Finding Signal From Noise
Finding Signal From Noise
Our world is bathed in a variety of signals and spectrums: acoustic, electromagnetic, chemical. The increasing digitization of data, commoditization of advanced sensors, and the use of AI to parse through complexity in search of signal, is allowing for increasingly clever ways of uncovering patterns in noise, with implications for privacy and communication. This is my living repository of some creative ways of finding signal.
Acoustic
Power grid: each power grid hums with a unique and fingerprintable acoustic signal, which can be used to timestamp any recording.
Digital voiceprinting: is someone's voice a unique fingerprint? (a bit skeptical, as it gives me graphology vibes i.e. human personality from signatures, but I don't know enough to judge its validity)
Animal translation: AI is being used to tackle the most difficult problems in linguistics - speaking to animals
Some of the most creative analyses in terms of extracting signal from noise are cybersecurity researchers who work on side-channel attacks e.g.
Using phone vibrations or computer fans from air-gapped devices
Using infrared lasers to input commands into smart home speakers like Alexa
Use ground-based seismometers to track atmospheric re-entry of satellite debris.
Electromagnetic
Using AI to estimate human pose from Wifi, with obvious implications for surveillance e.g. tracking someone in a crowded mall using ubiquitous Wifi or special operations e.g. knowing what position everyone is standing or sitting in inside of a room
See around walls: non-line-of-sight imaging allows one to indirectly image what is "around the corner" through extracting signal from diffuse light reflections
A mere photograph of someone's face can reveal what they're looking at through their retina reflection (using NERF techniques)
Covertly transmit information by rapidly switching a photodiode between forward-bias electroluminescence and reverse-bias negative luminescence, so that the time-averaged emission exactly matches the thermal background — invisible to any detector lacking sufficient bandwidth.
ODINI: malware that exfiltrates data from Faraday-caged, air-gapped computers by modulating low-frequency magnetic fields from CPU cores — magnetic fields pass straight through the metal shielding that blocks EMR (a compass still works inside a Faraday cage).
Biological
Environmental DNA: everyone is shedding microscopic amounts of DNA everywhere they go. Researchers are increasingly able to extract population-scale and individual-scale information, with surveillance implications. Funny enough, it originally originated from environmental sciences trying to study animal populations and movements
Finding the Golden State Killer: well-reported already but still worth emphasizing how biological information is a commons problem and just because you haven't played into the system, doesn't mean the system isn't playing with you. Pretty incredible the inferences we can make once we hit critical mass of population-scale data c.f. China
Fingerprinting agricultural products for supply chain integrity: using microscopic DNA strands as "physically unclonable functions" to verify the authenticity of seeds, cotton products, etc which is just the physically instantiated version of public key encryption from cybersecurity [NYT]
Measuring heart rate from cameras by detecting blood flow i.e. photoplethysmography
Recovering audio from crumpling chip bag through pixel fluctuations
Plants as surveillance: DARPA program exploring using plants as chemical sensors e.g. nuclear non-proliferation, chemical warfare. Benefits include that plants can be easily inserted via seed drops, blend in as sensors with actual plants, and can provide detection signals easily e.g. through dying, leaf color change, etc
Unrelatedly, I just want to say plants are very cool. They have a variety of signalling and networking mechanisms that we barely understand to communicate with another and not enough has been done to explore these abilities. They scream in ultrasound, communicate through the emission of volatile organic compounds, and have networks of fungi connecting them to each other in unknown ways. Also, we can use certain plants to mine for critical minerals aka phytomining.
Adversarial Noise
The inverse problem: noise that masquerades as signal, or signal quietly corrupted into noise — sometimes by accident, sometimes on purpose.
Trace palladium is everywhere: catalytic converters across 1.4 billion cars have flecked Pd onto every surface on Earth, from Antarctica to bench glassware. The result is a long lineage of "metal-free catalysis" papers that turn out to be parts-per-billion of contaminating Pd doing all the work — an unintentional adversary, but an adversary all the same, that fakes the signal you're hunting for.
Fast16: state-sponsored malware that surgically patched floating-point operations inside precision simulation tools like LS-DYNA, subtly corrupting outputs from crash testing, structural analysis, and nuclear research. Not data theft, not denial-of-service — just quietly degraded numerical accuracy in pipelines where small errors compound into real-world consequences. The corrupted signal looks completely valid; you only catch it if you know to compare against an uninfected machine.
Disappearing polymorphs: crystals can have polymorphs — chemically identical crystal forms with drastically different properties — which can serve as seeds that dominate solutions. Forced a $250M reformulation by Abbott after their Ritonavir production lines were contaminated by an unknown polymorph. Worse than particulate or biological contamination, which can be filtered or detected: polymorphs pass every chemical purity test because they are the target molecule, and self-propagate from a single seed into every subsequent batch.