Introducing the Atoms to Intelligence Podcast.
For the inaugural episode, I sat down with Manoj Gopalkrishnan @manoj_333 to talk about our shared history, molecular computing, AI, and his journey from computer science to DNA computing to building Algorithmic Biologics @AlgoBiologics.
We get into how cells are computing, implementing learning algorithms with chemistry, transformers vs HMMs, a possible Moore’s law for molecular computers, and his idea that intelligence may be limited by a second axis: stability.
0:00 Introduction
1:22 From Computer Science to DNA Computing
6:08 Early AGI Predictions and the Importance of Scale
8:08 Can Molecules Learn?
13:52 What Does It Mean for a Cell to Compute?
17:53 Chemical Computing and Federated Learning
21:22 The EM Algorithm: Rationalists vs Empiricists
25:25 Transformers vs Hidden Markov Models
28:54 Computing Statistics with Chemistry
31:04 Are “Futile Cycles” Actually Machine Learning?
32:23 AGI, Intelligence and the Missing Axis of Stability
39:26 When Will Molecular Computing Become Commercial?
41:21 Leaving Academia to Build Algorithmic Biologics
44:36 Compressed Sensing and 10× More Diagnostic Tests
46:46 From Sample Pooling to 200-Target Diagnostics
48:12 Molecular Agents and Synthetic Immune Systems
50:43 What Should Molecular Computers Be Made Of?
52:46 A Moore’s Law for Molecular Computing
53:52 Scaling Laws, Biology and Bottom-Up Engineering
57:27 AGI: Utopia, Dystopia and Stability
59:14 The Experiment Manoj Would Fund With Unlimited Resources
1:01:15 What Will the First Consumer Molecular Computer Look Like?
Simulations are instruments of discovery. A constructive apparatus most alive where existing knowledge reaches its edge. They do not merely imitate a truer reality from a lesser distance. Rather, they dynamically recompose the motion of complex systems.
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