So far scientists haven't found anything special about the human brain that can't be mimicked by a machine. Given enough neural connections, and a large enough data set, and a long enough training period there is no reason to think that a machine can't do everything a human brain can do.
Put another way there is nothing magical about a child learning about the world. A child's brain is just a large neural network being fed patterned data over the course of many years by a variety of extremely high resolution analog sensors. Eventually the child begins to respond to the patterns.
Not really, there are clearly epigenetic changes to neuron DNA w/r to memory formation and I don't think anyone has estimated what kind of computational firepower that represents.
Second, the 3D topology of a neuron is IMO more complex than reducing it to an FP32 activation threshold (all IMO of course).
Finally, I have to admit as a former biologist, I'm intrigued by microtubule activity and it seems like Dileep George and even Geoffrey Hinton are heading towards smarter but fewer neurons as opposed to just increasing the neuron count. Not surprisingly, the deep learning digerati are resisting this notion mightily just like the SVM peeps harped on neural networks until they kicked them in the keester.
TLDR: It's still early, and I'm biased that there are some interesting twists and turns yet to unfold here.
If you can computationally define how different common neurotransmitters affect the function of neurons at a broad, high level, then you can create your "psychoactive drug" by just writing a routine that excessively applies the function that those neurotransmitters represent.
An artificial serotonin reuptake inhibitor would just allow the serotonin-like activity to more active in the model.
The parameter Karpathy call 'temperature' seems not dissimilar in effect to a psychoactive drug, low temperature corresponding roughly to sober and high to being a bit, well, high.
> A child's brain is just a large neural network being fed patterned data over the course of many years by a variety of extremely high resolution analog sensors. Eventually the child begins to respond to the patterns
Seems a bit early to jump to the conclusion that we understand cognition. We don't. I agree that there is nothing exotic or metaphysical about brain meat, but really we're still feeling around in the dark with respect to how thinking occurs.
I'm confident that we'll get there eventually though.
My guess is that it's probably a bit like evolution in that fairly simple pressures and rules carried out by an astronomical number of times across a huge number of individuals interacting yields surprisingly complicated outcomes.
> So far scientists haven't found anything special about the human brain that can't be mimicked by a machine.
mimicking the brain's power-consumption-to-compute-power ratio is difficult, if not impossible, with today's technology.
an aside: since reading an article about the potential role of quantum mechanics in photosynthesis, i've wondered, as a lay person, whether quantum mechanics play a role in human cognition.
Theoretical physicist Roger Penrose is a proponent of this view, but theoretical computer scientist Scott Aaronson presents a rebuttal of his points [1]. Another article claims that the distance between synapses is two orders of magnitude too big for quantum mechanical effects to be effective, which seems like a plausible rebuttal to me [2].
There's regular quantum mechanics which underlies all chemistry and that you can use to calculate molecular properties and then the woo woo kind which Penrose seems to propose as behind consciousness on the basis that both are a bit mysterious so maybe one causes the other.
Put another way there is nothing magical about a child learning about the world. A child's brain is just a large neural network being fed patterned data over the course of many years by a variety of extremely high resolution analog sensors. Eventually the child begins to respond to the patterns.