledge base, the knowledge acquisition system, expert interface and user interface, knowledge engineer, inference engine, explanatory system, working memory, shell, and heuristics.
  • Expert systems use fuzzy logic to handle uncertainty and imprecision in knowledge, which makes them more suitable for real-world applications where information is not always clear-cut.

AI Usage of Big Data

  • AI technologies, such as machine learning, rely on large datasets to make decisions and predictions.
  • Al systems collect data from various sources like sensors, internet, and user interactions.
  • Data needs processing to make it suitable for analysis which may involve removing outliers, handling missing values, and restructuring the data. Data analysis in AI involves multiple types including descriptive, diagnostic, predictive, and prescriptive.

Ethical Considerations in AI

  • Data bias and algorithmic bias are significant factors.
  • Systems are trained using data that can reflect prejudices leading to unfair outcomes.
  • AI systems can raise privacy concerns and questions regarding access and legality and potential impact on employment.
  • Understanding and mitigating these issues are key ethical considerations for AI.

Social Considerations in AI

  • AI systems have several positive and negative implications on society.
  • Use of AI and large data sets raise issues like disenfranchisement, discrimination, removal of ethical considerations, privacy concerns, and large scale unemployment.
  • How bias can exist in data and implications on decision making are also important to consider.

Data Analytics

  • Data analytics is the computational analysis of data to uncover trends and insights.
  • Different types of data analysis (Descriptive, Diagnostic, Predictive, and Prescriptive) are used for various purposes.
  • Data analysis methods are integral to decision-making.

Data Flow Diagrams

  • Data flow diagrams are graphical representations that show how data flows through a system.
  • They identify the origin, storage, and final destination of data through processes, which help with clear representations and understanding.
  • Several key parts, such as external entities, data stores for storage, processes, and data flows, are fundamental in constructing a diagram to depict the data flows.

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Description

This quiz covers key concepts related to data repositories, processes in data flow diagrams, and neural networks. Test your understanding of how neural networks learn, their applications, and the structural components involved. Dive deep into the intersection of data processing and artificial intelligence.

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