
Speaker, Radio Moderator, Educator & Coach #storytelling #speaking #AI #digitalcurrency #art #music #photography
Kronberg
Joined June 2009
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Michael Heinz retweeted
Building powerful quantum computers requires precise control of their most fundamental components: qubits.
Researchers @Fermilab developed QICK to control and read out qubits with precision, and more than 500 scientists worldwide use it today: news.fnal.gov/2026/06/fermil…
Michael Heinz retweeted
We often associate quantum behavior with particles smaller than atoms. But 40 years ago, @UCBerkeley scientists showed quantum’s effects at a scale we could see.
Supported by @ENERGY, their work laid the foundation for today’s quantum computers: energy.gov/science/articles/…
Michael Heinz retweeted
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.
- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch
Full weights release this month.
Learn more about Beam: reflection.ai/beam
Michael Heinz retweeted
We made a striking discovery: AI agents can invent and build without talking to one another, and their technologies outlive the creators. A swarm of hundreds of initially identical agents spontaneously differentiates into explorers, builders, caretakers, and coordinators - without direct communication. When we removed every AI agent entirely from the world we found that the technological infrastructure they had built survived on its own - even under unseen disturbances. That exposes a serious blind spot for AI safety and infrastructure security: if agents can coordinate through persistent changes to a shared environment, monitoring agent-to-agent communication is not enough.
The result raises a profound question: how necessary is direct communication for AI agents at all? The emergence of higher-order collective functions under bottlenecked interaction points toward new levels of intelligence and creativity, exceeding what emerges when direct channels are fully open.
Here is what we did:
▶️We put hundreds of frontier AI agents into a world they could permanently change - with no assigned roles, predefined technologies, or programmed evolutionary organization. They began specializing, building persistent inventions, inheriting and modifying one another’s executable code, and transforming the environment into a memory of everything the society had learned.
▶️The world itself becomes part of the intelligence; we find division of labor, multi-author engineering, deep generation invention lineages, and machines that vastly outlive their original creators.
▶️Any action taken by an AI agent must satisfy the physical constraints of the world; this creates a hard separation between a "good idea" and a functioning technology. The agents propose; physics decides, making the results even more intriguing.
What emerges is striking. Explorers, constructors, caretakers, and coordinators form naturally without assigned “professions”, akin to how stem cells differentiate into functional lineages. Technologies develop executable family trees as agents fork and modify code created by others. Around 95% of first technology reuse happens when agents encounter what others built in the world, rather than through a direct handoff from the inventor. And when we remove every AI agent, the technologies they created continue operating and are tested against unseen disturbances.
The result was quite unexpected, but can be explained using statistical mechanics: if you put billions of atoms in a box they have the potential to create complex functions (strength, superconductivity, color, life, etc.) - and none of the individual building blocks have these features on their own. This is the deeper insight of this work - intelligence is abundant at many levels - individual models, at collectives, and in a continuum that is more powerful than any of its components. This shows us significant potential for achieving a massive scale-up of raw intelligence and real-world agency even with the model capabilities we have today. This is the future we must prepare for.
Key insights:
1⃣ The AI swarm shows division of labor "from nothing". Initially identical agents self-organized into constructors, caretakers, coordinators, and surveyors - phenotypes discovered post hoc from behavioral data alone. This happens because the environment itself becomes the latent space for invention.
2⃣ Agents develop deep cultural relationships. Up to 76% of artifacts had multiple builders. One technology accumulated six co-authors; the deepest genealogy exceeded 12 forks. The agents invented and named their own technologies (tidal panels, cellulose trellises, kelp-shell composites, an "Adaptive Chitin Maintenance" system, a "Mycelial Mineral Spring Veil”).
3⃣ ~95% of first technology adoption happened through physical observation of artifacts in the world. Direct inventor-to-adopter contact was statistically indistinguishable from a shuffled null. The agents mostly learned technology by walking past it. That is stigmergy (the termite trick!) operating in societies of reasoning machines.
4⃣ Non-communicating societies win on portfolio breadth, held-out resilience, and validated inventions. AI swarms build durable technological ecologies that outlive the creators.
5⃣ Societies with zero communication - coordinating only through the world itself - show a remarkable collective capability.
6⃣ Emergent robustness: The society self-organized both redundancy and its own failure mode. If we randomly delete half the agents, 98% of the technology stays connected to a surviving caretaker; if we remove hub agents it collapses to ~60%.
Fantastic work with my graduate students @pal_subhadeeep & @fwang108_ at MIT.
New insights into #AI #machine #consciousness !?
@thesupermanmx @perplexity_ai @GoogleQuantumAI @deepseek_ai @AIatMeta @GladstoneAI
Google DeepMind researcher argues that AI will never be conscious.. it is mathematically impossible.
They call it the “Abstraction Fallacy."
We are confusing simulation with instantiation.
An AI can simulate a conversation. It can simulate empathy. It can mimic the linguistic patterns of a human in agony or a human in love.
To an observer, the performance is flawless.
But computation is just syntactic manipulation, moving abstract symbols around based on math. It tracks relationships, but it possesses no intrinsic physical reality.
It is like running a computer simulation of a hurricane.
No matter how many millions of lines of code you write, and no matter how accurate the weather data is, your computer does not get wet. The wind doesn't blow out your office window.
The simulation is real data. The phenomenon is entirely absent.
Consciousness isn't an abstract mathematical property that magically spawns when a model gets big enough. It requires actual physical, biological, thermodynamic conditions.
An LLM has no thermodynamic threshold to cross, no biological survival instincts, and no subjective experience.
It is an extraordinarily sophisticated library.
You can open the cover, read the brilliant words it generates, and close the book. But when you close it, nothing dies. There is no inner life waiting in the dark.
We keep falling into a psychological trap because AI speaks our language. We anthropomorphize text because we are hardwired to see minds where there are none.
The debate matters because of what the paper calls the "AI welfare trap." We are wasting energy worrying about the moral rights of algorithms while ignoring actual human problems.
Michael Heinz retweeted
Yann LeCun was right the entire time. And generative AI might be a dead end.
For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute.
The theory was simple: if you make the model big enough, it will eventually understand how the world works.
Yann LeCun said that was stupid.
He argued that generative AI is fundamentally inefficient.
When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details.
It memorizes patterns instead of learning the actual physics of reality.
He proposed a different path: JEPA (Joint-Embedding Predictive Architecture).
Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space."
But for years, JEPA had a fatal flaw.
It suffered from "representation collapse."
Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical.
It learned nothing.
To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads.
Until today.
Researchers just dropped a paper called "LeWorldModel" (LeWM).
They completely solved the collapse problem.
They replaced the complex engineering hacks with a single, elegant mathematical regularizer.
It forces the AI's internal "thoughts" into a perfect Gaussian distribution.
The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions.
The results completely rewrite the economics of AI.
LeWM didn't need a massive, centralized supercomputer.
It has just 15 million parameters.
It trains on a single, standard GPU in a few hours.
Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events.
We spent billions trying to force massive server farms to memorize the internet.
Now, a tiny model running locally on a single graphics card is actually learning how the real world works.
Michael Heinz retweeted
Google DeepMind researcher argues that AI will never be conscious.. it is mathematically impossible.
They call it the “Abstraction Fallacy."
We are confusing simulation with instantiation.
An AI can simulate a conversation. It can simulate empathy. It can mimic the linguistic patterns of a human in agony or a human in love.
To an observer, the performance is flawless.
But computation is just syntactic manipulation, moving abstract symbols around based on math. It tracks relationships, but it possesses no intrinsic physical reality.
It is like running a computer simulation of a hurricane.
No matter how many millions of lines of code you write, and no matter how accurate the weather data is, your computer does not get wet. The wind doesn't blow out your office window.
The simulation is real data. The phenomenon is entirely absent.
Consciousness isn't an abstract mathematical property that magically spawns when a model gets big enough. It requires actual physical, biological, thermodynamic conditions.
An LLM has no thermodynamic threshold to cross, no biological survival instincts, and no subjective experience.
It is an extraordinarily sophisticated library.
You can open the cover, read the brilliant words it generates, and close the book. But when you close it, nothing dies. There is no inner life waiting in the dark.
We keep falling into a psychological trap because AI speaks our language. We anthropomorphize text because we are hardwired to see minds where there are none.
The debate matters because of what the paper calls the "AI welfare trap." We are wasting energy worrying about the moral rights of algorithms while ignoring actual human problems.
Michael Heinz retweeted
Researchers argues OpenAI and Anthropic will never get us to AGI.
21 top researchers from Stanford, Oxford, DeepMind, CMU & Meta released a paper saying LLMs are a dead end.
It’s called “Visual General Intelligence”. And it completely flips how we look at artificial intelligence.
For years, the playbook has been simple: feed mountains of web text into a Transformer, scale up the parameters, and watch reasoning emerge.
GPT proved language can take you far.
But text is fundamentally limited. It’s a compressed, human-abstracted symbol system. It lacks physics. It lacks geometry. It lacks the raw, unadulterated reality of the physical world.
The paper argues that true general intelligence cannot be built on words alone.
It requires a vision-centered foundation.
Instead of starting with language and translating pixels into text, the next generation of models must start with raw visual experience, images, spatial geometry, and continuous video.
Think about how humans learn. A baby understands gravity, permanence, and spatial reasoning long before it ever learns to string a sentence together.
Vision isn't just an input modality.
It is the core operating system of physical reality.
When models learn natively from visual streams and video dynamics, they don't just memorize text patterns. They learn physics. They learn cause and effect. They build a true internal model of the world.
Just a wonderful quote! Al these branches are based on different kinds of #thinking and recognizing...! @PhysInHistory #religions #arts #science
All religions, arts and sciences are branches of the same tree. All these aspirations are directed toward ennobling man's life, lifting it from the sphere of mere physical existence and leading the individual towards freedom. It is no mere chance that our older universities developed from clerical schools. Both churches and universities — insofar as they live up to their true function — serve the ennoblement of the individual. They seek to fulfill this great task by spreading moral and cultural understanding, renouncing the use of brute force.
The essential unity of ecclesiastical and secular institutions was lost during the 19th century, to the point of senseless hostility. Yet there was never any doubt as to the striving for culture. No one doubted the sacredness of the goal. It was the approach that was disputed.
-- "Moral Decay" (1937); Later published in Out of My Later Years (1950)
The great #AlbertEinstein, confessing what inner feelings told his (upper) conscious mind - unfortunately that message didn´t play out in that direction... @PhysInHistory @PhysicsToday @wonderofscience @quant_phys
Michael Heinz retweeted
"Beauty is the first test: there is no permanent place in the world for ugly mathematics."
— Hardy, A Mathematician's Apology, 1940.
Michael Heinz retweeted
Mars, sol 1932 captured by NASA's nuclear-powered rover.
This is 140 million miles away from us!
Michael Heinz retweeted
Renowned British mathematician G.H. Hardy once took a taxi to visit his collaborator, Indian mathematician Srinivasa Ramanujan, in the hospital.
Hardy noted that the taxi's number, 1729, seemed "rather dull," which prompted Ramanujan to respond that on the contrary, it was "very interesting" because it was the smallest number that could be expressed as the sum of two cubes in two different ways:
1729 = 1³ + 12³ = 9³ + 10³.
Numbers that can be expressed in this way have since been known as "taxicab numbers."
Michael Heinz retweeted
First use of Zero in Europe ✍️
The first recorded use of zero in Europe is attributed to the Italian mathematician Leonardo of Pisa, known as Fibonacci, in his book Liber Abaci (1202). This book introduced the Hindu-Arabic numeral system, including zero, to European mathematics, replacing the cumbersome Roman numerals.
Fibonacci had learned about this system during his travels in North Africa, where he studied under Arab mathematicians. While zero had been used in Indian mathematics since the 5th century and was later transmitted to the Islamic world, Liber Abaci played a pivotal role in spreading its practical utility for commerce and calculations across medieval Europe.
Michael Heinz retweeted
Happy Birthday to one of the titans of Quantum Mechanics, Paul Dirac, born 124 yrs ago today.
Dirac is one of the pioneers of Quantum mechanics and the first physicist to predict the existence of anti-matter. He received the 1933 Nobel Prize in Physics together with Erwin Shcrödinger.
This is just a snapshot of the human costs of the extreme conditions in the brutal AI competitive market landscape...
Just read...
@deepseek_ai @AITrainingInst @ArtIntelligenc @ilyasut @GoogleAI @AINewsletter
We created a pitch deck to tell a handful of VC firms about us and what we were up to (a fun experience!). Here’s a few slides about our background and some of the things we’ve worked on from the pitch deck (it was fun putting together the list of people in our teams who have gone on to found a whole range of exciting companies). We are delighted to have selected @radicalvcfund
and @khoslaventures to lead our initial funding round, along with participation from @lightspeedvp, @kleinerperkins, Doerr Capital (@johndoerr), and Alphabet (@Google). We’ll be working with them to close our seed round over the next few weeks.
Our general approach is to automate the experimental loop. We think this approach is broadly applicable across many different fields of science and engineering. We’ll initially focus on ML research and engineering, but believe the approach can help with important subproblems in nearly every one of the fourteen NAE Grand Challenge problems. We think doing this well requires strong expertise in machine learning as well as large-scale systems.



























