Pal Nikhil R
Indian Statistical Institute, Electronics and Communication Sciences Unit, The Centre for Artificial Intelligence and Machine Learning, Calcutta, India.
Front Robot AI. 2020 Jun 19;7:76. doi: 10.3389/frobt.2020.00076. eCollection 2020.
At present we are witnessing a tremendous interest in Artificial Intelligence (AI), particularly in Deep Learning (DL)/Deep Neural Networks (DNNs). One of the reasons appears to be the unmatched performance achieved by such systems. This has resulted in an enormous hope on such techniques and often these are viewed as all-cure solutions. But most of these systems cannot explain why a particular decision is made (black box) and sometimes miserably fail in cases where other systems would not. Consequently, in critical applications such as healthcare and defense practitioners do not like to trust such systems. Although an AI system is often designed taking inspiration from the brain, there is not much attempt to exploit cues from the brain in true sense. In our opinion, to realize intelligent systems with human like reasoning ability, we need to exploit knowledge from the brain science. Here we discuss a few findings in brain science that may help designing intelligent systems. We explain the relevance of transparency, explainability, learning from a few examples, and the trustworthiness of an AI system. We also discuss a few ways that may help to achieve these attributes in a learning system.
目前，我们目睹了人们对人工智能（AI），尤其是深度学习（DL）/深度神经网络（DNN）的极大兴趣。其中一个原因似乎是此类系统所取得的无与伦比的性能。这引发了人们对这些技术的巨大期望，并且这些技术常常被视为万能解决方案。但大多数此类系统无法解释为何做出特定决策（黑箱问题），而且在其他系统不会失败的情况下，它们有时会惨败。因此，在医疗保健和国防等关键应用中，从业者不太愿意信任此类系统。尽管人工智能系统的设计常常从大脑获取灵感，但并没有太多真正从大脑中获取线索的尝试。我们认为，要实现具有类似人类推理能力的智能系统，我们需要利用脑科学知识。在此，我们讨论一些脑科学中的发现，这些发现可能有助于设计智能系统。我们解释了人工智能系统的透明度、可解释性、从少量示例学习以及可信度的相关性。我们还讨论了一些可能有助于在学习系统中实现这些特性的方法。
