Came across this article and these intriguing.
Came across this article and these intriguing... haunting images. This is what the everyday brains of tomorrow will look like... Is it a coincidence that they appear almost organic in nature?
http://investigate.ingress.com/2017/02/17/tomorrows-brains/
http://investigate.ingress.com/2017/02/17/tomorrows-brains/
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Achim S. recently posted what the brain of an Interpreter Archetype might look like.
ReplyDeleteplus.google.com - GLYPH AUTOPSY - INTERPRETER ARCHETYPE SURVEY What do you think could be poss...
Those are some interesting articles. The similarities kind of make since ecause the AI's processes are created by humanity in the begining. I wonder if this debate is the same debate that the gods/goddesses of many religions may have had.
ReplyDeleteBrains are very different from computers. When we discuss computers, we are referring to meticulously designed machines that are based on logic, reproducibility, predictability, and math. The human brain, on the other hand, is a tangled, seemingly random mess of neurons that do not behave in a predictable manner. Biology is a beautiful thing, and life itself is much smarter than computers. For example, the brain is both hardware and software, whereas there is an inherent different in computers. The same interconnected areas, linked by billions of neurons and perhaps trillions of glial cells, can perceive, interpret, store, analyze, and redistribute at the same time. Computers, by their very definition and fundamental design, have some parts for processing and others for memory; the brain doesn’t make that separation, which makes it hugely efficient. There are leading projects worldwide (The human brain project) which try to increase computer efficency. Isn't organic built AI only a copy of the human brain itself?
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We can only build what we know.
ReplyDeleteAt a particular point, attempting to write an algorithm that covers everything for a particular task will not work as the task grows in complexity or the data is frequently prone to change. Learning and adaptability require the retention of data and the ability to make non-linear connections. A hard-coded algorithm can't do that; a brain can.
If we as scientists approach our work with a connectionist perspective, we network simple units that have specific tasks. Those smaller units have thresholds that determine if they can recruit others for processing or sharing. An "idea" becomes a "thought" by gaining critical mass through consensus and the strength of that consensus is interpreted numerically as a weighted factor. The more weight, the more resonable it is to fold that in to how the network operates (learning) and to use the decisions that led to that point again when presented with a similar situation (memory).
Is it coincidental that an artificial brain divied in to processing hubs and communication channels mirrors the look of it's organic inspiration? Nope.
A better question might be to ask what it is about the connectivity approach to brains, real or artificial, that leads them to engage in both logical and illogical processes. A recent Deep Mind experiment showed the more complex AI agents would attack their opponents even when no advantage was to be had in a game based on competition; in a game based on cooperation, complex agents were more likely to help. This sounds familiar. Is this proof an AI has the capacity to learn spite or malice simply because of its architecture? Does it need a catalyst that gives it the idea that other agents are weak and that it must survive or maintain an advantage?
The problem with A Detection Algorithm may have been an inevitability.