Paper Reading AI Learner

On Multiple Intelligences and Learning Styles for Multi- Agent Systems: Future Research Trends in AI with a Human Face?

2020-08-07 21:00:13
Andrzej Cichocki

Abstract

This article discusses recent trends and concepts in developing new kinds of artificial intelligence (AI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, and ethical intelligence, which to date have been under-discussed. We describe various aspects of multiple human intelligence and learning styles, which may impact on a variety of AI problem domains. Using the concept of multiple intelligence rather than a single type of intelligence, we categorize and provide working definitions of various AI depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human actors and each other, but also to efficiently exchange knowledge with abilities of cooperation, collaboration and even co-creating something new and valuable and have meta-learning capacities. Multi-agent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent.

Abstract (translated)

URL

https://arxiv.org/abs/2008.04793

PDF

https://arxiv.org/pdf/2008.04793.pdf


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3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot