What is Computing?
Simply speaking, computing uses technology to process information – both on a software and hardware level.
It includes the study of computers (the ‘computing machines’), how to engineer hardware to perform a computation, as well as data structures, algorithms, and, in general, software structuring the computation. Here, we use computing synonymously with calculating and counting.
Nowadays, computing is mostly based on metal-oxide-silicon field-effect transistors (MOSFETs) on the hardware side, juggling zeros and ones to run programs written in high-level programming languages on the software side.
However, new computing paradigms are emerging that go beyond binary data and exploit new physical effects for computing.
Future Computing Technologies
Artificial intelligence opens up a new paradigm for computing: Rather than instructing computers explicitly, they get to ‘learn’ from data to do useful things.
Andrej Kaparthy goes as far as speaking of software 2.0, where ‘coding’ essentially becomes ‘labeling data’.
Related keywords are machine learning, computer vision, evolutionary computing, and many more.
Quantum computing goes beyond binary bits by harnessing quantum states, so-called qubits, for computing.
Thereby, quantum computers may give exponential speedups (“quantum supremacy”) over classical computers, e.g. for simulating quantum systems.
Related keywords include quantum machine learning, quantum cryptography, graphene-based transistors, quantum algorithms, and many more.
Neuromorphic computing is emulating neural networks by hardware design – mimicking neurons and synapses through electronic components and analog circuits.
Collocating processor and memory avoids shuffling data between both, which is energetically costly. Thus, they allow for energy-efficient inference from neural networks.
Related keywords include memristors/memcapacitors, reservoir computing, and many more.
Biological computing studies both, how to use biological components to process and store information and to develop new algorithms for complex problems based on evolution.
Biocomputers are made e.g. of living cells that use chemical inputs to perform calculations.
Keywords include DNA-based data storage, nano-computers, evolutionary & genetic algorithms, and many more.
Optical computing uses photons, the quanta of light produced by a laser or diode, for performing calculations – promising higher bandwidths.
Yet, most projects focus on optical components that could be integrated into digital computers to process binary data.
Related keywords are photonic chips, photonic crystals, optical quantum computing, silicon photonics, and many more.
Distributed computing deals with connecting multiple computers to a cluster and sharing their data storage and computing power capabilities.
It has advantages for processing large amounts of data, where the computational load can be balanced between several computers or data could be kept local for privacy reasons.
Related keywords include edge computing, cloud computing, federated learning, and many more.
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