Introduction: AI & Biology Convergence
- The podcast explores the intersection of AI, biology, computer science, and philosophy, focusing on a recent paper by Levin and Lopez.
- The paper integrates multiple biological datasets (genes, drugs, diseases) into a unified network model, demonstrating a novel link between Gaba and cancer.
Multimodal Data Integration and Network Embedding
- Challenge: Integrating diverse “omics” data (gene, drug, disease interactions) into a unified representation.
- Solution: Developed a “universal multi-layer network” approach, enabling the combination of various data types and scales. Existing, independent datasets for Genes (protein interactions), and drugs (combinations of uses), and Disease (symptom commonality) were reconciled into a single, cross-referencable dataset.
- Network Embedding: Translated network nodes (genes, drugs, diseases) into vectors using a similarity measure based on “random walk with restart.”
- Random Walk with Restart: An algorithm that explores the network from a seed node, creating a probability distribution representing node similarity. This distills relationships (connections, probabilities of joint walks) from multiple datasources down into simplified repreesntation of data proximities.
- Machine Learning Application: Enabled machine learning on network data, which usually requires vector representations, facilitating tasks like link prediction.
- Process: a link is chosen and walks (of differnet lengths?) happen at randon. those that involve crossing the selected link (in this sense ‘linking’ them) causes them to get pulled closed, such that links become predictive of path similarities.
- Training and validation: 70% for training, 30% hold-out data for making novel link predictions between Nodes (e.g. ‘what is predicted drug for unknown sickness’)
Gaba-Cancer Link and Validation
- Prediction: The model predicted a significant link between the neurotransmitter Gaba (specifically the Gaba A receptor) and cancer (melanoma).
- Mechanism: Gaba A is a chloride channel that regulates cell electrical state. Perturbing Gaba signaling can disrupt cell communication.
- Experimental Validation: Using memantine (a Gaba A agonist) on melanocytes (pigment cells) induced a melanoma-like phenotype.
- Unlike usual tumors: *No primary tumor.* All melanocytes converted at the same time. This demonstrates the cancer phenotype can result *without genetic damage*, resulting instead solely by changing electrical networking capability.
- Significance: Demonstrates cancer initiation *without genetic damage*, only by altering cell-cell electrical communication (physiological change, *not* genetic.)
AI for Biology: Black Box Modeling and Data Limitations
- Grand Challenge of Biology: Understanding complex biological interactions (the “black box”). Current drugs act *when* and *after* mechanisms is known; ML seeks *preemptive*, mechanism-agnostic methods.
- Black box challenge: most interventions rely on knowing *exactly* where in a mechanisms one wants to act, as opposed to a *model* predicting interactions and cascading causal paths (downstream mechanisms).
- Emergent Models: Some AI models (like Evo) learn higher-order concepts directly from raw data (e.g., DNA), potentially showing less “inductive bias”.
- Levin: “Our [paper/approach] is Data-Driven” … aggregating a ton of ‘BigData’, and seeking correlations/predictions to study further and extract “Theory” out of them (Data first, theories second).
- Bootstrapping Knowledge: AI-driven predictions, validated by experiments, could iteratively refine biological knowledge.
- Data Limitation: Lack of standardized, easily-integratable and outcome-focused data is a major hurdle, as opposed to the availability of a ton of small, micro, data of individual components (such as single cells)
- Need data focused on larger level: Beyond just having raw DNA and protein interactions. Biology lack large scale collections that document overall body states, and how it changes in a body (example, the changes in location of your *Bones*, or amount of Bones), in respons eto various experimental changes.
- ‘bioinformatics of *Shape*.’ describes anatomical+functional outcome measurements, in response to some change/perturbation to get cause->effects. Example, given *This* stimulis -> This change. Current amount of public data only “in the hundreds.”
- Bioelectrical Data Missing: Current datasets largely lack crucial bioelectrical data, essential for cell communication.
- Publication challenges: copying methodology and introduction paragraphs from papers you’ve already published is consider self-plagiarism, working *against* standardization (which needs consistency and no variation for parsing reasons)
Multiscale Competency, Intelligence, and Communication
- Collective Intelligence: Biology exhibits collective intelligence across scales, with problem-solving (not just complexity) at each level (pathways, cells, tissues, organs).
- Pathways Alone Have learning: habituation sensitization and pavlonian learning
- Beyond Micromanagement: AI’s role may be less about precise, “bottom-up” pathway manipulation and more about “top-down” communication with biological systems. “Train a System” instead of ‘tweaking/clamping/forcing/fighting/hacking’ it.
- Cognitive tools will be powerful in helping comunicate to intelligent (cognitively rich, problem-sovling, but unintellegent *like us*) systems, than any attempt to micro-manage the whole network.
- Dogs vs Horses training analogies: successful use of ‘non-biomedical’ intelligence. Not micromanaging and fiddling with biology, but *communication/learning* via training, instead of ‘understanding what all neurons do’.
- Theory of Mind: AI could help build a “theory of mind” of biological systems, understanding their “proto-cognitive” properties (goals, preferences). This would revolutionize how we study them.
- No goal is “better” micromanging molecules. Real power in using these (learning algorithms?) as “Communication devices.”,” *Translators*”, “finding, unbiassed patterns and becom[ing] a translation tool”.
Data Limited Models vs Humans
- Wellness prediction challenge (like next tokens), taking microlevel (“your strava runs” and food intake etc), and subjective well-being measure, can make wellness predictions difficult:
- Paradox: delivering wellness predictions (of being in good moods or etc), might make someone immediately depressed.
- What, actually are we optizing: we “don’t actually sure what to opimize” because long-run or short run, one value conflict. “multi-reward designs”. “scale reward not enough”, at minim, must take “long run” and short run trade-off”
- Human limitations: “split brain patients” and multi-agent in the “the same person”: shows there “there exists multiple different opinions [..] and sometimes conflicting beliefs”. Not one solid person, but muliple and varied.
- Current biological layers and computing: each computationallayer assumes other ones *won’t fail or vary* with *precision.* It makes it fragile. This assumption of no degradation in layers below limits power to handle dynamic, adaptive changes.
Implications for AI Architecture and Alignment
- Evolution created solving machines *in various spaces*, like behavioral, etc.
- Multiscale Architectures: Current AI (like transformers) often lacks the multiscale architecture of biological systems. Biological reliability requires *interpretion.* This gives rise to more “Problem solving” at various spaces.
- Bio-Inspired AI: Future AI may need to embrace unreliable components, redundancy, and communication between layers with different competencies.
- Cancer and the robots: “White robots don’t get Cancer.”. The AI itself can “go off and go do things”.
- Giving up control and modularity: in Bioology the body wants easy controls of modules to act on, BUT also, an opposing drive makes things too “easy to control” and then you become easily ‘hackable’, from diseases (etc).
- There is no way for us to reach an “*alignement*”, becuase *we (humans)* have and display fundamental disagrement across time and location; a “a single alignment goal, “doesnt exit for humans””
- Robustness Through Noise: Biological systems thrive despite noise and defects; AI might learn from this resilience (e.g., Dropout in neural networks is a step).
- Self modeling helps (be *more simple and easy*). Helps “robusteness,” “generailizations” “other” benefits, when you have more jobs (“multitasking”), and that helps you stay relevant despite small noices or small changes (“augemntation”)
- Alignment Challenges: The concept of “alignment” is problematic due to inherent disagreements within humans and between different groups. Moving towards AI systems *more organic, and more integreated*.
Future of Digital Life, Agency, and Goals
- Digital Life: Exploring the potential (and risks) of digital life and an “ecology of AI” is crucial.
- The role of AI tools: to recognize communication/relationship with diverse forms of intelligences all around. Prosthetics, recognition.
- Prosthetics and Outsourcing: Humans have *already* outsourced significant aspects of life, and AI will continue this trend, raising ethical questions about what it means for how our lives are defined by, versus technology (will power, relatioship guiders..).
- This already exists! “tothbrush, education,” are outsourced. “The idea of having giving up our core competencices [..] this alreayd happened”.
- Painting positive futures: We have to get clearer, as a group. Not juts a negative-voiding goals (current “ai safty”): “Everybody doing. Everyone specify.. “we are closter to to achieve.”
- Agency and Goals: Understanding the origins and management of goals in collective systems is a fundamental and possibly existential challenge.