Digging deeper on "deep" learning: A computational ecology approach. (10th November 2017)

• Record Type: Journal Article
• Authors: Buscema, Massimo; Sacco, Pier Luigi
• Is Part Of: Behavioral and brain sciences. Volume 40(2017)
• Publication Date: 2017-11-10
• Subjects: Psychophysiology -- Periodicals; Psychology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Brain -- Periodicals; 616.89142
• DOI: 10.1017/S0140525X1700005X
• Languages: English
• ISSNs: 0140-525X

Abstract

Abstract: We propose an alternative approach to "deep" learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform "new" artificial intelligence.