2015/02/16 by Emanuel Diamant, Diamant, Emanuel · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Neurons and Cognition (q-bio.NC) #cs.AI #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1502.04791
The paper was submitted to IJCAI-15 conference, but was prudently rejected. Thus, replenishing the collection of my repudiated papers
openalex publication_date 2015/02/16 · arxiv created 2015/02/17 · arxiv updated 2015/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Over the past decade, AI has made a remarkable progress. It is agreed that this is due to the recently revived Deep Learning technology. Deep Learning enables to process large amounts of data using simplified neuron networks that simulate the way in which the brain works. However, there is a different point of view, which posits that the brain is processing information, not data. This unresolved duality hampered AI progress for years. In this paper, I propose a notion of Integrated information that hopefully will resolve the problem. I consider integrated information as a coupling between two separate entities - physical information (that implies data processing) and semantic information (that provides physical information interpretation). In this regard, intelligence becomes a product of information processing. Extending further this line of thinking, it can be said that information processing does not require more a human brain for its implementation. Indeed, bacteria and amoebas exhibit intelligent behavior without any sign of a brain. That dramatically removes the need for AI systems to emulate the human brain complexity! The paper tries to explore this shift in AI systems design philosophy.