Basically yes. But that's not only incredibly effective, it's quite possibly how real brains work too. A lot of people do believe it is a path to AGI.

> It's only incredibly effective in a static world. The world is not static nor is the human brain. Nor is the interplay between a human brain and the world and A.I. It's a very disjoint, dynamic, and interdependent relationship with far more complexities than could ever be represented in a statistical flow map much less in the incomplete mathematics and statistics that underly them. I have no doubt that people believe they can make a statistical map of the world. It wont be the first nor will it be the last time people try to make an 'effective' one. The dynamics of the world will change and they will be invalid as they in no way are structured based on a true understanding of what's going on. Nor is there any awareness of what's going. Aren't the overly complex and flawed risk models that no one could explain what caused the crash in 2007/2008? You think the dynamics in the world are less or more? So, I say to people subscribed to this provenly flawed thinking : Good luck.

That's not an accurate description at all, there are no "agents". In fact your whole description of NNs sounds off.
And backprop wasn't invented or named in silicon valley. In fact it's been around since the 80's.

> Agents/node.. Tomato/Tomato .. they are partial computation nodes receiving and instantiating fed back partial derivatives based on computing an error between expected/actual. Where's the intelligence? Hindsight is 20/20 ... You're brute forcing the partial elements that contribute to a desired answer by slamming a cheese grater (NN) in forward and reverse flow ... hoping the important stuff sticks somewhere. Don't try to make it seem any more complex than that. Curve fitting at its finest. Constraint optimization. Gradient Descent. Regression. statistics all packaged up with fancy buzzwords.

https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Re...

The Backpropagation algorithm is used to find a local minimum of an error function. Flashback to grad school where there were a laundry list of methods in constraint optimization courses. There's nothing special about it. What is special is the thinking behind it.

You rightfully stated, most of these methods were developed in the 70s' 80s'. While people are off copying and instantiating the works of that time and relabeling it with buzzwords, there is little attention being paid to the thinking that yielded those methods. That's what matters .. the actual intelligence and thinking.. not what pops out the other end.

With little focus/money being put into expanding upon the thinking of that time period, those focused on it are not going to get any further than they .. Even worse, you maintain no understanding as to why they did what they did. Which is why no one can tell you how NN works. The intelligent people who defined them are dead.

Every "AGI" project is a bunch of pseudoscience. They have no idea how to build an intelligence. They have no idea how the brain works. They have no results to show with their algorithms, they aren't beating benchmarks. The theories are always vague and ad hoc and include a million special cases to make their systems do anything.

> People in the weak A.I space may have the lay person fooled by slapping buzzwords and A.I on everything. However, anyone who has spent anytime doing grad work in this area before it took on fancy names knows better... It's distributed gradient descent. The objective function is broken down into partial forms and instantiated at the distributed computational points in the gradient descent flow path defined by a NN. You slam it in forward and reverse and eventually enough stuff gets jammed into the lines for future flows.

I recall something named Genetic algorithms/evolutionary programming that were supposed to be the keys to the future...

So, Strong A.I ... AGI. I'm thinking those who have the best shot at it are people who know how NNs work on down to the mathematics and statistics, theory, philosophy and pseudoscience. Given this understanding, they have the ability to formulate new math/statistics/computational models and frankly whatever else it takes to represent a true form of intelligence.

I imagine they are working hard at doing that very thing in the shadows while others busy themselves trying to beat cooked benchmarks and fight over coin and the spotlight.

So, you go down your path and they will seemingly go down their path... But don't for a second think they don't understand exactly where your path is likely to lead you.. Many of them have gone down it and found nothing of value at the end. I guess the new crop of individuals who have no understanding of the thinking behind these algorithms they're copying/instantiating have to take this journey for themselves.

Call AGI a bunch of pseudoscience and foolishness. I have no doubt a good number of people will be praising and following it like religious zealots much like the work of those 'crazies' from the 70s'/80s that everyone laughed at but now can't wait to copy/relabel and call their own.

*Cheers and enjoy the journey