Important AI Terms Explained
1. Artificial Intelligence (AI)
Answer:
The simulation of human intelligence in
machines that are programmed to think and learn like humans.
2. Machine Learning (ML)
Answer:
A subset of AI that allows systems to learn
from data and improve their performance without being explicitly programmed.
3. Deep Learning
Answer:
A subset of machine learning that uses
neural networks with many layers (deep neural networks) to analyze various
factors of data.
4. Generative AI
Answer:
A subset of AI that focuses on creating new
content, such as images, text, or music, using models like Generative
Adversarial Networks (GANs) or transformers.
5. Neural Network
Answer:
A network of artificial neurons designed to
recognize patterns. It consists of an input layer, hidden layers, and an output
layer.
6. Supervised Learning
Answer:
A type of machine learning where the model
is trained on labeled data to make predictions or classifications.
7. Unsupervised Learning
Answer:
A type of machine learning where the model
is trained on unlabeled data to find hidden patterns or data structures.
8. Reinforcement Learning
Answer:
A type of machine learning where agents
learn by interacting with their environment and receiving feedback through
rewards or punishments.
9. Natural Language Processing (NLP)
Answer:
A field of AI that focuses on the
interaction between computers and human language, enabling machines to
understand, interpret, and generate human language.
10. Computer Vision
Answer:
A field of AI that enables machines to
interpret and make decisions based on visual data, such as images or videos.
11. Training Data
Answer:
The dataset used to teach machine learning
algorithms, providing examples that help the model learn patterns.
12. Bias
Answer:
The tendency of a model to make systematic
errors or assumptions that may favor one outcome over others, leading to
inaccurate predictions.
13. Accuracy
Answer:
A metric used to evaluate the performance
of a machine learning model, measuring the proportion of correct predictions
made.
14. Precision
Answer:
The proportion of positive predictions that
were actually correct, measuring the quality of positive predictions.
15. Recall
Answer:
The proportion of actual positive cases
that were correctly identified by the model, measuring the model’s ability to
find all positive instances.
16. F1-Score
Answer:
A metric that combines precision and recall
into a single value by calculating their harmonic mean, providing a balance
between the two.
17. Overfitting
Answer:
A situation in machine learning where the
model learns the training data too well, including noise and outliers, which
harms its performance on new, unseen data.
18. Underfitting
Answer:
A situation where the model is too simple
and fails to capture the underlying patterns in the data, resulting in poor
performance.
19. Algorithm
Answer:
A set of instructions or rules followed by
a machine learning model to process data and make predictions or decisions.
20. Generative Adversarial Network (GAN)
Answer:
A type of neural network architecture where
two models (generator and discriminator) are trained simultaneously, with the
generator creating data and the discriminator evaluating its authenticity.
21. Support Vector Machine (SVM)
Answer:
A supervised machine learning algorithm
used for classification and regression tasks, which works by finding the
hyperplane that best separates different classes in the data.
22. Decision Tree
Answer:
A model used for classification and
regression tasks, where decisions are made based on the features of the input
data, visualized as a tree structure.
23. Random Forest
Answer:
An ensemble learning method that combines
multiple decision trees to improve accuracy and reduce overfitting.
24. K-Nearest Neighbors (KNN)
Answer:
A simple machine learning algorithm used
for classification and regression tasks, where the output is determined by the
majority vote or average of the nearest neighbors.
25. Gradient Descent
Answer:
An optimization algorithm used to minimize
the cost function by iteratively adjusting the parameters of the model in the
direction of the negative gradient.
26. Convolutional Neural Network (CNN)
Answer:
A class of deep learning algorithms
specifically designed for processing structured grid data like images, commonly
used in computer vision tasks.
27. Transfer Learning
Answer:
The process of taking a pre-trained model
on one task and fine-tuning it for a different but related task, saving time
and resources in training.
28. Autoencoder
Answer:
A type of neural network used for
unsupervised learning, primarily for data compression and dimensionality
reduction by encoding and decoding input data.
29. AI Ethics
Answer:
A field of study that focuses on the ethical
implications of AI technologies, including fairness, transparency, privacy, and
accountability.
30. Explainability
Answer:
The degree to which a model's decisions can
be understood by humans, important for ensuring trust and accountability in AI
systems.