Self-Assembling Brain

What Hacker News says

1. upghostDec 2024
   Wow, it is really interesting the difference in comments between ALife and AI stories on HN.

   For some of you out there, there's a great book that really hasn't gotten enough attention called "The Self-Assembling Brain" [1] that explores intelligence (artificial or otherwise) from the perspectives of AI, ALife, robotics, genetics, and neuroscience.

   I hadn't realized the divide was a sharp as it is until I saw the difference in comments. i.e. this one[2] about GPT-5 has over 1000 comments of emotional intensity while comments on OP story are significantly less "intense".

   The thing is, if you compare the fields, you would quickly realize that which we call AI has very little in common which intelligence. It can't even habituate to stimuli. A little more cross disciplinary study would help is get better AI sooner.

   Happy this story made it to the front page.

   [1]: https://a.co/d/hF2UJKF

   [2]: https://news.ycombinator.com/item?id=42485938

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2. upghostNov 2024
   No, not even close. We are not "tabula rasa"[1] or blank slate*. If you would actually like to understand why, some good books about this are "The Self Assembling Brain"[2] and "The Archaeology of Mind"[3].

   [1] https://en.wikipedia.org/wiki/Tabula_rasa

   [2] https://press.princeton.edu/books/hardcover/9780691181226/th...

   [3] https://www.amazon.com/Archaeology-Mind-Neuroevolutionary-In...

   [*] One of the things that frustrates me the most in the discourse on LLMs is that people who should know better deliberately mislead others into believing that there is something similar to "intelligence" going on with LLMs -- because they are heavily financially incentivized to do so. Comparisons with humans are categorical errors in everything but metaphor. They call them "neural networks" instead of "systems of nonlinear equations", because "neural network" sounds way sexier than vectorized y=f(mx+b).

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3. upghostOct 2024
   I've been trying to figure out to respond to this for a while. I appreciate the fact that you are pretty much the lone voice on this thread voicing this opinion, which I also share but tend to keep my mouth shut since it seems to be unpopular.

   It's hard for me to understand where my peers are coming from on the other side of this argument and respond without being dismissive, so I'll do my best to steelman the argument later.

   Machine learning models are function approximators and by definition do not have an internal experience distinct from the training data any more than the plus operator does. I agree with the sentiment that even putting it in writing gives more weight to the position than it should, bordering on absurdity.

   I suppose this is like the ELIZA phenomena on steroids, is the only thing I can think of for why such notions are being entertained.

   However, to be generous, lets do some vigorous hand waving and say we could find a way to have an embodied learning agent gather sublinguistic perceptual data in an online reinforcement learning process, and furthermore that the (by definition) non-quantifiable subjective experience data could somehow be extracted, made into a training set, and fit to a nicely parametric loss function.

   The idea then is that could find some architecture that would allow you to fit a model to the data.

   And voila, machine consciousness, right? A perfect model for sentience.

   Except for the fact that you would need to ignore that in the RL model gathering the data and the NN distilled from it, even with all of our vigorous hand waving, you are once again developing function approximators that have no subjective internal experience distinct from the training data.

   Let's take it one step further. The absolute simplest form of learning comes in the form of habituation and sensitization to stimuli. Even microbes have the ability to do this.

   LLMs and other static networks do not. You can attempt to attack this point by fiatting online reinforcement learning or dismissing it as unnecessary, but I should again point out that you would be attacking or dismissing the bare minimum requirement for learning, let alone a higher order subjective internal experience.

   So then the argument, proceeding from false premises, would claim that the compressed experience in the NN could contain mechanical equivalents of higher order internal subjective experiences.

   So even with all the might vigorous hand waving we have allowed, you have at best found a way to convert internal subjective processes to external mechanical processes fit to a dataset.

   The argument would then follow, well, what's the difference? And I could point back to the microbe, but if the argument hasn't connected by this point, we will be chasing our tails forever.

   A good book on the topic that examines this in much greater depth is "The Self Assembling Brain".

   https://a.co/d/1FwYxaJ

   That being said, I am hella jealous of the VC money that the grifters will get for advancing the other side of this argument.

   For enough money I'd probably change my tune too. I can't by a loaf of bread with a good argument lol

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4. vavooomNov 2022
   If you are interested in learning about the intersection of Artificial Neural Networks and Biological Neural Network research, I recommend "The Self-Assembling Brain - How Neural Networks Grow Smarter" by Peter Robin Hiesinger. He attempts to bridge research from both fields of study to identify where there are commonalities and differences in the design of these networks.
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5. procgenJul 2021
   I recommend Peter Hiesinger's 'The Self-Assembling Brain' if you're interested in a neurobiologist's perspective on this problem and its importance to AI.
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