Neural networks of neural networks doesn’t begin to describe it. We haven’t even scratched the tip of the tip of the iceberg in understanding this stuff.
Neural networks of neural networks doesn’t begin to describe it. We haven’t even scratched the tip of the tip of the iceberg in understanding this stuff.
In fact, I think you're probably dead wrong. Certainly hormone interactions and feedback loops in the body have some consequence on an organism, but these things take time. Lets say that some feedback loop is extremely fast, say 10 seconds. It seems reasonable to assume that you can't have a complex feedback loop with the body much faster than that, just because it takes time for chemicals to physically move. Cognition is much, much faster than that. There are many situations where you can learn, and then apply that learning in less than a second. Now if we were instead talking about what it takes to build a complex organism that can satisfy it's own needs, then certainly these complex hormonal feedback loops are essential. You can't have an organism survive when it doesn't look for food when it's body is out of fuel or keeps eating poisonous berries because it's body can't send the reasonable feedback to it's brain, and you can forget about dropping everything and competing for mates at the correct time without some kind of behavior modifying hormonal signals!
I would say that your assertion is not significantly different from saying "you can't understand the workings of the brain without considering the complex interactions of the brain with the lungs, since without oxygen the brain can't work". So while I agree that the brain exists as part of the body, I don't see why you would automatically assume that the hormonal feedback loops with the body are at all necessary for cognition rather than a way to tell the brain when it's necessary to find food, when it's adaptive to conserve energy and be lethargic, or what have you.
My point is that we may or may not find that there is some critical ingredient of cognition hiding in this place or that, and you can't very well tell us where that will be if nobody knows if it's there.
My point was that we can't just determine the typical time constant for a hormone's effect and run home with it since there are chemicals which have significantly lower time constants and some of them act as endocrine hormones as well.
Furthermore, an immediate feedback loop isn't the only possible way the body could play a role in cognition, though I suppose the OP is aware of this and was talking about moment-by-moment interaction with the body on purpose.
So yes I think we agree that it’s all more complicated than anyone can even comprehend just yet.
It's like the parable of the Drunken man looking for his lost keys under the street-lamp. He only looks under the lamp for his keys because that's the only place the light is.
Similarly, in neuroscience, we only have a few tools to look at the brain. By some miracle, the brain happens to be electrically active to a degree that we can shove wires into it and pick up signals. So, dutifully, grad students across the world have been shoving needles into brains and trying to pick up signals and then tell advisers what that means.
Another miracle is that blood has a ferromagnet in it and the brain has a fair bit of blood in it. So, after a lot of trial and error, we've been able to build very complicated fMRI machines that can give grad students an idea of the usage of blood in the brain. This is then correlated with other measurements to find out what is happening in the brain.
Recently, we've been able to combine two different little miracles together. One is opto-genetics, a little serendipity we pulled out of the chromatic hot-pools in Yellowstone. Essentially, when you shine light on a little bacteria, it's cell wall opens up and you can squeak certain ions through the hole. The other little miracle is CRISPR-CAS9. Here, you can again use some tricks bacteria have made up to more easily copy-paste in the genetic code. Using these tricks and a lot of help from post-docs, yet more grad students have been able to report results in lab meetings to their advisers.
More techniques exist, of course, but my point is that the vast majority of neuroscience is still in the dark, away from the drunken man searching for his keys in the light.
Look at astrocytes. These guys make up ~50% of the brain. Very recent research says that they play something of a role in the synapse, moving NMDA receptors around the cleft, recycling, phospho-tagging recpetor, etc. It's super recent work. But also super important. Before, we thought that only the neurons were involved in the synapse, but now we have good evidence that other, action-potential lacking, cells are also deeply involved.
That vein of research is also really hard to do and preform experiments in. As such, most grad students are wise not to do their work in that kinda lab. It's hard and has a high potential of failure, and therefore no degree.
So, wait and see. Our understanding of the brain is still very much in it's infancy. We've not really even got a good accounting of all the cell types and connections in the brain yet.
What we are really building are pretty good pattern recognition machines, often with surprising capabilities. But we overreach when we equate that with intelligence.
At least it's useful to discuss the ethics due to buzzwords, media hype and industry demands before we are actually able to produce something truly artificially intelligent.
Until then, we continue to add DL algorithms to our Madam Tousseau-esque collection of artificial intelligence.
Surely there’s going to be some sort of crash when they collectively realise that’s not the case.
Domain-specific AI is often already superhuman in performance, once trained. What we lack seems to be: (1) generalisation, as a trained AI is often useless in separate tasks; (2) efficient training, as AI take far more data to reach the same performance as a human (I have seen suggestions this is related to (1)); and (3) any idea at all what counts as self-awareness/conscious/qualia/etc., which may not be important from a performance point of view, but very much influences how people regard the AI and their future potential.
What is really the case is that some AI systems (in particular, deep neural net classifiers) have superhuman accuracy and that again only in classification tasks.
Edit: I'm not being contrary for the sake of it. I think there is a very useful insight to draw from the success of deep neural network classifiers: that it is possible to perform classification without any kind of understanding of the objects, or their classes, at all. In the past, AI researchers operated under the assumption that reasoning and inference would be required to perform this task, but we know now that such abilities are not necessary if the task is only to classify objects. It's also clear that in some cases dumb classification can replace reasoning- as long as a reasoning problem can be reduced to dumb classification.
However- humans clearly have the ability to reason and draw inferences (regardless of how often we do that successfully). There must be some explanation for this. Why do we have an intelligence that goes beyond simple object recognition? What is the point of having a broad intelligence? It must mean that there is more to intelligence than tasks that can be reduced to classification. So reducing "performance" to "accuracy" (of classification) risks fudging an important difference between the "superhuman" abilities of machines and human abilities.
In the end, the question is what are humans good at and why can't machines do the same things, even though they can beat us roundly in other tasks? What is the difference between tasks that are easy for machines and tasks that are easy for humans?
I don't think we can say for sure that there is more, though. If and when we finally understand how it all works, I wouldn't be completely surprised to find that it is just many layers of pattern matching and associated feedback loops.
We just don't know enough yet.
However, I think you’re making a distinction without a difference going between classification accuracy and performance. That said, I am running on 1.5 hours less sleep than I need today — the only other important metric I can think of right now is the kind of skill which humans book-learn and which get implemented on a computer as an explicit and deliberate algorithm instead of being learned, and I simultaneously don’t count those as AI and think machines have beaten us for decades in that domain.
I do have one other question though: do you regard Alpha(Go|Zero|Star) as nothing more than classification?
I believe in AlphaZero etc the deep neural net component was used to identify moves with a high probability of leading to a winning board position. That's a good example of a problem we used to think would require inference or reasonging but that can, after all, be solved by classification.
Although that's not to say that the same problems can't be solved by inference, plus powerful computation. That remains to be seen.
Edit: Maybe I shouldn't have called it "dumb" classification; that sounds dismissive. There's no doubt that neural net classifiers are impressive in what they do. I mean it to say that they have no understading of their domain, or ability to reason etc.
AI systems are better than humans at making decisions. AI systems can scale to millions of decisions per second and beyond. AI systems don't need to spend 3/4 or their time sleeping or relaxing. AI systems don't need 20 years of training to become experts in their fields, they can be replicated almost instantly.
While the scope of decisions the AIs can do is somewhat limited at the moment, the trend is clear: in the not so distant future AIs will make all economically relevant decisions.
I don't doubt that, just doubt that they will make unanimously good decisions, as expected of a superhuman system. AI systems are yet to be "intelligent" which makes half of their name a PR gag.
Pattern recognition is simply not equivalent to intelligence. It is certainly a pillar of intelligence, however.
> AI systems are better than humans at making decisions
Are they? Dermatology, transportation, face recognition, NLP... in all these fields we are yet to reach human-like performance outside of ultra-specific tasks. There is a difference between recognizing a human face every time, even if obscured by sunglasses or half hidden behind a pint of beer, and recognizing 100.000 human faces per second with an error rate of 8% because of shadows.
Decent performance can be reached when you train AI to do one hyper-specific thing under just the right circumstances with mountains of data which need to be prepared just right. Otherwise you end up with bias and other issues. I wonder how AlphaGo would have performed if it was required to suddenly switch to Mahjong or cooking but I suppose that wasn't its purpose.
Please also mail me a link for systems that prepare training data to automatically and reliably prevent bias and other major issues that lead to malperforming systems.
Demonstrations optimized for PR, AI companies which employ humans as AI-pretenders, and catastrophically failing systems are the reality of AI today. I don't doubt that we will improve but superhuman AI requires more than mere linear improvement from where we are right now.
Even a few years ago, I worked as a student at a Big4 and these firms ran out for "technology consulting" and told their clients about AI this, AI that and it's gonna be robots taking it all over. Of course those were advertisements, ruses even, for getting audit & tax customers via the "compliance" angle (e.g. cloud compliance laws).
But anyway, their is a true craze about AI and autonomous driving which holistically ignores all the major road blocks that we are facing now. It's buzzwords and marketing all over. It's the Gluten-intolerance of technology.
According to general public media we are 2-5 years away from level 5 autonomy. Ask people at Waymo about the issues they face...
If you think gene expression within neurons as small RNN, it's neural networks all the way down. RNN within neuron reacts to hormones and chemical signals altering the functioning of individual neurons. There is also feedback loop to other direction.
RNN nodes represent genes and connections between them are regulatory feedback loops in gene expression.
To anyone interested in this, I would highly recommend picking up a copy of “Principles of Neural Design”. It’s a look at how and why brains operate the way they do, from thermodynamic and information theory axioms up. A lot of the biology went a good bit over my head, but the authors do a great job of developing a set of hardware-agnostic principles that all brains follow.
The problem is that the biology of the brain is incredibly complicated. Dynamic instability of microtubules, contribution of extracellular factors, neuron biochemistry, etc etc... Sure, we've extracted signal to reconstruct primitive visual features from the basal ganglia of mammals, but that's a long way off from what we'd need for humans. (I can't easily cite atm, but I'll be happy to come back and put in references if desired.)
The other big issue is that it's quite invasive to get data out of this system. I imagine we'll be making progress on human cloning and artificial organs long before we crack this nut simply because of how disruptive and insufficient current techniques are.
All that said, I'm pretty sure we're all going to die without any archival backup of our brain-encoded memories. Progress will be made, but not in time for us.