Chapter 24 — The Second Watershed
42 references
| # | Reference | Links | Status |
|---|---|---|---|
| 1 | Figure 24.5 — The bi-metal thermostat: a threshold frozen at the factory commons.wikimedia.org | Source | 🟡 No archive |
| 2 | Figure 24.6 — Blind men appraising an elephant commons.wikimedia.org | Source | 🟡 No archive |
| 3 | Figure 24.8 — Vineyards in Napa Valley commons.wikimedia.org | Source | 🟡 No archive |
| 4 | Figure 24.9 — The collapsed Ponte Morandi spurred advanced structural metrics commons.wikimedia.org | Source | 🟡 No archive |
| 5 | Figure 24.10 — The grid's blast radius: a city street during a power cut en.wikipedia.org | Source | 🟡 No archive |
| 6 | Figure 24.11 — Same digit, same neighborhood: a t-SNE of handwritten digits (digit labels added arbitrarily for the illustration) commons.wikimedia.org | Source | 🟡 No archive |
| 7 | Figure 24.12 — Unstructured 3D: a LiDAR point cloud of a San Francisco intersection commons.wikimedia.org | Source | 🟡 No archive |
| 8 | Figure 24.13 — Sensor fusion in the hand: the infrared dot pattern Face ID projects to build a depth map of the face commons.wikimedia.org | Source | 🟡 No archive |
| 9 | Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer, "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot," arXiv preprint 2302.06590, February 2023 arxiv.org | Source | 🟡 No archive |
| 10 | Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, "Generative AI at Work," Quarterly Journal of Economics 140, no. 2 (2025); originally NBER Working Paper 31161, April 2023 nber.org | Source | 🟡 No archive |
| 11 | Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim Lakhani, "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and… papers.ssrn.com | Source | 🟡 No archive |
| 12 | Eaton Corporation, "About Us eaton.com | Source | 🟡 No archive |
| 13 | "Eaton's Generative AI Cuts Product Design Time by 87%," aPriori case study, 2024 apriori.com | Source | 🟡 No archive |
| 14 | Augury machine-health customer case studies. Colgate-Palmolive – "Superior Machine Insights Help Colgate-Palmolive Reimagine a Healthier Future for All," augury.com | Source | 🟡 No archive |
| 15 | PepsiCo – "PepsiCo's AI Innovations Boost Manufacturing Efficiency," augury.com | Source | 🟡 No archive |
| 16 | The √N rule is a standard result in signal processing: averaging N independent measurements of a common signal raises the signal-to-noise ratio in proportion to √N, because the coherent signal grows with N while uncorrelated noise grows only with √N. See "Signal averaging," Wikipedia en.wikipedia.org | Source | 🟡 No archive |
| 17 | Testing many candidate patterns inflates false positives, so the detection threshold must rise to compensate – but only with the square root of the logarithm of the number of hypotheses tested ($\propto \sqrt{2\ln M}$). On controlling false discoveries across many tests, see Yoav Benjamini and… doi.org | Source | 🟡 No archive |
| 18 | The same search penalty appears in physics as the look-elsewhere effect: Eilam Gross and Ofer Vitells, "Trial Factors for the Look Elsewhere Effect in High Energy Physics," European Physical Journal C 70 (2010): 525–530 doi.org | Source | 🟡 No archive |
| 19 | On detecting departures from learned-normal behavior with no predefined fault signature, see Varun Chandola, Arindam Banerjee, and Vipin Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys 41, no. 3 (2009): article 15 doi.org | Source | 🟡 No archive |
| 20 | John Deere reports more than one million connected machines and roughly 500 million "engaged acres" across its Operations Center; "Connectivity and Digitalization," John Deere about.deere.com | Source | 🟡 No archive |
| 21 | Its See & Spray computer-vision system, trained on millions of labeled field images, cut customers' non-residual herbicide use by an average of nearly 50% in the 2025 season – nearly 31 million gallons of spray mix across more than five million acres; "Deere Customers Use See & Spray Technology… deere.com | Source | 🟡 No archive |
| 22 | The data advantage has a liability twin: the FTC and several states sued Deere in January 2025 over its control of equipment data and repair; "FTC, States Sue Deere & Company," Federal Trade Commission, January 2025 ftc.gov | Source | 🟡 No archive |
| 23 | For an accessible overview of emergence in complex systems – how collective behavior arises from the interactions between parts and is not reducible to any part alone – see Andy Martin and Kristian Helmerson, "Emergence: The Remarkable Simplicity of Complexity," The Conversation, October 1, 2014 theconversation.com | Source | 🟡 No archive |
| 24 | Dietrich Braess, "Über ein Paradoxon aus der Verkehrsplanung," Unternehmensforschung 12 (1968): 258–268; English translation, Dietrich Braess, Anna Nagurney, and Tina Wakolbinger, "On a Paradox of Traffic Planning," Transportation Science 39, no. 4 (2005): 446–450 pubsonline.informs.org | Source | 🟡 No archive |
| 25 | Bouchaine Vineyards equipped its 104-acre Carneros estate with Cisco Industrial Asset Vision sensors, gathering temperature, light, humidity, wind, and water block by block. "Growing quality wine grapes with less water," Cisco Blogs blogs.cisco.com | Source | 🟡 No archive |
| 26 | "Napa Valley's Bouchaine Vineyards Utilizes Cisco IoT Sensors to Measure Vineyard Data in Real Time," Wine Industry Advisor, September 14, 2021 wineindustryadvisor.com | Source | 🟡 No archive |
| 27 | Cisco Industrial Asset Vision is a LoRaWAN-based system – its AV-series sensors communicate via the LoRaWAN protocol to a wireless gateway and cloud dashboard; "Cisco Industrial Asset Vision Data Sheet," Cisco integral-system.fr | Source | 🟡 No archive |
| 28 | The Genoa San Giorgio bridge, opened in 2020 to replace the Morandi viaduct (whose 14 August 2018 collapse killed 43), carries a permanent monitoring system – sensors for vibration, strain, inclination, and displacement, fiber-optic sensing, a digital twin, and inspection-and-cleaning robots –… webuildvalue.com | Source | 🟡 No archive |
| 29 | "Genoa-Saint George Bridge," Wikipedia en.wikipedia.org | Source | 🟡 No archive |
| 30 | A synchronized AC grid must balance generation and load continuously, so a local disturbance can cascade across an interconnection in seconds – as in the 14 August 2003 Northeast blackout, which cut power to roughly 50 million people across the United States and Canada; analysts hold that wide-area… en.wikipedia.org | Source | 🟡 No archive |
| 31 | On synchrophasor wide-area monitoring and machine learning over phasor measurement unit (PMU) data streams, see "Synchrophasor Technology Fact Sheet," NASPI naspi.org | Source | 🟡 No archive |
| 32 | and "Machine Learning for Synchrophasor Analysis," PNNL-30452 pnnl.gov | Source | 🟡 No archive |
| 33 | The Embedding Projector is an open-source, browser-based tool for visualizing high-dimensional embeddings (via PCA, t-SNE, or UMAP) projector.tensorflow.org | Source | 🟡 No archive |
| 34 | see "Open Sourcing the Embedding Projector," Google Research, December 2016 research.google | Source | 🟡 No archive |
| 35 | GraphRAG uses a language model to build a knowledge graph from unstructured text, then queries that graph. "GraphRAG: New Tool for Complex Data Discovery Now on GitHub," Microsoft Research, July 2024 microsoft.com | Source | 🟡 No archive |
| 36 | GraphRAG uses a language model to build a knowledge graph from unstructured text, then queries that graph. "GraphRAG: New Tool for Complex Data Discovery Now on GitHub," Microsoft Research, July 2024, ; code at github.com | Source | 🟡 No archive |
| 37 | Awni Y. Hannun, Pranav Rajpurkar, et al., "Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network," Nature Medicine 25 (2019): 65–69 nature.com | Source | 🟡 No archive |
| 38 | trained on 91,232 single-lead ECG records from 53,549 patients, matching board-certified cardiologists across twelve rhythm classes. Code github.com | Source | 🟡 No archive |
| 39 | A raw point cloud is only coordinates – no edges, surfaces, or objects. Processing recovers that structure: estimating surface normals, detecting edges and boundaries, segmenting objects, and reconstructing a 3D surface model, which mapping systems georeference to real-world coordinates, using… sciencedirect.com | Source | 🟡 No archive |
| 40 | and "A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds," arXiv:2301.13656 arxiv.org | Source | 🟡 No archive |
| 41 | Potree is an open-source WebGL renderer for very large LiDAR point clouds in the browser potree.github.io | Source | 🟡 No archive |
| 42 | Apple's TrueDepth system projects more than 30,000 infrared dots to build a depth map of the face; a portion of the Neural Engine, inside the Secure Enclave, transforms that depth map and an infrared image into a mathematical representation, matches it against the enrolled data, and adapts that… support.apple.com | Source | 🟡 No archive |