Self-replicating machines for easily scalable production

The ability to create self-replicating machines can give some very useful benefits. So what is the problem with creating this type of stuff?

Let’s say we have two pieces of equipment – 3d printers and robotic arms. These items are already available and are easy to create.

It looks like they are enough to create self replicating machines. 3d printers are able to print any details for arms and printers. Robotic arms are able to assemble other arms and printers. Both equipment items are able to create almost any other kind of stuff.

Basically, both arms (i.e. manipulators) and 3d printers consist of servomotors, wires, chips and structural mechanical elements. They all can be easily 3d printed, that’s no doubt I guess.

Continue reading “Self-replicating machines for easily scalable production”

Neural networks in models and in reality

I have recently read a modern book on neural systems in biology and found a lot of misconceptions between current models and real systems.

At first, real neurons use both inhibitory (negative, -) and motor (positive, +) actions, that corresponds to both negative and positive neuronal weights (between -1 and 1). But it seems like in lots of models neurons use only motor actions in range (0;1).

Also it looks like real neural systems are predefined by design using genes. For example, all sensory data (audio, visual and somatosensory) use the same pathways – at first to talamus, then to primary cortex areas (like V1 for vision) using the same pattern between standard 6 neuronal layers in cortex. This talamus-cortex path pattern always send (+) data to layer #4, it sends to processing layers 1-3. Layers 1-3 sends (+) data to layer #5 that resends it (+) to layer #6, which in turn is able to send both (+,-) data to layer #4 and back to talamus (regulating feedback).

Continue reading “Neural networks in models and in reality”