Showing posts with label neuromorphic. Show all posts
Showing posts with label neuromorphic. Show all posts

Wednesday, January 10, 2024

Make Everything a Computer Again


AKA The Atoms Themselves Are Computers Part 2

Tiny device mimics human vision and memory abilities
Jun 2023, phys.org

There won't be any computers one day. Somehow things will compute by themselves because of the way they're designed. Each thing will compute differently because it will be made of different things and arranged in different ways. There won't be all-purpose computers anymore; some things will see, some will hear, some will count, maybe some will smell.

A neuromorphic vision device -- a single chip enabled by a sensing element, doped indium oxide,  thousands of times thinner than a human hair and requires no external parts to operate, captures, processes and stores visual information.

The device mimics a human eye's ability to capture light, pre-packages and transmits information like an optical nerve, and stores and classifies it in a memory system like the way our brains can.

via Royal Melbourne Institute of Technology RMIT, Deakin University and University of Melbourne: Aishani Mazumder et al, Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In‐Sensor Neuromorphic Computation, Advanced Functional Materials (2023). DOI: 10.1002/adfm.202303641



Physicists design metamaterials with built-in frustration for mechanical memory
Jun 2023, phys.org

The future of computing where everything is a computer -- "metamaterials are materials whose responses are determined by their structure rather than their chemical composition"

But this metamaterial now has memory -- To construct a metamaterial with mechanical memory, they realized that its design needs to be "frustrated," and that this frustration corresponds to a new type of order, which they call non-orientable order. These materials naturally want to be ordered, but something in their structure forbids the order to span the whole system and forces the ordered pattern to vanish at one point or line in space. There is no way to get rid of that vanishing point without cutting the structure, so it has to be there no matter what.

(A simple example of a non-orientable object is a Möbius strip)

via University of Amsterdam: Xiaofei Guo, Non-orientable order and non-commutative response in frustrated metamate, Nature (2023). DOI: 10.1038/s41586-023-06022-7


New type of computer memory could greatly reduce energy use and improve performance
Jun 2023, phys.org

Processes data in a similar way as the synapses in the human brain, and based on hafnium oxide.

"In conventional computing, there's memory on one side and processing on the other, and data is shuffled back between the two, which takes both energy and time."

Conventional memory devices are capable of two states: one or zero. A functioning resistive switching memory device however, would be capable of a continuous range of states

At the atomic level, hafnium oxide has no structure, with the hafnium and oxygen atoms randomly mixed, making it challenging to use for memory applications.

However, the researchers found that by adding barium to thin films of hafnium oxide, some unusual structures started to form (and which allow electrons to pass through), perpendicular to the hafnium oxide plane, in the composite material.

via University of Cambridge: Markus Hellenbrand et al, Thin-film design of amorphous hafnium oxide nanocomposites enabling strong interfacial resistive switching uniformity, Science Advances (2023). DOI: 10.1126/sciadv.adg1946

AI Art - Nanotechnology Activated by a Frequency in the Human Body - 2023

The catch-22s of reservoir computing: Researchers find overlooked weakness in powerful machine learning tool
Sep 2023, phys.org

Sante Fe Institute is always far out:

Reservoir computing is effective in predicting the trajectory of chaotic systems after seeing very little training data, and can even determine where the system would end up just from its initial conditions.

"In a sense, you have this kind of information sneaked in before the training begins," he says. And if they perturbed the model? "Generally, it performed really poorly," Zhang says. That suggests that the model cannot make accurate predictions unless key information about the system being predicted was already built in. For RC, the duo observed that in order to correctly predict the system, the model requires a lengthy "warm-up" time that's almost as time-consuming as the dynamic movements of the magnet itself.

via Santa Fe Institute and Toronto Metropolitan University: Yuanzhao Zhang et al, Catch-22s of reservoir computing, Physical Review Research (2023). DOI: 10.1103/PhysRevResearch.5.033213


New 'assembly theory' unifies physics and biology to explain evolution and complexity
Oct 2023, phys.org

Assembly Theory - developing as empirically validated approach to life detection, with implications for the search for alien life and efforts to evolve new life forms in the laboratory.

In prior work, the team assigned a complexity score to molecules called the molecular assembly index, based on the minimal number of bond-forming steps required to build a molecule. They showed how this index is experimentally measurable and how high values correlate with life-derived molecules.

The new study introduces mathematical formalism around a physical quantity called "assembly" that captures how much selection is required to produce a given set of complex objects, based on their abundance and assembly indices.

"Assembly theory provides a completely new lens for looking at physics, chemistry and biology as different perspectives of the same underlying reality," explained lead author Professor Sara Walker, a theoretical physicist and origin of life researcher from Arizona State University.

via University of Glasgow and Arizona State University: Leroy Cronin, Assembly theory explains and quantifies selection and evolution, Nature (2023). DOI: 10.1038/s41586-023-06600-9. 

Monday, March 14, 2022

Programmable Matter and Ubiquitous Intelligence


Intelligence is going to be embedded in everything -- smart clothes, smart furniture, smart air.

I still think of a computer as a piece of hardware, a metal box with "electronics" inside. But if you told me that a cup of water could also be a computer, I'd have a hard time imagining that. It's one of those paradigm shifts that separates us from the future. Like if you just discovered fire, but then someone tells you there's another way to "cook" food in the slow fire of fermentation.

You would realize that food is cooking all the time, without our intervention. We just learned how to control it. 

If I try to imagine that a river can be a computer, it's hard. You mean the weather itself can be a computer that we can then use to forecast the weather? Yes, something like that, but not really (Gödel might want a word). 

Image credit: Efoia via Fractal Forums - Pseudo-kleinian folded with sphere inversion rendered in Oak Fractal Sandbox with Monte Carlo path tracing - 2017 [link]


How simple liquids like water can perform complex calculations
Jan 2022, phys.org

Reservoir computing is a relatively recent idea in computing. Instead of traditional binary programs run on semiconductor chips, the reactions of a nonlinear dynamical system—the reservoir—are used to perform much of the calculation. Various nonlinear dynamical systems from quantum processes to optical laser components have been considered as reservoirs.

"It turns out that deionized water is best for solving second-order nonlinear problems." The good performance of these solutions demonstrates their potential for more complicated tasks, such as handwriting font recognition, isolated word recognition, and other classification tasks", says Professor Akai-Kasaya.


I'm having visions of Stanislaw Lem's Solaris (1961), which featured an extra-terrestial intelligent terrestrial, aka a planetary superorganism. Considering that Lem intended to explore "the limitations of human rationality", I imagine he would enjoy seeing this branch of science develop.

via Osaka University: Shaohua Kan et al, Physical Implementation of Reservoir Computing through Electrochemical Reaction, Advanced Science (2021). DOI: 10.1002/advs.202104076


Researchers find a single-celled slime mold with no nervous system that remembers food locations
Feb 2021, phys.org

The researchers discovered that the organism weaves memories of food encounters directly into the architecture of the network-like body and uses the stored information when making future decisions.

"Past feeding events are embedded in the hierarchy of tube diameters, specifically in the arrangement of thick and thin tubes in the network," says Mirna Kramar, first author of the study. 

via Max Planck Institute for Dynamics and Self-Organization and Technical University of Munich: Mirna Kramar et al. Encoding memory in tube diameter hierarchy of living flow network, Proceedings of the National Academy of Sciences (2021). DOI: 10.1073/pnas.2007815118


Thinking without a brain - Studies in brainless slime molds reveal that they use physical cues to decide where to grow
Jul 2021, phys.org

Physarum polycephalum uses its body to sense mechanical cues in its surrounding environment, and performs computations similar to what we call "thinking".

via Wyss Institute at Harvard University and the Allen Discovery Center at Tufts University: Advanced Materials (2021). DOI: 10.1002/adma.202008161

Saturday, September 14, 2019

On the Brains of Machines


This picture is kind of like an infrared camera but for algorithms.

It's a heat map for the eyeballs of a computer; what is it looking at, what are its clues?

In this case, it's looking at the water, not at the ship, in order to identify the image as a ship. (We're also assigning agency to this thing, in case anyone's keeping track.)

Neural nets are a big deal these days, but they come with a new problem. We don't know what they're doing, because the thing that makes them so special is that they figure out their own algorithm. (Agency again.) Computer programmers are not writing the programs; the networks write the programs using trial and error. Machine Learning is another name for this idea of iterative development.

There's a lot of people who would like to know what's going on in there, mostly to see how these things are getting their answers, and to make sure that the algorithms don't cheat to get their answers. Some learn bad habits, like detecting "ships" in pictures with water (which means they're good at detecting water, not ships), or by skimming metadata, which means they're good at classifying metadata, not pictures of stuff. These heat maps, and more importantly the forensics-like algorithms that inform them, are very helpful. They let us see inside the brains of the machine.

***
Speaking of disembodied brains, here's the artificial synapse. It uses a new type of hardware memory system that works more like a brain does, in an array, where they can do their computing business simultaneously. Neuromorphic computing.

And if you want to grow those artificial synapses in a 3-D tissue culture (brains in a dish), call these guys.

Cerebral organoids -- they're more for studying how the brain works than they are about making artificial brains. At least they're not using human brains, right?

Wrong; there are ethical concerns that these organoids might develop consciousness, or have already developed consciousness. 

Notes:

What is it like being a brain in a computer?
Clarifying how artificial intelligence systems make choices
Mar 2019, phys.org

Sebastian Lapuschkin et al, Unmasking Clever Hans predictors and assessing what machines really learn, Nature Communications (2019). DOI: 10.1038/s41467-019-08987-4

Fast, efficient and durable artificial synapse developed
Apr 2019, phys.org

Elliot J. Fuller et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing, Science (2019). DOI: 10.1126/science.aaw5581

Researchers grow active mini-brain-networks
Jun 2019, phys.org

Stem Cell Reports, Sakaguchi et al.: "Self-organized synchronous calcium transients in a cultured human neural network derived from cerebral organoids"
https://www.cell.com/stem-cell-reports/fulltext/S2213-6711(19)30197-3
DOI: 10.1016/j.stemcr.2019.05.029