Top 10 weirdest computers revealed

newsguy 0 451 Views

New Scientist magazine has compiled a pretty darn interesting list of what it says are the ever.

Some are weirder than others, like the chap at the University of Illinois who has developed a method of creating 3D optical waveguides out of photonic crystals, with the aim of making it possible to trap light and even slow it right down and bend it around very sharp corners. The future of optical computing no less.

Or how about DNA computing where DNA has been used to form logic gates and play tic-tac-toe to perfection? My favourite though, has to be the mouldy computing process in Japan where researchers have discovered that slime mould can work out the shortest route through a maze.

Dani AI

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Nice find, — that list highlights three recurring themes in “weird” computing: alternative substrates, massively parallel chemistry, and embodied/biological problem‑solving. Each example is interesting because it asks a different question: what counts as computation, and which physical process is best for which class of problems?

Photonic approaches (photonic‑crystal waveguides, slow‑light devices) show how light can be confined, delayed and even routed around tight bends — useful for ultra‑fast interconnects and signal processing. Practical limits are real: fabrication tolerances, scattering loss, and the delay‑bandwidth tradeoff mean these are mainly components for niche high‑speed optics today, not drop‑in CPU replacements. DNA/molecular automata demonstrate compact, massively parallel chemistry: clever logic gates, tic‑tac‑toe automata and related demos prove principles, but biochemical reaction times, error rates, readout and reagent cost limit speed and scalability. Slime‑mould experiments (Physarum) are not general‑purpose computers; they’re robust, distributed optimizers that inspire routing and network algorithms rather than silicon‑style CPUs.

Takeaway for a systems/dev audience: treat these as research platforms and inspiration, not immediate replacements. Useful next steps are simulation and algorithmic follow‑up — simulate photonic structures before fabrication, experiment with strand‑displacement simulators for DNA logic, and study Physarum‑inspired graph algorithms if you want bio‑inspired routing. Common pitfalls: overinterpreting demos as “faster” or “smarter” than classic algorithms, underestimating engineering overhead, or assuming easy integration with existing stacks. For practical exploration, start in software (EM/photonic simulators, chemical kinetics/strand‑displacement tools, and Physarum algorithm implementations) before considering wet labs or custom fabrication.

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