Convergent Evolution: The Co-Revolution of AI & Biology with Professor Michael Levin & Staff Scientist Leo Pio Lopez
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What is the main topic discussed in this episode?
We are interested in cancer obviously because of the biomedical importance of the disease, but also because it teaches us about uh multicellularity and the failures of the collective intelligence of cells. There's nothing genetically wrong with any of these uh cells. It's a purely physiological change. What you've altered is the ability of the cells to uh 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.
We have so many biological data right now. But we don't have maybe enough data on bioelectricity or the but the main problem is maybe not but the data also, it's about how we transform this data into information and knowledge.
The power of AI is to build a theory of mind of the system. We're not trying to make a black box that uh tells us how to control it bottom up. We're trying to get the system to learn what is the kind of protocognitive system that we're dealing with.
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 LeBenz, joined by my co-host, Eric Tornberg. 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 doctor 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 datasets, 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 experiments.
In this episode, we first discuss 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 touched 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.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–7:59
2
What is the groundbreaking paper that combines multi‑omics data into a unified disease network?
7:59–11:54
3
How did the researchers integrate genes, drugs, and diseases into a single multilayer network?
11:54–17:30
4
What is the random‑walk‑with‑restart technique and why is it used for network embedding?
17:30–31:13
5
How did the network predict a causal link between GABA signaling and melanoma?
31:13–38:49
6
Why do the guests say AI for biology is currently data‑limited rather than algorithm‑limited?
38:49–47:25
7
What is multi‑scale intelligence in biological systems and how does it differ from emergent complexity?
47:25–59:45
8
How might AI‑driven models enable personalized human‑wellness predictions?
59:45–1:10:26
Speakers
2 identifiedMore from "The Cognitive Revolution"
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