Artificial Intelligence, Systems Biology and Philosophy of Mind
Some years ago, after finishing a bachelor in Computer Systems Engineering, I moved to Madrid to study a Master in Artificial Intelligence. I started to learn about how bots, neurons or ant swarms could develop emergent properties. During a a couple of years I was coding basic-AI software, such as agent-based models and cellular automata. Coding software in which something we called intelligence could be observed.
I also discovered the field of Philosophy of Mind and how concepts like cognition, consciousness, self, complexity and self-organization could be studied from a scientific and analytical perspective.
Fascinated by this new field, I realized that there was an available position in college to study artificial consciousness. Unfortunately, the main researcher never replied my emails. So I finally started to work as a researcher in a synthetic biology project within the context of an unconventional computing lab.
Eventually, I found myself working in an research group, exploring communication mechanisms between bacteria. Our goal was to build a biological computer in which bacteria were "reprogrammed genetically" in order to perform human-defined tasks like an electronic computer would do. My work basically consisted in the implementation of a simulator in which we could test how those bacteria would behave and predict the evolution of the system.
After some time working in the lab I would left the systems biology field to work as software engineer for different companies, universities and organizations. However, that experience changed my perception about reality forever. The complexity of communicaton mechanisms and the nature of interdependence in living organisms would stick in my mind for the rest of my life.
Computer Science and Biology were not that different: both explored information processing systems. During the next years, complex networks became the most fascinating of the methapores to understand how the world works.
From Bacteria to Humans: the discovery of Complex Adaptive Systems
Developing my career as software engineer and doing some side research was a priority at the time. But I was a young guy, ready to change the world. Worried about inequality and social justice, I pursued another degree in Cooperation for Development, something not related at all with my former academic education.
Some time after traveling to Bolivia as a volunteer and visiting some NGOs' projects in Senegal, I started to see how social systems could be conceptualized under the same umbrella than biological and computational systems: complex adaptive systems.
In my mind everything started to make sense. As information-processing beings, cells, individuals and societies could be understood as the foundation of several layers of interdependent elements, of emergent properties, of complexity.
The building blocks of life, individuals and societies were just self-organized units with an autopoietic character, a combination of functional diversity and decentralized communication infrastructures. Physical units exchanging flows of ions, chemicals, bits, words or photons.
Some years later, I got a PhD in Information Science after doing some computational modeling of complex adaptive systems and writing several articles, book chapters and a dissertation about peer-to-peer systems.
Some of the theoretical and formal justifications which are the basis of 'Autopoiesis' can therefore be found in my doctoral dissertation, full of artificial life theory, P2P networks, bacteria-based algorithms, cognitive science & learning theories, and (of course) sociological models. My dissertation was a formal and theoretical defense of P2P dynamics and was controversial at the time, some years before Blockchain technologies, protocols like Dat or IPFS and the new ideas about the distributed web started to become more mainstream.
Finding a multidisciplinary path at the University of Toronto
I had the chance of staying as Visiting Researcher at the Critical Making Lab of the University of Toronto (UofT) during the fall of 2014. I arrived without a clear project in mind, looking for an immersive experience in order to open my research to new perspectives while finishing my PhD.
At the time, I was studying how peer-to-peer (P2P) dynamics could be a source of collective production of knowledge. So I thought that a short period as a participant in such a specific environment, full of Canadian critical thinkers and makers, would help me to understand the Critical Making approach and its possible implications for a P2P society.
Coming from the field of Computer Science, an specifically from Artificial Intelligence, my research practices and methodological frameworks were mostly quantitative. A large part of my time before going to Toronto I was focused on designing models and algorithms, coding them and analyzing resulting data. In that sense, my study of social phenomena was partially reduced to the analysis of mathematical models and their computational implementations, that is, the simulation of artificial minds and artificial societies.
The focus of my dissertation was far away from actual societies, as a result of considerable simplifications to fit in the criterion of scientific falsifiability. Popper’s legacy sometimes implies a reductionist perspective; and when the object of study is the social dimension of the human being, the researcher has to dramatically reduce the number of variables.
I was already a guy who rejected the compartmentalization of knowledge and embraced a multidisciplinary project of life. In fact, while working as consultant and developer I studied a wide variety of topics, from Learning Theories to Philosophy of Mind, Cognitive Science or Evolutionary Biology. I had meetings with cognitive psychologists, economists, biologists and physicists. And I learned a lot but, as said, my approach was always quantitative.
However, the experience at Semaphore Research Cluster of the University of Toronto implied a completely different mindset, a chance to “experience” without methodological constraints. The result was kind of an anarchical grounded theory approach that allowed me to observe and participate as a member of the team.
UofT was a game-changer. I had meetings with philosophers, artists, historians, sociologists, designers,… even a cartoonist and a film maker. And were those human-based P2P dynamics, more than my computational simulations, what really opened my mind to the actual meaning of the word “knowledge".
I learned the basis of 3D printing and physical computing. I learned about situated learning and could see how artists and academics with a background in humanities explored new materials, electronic devices and micro-controllers with extraordinary skills. I tested sensors and actuators and got a sense of what it is possible to make, discussing with everyone about potential projects, critical issues and cultural engagements.
In a sense, the Critical Making Lab destroyed part of my mental constraints. It provided new intellectual and hands-on tools, new human experiences and an open and critical mindset. It also gave me a clue about what information means and about the implications of technology in critical reflection and social transformation.
My research and my projects never again could fit in one specific field. No technology without humanities. No science without arts.