@hex6c
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Teaching Data & Network Science | Creating Generative & Data Art | NFT Original Gangster
Joined January 2011
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hex6c retweeted
The pioneering crypto artist @hex6c coded a variant of Conway's Game of Life called Game of Pixel.
hex6c.medium.com/game-of-pix…
Broken Beauty (2018)
In the Game of Christ (2018)
2/ John Conway’s Game of Life is a mathematical model in which every cell on a grid is either alive or dead. At each step, its next state is determined by the living cells around it.
Depending on the starting arrangement, these simple rules can produce structures that remain stable, oscillate or move across the grid.
No cell directs the whole. Complex behavior emerges from local interactions.
Can data be more than something we analyze or visualize?
I’ve put together 10 defining questions about data art.
The central idea:
Data can be not only information, but also artistic material—something to transform into visual, sonic, physical, interactive, or computational experiences.
[Link in the first comment]
Data Artwork: Giorgia Lupi, Stefanie Posavec. Dear Data: Week 7 (Musical Complaints / A Week of Complaints), 2014
P vs NP asks a deceptively simple question: if a solution to a problem can be checked quickly, can it also be found quickly?
This is one of the seven (six?) Clay Millennium Problems still open.
Will AI solve it? There are roughly three possible scenarios:
1. AI as an amplifier. A human mathematician develops the crucial conceptual idea, while AI discovers lemmas, checks cases, searches literature, formalizes proofs, and explores variants.
2. Human–AI co-discovery. An AI notices some structural phenomenon—for example in circuit lower bounds—that humans had overlooked, and humans turn it into a theory. At that point it becomes difficult to say who "solved" the problem.
3. Autonomous discovery. An AI invents a new mathematical framework producing a proof that humans subsequently verify.
And P vs NP may actually be an unusually revealing benchmark for advanced mathematical AI. Current systems can increasingly succeed by extending existing mathematics. P vs NP seems likely to require something closer to creating new mathematics. If an AI independently crossed that boundary, the significance would extend far beyond complexity theory.
An interactive real-time demo of Conway's Game of Life rendered with infinite recursion.
Each cell at one level is itself a full Game of Life simulation at the next.
Chapeau!
I am designing an AI-aware Data Science teaching/learning model.
Modern Data Science education must adapt to a landscape where artificial intelligence is both ubiquitous and powerful.
Because traditional assignments no longer guarantee that a student has actually learned the material, educators need to redesign their teaching models to address this reality.
A proposed social contract shifts the focus from simply producing final answers to fostering genuine cognitive development and critical thinking. This framework relies on a stepped progression that begins with solo human effort, moves through group collaboration, and culminates in the thoughtful integration of technology.
Ultimately, students are evaluated across multiple layers to ensure they retain essential judgment while augmenting their capabilities with modern tools rather than blindly automating their coursework.
hex6c retweeted
Researchers proved every LLM trained on AI-generated content develops an irreversible genetic disorder.
They call it "Model Collapse"
When you train an AI on internet data, it learns the patterns of human language.
When the internet fills up with AI-generated text, future AIs start training on that synthetic data.
Then the next generation trains on the AI's version of the AI.
It is the digital equivalent of inbreeding.
With every single generation of recycling, the model loses touch with reality.
Rare events vanish entirely. The tails of the distribution get chopped off.
The AI forgets what normal human writing actually looks like, and the output degenerates into pure, repetitive statistical gibberish.
The scariest part?
It is completely irreversible.
Once a model goes through collapse, you cannot patch it by throwing clean data back into the mix. The underlying architecture's genetic code is permanently corrupted.
We are actively flooding the internet with synthetic content every single day.
We are poisoning the well that the next generation of models has to drink from.
If the future of the internet is just AI talking to AI, the data supply chain is about to rot from the inside out.
——
to clarify real quick: it's not a literal biological "genetic disorder" since ai doesn't have dna, but researchers actually do call it "ai inbreeding," "habsburg ai," or "mad" (model autophagy disorder) because the mathematical effect is basically the exact same thing.
AI is extraordinarily adept at extracting patterns from the past in order to make predictions; human beings, on the other hand, possess the ability to formulate causal theories about things that do not yet exist, to act upon reality in order to test them and, in so doing, to generate new data and new knowledge.
Every subject taught [at MIT] will likely need to be reexamined and potentially revamped to make sure that how students are being taught, what they’re learning, and how they’re assessed are “AI-aware.”
aiandeducation.mit.edu/repor…
hex6c retweeted
The sometimes organic quality in my work does not come from trying to imitate nature visually, but from allowing rules to interact until their behaviour becomes too complex to read as mechanical
hex6c retweeted
Oxford researchers argue that LLMs can never invent anything.
It is mathematically impossible.
They published a paper called “Theory Is All You Need" and it argues against the claim that computational models can generate genuine novelty or new knowledge.
They analyzed the limits of generative ai, and the results are a brutal reality check for the idea that ai will replace human decision making under uncertainty.
Here is why AI is stuck and human cognition wins:
backward-looking vs forward-looking.. llms are probability machines that look backward at existing data. human cognition is forward-looking and capable of generating genuine novelty. human cognition operates theoretically "top-down" rather than "bottom-up" from data.
the "data-belief asymmetry".. the researchers use the invention of "heavier-than-air flight" to illustrate this concept. an ai relies on data-based prediction, which is largely imitative. humans, however, use theory-based causal logic that allows them to hold beliefs that go beyond existing data.
the intervention gap.. humans don't just process information; we use theory to practically "intervene" in the world. we engage in directed experimentation to generate entirely new data. ai-based models are theory-free and place primacy on existing data and prediction.
tldr?
AI uses a probability-based approach to knowledge and ia largely imitative. It can process data and make predictions, but human cognition relies on theory-based causal reasoning.
The decades-old analogy comparing human minds and computers to mere "input-output" devices is fundamentally flawed.
I’m therefore proposing a three-step plan with the goal of pacing the frontier: building AI at a balanced rate that aims to ensure its safety while still achieving its benefits and grappling with important geopolitical dilemmas. To be clear, pacing does not mean halting model training or technical progress, but ensuring companies take adequate time to align and safeguard their models, and for third party evaluators to confirm this.
darioamodei.com/post/we-must…
hex6c retweeted
MIT published a brutally honest report on what AI is doing to students.
A committee of professors and students spent five months studying how AI changed learning on campus, and the findings read like a warning to every university on the planet.
Study groups are disappearing. Office hours are emptying out. Problem sets and take-home exams no longer prove anything, because AI can produce credible solutions to almost any written assignment in the undergraduate curriculum. Students who lean on chatbots lose mastery and confidence, and some slip into what the report calls cognitive surrender, reaching for AI at the first hint of struggle.
The numbers are rough. 46 percent of surveyed MIT undergrads use LLMs daily. 90 percent worry about their own overreliance. Undergrads who feel AI makes them replaceable now outnumber those who feel it makes them capable.
The committee's answer surprised me. They refused to fight AI with surveillance. The report calls AI detectors unreliable, says lockdown browsers feel like spying, and warns that policing students builds a classroom atmosphere of mutual distrust.
Instead, MIT wants to rebuild education around the things AI can't replace. That means oral exams, semester portfolios, in-person project work, and a required social component in every subject. The report even floats the idea of rethinking grades entirely, since without a GPA to optimize, much of the incentive to cheat with AI evaporates.
The committee warns professors against replacing undergrad research assistants with AI agents just because they're cheaper, because a university exists to grow people, not output.
The most famous tech school on earth admitted the machines broke its way of teaching. Its answer is more humans, not more software.
