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General Artificial Intelligence will be Smarter

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General Artificial Intelligence is a term used to describe the type of artificial intelligence that we think is a human being as intelligence. We cannot even come up with a complete definition of intelligence, but we are already on the verge of building a lot. The question is whether the artificial intelligence we build will work for us or if we work for it.

If we are to understand the concerns, we will first need to understand the information and then plan where we are in the process. Intelligence can be said to be the process necessary to formulate information based on the information available. These are the basics if you can create new information based on the existing information, it is smart.

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Because it’s more scientific than spiritual, let’s talk about science. I will try not to put too much scientific terminology so that an ordinary man or woman can easily understand the content. A term that involves the construction of an artificial intelligence. He claims that the Turing test The Turing test is an artificial intelligence test to determine if we can identify it as a computer or if we see no difference between that and human intelligence. According to the evaluation of the test, if you communicate with artificial intelligence and forget during the process that you remember that this is actually a computer system and not a person, the system passes the test. That is, the system is really artificially intelligent. Today, many systems can pass this test in no time. They are not artificially artificial, because we remember that it is a computer system throughout the process.

Jarvis is an example of artificial intelligence in all Iron Man and Avengers movies. It is a system that understands human communication, predicts human nature and even becomes frustrated at times. This is what the computing community calls or the general community of artificial intelligence coding.

To express it regularly, you can communicate with this system as you would with someone and the system will interact with you as a person. The problem is that people have limited knowledge or memory. Sometimes we do not remember certain names that we know we know about the other guy’s name, but we cannot get it in time. We will remember one way or another, but in another case. This is not what is known as parallel computation in the world of coding, but it is a little similar. Our brain function is not fully understood, but our neurological functions are first understood. This is equivalent to saying that we do not understand computers but we understand transistors. Because transistors are the central components of all computer memory functions.

When a human can process information in parallel, we call it memory. When we talk about something, we remember something else. We say, “By the way, I forgot to tell you,” and then we go on to another topic. Now imagine the performance of the computer system. They never forget something. This is the most important part. The higher the processing capacity, the better the processing of your information. We are not like that. It seems that the human brain has a limited capacity for treatment. On average

The rest of the brain is memory. Some people have changed their skills in the back. You may have met people who are too poor to remember something, but who know how to do math just right. These people have actually affected parts of their brains regularly assigned to memory processing. This allows them to work better, but they lose the memory part.

The human brain is of medium size and the number of neurons is therefore limited. It is estimated that there are about 100 billion neurons in an average human brain. It’s at least 100 billion connections. I will refer to the maximum number of connections later in this article. So if we wanted to have about 100 billion transistor connections, we would need about 33,333 billion transistors. Indeed, each transistor can contribute to 3 connections.

Back to the point; we reached this level of computing around 2012. IBM had managed to simulate 10 billion neurons to represent 100 trillion synapses. You must understand that a computer synapse is not a biological neuronal synapse. We cannot compare a transistor to a neuron because neurons are much more complicated than transistors. To represent a neuron, we need several transistors. In fact, IBM had built a supercomputer with 1 million neurons representing 256 million synapses.

You can now understand how the real human neuron should be complicated. The problem is that we have not been able to build an artificial neuron at the hardware level. We built transistors and then integrated software to manage them. Neither a transistor nor an artificial neuron can support itself. But a real neuron can. Then, the computational capacity of a biological brain begins at the level of neurons, but artificial intelligence begins at much higher levels after several thousand or more fundamental units or transistors.

The interest of artificial intelligence lies in the fact that it is not confined in a skull subjected to a constraint of space. Once you understand how to connect 100 billion neurosinaptic nuclei and have enough facilities, you can use it to build a supercomputer. You cannot do that with your brain; your brain is limited to the number of neurons. According to Moore’s law, computers will eventually support the limited connections of the human brain. This is the critical moment when the uniqueness of information is reached and computers become much smarter than humans. This is the general thinking about it. I think it’s wrong and I’ll explain why I believe it.

By comparing the growth in the number of transistors in a computer processor, computers should be able to process by 2015 at the brain level of a mouse; A real biological mouse. We have reached this point and we are moving there. It’s the computer in general and not the supercomputer. Supercomputers are actually a combination of interconnected processors in order to process information in parallel.

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