RE: https://mathstodon.xyz/@gconstantinides/116448779816230899
We're hiring for a project on #SpikingNeuralNetworks and #neuromorphic computing, to start in October this year, for 36 months. Can hire at pre- or post-PhD level. Feel free to email me informally, or apply at the link below. Please do share with your networks if you know someone who would be interested.
#ComputationalNeuroscience
#Zoomposium with #ThomasMetzinger Part 2: 🤖 #Self-Models, #neuromorphic #AI & the Future of Our #CultureofConsciousness đź§
What is the “self”—and could a #machine one day develop its own #consciousness?
In the second part of the discussion, the #SelfModelTheory of #Subjectivity takes center stage.
🎥 https://youtu.be/Ibf2mrZ3uzM
đź“Žhttps://philosophies.de/index.php/2026/03/15/selbstmodelle-metzinger/
#ArtificialIntelligence #PhilosophyOfMind #Neuroethics #KIEthics #TechnologyAndSociety #FutureOfAI #Philosophy
Been working on something for a while and finally put it out there, a public security challenge against a threshold cryptography system I built for my own infrastructure.
Four servers, four countries, four hosting providers. The group signing key was generated distributedly (Pedersen DKG), no single server holds the full secret. I literally can't extract it myself. The challenge is to forge a valid FROST Ed25519 signature against today's published challenge string.
What makes it different from a typical CTF:
→ It's not a weekend event. It runs 24/7 for 90 days. The servers are real production boxes running real software (Nextcloud, Gitea, a team API, Grafana). Not docker containers with planted vulns.
→ Post-quantum hybrid. The audit chain carries ML-DSA-44 signatures alongside the FROST threshold sigs, with a downgrade-detection flag baked into the signed payload. Stripping the PQ signature invalidates the classical one.
→ There's a spiking neural network watching the cluster. 258 neurons with STDP learning and four neuromodulators (dopamine, noradrenaline, acetylcholine, serotonin). It processes DAG events, network metrics, and system telemetry as spike trains. A local LLM reads the brain's internal state every five minutes and reports what it observes. Currently it says the cluster is calm. I want to see what it says when someone's actually poking around.
The detection layer is consensus-based. Cross-peer Merkle verification, honey ports, file canaries, DNS sentinels — but quarantine requires multiple observers to agree before acting. One node can't panic the cluster on its own.
I've already broken it myself twice during deployment. Rolled a binary update and got cascade-quarantined by my own Merkle checker. Tripped a file canary rotating honeypot credentials. Those incidents are published. The system catches real mistakes.
Five tiers from foothold to crown jewel. No cash bounty, just your name on the board, CVE attribution, and write-up rights. Safe harbour under disclose.io terms.
#infosec #security #cryptography #thresholdcrypto #ctf #FROST #postquantum #pentest #redteam #hacking #spikingneuralnetwork #neuromorphic
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The Constraints: Physics, chemistry, energy consumption.
The Emergent Architecture: The Octopus.
The paper is essentially trying to turn the "Jazz Band" chaos of evolution into a repeatable engineering formula. It’s "old wine in a new bottle," but the bottle is now high-performance computing.
#AI
#neuromorphic
#DistributiveIntelligence
#evolution
#octopus
8/
3. The "Neuromorphic" Hardware Angle
The "new" part is often the Hardware (Neuromorphic). We are finally building chips that can actually handle these distributed rules without a central "Boss" CPU bottlenecking everything.
The "Mind Club" Connection
If you feel like you’ve heard this before, it’s because you have—it’s the story of Evolution.
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2. Declarative vs. Procedural
Most AI today is "Procedural"—it’s fed millions of examples to learn a procedure. A "Declarative Constraint" system is different. It’s like telling a robot: "You are not allowed to touch the walls," and letting it figure out navigation, rather than showing it 10,000 videos of robots not touching walls.
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1. From "Bio-Inspiration" to "Mechanical Necessity"
Usually, AI researchers try to mimic the brain (Neural Networks). Kinney is suggesting we stop trying to copy the brain's look and instead copy its limitations. He’s arguing that if you set the right "Universal Constraints," the system is forced to develop a brain-like structure because that's the only mathematically efficient way to solve the problem.
5/
The core idea of emergence from rules is nothing new. It’s what John Conway was doing with the Game of Life in 1970, and it’s how Stephen Wolfram has viewed the universe for decades.
What makes this specific paper by Kinney "new" (or at least a fresh take) isn't the concept of emergence itself, but the application:
4/
The Big Idea
Stephen Kinney is essentially arguing that we can create "Artificial Life" (or at least more flexible AI) by focusing on Limitations (Constraints) rather than Commands.
Dan Goodman
Philo Sophies
beesweater
DirtRonin
earthling