wildiris (@wildiris19) profile picture

Retired engineer: embedded systems hardware design. Education: physics and math. Interests range from Agricultural Robotics to Origin-of-Life questions.

California, Monterey Bay
Joined May 2022
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wildiris retweeted
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wildiris retweeted
Replying to @fkasummer
Polanyi too: “scientific tradition derives its capacity for self-renewal from its belief in the presence of a hidden reality”
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String theory is what happens when an intelligent system is starved of sensory input and starts hallucinating. Intelligence needs grounding. For this reason, intelligence is practically never the bottleneck to solving impactful problems. The bottleneck is iterated contact with reality.
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This is what I have been working on and although the research focuses on literary creativity I strongly suspect that it also applies to inventiveness in general including the mathematical intuition that leads to new discoveries. The Goblin Realms: Topological Mapping of Human and AI Semantics The "Goblin Realms" framework identifies a fundamental topological divide between the way humans and artificial intelligence navigate the landscape of meaning. Using high-dimensional mapping, the research reveals that human creativity relies on a high "leap rate," where authors jump across semantic voids to connect distant concepts. In contrast, AI models perform an "algorithmic crawl," sticking to predictable, precomputed paths even when instructed to be original. This distinction is quantified through the "Goblin Criterion," which focuses on versatile, low-friction words that serve as hubs for these creative jumps. The study further demonstrates that reading difficulty is driven by these cognitive leaps rather than simple vocabulary rarity. Ultimately, neurobiological data confirms that while AI mirrors standard human logic, it fails to replicate the unique neural patterns associated with true divergent thinking. I still have more multilingual experiments to run with Large Language Models, then human experiments ranking results (if I can get funding), then I hope to find the holy grail, a harness for LLMs that lets them make genuinely creative leaps in the same way humans that do. And then formally write it all up, so the AI's can read it, because a lot of humans don't read anything these days. 😆
🤖 Made with AI
The reason why the Goblin phenomena in AI outputs exists is actually very interesting and tell us some important things about how they, and how human language works.
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wildiris retweeted
It is so strange how often theology comes up in purely scientific investigations. History of science says it’s not uncommon but still very strange.
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I'm so pleased to finally finish and deliver the piece commissioned by @ThePersonPod , a brave man he chose to let me create it with next to zero oversight on his part. There's a lot to the little piece so I'm planning at some point to have a chat about what went into it (physically and metaphysically). Is this what one might call "metagelical iconography" @PaulVanderKlay ?😉 We'll see if I can't get a critique or a like from @PageauJonathan 🙃 @klwong43 @AHomelyHouse @ashley_lande @mattcrotts (fitted, carved oak; painted and finished)
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A must watch video. The most hilarious experiment / expose of just how cringe and technically terrible these chatbots that we call "AI" really are. Chatbots attempt beatboxing and rap.
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wildiris retweeted
i think the biggest problem with tech is that most people in it have never had real jobs this isn’t an indictment — it’s a prescription. you can fix your broken thinking if you become a welder or carpenter for a couple years and learn the difference between tools and people
Jacob is correct here—we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.
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Consciousness is stochasticity at the cellular level, says Denis Noble. Yes, because consciousness is stochasticity at ALL levels. Free choice of a system feels conscious from the inside and looks stochastic from the outside. That's the ontological difference between machines and living systems, the pinch of indeterminism from outside = choice we keep till we die. Noble's Darwin/Wallace split is this from the two sides. Wallace's chance and Darwin's choice are one event seen from its two aspects: the peahen's preference is a selection coefficient to the geneticist. Noble's other cut, water versus silicon, is the wrong one. Silicon is as indeterminate as water at the electron level; a chip's determinism is engineered above the physics by thresholds, clocks and error correction, so that no fluctuation from below reaches the computation. A cell does the opposite: it keeps the fluctuations and selects among them, Noble's own immune example. The difference is who owns the noise. An LLM's temperature is a knob the operator sets, and at zero the machine still runs. An organism has no temperature zero; a heart without jitter is a dying heart. What ends at death is the system's indeterminism; the water keeps jittering in the corpse. And "all levels" means: at every level, the noise seen from above is choice from below. A fed cat's afternoon is random from the whole cat's point of view; its parts are choosing. Across aspects, the pinch of indeterminism outside = choice inside; across levels, noise from above = choice from below.
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Replying to @saranormous
Working on some content right now about modern open source RTL-to-tapeout (LibreLane/OpenRoad)! But would love to read about the proprietary systems too! There's not much out there, folks would benefit from being exposed to more chip content imo 🙂
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Starting to like where this is going
Playing around with ASIC render concepts. Need to work on visually differentiating the "active" module a bit more, but I think the bones are here.
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Playing around with ASIC render concepts. Need to work on visually differentiating the "active" module a bit more, but I think the bones are here.
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wildiris retweeted
I was wondering about this…
This paper gives a fancy name to a problem you can already feel in your bones "The Tragedy of the Cognitive Commons." Checking AI output requires deep expertise. Deep expertise comes from doing grunt work for years. And grunt work is the first thing AI eats So we're building systems that need expert supervision while dismantling the only known process for making experts The paper calls the shared pool of human expertise the "cognitive commons." Every profession drinks from it. Nobody's refilling it. By eliminating junior roles, each company is acting totally rationally, and the collective result: a profession that can't catch AI's mistakes anymore because it never learned to do the work in the first place Who checks AI's homework in 15 years?
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Replying to @nikitabier
Hello Nikita, There’s something seriously wrong with our account. A hate wave two or so years ago focused against the account with mass blocks and mutes brought it to a grinding halt. It was not justified. We’re nice people. We swear. We’re doing fine on other platforms. Just not on the one literally owned by @SpaceX. Could someone please look into why our metrics are excellent, but reach is minimal? Repeated attempts to reach any support on X have turned up no solution. Thank you for your time! All the best The WAI Team P.S.: Game Over 🫡
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Dive into #ASIC design this summer with the Zero to ASIC course! The Zero to ASIC courses are the best known, highest rated and still the only ones to include a guaranteed tapeout. Use code summer26 to get $100 off the course until the 10th August. zerotoasiccourse.com/
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every robotics company is modeling unit economics per robot and nobody's asking who's gonna fix them when they break. there are maybe 200 people on earth qualified to maintain humanoid robots at scale. your deployment math is a fantasy if your maintenance workforce literally doesn't exist yet.
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wildiris retweeted
Modern compute is performed using electricity, historically compute has been done mechanically or hydraulically, some have experimented using optics. But, did you know you can replicate any digital circuit using knitted circuits? You can literally write your "fabric".
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Who knew you could build your own asic for under 1000$. Suprised to see the service available for anyone to get in on the silicon rush. @tinytapeout Checkout the gds file for tapeout. My first attempt at making a basic beamformer. Github pages: JJassonn69/ttsky-beamformer
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The green traces show a chip designer’s worst nightmare! If they take too long to settle, it causes a serious problem that can’t be fixed after a chip is manufactured. Try out my updated metastability explorer tool here: zerotoasiccourse.com/metasta…
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