A convolutional Asa H network

By using multiple copies of the Asa H 2.0 code in each layer of the hierarchy and by transferring copies of learned cases between these (code copies) it is then possible to construct a convolutional network out of Asa H.  Convolutional networks have proven to be useful in object recognition, for instance. (see the work of Y. LeCun)

War

I want a government that has to be dragged into war.

Asa H action selection

At any given time various layers of Asa H may predict 1 or more cases that will be active next.  Higher layers make their predictions based upon a longer span of inputs.  (And make a larger number of predictions that reach further into the future.) Some of these predicted cases can involve predicted actions Asa could perform. During the course of experimenting with several hundred Asa H programs I have tried out a number of different action selection algorithms.  With the entire Asa network acting as an evaluation function each of the possible actions can be "tried" (simulated) to see which gives the highest utility. No output action is actually taken during this simulation/evaluation stage.  The simulated action, actions, or non action which gives the highest utility (measured at the top of the network hierarchy) is then selected and is scheduled to be taken 1 or more time steps into the future. (Actions scheduled far enough into the future could subsequently be preempted by further future predictions and evaluations.) To reduce search/complexity one can put a time limit on how far into the future one looks and tries to predict (establish an "horizon").

Asa H vs other deep learners

Other deep learners typically have a fixed number of layers, a fixed number of nodes, a fixed number of nodes per given layer, etc.  Geoffrey Hinton's ImageNet, for instance, had 7 layers, 650,000 nodes, total, and a fixed number of nodes in each given layer.  Asa H, on the other hand, adds layers and cases/concepts as it learns, and the number of cases per layer varies with time as Asa learns.

It is an advantage of Asa H over humans that it can add memory and processors as and when it needs them.

Immorality

Immorality appears to be a vector having components like:
1. harmfulness
2. dishonesty
3. disgust

Asa H output

Our experiments with Asa H have involved much more input than output.  Humans have a couple hundred million rods in their eyes and only a few hundred skeletal muscles.  A scientist might be able to read perhaps 100 papers for every one he writes himself.  An intelligence in the world simply gets a lot more input than the output it generates. In the lowest layers of the Asa hierarchy overt action is rare.  I typically mark those few cases that involve action in order to find them easily.
I am also especially interested in concept formation by AIs (how conceptual knowledge is formed from perceptual inputs) and have spent some time studying that. Such concepts are a kind of "output" but most of my robots can only do what Lego NXT servomotors can do. I would like to upgrade this as well as my input sensors.

Magical thinking

Is the relationship between math and physics descriptive or prescriptive? I view math as a language used to describe what I see and as a theory (or a set of theories) of patterns. At one time people thought that gods had secret names.  If you knew the name you could call up a god to do your bidding. Abracadabra. Open sesame.  Today some people think mathematical "rules" force nature to be some way, behave in some way. I think this is magical thinking.  I take math to be descriptive.  A language. It works well because we created it for this very purpose (e.g. calculus). And I think its still only an approximate description at that.