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Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), is a field of research within artificial intelligence (AI) that explores methods that provide humans with the ability of intellectual oversight over AI algorithms.
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 ...
Explainable Neural Networks ... Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses ...
Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. [126] It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. [127]
He contends with their concerns: “Humanity faces risks and we have to manage them,” he said, “but that doesn't mean we completely forgo the benefits of especially powerful technologies like ...
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
10 things not to say to AI. Passwords or login credentials: A major privacy mistake. If someone gets access, they can take over your accounts in seconds.
Microsoft said earlier this month it would invest around $80 billion in AI data centers in fiscal 2025 to meet growing computational needs. Applied Digital is set to report its second-quarter ...