IB Digital Society - Artificial Intelligence

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Artificial Intelligence

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20 Terms

1

Artificial Intelligence

The simulation of human intelligence processes by machines, especially computer systems.

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2

Narrow AI

AI systems designed to perform a specific task or a narrow range of tasks.

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3

General AI

Hypothetical AI systems with generalized cognitive abilities that can understand, learn, and apply knowledge across a wide range of tasks.

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4

AI agent

The learner or decision-maker in a reinforcement learning system.

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5

Reinforcement Learning

A type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve maximum cumulative reward.

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6

Neural Network

A computational model inspired by the human brain, consisting of layers of interconnected nodes or neurons.

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7

Deep Learning

A subset of machine learning involving neural networks with many layers that can model high-level abstractions in data.

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8

Supervised Learning

A type of machine learning where the model is trained on labeled data.

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9

Unsupervised Learning

A type of machine learning where the model learns from data without labeled responses, identifying patterns and structures.

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10

Convolutional Neural Network

A type of deep learning model specialized for processing grid-like data, such as images.

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11

Activation Function in a Neural Network

A function that introduces non-linearity into the network, allowing it to learn more complex patterns.

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12

Turing Test

A measure of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.

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13

Reinforcement Learning Policy

A strategy used by the agent to decide which actions to take, given the current state.

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14

Bias in AI

Systematic and unfair discrimination against certain groups within AI algorithms, often due to biased training data.

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15

Explainable AI

AI systems designed to be transparent and understandable, allowing users to comprehend how decisions are made.

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16

Alignment problem in AI

Ensuring that advanced AI systems’ goals and actions are aligned with human values and interests.

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17

Autoencoder

A type of neural network used for unsupervised learning tasks such as dimensionality reduction or denoising.

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18

Generative Adversarial Network

A type of neural network consisting of two networks, a generator and a discriminator, that compete with each other to create realistic data.

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19

Overfitting

When a model learns the training data too well, including noise and details, resulting in poor generalization to new data.

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20

Loss Function

A function that measures the difference between the predicted output and the actual output, guiding the training process.

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