Downvoters please explain what's so stupid about my question.
"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.
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.
Still, please note that the critiqued comment did not even go as far as to even suggest an idea about the mechanisms involved beyond (implicitly, not even explicitly) "somewhat like in the brain". There's just not enough meat there to even do a proper critique, and on this basis I pulled the "Not even wrong"-card, which is of course at bit harsh, but that was the whole point about trying to put myself into the position of a downvoter.
It really isn't; what's reasonable is to always assume whatever you thought of in 10 seconds, has been deeply researched regardless of whether you're aware of it or not. What's reasonable is to assume researchers are vast leagues ahead of whatever shallow thoughts you have on a subject since you "know" you have a shallow understanding. The reasonable person simply asks for links to read deeper about a subject, they don't criticize something they have a shallow understanding of and ask why no one is doing X.
Of course I made the mistake to use “only“ where it should have been “so much“ or something. Sorry again.
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
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-addic...
[1] http://slatestarcodex.com/2017/09/12/toward-a-predictive-the...
[2] 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..., ctrl-f "biological"
[4] https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomol...
[0] I did my PhD on this
[1] https://en.wikipedia.org/wiki/Parkinson%27s_disease#Pathophy...