A Commitment to Sustainable Development and Ethical Governance In an attempt to address international governance of artificial intelligence (AI), the United ...
In the field of machine learning (ML), fairness and ethics have become central concerns, evolving from theoretical considerations to indispensable aspects in...
- Objective: Please translate the following text from Spanish to English with the highest accuracy possible, paying special attention to maintaining the orig...
Artificial Intelligence (AI) has seen an exponential growth in its capabilities, thanks to the advancement of language models such as GPT-3, BERT, and more r...
Video analysis applies computer vision to image sequences over time. We separate its tasks (classification, detection, tracking, segmentation, temporal understanding), survey the models from 3D convolutions to video transformers and video-language systems, and its risks of bias and surveillance.
Semi-supervised learning combines few labels with much unlabeled data. We explain when it works (smoothness, cluster and manifold assumptions), its methods (pseudo-labeling, consistency, graphs), why it is not the same as self-supervised learning, and its risk of confirmation bias.
Deep learning uses many-layered networks to learn hierarchical representations of data, without hand-designed features. We review its fundamentals (backpropagation, representation learning), its architectures and scaling, and its limits: data, compute, robustness, interpretability, hallucinations and bias.
Artificial Intelligence (AI) has transcended the realm of science fiction to become a tangible reality that increasingly permeates various aspects of our eve...
«Bias» names at least four distinct concepts in AI: a neuron's intercept, an estimator's bias, the bias of the bias-variance tradeoff, and algorithmic bias. This entry pulls them apart one by one.
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