[1Q3-OS-7a] 統合AIへの展望

[1Q3-OS-7a-01] A Study on Collaborative AI Models Based on Nature of Life

〇Masaru Hirakata¹, Chong Ma¹, Kiwamu Kase² (1. National Institute of Maritime, Port and Aviation Technology, 2. RIKEN Center for Advanced Photonics)

Keywords:Artificial General Intelligence (AGI), intelligence development, neural network architecture

In order to realize DX in the manufacturing industry, in addition to improving operational efficiency through the introduction of third-generation AI (Narrow AI), there is a need for collaboration that enables dialogue with humans and literature/data dialogue (subjective reading by AI). The collaborative AI (Artificial General Intelligence) is expected. Although the current AI has become able to respond habitually (based on statistical relationships), it is not at the stage where it can proactively learn and have a dialogue based on meaning. The abilities and functions required of the fourth-generation AI, which is beyond the problem-solving of the third-generation AI (Narrow AI), are proactive actions, i.e., predictive actions performed by humans, planning actions, and interpretation (using images and knowledge). It is to equip them with so-called intelligence (thinking mainly of non-cognitive skills), such as reading while supplementing. In this paper, we systematically organize behavior/learning (neural network architecture)from the perspective of intelligence development. We report on the prospects of fourth-generation AI (neural network system) models.