Much of this progress has come from advances in “deep learning,” which refers to multilayer network-style models that emulate the working principles of the brain. The development of artificial intelligence (AI) and its application to the engineering has been tremendous in the 2000s, and particularly during the past 10 years. Both development of AI and understanding of human behavior go hand in hand. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. We describe these solutions by reviewing the current research from this perspective. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. First, intuitive models support an individual to use information meaningful ways in a current context. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We should build similar intuitive models and Bayesian algorithms for the new AI. In addition, we follow Bayesian inference to combine intuitive models and new information to make decisions. Human can build intuitive models from physical, social, and cultural situations. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. This study focuses for explaining human behavior from intuitive mental models’ perspectives. 2Competences, RDI and Digitalization, Haaga-Helia University of Applied Sciences, Helsinki, Finlandĭespite the success of artificial intelligence (AI), we are still far away from AI that model the world as humans do.1NeuroLab, Laurea University of Applied Sciences, Vantaa, Finland.
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