Sublime

AI-driven design → build → test → learn loops are changing synthetic biology. CLASSIC compresses gene-circuit design into Golden-Gate pools + nanopore barcodes + Illumina -> reads map 10^5 circuits in one go. Train on just 7 % of that data and an MLP predicts behavior for 3.4 billion logic... See more

On ML Ops

What's really going on in machine learning? Just finished a deep dive using (new) minimal models. Seems like ML is basically about fitting together lumps of computational irreducibility ... with important potential implications for science of ML, and future tech... https://t.co/OfevpJezi7

For the hardest problems—the ones we really want to solve but haven’t been able to, like curing cancer—pure nature-inspired approaches are probably too uninformed to succeed, even given massive amounts of data. We can in principle learn a complete model of a cell’s metabolic networks by a combination of structure search, with or without crossover,

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ata Collection Experimentation Evaluation and Deployment Monitoring and Response Metadata Data catalogs, Amundsen, AWS Glue, Hive metas-tores Weights & Biases, MLFlow, train/test set parameter configs, A/B test tracking tools Dashboards, SQL, metric functions and window sizes Unit Data cleaning tools Tensorflow, ML-lib, PyTorch, Scikit-learn,... See more

FUTURE of augmented SCIENCE discovery is imminent. Humans programming code -> prompting experiment design -> automated runs of experiments -> AI/ML analysis of results -> machine prompted (human in loop) iterated experiment design -> LOOP = growth in experiments + knowledge... See more