可解释人工智能(xAI)方法和深度元学习模型

Evren Daglarli
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引用次数: 17

摘要

可解释的人工智能(xAI)是最近出现的有趣问题之一。许多研究人员正试图从不同的维度和有趣的结果来处理这个问题。然而,我们在理解这些类型的模型方面仍处于起步阶段。预计未来几年将是讨论深度学习模型开放性的几年。在经典的人工智能方法中,我们经常遇到今天可用的深度学习方法。根据数据集的大小、数据集的质量、特征提取的方法、深度学习模型中使用的超参数集、激活函数和优化算法,这些深度学习方法可以产生非常有效的结果。然而,目前的深度学习模型还有一些重要的不足之处。这些基于人工神经网络的模型是黑箱模型,它对传输给它的数据进行泛化,并从数据中学习。因此,投入和产出之间的关系联系是不可观察的。这是人工神经网络和深度学习模型的一个重要突破点。因此,有必要对黑箱模型的可解释性和可解释性进行认真的研究。
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Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models
The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.
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