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What is AI? Understanding Artificial Intelligence and Its Applications

AI is no longer a thing of the future, we are seeing how it is now integrated into almost everything. Generative AI has become more popular with the rise of OpenAI products such as ChatGPT for text generation and DALL-E for Image generation. Their API has enabled everybody can create, and query their own data to produce customized ChatGPT. Also, Following by success of these products we have now many products, and the era of AI has begun.

AI has reached an exceptional pinnacle of advancement. It is a better time to look into some basic knowledge about AI. It can be beneficial for developers and users to understand and use AI for more purposes. Today, we are going to look into the following topics:

  • History of AI
  • What is an AI?
  • Concepts of AI
  • Application of AI

So, let’s get started.

History of AI

Before understanding AI, let’s have a quick history of it.

  • Early Concepts (1950s): The idea can be traced back to the 1950s. The idea was to create a machine that has human-like intelligence. During this period, John Carthy 1956 coined the term “Artificial Intelligence”. It was very early, and researchers only exploring the possibilities of AI.
  • Early Successes (the 1950s-1960s): The Logic Theorist was a program developed by Allen Newell and Herbert Simon that used formal logic to prove mathematical theorems. Also, General Problem Solver(GPS) helped in solving problems by searching for possible solutions.
  • AI Winter(1970-1980s): During this period, the interest and funding for AI research has fallen down. The advancement wasn’t much in this period.
  • Expert System(1980s-1990s): Due to failure in the past period, researchers shifted their focus towards more practical applications. Expert systems were introduced to mimic the decision-making of human experts.
  • Machine Learning Resurgence(2000s-2010s): With the rise in machine learning, the availability of programs to handle large databases has become efficient. This helped in a resurgence in AI. Techniques such as neural networks and deep learning gained prominence, leading to breakthroughs in areas like image and speech recognition.
  • Modern AI Advancements(2010s-present): From the past decade to these days, AI has just seen unprecedented growth. AI has become more capable with Image recognition and Natural language understanding. A model like GPT-3 was able to demonstrate their capabilities to generate text. ## What is an AI?

After going through the history of AI, we can define AI as a simulation of human intelligence processes by computer systems. These human processes include learning, reasoning, problem-solving, perception, and language understanding. Most of the AI today, can deal with most human processes.


AI is mainly divided into two major categories:

Narrow or Weak AI

This kind of AI is designed and trained on particular data for a task to perform. It can perform a limited or narrow set of tasks. It uses data to perform tasks and it lacks human intelligence. ChatGPT is a good example of Narrow AI as it is trained on data to produce human-like text but it lacks human intelligence to answer without the data. Siri and Alexa also fall into this category.

General or Strong AI

This is the concept where a machine is fully able to replicate human intelligence. It should have the ability to understand, learn, and perform any intellectual task that a human being can. There is no present example of general AI as it is more in the realm of science fiction now.

Technical Core of AI

Let’s look into some of the technical concepts that revolve around AI.

Programming Language

AI can be built in any language but there are some language that gives more flexibility in term of library and performance. Python is such language that is widely used for such development. Libraries such as NumPy, TensorFlow, and scikit-learn give an edge to Python over other programming languages. R and Julia are also capable of building AI.

Basic Algorithms

There are some basic algorithms that are associated with the AI. This can be helpful in predicting, clustering, and generating text. Here are those algorithms:

  • Linear Regression: It is used for modeling the relationship between a dependent variable and one or more independent variables. It can be used to predict future values based on previous relations.
  • K-Means Neighbors(KNN): It is an algorithm used for classification and regression tasks. It does this by finding the nearest neighbor of a data point in a dataset. It can be used for prediction, text categorization, and spam detection
  • K- means Clustering: It is a popular algorithm used for grouping data points. It groups data based on similarity. The goal of the algorithm is to partition data into clusters so that the data points having similar data are grouped in a cluster. It can be used for segmenting customers, image compression, and anomaly detection. ## AI Frameworks and Libraries

TensorFlow is a framework available in JS and Python both for the development of AI. It provides tools to create, build, and deploy AI models easily. Along with that PyTorch is used for dynamic computation graphs. scikit-learn simplifies machine learning tasks, making it accessible to developers of all levels.

Cloud AI Services

AWS, Azure, and Google Cloud are the major cloud providers that offer AI services. They can help you harness the power of the AI without worrying a lot about the infrastructure management. They provide services from speech recognition to image analysis.

Some concepts of AI

There are various concepts that are associated with AI. These concepts are part of AI that require understanding to build and understand AI. Let’s look into those:

  • Machine Learning: It is a subset of AI that deals with the development of algorithms. It enables computers to learn patterns from data. It is further divided into supervised learning, unsupervised learning, and reinforcement learning.
  • Deep Learning: It is a subset of machine learning that deals with artificial neural networks. It is inspired by the structure and function of the human brain. It is useful in image and speech recognition.
  • Natural Language Processing (NLP): It deals with the interaction of human and computer language. It helps computers to understand, interpret, and generate human language. It is widely used in language models that deal with translation, chatbots, and text generative.
  • Computer Vision: As the name suggests, it helps the computer to interpret and process visual information. It is used in image recognition, object detection, and facial recognition systems.
  • Robotics: AI-driven robots can help in performing tasks semi to full autonomously. It helps in manufacturing and assembly, complex surgeries, and space explorations.
  • Expert System: As discussed earlier, it is used to mimic the decision-making abilities of a human brain. They use a knowledge base of facts and rules to offer recommendations or solutions. ## Challenges of AI

As with every technology there will some challenges will come. Here are some of the challenges of AI:

  • Bias Concerns: Data is a crucial part of building any AI, as it is required to train AI on the data. If there is any bias in the data then the AI will lead to a bias or discriminatory outcomes. It can also make ethical decisions that can be questionable.
  • Transparency: AI models especially the deep learning algorithms are seen as black boxes. It simply means that the model is not transparent or easily interpretable. This can lead to making it difficult to understand how they arrive at the solution.
  • Privacy and Security: AI models require a large set of data to be trained. ChatGPT is trained on 570GB of text data including books, articles, websites, and other sources. Since data sources are not known, it raised a question of where the data has come from. Also, with security, it is vulnerable to attacks and manipulation. Thus posing a risk to critical systems and data.
  • Regulatory and Legal Challenges: AI is developing and involving at a pace that is no match today. It leads to a lack of time for the authority to make regulations to it. Thus there is uncertainty in legal and ethical standards.
  • Safety in Autonomous Systems: Self-driving cars and other autonomous are need to be ensured with safety, as it is a critical sector. Robotics in healthcare, manufacturing, and other industries need to operate safely with humans. ## Applications of AI

AI is widely used in various fields to automate tasks, analyze large data, and make intelligent decisions. Let’s look into some:

  • Using NLP, chatbots and virtual assistance has helped us answer our queries. It also helped in translating the text into different languages.
  • Facial recognition and object detection have become possible due to computer vision. It has also been implemented in medicine to assist in finding diseases and anomalies through X-rays and MRIs.
  • AI-driven algorithms analyze market data to help in making quick decisions in trading. It also helped in reducing financial fraud by finding patterns in SMS and emails.
  • Self-driving cars use different sensors to collect data and based on that a real-time decision is taken. It is helping to enhance the safety of cars.
  • AI is helping in the education sector by adapting individual students’ needs and learning styles to provide the best course structure.
  • The creative field is not being touched by AI. With Dalle,, and other image generation tools it creates beautiful images. GPT-3 has enabled to generation of text. It can be used for creative writing for essays, short films, and others.

It’s quite hard to cover all the domains in which AI is making its advancement as it is expanding in every field. But this can give you quite an idea of how it is helping in different sectors.


AI realized its potential when it started as a concept in the 1950s. As we can see it’s applied in almost every sector. Today, we are seeing how it is being integrated into every application to improve the user experience and accessibility. We can see how GPT3 is integrated with the application to provide results and suggestions to the users.

There is more to come to the domain of AI. As we are only able to achieve narrow AI. If we are able to create general AI then it will open huge possibilities.

I hope this article has helped you understand the journey of AI better. Thanks for reading the article.

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