A Self-Made Machine

RepRap, a new open-source hardware project, goes a long way toward fulfilling the dream of self-replicating machines

2 min read

More than 50 years ago, computer pioneer John von Neumann conceived of a self-reproducing machine. It would mine its own ore, smelt it into metal ingots, machine the ingots into parts, and assemble the parts into a copy of itself. During the 1980s, nanotechnology evangelists worked out the same idea on a much smaller scale, prompting critics to envision a horror scenario in which molecule-size bots reduce the entire world to a featureless mass dubbed ”gray goo.”

Today there’s RepRap. Unlike gray goo or von Neumann’s idealized machines, RepRap (short for ”replicating rapid-prototyper”) doesn’t harvest its own materials. But also unlike them, it’s entirely real. For about US $725 in parts, this self-reproducing machine, spawned by a global band of engineers and hobbyists, will squirt out complex three-dimensional patterns of molten plastic filaments that will solidify into most, if not all, of the mechanical parts for another RepRap (see sidebar, ”Self-Reproduction Is Hard; Selfâ¿¿Assembly Is Harder”).

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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