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Artificial intelligence, working, type of Artificial intelligence, tools, Languages used and applications in Industry

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

Way of working AI:

As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply just one component of AI, which is machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. There isn’t just one programming language which is synonymous with AI, some of the popular ones are Python, R and Java.In general, AI systems work by ingesting large amounts of labelled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.

AI programming focuses on three cognitive skills: learning, reasoning and self-correction.

Advantages and disadvantages of AI:


·         Reduction in Human Error

·         Takes risks instead of Humans:

·          Available 24x7

·          Helping in Repetitive Jobs

·          Digital Assistance

·         Faster Decisions

·         New Inventions

·         Reduced time for data-heavy tasks

·         Delivers consistent results

·         AI-powered virtual agents are always available


·         Expensive

·         Requires deep technical expertise

·         Limited supply of qualified workers to build AI tools

·         Only knows what it's been shown

·         Lack of ability to generalize from one task to another.


Artificial intelligence type:

Type 1: Reactive machines.These AI systems have no memory and are task specific. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to predict future ones.

Type 2: Limited memory.These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.

Type 3: Theory of mind.Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behaviour, a necessary skill for AI systems to become integral members of human teams.

Type 4:Self-awareness.In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.

Examples of AI and its current state:

There are 6 different of AI examples

·         Automation.When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes.

·         Machine learning.This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:

o    Supervised learning.Data sets are labelled so that patterns can be detected and used to label new data sets.

o    Unsupervised learning.Data sets aren't labelled and are sorted according to similarities or differences.

o    Reinforcement learning.Data sets aren't labelled but, after performing an action or several actions, the AI system is given feedback.

·         Machine vision.This technology gives a machine the ability to see.Machine vision captures and analyses visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.

·         Natural language processing (NLP).This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.

·         Robotics.This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in assembly lines for car production or by NASA to move large objects in space. Researchers are also using machine learning to build robots that can interact in social settings.

·         Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.

Future of Artificial Intelligence:

AI is used in all industries now a days. It has emerged as one of the most exciting and advanced technologies of our time. Robotics, Big Data, IoT, etc. are all fueled by AI. There are companies around the world conducting extensive research on Machine Learning and AI. At the current growth rate, it is going to be a driving force for a very long time in the future as well.

AI helps computers generate huge amounts of data and use it to make decisions and discoveries much faster as compared to a human. It already had a big of impact on our world. If used responsibly, it can end up massively benefiting human society in the future.


We can distinguish two types of AI applications to enhance people’s daily lives.


Voice assistants, image recognition for face unlock in cellphones, and ML-based financial fraud detection are examples of AI software currently being used in everyday life. Typically, just download the AI software from an online store and use it, no need for any other device.


Drones, self-driven vehicles, assembly-line robots, and the Internet of Things (IoT) are examples of AI implementation in hardware. This includes the development of specific devices that incorporate AI capabilities.

We need AI in our lives such as Artificial Intelligence call centres or Artificial Intelligence in games. So, let’s take a deep dive into the usage of Artificial Intelligence in everyday life.


AI and ML-driven software and devices emulate human thought processes to help society move forward with the digital revolution. AI systems recognize their surroundings, handle what they see, resolve issues, and take action to assist with chores to make daily life more comfortable.


People regularly check their social media accounts, including Facebook, Twitter, Instagram, and other platforms. AI is not only working behind the scenes to customize your feeds, but it’s also detecting and eliminating false news.


Twitter has started to rely on artificial intelligence behind the scenes to improve its product, from suggesting tweets to fighting offensive or racist material and improving the user experience. To learn in time what customer’s preferences are, they use advanced neural networks that process a large amount of data.


Deep learning assists Facebook in extracting value from a growing number of its unstructured data sets, acquired from almost 2 billion users updating their statuses 293,000 times a minute. Most of Facebook’s deep learning technology is based on the Torch framework, which focuses on deep learning and neural networks.


Instagram also makes use of big data and artificial intelligence to target advertising and combat cyberbullying, as well as remove abusive comments. As the number of posts on the platform increases, artificial intelligence is becoming increasingly important in showing people information they might be interested in, removing spam, and improving user experience.



Chatbots are AI programs that can answer questions and deliver relevant content to consumers with common queries. Sometimes, chatbots are so successful that it appears as if you’re interacting with a real person.


Drones, or unmanned aerial vehicles (UAVs), are already present in our skies, conducting surveillance and providing delivery services in various plans, among them the delivery of medicines and necessities to confined-to-home elderly persons COVID-19 restricts their mobility.


Virtual assistants like Siri, Cortana, Google Assistant, and others have made our lives easier. They’ve acted as a fantastic friend, reminding us to pick up a package and telling us jokes. This software can recognize speech patterns and offers natural language processing functionality. It also has the potential to learn about the user via monitoring working hours, screentime, and other related data. Because of its use of artificial intelligence, it can practice learning and listen like a person.


Around mealtime, tempting notifications about breakfast, lunch, and dinner frequently arrive from online ordering applications and sites. This is made possible by artificial intelligence software that keeps track of the time when you’re most likely to order food. Not only that but AI also keeps track of the types of foods you enjoy eating and offers comparable cuisines based on your preferences.


Other excellent examples of AI are the music and video streaming services that we use daily. AI makes judgments for you when you use Spotify, Netflix, or YouTube. These platforms provide suggestions based on your preferences.


While we’re on the subject of banking, let’s speak about fraud for a moment. Every day, a bank handles millions of transactions. It’s tough for a typical person to keep track of all that and analyze it.

Furthermore, the appearance of fraudulent transactions varies on a daily basis. With AI and machine learning algorithms, you may process thousands of transactions in a second. Furthermore, they may be trained to recognize what problematic activities might appear like and prepare for future problems.

Finally, when you apply for a loan or obtain a credit card, a bank must verify your application. The software can now handle numerous elements, such as your credit score, financial history, etc. This means that approval waiting periods are reduced, and less room for error.


Consumer’s online shopping experience is becoming more personalized and streamlined, owing to artificial intelligence (AI) technologies such as machine learning.

AI-powered automated warehousing and supply chain management systems assist commercial enterprises in better managing their logistics. At the same time, sentiment analysis allows them to better understand and react to their consumers’ needs and behavior.


The job of AI engineers behind navigation apps like Google Maps and Waze never ends. Only satellite images, which are updated every second, can effectively be cross-checked by ML algorithms unleashed on them.


Vehicle rentals, such as Uber and Lyft, are extremely useful since they can provide you with a car nearly every time you need one. However, we underestimate the AI-based programs that operate on them. We frequently get a notification to hail a cab before leaving for home from the workplace. How do these applications know when we need a taxi? Because these apps use deep learning technology and have already identified our routine behavior, they can do so. Artificial Intelligence in everyday life is increasing day by day.


In video games, we often compete with a virtual competitor. This virtual player is an embodiment of a real gamer and is designed to duplicate the gaming behavior throughout time with the help of machine learning and programming, providing the user with a delightful gaming experience.


The email communication system is a great deal of fun. Unwanted emails are immediately filtered out and tagged as spam or non-urgent. When creating a new email, the software suggests possible replies. Some email systems also include functions that notify users when it’s time to submit their messages. Artificial intelligence is required for all of these useful features to existing.


Many job-search engines are employing deep learning to understand more and more about the user and his needs. These applications use software that allows consumers to discover the finest possibilities by suggesting jobs, roles, employees, and other pertinent information.


Have you ever wondered how a non-living device responds to your voice command and converts it into text? What happens to the sound of your voice when it’s converted to text? Why don’t we encounter any language difficulties while interacting with our digital assistants? The most popular response to all of these inquiries is that speech recognition and natural language processing are utilized to develop these programs. As we said before, Artificial Intelligence Technology is largely based on these two technologies. As a result, it gives us a variety of intelligent characteristics.


Every country has its own set of rules, but whether or not you think it’s a good idea to have a massive surveillance system is up for debate. We may all disagree about the ethics of utilizing such a system, but there’s no doubt that it’s being utilized, and AI plays an important role in this.


Whenever you try to look something up on Google, you’ve undoubtedly seen a few of the auto-populated search terms in the search bar. This is Google’s Autocomplete feature, which displays prognostications as you type each character of your query.

Google uses various technologies in its search engine, including Neural Networks, Deep Learning, Machine Learning, and Artificial Intelligence. It would be hard to imagine life without Google’s search algorithms because they are effective.


The confluence of AI and the Internet of Things (IoT) provides a wealth of possibilities for creating smarter home technologies that need less human intervention to function. The AI component, on the other hand, aids these gadgets in learning from data.

Create, communicate, aggregate, analyze, and act are the five major phases in IoT-enabling. The effectiveness of the “act” step is dependent on how thorough the analysis was; AI adds a lot of value to this process.

The value of data gathered by IoT devices is unlocked via IoT’s adaptability. Learnings from this information over time help IoT technology to respond to human signals and need more effectively.


AI has the potential to improve various things, including home management. However, it may also assist with various activities outside the realm of typical AI research. For example, AI might be used to help chefs prepare meals more efficiently.


Autocorrect does more than just correct typos; it also suggests the user with the next word in a sentence, making it an essential aid for anyone who types quickly. Have you ever wondered how autocorrect understands what you want to write? It’s simple: With the help of pattern recognition (AI), it can save your most frequently used letters and send you the most frequent texts. This establishes a pattern in the device’s artificial memory, which is later used for suggestions and forecasts.


The medical application of AI is incredible. Devices are being utilized to identify and treat damaged tissues, including Google AI Eye Doctor, which works with an Indian eye health chain to develop a cure for diabetic retinopathy, a disease that causes blindness. IBM Watson Health also uses computer algorithms to monitor patients’ health. With AI and cutting-edge cognitive capabilities like data processing, healthcare has been revolutionized extraordinarily..

Artificial intelligence and its impact on everyday life

In recent years, artificial intelligence (AI) has woven itself into our daily lives in ways we may not even be aware of. It has become so pervasive that many remain unaware of both its impact and our reliance upon it. 

From morning to night, going about our everyday routines, AI technology drives much of what we do. When we wake, many of us reach for our mobile phone or laptop to start our day. Doing so has become automatic, and integral to how we function in terms of our decision-making, planning and information-seeking.

Once we’ve switched on our devices, we instantly plug into AI functionality such as:

·          face ID and image recognition

·          emails

·          apps

·          social media

·          Google search

·          digital voice assistants like Apple’s Siri and Amazon’s Alexa

·          online banking

·          driving aids – route mapping, traffic updates, weather conditions

·          shopping

AI touches every aspect of our personal and professional online lives today.


Working of AI:


AI is built upon acquiring vast amounts of data. This data can then be manipulated to determine knowledge, patterns and insights. The aim is to create and build upon all these blocks, applying the results to new and unfamiliar scenarios.

Such technology relies on advanced machine learning algorithms and extremely high-level programming, datasets, databases and computer architecture. The success of specific tasks is, amongst other things, down to computational thinking, software engineering and a focus on problem solving.

Artificial intelligence comes in many forms, ranging from simple tools like chatbots in customer services applications, through to complex machine learning systems for huge business organisations. The field is vast, incorporating technologies such as:

Machine Learning (ML): Using algorithms and statistical models, ML refers to computer systems which are able to learn and adapt without following explicit instructions. In ML, inferences and analysis are discerned in data patterns, split into three main types: supervised, unsupervised and reinforcement learning.

Narrow AI. This is integral to modern computer systems, referring to those which have been taught, or have learned, to undertake specific tasks without being explicitly programmed to do so. Examples of narrow AI include: virtual assistants on mobile phones, such as those found on Apple iPhone and Android personal assistants on Google Assistant; and recommendation engines which make suggestions based on search or buying history.

Artificial General Intelligence (AGI): At times, the worlds of science fiction and reality appear to blur. Hypothetically, AGI – exemplified by the robots in programmes such as Westworld, The Matrix, and Star Trek – has come to represent the ability of intelligent machines which understand and learn any task or process usually undertaken by a human being.

Strong AI: This term is often used interchangeably with AGI. However, some artificial intelligence academics and researchers believe it should apply only once machines achieve sentience or consciousness.

Natural Language Processing (NLP): This is a challenging area of AI within computer science, as it requires enormous amounts of data. Expert systems and data interpretation are required to teach intelligent machines how to understand the way in which humans write and speak. NLP applications are increasingly used, for example, within healthcare and call centre settings.


Deepmind: As major technology organisations seek to capture the machine learning market. they are developing cloud services to tap into sectors such as leisure and recreation. For example, Google’s Deepmind has created a computer programme, AlphaGo, to play the board game Go, whereas IBM’s Watson is a super-computer which famously took part in a televised Watson and Jeopardy! Challenge. Using NLP, Watson answered questions with identifiable speech recognition and response, causing a stir in public awareness regarding the potential future of AI.


List of AI Tools & Frameworks:

From the dawn of mankind, we as a species have always been trying to make things to assist us in day today tasks. From stone tools to modern day machinery, to tools for making the development of programs to assist us.Some of the most important tools and frameworks are:



·         TensorFlow

·         Theano

·         Caffe

·         MxNet

·         Keras

·         PyTorch

·         CNTK

·         Auto ML

·         OpenNN

·         H20: Open-Source AI Platform

·         Google ML Kit

·         Sckit learn


Programming lanuages for AI:


Java, Python, Lisp, Prolog, and C++ are major AI programming languages used for artificial intelligence capable of satisfying different needs in developing and designing software. It is up to a developer to choose which AI languages will gratify the desired functionality and features of the application requirements.




Python is among developers’ favourite AI programming languages in development because of its syntax simplicity and versatility. Python is very encouraging for machine learning for developers as it is less complex compared to C++ and Java. It is also a very portable language used on platforms including Linux, Windows, Mac OS, and UNIX. It is likable of its features such as Interactive, interpreted, modular, dynamic, portable, and high level, making it more unique than Java.

Moreover, Python is multi-paradigm programming supporting object-oriented, procedural, and functional programming styles. AI programming with Python supports neural networks and the development of NLP solutions thanks to its simple function library and, more so, ideal structure.



  • Python has a wide variety of libraries and tools.
  • Supports algorithm testing without having to implement them.
  • Python supporting object-oriented design increases a programmer’s productivity.
  • Compared to Java and C++, Python is faster in development.



  • Developers accustomed to using Python face difficulty adjusting to completely different syntax when they try using other languages for AI coding.
  • Unlike C++ and Java, Python works with the help of an interpreter, which makes compilation and execution slower in AI development.
  • Not suitable for mobile computing. Python is unsuitable for AI meant for mobile applications due to its weak language for mobile computing.



C++ is the fastest computer language. Its speed is appreciated for AI programming projects that are time-sensitive. It provides faster execution and has less response time which is applied in search engines and the development of computer games. In addition, C++ allows extensive use of algorithms and is efficient in using statistical AI techniques. Another important factor is that C++ supports the re-use of programs in development due to inheritance and data-hiding, thus efficient in time and cost-saving. C++ is appropriate for machine learning and neural network.



  • Good for finding solutions for complex AI problems.
  • Rich in library functions and programming tools collection.
  • C++ is multi-paradigm programming that supports object-oriented principles and is thus useful in achieving organized data.



  • Poor in multitasking; C++ is suitable only for implementing core or the base of specific systems or algorithms.
  • It follows the bottom-up approach, thus, highly complex, making it hard for newbie developers to use it for writing AI programs.




Java is another programming language to answer ‘which computer language is used for artificial intelligence. Java is also a multi-paradigm language that follows object-oriented principles and the principle of Once Written Read/Run Anywhere (WORA). It is an AI programming language that can run on any platform that supports it without recompilation.

Java is one of the most commonly used and not just in AI development. It derives a major part of its syntax from C and C++ in addition to its lesser tools. Java is not only appropriate for NLP and search algorithms but also neural networks.



  • Very portable; it is easy to implement on different platforms because of Virtual Machine Technology.
  • Unlike C++, Java is simple to use and even debug.
  • Java has an automatic memory manager, which eases the work of the developer.




  • Java is, however, slower than C++; it has less speed in execution and more response time.
  • Though highly portable, Java would require dramatic changes in software and hardware to facilitate on older platforms.
  • Java is a generally immature programming AI language as there are still some developments ongoing.




Lisp is another language used for artificial intelligence development. It is a family of computer programming languages and is the second oldest programming language after Fortran. Lisp has developed over time to become a strong and dynamic language in coding.

Some consider Lisp as the best programming language for AI due to the favour liberty it offers developers. Lisp is used in AI because of its flexibility for fast prototyping and experimentation, which in turn facilitates Lisp to grow to a standard AI language. For instance, Lisp has a unique macro system that facilitates the exploration and implementation of different levels of Intellectual Intelligence.

Unlike most AI programming languages, Lisp is more efficient in solving specific as it adapts to the needs of the solutions a developer is writing. It is highly suitable for inductive logic projects and machine learning.



  • Fast and efficient in coding as compilers instead of interpreters support it.
  • An automatic memory manager was invented for Lisp; therefore, it has a garbage collection.
  • Lisp offers specific control over systems resulting in their maximum use.



  • Few developers are well acquainted with Lisp programming.
  • Being a vintage programming language artificial intelligence, Lisp requires the configuration of new software and hardware to accommodate its use.






Prolog is one of the oldest programming languages, thus suitable for the development of programming AI. Like Lisp, it is also a primary computer language for artificial intelligence. It has mechanisms that facilitate flexible frameworks developers enjoy working with. It is a rule-based and declarative language as it contains facts and rules that dictate its artificial intelligence coding language.

Prolog supports basic mechanisms such as pattern matching, tree-based data structuring, and automatic backtracking essential for AI programming. Other than its extensive use in AI projects, Prolog is used to create medical systems.




  • Prolog has a built-in list handling essential in representing tree-based data structures.
  • Efficient for fast prototyping for AI programs to be released modules frequently.
  • Allows database creation simultaneous with the running of the program.



  • Despite Prolog’s old age, it has not been fully standardized in that some features differ in implementation, making the developer’s work cumbersome.



Thanks to the continuous increase in data available to machine learning algorithms, Artificial Intelligence is evolving. This allows Artificial Intelligence to function more effectively because trained ML models may be modelled to work better with users’ wide array of real data. The following are few of the most prominent examples of AI in everyday life that are widespread and easy to use. This proves that AI is already changing our lives by allowing us to be more productive while also putting our efforts toward actual problems.

The future is now. AI technology will only accelerate, expand, and become more important to all industries and virtually every aspect of our everyday lives in the future

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