Emergence

Einstein’s brain, like all brains, is composed of neurons and glial cells. The neurons transmit electrochemical signals to each other, and glial cells provide the neurons with various forms of support and insulation. It is common knowledge that a neuron can only transmit a signal in one direction; however, recent research suggests otherwise. Whether or not synaptic transmission is bi-directional, neurons transmit signals very quickly – generally believed to be at speeds greater than 200 mph. Each neuron can be connected to more than just two other neurons. While some make only a few connections, most make thousands. The vast network of synaptic connections coupled with signal transmission speed is one of the reasons why the brain is so efficient. Another important point is that a neuron is a single cell. The brain is composed of roughly 100 billion neurons. Each neuron doesn’t think for itself. A person’s thoughts emerge from the brain’s neuronal networks.

A brain composed of Einsteins, on the other hand, is probably not as efficient as Einstein’s brain. If each neuron is replaced with an Einstein, then each neuron is replaced with a brain containing 100 billion neurons. This certainly seems like it would make for a more powerful brain, and this may be true if each neuron was replaced with 100 billion more neurons, though it would take much longer for information to travel. Unfortunately, Einstein is not just 100 billion neurons. Something must house those neurons, specifically, the human body. Assuming the Einsteins don’t possess telepathic powers, they must communicate via the five senses, perhaps using verbal speech or body language or sign language or pheromones. It would take much, much longer for one Einstein to communicate something to the next Einstein or thousands of other Einsteins. Even though each neuron is an Einstein, each Einstein does not necessarily think the same things as the other Einsteins at the same time. Identical twins theoretically have the same DNA (it’s almost the same, but some genes can be expressed on one that aren’t on the other due to environmental factors), but they don’t have the same thoughts. So an Einstein might try to communicate something to another Einstein, but the receiving Einstein might not understand what the transmitting Einstein is trying to say, so the transmitting Einstein must further explain until the receiving Einstein understands, otherwise the signal cannot be passed down to the next Einstein. But each Einstein is likely to be communicating the signal to thousands of other Einsteins, many of which could be confused. It would take an excruciatingly long time for just one signal to be transmitted. One can only imagine how long it would take if some of the Einsteins were uncooperative. The brain would probably be a massive web of confusion.

Albert Einstein may have been a genius, but a person whose brain is composed of Einsteins might have trouble staying alive.

The paper says the connectionist network is a model that is “based loosely on neural architecture”; however, after reading the paper, I found many similarities between the connectionist network and a neural network. Each unit has an activation value, which are passed between units in only one direction. This is exactly like the action potential of a neuron. In a connectionist network, there are three types of layers: input, hidden, and output, which are exactly like the three types of neurons: afferent, inter, and efferent. In both networks, the input/afferent sends a signal to the hidden/inter, which sends a signal to the output/efferent. In neural networks, however, inter neurons usually send many signals to each other before sending a signal to an efferent neuron, if they even do. Also, the paper says that the connectionist network can learn and recognize patterns to generalize. The brain generalizes a lot of information, which is one of the reasons why it is so efficient. Back-propagation is like the feedback loops found not just in the brain, but anywhere in the body. Because of these similarities to the brain, I was able to mostly understand this paper, which was a first in the computer science-y readings we’ve had to do for this class. And since I could see it in those terms, I think I have a little more respect for cognitive models, such as Metacat.

I thought the program sounded intriguing (sorry, I had to say it!); and it sort of reminded me of those text adventure games because it has a running commentary. After playing with it, I got kind of bored; it’s just letters that you type into the program to see what patterns it can make. I didn’t see how it could be helpful, so I reread the paper and found this: “Rather, strings should be viewed as representing idealized situations involving abstract categories and relations” (8). However, I’m still having trouble thinking of what sort of situations this would entail… Is it just what we talked about in class regarding the analogy web of Nixon-war-hippies-drugs?

Also, I was disappointed when the program couldn’t come up with a satisfactory (by its own standards) answer to the first problem with which I presented it – I think it may have been something really similar to the first example shown in the paper, a string from the xyz family. Unfortunately I can’t remember exactly what input I gave, but I know that it didn’t respond with what I thought was an obvious answer. I thought that the program was supposed to give obvious answers and also clever answers that aren’t immediately apparent to us.

Overall, though, I was impressed with the program’s ability of self-awareness, and that it can change its own concepts and analogies, which are stored in its memory (also impressive), into ones that it finds more helpful as it continues to run.