ARTIFICIAL INTELLIGENCE I

ARTIFICIAL INTELLIGENCE

What is Artificial Intelligence?

How do we explain Artificial Neural Networks(ANN)?

Compare Intelligence in Machines, Humans and Animals

What is artificial intelligence?

When a child is born, His/her mind looks like that of a blank paper. It is spotless. However, the environment in which he/she lives influences how they behave, speak and talk. If the child is raised in a quiet place, he/she normally becomes quiet and vise versa. This means the training a child receives defines greatly who he/she becomes. 

In the same vein, Computers are fed with data, it analyses the data and refines it and brings out information when it needs to do. 

Artificial Intelligence refers to a computer system that thinks like a human, understand and interpret data, make decisions and take actions and improve its performance over time. 

This means, computers are fed with data over a long period of time, it is then programmed to behave like a brain and interpret the data and make decisions or take actions based on the data.

What are neurons?

They are messengers in the body that helps us to communicate with every part of the body. 

They contain Dendrites (ears) that listens to messages from other neurons. 

The cell body processes the messages and 

The axon sends the messages to other neurons. 

What are Artificial Neuron Networks?

Just like the neural network in the human brain, Artificial neural network refers a computation model that is based on the structures and functions of biological neural networks. 

It is based on the input and output of data that is fed into the machine. 

What are Artificial Neuron Networks?

When we feed computers with information, it processes them and brings it out as information. As there is an advance in technology, there is a constant learning by the computer and it continues to use these information over a long period of time. 

This helps in various sectors such as facial recognition software, chatbot, natural language processing, speech recognition, self – driving cars and medical diagnosis.  

In ANN, there are stages. These includes

Input layer (Dendrites): This is where we feed the machine (computer) with data. So the machine receives the data. 

Hidden layers: Processes the information given to the machine and 

Output layer: Produces predictions or decisions just like the human brain. 

How ANNs works

Artificial neural networks work through these processes. 

Training: It learns from the data given to it

Forward pass: Input flows through nodes producing outputs

Backward Pass: Errors calculated, weights adjusted.

Iterations: Process, repeats, improving accuracy. 

By mimicking biological neurons, ANNs enable machines to learn, adapt and make decisions, revolutionizing many fields. 

Differences between Biological and Artificial Neural networks

Hardware: ANNs run on computers, biological neurons on biological tissue

Scale: ANNs can have millions of nodes, biological neurons have billions of nodes.

Processing: ANNs use algorithms, biological neurons use electrochemical signals. 

APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

ANNs are paving way for life changing applications to be developed and use in all sectors. 

It helps translate web pages into other languages

E-mail service providers detect and delete spam from users inbox through ANNs

Asset managers use ANNs to predict the direction of a company's stock

E-commerce platforms use it to personalize recommendations to their audience.

Chatbots are developed for natural language processing, deep learning algorithms use ANN to predict the likelihood of an event

ADVANTAGES OF ARTIFICIAL NEURAL NETWORKS

• Information is stored on the entire network instead of on a database. This means when a few pieces of an information is missing in one place, it does not restrict the network from functioning
• It has the ability to work with inadequate information. After it is trained, ANN may produce information even with incomplete information
• Fault tolerance: When one or more cells are corrupted, it does not prevent the machine from producing an output
• Having distributed memory: ANN learns by using examples fed into it and how it is taught to give desired outputs. 
• Gradual corruption: A network does not corrode immediately but takes a longer time
• Ability to train machines: ANNs have the ability to learn from previous events and make decisions accordingly
• Parallel processing Ability: ANNs have numerical strength and can perform more than one job at the same time. 

DISADVANTAGES OF ARTIFICIAL NEURAL NETWORKS

• Unexplained function of the network: When you get an answer from ANN, it does not give explanations as to why and how it came by that. It causes people not to trust the answers
• Assurance of proper network: there is no rule on how the neural network is built and the correct structure needed can only be obtained through trial and error. 
• The difficulty of showing the problem to the network: ANN works with numerical information (0s and 1s). When solving a problem, it has to be translated into numerical values before being introduced to an ANN. This makes it difficult for a user who is not properly skilled to get desired results
• The duration of the network is unknown: When a network is reduced to a certain value of error, it means that the training is completed. This does not give us optimum results

Machine, Human and Animal Intelligence

A machine intelligence enables a machine to interact with its environment in an intelligent way. This means the machine can only talk about subjects based on the data fed to it. 

Machines are created by humans to make work easier and faster. 

Humans therefore depends now mostly on machines for most of our tasks. 

Humans have feelings and emotions machines lack. Humans can make decisions based on feelings and emotions but machines make decisions based on data present

Humans behave according to our consciousness and machines behave the ways they are taught. This means the brilliance of a machine depends on the humans that created it. But human intelligence is based on experiments, feelings and emotions

Humans perform original tasks whiles machines mimic it. This means machines have limitations and cannot be original. Machines don’t have original thoughts but humans do. 

STRENGTHS OF AI

It can do a lot in a small time

It can talk

It learns from past events

WEAKNESSES OF AI

It is limited to how it is programmed. 

It does not engage in deep thinking conversations

The learning is designed towards the job it is to do only.