Techniques for AI 

We all know humans have a super powerful brain that can think, feel and predict things. 

For instance: When we see a night sky full of stars, we can say it is going to be sunny the next day morning. This prediction is based on our years of experience.

While humans learn from experience, can computers do the same? - The answer is "yes", this is what is called machine learning.

Artificial intelligence is the study and application of many different techniques - let's see some of them.

Machine learning:

Machine learning is a technique that is used by machines to learn and improve their performance and predictions from their experience based on computational methods and algorithms. It is one of the most rapidly growing fields in technology.

Deep Learning: 

This is a branch of machine learning. computers are programmed to automatically learn complicated concepts from multiple layers of representation by building them from simple ones.

Deep learning methods are inspired by human brains, where machines learn from data by using artificial neural networks. 

Forms of Learning for machine model

Supervised learning:

 In this kind, the machine undergoes trained data that is mostly guided by a human.

Unsupervised learning - But sometimes the data does not contain any historical information or may contain extra factors.

Reinforcement learning:

In this kind of learning the machine is programmed to learn by its experience with a way programming agent by reward and punishment without needing to specify how the task is to be achieved

Natural Language Processing (NLP):

NLP  is a spectrum of theory-based computer techniques that focuses on enabling computers to understand, interpret, and respond to human language in a way that is both meaningful and contextually appropriate. 

It uses computational techniques from various fields such as computer science, artificial intelligence, linguistics, and data science to enable computers to understand human language, both written and spoken.

For example - technology such as chatbots, speech recognition products like Amazon’s Alexa or Apple’s Siri, or Google's "Hey Google"

Computers are programmed to interpret and process human spoken language in the form of text or sound.

Subtypes of NLP

Natural Language Understanding (NLU):

NLU establishes an associated ideology, a data structure that specifies the relationships between words and phrases.

For example, I hit my left arm because I left my glasses on the table

In the sentence above, the word "left" in the first part of the sentence has a completely different meaning than the word "left" in the second part of the sentence. People easily distinguish between homonyms and homonyms, which alters the nuances of spoken language. 

Translating human language into a computer-understandable representation is not trivial. Because in language the same group of words can have different meanings depending on the context.

Another example - the phrase "What?" when used with different emotions can mean different.

• When a person in Shock - will say the phrase "What?" with eyes widely open.
• When a person is confused - will say the phrase "What?" with eyebrows shrunk or tilted.
• When a person is surprised - will say the phrase "What?" with his mouth open and wide eyes.

We, humans, can read facial expressions and phrases to understand a sentence with emotion - but for machines, this is still a challenge and needs more explanation of how to capture words with emotions.

This survey perspective is often used in data mining to understand customer feedback. Sentiment analysis allows brands to monitor customer feedback more closely and take corrective action.

Natural Language Generation (NLG):

This is another area of NLP in which machines are programmed to understand human language and produce human-readable responses in text form, which can also be connected to speech using the "Text-to-speech" conversion method.

NLG has the ability to generate an overview of its given input documents while maintaining the integrity of the entered information.

Example - ChatGPT 

This area of study involves understanding techniques and developing new processes for machines to perform analysis and manipulation in human languages

Computer Vision:

Computer Vision also known as Machine Vision is the study of artificial intelligence that focuses on making machines interpret and understand visual data. 

Its goal is to give computers the ability to extract high-level understanding from digital images and videos. 

This might seem easy but for computers, it's not so, unlike humans machines do not have the gift of vision and perception

So for machines an image looks like a array of massive number of integers each representing a color and intensity.

Algorithms are designed to use machine learning in order to train the machine to understand an image.

Computer vision algorithms used today are based on Pattern Recognition and typically rely on Convolutional Neural Networks (CNNs). In this computers are trained with enormous about data using machine learning techniques to find patterns in the image, for example, to identify a face in an image.

A popular example using the case of Computer Vision is "Object Recognition", one of its applications is identification detection.

Robotics:

This is another branch of Artificial Intelligence that consists of studies to produce machines called Robots to substitute human actions, in another way you can say the end product of Robotics is called Robots that use programmable machines and mimic human actions.

Robotos were initially built to perform repetitive and monotonous tasks like "assembling cars" but today the area of their application has expanded, we even use Robots in our homes to vacuum.

The limitation of Robots so far has been that a Robot can perform the ONLY task for which it is programmed and nothing else. This field is expanding with more exploration of how to make robots deal with their environment and perform tasks like humans.

Conclusion :

In short, AI consists of several techniques of its applications, and each branch itself is vast and requires more exploration. AI has a huge impact on our lifestyle.  

Considering the field of AI, this is definitely a very fast-growing and demanding field. As of 2023, If you are looking to build a career in computer science, then definitely there is no other field like AI. This is the most promising area.