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As part of my analysis for Provocation 2, we will construct a concise, non-linear history of the technology I have mapped.

In doing so, we will:

Identify the key precursors and foundational innovations that contributed to its development,

And examine the diverse trajectories it has taken—technological, social, and cultural—since its emergence.

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As part of our analysis, we constructed a concise, non-linear history of facial recognition technology (FRT).

• Key precursors and foundational innovations include early computer vision research in the 1960s, the development of neural networks in the 1980s, and the rise of deep learning and big data in the 2010s. These set the stage for modern FRT systems.

• Since its emergence, FRT has taken diverse trajectories:

• Technological: Evolved from static image matching to AI-powered real-time recognition.

• Social: Widely adopted in smartphones and public security, but raised concerns about surveillance and discrimination.

• Cultural: Accepted in some societies as convenient, but viewed critically in others due to ethical concerns.

• Governmental: Governments have played a key role—some promoting FRT for law enforcement and border control, while others have introduced strict regulations to protect privacy and civil rights.

This non-linear history highlights how FRT is shaped not only by technical advances, but also by shifting social values, cultural norms, and political decisions.

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Key precursors:

1) Tuning machine - First model of algorithms that compute calculations

2) First artificial neural network (ANN) SNARC

3) University of Montreal researchers published "A Neural Probabilistic Language Model," which suggested a method to model language using feedforward neural networks.

Trajectory:

Computing calculations to language models to image generation using information of the internet and creating variations of it, remixes. In the 21st century, humanity demands AI to shape future jobs, thinking, and functional capabilities.

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The history of self-driving cars can be traced back to the 1950s, when people began to think about automated driving systems. With the development of computer technology, the "smart car" project in the 1980s became its initial attempt. After 2000, with the breakthrough of AI and sensor technology, self-driving technology entered a rapid development and multiple stages: Tesla launched semi-autonomous driving functions, Google's Waymo developed driving systems, and discussions between companies on regulations and ethical issues continued to promote technological progress. Today, self-driving cars are still undergoing the integrated development of technology, government, and social adaptation.

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Foundations & Precursors

- Early biometrics like fingerprints and facial measurements (1800s–1900s) were used for criminal identification.

- In the 1960s, early computers were used to map facial features.

- CCTV cameras and digital databases in the 1990s made it easier to collect face data.

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Different Trajectories

Government & Security

- After 9/11, FRT was used in airports, borders, and public surveillance.

- It became a tool for law enforcement and crowd control.

Consumer Technology

- Apple’s Face ID (2017) and Facebook’s auto-tagging made FRT part of everyday life.

AI & Deep Learning

- New AI models (2012+) made FRT more accurate — but also raised concerns about bias and fairness.

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Social & Cultural Reactions

- Some cities banned police use of FRT over privacy and racial bias concerns.

- Activists and researchers (like Joy Buolamwini) showed FRT often works worse for people of color.

- Today, people are asking: Who controls FRT, and who does it benefit?

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In 1925, Houdina Radio Control demonstrated a radio-controlled car

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In 1925, Houdina Radio Control demonstrated a radio-controlled car

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In 1958, the American Chrysler Company tested an electromagnetic induction guidance system on a test highway

In 1977, the University of Tsukuba in Japan developed a self-driving car that relied on cameras to track road markings

In 1986, the VaMoRs vehicle at the Bundeswehr University in Munich, Germany, used cameras and computer algorithms to achieve simple computer vision autonomous driving.

In 2004, the Defense Advanced Research Projects Agency (DARPA) promoted the development of unmanned vehicles

In 2009, Google launched the driverless car project (which later evolved into Waymo), which became the beginning of commercialization

2015-2020: Level 3/ Level 4 autonomous driving test

2020-present: L4/L5 level automated driving commercial

Evolution of laboratory research → military promotion → Technology giant competition → commercial landing.