The meaning of Super AICP Blockchain is much more complex

 Blockchain field of AI Artificial intelligence blockchain is a very wide and complex chain that has many fields, some of its fields are: Ruleless Framework Machine Learning Ruleless Algorithms Neural Network Experts System Fuzzy Logic Natural Language Processing


Different levels of artificial intelligence blockchain AICP blockchain has two levels based on what it perceives from the outside world and can respond to: 1. Limited AICP Blockchain 2. Super AICP Blockchain

The meaning of Super AICP Blockchain is much more complex. This word refers to a system that can do whatever is assigned to it like a super artificial intelligence. The super blockchain artificial intelligence ideal is to be able to have an experimental understanding and general knowledge of the environment in which it is placed and also to be able to process the data and information that is given to it at higher speed compared to other blockchains. Therefore, we can say that artificial intelligence super blockchain systems will be stronger than other blockchains in terms of knowledge, cognitive ability and processing speed. The important point is that this system is born from the brain and human science, but with the use of ruleless AICP algorithms, it will be a new evolution in the field of artificial intelligence and blockchain.

With all the explanations that were given about the types of blockchain, we described the limitations of this technology and demonstrated how difficult it is to achieve the artificial intelligence blockchain and it’s certainly hard to say that a single individual can achieve artificial intelligence without the help of other experts. We intend to take a small step towards creating an artificial intelligence blockchain. Our main goal is to create a blockchain artificial intelligence. We are trying to create a new definition and a new step between normal blockchains and artificial intelligence blockchains and create the first artificial intelligence blockchain

How the AICP blockchain teaches itself Artificial intelligence systems become intelligent with the help of machine learning and deep learning and can learn and be trained with the help of technology and techniques as follows: 1. Machine Learning 2. Deep learning 3. Learning ruleless algorithms 4. AICP Natural Language Processing Algorithm (NLP AICP)

Machine Learning Machine learning is a subset of artificial intelligence that allows systems to learn and improve automatically without the need for a specific program to perform that specific learning. The main focus of machine learning is to develop programs that can access data and automatically use it to learn the system by itself. In machine learning, the learning process begins with observations or data, and the system uses examples, direct data, recipes or other forms of data to reach a certain pattern and start making decisions and solving problems based on that pattern. 

The main goal of machine learning is to allow computers to learn automatically without human intervention and help, and to be able to adjust their behavior based on observations and data. There are many different algorithms for machine learning and every day hundreds of new algorithms are produced in this field. Usually, these algorithms are grouped by learning style or according to their similarity in form and function. Regardless of any grouping, all machine learning algorithms usually operate in the following areas: 1. Representation: A set of classifiers or a language that a computer understands. 2. Evaluation: Also known as objective performance scoring. 3. Optimization: Search method; Often the classifier with the highest score. The fundamental goal of machine learning algorithms is to successfully interpret data and generalize learning beyond the trained examples.

LINK INFORMATION :

https://aicprime.com/

https://t.me/aicprimechat

https://twitter.com/aicprime

https://www.linkedin.com/company/aicprimeco/

https://github.com/aicprime

https://medium.com/@aicprime

https://aicprime.com/AICPrimeWhitepaper.pdf

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