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Neural relational inference is a machine learning method aimed at discovering latent interaction structures within complex dynamical systems. It combines neural networks with relational modeling to infer the underlying graph of interactions among entities from observational data.
AI in finance refers to the application of artificial intelligence technologies to improve financial services, including risk management, trading, and customer service. It enables automation, enhanced data analysis, and predictive modeling within the financial industry.
Predictive coding networks are computational models inspired by the brain’s predictive processing framework, designed to minimize prediction errors by continuously updating internal representations. They are used in neuroscience and machine learning to explain sensory perception and to improve artificial intelligence systems.
An activation function is a mathematical function used in artificial neural networks to introduce non-linearity into the model. It determines the output of a neural node or neuron based on its input, enabling the network to learn complex patterns.
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DeepNash is an artificial intelligence system designed to play Stratego, a complex strategy board game. It utilizes advanced game-theoretic and reinforcement learning techniques to achieve high-level play against human and AI opponents.
XGBoost is an open-source machine learning library that provides an efficient and scalable implementation of gradient boosting framework, widely used in data science.
Natural language processing (NLP) is a field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. It combines computational linguistics, machine learning, and deep learning techniques to facilitate interactions between humans and machines using natural language.
AI in law refers to the application of artificial intelligence technologies to support, automate, or enhance various legal tasks. These include document review, legal research, contract analysis, and even predictive analytics for case outcomes. AI aims to improve efficiency and accuracy in legal processes while raising important ethical and regulatory considerations.
BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking language model developed by Google that significantly improves natural language understanding and processing.