Artificial interlligence part - 2

            ARTIFICIAL NATURAL NETWORKS

    This article is part of a series on artificial intelligence, and in this installment, we'll be discussing artificial neural networks.

Introduction and how the brain works 

              

           Before diving into the topic, it's important to understand the significance of the development of intelligent machines. In May 1997, the world chess champion, Garry Kasparov, was defeated by an IBM supercomputer called Deep Blue. This marked a turning point in the quest for intelligent machines, as Deep Blue was able to analyze 200 million positions per second and appeared to display intelligent thoughts. This achievement paved the way for machine learning, which involves adaptive mechanisms that enable computers to learn from experience and improve their performance over time. The most popular approaches to machine learning are artificial neural networks and genetic algorithms, and this article is dedicated to exploring neural networks.

                         

                        

                                      A neural network is a model of reasoning based on the human brain, which consists of nerve cells called neurons. The brain incorporates nearly 10 billion neurons and 60 trillion connections, or synapses, between them. While each neuron has a simple structure, an army of these elements constitutes tremendous processing power. Signals are propagated from one neuron to another through complex electrochemical reactions.substances called neurotransmitters are released from the synapses, leading to a change in the electrical potential of the cell body.. When the potential reaches its threshold, an electrical pulse, or action potential, is sent down through the axon. The electrical pulse then spreads out from the neuron and reaches its connections with other neurons, called synapses, causing them to either strengthen or weaken their connections.. Neural networks exhibit plasticity, meaning they can change in response to stimulation patterns. Neurons can form new connections with other neurons and demonstrate long-term changes in the strength of their connections. These mechanisms form the basis for learning in the brain, and artificial neural networks aim to emulate this natural process.

                            

                       

                           

                                     Although present-day artificial neural networks still have a long way to go before they can resemble the human brain, they are capable of learning through experience and using that experience to improve their performance. When exposed to a sufficient number of samples, they can recognize patterns, identify words in human speech, detect explosives at airports, and more. They can even observe patterns that human experts fail to recognize.As an illustration, a financial institution utilized a neural network to analyze data related to fraudulent credit card transactions and found that purchases of women's shoes within the price range of $40 to $80 were the most questionable.

                                      

                                  In summary, artificial neural networks are a fascinating field of study that offers a glimpse into the possibilities of intelligent machines. In the next article of this series, we'll explore this topic in greater detail.

Here is the Q&A to get a good idea about the above lesson.

Q: What is a neural network?

A: A neural network is a type of computing system modeled after the structure of the human brain.

Q: How does information travel through a neuron?

A: Information travels through a neuron as an electrical signal, which is generated by the movement of ions across the cell membrane.

Q: What is a synapse?

A: A synapse is the junction between two neurons, where information is transmitted from one neuron to another.

Q: What happens when an electrical signal reaches a synapse?

A: When an electrical signal reaches a synapse, it triggers the release of chemical substances, which cause a change in the electrical potential of the cell body.

Q: How are neural networks used in real-world applications?

A:  Neural networks are utilized in a broad range of applications, such as identifying images, processing natural language, and detecting fraudulent activities.

                

        

Accordingly, expect the rest of this in the next article.

Thank you.

have a nice day.