## 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