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Reading about rewards in AI context makes me wonder, why current AI only focuses on the network topology aspect of natural intelligence. What about the neurotr

• mh-cx 9y ago
  Downvoters please explain what's so stupid about my question.
  • 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
• eru 9y ago
  Current AI approaches don't have that much to do with the biological brain. Despite the name 'neuronal networks' that we have history to thank for, it's better think of them as just long compositions of functions with a lot of parameters one can tweak. Historically researchers looked at activation functions that are at least somewhat justified by nature, like the sigmoid. More recently people have focused on the mathematical aspects and figured out that the rectified liner unit `ReLU(x) = max(x, 0)` works just as well if not better for most applications. Maxout networks (https://arxiv.org/pdf/1302.4389.pdf https://arxiv.org/pdf/1302.4389.pdf) are another interesting development in that direction. There's still lots we can learn from biology, of course; but the last decades rapid advances we saw from deep learning at most barely relate to real brains.
  • orbifold 9y ago
    Actually ReLU(x) = max(x,0) can be thought after a change of parameters as the firing rate response of a leaky integrate and fire neuron at least for a certain range of parameters. So it is not completely far fetched, but of course nature does not use rate coding exclusively.
    • eru 9y ago
      Yes. Though I think they stumbled on ReLU just from technical considerations and not taking nature into account? That happens fairly often in engineering: looking around enough one can often find natural precedents for our artifices. But directly trying to copy nature did not lead to the plane or helicopter.
      • zardo 9y ago
        Wikipedia list this as the first paper involving ReLU in an ANN: [1] R Hahnloser, R. Sarpeshkar, M A Mahowald, R. J. Douglas, H.S. Seung (2000). Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit.
  • mannigfaltig 9y ago
    > Despite the name 'neuronal networks' that we have history to thank for, it's better think of them as just long compositions of functions with a lot of parameters one can tweak. Actually, the name is not so misleading because `ReLU(Wx + b)` is an approximation of the response of a large ensemble of integrate-and-fire neurons in terms of the firing rate [1] (it models populations of pyramidal neurons in the cortex). The ReLU activation function basically approximates a logarithmic activation function [2]. Obviously many kinds of computations are left out in this model, for example in pyramidal neurons, the integration of the incoming spikes is often sublinear rather than linear; the branches of the neurons that receive signals (dendrites) can themselves perform complex computations such as spatial clustering and logical operators basically sending small (dendritic) spikes forward and backward within an neuron [3]; short-term plasticity is left out entirely and so are countless types of interneurons which inhibit other neurons nearby and measure mean activity across populations of neurons etc. [1] https://web.stanford.edu/group/brainsinsilicon/documents/Wong_Wang_2006.pdf https://web.stanford.edu/group/brainsinsilicon/documents/Won... [2] https://www.utc.fr/~bordesan/dokuwiki/_media/en/glorot10nipsworkshop.pdf https://www.utc.fr/~bordesan/dokuwiki/_media/en/glorot10nips... [3] https://neurophysics.ucsd.edu/courses/physics_171/annurev.neuro.28.061604.135703.pdf https://neurophysics.ucsd.edu/courses/physics_171/annurev.ne...
• kortex 9y ago
  Glutamate is the primary "connection" neurotransmitter in the brain ("It is used by every major excitatory function in the vertebrate brain, accounting in total for well over 90% of the synaptic connections in the human brain."). DA and SER are far more sparse and unevenly distributed, concentrating in areas with heavy interconnectivity, such as the limbic system and hippocampus. These monoamines are thought to primarily serve in a modulatory role, affecting performance and stability of neural connections in general, in an on-line fashion. By comparison, ANNs have many meta-parameters (parameters besides the actual weights), such as learning rate and momentum, affecting stability and performance. Being able to tune these on the fly would likely improve performance. The "ANNs are not like the brain!" camp like to point out that the ANN is a mathematical model bearing (on one level) little similarity to the human brain, but I think this glosses over the fact that key features (e.g. layers of connections, CNNs) were directly biologically inspired, and we can still glean a lot by studying the best example in our world of a learning system.