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Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI. [61] Explainable AI has been recently a new topic researched amongst the context of modern deep learning.
Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to summarizing neural network behavior and building user confidence, while interpretability is defined as the comprehension of what a model has done or could do.
Empowerment (artificial intelligence) Enterprise cognitive system; Environmental impacts of artificial intelligence; Epistemic modal logic; Evolutionary developmental robotics; Explainable artificial intelligence; Extremal optimization
The field of Explainable AI seeks to provide better explanations from existing algorithms, and algorithms that are more easily explainable, but it is a young and active field. [ 18 ] [ 19 ] Others argue that the difficulties with explainability are due to its overly narrow focus on technical solutions rather than connecting the issue to the ...
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]
A white box (or glass box, clear box, or open box) is a subsystem whose internals can be viewed but usually not altered. [1] The term is used in systems engineering, software engineering, and in intelligent user interface design, [2] [3] where it is closely related to recent interest in explainable artificial intelligence.
Alibaba says the latest version of its Qwen 2.5 artificial intelligence model can take on fellow Chinese firm DeepSeek's V3 as well as the top models from U.S. rivals OpenAI and Meta.
Approaches for integration are diverse. [10] Henry Kautz's taxonomy of neuro-symbolic architectures [11] follows, along with some examples: . Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models.