What is AI?

Artificial Intelligence is development of computer systems and machines that can perform tasks requiring human intelligence like humans.

Artificial Intelligence has enabled computer and machines to perform various tasks like learning from machines, recognizing speech, problem solving, understanding natural Language and making decisions and predictions.

There are Basic two Classifications of AI

Narrow AI (Weak AI): Narrow AI can handle specific tasks like virtual assistance, recommendation algorithms. Narrow AI cannot generalize across set tasks. Siri, Alexa and autonomous vehicles are the examples of Narrow AI.

General AI: It is the strongest Form of AI and can perform any task just like humans. This form of AI can perform complex tasks like understanding and learning across various domains.

Artificial Intelligence is covering all fields from healthcare to finance, transportation to entertainment with different AI techniques including machine learning, deep learning, neural networks, natural language processing and more.

How Old is AI?

To answer the question how old is AI? We have to go back in the history of AI. Then we can find answers to the questions like who created AI, History of AI and how long AI has been around us?

Origin of Artificial Intelligence

The idea of Artificial intelligence is thousands of years back. Ancient philosophers pondered life and what it means for something to be alive. In those times inventors created mechanical devices called “automatons” which could move on their own without human help. The world “Automaton” comes from Greek language which means acting on one’s own.

400 BCE a mechanical Pigeon was made by friend of the philosopher Plato. In 1495, Leonardo da Vinci built a famous automaton. These were the earliest sign of AI.

First Initials of Artificial Intelligence [1900-1950]

In early 1900s the idea of artificial intelligence was appearing in many stories; scientists were asked questions like “can we create an artificial brain?” This was the time when people started making simple robots. At that time these simple robots were powered by steam, and were able to make basic movement like walking and changing facial expressions.

1921: Czech Playwright Karel Capek wrote a play with name Rossum’s Universal Robots[¹]. This play introduced world to a word “Robot” with a concept of artificial people.

1929: Makoto Nishimura from Japan built Japan’s first robot called Gakutensoku[²].

1949: Edmund Callis Berkey a computer scientist published Giant Brains, or Machines that Think, in these books he compared computers with human brains.

Birth of AI: 1950-1956

When we ask ourselves that who created artificial intelligence then we find some names from time span of 1950-1956 who can fit in the answer to this question. Because it was the time when interest of people in AI grew rapidly.

1950: Alan Turing Published his paper introducing Turing test to machine intelligence.

1952: Arthur Samuel developed a program that learn to play checkers on its own. This is one of the earliest example of machine learning.

1955: John McCarthy used artificial intelligence for the first time at workshop at Dartmouth college.

Artificial intelligence pioneers: 1957-1979

From 1957 to 1979 AI went through growth and challenges at the same time.

1958: John McCarthy developed LISP, a programming language for AI which is still in use today.

1959: Arthur Samuel introduced the term machine learning with development of machines that can play chess better than humans.

1961: First industrial robot was made with name “Unimate”. Unimate started its working in New Jersey with performing dangerous task like welding and moving heavy parts.

1965: Edward Feiganbaum and Joshua Lederberg created first expert system. This AI program was designed to mimic the thinking of human experts.

1966: the first chatbot “ELIZA” was built by Joseph Weizenbaum based on natural language processing to simulate conversations like psychotherapist.

1968: Alexey Lvakhnenko introduced the basics of Deep Learning.

1979: The American Association of Artificial Intelligence was founded which is now known as Association for the Advancement of Artificial Intelligence.

When did AI start becoming popular? [1993-2011]

From early 1990s AI started facing some major advancements. It was the time when an AI system defeated world champion chess player for the first time. During this era, AI began to appear in everyday life through different devices but in its basic form.

1997: IBM’s Deep Blue defeated world champion chess player Gary Kasparov.

1997: Window released its first speech recognition software, which was developed by Dragon System.

2000: Professor Cyntia Breazeal created Kismet the first robot that could mimic human emotions with facial features like eyes, eyebrows ear and mouth.

2002: Roomba the first AI-powered robotic vacuum was launched.

2003: NASA launched Spirit[³] and Opportunity[⁴] on Mars who explored Planet’s surface without human control.

2006: Twitter, Facebook and Netflix started using AI to improve advertising and user experience through AI based algorithms.

2010: Xbox 360 Kinect was released by Microsoft, the first gaming system to recognize body movements.

2011: Apple launched Siri, world’s first most widely used virtual assistant in mobile phones and tablets.

2011: IBM’s Watson defeated two former champions on TV game show Jeopardy in question answer competition.

Artificial Intelligence 2012-present

From 2012 to present is about the most recent advancements in AI. During this era world has witnessed a big rise in AI tools that people use every day.

2012: Jeff Dean and Andrew Ng developed a neural network to recognize cats by showing unlabeled images without any extra information.

2015: Steve Wozniak, Stephen Hawking, Elon Musk and over 3,000 other people signed a letter urging governments to ban autonomous weapons for warfare.

2016: Hanson Robotics Created Sophia[⁵], the first humanoid robot with emotions and communications like humans.

2017: Facebook’s AI chatbots developed their own language without being programmed to do so.

2018: Alibaba’s AI for language processing outperformed humans in a Standford reading and comprehension test.

2019: Google’s AlphaStar[⁶] reached the Grandmaster level in StarCraft 2, by outperforming 99.8% of human players.

2020: OpenAI launched GPT-3 an AI model that can write texts and codes in minutes just like humans.

2021: OpenAI developed DALL-E an AI system that can understand images and generate captions. This AI system can understand the visual world.

From 2021 to till data artificial intelligence is continuously growing with new updates and developments.

How does AI Work?

Artificial Intelligence usually works by consuming large amount to data, study and analyze the data to find patterns and connections and then make predictions about future situations on the basis of data.

This process comprises of various techniques such as machine learning, deep learning, and natural language processing. We can understand how does AI work in the following organized segments:

Data Input

AI systems require large amount of data to learn and make decisions. This data input included texts, images, audio and video data.

Learning Process

AI uses a set of rules or algorithms to find data patterns. The AI systems is shown examples of patterns and it learns by adjusting its internal settings to make prediction. This process in machine learning in artificial intelligence nowadays. Machine learning can be

Supervised learning: the AI is trained with labeled data to make predictions.
Unsupervised learning: AI is given data without labels and has to figure out patterns by itself.

Deep Learning (Advanced AI)

AI uses artificial neural network which works similar to human brain. It is complex system of interconnected nodes through which AI system process and pass information. Through Deep learning AI systems are able to handle huge amount of data and can make complex decisions.

Decision Making (Inference)

Once the AI system is trained then it can make decisions and prediction for new data. This can be seen in Chatbots that analyze user question data and give appropriate answers based on the patterns it learned during training. Even AI death calculators can estimate the life expectancy of people based on their previous health data.

Improvement (Feedback Loop)

AI system also works on improvement on the basis of feedback which we provide on its results. We can mention the mistakes so that AI will avoid repeating such mistakes in the generated results. This is how AI gets better as it consumes data in the form of feedback as well.

Types of AI Models

Based on technology there are Six Major Types of AI models

Machine Learning Models
Deep Learning Models
Natural Language Processing Models
Reinforcement Learning Models
Ensemble Models
Hybrid Models
Machine Learning Model

This model of AI allows computer and Machines to learn from data without being explicitly programmed. Machine learning model is further divided into

Linear Regression Model: which provides continuous outcomes based on input data. For example, predictions for house pricings.

Logistic Regression: used for classification tasks of data.

Decision Tree: This model splits data into branches for both classification and regression tasks.

Deep Learning Model

Deep Learning Model is based on neural networks to analyze and process complex data. Deep learning model is further divided into

Artificial Neural Networks: used for pattern recognition and classification.

Convolutional Neural Network: used in facial recognition and object detection.

Recurrent Neural Network: is used for language translation and speech recognition.

Generative Adversarial Networks: is used in image generation and data augmentation.

Natural Language Processing Models

This model is used for understanding and generating human language.

Bag of words: is used for text classification and sentiment analysis.

Word2vec: is used for word embeddings and semantic analysis.

Reinforcement Learning Models

In this model AI learns to make decisions by interacting with environment and receiving feedback.

Q-Learning: is used in game Playing and robotic control.

Deep Q-Networks: used in playing video games and driving autonomous driving. Policy Gradient Methods: this model improves decision making function in Reinforcement learning models.

Ensemble Models

These models are combination of different learning models to improve accuracy and performance of AI.

Hybrid Models

Hybrid Models are combination of techniques from different AI areas. Hybrid model can be combination of Reinforcement learning and NLP with image recognition to tackle complex and multi-domain tasks for AI.

Types of AI Models Based on Functionalities

Reactive Machines

These AI models are programed to respond according to current and different situations. These models do store data for future actions. IBM’s Deep Blue models is best example of Reactive Machines.

Limited Memory Model

This model can be seen in different modern-day applications and software. This model can make informed decision based on the stored information and data.

Theory of Mind

This model works around understanding human emotions and behavior. This model enables machines and computers to predict human behavior and responses.

Self-Aware AI Model

This model is about self-improvement and self-consciousness of machines. This model enables machines to understand their current situation and through self-analysis machines can make informed decisions for themselves.

Best AI Apps for Business

Artificial Intelligence Apps for business are very helpful in assisting work and data management. AI apps for business are effective for streamline operations, enhancing decision making and improving productivity across various platforms.

ChatGPT (Open AI)

ChatGPT is best AI for work. It supports business owners in customer support, content generation and research assistance. Due its advanced conversational capabilities ChatGPT is valuable of business related to e-commerce, online marketing and customer service.

Jasper AI

Jasper creates AI-driven content for blog posts, ads, email and more. In this way it best AI app for business as it creates engaging content according to marketing terms and products of business.

Grammarly Business

Grammarly is AI-powered spelling, style and tone checker of content. This AI app is useful for improving communication in reports and emails.

Fyle

AI powered data management app. It helps in data management of employees regarding their expense and all. This app is available on both mobile and desktop.

Gemini

Is the latest AI based Chatbot. It can respond to visitors and customers queries according to their questions. This AI app is very helpful for business owners and it is compatible with mobile phones, android, iPhone and iPads.

Salesforce CRM

This is most frequently used AI-powered data management tool. This tool helps the companies and businesses to manage the record of each and every employee. From their personal record to professional performance records each and every thing is managed and analyzed through this app.

Companies Using AI for Marketing

Many Companies around the world are using AI for marketing to enhance customer engagement, personalization and increasing overall efficiency. With the help of AI companies are making best AI ad campaigns. There are several brands using AI for marketing.

Amazon

Amazon uses AI based algorithms to generate recommendations and suggestion of products for users. AI powered algorithm provide most suitable recommendation for users according to their browsing and purchasing history.

Netflix

Netflix is one of the largest brands using AI to analyze viewing patterns and recommend shows or movies to users. The benefit of this AI based analysis is to increase the user retention.

Coca-Cola

Coca-Cola has also experimented AI to create social media content and advertisement to resonate with specific audience’s needs.

Alibaba

Alibaba is one of the largest e-commerce companies. Alibaba is also using Al-powered algorithms analyze data of more than 1.3 billion active users to optimize sales.

Unilever

Unilever uses AI-driven tools to analyze social media trends which helps the company to shape their marketing strategy.

Nike

Nike has developed AI-Powered Fit APP [⁷] which is available in Play store. This App powered app creates digital picture of customer foot using with the help of AI. Through this very accurate picture the app shows the right and most suitable recommendation to customer.