An example of artificial intelligence would be the Jarvis in all Iron Man movies and the Avengers movies. It is a system that understands human communications, predicts human natures and even gets frustrated in points. That is what the computing community or the coding community calls a General Artificial Intelligence.
When a human can parallel process information, we call it memory. While talking about something, we remember something else. We say “by the way, I forgot to tell you” and then we continue on a different subject. Now imagine the power of computing system. They never forget something at all. This is the most important part. As much as their processing capacity grows, the better their information processing would be. We are not like that. It seems that the human brain has a limited capacity for processing; in average.
The rest of the brain is information storage. Some people have traded off the skills to be the other way around. You might have met people that are very bad with remembering something but are very good at doing math just with their head. These people have actually allocated parts of their brain that is regularly allocated for memory into processing. This enables them to process better, but they lose the memory part.
Human brain has an average size and therefore there is a limited amount of neurons. It is estimated that there are around 100 billion neurons in an average human brain. That is at minimum 100 billion connections. I will get to maximum number of connections at a later point on this article. So, if we wanted to have approximately 100 billion connections with transistors, we will need something like 33.333 billion transistors. That is because each transistor can contribute to 3 connections.
Coming back to the point; we have achieved that level of computing in about 2012. IBM had accomplished simulating 10 billion neurons to represent 100 trillion synapses.
You have to understand that a computer synapse is not a biological neural synapse. We cannot compare one transistor to one neuron because neurons are much more complicated than transistors. To represent one neuron we will need several transistors. In fact, IBM had built a supercomputer with 1 million neurons to represent 256 million synapses.
Now you can understand how complicated the actual human neuron should be. The problem is we haven’t been able to build an artificial neuron at a hardware level. We have built transistors and then have incorporated software to manage them.
Neither a transistor nor an artificial neuron could manage itself; but an actual neuron can. So the computing capacity of a biological brain starts at the neuron level but the artificial intelligence starts at much higher levels after at least several thousand basic units or transistors.
Emotion in AI
The more intelligent an organism is, the more it gets emotional. There would be a point where some animals would behave in a way that we cannot conclude whether they are emotions or reactions. That is the point where intelligence starts making emotions. If you take the evolutionary path of organisms, this would be somewhere at the reptiles. If you watch the reptiles, the lower evolved ones would be merely reacting to stimuli but the higher evolved ones like crocodiles would have emotions. So, I think I have reason to think that emotion would be a function of intelligence.
[blockquote align=”none” author=”Albert Camus”]Do you like AI?[/blockquote]
Future of AI Tech
Either way, we are headed towards one conclusion; the end of humans as we know it. We have to accept the fact sometimes even if it is not very juicy. Sometimes we have to accept that we are going to fail. This is such a situation that we have to first understand that we are headed in a one way route where there is only one possibility. We are headed towards altering the human species.
If we do not understand, then we cannot make a decision about it. If we understand it, then we might be able to accept it. It is nothing different than we accepting electronics, cars, computers, internet and the mobile phones in the past. The only difference is that this time it is going to be within us. Source