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Among them is the AI chatbot Wysa (20$ million in funding), BlueSkeye that is working on improving early diagnosis (£3.4 million), the Upheal smart notebook for mental health professionals (€1.068 million), and the AI-based mental health companion clare&me (€1 million).
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.
Artificial intelligence in healthcare is the application of artificial intelligence (AI) to analyze and understand complex medical and healthcare data. In some cases, it can exceed or augment human capabilities by providing better or faster ways to diagnose, treat, or prevent disease.
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.
DARPA established the XAI ("Explainable Artificial Intelligence") program in 2014 to try to solve these problems. [262] Several approaches aim to address the transparency problem. SHAP enables to visualise the contribution of each feature to the output. [263] LIME can locally approximate a model's outputs with a simpler, interpretable model. [264]
Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used for solving complex problems. The justification for knowledge representation is that conventional procedural code is not the best formalism to use to solve complex problems.
This has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence. [69] 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 ...
Q-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy. [ 2 ] "Q" refers to the function that the algorithm computes: the expected reward—that is, the quality —of an action taken in a given state.
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