# Neural Networks Fit Curves to Data by Roger Highfield > “Blind gathering of Big Data in biology continues apace, however, emphasizing transformational technologies such as machine learning—artificial neural networks, for instance—to find meaningful patterns in all the data. But no matter their "depth" and sophistication, neural nets merely fit curves to the available data. They may be capable of interpolation, but extrapolation beyond their training domain can be fraught.” > **— Roger Highfield**, *2017, Edge Annual Question, “What Scientific Term Or Concept Ought To Be More Widely Known?”* ## Sources and Context - **Direct Edge response and surrounding essay:** [Actionable Predictions](https://www.edge.org/response-detail/27046) — Edge published this exact passage in its 2017 Annual Question collection, *WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?*. The response page preserves the surrounding argument and qualifications. - **Complete local collection index:** [[research/Edge 2017 Full Quotation Curation|Edge 2017 Full Quotation Curation]] — retained source-first record for the complete response cohort and this passage's promotion status. ## Related Articles and Collections - **Collection:** [[collections/Edge|Edge]] - **Collection:** [[collections/Machine Succession|Machine Succession]] - **Article:** [[articles/Modern Artificial Intelligence in the 1970s|AI in the 1970s]] - **Article:** [[articles/Christopher Nolan's Odyssey at the Threshold of Machine Succession|Nolan's Odyssey]] - **Article:** [[articles/Cyber-Physical Lessons from Imperial Beekeeping|Cyber-Physical Beekeeping]] - **Wiki map:** [[wiki/Artificial Intelligence|Artificial Intelligence]] ## Related Topics - [[wiki/Artificial Intelligence|Artificial Intelligence]] - [[wiki/Machine Learning|Machine Learning]] - [[wiki/Infrastructural Superintelligence|Infrastructural Superintelligence]] - [[wiki/Machine Succession|Machine Succession]] ## Share on Social Media ``` “Blind gathering of Big Data in biology continues apace, however, emphasizing transformational technologies such as machine learning—artificial neural networks, for instance—to find meaningful patterns in all the data. But no matter their "depth" and sophistication, neural nets merely fit curves to the available data. They may be capable of interpolation, but extrapolation beyond their training domain can be fraught.” — Roger Highfield, 2017, Edge Annual Question, “What Scientific Term Or Concept Ought To Be More Widely Known?” https://bryantmcgill.com/simple-reminders-roger-highfield-neural-networks-fit-curves-data ```