I usually don't post artist renderings like this, but this is what I see when imagining everything made of computers, using ambient energy like light to control different particles each designed to take it and do different things with it but all in one jumble of matter, like an intelligent matter: Above image: An artistic depiction of a wavelength-multiplexed diffractive optical processor for 3D quantitative phase imaging.
Credit: UCLA Engineering Institute for Technology Advancement [link]
On what could be called "ubiquitous computing", a legend of artificial intelligence (Hinton) describes it really well:
(What's next in computing?) My last years at Google I was thinking about analog computing ... run these big language models in analog hardware ... if you're gonna use that low power analog computation, every piece of hardware is gonna be a bit different. And the idea is that the learning is gonna make use of the specific properties of that hardware.
--Geoffrey Hinton interview, "On Working w Ilya, Choosing Problems, and the Power of Intuition", July 2024 30min?
Researchers use 'smart' rubber structures to carry out computational tasks
May 2024, phys.org
"We now know how to design simple materials so they can process information."
The research team created a rubber computer that can act as a two-bit binary counter using slender rubber elements as mechanical bits, and assembling multiple bits together in a metamaterial.
Note: The title of their demonstration video is "Can Rubber Compute?" and I now see it all as a series of experiments like the Will It Blend series, where they just do it to everything - can crystals compute? (Yes, we already know that) Can light compute? (Yes we already know that too) Can slime mold compute? But can salt compute? (Actually yes, like in a gradient of fresh water and salt water, but I was talking about a pile of table salt.) Can my sneakers compute? (I mean obviously) Can my front door compute? (Also obvious, its whole thing is to open and close like 1/0) I'm not talking about a computer screwed on top of my doorknob, I mean the door itself, the whole thing, is a computer, just by the way its materials are put together. The garbage can? Definitely garbage cans will compute.
via Leiden University and AMOLF: Jingran Liu et al, Controlled pathways and sequential information processing in serially coupled mechanical hysterons, Proceedings of the National Academy of Sciences (2024). DOI: 10.1073/pnas.2308414121
Using DNA origami, researchers create diamond lattice for future semiconductors of visible light
May 2024, phys.org
With headlines like that, there is no further explanation.
via Ludwig Maximilian University of Munich: Gregor Posnjak et al, Diamond-lattice photonic crystals assembled from DNA origami, Science (2024). DOI: 10.1126/science.adl2733
Also: Hao Liu et al, Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments, Science (2024). DOI: 10.1126/science.adl5549
Mechanical computer relies on kirigami cubes, not electronics
Jun 2024, phys.org
It's a mechanical computer, one that doesn't use electronics. Is that all we need to call it? A mechanical computer?
Historically, these mechanical components have been things like levers or gears. But cubes can have five or more different states. Theoretically, that means a given cube can convey not only a 1 or a 0, but also a 2, 3 or 4.
When any of the cubes are pushed up or down, this changes the geometry—or architecture—of all of the connected cubes. This can be done by pushing up or down on one of the cubes with a magnetic field. These 64-cube functional units can be grouped together into increasingly complex metastructures that allow for storing more data or for conducting more complex computations.
The cubes are connected by thin strips of elastic tape. To edit data, you have to change the configuration of functional units. That requires users to pull on the edges of the metastructure, which stretches the elastic tape and allows you to push cubes up or down. When you release the metastructure, the tape contracts, locking the cubes—and the data—in place.
"One potential application for this is that it allows for users to create three-dimensional, mechanical encryption or decryption"
via North Carolina State University: Yanbin Li et al, Reprogrammable and reconfigurable mechanical computing metastructures with stable and high-density memory, Science Advances (2024). DOI: 10.1126/sciadv.ado6476 , www.science.org/doi/10.1126/sciadv.ado6476
New material paves the way to on-chip energy harvesting
Jul 2024, phys.org
They utilize the waste heat generated during operation and convert it back into electrical energy, called "on-chip energy harvesting", and it works because they put tin in the germanium (Ge+Sn).
via Forschungszentrum Jülich and IHP—Leibniz Institute for High Performance Microelectronics in Germany, University of Pisa, University of Bologna, University of Leeds: Omar Concepción et al, Room Temperature Lattice Thermal Conductivity of GeSn Alloys, ACS Applied Energy Materials (2024). DOI: 10.1021/acsaem.4c00275
A first physical system to learn nonlinear tasks without a traditional computer processor
Jul 2024, phys.org
They made a contrastive local learning network where components evolve on their own based on local rules without knowledge of the larger structure, similar to how neurons in the human brain don't know what other neurons are doing and yet learning emerges.
"It can learn, in a machine learning sense, to perform useful tasks, similar to a computational neural network, but it is a physical object."
(Physical object, that's the key)
"Because the way that it both calculates and learns is based on physics, it's way more interpretable. You can actually figure out what it's trying to do because you have a good handle on the underlying mechanism. That's kind of unique because a lot of other learning systems are black boxes where it's much harder to know why the network did what it did.
via University of Pennsylvania: Sam Dillavou et al, Machine learning without a processor: Emergent learning in a nonlinear analog network, Proceedings of the National Academy of Sciences (2024). DOI: 10.1073/pnas.2319718121
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The optical era of science reporting where every picture has rainbows in it: Artistic depiction of diffractive information processing - Ozcan Lab at UCLA - Jul 2024 |
Scientists demonstrate chemical reservoir computation using the formose reaction
Jul 2024, phys.org
Good explanation by the writeup author here, Tejasri Gururaj: The field of molecular computing interests researchers who wish to harness the computational power of chemical and biological systems. In these systems, the chemical reactions or molecular processes act as the reservoir computer, transforming inputs into high-dimensional outputs. ...
The formose reaction is the only example of a self-organizing reaction network with a highly non-linear topology, containing numerous positive and negative feedback loops.
The researchers used a continuous stirred tank reactor (CSTR) to implement the formose reaction. The input concentrations of four reactants—formaldehyde, dihydroxyacetone, sodium hydroxide, and calcium chloride—are controlled to modulate the reaction network's behavior.
The output molecule is identified using a mass spectrometer, which allows them to track up to 106 molecules.
This setup can be used to do calculations, with the reactant concentrations being the input value to any function that needs to be computed.
The team showed that it could predict the behavior of a complex metabolic network model of E. coli, accurately capturing both linear and nonlinear responses to fluctuating inputs across various concentration ranges.
Furthermore, the system demonstrated the ability to forecast future states of a chaotic system (the Lorenz attractor), accurately predicting two out of three input dimensions several hours into the future.
via Institute for Molecules and Materials at Radboud University: Mathieu G. Baltussen et al, Chemical reservoir computation in a self-organizing reaction network, Nature (2024). DOI: 10.1038/s41586-024-07567-x
