Lana Sinapayen

As an Artificial Life researcher, I believe that the best way to understand a system is to copy and build it yourself. Accordingly, as an AI researcher, I try to understand cognition by building neural networks, and then these neural networks try to understand the world by rebuilding it through predictions. I apply a hybrid synthetic approach to the problem of cognition. Imitating the micro-scale properties of biological nervous systems has its limits, but so does imitating the superficial macro-scale properties of living things. I find inspiration in concepts from cognitive science to solve practical issues, but optimize pertinent solutions with no regard for biological relevance. This hybrid approach helps separate the side effects of cognitive processes from their actual goals. I believe that the resulting new algorithms can lead to valuable real-world applications.

[keywords] AI / artificial life / neural network / predictive coding / cognition / hybrid synthetic approach




Lana Sinapayen first joined the research world through an internship at the Honda Research Institute in 2012. In 2015, she completed a double-degree program, consisting of a degree in Information Sciences Engineering from the French National Institute of Applied Sciences, and an MS in Computer and Mathematical Science from the University of Tohoku in Japan. Shifting her focus from mathematical models to embodied cognition, she received her PhD in 2018 from the University of Tokyo, where her doctoral research focused on artificial life, and where she received the Ichiko Memorial Prize. She was also the recipient of a scholarship from the Integrated Human Sciences Program. In September 2018, she joined Sony CSL as an associate researcher. Her current ambition is to develop a new way of understanding cognition by embedding cognitive algorithms in real world applications.

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