u/Tobio-Star

▲ 106 r/psychologists_india+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

TLDR: François Chollet has been, to date, the most credible advocate for Neurosymbolic AI, with a lab dedicated to proving its potential for AGI research. Here, he further clarifies his "Symbolic descent" idea (also known as Program Synthesis), and why it could be more sample-efficient than even the human brain!

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➤Chollet's vision for AGI

Chollet is exploring a completely different path to AGI, based on a reinvented version of Machine Learning. He aims for "optimal AI", which he believes to be fundamentally superior to human intelligence, both in quality and efficiency.

The core of his vision is "program synthesis", a mechanism through which AI could build concise and efficient models of how the world works.

➤Turning a continuous reality into simple pieces

Symbolic descent (also called "program synthesis") works by "cutting" the world into discrete entities in order to best explain a task or observation. For instance, separating a cooking session or recipe into well-defined steps.

Instead of memorizing an infinite number of continuous patterns (the millisecond-by-millisecond muscle movements while cooking), the system looks for the underlying process that generated them. That process is a set of discrete steps, actions or objects like "mixing", "baking" or "ingredients".

➤Why simple representations matter

These discrete elements along with their relationships, form a much simpler model than the true chaotic real-life experience. It also leads to better generalization. According to the Minimum Description Length principle, a simple solution always generalizes better than a messy one.

Chollet's bet is that discretizing the world is a fundamentally more powerful approach to make sense of it than fitting those complicated deep learning curves on data. Said otherwise, he aims to replace the popular "input → complicated curve → output" pipeline with "input → symbolic model → output".

➤The architecture

Chollet's AI features two parts:

  • a "fluid intelligence" module (partly symbolic)
  • a knowledge base (entirely learned)

Analogy: AlphaGo used Monte Carlo Tree Search (symbolic model) to reason but applied to an ever-growing library of game experience.

This is not just naive Symbolic AI: the symbolic model would at least partially be learned, not handcrafted by humans. And being symbolic, it would also be far more sample-efficient than neural network-based systems (including the human brain).

➤A new form of reasoning

The fluid intelligence module's input would be the discrete elements automatically extracted by the system from the problem at hand (e.g. steps, actions, objects...). Then, to reason, it would perform a search over the space of possible combinations of those until it lands on one that accurately describes the situation.

Think of how to predict the position of Jupiter, astrophysicists sifted through a gigantic number of variables (mass, density, temperature, shape, velocity, ...) until they landed on this reduced, simple combination: position = f(initial_position) + f(velocity).

Similarly, this AI would autonomously extract various discrete variables about a given task (like cooking, chess or a math problem), reduce them to the most relevant ones and find the right way to combine them.

➤Handling computational complexity

This search process faces a major challenge: combinatorial explosion. For n variables, the number of possible combinations for a given problem is "n!" (which is worse than exponential!). To drastically reduce the search space, the AI would leverage messy curve fitting (i.e deep learning) to instruct the model on the most promising locations of the problem space to look at.

A chess player for example, doesn't literally try all possible moves in their head. They use their messy intuition built from previous games to guide their attention during reasoning. A cook doesn't take random actions: their choices are conditioned by life experience.

Chollet's AGI architecture is essentially an ambitious attempt to merge the symbolic and deep learning paradigms.

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OPINION

According to Chollet, his lab has started getting "good results" with this approach 6 months ago. However, I will remain skeptical until an actual paper is available. It's hard for me to see how Symbolic AI plays any role in the future of this field, even though Chollet's enthusiasm for this "revamped version of Machine Learning" is intriguing. On the bright side, this is the only "Neurosymbolic" advocate that I have seen with a somewhat coherent vision

MORE: If you want a more in-depth presentation of his ideas, this clip I posted a few months ago is fantastic: [Analysis] Deep dive into Chollet’s plan for AGI

SOURCE: https://www.youtube.com/watch?v=k2ZLQC8P7dc

u/Tobio-Star — 28 days ago

TLDR: Human insight is crucial for developing AGI. The idea that it holds systems back, and that scale, RL and search should be the only focus of AI research (as popularized by "The Bitter Lesson") is unreasonable and, at this point, outdated

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Basically, people have reduced it to “Don't think, just throw more money at the problem”, and made it this sacred principle that should never be questioned.

➤Reminder (for those who don't know)

The Bitter Lesson is an influential essay by Sutton, suggesting that the techniques in AI that eventually prevail aren't the ones researchers spent time and effort crafting manually but rather those that scale without human intervention.

Sutton made the point that humans should stay away from giving AI any form of pre-built representation or internal knowledge, and simply stick to designing a meta environment through which AI can learn on its own.

Basically, it's a case for Reinforcement Learning, Self-play and Search as the path to AGI (since these processes can be done completely autonomously).

➤1st counterargument: CNNs

Sutton argues that "adding human insight" and "looking for techniques that scale" are mutually exclusive. They simply are not.

CNNs drew inspiration from the human visual cortex and still heavily rely on scale and data to produce meaningful results. By the way, they are still the go-to for AI vision today (at least in systems for which speed is crucial, like cars, where ViTs are too slow).

➤2nd counterargument: RL has already shown limitations

  • RL has very clearly shown its limits when it comes to the physical world. We keep making systems that are impressive at demos but are brittle and never actually generalize. RL only works for relatively narrow domains like chess and Go, and formalizable ones (code, math). But for messy inputs like almost any real-world experience, using RL exclusively has been a massive failure in every way
  • Search is even more limited as a path to AGI. We learned decades ago with the "General Problem Solver" that intelligence is NOT just about search. Complexity theory is a thing. Most search spaces are exponentially big. There are a lot of inductive biases that make humans smart by making the job easier for our prefrontal cortex (see this thread). We don't have to think or perform search-like processes for many aspects of cognition.

➤LLMs do not align with the Bitter Lesson

Sutton has repeatedly insisted that LLMs do not fit the Bitter Lesson ideology since they rely on human-written text. They weren't designed to learn by experiencing the world on their own. In Sutton's model, apart from the meta-architecture of the system, the AI should contain no human trace at all (a position I completely disagree with, of course).

So people are using this principle like it's an absolute premise to justify spending an unreasonable amount of resources on a type of system that doesn't even fit the vision!

➤It's not a law

Like Moore's ""Law"", it's just an observation of trends from a specific era. But AI has proven to be a special field where every strong claim, like attempts to restrict intelligence to "just x" or "just y", has consistently failed. That tends to happen when the subject matter is as complex and ill-defined as intelligence.

Despite all the blind trust in the Bitter Lesson, AI today still falls short of human intelligence in many fundamental aspects. It only makes sense to update and start questioning it or at least the extent to which it should apply.

Inspiration from biology and neuroscience is obviously valuable when we are trying to replicate intelligence, i.e. the most complex phenomenon in the universe. We shouldn't restrict what should guide us on the path to AGI based on early observations (AI is still a relatively young field).

>!The Bitter Lesson was an important essay because it highlighted the importance of scale and self-learning as components of research: any idea needs to scale to be worth pursuing. But the overall hypothesis is way too strong!<

reddit.com
u/Tobio-Star — 1 month ago