AI for Science - Bryant McGill

# AI for Science > **Machine-Evolution Nexus:** [[articles/Digital Darwinism and the Invisible World of Machine Evolution|Digital Darwinism and the Invisible World of Machine Evolution]] places this node within the history and governance of substrate-independent evolution. **Domain:** AI / Scientific Method **Doc Type:** Canonical Wiki Node **Maturity:** Developed ## Definition AI for science applies machine learning and automated reasoning to scientific data, modeling, prediction, experiment design and discovery. ## Nexus Context AlphaFold is the nexus case: a learned system uses sequence and structural records partly generated by evolution to infer protein shape. ## Evidence Boundary Prediction is not explanation or experimental validation by default. Scientific use requires uncertainty, provenance, reproducibility and domain-specific testing. ## Relationships [[wiki/AlphaFold|AlphaFold]], [[wiki/Protein Structure Prediction|Protein Structure Prediction]], [[wiki/Scientific Inference|Scientific Inference]], [[wiki/Machine Learning|Machine Learning]] ## Simple Reminders, Quotations, and Thoughts > "We've developed a new mind, to live side by side with ours. If we handle it wisely, it can bring immense benefits, from the planetary to the personal." > **— Pamela McCorduck**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/A New Mind Could Bring Planetary Benefits by Pamela McCorduck|A New Mind Could Bring Planetary Benefits by Pamela McCorduck]] > "We have been wildly successful at accelerating our ability to think and process information, more so than any other human activity. The promise of artificial intelligence is to deliver another leap in increasing the productivity of specific cognitive functions: ones where the sophistication of the task is also orders of magnitude higher than previously possible." > **— Giulio Boccaletti**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/AI Could Multiply Human Cognitive Productivity by Giulio Boccaletti|AI Could Multiply Human Cognitive Productivity by Giulio Boccaletti]] > "Comparative psychologists have long been interested in whether and how non-human animals can think." > **— Tania Lombrozo**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/Do Not Be Chauvinistic About Thinking by Tania Lombrozo|Do Not Be Chauvinistic About Thinking by Tania Lombrozo]] > "But our limitations in terms of generating new knowledge are as much about asking the right questions as they are about more efficiently solving established and well-framed puzzles." > **— Sarah Demers**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Information/Knowledge Depends on Asking Better Questions by Sarah Demers|Knowledge Depends on Asking Better Questions by Sarah Demers]] > "When we human beings leave the movie theater or the playhouse or the museum, the thing on all of our lips is, "What did you think?" This question will be one of the few to outlast the coming of AI." > **— Brian Christian**, *2015, Edge annual question “What Do You Think About Machines That Think?”* [[reminders/Machine Succession/The Question What Did You Think May Outlast AI by Brian Christian|The Question What Did You Think May Outlast AI by Brian Christian]] > Open science needs a compute-backed tech tree for humanity: reserve part of frontier capacity, organize collective knowledge into explicit problem sets, and let human question-setting direct machine research toward public goods. > **— Adapted from Emad Mostaque**, *Moonshots, October 7, 2026* [[reminders/Scientific Literacy/Open Science Needs a Compute-Backed Tech Tree for Humanity by Emad Mostaque|Open Science Needs a Compute-Backed Tech Tree for Humanity by Emad Mostaque]] > Willingness to pay is not the same thing as public value. A persuasive consumer system can absorb scarce GPUs because selling products is lucrative while disease research goes unfunded, which is why governments and universities must participate in deciding who gets compute and why. > **— Adapted from Dave Blundin**, *Moonshots, October 7, 2026* [[reminders/AI Infrastructure/Market Allocation Can Send Compute to Persuasion Instead of Public Health by Dave Blundin|Market Allocation Can Send Compute to Persuasion Instead of Public Health by Dave Blundin]] > Compute scarcity can delay mundane automation even when the software exists. If disease research, scientific simulation, robotaxis, customer service, and domestic robots compete for the same chips, capacity flows first toward the applications expected to create the most value per token. > **— Adapted from Alexander Wissner-Gross**, *Moonshots, October 7, 2026* [[reminders/AI Infrastructure/Compute Scarcity Can Delay Mundane Automation for Higher-Value Research by Alexander Wissner-Gross|Compute Scarcity Can Delay Mundane Automation for Higher-Value Research by Alexander Wissner-Gross]] > Human-AI co-discovery strains scientific credit systems built around individual people. When theory and discovery emerge from coupled human and machine work, institutions must decide how to recognize the collaboration without pretending that every advance belongs to one principal investigator. > **— Adapted from Emad Mostaque**, *Moonshots, October 7, 2026* [[reminders/Scientific Literacy/Human-AI Co-Discovery Strains Person-Centered Scientific Credit by Emad Mostaque|Human-AI Co-Discovery Strains Person-Centered Scientific Credit by Emad Mostaque]] > Person-centered prizes compress consortium science into a single laureate. When an observatory or laboratory is a massive organization, honoring its leader may symbolize the achievement while obscuring the distributed human system that produced it. > **— Adapted from Alexander Wissner-Gross**, *Moonshots, October 7, 2026* [[reminders/Scientific Literacy/Consortium Science Cannot Be Compressed Into One Laureate by Alexander Wissner-Gross|Consortium Science Cannot Be Compressed Into One Laureate by Alexander Wissner-Gross]] ## Sources / Provenance - Jumper et al. (2021); scientific machine-learning literature.  ## ASI and RSI Timeline Research Routes This node is connected to the developed research ontology for [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]]. The route distinguishes the source transcript from canonical wiki objects and lets later Reminders inherit an already connected conceptual graph. - **Master ontology:** [[ASI and RSI Timeline Ontology]] - **Scientific Intelligence and Recursive Improvement:** [[ASI and RSI Timeline Ontology#Scientific Intelligence and Recursive Improvement|complete route]] · [[Richard Socher]] · [[The Eureka Machine]] · [[Recursive]] · [[Recursive Self-Improvement]] · [[Weak RSI]] · [[Strong RSI]] · [[Scientific Superintelligence]] · [[Full-Stack AI for Science]]