Science 2.0 Research Program
The Science 2.0 Research Program is an initiative proposed to restructure how science is funded, conducted, and shared. In light of rapid AI development and changes in federal research funding policies, this program offers an alternative path for pre-faculty researchers preparing for leadership in academia—one centered on intellectual freedom, transparency, and collaboration. The program is rooted in intrinsic motivation and researcher-defined purpose, rather than shaped by funder priorities or publisher expectations. Departing from competition-based selection, the program introduces a collaborative matching model that connects researchers with aligned resources and supports them in defining their own research trajectory, timeline, and milestones. The program committee serves as a facilitator, helping fellows navigate challenges and improve execution, rather than acting as a gatekeeper.
A core element of the program is its mandatory publication model: all research outputs must be published on a program-developed open-access platform and cannot be submitted to traditional journals. This requirement ensures that the full research process—not just selected outcomes—is shared transparently. The platform hosts a range of outputs, including formal reports, raw data, experimental logs, and research essays that document the actual research journey—capturing the reasoning, setbacks, decisions, and collaboration that shaped the work. All publications are released without selection bias, and commentary remains permanently open, enabling ongoing scholarly dialogue without time limits. Authorship is listed alphabetically, reflecting collective contribution without ranking. By prioritizing transparency and long-term engagement, this model challenges the constraints of conventional publishing and supports a more complete understanding of scientific work.
By reducing gatekeeping and prioritizing ethical, curiosity-driven work, the Science 2.0 Research Program promotes a sustainable, researcher-focused model. It shifts focus from career incentives to the value of discovery. As AI becomes capable in tasks like evaluation, synthesis, and strategic planning, it is positioned as a tool to support researchers, allowing them to concentrate on inquiry, leadership, and scientific understanding. This program offers a framework for a more open and effective research ecosystem.
We welcome sponsors and collaborators to power this mission.
With valuable contributions from Charles Yokoyama (Fujita Health University), Micah Altman (MIT), and Pierre Azoulay (MIT)
Sponsored by MIT School of Science—SQoL Grant (AY24-25 Fall #004106)
CephNeuroAI Initiative
The CephNeuroAI Initiative is a research-driven program aimed at developing a practical and effective model for convergence research, centered on the underexplored yet highly promising topic of uncovering the circuit-level and computational foundations of cephalopod behavior. Cephalopods—such as octopuses and squid—possess a unique combination of distributed nervous systems, locally autonomous neural processing, and advanced adaptive behaviors, making them an ideal model for exploring how cognition and decision-making emerge in both biological and artificial systems. By bridging neuroscience, artificial intelligence, biotechnology, social sciences, philosophy of mind, and additional science-related disciplines, the initiative provides a platform to examine the nature of intelligence beyond traditional vertebrate models, focusing on how decentralized systems integrate sensory, motor, and cognitive functions.
CephNeuroAI centers on three core objectives. First, it develops minimally invasive, wireless neurotechnologies that allow precise and reversible modulation of neural activity in freely behaving cephalopods—overcoming the limitations of existing genetic or surgical methods. Second, the initiative designs parametric behavioral tasks to evaluate how centralized and distributed circuits interact during real-time decision-making. Third, it constructs mechanistic computational models that simulate hypothesized circuit operations while capturing latent cognitive variables. Together, these components form a structured, interdisciplinary approach for decoding the neural logic behind cephalopod intelligence.
The initiative’s broader impact lies in establishing a comparative framework for adaptive biocomputation, expanding the boundaries of systems neuroscience and informing the development of distributed control systems in AI and robotics. CephNeuroAI serves as a collaborative hub for researchers across science, engineering, and philosophy, fostering a transdisciplinary culture of inquiry and innovation. By studying cephalopods as living models of non-hierarchical cognition, the initiative challenges conventional assumptions about control, agency, and intelligence—contributing new perspectives to both fundamental science, applied technologies, and philosophy of mind.
We welcome sponsors and collaborators to power this mission.
Collaborating with Joshua Rosenthal (MBL) and Horst Obenhaus (NTNU)
With valuable contributions from Matthias Michel (MIT)
Sponsored by MIT School of Science—SQoL Grant (AY24-25 Spring #004302)
Past Events
2025
CephNeuroAI Interdisciplinary Research Dialogue: MIT × MBL
MIT Sigma Xi – CephNeuroAI Initiative. May 30th, 2025 | Cambridge, MA, USA
Sponsored by MIT School of Science and MIT Picower Institute for Learning and Memory