ML
Machine Learning
Machine Learning
Development of algorithms and statistical models that enable computers to perform tasks without being explicitly programmed for each one.
Generality: 965
Algorithm
Step-by-step procedure or formula for solving a problem or performing a task.
Generality: 960
Linear Algebra
Branch of mathematics focusing on vector spaces and linear mappings between these spaces, which is essential for many machine learning algorithms.
Generality: 950
Training Data
Dataset used to teach a ML model how to make predictions or perform tasks.
Generality: 950
Human-Level AI
AI systems that can perform any intellectual task with the same proficiency as a human being.
Generality: 945
Universality
Concept that certain computational systems can simulate any other computational system, given the correct inputs and enough time and resources.
Generality: 941
Training
Process of teaching a ML model to make accurate predictions or decisions, by adjusting its parameters based on data.
Generality: 940
BNNs
Biological Neural Networks
Biological Neural Networks
Complex networks of neurons found in biological organisms, responsible for processing and transmitting information through electrical and chemical signals.
Generality: 940
Loss Function
Quantifies the difference between the predicted values by a model and the actual values, serving as a guide for model optimization.
Generality: 940
TensorFlow
Open-source software library for machine learning, developed by Google, used for designing, building, and training deep learning models.
Generality: 937
Neural Network
Computing system designed to simulate the way human brains analyze and process information, using a network of interconnected nodes that work together to solve specific problems.
Generality: 932
NLP
Natural Language Processing
Natural Language Processing
Field of AI that focuses on the interaction between computers and humans through natural language.
Generality: 931
Decomposition
Process of breaking down a complex problem into smaller, more manageable parts that can be solved individually.
Generality: 920
Tensor
Multi-dimensional array used in mathematics and computer science, serving as a fundamental data structure in neural networks for representing data and parameters.
Generality: 920
DNN
Deep Neural Networks
Deep Neural Networks
Advanced neural network architectures with multiple layers that enable complex pattern recognition and learning from large amounts of data.
Generality: 916
CNN
Convolutional Neural Network
Convolutional Neural Network
Deep learning algorithm that can capture spatial hierarchies in data, particularly useful for image and video recognition tasks.
Generality: 916
Compute
Processing power and resources required to run AI algorithms and models.
Generality: 915
Scalar
Single numerical value, typically representing a quantity or magnitude in mathematical or computational models.
Generality: 915
Functional AGI
Hypothetical AI technology that possesses the capacity to understand, learn, and apply knowledge across diverse tasks which normally require human intelligence.
Generality: 910
Dataset
Collection of related data points organized in a structured format, often used for training and testing machine learning models.
Generality: 905
DL
Deep Learning
Deep Learning
Subset of machine learning that involves neural networks with many layers, enabling the modeling of complex patterns in data.
Generality: 905
AGI
Artificial General Intelligence
Artificial General Intelligence
AI capable of understanding, learning, and applying knowledge across a wide range of tasks, matching or surpassing human intelligence.
Generality: 905
Unsupervised Learning
Type of ML where algorithms learn patterns from untagged data, without any guidance on what outcomes to predict.
Generality: 905
Cognitive Computing
Computer systems that simulate human thought processes to solve complex problems.
Generality: 900
Cybernetics
Interdisciplinary study of control and communication in living organisms and machines.
Generality: 900
Subsymbolic AI
AI approaches that do not use explicit symbolic representation of knowledge but instead rely on distributed, often neural network-based methods to process and learn from data.
Generality: 900
Clustering
Unsupervised learning method used to group a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups.
Generality: 900
Interpretability
Extent to which a human can understand the cause of a decision made by an AI system.
Generality: 900
Matrix Multiplication
An algebraic operation that takes two matrices and produces a new matrix, fundamental in various AI and ML algorithms.
Generality: 900
Connectionist AI
Set of computational models in AI that simulate the human brain's network of neurons to process information and learn from data.
Generality: 900
Bayesian Inference
Method of statistical inference in which Bayes' theorem is used to update the probability estimate for a hypothesis as more evidence or information becomes available.
Generality: 896
Goal
Desired outcome or objective that an AI system is programmed to achieve.
Generality: 896
Inductive Reasoning
Logical process where specific observations or instances are used to form broader generalizations and theories.
Generality: 895
Optimization Problem
Optimization problem in AI which involves finding the best solution from all feasible solutions, given a set of constraints and an objective to achieve or optimize.
Generality: 895
Natural Language
Any language that has developed naturally among humans, used for everyday communication, such as English, Mandarin, or Spanish.
Generality: 894
NLU
Natural Language Understanding
Natural Language Understanding
Subfield of NLP focused on enabling machines to understand and interpret human language in a way that is both meaningful and contextually relevant.
Generality: 894
Bias-Variance Dilemma
Fundamental problem in supervised ML that involves a trade-off between a model’s ability to minimize error due to bias and error due to variance.
Generality: 893
RNN
Recurrent Neural Network
Recurrent Neural Network
Class of neural networks where connections between nodes form a directed graph along a temporal sequence, enabling them to exhibit temporal dynamic behavior for a sequence of inputs.
Generality: 892
Generalization
Ability of a ML model to perform well on new, unseen data that was not included in the training set.
Generality: 891
Search
The process within AI of exploring possible actions or solutions in order to achieve goals or solve problems.
Generality: 890
Statistical AI
Utilizes statistical methods to analyze data and make probabilistic inferences, aimed at emulating aspects of human intelligence through quantitative models.
Generality: 890
Hash Table
Data structure that stores key-value pairs and allows for fast data retrieval by using a hash function to compute an index into an array of buckets or slots, from which the desired value can be found.
Generality: 890
Supervision
Use of labeled data to train ML models, guiding the learning process by providing input-output pairs.
Generality: 890
Backpropagation
Algorithm used for training artificial neural networks, crucial for optimizing the weights to minimize error between predicted and actual outcomes.
Generality: 890
Knowledge Representation
Method by which AI systems formalize and utilize the knowledge necessary to solve complex tasks.
Generality: 890
NLP
Neuro-Linguistic Programming
Neuro-Linguistic Programming
Techniques and methodologies for understanding and generating human language by computers.
Generality: 890
Numerical Processing
Algorithms and techniques for handling and analyzing numerical data to extract patterns, make predictions, or understand underlying trends.
Generality: 890
Overfitting
When a ML model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
Generality: 890