Category: AI Tutorial

Artificial Intelligence is developing with such incredible speed, sometimes it seems magical. There is an opinion among researchers and developers that AI could grow so immensely strong that it would be difficult for humans to control. Ironically, humans introduced AI into every possible task they could, for which they themselves now seem threatened.

Threat to Privacy

An AI program that recognizes speech and understands natural language is theoretically capable of understanding each conversation on e-mails and telephones, which leads to a society where people are constantly monitored.

Threat to Human Dignity

AI systems have already started replacing the human beings in a few industries. It should not replace people in the sectors where they are holding dignified positions that are pertaining to ethics, such as nursing, surgeon, judge, police officer, etc.

Threat to Safety

The self-improving AI systems can become so mighty compared to humans that it could be very difficult to stop them from achieving their goals, which may lead to unintended consequences.

Affects Decision Making

Humans intend to trust decisions made by machines, as they make clear and data-driven decisions, which simultaneously lead to similar solutions, decision-making patterns, and auto-adjustments.

Clearly AI is not capable of thinking outside the box, ethically or provide a unique perception. Additionally, as most algorithms use data to process, the algorithms generate discriminated outcomes of the data that have biases. This usually leads to unfair decisions in areas like hiring, lending, and law enforcement.

Autonomous Weapons

These are weapons that are capable of selecting a target without human intervention. They raise ethical concerns about accountability and the risk of unintended consequences.

Lack of Transparency

AI algorithms and models are complex and not easily understandable in how they operate and determine decisions, leading to accountability issues.

Dependency on Technology

Everyone depends on AI and technology for every tasks creating concerns about the loss of creativity, decision-making, problem-solving, and critical thinking skills.

Ethical and Regulatory Challenges

The rapid development of various domains in AI technology challenges on the revision of existing legal and ethical frameworks, making it difficult to ensure safety and privacy.

Legal Issues

Legal concerns around AI are still evolving. Some of the issues include liability, intellectual, and property rights. Legal issues concerning copyright frequently arise from the ownership of content produced by AI and its algorithm.

Building Trust

Trust in AI systems is essential for the extensive use and acceptance of them by people. The basis for trust relies on openness, dependability, and responsibility. Organizations must reveal the workings of AI to promote transparency and build trust. The outcomes of AI should be made more reliable and consistent.

Artificial Intelligence is the technology that has the ability to replicate human actions. Machines learn from past experiences, make decisions, and think similarly to humans. This technology has evolved considerably over the years, and still has a long way to go.

AI is used in a wide range of applications and has many advantages but also comes with concerns and limitations. Some of the key ethics and biases in AI are discussed below.

Bias is Artificial Intelligence

Usually machines shouldn’t be biased, as they don’t have experiences or memories. But that’s not the case with AI-based machines since they learn from data. Some of the common AI biases are −

Artificial Intelligence learns from the data, and if that data is incorrect or misleading, the outputs of the algorithms will also be inaccurate.

• Algorithm Bias − If the algorithm fed to the system itself is faulty, it affects the outcome of the algorithm as well.
• Sample Bias − If the dataset you picked is irrelevant and not accurate, the error will reflect on the results.
• Prejudice Bias − This is similar to sample bias, prejudice bias uses data that is influenced by social biases like prejudice and discrimination.
• Measurement Bias − This bias occurs when data is incorrectly collected, measured, and integrated.
• Exclusion Bias − This bias occurs when an important data point is left out of a dataset, often because of human negligence, which could intentional (not knowing the significance) or by mistake.
• Selection Bias − This bias occurs when the data used to train the algorithm doesn’t reflect the real-world distribution.

Prevention of Bias

Bias often causes inequality and regulatory challenges, To address these issues, organizations should take certain measures to develop ethical practices. Some of the key measures to prevent bias are −

• Most biases occur due to small or limited datasets. To avoid this, collect as much data as possible from multiple sources to diversify the dataset.
• Run multiple tests during the early stages of testing to check for biases and correct them.
• Continuously check for the data quality as time passes.

Ethics in Artificial Intelligence

Ethics in AI is a set of principles and considerations which enhance the development, deployment, and impact of AI technologies. The key ethical issues in AI include −

• Privacy − We feed the machines with the personal details of people to help it think and act like humans. But how do we know that the details are secure and private? Data privacy is one of the crucial concerns in the development and use of AI.
• Transparency − Transparency in AI ethics refers to the practice of making AI systems and their operations understandable to users, and the way to do this is disclosure.
• Accountability − Establishing a clear note of accountability is important, especially while operating in critical areas like healthcare or law enforcement. This allows users to learn who would be responsible for the outcomes of AI systems.
• Human Dependence − AI systems are capable of automating some tasks that humans previously used to perform, especially tasks with data. However, as AI will never take the responsibility and accountability, it is important that the task of decision-making should be denied.
• Social Impact − The use of AI over employment, social interactions, and power dynamics must be carefully considered to promote positive outcomes.

Artificial Intelligence is a technology that we interact knowingly and unknowingly every single day. AI has only been advancing and growing through the years reaching out to easy many tasks for people, lets dive into some of the real-life examples −

Virtual Assistants

Siri, Google Assistant, and Alexa are some of the most common voice assistants that help to perform tasks like a personal assistant. They usually take the voice command from the user as input and perform that particular task. Some of the tasks include setting reminders, playing music, and answering questions.

These virtual assistants are designed to facilitate a wie range of functions like voice interaction, task automation, information retrieval, smart home control, and entertainment. With benefits comes the challenges of integrating voice assistants that include privacy concerns, accuracy, limited contextual understanding, dependence on internet connectivity, and integration complications.

Predictive Analytics

Predictive Analytics include analyzing the data to predict the future. These AI systems are widely used in many industries like healthcare to analyze patient data to predict potential health issues and recommend preventive measures, banking to identify vulnerabilities, and marketing for customer segmentation.

Autonomous Vehicles

Companies like Telsa and Waymo designed self-driving cars with premium safety and navigation using AI. These self-driving cars do not require human intervention, where they understand the surroundings, navigate through the obstacles and reach the destination as set by the user.

Chatbots

The AI-algorithms is almost used by all the organizations providing a service or product like Zomato, H&M, Amazon, and many more. This is basically a automated customer support which can clear inquiry and issues raised my users. This tool improves the ease of solving an issue and user engagement.

Most companies opt for this as it is cost efficient, provides instant support, has the capability of multi-tasking, and 24/7 availability.

Recommendation Systems

Ever pondered over which movie to watch on Netflix, and wondered how does it accurately predict what movie you would enjoy? Well, thanks to recommendation systems.

A recommendation system is an artificial intelligence algorithm that uses big data to suggest or recommend products to consumers. These are based on various criteria like past purchases, search history, demographic information, and other factors.

Facial Recognition

Facial recognition uses AI to identify person which is especially used to unlock mobile phones. It uses a combination of techniques including Deep learning, Computer Vision, and Image processing to detect the face and to recognize the specific facial features. This is a uniquely secure way to access your personal information. Other than unlocking phones, airports and high-security institutions use facial recognition to validate your identity.

Robotics

Robots are mechanical humans which handle repetitive tasks. Though robots seem futuristic, in reality we have many existing in the world. For example, Mars rovers NASA are programmed to explore, gather samples and send transmissions back to earth to provide data from Mars which an astronaut cannot.

Navigation

Other than autonomous vehicles, there are many other tasks in transportation and navigation like traffic management systems that take real-time data about the road, weather, and traffic conditions to predict traffic flows and congestion, Direction apps such as Google Maps, and Apple Maps use location data collected from users to determine traffic., and Rideshare apps uses AI to predict time of arrival, road conditions, and set’s fare.

Search Engines

Search engine algorithms use AI to refine and show better results without the intervention of programmers. You can find this on Google if you search for a question. This algorithm gathers data on what people search the most and uses it to auto-complete answers when you type in the search bar, and shows two more related questions below. Some of the most popular search engines include Google, Yahoo, and Bing.

Artificial Intelligence is widely applied across various sectors. The demand for AI is increasing exponentially, as it can solve complex problems in an efficient way. The AI techniques are applied across the following major industries to make our lives easy and comfortable −

Healthcare

AI in Healthcare is used to simplify the lives of patients, doctors, and hospital management by performing tasks that were once done manually by humans, but in less time and cost.

It is used in a number of tasks, that include drug discovery, finding new links between genetic codes, surgery-assisting robots, automating administrative tasks, and providing personalized treatment.

For example, initially radiologists would manually analyze X-Rays, CT Scans, and MRI’s to identify the signs for conditions like tumors or fractures. This process was time-consuming and the relevance depends on the radiologist’s experience. With the integration of AI, certain algorithms help radiologists to quickly analyze images and highlight the areas of concern with great accuracy and faster than humans.

Finance

AI in Finance enables financial services organizations to better understand markets, customers, analyze and learn digital journeys.

It helps gain insights for performance measurement, predictions and forecasting, real-time calculations, customer servicing, and intelligent data retrieval.

For example, initially credit assessments were done manually, which involved extensive paperwork and human analysis. With the integration of AI algorithms, large amounts of data are analyzed quickly, automating the credit assessment process.

Automotive

AI in Automotive Industry is used to streamline operations and improve overall vehicle performance. Some of the applications of AI in this industry range from autonomous vehicles to advanced safety systems.

For example, Quality checks were initially conducted by workers through visual inspections of vehicles, which was time-consuming and often not reliable. With the integration of AI-powered vision systems, it is easy to find defects with high precision and reduced costs.

E-Commerce

AI in E-Commerce is quite impactful in various tasks like personalized product recommendations, chatbots and virtual assistants, fraud detection and prevention, inventory management, and dynamic pricing.

For example, customer support initially relied on human agents, which caused limited availability. After integrating AI-based Chatbots, the tasks of answering queries and resolving issues became quite easy as they provide instant customer support.

Agriculture

AI in Agriculture allows systems to make weather predictions, monitor agricultural sustainability, and assess farms to identify diseases or pests using data like temperature, precipitation, wind speed, and sun radiation.

For example, initially farmers depended on manual inspection to identify issues in crops like pests and diseases. Furthermore, with the integration of AI-based monitoring tools such as drones and satellites are used to monitor crop health in real-time.

Human Resources

AI in Human Resources allows organizations to automate time-consuming HR tasks like resume screening and employee engagement. Additionally, AI also recommends targeted training based on individual job performance metrics.

For example, initially HR professionals manually reviewed hundreds of resumes, which is time-consuming and leads to human bias. With the integration of automated resume screening, AI algorithms automatically screen resumes and filter the candidates based on required skills.

Law

AI in Law assists in document review, discovery, and drafting contracts. Additionally, it also optimizes legal research by gathering information on relevant case law, assessing case strategies, and scheduling court calendaring.

For example, earlier lawyers spent so much time reading documents to identify relevant information, which often led to human error or oversight. With the integration of AI, the task of document review and drafting became easy.

Gaming

AI in Gaming allows you to create interactive experiences and bring character to life with abilities. Additionally, complex algorithms assist with simulation-based training by generating virtual scenarios.

For example, game difficulty was set at a fixed level, often forcing users to choose either too easy or too hard with no dynamic adjustments. With the integration of AI, developers can analyze player performance and adjust the difficulty of the game in real-time, this will ensure tailored challenges that keep players engaged.

Education

AI in Education can assist students with personalized learning experiences on analyzing their strengths and weaknesses.

For example, teachers often offer the lesson at the same pace irrespective of the students capability and learning pace. With the integration of AI-driven platforms , it is easy to assess individual student performance and provide content based on each student’s need.

Artificial Intelligence allows us to perform tasks that were once considered possible only for humans, such as understanding, recognizing patterns, decision-making, and generating natural language. For the developers to develop models and algorithms, it is important that they have technical expertise on frameworks and libraries.

Frameworks are a collection of pre-built tools and resources that simplify developing AI-based applications. Some of the top AI frameworks and libraries include −

PyTorch

PyTorch is an open-source framework based on theTorch library and is widely used for applications in deep learning and artificial intelligence. It provides a flexible and dynamic computational graph, which makes it a popular choice. Developers use this for various tasks like Computer Vision and Natural Language Processing.

PyTorch is commonly used for building deep learning models, and applications like image recognition and language processing.

Scikit-Learn

Scikit-Learn is an open-source library in Python programming language. It simplifies the process of building and deploying Machine Learning models and algorithms. It is an user-friendly interface and has a comprehensive range of tools, especially for Data Mining and deep learning tasks.

Scikit-learn is primarily used for performing tasks like classification, regression, clustering, dimensionality reduction, feature selection, and data preprocessing.

TensorFlow

TensorFlow is an open-source deep learning framework developed by Google. It is flexible and scalable, and often used by developers to build and train machine learning models. It is well-documented and supports deployment on various platforms.

TensorFlow is used for developing machine learning models like image recognition, handwriting recognition, object detection, sentiment analysis, and machine translation.

Keras

Keras is an open-source high-level Neural Networks API that runs top of the TensorFlow Library and other frameworks. It is easy to learn and is user-friendly and is usually used for building and training deep learning models.

Microsoft Cognitive Toolkit

Microsoft Cognitive ToolKit (CNTK) is an open-source deep learning framework developed by Microsoft. It is designed to train deep neural networks and offers a wide range of features and capabilities, and supports multiple neural network types, including feedforward and recurrent networks.

CNTK is used to create machine learning prediction models, and also create deep neural networks, such as Cortana and self-driving cars.

LangChain

LangChain is one of the popular frameworks for large language model (LLM) applications. It integrates with various tools like OpenAI and Hugging Face Transformers and is used for many applications like chatbots, document summarization, and interacting with APIs.

LangChain allows developers to chain together tasks like data retrieval, processing, and LLM calls in a sequential manner.

Hugging Face

Hugging Face is an open-source platform where users can build, train, and deploy ML models. It uses a Python library called “Transformers,” which simplifies the process of downloading and training ML models. The platform also allows users to share resources and models to reduce model training time, resource consumption, and environmental impact of AI development.

PyBrain

PyBrain is an open-source library for implementing Machine Learning using Python. It is flexible, easy to use, and provides a variety of predefined environments to test and compare algorithms.

The library makes it easy for training algorithms for networks, datasets, trainers to train and test the network.

Theano

Theano is a Python library that allows you to define mathematical expressions used in Machine Learning, optimize these expressions, and evaluate those very effectively by decisively using GPUs in critical areas.

Caffe

Caffe is an open-source deep learning framework that is used to create and train neural networks and models. It is quite popular for its speed and efficiency in processing images and other data.

XGBoost

XGBoost (Extreme Gradient Boosting) is the optimized distributed gradient boosting toolkit that trains machine learning models in an efficient and scalable way. It implements an efficient version of the gradient boosting framework, which creates models progressively by merging several weak learners to generate a more robust predictor.

Before you deep dive into the concepts of artificial intelligence, it can be useful to first get familiar with some of the common terminology and definitions. The following list of AI words will provide a foundation on the key concepts of AI and machine learning −

Term	Definition
Artificial Intelligence (AI)	The technology that enable computers and machine to replicate human intelligence.
Machine Learning (ML)	A subset of AI that allows systems to learn from data and improve their performance over time.
Deep Learning	A specialized domain of machine learning that uses neural networks with many layers to analyze various forms of data.
Neural Networks	Computational models inspired by the functioning of human brain using neurons. This models consist of interconnected nodes to process the data.
Natural Language Processing (NLP)	The domain in AI which deals with interaction between computers and humans through natural language.
Computer Vision	A field of AI that allows machines to interpret and make decisions over visual data.
Reinforcement Learning	This is a type of machine learning in which an agent learns to make decisions based on actions in an environment.
Supervised Learning	A type of machine learning where the model is trained on labeled data to predict outcomes.
Unsupervised Learning	A type of machine learning where the model identifies patters and relationships from unlabeled data.
Semi-Supervised Learning	A hybrid machine learning method that combines small amount of labeled data and a large amount of unlabeled data to predict outcomes.
Data Mining	The process of discovering patterns and knowledge from large amounts of data using various techniques.
Agent	An entity that perceives its environment and takes actions to achieve specific goals.
Algorithm	A step-wise procedure or processes followed in calculations or problem-solving operations by a computer.
Training Data	The dataset used to train a machine learning model to recognize patterns and make predictions.
Model	A mathematical representation of a process which captures relationships in the data for predictive tasks.
Overfitting	A modeling error that occurs when a model learns the training data too well, capturing noise instead of the underlying patterns.
Underfitting	A modeling error that occurs when a model is too simple to capture the underlying trend in the data.
Cognitive Computing	An AI approach that mimics human through processes in a complex, human-like way.
Autonomous	Systems that operate independently without human intervention.
Large Language Models	AI models like GPT that are trained on large amounts of text data to understand and generate human-like data.
Artificial General Intelligence (AGI)	A theoretical form of AI that is capable of understanding, learning, and general intelligence throughout almost any task, quite similar to that of a human being.
Generative AI	AI capable of generating new content, be it text, images, or music, based on learned patterns.
Transfer Learning	A technique where the model trained on one task is adapted to work on a different related tasks.
Chatbot	A program designed to simulate conversation with human users.
Backward Chaining	An inference method where reasoning started from the goal and works backwards to find supporting data.
Forward Chaining	An inference method that started with available data and applied rules to extract more data until a goal is reached.
Environment	The surrounding context or scenario in which an agent operates and makes decisions.
Heuristics	Problem-solving strategies that use practical methods to produce solutions that may not be optimal but are sufficient for immediate goals.

Artificial Intelligence (AI) is a technology that enables computers to think and act like humans. These systems are trained to learn from past experiences to enhance speed, precision, and effectiveness. Further based on the following criteria artificial intelligence can be categorized into different types −

• Capabilities
• Functionality

Based on Capabilities

AI is classified into the following types based on capabilities −

Normal AI (Weak AI)

Narrow AI is a type of AI that enables to perform a specific task with intelligence. Narrow AI is trained only for a specific task and fails to perform beyond its limitations.

Voice assistants like AppleSiri, Alexa, and others are a good example of Narrow AI, as they are trained to operate within a limited range of functions. Some of the other examples of Narrow AI are chess games, facial recognition, and recommendation engines.

General AI (Strong AI)

General AI is a type of AI that enables to perform intellectual tasks as efficient;y as humans. The systems are trained to have the capability to understand, learn, adapt, and think like humans.

Though it seems efficient, the General AI still seems to be a theoretical concept that researchers aim to develop in the future. It is quite challenging, as the system should be trained to be self-conscious, to get aware of the surroundings, and to make independent decisions. The potential applications could be robots.

Super AI

Super AI is a type of AI that surpasses human intelligence and can perform any task better than humans. It is an advanced version of general AI, where machines make their own decisions and solve problems by themselves.

Such AI would not only perform tasks but also understand and interpret emotions and respond like humans. While it remains hypothetical, development of such models would be complex.

Based on Functionality

AI is classified into the following types based on the functionality −

Reactive Machines

Reactive Machines are the most basic type of artificial intelligence. These machines operate only on the present data and do not store any previous experiences or learn from past actions. Additionally, these systems respond to specific inputs with predetermined outputs and cannot be changed.

IBM’s Deep Blue is a great example of reactive machines. It is the first computer system to defeat a reigning world chess champion, Garry Kasparov. It could identify pieces on the board and make predictions but could not store any memories or learn from the past games.

Google’s AlphaGo is another example of a reactive machine, playing the board game Go with a similar method of pattern recognition without gaining knowledge from previous games.

Limited Memory

Limited Memory is the most used category in most modern AI applications. It can store past experiences and learn from them to improve future outcomes. These machines store historical data to predict and make decisions but do not have long-term memory. Major applications like autonomous systems and robotics often rely on limited memory.

Chatbots is an example of limited memory, where it can remember recent conversations to improve the flow and relevance. Additionally, self-driving cars is another example that observes the road, traffic signs, and surroundings to make decisions based on past experiences and current conditions.

Theory of Mind

Theory of Mind could understand the human emotions, beliefs, and intentions. While this type of AI is still in development, it has enabled machines to interpret emotions accurately and modify behavior accordingly so that machines could interact with humans effectively. Some of the possible applications of this type are probably collaborating robots and human-robot interaction.

Self-Awareness

Self-Aware AI represents the future of artificial intelligence with self-consciousness and awareness similar to humans.While we are far from achieving the goal of self-aware AI, it is an important objective for the development of AI. The applications of self-aware AI could be fully autonomous systems that could take moral and ethical decisions.

Artificial Intelligence is a technology that makes machines replicate human intelligence. These machines can learn, make decisions, adapt, and perform tasks similar to humans. The history and evolution of AI is a journey that spans several decades. This chapter gives you a concise overview of key milestones throughout the years.

There is an assumption that Artificial Intelligence is a recent technology in the market, but in reality the groundwork of AI dates back to early 1900s, while the biggest innovations weren’t made until 1950’s.

Foundation of AI

The early 1900’s, i.e., 1900-1950 is when there was a lot of buzz created regarding the idea of artificial humans. This made scientists of all sorts think if it was possible to create an artificial brain. Though most of them tried creating the simpler versions of robots. Some of the key milestones in this period are −

Year	Milestone
1921	Czech playwright Karel Capek released a science fiction play “Rossum’s Universal Robots”, where he introduced artificial people and named them robots.
1943	Warren McCulloch and Walter Pitts created the first conceptual model of a neural network.

Emergence of AI

The years from 1950-1956 marked the turning point for AI. Researchers and companies made Some of the key milestones in this period are −

Year	Milestone
1950	Alan Turing published “Computer Machinery and Intelligence” which proposed Turing test to measure the intelligence of a machine.
1952	Arthur Samuel is a computer scientist, who developed a program to play checkers, which improved its performance through experience.

AI Revolution

The period from 1957-1973 was also commonly known as “Golden Age” as most researchers showed interest and enthusiasm to achieve remarkable advancements in the field. Some of the notable milestones in this period are −

Year	Milestone
1957	Frank Rosenblatt introduced perceptron, which was one of the early innovation for artificial neural networks.
1958	John McCarthy created LISP, the first programming language for AI research.
1959	Arthur Samuel used the term “Machine Learning” and defined it as intellectual computers that surpass humans in any task.
1966	Joseph Weizenbaum created ELIZA, that used natural language processing to make conversations with humans.
1972	Alain Colmerauer and Philippe Roussel developed prolog programming language.

AI Winter

The initial AI winter occurred from 1974-1980 , which was quite a tough time for the improvement of AI. During this time, there was a substantial decrease in research funding which affected the interest on AI.

AI Boom

The time period from 1980-1987 showed a period of rapid growth and interest in AI. This happened because of both research breakthroughs and additional government funding to support the researchers. Some of the key milestones in this period are −

Year	Milestone
1980	The first expert systems, know as XCON came into the commercial market.
1981	The Japanese government alocated $850 million to the development of Fifth Generation Computer Project, to create computers that could translate, converse in human language and express reasoning on a human level.
1984	The AAAI warns about the incoming AI Winter, where the funding and interest would decrease significantly effecting the research.
1986	Ernst Dickmann and his team demonstrated the first self-driving cars, which drove up to 55kmph with no obstacles and human driver.

AI Stagnation

The second AI winter took place from the years 1987-1993, where again investors and government stopped funding due to high cost and no efficient results.

AI Agents

The years between 1993-2011, there was a significant growth in AI, especially with the development of intelligent computer programs. In this era, professional focused on developing software to match human intelligence for specific tasks. Some of the key milestones in this period are −

Year	Milestone
1997	Deep Blue was the first program to beat a human chess champion, Gray Kasparov.
2000	Professor Cynthia Breazeal the first robot named Kismetthat could simulate human emotions and had facial features similar to humans.
2003	NASA landed two rovers onto Mars, which navigated through the surface of the planet without human intervention.
2006	Companies such as Twitter, Facebook, and Netflix started using AI as part of advertising, business analysis, and user engagement.
2011	Apple released Siri, the first popular voice assistant.

Artificial General Intelligence

From 2011 to present, unfolded significant advancements within AI domain. These achievements can linked to extensive data application, and the ongoing interest on artificial general intelligence(AGI). Some of the key milestones in this period are −

Year	Milestone
2012	Google researchers Jeff Dean and Andrew Ng trained a neural network to recognize cats using unlabeled images without prior information.
2016	Hanson Robotics introduced Sophia, the first humanoid robot with realistic human features, emotion recognition, and communication abilities.
2017	Facebook programmed two AI Chatbot to communicate and learn to negotiate, but as the conversation went on they eventually stopped using English and started speaking their own language entirely on their own.
2018	A Chinese tech group Alibaba’s language − processing AI won over human intellect on a Standford reading and comprehension test.
2019	Google’s AlphaStar reached Grandmaster on the video game StarCraft 2 outperforming all but .2% of human players.
2020	OpenAI started beta testing GPT-3, a model that uses Deep Learning to create code, content, and other creative tasks.
2021	OpenAI developed Dall-E, which can generate images using the natural language as prompts.
2022	Dall-E was integrated with ChatGPT, showcasing AI’s capacity to generate text and relevant images.
2023	Multimodal is another major breakthrough in AI. These models process all the data types like text, image, video, and audio simultaneously.
2024	Devin is the first AI software engineer still under development and SORA is another innovation of OpenAI which is an text-to-video model.

Since the invention of computers or machines, their capability to perform various tasks has continued to increase rapidly. Humans have achieved the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time. A branch of computer science named Artificial Intelligence is to build machines or computers that are as intelligent as people.

What is Artificial Intelligence?

Artificial intelligence is the technology that allows systems to replicate human behavior and thoughts. At its core, AI uses algorithms to train datasets that will generate AI models that let computer systems perform tasks like recommending songs, googling route directions, or providing text translations betweentwo languages. A few examples of AI are ChatGPT, Google Translate, Tesla, Netflix, and many more.

According to the father of artificial intelligence, John McCarthy, it is The science and engineering of making intelligent machines, especially intelligent computer programs..

History of AI

Artificial Intelligence has evolved since its inception in the mid-20th century. Initially, AI focused on automating simple tasks, and with advancements in machine learning and deep learning, it made significant improvements in understanding and processing data. Today, AI influences various fields, including healthcare, finance, and automobiles. Some of the key milestones in the history of AI are −

Year	Milestone
1923	Karel apek play named Rossum’s Universal Robots (RUR) opens in London, first use of the word “robot” in English.
1956	John McCarthy, a professor at Dartmouth College coined the term “Artificial Intelligence”.
1966	Joseph Weizenbaum created ELIZA, that used natural language processing to make conversations with humans.
1997	Deep Blue was the first program to beat a human chess champion, Gray Kasparov.
2012	AlexNet is a convolution neural network (CNN) architecture that was designed by Alex Krizhevsky.
2020	OpenAI started beta testing GPT-3, a model that uses deep learning to create code, content, and other creative tasks.

Goals of AI

The potential of AI is basically to mimic human skills and traits and apply them to machines. While the main objective of AI is to create a core technology that is able to allow computer systems to process intelligently and independently. Below are the essential goals of AI −

• To Create Expert Systems
• To Implement Human Intelligence in Machines
• To Develop Problem-Solving Ability
• To Allow Continuous Learning
• To Encourage Social Intelligence and Creativity

What Contributes to AI?

AI is a field that combines various scientific and technological disciplines, which include Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. The main objective of AI is to develop computer programs that can perform tasks with reasoning, learning, and solving problems similar to human intelligence.

AI Programming vs. Traditional Coding

Below is the difference between AI programming and traditional coding −

AI Programming	Traditional Coding
Can deal with complex, undefined problems.	Can handle only well-defined, predictable problems.
Uses data-driven methods and algorithms.	Relies on explicit logic and rules.
Produces models that make predictions or decisions.	Generates specific functional software
Utilizes frameworks and libraries like TensorFlow, PyTorch.	Commonly uses languages like Python, Java.
Involves validation of model accuracy.	Focuses on debugging and unit testing.
Models learn patterns from data.	Programs execute pre-defined instructions.

What is an AI Technique?

AI techniques refer to methods and algorithms that are used to create smart systems that perform tasks requiring human-like intelligence. Some of these techniques are Machine Learning, Natural Language Processing, Computer Vision and others. These AI techniques use the knowledge efficiently in such a way that −

• It should be perceivable by the people who provide it.
• It should be easily modifiable to correct errors.
• Elevate the speed of execution of the complex program it is equipped with.

Applications of AI

AI has been dominant in the following fields −

• Gaming − AI plays a crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.
• Natural Language Processing − It enables machines to interact with humans in natural language.
• Expert Systems − It is an AI based software that enables decision-making ability similar to a human expert.
• Computer Vision − These systems understand, interpret, and comprehend visual input on the computer.
• Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in humans noise due to cold, etc.
• Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.
• Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as temperature, movement, and sound. They have efficient processors, and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to new environment.

Challenges in AI

The main challenges in implementing AI includes −

• Data Quality and Accessibility − AI requires large, high-quality, and relevant datasets for effective learning.
• Technical Expertise − Implementing AI algorithms and models requires skilled professionals.
• Ethical and Legal Concerns − It is important to make sure that the AI systems are fair, unbiased, and don’t harm anyone’s safety.
• Integration − Integrating AI with existing systems can be complex.
• Cost − Developing and maintaining of AI infrastructure can be expensive.

Future of AI

As technology advances, we could witness greater integration of AI into our lives, and a more interactive relationship between humans and AI. Along with technology advancement is the need for ethical and privacy considerations including bias, privacy, and job displacement to help ensure that AI is beneficial to society as a whole.

The four key trends that define the future of AI include −

• Rise of Multimodal
• Emergence of Agentic platforms for AI Deployment
• Optimization of AI’s performance
• Democratize AI Access.

Some of the other notable AI technologies that are going to shape various industries in the near future are rapid development of Generative AI and highlighting its transformative impact on various various industries.

This artificial intelligence tutorial provides introductory knowledge on Artificial Intelligence. It would be a great help if you are about to select Artificial Intelligence as a course subject. You can briefly learn about the areas of AI in which research is prospering.

This tutorial provides a good understanding of the workings of AI and its applications. This tutorial would be a good choice for someone who is a beginner and also for someone who would want to know more about it.

What is Artificial Intelligence?

Artificial Intelligence(AI) is that branch of computer science that creates intelligent machines that think and act like humans. It is one of the revolutionizing technologies that people are fascinated by because of its ability to relate to their daily lives. AI enables machines to think, learn and adapt, to enhance and automate tasks across industries.

Artificial Intelligence has many subsets that focus on different aspects of mimicking human beings. Machine learning is one of the popular subsets, whereas the others included are Deep Learning, Natural Language Processing, and Robotics.

Features of Artificial Intelligence

Artificial Intelligence is a technology that aims at replicating human intelligence. It has numerous applications across various sectors, from enhancing customer experiences to disease diagnosis. Some key features of AI are:

• Ability to learn − AI systems can improve their performance eventually by learning from data and past experiences.
• Logical decision making − AI systems are fed with large amounts of data to understand and recognize patterns for analysis and decision making.
• Adaptability − AI systems can adjust and adapt to changes in data.
• Efficient automation − AI would efficiently execute repetitive tasks and processes.
• Versatility − AI can be widely applied for various tasks across all fields like businesses, automotive, health, and many others.

Why to Learn Artificial Intelligence?

AI is one of the growing technologies, and learning AI would help build a career in a field that is high in demand. It opens up to various job opportunities and helps you stay intent with evolving technologies. It enhances problem solving skills, automates processes and can be applied to various industries, from business to health care.

Who Should Learn Artificial Intelligence?

This tutorial is prepared for students at the beginner level who aspire to learn Artificial Intelligence.

This course is also helpful for professionals aiming to enhance their skills or an entrepreneur trying to integrate AI with their business. Professionals in software development, data science, and engineering would find this artificial intelligence tutorial especially relevant to enhance their careers.

Applications of Artificial Intelligence

AI is transforming various industries with its ability to automate, make decisions, and enhance the efficiency of various tasks. As it is known for its versatility, some of its applications are:

• Health care − AI in healthcare is used to assist in tasks like diagnosing diseases, personalizing treatments and drug discovery.
• Finance − AI is used for fraud detection, trading and stock market analysis and customer service through chatbots.
• Manufacturing and Industries − AI optimizes production processes, improves quality and identifies machinery failure.
• Agriculture − AI helps combine technology with agriculture by analyzing soil conditions.
• Transportation − AI helps in designing autonomous vehicles. Some other tasks include traffic management and route optimization in maps.
• Customer Service − Chatbots and Virtual assistants are AI applications to improve user engagement.
• Entertainment and Media − AI helps in content creation, personalized content recommendations, and target advertising.
• Safety and Security − AI enhances threat detection and automates security measures.

Jobs and Opportunities

Many companies require highly skilled individuals in AI. As companies integrate AI into their operation, there will be a need for individuals to implement it. Some roles that companies hire are:

• Machine learning engineer
• Data Scientist
• AI Research Scientist
• Computer Vision Engineer
• NLP Engineer
• AI Product Manager
• AI Marketing Specialist

Prerequisites to Learn Artificial Intelligence

The basic knowledge of Computer Science is mandatory. Knowledge of science, mechanical engineering, or electrical engineering is a plus.

Before deep diving into Artificial Intelligence, there are a few skills and concepts that one should focus on, which includes −

• Mathematics and Statistics
• Knowledge of any programming language such as Python or R.
• Basics on Data Structures and Data Handling Techniques

Getting Started with Artificial Intelligence

Getting started to learn AI involves a few steps, which helps build a solid foundation. Here is a brief guide on the steps that can make you strong in the fundamentals of AI:

• Master Prerequisites − AI is complex, so you can only get deep into the technology if you have interest and enthusiasm. The first step is to master prerequisites, which include an understanding of basic mathematics and statistics along with learning a programming language and data structures.
• Learn AI algorithms −Artificial Intelligence is all about algorithms like searching and sorting. So if you get familiar with algorithms, you can ace AI.
• Getting to know AI tools and frameworks −The final step would be learning to handle AI frameworks. This is a practical step and requires prior theoretical knowledge. Some popular tools and libraries used to develop and deploy AI models are NumPy, Pandas, Matplotlib.
• Practice with Real data −Practicing AI algorithms on real data that is collected from various websites or APIs would help understand the working of AI in real-time scenarios.

Frequently Asked Questions about Artificial Intelligence

There are some very Frequently Asked Questions(FAQ) about Artificial Intelligence, this section tries to answer them briefly.What is Artificial Intelligence?

Why is artificial Intelligence important?

What are the types of artificial intelligence?

What are the applications of AI?

What is the future of AI?

How to Learn Artificial Intelligence

Can AI take over the world?

Who invented Artificial Intelligence?

How to Use Artificial Intelligence in Mobile Apps?

How is AI used in education?

What are the risks of artificial intelligence?

Artificial Intelligence Articles

You can explore a set of Artificial Intelligence articles at Artificial Intelligence Articles.