# 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]] ## Sources / Provenance - Jumper et al. (2021); scientific machine-learning literature.