Convergent Evolution: The Co-Revolution of AI & Biology with Professor Michael Levin & Staff Scientist Leo Pio Lopez

**Michael Levin** (0:00)
We are interested in cancer, obviously, because of the biomedical importance of the disease, but also because it teaches us about multicellularity and the failures of the collective intelligence of cells. There's nothing genetically wrong with any of these cells. It's a purely physiological change. What you've altered is the ability of the cells to work electrically with other cells. So it's a very interesting example of how the dysregulation of cancer can be initiated without any kind of genetic damage.

**Leo Pio Lopez** (0:27)
We have so many biological data right now, but we don't have maybe enough data on bioelectricity. But the main problem is maybe not about the data. Also, it's about how we transform this data into information and knowledge.

**Michael Levin** (0:39)
The power of AI is to build a theory of mind of the system. We're not trying to make a black box that tells us how to control it bottom up. We're trying to get the system to learn what is the kind of proto-cognitive system that we're dealing with.

**Nathan Labenz** (0:52)
Hello and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my co-host, Erik Torenberg. Hello and welcome back to The Cognitive Revolution. Today, I'm pleased to share a conversation with professor of Biology, Michael Levin, and staff scientist, Dr. Leo Pio Lopez of Tufts University. professor Levin's previous appearance on the show has become our most popular guest episode ever, and for good reason. His perspective on the ongoing convergence of biology, computer science, and philosophy are fascinating. I invited Michael and Leo again now because they recently published a groundbreaking paper that uses advanced embedding techniques to combine multiple biological data sets, spanning the modalities of genes, drugs, and diseases, into a single unified network model of disease. I've been watching out for new machine learning approaches that can help pry open the black box of biological interactions and causation, and I was impressed that this approach has already generated meaningful new insights, including most notably a predicted link between the neurotransmitter GABA and the cancer melanoma, which was subsequently validated by laboratory experiment. In this episode, we first discussed the technical details of this work, including the application of random walk with restart algorithms to the challenge of learning associations across a multimodal multi-layer network, as well as the potential for this approach to uncover additional new therapeutic targets in the future. From there, we zoom out to consider broader topics in AI for biology, including the current limitations in biological data collection and standardization, the shortage of data related to the aspects of health and development that matter to us most, and how emerging technologies like robot scientists and cloud labs might accelerate progress.
We also touch on the concept of multiscale intelligence in biological systems, a recurring theme in professor Levin's work, including the remarkable observations that even simple gene regulatory networks are capable of some forms of learning, that biological systems can often survive and thrive despite major defects in their own hardware, and that humans have historically domesticated and effectively trained wild animals despite knowing essentially nothing about their biology. We briefly explore how such results and other biological models might inspire more robust and adaptable AI architectures. Toward the end, we get into more philosophical territory as well, including the future of human enhancement and the ethical implications of outsourcing aspects of cognition to AI, the blurring of categories like living things and machines, often thought to be mutually exclusive, and the potential for digital life, the challenge of envisioning a positive future, and specifically what humanity might look like as a mature species, the potential incoherence of the concept of high-level AI alignment, considering the lack of alignment amongst humans, the possibility of transcending individual identity, but also the extreme risks associated with overly powerful collective intelligences, and finally, the existential importance of developing a new science of where agency and goals come from and how we can effectively manage them. Overall, it's a super thought-provoking episode which serves to remind us of how quickly the future is coming at us and how much work we still need to do to be ready for it. As always, if you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, and we always appreciate your reviews on Apple Podcasts or Spotify. We welcome your feedback via our website, cognitiverevolution.ai, and you can always DM me on your favorite social network. Now, I give you professor Michael Levin and Dr. Leo Pio Lopez on the intersection of AI and Biology and the future of intelligence. professor Michael Levin, professor of Biology at Tufts University, and Leo Pio Lopez, Staff Scientist at Tufts University. Welcome to The Cognitive Revolution.