Some of the earlier computers of the 20th centure were analog computers, not digital.
At some point analog died however, and "computer" basically by default started meaning just "digital computer".
As of the 2010's and forward, with the limit of Moore's law and the rise of machine learning, people have started looking again into analog computing as a possile way forward. A key insight is that huge floating point precision is not that crucial in many deep learning applications, e.g. many new digital designs have tried 16-bit floating point as opposed to the more traditional 32-bit minium. Some papers are even looking into 8-bit: dl.acm.org/doi/10.5555/3327757.3327866
As an example, the Lightmatter company was trying to implement silicon photonics-based matrix multiplication.
A general intuition behind this type of development is that the human brain, the holy grail of machine learning, is itself an analog computer.
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An analog computer is a type of computing device that uses continuous physical quantities to represent information. Unlike digital computers, which process data in discrete binary values (0s and 1s), analog computers work with real-world phenomena and can model variables such as voltage, current, mechanical movement, or fluid pressure. ### Key Characteristics of Analog Computers: 1. **Continuous Data Representation**: Analog computers represent data in a continuous form.