4 ms·

Downvoters please explain what's so stupid about my question.

by mh-cx 9y ago

pizza 9y ago
I upvoted you, and I'm actually glad you asked the question despite downvotes. I imagine those who did are of the opinion that essentially you're ascribing too much function to the neurotransmitters themselves, as opposed to the contexts in which the presence of the neurotransmitters are used as signals. Take a look at this brain schematic here [0]. I vehemently disagree with some of the presented-as-fact ideological aspects of the article, but the depiction of neurotransmitters working differently in different brain regions is illustrative. i.e., a dopamine molecule does not already constitute a meaningful coordinate system -- it doesn't have a meaning in and of itself, such as "the reward molecule", or euphoria/"feel-good"/"addiction trap"/excitement molecule) in the brain. Rather, the dopamine molecule has locally-interpreted semantics. To make this more concrete with examples of different aspects of dopamine alone: - "dopamine is known to encode the confidence level of motor predictions" - Scott Alexander, psychiatrist [1] - saturating the synaptic cleft between neurons with released (or non-recycled) dopamine is a part some euphoric processes (e.g. drugs, orgasm, gambling) in the striatum negra - 'excess' (not a simple thing to determine for individuals imo) dopamine is related with psychosis (not per se causally), and antipsychotics are often designed to blunt dopamine transmission However, - dopamine transmission is also fundamentally necessary for muscle movement, in the motor cortex - additionally, the conscious sensation of dopamine is highly dependent upon the frequency of dopaminergic neuron spikes - Parkinson's disease is (iirc, said to be,) the result of the degeneration of motor neurons; shakiness, problems sleeping; What does that mean for AI? Well, if we want to go beyond network topology, and start to use new varieties of component substrates, the individual types of signals they represent must either a) be fairly meaningless outside of a specific context or b) not be suited for learning from evidence and generalization of data. Check out [2], [3], and [4] for more. [0] http://www.nationalgeographic.com/magazine/2017/09/the-addicted-brain/ http://www.nationalgeographic.com/magazine/2017/09/the-addic... [1] http://slatestarcodex.com/2017/09/12/toward-a-predictive-theory-of-depression/ http://slatestarcodex.com/2017/09/12/toward-a-predictive-the... [2] https://www.edge.org/conversation/stephen_wolfram-ai-the-future-of-civilization https://www.edge.org/conversation/stephen_wolfram-ai-the-fut... (not that i am a huge fan of wolfram :P) [3] https://www.edge.org/responses/what-scientific-concept-would-improve-everybodys-cognitive-toolkit https://www.edge.org/responses/what-scientific-concept-would..., ctrl-f "biological" [4] https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomolecules https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomol...
- deleted 9y ago
  [deleted]
- chiggins 9y ago
  Not to derail this further, but your description of Parkinson's disease is incorrect. Dopamine plays a role, but it isn't degeneration motor neurons themselves. There are many effects, but the primary symptoms WRT motor control are caused by death of dopaminergic neurons whose cell bodies reside in the substantia nigra but innervate and regulate firing of corticostriatal pre-motor pathways. [0] I did my PhD on this [1] https://en.wikipedia.org/wiki/Parkinson%27s_disease#Pathophysiology https://en.wikipedia.org/wiki/Parkinson%27s_disease#Pathophy... [2] https://en.wikipedia.org/wiki/Nigrostriatal_pathway https://en.wikipedia.org/wiki/Nigrostriatal_pathway
  - pizza 9y ago
    TIL, thank you
_0ffh 9y ago
I didn't downvote, but I have an inkling. I'll share what some of these people may be thinking about your comment. I'll be hyper-critical there because you specifically asked about why someone might have downvoted, so don't take it personally. "Reading about rewards in AI context makes me wonder, why current AI only focuses on the network topology aspect of natural intelligence." Well, it doesn't. Not at all. It looks as if you conflate one very specific AI techique (deep neural networks) with all of AI. Because that one technique is currently insanely popular and has been widely mainstream reported and hyped that makes it look as if your education in that field did not go beyond reading a couple of mainstream media articles. Also, there's a whole huge subfield in AI which is actually defined by it's focus on learning from rewards, which you seem not to be aware of. "What about the neurotransmitters like dopamine or serotonine? Could they (or more specific: their effects) be key to an AI that feels more “natural“?" Not applicable as written, too broad and unspecific. "Not even wrong." Looks like someone with superficial knowledge is just throwing a few sticks into the blue in the hope that someone might be aroused to a reply which competently tries to make actual sense out of it.
- dwringer 9y ago
  If someone only has familiarity with AI from reading passing news stories, it is reasonable to think they would have such a perception of the field. I think your diagnosis of the downvoters might be correct, but "not even wrong" seems a bit extreme. AFAIK somebody out there is probably trying to do a research paper on simulated neurotransmitters as we speak. More interesting would be if anyone had links to existing research in that area. It may not be high-efficiency or high-performance technology, but as a learning tool for exploring theoretical models I think it could be very beneficial. The topic reminds me of a book I read (and was fascinated by at the time), The Muse in the Machine by David Gelernter. It seems he has moved away from AI research and into political punditry, however, and recently made headlines denying that the moon landing ever happened. Nevertheless, that book contained a lot of exposition around the idea of creating AI with simulated emotion. This is simultaneously in line with the idea of simulating neurotransmitter function, but also has parallels with more general mathematical techniques that are in widespread use. Thus, the book (and perhaps the idea itself) are a bit dated, but I don't think it makes them less relevant.
andrepd 9y ago
Edit instead of replying to your post.
- mh-cx 9y ago
  Couldn't. Maybe not enough karma.
miguelrochefort 9y ago
People assume you believe your idea is novel when it's not.
- deleted 9y ago
  [deleted]
kortex 9y ago
I suspect it comes across as slightly naive to this audience. The comparison between ANNs/AI and the actual human brain comes up a lot in the general media, and this leads to lots of misconceptions about AI/ANNs/deep learning, and this makes (my opinion) people in both the fields of neuroscience and machine learning a bit salty. Inescapably, much of the high level ideas in ANN-based machine learning ("connect a bunch of nodes", "integrate and fire", "convolutional neurons/ receptive field") are cribbed directly from neuroscience. Spiking Neural Networks (see SpiNNaker) and Hierarchical Temporal Memory (see numenta/nupic) aim for more biological plausibility, directly. *ANNs == artificial neural networks

Downvoters please explain what's so stupid about my question.

by mh-cx 9y ago

Downvoters please explain what's so stupid about my question.

I upvoted you, and I'm actually glad you asked the question despite downvotes. I imagine those who did are of the opinion that essentially you're ascribing too much function to the neurotransmitters themselves, as opposed to the contexts in which the presence of the neurotransmitters are used as signals. Take a look at this brain schematic here [0]. I vehemently disagree with some of the presented-as-fact ideological aspects of the article, but the depiction of neurotransmitters working differently in different brain regions is illustrative. i.e., a dopamine molecule does not already constitute a meaningful coordinate system -- it doesn't have a meaning in and of itself, such as "the reward molecule", or euphoria/"feel-good"/"addiction trap"/excitement molecule) in the brain. Rather, the dopamine molecule has locally-interpreted semantics. To make this more concrete with examples of different aspects of dopamine alone: - "dopamine is known to encode the confidence level of motor predictions" - Scott Alexander, psychiatrist [1] - saturating the synaptic cleft between neurons with released (or non-recycled) dopamine is a part some euphoric processes (e.g. drugs, orgasm, gambling) in the striatum negra - 'excess' (not a simple thing to determine for individuals imo) dopamine is related with psychosis (not per se causally), and antipsychotics are often designed to blunt dopamine transmission However, - dopamine transmission is also fundamentally necessary for muscle movement, in the motor cortex - additionally, the conscious sensation of dopamine is highly dependent upon the frequency of dopaminergic neuron spikes - Parkinson's disease is (iirc, said to be,) the result of the degeneration of motor neurons; shakiness, problems sleeping; What does that mean for AI? Well, if we want to go beyond network topology, and start to use new varieties of component substrates, the individual types of signals they represent must either a) be fairly meaningless outside of a specific context or b) not be suited for learning from evidence and generalization of data. Check out [2], [3], and [4] for more. [0] http://www.nationalgeographic.com/magazine/2017/09/the-addicted-brain/ http://www.nationalgeographic.com/magazine/2017/09/the-addic... [1] http://slatestarcodex.com/2017/09/12/toward-a-predictive-theory-of-depression/ http://slatestarcodex.com/2017/09/12/toward-a-predictive-the... [2] https://www.edge.org/conversation/stephen_wolfram-ai-the-future-of-civilization https://www.edge.org/conversation/stephen_wolfram-ai-the-fut... (not that i am a huge fan of wolfram :P) [3] https://www.edge.org/responses/what-scientific-concept-would-improve-everybodys-cognitive-toolkit https://www.edge.org/responses/what-scientific-concept-would..., ctrl-f "biological" [4] https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomolecules https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomol...

deleted 9y ago
[deleted]
chiggins 9y ago
Not to derail this further, but your description of Parkinson's disease is incorrect. Dopamine plays a role, but it isn't degeneration motor neurons themselves. There are many effects, but the primary symptoms WRT motor control are caused by death of dopaminergic neurons whose cell bodies reside in the substantia nigra but innervate and regulate firing of corticostriatal pre-motor pathways. [0] I did my PhD on this [1] https://en.wikipedia.org/wiki/Parkinson%27s_disease#Pathophysiology https://en.wikipedia.org/wiki/Parkinson%27s_disease#Pathophy... [2] https://en.wikipedia.org/wiki/Nigrostriatal_pathway https://en.wikipedia.org/wiki/Nigrostriatal_pathway
- pizza 9y ago
  TIL, thank you

_0ffh 9y ago

I didn't downvote, but I have an inkling. I'll share what some of these people may be thinking about your comment. I'll be hyper-critical there because you specifically asked about why someone might have downvoted, so don't take it personally. "Reading about rewards in AI context makes me wonder, why current AI only focuses on the network topology aspect of natural intelligence." Well, it doesn't. Not at all. It looks as if you conflate one very specific AI techique (deep neural networks) with all of AI. Because that one technique is currently insanely popular and has been widely mainstream reported and hyped that makes it look as if your education in that field did not go beyond reading a couple of mainstream media articles. Also, there's a whole huge subfield in AI which is actually defined by it's focus on learning from rewards, which you seem not to be aware of. "What about the neurotransmitters like dopamine or serotonine? Could they (or more specific: their effects) be key to an AI that feels more “natural“?" Not applicable as written, too broad and unspecific. "Not even wrong." Looks like someone with superficial knowledge is just throwing a few sticks into the blue in the hope that someone might be aroused to a reply which competently tries to make actual sense out of it.

dwringer 9y ago
If someone only has familiarity with AI from reading passing news stories, it is reasonable to think they would have such a perception of the field. I think your diagnosis of the downvoters might be correct, but "not even wrong" seems a bit extreme. AFAIK somebody out there is probably trying to do a research paper on simulated neurotransmitters as we speak. More interesting would be if anyone had links to existing research in that area. It may not be high-efficiency or high-performance technology, but as a learning tool for exploring theoretical models I think it could be very beneficial. The topic reminds me of a book I read (and was fascinated by at the time), The Muse in the Machine by David Gelernter. It seems he has moved away from AI research and into political punditry, however, and recently made headlines denying that the moon landing ever happened. Nevertheless, that book contained a lot of exposition around the idea of creating AI with simulated emotion. This is simultaneously in line with the idea of simulating neurotransmitter function, but also has parallels with more general mathematical techniques that are in widespread use. Thus, the book (and perhaps the idea itself) are a bit dated, but I don't think it makes them less relevant.

andrepd 9y ago

Edit instead of replying to your post.

mh-cx 9y ago
Couldn't. Maybe not enough karma.

miguelrochefort 9y ago

People assume you believe your idea is novel when it's not.

deleted 9y ago
[deleted]

kortex 9y ago

I suspect it comes across as slightly naive to this audience. The comparison between ANNs/AI and the actual human brain comes up a lot in the general media, and this leads to lots of misconceptions about AI/ANNs/deep learning, and this makes (my opinion) people in both the fields of neuroscience and machine learning a bit salty. Inescapably, much of the high level ideas in ANN-based machine learning ("connect a bunch of nodes", "integrate and fire", "convolutional neurons/ receptive field") are cribbed directly from neuroscience. Spiking Neural Networks (see SpiNNaker) and Hierarchical Temporal Memory (see numenta/nupic) aim for more biological plausibility, directly. *ANNs == artificial neural networks