Nature is an ultimate inspiring source of imagination and innovation. For decades, biologically-inspired computing have been a vital branch of the Artificial Intelligence family, from Neural Network, Ant Colony Optimization, Swarm Intelligence, to Genetic Algorithms. The biomimicry algorithms have been integrated into broader engineered complex systems such as designing satellite antennas and space mobile robots that walk and climb over rough terrain without a world model, or traditional planning or reasoning. Emerging theories of computation biomimicry aim to look into the fundamentals of commonsense behind natural phenomena and pave the way for futurist innovations. More than ever, we need systematic thinking for designing cyber-physical systems. We need more innovative theories for artificial intelligence, cybernetics, ethology, human-computer interaction, data science, computer science, security and privacy, social media, and autonomous robots.

We are in the era of the Renaissance of Biologically Inspired Computing. Our society has been increasingly digitized and become more and more complex. It needs more effective algorithms. For example, how to incorporate an artificial immune system to improve cyber security? How do we use evolutionary algorithms to solve the scalability problem in a Blockchain system? How to detect Deep Fake media content? On the other hand, modern biological discoveries provide new computational models for problem-solving, for example, CRISPR therapy and RNA vaccine, et al. Furthermore, modern computing technologies enable more powerful means to implement biomimicry algorithms, for example, biomorphic chips.

This leads us to the present where a confluence of biological theory, economics, engineering, and technologies can potentially enable a deeper understanding of signaling behaviors in an ever more connected world. The connections between systems and biology and economics, signaling theory may herald new approaches and insights to multidisciplinary subjects: signaling systems, signaling game theory, protocol designs, cellular signaling, signaling control, honest signaling, coding and modulation, regulatory signaling, narratives and virology, costly signaling, and so forth. An approach that models signaling to include the vast array of known biological signaling types may be beneficial to anticipate the signaling that could actually be enabled by new and novel communications and computer technologies. In general, the vast array of signaling strategies as it informs novel systems is a timely application of bio-inspired communication technologies that is well suited for current engineering challenges including improving trust and making communication technology more trustworthy.

EAI BICT 2026 is a brainstorm of multidisciplinary researchers and developers in the area. It contains invited lectures by the pioneers of bio-inspired computing and robotics, research presentations, demos, and lab tours.