What you say about people reflects cooperative behavior that drives reproductive success for shared gene complexes.
In fact what nature invented was a persistent colony organism with external memory.
Wild solo humans are only a little smarter than wolves individually, but being able to share and externalise invention and learning created a massive advantage.
Humans are successful because although only a tiny minority of individuals are any good at invention, the fact that information persists and is shared means the entire population benefits.
The problem for AI is modelling the learning and invention process. Classifiers and recognisers are getting better, but they're not really learning in the human sense, which is a combination of abstraction, mimicry, and occasional invention.
IMO there's no chance of AGI developing until there's a persistent, transferable, abstracted model generated as an output from classifier systems and other learning machines which is a symbolic - not just a statistical - summary of the learning.
It can be compressed to "until AIs can talk".
Another way of putting it - the source of meaning is life, or death (prolonging life, avoiding death as much as possible). Reproduction is just the start of life. From this game of life and death come reward signals that teach us how to act in the world (our values).
In a neural net we do just that - maximize diversity by splitting the signal over many neurons, each with different weights, computing different things. Integration is maximized by the mixing together of signals from other neurons and training them together with a common loss function.
Even the internet as a medium requires diversity and integration to be successful. For example, net neutrality is related to diversity. Integration is related to national firewalls, copyright barriers, filter bubbling effect (where one sees only content from parts of the internet they agree with), walled gardens (like the app stores), and other things that cut the connection between people.
You can apply diversity and integration to other fields as well, for example, in politics/governance. We can compare a federal system (more diversity) with a centrally planned system (less diversity) and see the effects. With integration - we can compare free trade with regulated trade. The same principles apply to free speech - where diversity and integration are basically promised by the constitution.
Insects and especially social insects like ants are good examples of very successful survivors with very little general intelligence.
> Wild solo humans are only a little smarter than wolves individually, but being able to share and externalise invention and learning created a massive advantage.
This seems to resonate with a strain of Philosophy of Mind https://plato.stanford.edu/entries/content-externalism/ which deals with our mental content being distributed not only around the brain and body, but on paper, computers and relations with other people.
The starting place is simulation. We use games (and other kinds of sims) to learn to act intelligently in a virtual environment. In such a place we can define many tasks and a learning curriculum.
- The former can be debugged. If your image analysis system embarrassingly tags black people as gorillas, you can go in and find the bug and fix it. That's not so easy if you're using a black box model.
- The black box approach is tremendously enervating. You code up a neural network architecture, launch a bunch of cloud GPU instances, and start training. If the results are bad... you try a new architecture. In the function decomposition world, you can actually use your knowledge and understanding of the system as an engineer to figure out what went wrong and why.
Evolution created a goal from nothing, which is self-replication. It works on many levels - self replicating DNA, self replicating cells, self replicating ideas (memes), self replicating ecosystem, even the economy has become a self replicator.
Neuroscience-Inspired Artificial Intelligence http://www.cell.com/neuron/abstract/S0896-6273(17)30509-3
Saw their Ted talk, looks promising, but seems they delayed a lot.
In some ways it is a search through program space. But then I think we as intelligences do a guided search through program space as we develop. It seems important to use what information you can to guide you e.g. culture and copying other people who have already been navigating the space of programs to be.
<rant>This exemplifies the issue I have with current day philosophy. It's too blissfully unaware of the discoveries in AI. While they redefine consciousness the 1000th time, the AI researchers make "Reinforcement Learning Agents" that play Go, drive cars, paint, draw and can take a pizza order from you. Philosophers, get more concrete.</>