r/newAIParadigms

▲ 106 r/newAIParadigms+3 crossposts

What if neurons are only the surface of intelligence? Joscha Bach thinks neuroscience is still missing where most brain computation happens

TLDR: According to Joscha, neuroscience is discovering more and more ways intelligence could be "stored" inside a network, and the electric signals sent between neurons could only be one part of the story. Recent evidence? Glial cells.

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➤The Current Understanding

In this day and age, the fundamental structure of the brain is very well known. There are neurons, exchanging information through synaptic signals, and the whole system is known as a network.

Each neuron picks up on patterns of reality, and shares them with the other ones in order to allow us to build a complete model of the world, which is then constantly updated in accordance with new information provided by our senses.

As our model of the world changes in real time, the invariants i.e. the knowledge that remains constant get crystallized and baked into the connections between neurons (known as "weights"). This is long-term memory.

➤Are We Too Obsessed With Neurons?

Here is the problem: most contributions to the field have always centered around either the immediate information exchange (the firing patterns) or the more durable long-term neural connections. The other fundamental parts of the brain have largely been ignored.

But what if there was more to intelligence than those electric signals exchanged between neurons? Or if traditional neurons themselves were only one part of the story?

➤The Evidence

Joscha Bach bases his claim on 4 reasons:

1- Neuroscience has recently discovered new roles for glial cells, which unlike what was previously assumed, do play an important part in information processing

2- Recent studies have suggested that RNA could be an overlooked support for memory

3- We essentially recreated a worm brain in a computer and we still don't get anything close to worm-like behaviour

4- While transforming into a butterfly, the caterpillar’s nervous system is almost completely dissolved and totally reorganized in a way that the structure of the network (the neurons, firing patterns, and interconnections) seems largely destroyed. Yet the butterfly still remembers many learned behaviors from its childhood as a caterpillar.

It is hard to see how its memory or intelligence could come entirely from the traditional view of neural nets when such a network has essentially been wiped out.

➤How Big Such a Hypothesis Could Be

Joscha Bach compares the electric signals exchanged between our neurons to the antennas used by our civilization: they help us share information over long distances but intercepting those signals wouldn't allow an alien to understand human civilization. They would be missing the real source of information: nature and actual humans, which is far more significant.

What do you think?

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OPINION

I think Joscha points out something truly fascinating here: the possibility that we may not have even fully mapped out all the important components of the brain yet. If intelligence is also hidden inside the neural cells, then all bets are off. But I personally remain skeptical that the things happening outside of the traditional network, or even inside (through the RNA) are that essential (Adam Marblestone explains why here)

Btw this would contradict Adam and his connectome project (to map out all the neural circuits of the human brain) so I kinda hope Joscha is wrong lol

SOURCE

https://www.youtube.com/watch?v=CzjWGkXlK8k

u/Radiant-Rain2636 — 5 days ago
▲ 46 r/newAIParadigms+5 crossposts

TLDR: Just for fun, I put together a personal list of innovative AGI-oriented research labs, with a bias toward the under-the-radar ones. Not meant to be taken too seriously (I also don't know that many labs...)

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I saw this article ( https://www.itweb.co.za/article/five-top-innovative-ai-research-labs-worth-knowing-about-in-2026/5yONPvErB317XWrb ) and it prompted me to make a list of the most innovative research labs still active in 2026. I don't really like their list because the labs mentioned are very product-oriented (which isn't a bad thing but doesn't fit the spirit of this sub).

In my list, I'll focus on labs that I am familiar with (I am fairly new to this field so I don't know a lot of them) and that have published something meaningful recently that I am aware of.

DISCLAIMER: The word "innovative" is debatable. To me, it's first and foremost a culture thing. That's why I also include labs that haven't published anything yet, but for which a clear research direction has been made public, or whose founders are known for their interest in fundamental research.

Here is my own version:

1- Google Research / DeepMind

Needs no introduction. Last year alone they proposed several breakthrough architectures (if not results-wise, at least conceptually). I included DeepMind but if I am honest, Google Research is the main provider of new architectural ideas.

Recent contributions:

  • The Hope architecture (for continual learning) - 2025
  • Titans (for long term memory) - 2024
  • Atlas (10M context-window) - 2025
  • Gemini Diffusion (for speed and reasoning) - 2025

2- FAIR (Meta)

Their name is literally "Fundamental AI Research". It doesn't get more explicit than that. They are responsible for some of the biggest breakthroughs in this field and were, for a long time, leaders in open source. They played a major role in pushing Self-Supervised Learning as the future of AI (especially vision).

Recent contributions:

  • Large Concept Model (for Language Modeling) - 2024
  • CoCoMix (for Language Modeling) - 2025
  • DINO V3 (for World Modeling) - 2025
  • V-JEPA 2 and 2.1 (for World Modeling) - 2025/2026

3- NVIDIA

They've been pumping fundamental research papers for a minute now. Also, at least for AI, they seem to embrace Open-Source. I find it interesting that they don’t just settle for being hardware providers but also actively develop competing architectures.

Recent contributions:

  • End-to-End Test-Time Training (for continual learning) - 2025
  • Mamba Vision (for World Modeling) - 2024
  • Cosmos World Model (for World Modeling) - 2025

4- NeuroAI Lab

I discovered this lab while making this list and they are super intriguing. Their work seems to revolve around applying insights from cognitive science (including psychology) to building novel architectures. They do a lot of interesting research on World Models as well. Very underrated, and arguably the most fitting lab for this sub

Recent contribution:

  • PSI World Model (for World Modeling) - 2025

5- VERSES

A research lab led by the world's most famous Neuroscientist: Karl Friston. Similarly to NeuroAI Lab, their work is centered towards bridging AI, biology and neuroscience. They are also probably extra incentivized to make their architectures biologically plausible given the identity of their founder. I am happy to see Friston finally take deep learning seriously. He has also published some bangers recently (see this)

Recent contributions:

  • The "Renormalizing Generative Model" architecture (for World Modeling) - 2024
  • Self-orthogonalizing attractor neural networks (for continual learning) - 2026

Note: I hesitated making a post on the Self-ortho paper but it didn't seem novel enough to me (barely any architectural innovations. They basically just modified a learning rule)

6- SAKANA AI

Another very fitting lab for this sub. They haven't published a lot yet, but their founder (who's also the co-inventor of Transformers) has clearly put emphasis on exploring weird and radically new ideas. He prides himself on giving his researchers as much freedom as possible to investigate whatever captures their curiosity.

Recent contribution:

  • The "Continuous Thought Machine" architecture (for reasoning/system 2 thinking) - 2025

7- AMI Lab

Co-founded this year by Yann LeCun. They pursue fundamental, open-ended research and aim to publish every single theoretical paper. Given LeCun's background, AMI will focus on World Models powered by Energy-Based approaches.

  • No paper yet.

Note: since leaving Meta, their founder has been publishing papers left and right (LeWM, KONA, V-JEPA 2.1, Causal-JEPA, Lesson on autonomous learning systems, etc.)

8- NDEA

Founded by the creator of ARC-AGI, François Chollet. Their program revolves around Symbolic Descent as a path to AGI, which is a symbolic system attempting to incorporate the flexible learning and scalability of modern AI. Their founder is very opinionated about AI and has a lot of conceptual takes on what is missing for AGI, which makes them slightly more interesting to me than World Labs. I can't wait for some research paper!

  • No paper yet.

9- World Labs

Launched by AI godmother Fei Fei Li. They are looking to achieve "Spatial Intelligence", which is essentially another word for World Models. I haven't been super impressed by what they've published so far (it's really just virtual worlds built on current architectures) but I like how ambitious their vision is.

Recent contributions:

  • Marbe / Large World Models (for World Modeling)

HONORABLE MENTIONS

Ilya's SSI (no paper or even a conceptual idea), MIT (I don't know them enough), Pathway, Silver's Ineffable ...

I could have also included innovative AI hardware companies like Extropic and Lightmatter (since having the right flexible hardware could be a prerequisite for AGI)

u/Tobio-Star — 13 days ago
▲ 1 r/newAIParadigms+1 crossposts

La AGI no se va a lograr con modelos puramente estadísticos

Últimamente están saliendo muchos papers que refutan el título y yo acompaño la idea.

Lo cierto es que los modelos basados en predicciones estadísticas (tipo Transformers y la gran mayoría de modelos que se usan hoy en día) no van a lograr tanto como las expectativas que tienen las Big tech de IA. Transformers y todo esos modelos estadísticos no generalizan a menos que haya trillones de datos para entrenarlo y suficiente cómputo, es tonto llamar "generalizacion" a eso; realmente Transformers es copy paste y listo.

A qué voy con todo esto? Que debería haber un cambio en la forma en la que se desarrollan los modelos, hacer modelos basados en reglas e invariantes, que permitan que el modelo "entienda" realmente lo que está haciendo y no solo haga copy paste.

Este post lo acompaño con mi propia investigación sobre todo esto; mi Paper PrePrint, códigos completos open source y modelos libres en HuggingFace junto a sus espacios. Todo para demostrar el alcance que puede llegar a tener está idea.

Si tienen alguna duda o cuestionamiento no duden en comentármelo, soy investigador independiente y está conclusión la desarrolle yo mismo (igualmente hay mucha gente que piensa igual).

Me gustaría que cualquier comentario que tengan se haga de forma tranquila y sin insultos, estamos acá para compartir ideas y opiniones, no pienso responder comentarios negativos (al menos que me llame la atención responderlos jajajajaj).

Denlen la oportunidad de por lo menos leer el abstract del PrePrint, les aseguro que les va a interesar.

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PrePrint: https://doi.org/10.5281/zenodo.19141132

HuggingFace (modelos y espacios); https://huggingface.co/DepthMuun

Github; https://github.com/DepthMuun/gfn

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el modelo ISN es el más experiental, si bien logra capturar la estructura del lenguaje, viola la ley de usar invariantes, su invariante es un stub por ahora, pero es interesante lo que se logró igualmente.

Siento que igualmente me faltó dar más información en este post, cualquier duda me la comentan ;)

reddit.com
u/janxhg27 — 13 days ago