FTA: “The team showed that indeed they express “D1” receptors for the neuromodulator. Commensurate with the degree of dopamine connectivity“
There are receptors specifically for dopamine on the amygdala neurons. Dopamine molecules are released by the pre-synaptic neurons, travel across the synapse, and bind to these receptors.
Dopamine’s role in the nervous system is not simply an intermediate on the pathway to produce epinephrine or norepinephrine. If you thought like this you’d reach the conclusion that testosterone is simply a precursor to estrogen because the pathway to convert it exists in some tissues of the body.
You’re not dumb, it’s an incredibly complex topic with convoluted and contradictory messaging everywhere.
The way that I’ve learned to think about it is that the brain is made up neurons, and they perform specific functions, technically individually but more usefully understood in regional groupings (primarily figured out via fMRI/blood flow studies and lesion experiments).
Each neuron’s activity is regulated by specific neurotransmitters, and the type of receptors expressed in neurons also correlates with these functional areas (figured out through PET/radio-tagged molecule scans and biopsies). Regarding dopamine specifically, the area that is responsible for effortful attention (prefrontal cortex) as well as for reward (in a general sense, broader “good” not only simpler treats) processing (nucleus accumbens) have high concentrations of dopamine receptors.
Therefore, drugs that interact with dopamine receptors or with chemical chains that involve dopamine can affect these functions.
Neurotransmitters are just chemicals and they go through many complex and interrelated metabolic chains, and at baseline (in a typical individual, barring specific genetic differences) it is often most useful to assume they’re all there and instead understand where they’re used.
This comment might not be the most succinct and I’m just started my education on the subject so I’m sure there are inaccuracy’s and I’d be happy if they’re pointed out, but I do hope that it helps you get a somewhat clearer picture and realize that you’re not dumb for being confused about this.
Said comment, so other's don't have to dig around in your history:
"Since google, everyone trying replicate this feature... (OpenAI, HF..)
It's powerfull yes, so as asking an A.I and let him sythezise all what he fed.
I guess the air is out of the ballon from the big players, since they lack of novel innovation in their latest products."
I'd say the important differences are that simonw's comment establishes a clear chronology, gives links, and is focused on providing information rather than opinion to the reader.
Why are you using 2 seconds? The commenter you are responding to hypothesized being able to do 250/s based on "100 parallel inference at 5 at a time". Not speaking to the validity of that, but find it strange that you ran with the 2 seconds number after seemingly having stopped reading after that line, while yourself lamenting people don't read and telling them to "read again".
Ok, let me dumb it down for you: you have a cockroach in your bathroom and you want to kill it. You have an RPG and you have a slipper. Are you gonna use the RPG or are you going to use the slipper? Even if your bathroom is fine after getting shot with an RPG somehow, isn't this an overkill? If you can code and binary classifier train a classifier in 2 hours that uses nearly 0 resources and gives you good enough results(in my case way above what my targets were) without having to use a ton of resources, libraries, rags, hardware and hell, even electricity? I mean how hard is this to comprehend really?
But then they said 250/second when running multiple inference? Again I don't know if their assertions about running multiple inference are correct but why focus on the wrong number instead of addressing the actual claim?
Your comment made me curious so I looked at his posts and he has a one about leaving academia because he wasn't happy in 2022, and a more recent one about rejoining it some months ago.
I didn't see the more recent post, so thanks for the link.
I should say that I'm still in grad school (nearing the end), so the decision hasn't been made yet. The direction I'm thinking is away from academia.
I love the academic environment, access to university resources and close proximity to lots of domain experts. However my experience as of late has been pretty isolating, as my group is almost fully remote despite nearly everyone living in the same town making motivation difficult some times. I also sometimes miss exercising my practical engineering skills, as my current work is entirely analytical/simulation. Overall its been less rewarding than I had hoped.
This isn't really a meaningful prediction unless you define clearly your idea of what being "as intelligent as a precocious child" is, and how you would assess an LLM or any other system against that metric. Though I suppose you avoid the risk of having to move the goalposts later if you never set them up in the first place.
