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v. t. e. Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...
Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this. [ 1 ][ 2 ] The main focus is usually on the reasoning behind the decisions ...
A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 Transformer. A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention ...
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [1][2][3] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively ...
Foundation model. A foundation model, also known as large AI model, is a machine learning or deep learning model that is trained on broad data such that it can be applied across a wide range of use cases. [ 1 ] Foundation models have transformed artificial intelligence (AI), powering prominent generative AI applications like ChatGPT. [ 1 ]
The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. [1] System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction. A common approach is to start from ...
How deep learning is a subset of machine learning and how machine learning is a subset of artificial intelligence (AI) The deep learning revolution started around CNN- and GPU-based computer vision. Although CNNs trained by backpropagation had been around for decades and GPU implementations of NNs for years, [ 112 ] including CNNs, [ 113 ...