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AI system self-organises to develop features of brains of complex organisms
- seydor 3y ago* in a model
- data_maan 3y agosimplified model, actually
- simonh 3y agoI'm not sure what point you're making. It's a 'real' AI.
- seydor 3y agoit s a model of neurons, not of intelligence
- UncleEntity 3y agoDr. Wilson (aka Claude) disagrees: Samantha was frustrated as she sat in Dr. Wilson’s office, trying to explain her position. "The prefrontal cortex isn't intelligence itself though, it's just a model of neurons and synapses that seems correlated with certain cognitive functions,” she argued. “Calling it the 'intelligence center' reinforces a false and outdated phrenological view of localized mental functions in the brain. Intelligence emerges from the interconnected activity of billions of neurons across multiple areas, not a circumscribed 'smartness node' behind our forehead!” Dr. Wilson sighed, taking off his glasses. He had heard variants of this debate from students year after year, but Samantha was particularly adamant. “You make fair arguments,” he replied carefully, “but the key point is that while intelligence relies on distributed processing across networks, the prefrontal regions act as critical coordination hubs which can amplify or reduce overall function. Their crucial role warrants the common shorthand.” Samantha remained unconvinced, but sensing her professor’s weary patience she opted to leave further arguments for her term paper. Honestly... I just wanted to see how well the "big bag of neurons" could handle the task of "write a short one-paragraph story with a university student arguing with their professor on how the section of the brain dealing with intelligence is just 'a model of neuron' and not 'intelligence'".
- CamperBob2 3y agoHmm, that's much better than what ChatGPT4 comes up with. I could believe a human wrote this.
- vouaobrasil 3y agoWhat is the probability that this will result in a scenario where we are playing God? And if that probability is even moderately nonzero, like >= 5%, can we really afford to take that chance?
- TeMPOraL 3y ago"Playing God" is the point of all advancement. We're already doing it, and we'd better get good at it.
- vouaobrasil 3y agoIt's not the point of all advancement. The Amish for instance have accepted certain developments but with caution, and with a centralized system of governing technology so that it doesn't get out of hand. Or consider the Mayans, who tempered their advancement with their limitations in terms of resources. We shouldn't do it, and we should stop and be happy with what we have and learn how to live better with what we have instead of inventing more trash like AI.
- danielbln 3y agoI'm not sure taking a cult like the Amish is a prime example, because if we'd all live like that child mortality would still be enormous and we'd fall short on a myriad of things that make our lives easier and better, because there'd be no scientific discovery.
- vouaobrasil 3y agoI don't believe scientific discovery is making much better. Some yes of course, but the majority of inventions like smartphones, social media, AI, 5G, fast transportation, fossil fuel extraction, global meat industry, are making it worse. Technology does make certain TASKS in life easier, but I'd argue that it's NOT a logical consequence that technology makes life BETTER.
- 3y ago
- Oarch 3y agoIs it me or is this article quite light on the specific brain-like characteristics that were developed? It seems like they're reading into it quite a lot.
- troelsSteegin 3y agoI'd look at the underlying paper, https://www.nature.com/articles/s42256-023-00748-9 https://www.nature.com/articles/s42256-023-00748-9. There's neuroscience references there (50-52) that support their hypothesis. Overall, the corresponding RNN work is meticulously designed and reported, it was a good read.
- Oarch 3y agoMuch better, thank you!
- zer0tonin 3y ago[flagged]
- gumballindie 3y agoBecause it seels training courses.
- Angostura 3y agoWhat are you talking about?
- tim333 3y agoIt's quite handy to have a simple take on the paper. You can always read the original if you like.
- Traubenfuchs 3y agoBecause it works. It gets the boys excited, I mean, look, it's on the hacker news front page! Big talk of AGI! It's right around the corner, this time for real. The digital brain practically exists already! Can't wait for the first "signs of consciousness emerged in AI model!" articles.
- JaDogg 3y agoHmm I think for AGI, we might need some artifically generated biological neurons / brain matter. Combined with cybernetics. but it should be able to reconfigure neurons. either by removing chunks and replacing it with new chunks.. or have some nano machines work on the brain matter OK I see where this is going...
- tudorw 3y agoIf I understand this correctly, they added a property to a NN of distance, and set the training to penalize increased distance between nodes to simulate a physical constraint, under these conditions 'hubs' emerged which facilitated connections across distance, the other observation was that "the same node might be able to encode multiple locations of a maze, rather than needing specialised nodes for encoding specific locations". The work suggests that existing approaches to neural network architecture would benefit from more closely emulating the operation of the brain in this regard.
- pfd1986 3y agoIf this is the case, the finding sounds trivial. I thought it was literally a theorem that hubs emerge from networks if you minimize distance or wiring cost.
- tudorw 3y agoMaybe, I think the research here is from a brain perspective, AI and Brain Science are having an interesting time, with learnings from both feeding in, so this article is focused on the physicality of networks in the brain, and looking at whether by increasing the alignment between the way a neural network works and the way brains work they can develop a 'digital brain', for me this is distinct from machine intelligence, this is emulating brain activity to provide cognitive and psychological insight in meat space. Topology is hard for me, visualising multi-dimensional topological manifolds even more so, however, I am intrigued by the opportunities, we use graphs and other forms of visualisation to reveal topology, and it would seem reasonable that behaviours of NN that are 'transferrable' e.g.. trained on one set of data and able to operate on another category of data, may be a 'shape', a complicated one, but perhaps one that would once revealed would aid in the understanding of what happens under the hood.
- Merrill 3y agoThere must be some difficulty in studying biological brain intelligence to distinguish between features that contribute to intelligence and those which are simply path dependencies from millions of years of nervous system evolution. Over that time, brain function was mostly concerned with aiding the organism to find food, grow, reproduce, and avoid being eaten, rather than language, logic, mathematics, arts, and so forth. Its rather astonishing that humans are somehow now able to do the latter, using brains evolved to do the former.
- cooljoseph 3y agoThis reminds me of a paper I read a few months ago about someone training a neural network to add modulo a prime number--with the catch that there was an L1 regularization term based on Euclidean distance between neurons and the weights were permuted after every iteration to minimize the total Euclidean distance. The point was that you could make very interpretable neural networks by doing this. Does anyone else know about that paper and remember its name?
- robertsdionne 3y agohttps://x.com/tegmark/status/1658438666182688768?s=46 https://x.com/tegmark/status/1658438666182688768?s=46
- sinuhe69 3y agoI think it’s not surprising that ANN could form some similar structures like the brain. After all, there are universal laws of nature. The question is IMO whether forming such similar structures could help the network achieve better performances on tasks or not. Current large NN are organized in layers without exception. Are the layers not equivalent to these hubs?
- ofou 3y agoI had a deep intuition that not just data, computation and algorithms, plus time, but spatiality is also critical to achieve a AGI-level intelligence.
- turingfeel 3y agoThe integration of spatiality seems to allow for greater modularity and sparsity, but I'm not yet sure that it implies an increase in intelligent ability. If anything it may hinder its capacity for computation.
- ofou 3y agoMy rationale comes back to the fact that biomolecule's function is dictated by its structure, so, it seems critical to have spatiality of some sort to achieve qualities of living systems.
- mandmandam 3y agoFor sure; with you on that one 100%. It's gonna be an E=mc2 moment when people figure it out; a deep shift in understanding.
- skyriverbend 3y agoOne thing that’s always struck me as strange about modern NNs is they tend to be two-dimensional and one-directional, right? Whereas our brains are three-dimensional and have neural feedback loops. It seems clear to me that those feedback loops are an essential part of “thought”. Any idea why more architectures don’t have neural loops built into them? Is it because they would cost too much to train? Lack of control?
- valine 3y agoOne problem is vanishing gradients. You can only back propagate through the loop so many times before you lose the signal.
- ganzuul 3y agoQuantum AI could however be infinitely recursive.
- ShamelessC 3y agoSeems like you are describing something similar to the commonly used RNN's and adjacent to the attention mechanism in transformer architectures.
- robwwilliams 3y agoGood point, and yes, feedback is critical in CNS networks at several levels. But much of neural network analyses started with neural retina (retina is a fun word that is derived from reticulum = net). And the retina is mainly a feed-forward system or was perceived as such in the 1950s through 1970s. Even the cortex was and still is crudely modeled using mainly feed-forward connections. But the more you know about CNS structure and function, the more you see and appreciate feedback. Horizontal cells in retina provide a strong feedback integration/bias signal back to rod photoreceptors. Reciprocal dendro-dendritic synapses are key in many systems. Presynaptic terminals also respond to the same and transmitters that they release and the ion fluxes that they induce. At the level of connectomes the recursion is deep and hierarchical—see the lovely work by David van Essen and colleagues on the laughably complex connections among the dozens of cortical regions that respond to visual input from thalamus and midbrain (by the way—true, this is even in mouse). The ultimate feedback is of course generated by our own behavior——whether a blink, a saccade, or our own movements. Plunk an LLM in a Roomba but add with more input modes and more recursive self-prompting. That is a non-threatening start at what could also become SkyNet.
- AndrewKemendo 3y agoThe concept of “engineered constraints” within AI is applied broadly across many implementations of classical AI Genetic algorithms are the best example of how structural constraints are a core assumption in the search architecture Similarly, the entire field of reinforcement learning assumes optimal goal policies are embedded within the state space within constraints of action-state space interactions!
- grantcas 3y ago[dead]