OVERVIEW OF ARTIFICIAL INTELLIGENCE

AND APPLICATIONS

K J SARMA

Freelance Research

Retired Professor

kjsarmalsm@ieee.org

ABSTRACT: Following the reasoning process, humans were able to land themselves into something artificial. This intelligence is the simulation of human intelligence processes which is done by machines, especially computer systems.

These processes include learning, reasoning, and self-correction. Its applications consist of expert systems, speech recognition, and machine vision. Artificial Intelligence is advancing dramatically. It is already transforming our world socially, economically and politically. The process of finding solutions in a massive array of data can still be referred to as AI, even though the training of the system using samples of training data and choosing from a variety of paradigms seems too slavish to call it intelligence.  Artificial Intelligence is prominent in people’s life. Artificial intelligence was developed during the turn of the twenty-first century, significantly expanding the application of technology in a variety of fields.  We have traced the basic definitions, understand and the genesis of the development of the AI system. Also we have outlined the  applications in several fields

KEY WORDS:  A.I., M.L., Special definitions, history, division of a.i., stages in implementation, core practices, creating a system and applications,

INTRODUCTION:

Artificial Intelligence (AI) is the machine-displayed intelligence that simulates human behavior or thinking and can be trained to solve specific problems. AI is a combination of Machine Learning techniques and Deep Learning.  Types of artificial intelligence models are trained using vast volumes of data and have the ability to make intelligent decisions.

AI planning and forecasting is a field of artificial intelligence used to make scientific predictions. AI planning and forecasting uses algorithms to make scientific predictions. A I  is also used to  forecast trends about the future without requiring oversight or  human judgment.  Of course causes far less error and often outperforms data scientists. Studies comparing AI predictions with expert predictions from humans almost always showed artificial intelligence as predictions and estimates are more close to real life. While algorithms and AI will not replace human intelligence in the future, their ability to analyze data will always be an aid to data scientists and forecasters.  AI planning tools use time series data to estimate future developments for many industries, like sales, healthcare, financial services, and manufacturing.

The main characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning (ML). This refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.   In a more concise fashion

*Artificial intelligence (AI) refers to the simulation or approximation of human intelligence in machines.

*The goals of artificial intelligence include computer-enhanced learning, reasoning, and perception.

*AI is being used today across different industries from finance to healthcare.

*Weak AI tends to be simple and single-task oriented, while strong AI carries on tasks that are more complex and human-like.

*Some critics fear that the extensive use of advanced AI and may  have a negative effect on society. AI  learns reasons and does self-correction.

Starting with the identifying pattern, it has made data more efficient. It has become more important  to gain more insight of the situation  from  the data and businesses which have changed a lot in the course of time. Human related  data is getting analyzed to map poverty and climate change.

The automation in agricultural practices and irrigation is happening at a fast pace. How can we forget the healthcare sector and predictions of consumption patterns, energy-usage and waste-management? We need more knowledge reasoning, Planning, Machine Learning, Natural Language Processing, Computer Vision, Robotics, and Artificial General Intelligence

AI is being applied in a variety of fields to get insights into user behavior and make data-driven suggestions. Google’s predictive search algorithm are used to analyze user data from the past to forecast what a user would put next in the search field. Netflix leverages previous user data to suggest what movie a user should watch next, keeping them hooked on the platform and increasing their viewing duration. Facebook uses historical user data to automatically propose tags for your friends based on their facial traits in their photos. Large corporations employ AI to make the lives of their customers easier.

The uses of Artificial Intelligence would broadly fall under the data processing category, which would include Self-driving cars; Smart Assistants; Disease mapping; Manufacturing robots; Virtual Travel booking agent.

Also one would be interested to learn, what is and what AI isn’t:     Handwriting recognition and voice recognition used to be called AI. However, with the availability of commercial systems to do these things, they no longer fall under the AI umbrella. AI always seems to incorporate the unknown and mysteries. In simple words Artificial Intelligence is made up of the phrases Artificial and Intelligence, with Artificial referring to “man-made” and Intelligence referring to “thinking power,” so AI refers to “a man-made thinking power.”

SEVERAL DEFINITIONS OF A.I.

1.  The science and engineering of making intelligent machines as intelligence is the computational part of the ability to achieve goals in the world (actually John McCarthy coined the term ‘Artificial Intelligence’ in 1955)

2. Makes a machine behave in ways that would be called intelligent, if a human were so behaving”

3. It is the science of making machines do things that would require intelligence if done by men. This definition is by A.I. pioneer Marvin Minsky in 1968.

4. The science of making machines smart” (Demis Hassabis, CEO and founder of Deep Mind, now part of Google)

5.  An intelligent machine (Google’s Avinash Kaushik)

6.  The next, logical step in computing: a program that can figure out things for itself. It’s a program that can reprogram itself by Jim Sterne, author of Artificial Intelligence for Marketing

7. Anything a machine does to respond to its environment to maximize its chances of success” Steven Struhl, author of Artificial Intelligence Marketing and Predicting Consumer Choice

8. Technologies emerging today that can understand, learn, and then act based on that information” (PwC‘s definition)

9. Anything that makes machines act more intelligently” (IBM‘s definition)

10.  A constellation of technologies that extend human capabilities by sensing, comprehending, acting and learning – allowing people to do much more” (Accenture‘s definition)

11. Getting computers to do tasks that would normally require human intelligence” (Deloitte’s definition)

12. The ability of machines to exhibit human-like intelligence”  (McKinsey‘s definition)

13. A field of computer science that focuses on creating machines that can learn, recognize, predict, plan, and recommend — plus understand and respond to images and language (Salesforce‘s definition).

14. Technology that thinks and acts like humans” (popular business use of the term as reported in a 2015 Narrative Science survey)

15. A subfield of computer science aimed at the development of computers capable of doing things that are normally done by people — in particular, things associated with people acting intelligently” (Definition in Practical A.I. for Dummies)

16. The replication of human analytical and/or decision-making capabilities“ Steven Finlay (Author of Artificial Intelligence and Machine Learning for Business, 2017)

17. The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings” (definition in Encyclopedia Britannica by Prof. B.J. Copeland)

18. A set of computer science techniques that enable systems to perform tasks normally requiring human intelligence” Economist Intelligence Unit‘s definition

19. A computerized system that exhibits behavior that is commonly thought of as requiring intelligence” US Government definition (NSTC)

20. Intelligence demonstrated by a machine or by software…[where] intelligence measures an agent’s general ability to achieve goals in a wide range of environments” (Calum Chase, author of Surviving A.I.)

21. intelligence exhibited by machines, rather than humans or other animals (natural intelligence, NI)” (Wikipedia definition 1)

22. Intelligence exhibited by machines or software” (Wikipedia definition 2)

23.The study of agents that receive percepts from the environment and perform actions” (the classic textbook definition from Peter Norvig and Stuart J. Russell that classifies A.I. into four areas (and focuses on the fourth), agents that 1) think like humans, 2) act like humans, 3) think rationally, 4) act rationally – “a rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best-expected outcome”.

 

UNDERSTANDING ARTIFICIAL INTELLIGENCE:

When most people hear the term artificial intelligence, the first thing they usually think of is robots. That's because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined and mimicked so that it executes the tasks, from the most simple to more complex. The goals of artificial intelligence include mimicking human cognitive activity. Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning, and perception which can be concretely defined.

As technology is advancing old benchmarks which defined artificial intelligence got outdated. For example, machines that calculate basic functions or recognize text through optical character recognition are no longer considered. In other words   AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approaches based on mathematics, computer science, linguistics, psychology, and more.

The first wave of early AI techniques is known as 'symbolic AI' or expert systems. Here, human experts create precise rule-based procedures – known as 'algorithms' – which a computer can follow, step by step, to decide how to respond intelligently to a given situation. Fuzzy logic is a variant of the approach that allows for different levels of confidence about a situation, which is useful for capturing intuitive knowledge. Thus the algorithm can make good decisions in the face of wide-ranging and uncertain variables which interact with each other. Symbolic AI is at its best in constrained environments which are not temporal, where the rules are strict and the variables are unambiguous and quantifiable. These methods remain very relevant and are still applied successfully in several domains.

The second wave of AI comprises more recent 'data-driven' approaches which have developed rapidly over the last two decades and are largely responsible for the current AI resurgence. These automate the learning process of algorithms, bypassing the human experts of first wave AI. Artificial neural networks (ANNs) are inspired by the functionality of the brain. Inputs are translated into signals which are passed through a network of artificial neurons to generate outputs that are interpreted as responses to the inputs. Adding more neurons and layers allow ANNs to tackle more complex problems.

Deep learning simply refers to ANNs with several layers. Machine learning (ML) refers to the transformation of the network, so that these outputs are considered useful. ML algorithms can automate this learning process by making gradual improvements to individual ANNs, by applying evolutionary principles so that they result in  gradual improvements in large populations of ANNs.

The third wave of AI refers to speculate possible future waves of AI. The first and second wave techniques are described as 'weak' or 'narrow', in the sense that they behave intelligently in specific tasks. The ‘strong’ or 'general' AI refers to algorithms that can exhibit intelligence in a wide range of contexts and problem spaces. Such artificial general intelligence (AGI) is not possible with current technology and would require paradigm shifting advancement. Some potential approaches being advanced evolutionary methods, quantum computing and brain emulation. Other forms of speculative future AI such as self-explanatory and contextual AI can seem modest in their ambitions.

AI implementation requires considerable investment in the team. The team typically consists of data engineers, data scientists & domain experts to build good mathematical algorithms. These algorithms are translated into software solutions by product development teams.

Stuart Russell and Peter Norvig state that AI tech is “the study of agents that receive percepts from the environment and perform actions.” The four approaches to AI tech, as defined by them are: 1. Thinking humanly, 2. Thinking rationally, 3. Acting humanly, 4. Acting rationally.

HISTORY AND TIME LINE OF THE DEVELOPMNT OF A.I. :

This technology is much older than you would imagine. Even there are the myths of Mechanical men in Ancient Greek and Egyptian Myths. Following are some milestones in the history of AI which defines the journey from the AI generation to till date development.

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The seeds of modern AI were planted by philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols.

This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

Year 1943: The first work which is now recognized as AI was done by Warren McCulloch and Walter pits in 1943. They proposed a model of artificial neurons.

Year 1949: Donald Hebb demonstrated an updating rule for modifying the connection strength between neurons. His rule is now called Hebbian learning.

Year 1950: The Alan Turing who was an English mathematician and pioneered Machine learning in 1950. Alan Turing publishes "Computing Machinery and Intelligence" in which he proposed a test. The test can check the machine's ability to exhibit intelligent behavior equivalent to human intelligence, called a Turing test.

The birth of Artificial Intelligence (1952-1956)  : Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program"Which was named as "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and find new and more elegant proofs for some theorems.

Year 1956: The word "Artificial Intelligence" first adopted by American Computer scientist John McCarthy at the Dartmouth Conference. For the first time, AI coined as an academic field.  At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the enthusiasm for AI was very high at that time.

The golden years-Early enthusiasm (1956-1974):  Year 1966: The researchers emphasized developing algorithms which can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named as ELIZA.  Year 1972: The first intelligent humanoid robot was built in Japan which was named as WABOT-1.

The first AI winter (1974-1980): The duration between years 1974 to 1980 was the first AI winter duration. AI winter refers to the time period where computer scientist dealt with a severe shortage of funding from government for AI researches.  During AI winters, an interest of publicity on artificial intelligence was decreased.

A boom of AI (1980-1987):  Year 1980: After AI winter duration, AI came back with "Expert System". Expert systems were programmed that emulate the decision-making ability of a human expert.  In the Year 1980, the first national conference of the American Association of Artificial Intelligence was held at Stanford University.

The second AI winter (1987-1993):  The duration between the years 1987 to 1993 was the second AI Winter duration.   Again Investors and government stopped in funding for AI research as due to high cost but not efficient result. The expert system such as XCON was very cost effective.

The emergence of intelligent agents (1993-2011):  Year 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and became the first computer to beat a world chess champion.   Year 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner.   Year 2006: AI came in the Business world till the year 2006. Companies like Facebook, Twitter, and Netflix also started using AI.

Deep learning, big data and artificial general intelligence (2011-present) :  Year 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where it had to solve the complex questions as well as riddles. Watson had proved that it could understand natural language and can solve tricky questions quickly.  Year 2012: Google has launched an Android app feature "Google now", which was able to provide information to the user as a prediction.  Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in the infamous "Turing test."  Year 2018: The "Project Debater" from IBM debated on complex topics with two master debaters and also performed extremely well.

Google has demonstrated an AI program "Duplex" which was a virtual assistant and which had taken hairdresser appointment on call and lady on other side didn't notice that she was talking with the machine.

Now AI has developed to a remarkable level. The concept of Deep learning, big data, and data science are now trending like a boom. Nowadays companies like Google, Facebook, IBM, and Amazon are working with AI and creating amazing devices. The future of Artificial Intelligence is inspiring and will come with high intelligence.

In 2018-2019,  Key themes are singled out by these experts include deep learning advancements, transfer learning, the limitations of machine learning, the changing landscape of natural language processing, and much more.

Key trends expected in 2019 being auto ML, Market automation.  Real-time speech generation on mobile devices, and  Self-driving taxis remain in the test/ PoC phase.

The needs of our clients, who now have access to terabytes of data thanks to the Internet and the IoT, have evolved. We have  chosen to put ourselves in a position  to accompany them in the management. And exploitation of their dataIn 2- team around a  core workforce of 75% Data Scientists, whereas more traditional firms  have invested more in Data Analysts.  Heka’s positioning is therefore closer to the pure players in AI. We have  completed this Data Science expertise with profiles of Data Engineers, Devops Engineers, Web Developers, UI/UX Designers during 2022.

Over the next 18 months, we plan to double the size of the team. This will allow us to address clients in new geographies (Middle East, Asia, etc.) and to offer an even wider range of products and services (quantitative and actuarial services, etc.). We will also invest in the development of emerging technologies: Block chain, Cryptos,  Quantum Computing, etc.

FUNDAMENTAL DIVISIONS OF A.I. BASED ON FUNCTIONALITIES: 

1. Reactive Machines: Reactive machines use past experiences to determine future actions. They don’t refer to past experiences, and cannot improve with practice. Reactive machines simply perceive the world and react to it. They have no memory and cannot use past experiences to inform future ones. These are the most basic types of AI systems that can’t form or use past experiences to make future decisions. An example of a reactive machine is Deep Blue, IBM’s supercomputer that rose to prominence, when it beaten the chess world champion, Garry Kasparov, in 1990. The computer identifies chess pieces and knows how to move every one of them. It can also make predictions about an opponent’s move, based on that knowledge.

2. Limited Memory:  Limited memory machines retain data for a short period of time and use data for a specific period of time. They cannot add it to a library of their experiences and the decision-making functions are used in autonomous vehicles.  A lot of self-driving cars use Limited Memory technology, store data such as the recent speed of nearby cars, the distance of such cars, the speed limit, and other information that can help them navigate on roads.  Artificial systems of this type have the ability to look past the present moment and learn from experiences. An excellent example of these systems is self-driving cars. By learning, observing direction and speed from other cars over a prolonged period of time, they add the derived knowledge of their preprogrammed understanding, traffic lights and other vital elements of driving.

3. Theory of Mind:  Theory of mind is all about imitating the mental models, of  the human brain. It forms representations about the world, starting from thoughts, emotions, and memories.   AI tech systems in this category can form their own representations of the world and the entities that make it. Theory in mind is just that — understanding that living entities in the world have thoughts and emotions and that their behavior is impacted by them. Artificial intelligence systems that have this ability can understand motivations, intentions which are the foundation of the next-gen of artificial intelligence machines.

4. Self-awareness:  Self-awareness in machines is when they understand the current state and can use the information to infer what others are feeling. This type of AI will be in use in the near future.  AI only exists hypothetically. Such systems understand their internal traits, states, conditions and perceive human emotions. These machines will be smarter than the human mind. This type of AI will not only be able to understand and evoke emotions in those it interacts but also have emotions, needs, and beliefs of its own.

Also in the future, predictive analytics and artificial intelligence will play an even more fundamental role in content creation and also in the software fields.   The  open-source information, artificial intelligence, opportunities across the globe are  for future domains related to  health, environment, public safety, and security.

Thus Artificial Intelligence (AI) is a method of programming a computer, robot, or other object to think like a smart human. AI is the study of how the human brain thinks, learns, makes decisions, and works to solve problems. The goal of artificial intelligence is to improve computer functions that are linked to human understanding, such as reasoning, learning, and problem-solving.

A subset of AI called Machine Learning (ML) is most typically utilized when firms install artificial intelligence systems nowadays. Both phrases, AI and ML, are often used interchangeably and ambiguously. Machine learning is an artificial intelligence area that allows computers to learn without being explicitly taught.

Machine learning (ML) is a subfield of artificial intelligence that allows machines to learn without being explicitly programmed. The majority of artificial intelligence in this category is paired with big data. Applications that make suggestions, such as Netflix or Spotify, use this approach.  Learn more about Machine Learning (ML) and how it works in detail in our article Machine Learning: What is

Further a subset of Machine learning Deep Learning (DL) is a type of machine learning that trains a computer to perform human-like tasks, such as speech recognition, image identification, or predictions. The computer is prepared to learn by recognizing patterns using many processing layers.  Using deep learning, a computer model learns to execute categorization tasks from images, text, or sound. Deep learning models can achieve cutting-edge accuracy, sometimes even outperforming humans. A vast set of labeled data and neural network topologies with multiple layers are used to train models.

TYPES OF ARTIFICIAL INTELLIGENCE: 

Weak AI is also known as narrow AI. It is an AI system that is designed and trained for a specific type of task. For example – Siri and Alexa are weak AI. This categorization happens with the help of unsupervised programming.  It is because they already have programmed responses and therefore they classify things accordingly. So, observe carefully when you ask Alexa to play a song. The algorithm will respond by playing a song, but it is only responding to its programming.  Strong AI is more like the human brain and is also known as artificial general intelligence. It has cognitive abilities that help to perform unfamiliar tasks and commands. It can find the solution to a problem and works beyond a preprogrammed algorithm.  Visual perception, speech recognition, decision making, and translations between languages, are all examples of strong AI.   There is another method of division in which 3 types of artificial Intelligence are suggested, they are

Artificial Narrow Intelligence (ANI): The only sort of artificial intelligence we have effectively generated yet is artificial narrow intelligence (ANI), often known as weak AI or narrow AI. Narrow AI is goal-oriented, designed to accomplish a single task – such as facial recognition, speech recognition/voice assistants, driving a car, or surfing the internet – and is extremely competent at fulfilling the objective.

Artificial General Intelligence (AGI): AI whose purpose is general and whose efficiency can be applied to diverse tasks. This type of artificial intelligence can improve itself by learning and is the closest to the human brain in terms of capacities.   This type of knowledge is found in speech recognition systems and voice assistants.  Artificial general intelligence (AGI), also known as strong AI or deep AI, is the idea of a machine with general intelligence that can learn and apply its intelligence to solve any problem.   AGI can think, understand, and act in a manner that is indistinguishable from that of a human.

Artificial Super Intelligence (ASI): This AI concept is way more sophisticated than any other artificial intelligence system or even a human brain. The main trait of ASI is that it can contemplate about abstractions of which humans are unable to think. Its neural network exceeds that of humans’ billions of neurons. Artificial super intelligence (ASI) is a hypothetical AI that does more than mimic or understands human intelligence ASI is related to  self-awareness  and exceeds human intelligence and ability.

 

INTELLIGENCE HARDWARE:

There are also chips optimized for different parts of the machine learning pipeline. These may be better for creating the model because it can juggle large datasets — or, they may excel at applying the model to incoming data to see if the model can find an answer in them. These can be optimized to use lower power and fewer resources to make them easier to deploy in mobile phones or places where users will want to rely on AI but not to create new models.

Additionally, there are basic CPUs that are starting to streamline their performance for ML workloads. Traditionally, many CPUs have focused on double-precision floating-point computations because they are used extensively in games and scientific research.

Lately, some chips are emphasizing single-precision floating-point computations because they can be substantially faster. The newer chips are trading off precision for speed because scientists have found that the extra precision may not be valuable in some common machine learning tasks — they would rather have the speed.  Many of the chips designed for accelerating artificial intelligence algorithms rely on the same basic arithmetic operations as regular chips. They add, subtract, multiply and divide as before. The biggest advantage they have is that they have many cores, often smaller, so they can process this data in parallel.

The architects of these chips usually try to tune the channels for bringing the data in and out of the chip because the size and nature of the data flows are often quite different from general-purpose computing. Regular CPUs may process many more instructions and relatively fewer data. AI processing chips generally work with large data volumes.

 The main advantage of AI hardware  is speed. It is not uncommon for some benchmarks to show that GPUs are more than 100 times or even 200 times faster than a CPU. Not all models and all algorithms, though, will speed up that much and some benchmarks are only 10 to 20 times faster. A few algorithms aren’t much faster at all.

 In the right combinations, GPUs and TPUs can use less electricity to produce the same result. While GPU and TPU cards are often big power consumers, they run so much faster that they can end up saving electricity. This is a big advantage when power costs are rising. They can also help companies produce “greener AI” by delivering the same results while using less electricity and consequently producing less CO2.

The specialized circuits can also be helpful in mobile phones or other devices that must rely upon batteries or less copious sources of electricity. Some applications, depend upon AI hardware for very common tasks like waiting for the “wake word” used in speech recognition.  Faster, local hardware can also eliminate the need to send data over the internet to a cloud. This can save bandwidth charges and electricity when the computation is done locally.

 

 

MAIN STAGES OF IMPLEMENTING OF AN AI-BASED PROJECT

Firstly we must know the problem and the customer’s expectations. At the very beginning, we create a business hypothesis (hypothetical business case) which will be verified at the next stage of the process. Based on an interview with the customer, we define the problem; determine the initial assumptions of the project and effects that would be satisfactory for the customer in terms of quality, and return on investment (ROI) like measurable profits and benefits.

The AI solution which customer thinks would be a good success will be determined.   It may be noted that the benefits do not have to end with the savings resulting from the use of an AI solution. The calculations should also take into account indirect benefits – resulting from the fact that, for example, people involved in the process, which will now be handled by the AI solution. We can also look for profits in other places in the company, other processes that will  indirectly improve by  artificial intelligence.

Projects using AI are burdened with risk, so we always try to find alternative solutions that might work better in a given case. Just because a client approaches us with an idea to solve their problem with the help of artificial intelligence does not yet mean that it is the best option. Due to the time-consuming character, costs, and high uncertainty of AI projects, we recommend such implementations only in cases where they are actually likely to benefit the customer much more than other alternatives.

It is worth noting, however, that much depends on the strategy of the customer – one company may not decide to use AI because it will be able to achieve a similar return on investment by more conventional means, while another, despite similar conditions will decide to go forward with the AI/ML implementation as they will consider the prestige that comes with having such solution an important factor in their business strategy.

We also take an initial look at the data that the customer has, which would later be used to feed the AI system. Among other things, we check:  how much data they have at their disposal; for how long they have been collecting data; at what rate and in what quantities it can be collected.

At the very beginning of the cooperation, in which we de facto only recognize the topic, the customer gains awareness of:   what the process of implementing an AI/ML solution looks like ; what are the possible outcomes at each stage of the process;  what potential risks are associated with each of them.

In automating the process of entering data contained in documents into the system

1. Define the areas that need automation:  What are, and where are the barriers?,  In what areas are data and technology prevailing?  Which areas are flexible enough for fast innovation implementation?,  What resources do we have, and do we need to implement AI?  Even though there are  multiple areas where AI could improve things – but you must focus on the top priorities first – the low-hanging fruit. The  priorities to be  considered are -- Increase profits and reduce costs,  Optimize processes key to business success,  Launch more data-driven processes,  Improve interaction with customers and employees

2. Staff of AI team:  People responsible for AI implementation in your company should have different functions and be capable of efficiently managing the processes they’re responsible for. Managers must ensure that team members are properly integrated into the new initiative and deal with potential barriers to successful implementation. 

3. Rethinking of business:     The AI implementation plan is to reimagine the  business.  We need to Find a goal and investigate how you may achieve it, describing the process in detail. For example, a vast HR consulting company needs the employees to log their time in one click – how do you achieve this? To develop AI solutions or reinvent the current inconvenient platform with some ML components. 

4. Higher level management must Support new AI-based processes with agility -- Companies should define AI technologies that will speed up the development of new business capabilities as much as possible and then move on to channel additional investments into other priority areas in the business.  Further  

5. Build data fluency:  Practical conversations about AI require a basic understanding of how data powers the entire process. "Data fluency is a real and challenging barrier -- more than tools or technology combined," said Penny Wand, technology director at IT consultancy West Monroe. In a 2020 report, Forrester Research found that 90% of data and analytics decision-makers surveyed see increased use of data insights as a business priority, yet 91% admitted that using those insights is a challenge for their organizations. Forrester further reported that the gap between recognizing the importance of insights and actually applying them is largely due to a lack of the advanced analytics skills necessary to drive business outcomes. "Executive understanding and support," Wand noted, "will be required to understand this maturation process and drive sustained change."

6. Define your primary business drivers for AI:  "To successfully implement AI, it is critical to learn what others are doing inside and outside your industry to spark interest and inspire action," Wand explained. When devising an AI implementation, identify top use cases and assess their value and feasibility. In addition, consider your influencers and who should become champions of the project, identify external data sources, determine how you might monetize your own data externally, and create a backlog to ensure that the project's momentum is maintained.

7. Identify areas of opportunity:  Focus on business areas with high variability and significant payoff, advised Suketu Gandhi, a partner at digital transformation consultancy Kearney. Teams comprising business stakeholders who have technology and data expertise should use metrics to measure the impact of an AI implementation on the organization and its people.

8. Evaluate your internal capabilities:  Once use cases are identified and prioritized, business teams need to map out how these applications align with your company's existing technology and human resources. Education and training might help bridge the technical skills gap internally, while corporate partners can facilitate on-the-job training. Meantime, outside expertise could help accelerate promising AI applications.

9. Identify suitable candidates:  It's important to narrow a broad opportunity to a practical AI deployment -- for example, invoice matching, IoT-based facial recognition, predictive maintenance on legacy systems or customer buying habits. "Be experimental," Carey said, "and includes as many people [in the process] as you can."

10. Pilot an AI project:  To turn a candidate for AI implementation into an actual project, Gandhi believes a team of AI, data and business process experts is needed to gather data, develop algorithms, deploy scientifically controlled releases and measure impact and risk. 

11. Establish a baseline understanding:  The successes and failures of early AI projects can help increase understanding across the entire company. "Ensure you keep the humans in the loop to build trust, and engage your business and process experts with your data scientists," Wand said. Also recognize that the path to AI starts with understanding the data and good old-fashioned rearview mirror reporting to establish a baseline of understanding. Once a baseline is established, it's easier to see how the actual AI deployment proves or disproves the initial hypothesis.

12. Scale incrementally:  The overall process of creating momentum for an AI deployment begins with achieving small victories, Carey reasoned. Incremental wins can help build confidence across the organization and inspire more stakeholders to pursue similar AI implementation experiments from a stronger, more established baseline. "Adjust algorithms and business processes for scaled release," Gandhi suggested. "Embed [them] into normal business and technical operations."

13. Bring overall AI capabilities to maturity:  As AI projects scale, business teams need to improve the overall lifecycle of AI development, testing and deployment.

14. Continuously improve AI models and processes: Once the overall system is in place, business teams need to identify opportunities for continuous improvement in AI models and processes. AI models can degrade over time or in response to rapid changes caused by disruptions like the COVID-19 pandemic. Teams also need to monitor feedback and resistance to an AI deployment from employees, customers and partners.

Co-existing with machines:   During each step of the AI implementation process, problems will arise. "The harder challenges are the human ones, which has always been the case with technology," Wand said.

A steering committee vested in the outcome and representing the firm's primary functional areas should be established, she added. Instituting organizational change management techniques to encourage data literacy and trust among stakeholders can go a long way toward overcoming "human" challenges.  "AI capability can only mature as fast as your overall data management maturity," Wand advised, "so create and execute a roadmap to move these capabilities in parallel."

THE APPROACHES FOR AI PLANNING:  There are three approaches which dominate artificial intelligence today, including models and methods for types of AI planning and forecasting in business:

Bayesian Networks: Bayesian Networks, one of the oldest forms of AI, scale extremely well for many types of problems and are probably the most widely deployed and influential AI technologies. They aptly perform many highly-diverse AI tasks, such as spam filters that protect your email inbox, the order planning tools in cutting-edge POS systems, and military hardware used in detecting threats to national defense.

Evolutionary Algorithms: Though the inspiration for evolutionary algorithms is ancient, evolutionary algorithms are one of the newer AI technologies. Mimicking the natural evolution process of mutation, recombination, and performance competition to determine the best adapted to continue evolving. Evolutionary algorithms are less heavily represented in AI planning because they are a more natural fit for engineering problems where conditions are complex but very well-understood. They are still very useful in some forecasting and planning tasks, though, as they are innovative and novel optimizers that can find solutions humans typically don’t consider.

Deep Learning: This is the youngest AI discipline but by far the most computationally-intensive of the AI disciplines. What it lacks in the refinement of age, it often more than makes up for in its close correlation with the processes found in the true, naturally-occurring intelligence of humans. It takes unstructured data and passes it through a network of specialized processes that work on smaller parts of the data before recombining their individual characterizations until the full data set has been analyzed. Because each component in a deep learning system progressively refines its task by “learning” every time it runs, it is very well-suited to judgments on tasks that are not easily defined by simple rules. This can be a critical strength when forecasting or planning around human behavior or preferences. 

Requirement for the successful implementation of AI:    There are Seven key steps to implementing AI used in business are   1: Understand the difference between AI and ML;    2. Define your business needs;   3: Prioritize the main driver(s) of value;   4. Evaluate your internal capabilities; 5. Consider consulting a domain specialist; 6. Prepare your data; 7. You’re ready to start, but start small.

CREATING AN ARTIFICIAL INTELLIGENCE SYSTEM:     Some of the major subfields of AI tech that present methodological and theoretical grounds are:

a) Neural networks: units that are interconnected and provide machines with learning ability by processing info gathered from external inputs.

b) Machine learning: uses neural networks, physics and stats to find insights and learn from them without being programmed for the ability to make conclusions.

c) Cognitive computing: human-like interaction with machines whose ultimate goal is to simulate human processes by interpreting speech and images.

d) Natural Language Processing (NLP): machine’s ability to analyze, comprehend and even recreate human language and speech.

e) Deep learning: a higher form of machine learning that uses computing power to learn intricate patterns in significant amounts of data. Image and speech recognition arises from this. 

AI implementation must begin with developing a carefully planned strategy.   It is better to Tie up AI implementation strategy to the overall company strategy and then orient in investments. Organizations with good profits from AI implementation follow both core and more advanced best practices.

 

 

 

CORE PRACTICES AND CAPABILITIES FOR SUSTAINED SUCCESS OF AI:

1.                   Build a modern data platform that streamlines how to collect, store and structure data for reporting and analytical insights based on data source value and desired key performance indicators for businesses.

2.                   Develop an organizational design that establishes business priorities and supports agile development of data governance and modern data platforms to drive business goals and decision-making.

3.                   Create and build the overall management, ownership, processes and technology necessary to manage critical data elements focused on customers, suppliers and members. Further

4.                   A properly designed process of implementing AI-based solutions, has to be  designed in a way that will most accurately determine the needs and expectations of customers and minimize the risk of project failure.

 

 

The steps of an AI based solution implementation project are

Step 1-- Business hypothesis: Learning about the customer’s problem; Formulating a business hypothesis for verification; Calculating costs and ROI; Considering alternatives to AI/ML; Overview of the data available to the customer

Step 2 - Creation & verification of business case:  Customer data analysis; Building and testing a prototype AI solution model based on the customer’s data; Summary of the potential contained in the data; (Positive) result

of business hypothesis verification ; Initial solution design and offer ;  Initial decision on implementation .

Step 3  -  Deployment :  Building of the AI/ML solution;  Gathering test and teaching data;  Creating AI/ML models ;  Testing & tuning of the models ;  Deployment in the production version ; Integrations and changes in the business processes;  Training ;  Deployment on production ;

Step 4 - Maintenance:  Supervision of the proper functioning of the solution; Periodic model training and quality improvement; Possible paths of an AI solution implementation project  ; 

Above we have described the typical stages of conducting an AI solution implementation project with a positive scenario, i.e. when the customer decides to implement AI, the business case passes the verification, and at no stage do we encounter contraindications to carrying out the implementation.   It may e noted that many projects do not go through all these stages.

 

AI IS USED IN THE FOLLOWING FIELDS:

1. AI in Astronomy:  Artificial Intelligence can be very useful to solve complex universe problems. AI technology can be helpful for understanding and exploring the universe such as how it works, origin, etc.

2. AI in Healthcare:  In the last ten years, AI is becoming more advantageous for the healthcare industry and going to have a significant impact on this industry.  Healthcare Industries are applying AI to make a better and faster diagnosis than humans. AI can help doctors with diagnoses and can inform when patients are worsening so that medical help can reach to the patient before hospitalization.

3. AI in Gaming:  AI can be used for gaming purpose. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places.

4. AI in Finance:  AI and finance industries are the best matches for each other. The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes.

5. AI in Data Security:  The security of data is crucial for every company and cyber-attacks are growing very rapidly in the digital world. AI can be used to make your data more safe and secure. Some examples such as AEG bot, AI2 Platform, are used to determine software bug and cyber-attacks in a better way.

6. AI in Social Media:  Social Media sites such as Facebook, Twitter, and Snap chat contain billions of user profiles, which need to be stored and managed in a very efficient way. AI can organize and manage massive amounts of data. AI can analyze lots of data to identify the latest trends, hash tag, and requirement of different users.

7. AI in Travel, Tourism & Transport:  AI is becoming highly demanding for travel industries. AI is capable of doing various travel related works from making travel arrangement to suggesting the hotels, flights, and best routes to the customers. Travel industries are using AI-powered chatbots which can make human-like interaction with customers for better and fast response.

8. AI in Automotive Industry: Some Automotive industries are using AI to provide virtual assistant to their user for better performance. Tesla has introduced Tesla Bot, an intelligent virtual assistant. Various Industries are currently working for developing self-driven cars which can make the journey more safe and secure.

9. AI in Robotics: Artificial Intelligence has a remarkable role in Robotics. Generally robots are programmed such that they can perform some repetitive tasks, but with the help of AI, we can create intelligent robots which can perform tasks with their own experiences without pre-programmed.  Humanoid Robots are best examples for AI in robotics, recently the intelligent Humanoid robot named as Erica and Sophia has been developed which can talk and behave like humans.

10. AI in Entertainment:  We are currently using some AI based applications in our daily life with some entertainment services such as Netflix or Amazon. With the help of ML/AI algorithms, these services show the recommendations for programs or shows.

11. AI in Agriculture:  Agriculture is an area which requires various resources, labor, money, and time for best result. Now a day's agriculture is becoming digital, and AI is emerging in this field. Agriculture is applying AI as agriculture robotics, solid and crop monitoring, predictive analysis. AI in agriculture can be very helpful for farmers.

12. AI in E-commerce:  AI is providing a competitive edge to the e-commerce industry, and it is becoming more demanding in the e-commerce business. AI is helping shoppers to discover associated products with recommended size, color, or even brand.

13. AI in education:  AI can automate grading so that the tutor can have more time to teach. AI chat-bot can communicate with students as a teaching assistant.  AI in the future can be a personal virtual tutor for students, which will be accessible easily at any time and any place

 

AI is being applied in a variety of fields to get insights into user behavior and make data-driven suggestions. Google’s predictive search algorithm, for example, analyzed user data from the past to forecast what a user would put next in the search field. Facebook uses historical user data to automatically propose tags for your friends based on their facial traits in their photos. Large corporations employ AI to make the lives of their customers easier.

 

The uses of Artificial Intelligence would broadly fall under the data processing category, which would include     Self-driving cars, Smart Assistants,   Disease mapping, Manufacturing robots; Virtual Travel booking agent.

 

APPLICATIONS OF A.I. IN DETAIL IN  DIFFERENT DOMAINS:   

 

1. AI Application in E-Commerce:  Personalized Shopping:  Artificial Intelligence technology is used to create recommendation engines through which you can engage better with your customers. These recommendations are made in accordance with their browsing history, preference, and interests. It helps in improving your relationship with your customers and their loyalty towards your brand.

Personalizing users’ experience has become the latest pantheon for all the leading tech giants. Ecommerce stores aren’t behind either, and have been the biggest platforms to implement the personalization domain of AI. The latest artificial intelligence applications use AI-powered algorithms to curate the list of buying recommendations and filtrations for the users.

They first collect the user data by going through the user’s most recent search history. Then, these AI algorithms create a list of the products that fit the criteria of being useful or similar, that the users might be interested in looking at and potentially buying in similar and different categories. For instance, if a user has purchased a smartphone, the AI algorithms suggest various add-on products for the same model they purchased like screen guards, back covers, earphones, etc.

Through this personalization, users get products that they actually might be interested in to choose from. Amazon’s recommendations are a great example of smart AI implementation in e-commerce.

AI-powered Assistants:  Virtual shopping assistants and chatbots help improve the user experience while shopping online. Natural Language Processing is used to make the conversation sound as human and personal as possible. Moreover, these assistants can have real-time engagement with your customers. Did you know that on amazon.com, soon, customer service could be handled by chatbots?

Fraud Prevention:  Credit card frauds and fake reviews are two of the most significant issues that E-Commerce companies deal with. By considering the usage patterns, AI can help reduce the possibility of credit card frauds taking place. Many customers prefer to buy a product or service based on customer reviews. AI can help identify and handle fake reviews.  This is one of the Artificial Intelligence Applications that’s found to be widely used.

Different departments of E-commerce including logistics, predicting demand, intelligent marketing, better personalization, use of chatbots, etc. are being disrupted by AI.

The E-Commerce industry, a prominent player being Amazon is one among the primary industries to embrace AI. E-commerce retailers are increasingly turning towards chatbots or digital assistants to supply 24×7 support to their online buyers. Built using AI technologies, chatbots are becoming more intuitive and are enabling a far better customer experience.

We’ve seen AI Applications in various domains. There are a number of industries which are on the verge of transformation by AI.   It may be noted that this is often in no way an exhaustive list but probably the foremost plausible ones within the near future.

2. Applications Of A.I. in Education:  Although the education sector is the one most influenced by humans, Artificial Intelligence has slowly begun to seep its roots in the education sector as well. Even in the education sector, this slow transition of Artificial Intelligence has helped increase productivity among faculties and helped them concentrate more on students than office or administration work. Some of these applications in this sector include:

Administrative Tasks Automated to Aid Educators:  Artificial Intelligence can help educators with non-educational tasks like task-related duties like facilitating and automating personalized messages to students, back-office tasks like grading paperwork, arranging and facilitating parent and guardian interactions, routine issue feedback facilitating, managing enrollment, courses, and HR-related topics. 

Creating Smart Content:  Digitization of content like video lectures, conferences, and text book guides can be made using Artificial Intelligence. We can apply different interfaces like animations and learning content through customization for students from different grades.    Thus Artificial Intelligence helps create a rich learning experience by generating and providing audio and video summaries and integral lesson plans.

Voice Assistants:  Without even the direct involvement of the lecturer or the teacher, a student can access extra learning material or assistance through Voice Assistants. Through this, printing costs of temporary handbooks and also provide answers to very common questions easily.

Personalized Learning:  Using top AI technologies, hyper-personalization techniques can be used to monitor students’ data thoroughly, and habits, lesson plans, reminders, study guides, flash notes, frequency or revision, etc., can be easily generated.

A number of problems which will be solved by the implementation of AI  being  automated marking software, content retention techniques and suggesting improvements that are required.

It must be very tedious for a teacher to grade homework and tests for large lecture courses. A significant amount of time is consumed to interact with students, to prepare for class, or work on professional development. But, this will not be the case anymore.

Though it can never replace human work, it is pretty close to it. So, with the automated grading system checking multiple-choice questions, fill-in-the-blank testing and automated grading of students can be done in a jiffy. It can tell the areas, where there is a need for improvement –

A lot of times, it happens that the teachers may not be aware of the gaps that a student might face in the lectures and educational material, the system alerts the teacher and tell what is wrong. It gives students a customized message which offers hints to the correct answer.  This thus ensures that students are building the same conceptual foundation.

This can help the teachers monitor not just the academic but also the psychological, mental and physical well being of the students but also their all-round development. This would also help in extending the reach of education to areas where quality educators can’t be present physically.  For Example, Case-based simulations offered by Harvard graduate school is one such use.

3. Applications of A.I. in Lifestyle: Artificial Intelligence has a lot of influence on our lifestyle. Let us discuss a few of them. Autonomous Vehicles:  Automobile manufacturing companies like Toyota, Audi, Volvo, and Tesla use machine learning to train computers to think and evolve like humans when it comes to driving in any environment and object detection to avoid accidents.

Spam Filters:  The email that we use in our day-to-day lives has AI that filters out spam emails sending them to spam or trash folders, letting us see the filtered content only. The popular email provider, Gmail, has managed to reach a filtration capacity of approximately 99.9%.

Facial Recognition:  Our favorite devices like our phones, laptops, and PCs use facial recognition techniques by using face filters to detect and identify in order to provide secure access. Apart from personal usage, facial recognition is a widely used Artificial Intelligence application even in high security-related areas in several industries.

Recommendation System:  Various platforms that we use in our daily lives like e-commerce, entertainment websites, social media, video sharing platforms, like YouTube, etc., all use the recommendation system to get user data and provide customized recommendations to users to increase engagement. This is a very widely used Artificial Intelligence application in almost all industries.

4. Applications of A.I. in Navigation:   Based on research from MIT, GPS technology can provide users with accurate, timely, and detailed information to improve safety. The technology uses a combination of Convolutional Neural Network and Graph Neural Network, which makes lives easier for users by automatically detecting the number of lanes and road types behind obstructions on the roads. AI is heavily used by Uber and many logistics companies to improve operational efficiency, analyze road traffic, and optimize routes.

Travel companies are using AI for several tasks. Apart from enhancing their customer support (which we’ve already discussed), they are also using AI tools for determining prices for various different locations by analyzing historical and real-time data from the various accessible data silos.

AI and machine learning applications help travel companies in calculating profitable yet affordable prices according to various factors to attract in a multitude of customers.

A.I. in Tourism and Travel around with AI. Competition in the travel and tourism industry is very high. You must have seen that prices keep on fluctuating and change often.

You might have also booked a flight ticket in advance or have waited just before the departure date to find cheaper tickets. Everyone does that, but the struggle is minimized with AI.

With predictive analytics driven by artificial intelligence, the price can be predicted. The application is able to predict price patterns and alert travelers when to buy the tickets. So, the cheapest rate can be known before you book the flights to your destination.

The price trend is analyzed on the basis of the recorded data on each route. So, you get notifications of when to book your flight. Book it at the right time and at the right price and say thanks to artificial intelligence.

AI is also used to evaluate the level of traffic on the roads and give an accurate estimate for the trip for travel agencies and enterprises. It can also calculate the estimated time it will take you to reach a particular point through different modes of travel, including bus, trains, and flights.

5. Applications of A.I. in Robotics:  Robotics is another field where artificial intelligence applications are commonly used. Robots powered by AI use real-time updates to sense obstacles in its path and pre-plan its journey instantly.   It can be used for - Carrying goods in hospitals, factories, and warehouses; Cleaning offices and large equipment;  Inventory management.

 With increasing developments within the field of AI, robots are becoming more efficient in performing tasks that earlier were too complex.

The idea of complete automation are often realized only with the assistance of AI, where the system can’t just perform the specified task but also monitor, inspect and improve them without any human intervention.

AI in robotics helps the robots to learn the processes and perform the tasks with complete autonomy, without any human intervention. This is because robots are designed to perform repetitive tasks with utmost precision and increased speed.

AI has been introducing flexibility and learning capabilities in previously rigid applications of robots. These benefits are expected to reinforce the market growth.

6. Applications of A.I. in Human Resource:  Companies use intelligent software to ease the hiring process? Artificial Intelligence helps with blind hiring. Using machine learning software, you can examine applications based on specific parameters. AI drive systems can scan job candidates' profiles, and resumes to provide recruiters an understanding of the talent pool they must choose from.  

7. Applications of A.I. in Healthcare: Artificial Intelligence finds diverse applications in the healthcare sector. AI applications are used in healthcare to build sophisticated machines that can detect diseases and identify cancer cells. Artificial Intelligence can help analyze chronic conditions with lab and other medical data to ensure early diagnosis. AI uses the combination of historical data and medical intelligence for the discovery of new drugs.  Do you wish to accelerate your AL and ML career? Join our PG Program in AI and Machine Learning and gain access to 25+ industry relevant projects, career mentorship and more.

One of the foremost deep-lying impacts which AI has created is within the Healthcare space.

A device, as common as a Fitbit or an iWatch, collects a lot of data like the sleep patterns of the individual, the calories burnt by him, heart rate and a lot more which can help with early detection, personalization, even disease diagnosis.

This device, when powered with AI can easily monitor and notify abnormal trends. This can even schedule a visit to the closest Doctor by itself and therefore, it’s also of great help to the doctors who can get help in making decisions and research with AI.

It has been used to predict ICU transfers, improve clinical workflows and even pinpoint a patient’s risk of hospital-acquired infections.

8. Applications of A.I. in Agriculture:  Artificial Intelligence is used to identify defects and nutrient deficiencies in the soil. This is done using computer vision, robotics, and machine learning applications, AI can analyze where weeds are growing. AI bots can help to harvest crops at a higher volume and faster pace than human laborers.  One of the latest artificial intelligence applications, Agriculture, has also shown a significant impact on the industry.

With the increased demand for food, organizations are using automation and robotics technology with AI embedded in it to help farmers find more efficient ways to protect their crops from various elements like weather, weeds, market consumption rates, and much more.

Issues such as climate change, population growth, and food security concerns have pushed the industry into seeking more innovative approaches to improve crop yield and targeted production.

The latest artificial intelligence applications in the form of image recognition identify possible defects in the crops through images captured by the user’s smartphone camera. Users are then provided with soil restoration techniques, tips, and other possible solutions to deal with the identified defects.

Artificial Intelligence is changing the way we do one among our most primitive and basic professions which is farming.  The use of AI in agriculture is often attributed to agriculture robots, predictive analysis, and crop and soil monitoring.

In addition, drones are also used for spraying insecticides and detecting weed formation in large farms. This is getting to help firms like Blue River Technologies, better manage the farms.  AI has also enhanced crop production and improved real-time monitoring, harvesting, processing and marketing.

9. Applications of A.I. in Gaming:  Another sector where Artificial Intelligence applications have found prominence is the gaming sector. AI can be used to create smart, human-like NPCs to interact with the players.

It can also be used to predict human behavior using which game design and testing can be improved. The Alien Isolation games released in 2014 uses AI to stalk the player throughout the game. The game uses two Artificial Intelligence systems - ‘Director AI’ that frequently knows your location and the ‘Alien AI,’ driven by sensors and behaviors that continuously hunt the player.

Over the past few years, artificial intelligence has become an integral part of the gaming industry. In fact, one of the biggest accomplishments of AI is in the gaming industry.

The actions taken by the opponent AI are unpredictable because the game is designed in such a way that the opponents are trained throughout the game and never repeat the same mistakes.

Their abilities to perform in the game get better as the game gets harder. This makes the games very challenging and prompts the players to constantly switch strategies and never use the same tactics again.

The gaming world is the best example of intelligent applications of artificial intelligence as it is at this platform where there are a lot of alterations in the purpose. AI is used for designing the game, developing the characters, and also frame the story to a certain extent.

 In the gaming industry also, computer game Systems powered by AI is ushering us into a replacement era of immersive experience in gaming.

AI is employed to get responsive, adaptive or intelligent behaviors primarily in non-player characters (NPCs) almost like human-like intelligence in video games. It serves to enhance the game-player experience instead of machine learning or deciding.

AI has also been playing a huge role in creating video games and making it more tailored to players’ preferences.  Matthew Guzdial from the University of Alberta and his team are working towards leveraging AI’s power to assist video gamers create the precise game that they need to play.

10. Healthcare:  The Healthcare sector has been amongst the top adopters of AI technology. It boils down to the power of AI to crunch numbers fast and learn from historical data, which is critical in the healthcare industry.   AI has taken a critical step in helping people with looking after patients as well. The automated bots and healthcare applications ensure proper medication and treatment of patients in the facilities.   In certain cases, AI applications have also been known to provide operating assistance to the doctors.

This is the most important thing that humans need in today’s generation. Health is wealth, and the rate at which humans are compromising on it is really shocking.  With AI, natural language is a boon. It helps to respond to the questions that are asked for. It enables workflow assistants who help the doctors to free up their schedules and also reduce the time and cost by streamlining processes.

They also open new avenues for the industry. With that, AI-powered technology helps pathologists in analyzing samples of tissue and helps the diagnosis to be more accurate.

1.         It helps to support decision making and research.

2.         Help to integrate activities in medical, software and cognitive sciences.

3.         Help to offer a content-rich discipline for the future scientific medical communities

10. Applications of A.I. in Automobiles:  Artificial Intelligence is used to build self-driving vehicles.  AI can be used along with the vehicle’s camera, radar, cloud services, GPS, and control signals to operate the vehicle. AI can improve the in-vehicle experience and provide additional systems like emergency braking, blind-spot monitoring, and driver-assist steering.

Self-driving cars are the most common existing example of applications of artificial intelligence in real-world, becoming increasingly reliable and ready for dispatch every single day. From Google’s self-driving car project to Tesla’s “autopilot” feature, it is a matter of time before AI is a standard-issue technology in the automotive industry.

Advanced Deep Learning algorithms can accurately predict what objects in the vehicle’s vicinity are likely to do. The AI system collects data from the vehicle’s radar, cameras, GPS, and cloud services to produce control signals that operate the vehicle. Moreover, some high-end vehicles come with AI parking systems already. With the evolution of AI, soon enough, fully automated vehicles will be seen on most streets.

 Long-range radar, cameras, and LIDAR, a lot of advancement has been made in the autonomous vehicle segment. These technologies are used in different capacities and each of them collects different pieces of information.  This is where artificial intelligence is used and where it can be compared to the human brain. Some of uses of AI in autonomous vehicles are:

•           Directing the car to the gas station or recharge station when it is running low on fuel.

•           Adjust the trip’s directions based on known traffic conditions to find the quickest route.

•           Incorporate speech recognition for advanced communication with passengers.

•           Natural language interfaces and virtual assistance technologies.

At this stage where automobiles changing from an engine with a chassis around it to a software-controlled intelligent machine, the role of AI cannot be underestimated.

The goal of self-driving cars, during which Autopilot by Tesla has been the frontrunner, takes up data from all the Tesla’s running on the road and uses it in machine learning algorithms. The assessment of both chips is later matched by the system and followed if the input from both is the same.

11. Applications of A.I. in Social Media 

Instagram:  On Instagram, AI considers your likes and the accounts you follow to determine what posts you are shown on your explore tab. 

Facebook:  Artificial Intelligence is also used along with a tool called DeepText. With this tool, Facebook can understand conversations better. It can be used to translate posts from different languages automatically.

Twitter: AI is used by Twitter for fraud detection, removing propaganda, and hateful content. Twitter also uses AI to recommend tweets that users might enjoy, based on what type of tweets they engage with.

 Instagram, Snapchat, Facebook, Twitter, the world today is changing and everyone is using these social media apps to stay connected with the virtual world. But, are you aware of the fact that a majority of your decisions are being influenced by artificial intelligence.

Starting from notifications, to up-gradations, everything is curated by AI. It considers all the past web searches, behaviors, interactions, and much more. So, while you visit these websites, your data is being stored and analyzed and thus you are served with a personalized experience.

Social media is something that has established itself as an essential element for the current generation. We’ve been generating an immeasurable amount of data through chats, tweets, posts, and so on.

In the most common understanding of the statement, wherever there is an abundance of data, AI and machine learning are always involved. The most common use of AI in social media is for face verification and to detect facial features.

AI in social media can be associated with big data and machine learning where deep learning is used to extract every minute detail from an image by using a bunch of deep neural networks. On the other hand, machine learning algorithms are used to design your feed based on your interests.

All of us love Social Media, don’t we?

Social Media is not just a platform for networking and expressing oneself. It subconsciously shapes our choices, ideologies, and temperament.

All this due to the synthetic Intelligence tools which work silently within the background, showing us posts that we “might” like and advertising products that “might” be useful based on our search and browsing history.

 

For example, recently Instagram revealed how it’s been using AI to customize content for the Explore Tab.

This helps with social media advertising because of it’s unprecedented ability to run paid ads to platform users based on highly granular demographic and behavioral targeting.

Did you know, we also have AI tools that will actually write Facebook and Instagram ads for us. Another huge benefit of AI in social media is that it allows marketers to analyze and track every step that they take.

 

12. Applications of A.I. in Marketing: Artificial intelligence applications are popular in the marketing domain as well.    By Using AI, marketers can deliver highly targeted and personalized ads with the help of behavioral analysis, and pattern recognition in ML, etc. It also helps with targeting audiences at the right time to ensure better results and reduced feelings of distrust and annoyance.

AI can help with content marketing in a way that matches the brand's style and voice. It can be used to handle routine tasks like performance, campaign reports, and much more.  

 

Chatbots powered by AI, Natural Language Processing, Natural Language Generation, and Natural Language Understanding can analyze the user's language and respond in the ways humans do. 

AI can provide users with real-time personalizations based on their behavior and can be used to edit and optimize marketing campaigns to fit a local market's needs. 

 

One of the greatest artificial intelligence examples applications have been a key area for improvement and the latest trends in AI. The early 2000s were not so great in terms of AI’s implementation on the marketing domain online. Yes, e-commerce existed, but the search wasn’t that great. It was hard to find anything in a store if you didn’t know the exact name. It is due to the improvement in AI that smart suggestions are way more effective now.

Moreover, AI has also made its way into many software and hardware used by the marketing individuals to help them calibrate the huge amount of data and comprehensively analyze it. Big Data and Machine Learning have been the major players in the domain where AI has shined and effectively elevated the various processes involved in handling data. Taking away a load of performing monotonous and mundane tasks, AI’s implementation in the marketing sector has elevated productivity of the domain up to many notches.

13. Applications of A.I. in Chatbots  :  AI chatbots can comprehend natural language and respond to people online who use the "live chat" feature that many organizations provide for customer service. AI chatbots are effective with the use of machine learning, and can be integrated in an array of websites and applications. AI chatbots can eventually build a database of answers, in addition to pulling information from an established selection of integrated answers. As AI continues to improve, these chatbots can effectively resolve customer issues, respond to simple inquiries, improve customer service, and provide 24/7 support. All in all, these AI chatbots can help to improve customer satisfaction.

These days, virtual assistants are a very commodity possessed by various industries. Almost every household has a virtual assistant that controls their appliances at home. A few examples include Siri, Cortana, and Alexa, which are gaining popularity because of the user experience they provide.

Amazon’s Echo is an example of how artificial intelligence can be used to translate human language into desirable actions. This device uses speech recognition and NLP to perform a wide range of tasks on your command.

It can do more than just play your favorite songs. It can be used to control the devices at your house, book cabs, make phone calls, order your favorite food, check the weather conditions, and so on.

There are many websites now that offer customers the ability to chat with customer support. It’s one of the most general applications of artificial intelligence.  These chat support bots are little more than automated responders.

The more advanced customer service chatbots are able to extract information from the site and present it to you on request. Chatbots are needed to adapt as per the natural language.

Teaching a machine to understand the human language is not easy. Rapid advances in natural language processing (NLP) means they are evolving by consuming information all the time.

14. Applications of A.I. in Finance:  It has been reported that 80% of banks recognize the benefits that AI can provide. Whether it’s personal finance, corporate finance, or consumer finance, the highly evolved technology that is offered through AI can help to significantly improve a wide range of financial services. For example, customers looking for help regarding wealth management solutions can easily get the information they need through SMS text messaging or online chat, all AI-powered.

 Artificial intelligence can also detect changes in transaction patterns and other potential red flags that can signify fraud, which humans can easily miss, and thus saving businesses and individuals from significant loss. Aside from fraud detection and task automation, AI can also better predict and assess loan risks.  Thus Artificial Intelligence is revolutionizing the industries with its applications and helping to solve complex problems.   

15. Enhanced Images: Cameras and apps use AI for applying different effects on images, refining their quality, and even suggest how to click them live!

AI can help in object identification in images and also enhance the photograph to the maximum extent by identifying the depth, lighting, and scope of the picture, and helping capture every element in as much detail as possible. By using this feature, many apps and cameras let you add a variety of effects in your pictures.

Artificial intelligence features allow the users to blur out the background, increase focus on a particular object, add filters, and do a plethora of other amazing experiments on the clicked picture.

Moreover, Google Photos also uses AI to let users look up photos of particular people in their contact lists or tags. It identifies the faces of different people in your pictures and enables you to tag them or search accordingly.

16.  Surveillance:  Traditional security camera monitoring is usually conducted by a human operative. Humans are susceptible to make mistakes due to various different reasons, and human error in this domain can be a dangerous affair. Accidents are bound to happen due to trouble in tracking multiple monitors simultaneously.

Like other applications of artificial intelligence in the real world, AI can also be trained using supervised exercises, developing security algorithms, identification protocols, and much more, to take input from security cameras. Eventually, AI can identify potential threats and warn human security officers to investigate further.

AI has significantly evolved in the surveillance domain and can identify many types of threats such as intruders, invalid access, unidentified individuals on defined premises, etc. Although limited in its capabilities, AI is expected to be a major asset across the world in the surveillance domain in the next 10 years.   AI has made it possible to develop face recognition Tools which may be used for surveillance and security purposes.

As a result, this empowers the systems to monitor the footage in real-time and can be a path breaking development in regards to public safety.

Manual monitoring of a CCTV camera requires constant human intervention so they’re prone to errors and fatigue. AI-based surveillance is automated and works 24/7, providing real-time insights.

According to a report by the Carnegie Endowment for International Peace, a minimum of 75 out of the 176 countries are using AI tools for surveillance purposes.  Across the country, 400 million CCTV cameras are already in situ , powered by AI technologies, primarily face recognition .

The most advanced form of applications of artificial intelligence in the real-world are being implemented in homes, and are becoming smarter every day. Various devices like smart locks, smart switches, ect., are increasingly becoming compatible with various devices, and the application of smart homes is becoming more accessible to the general population every day.

The past few years have witnessed many smart devices that can now learn your behavior patterns and help you save money by saving energy, suggesting steps that can potentially save your time and resources, and implementing cost-optimized operations. These devices help you with a smarter way of living.  Thermostats and building management systems can help automate building heating and cooling, for instance. In effect, they learn and can predict when to turn your boiler on or off for optimal comfort, whilst factoring in outside weather conditions as well.

17.  Banks:  A lot of banks have already adopted AI-based systems or software to provide customer support and detect anomalies and credit card fraud.

Another use of AI for banking, which is of far higher value for banks, is in fraud detection. It can be hard for humans to understand patterns, but machines are good at it. This is where fraud prevention AI comes into play.

By tracing card usage and endpoint access, security specialists are more effectively preventing fraud. Organizations rely on AI to trace those steps by analyzing the behaviors of transactions.   One of the early adopter of Artificial Intelligence is the Banking and Finance Industry.

From Chatbots offered by banks, for instance , SIA by depository financial institution of India, to intelligent robo-traders by Aidya and Nomura Securities for autonomous, high-frequency trading, the uses are innumerable.

Features like AI bots, digital payment advisers and biometric fraud detection mechanisms cause higher quality of services to a wider customer base.  The adoption of AI in banking is constant to rework companies within the industry, provide greater levels useful and more personalized experiences to their customers reduce risks as well as increase opportunities involving financial engines of our modern economy.

18. Space Exploration:  Space expeditions and discoveries always require analyzing vast amounts of data. Artificial intelligence and machine learning is the best way to handle and process data on this scale.

For instance, after rigorous research, astronomers used Artificial Intelligence to sift through years of data obtained by the Kepler telescope in order to identify a distant eight-planet solar system.

Artificial intelligence is also being used for NASA’s next rover mission to Mars, the Mars 2020 Rover. The AEGIS, which is an AI-based Mars rover, is already on the red planet. The rover is responsible for the autonomous targeting of cameras in order to perform investigations on Mars.

AI systems are being developed to scale back the danger of human life that venture into the vast realms of the undiscovered and unraveled universe which is a very risky task that the astronauts need to take up.  As a result, unmanned space exploration missions just like the Mars Rover are possible due to the utilization of AI.

It has helped us discover numerous exoplanets, stars, galaxies, and more recently, two new planets in our very own system.  NASA is also working with AI applications for space exploration to automate image analysis and to develop autonomous spacecraft that would avoid space debris without human intervention, create communication networks more efficient and distortion-free by using an AI-based device.

Space exploration, microbiological research, archaeological advancements, and much more have become much easier tasks than they were 10 years ago due to AI. And the scope will only grow with time, with Virtual Reality (VR), training simulations, intuitive chatbots, and exploration technologies already paving the way for the future with the help of AI. All that we, as humans, can do is observe this advancement and let it help us in building a better future.

19. A.I. in Business:  A business heavily relies on real-time reporting, accuracy, and processing of large volumes of quantitative data to make crucial decisions. With this efficiency and effectiveness of a business, it is quickly able to implement machine learning.  The adaptive intelligence, chat bots, automation helps to smoothen out the business process.   Let’s take an example of the Help Desk. AI there is used in online help centers. If you’ve visited a website, you must have seen that the chat window pops up. You can then ask questions there directly and they revert to your problem or query in no time.

This happens with the help of robotic process automation. This thus reduces the repetitive tasks that are normally performed by humans. The algorithms are integrated into analytics and CRM (Customer relationship management) platforms that uncover information on how to better serve the customers.

20. A.I. for a Better World:  Many people say that technology is snatching away their jobs and with the machine, there is no need for humans. But, do you know that it is these machines that are making the world a better place to live in.

It is this AI that is helping us to prevent future damage. It understands the needs and addresses developmental needs while focusing on sustainability.  Do you know that companies like Microsoft are using AI to study land-use patterns with terrain maps? By understanding these patterns in-depth, better decisions related to land are taken.  This is helping in implementing proper preservation techniques. Scientists are using the information obtained to preserve biodiversity and the ecosystem.

The term can also be applied to any machine that exhibits traits related to a person’s mind like learning and problem-solving. A lot of development has been made in the field of Artificial Intelligence and the progress can be seen.  AI is a dynamic tool used across industries for better decision making, increasing efficiency and eliminating repetitive work.   Here we have some of the Artificial Intelligence Applications in real world.

21. Entertainment:  The show business, with the arrival of online streaming services like Netflix and Amazon Prime, relies heavily on the info collected by the users.

This helps with recommendations based upon the previously viewed content. This is done not only to deliver accurate suggestions but also to create content that would be liked by a majority of the viewers.  With new contents being created every minute, it is very difficult to classify them and making them easier to search.  AI tools analyze the contents of videos frame by frame and identify objects to feature appropriate tags. AI is additionally helping media companies to form strategic decisions.

22.Forecsting: AI planning and forecasting uses algorithms to make predictions and forecast trends without human judgment, which leads to far less error and often outperforms data scientists and experts. Studies comparing AI predictions with expert predictions from humans almost always showed artificial intelligence as the victor. While algorithms and AI will not replace human intelligence in the future, their ability to analyze data will always be a welcome aid to data scientists and forecasters. 

Since businesses know many pieces of specific data, like how long a consumer waits on average or how many products can be manufactured in a day, but with millions of bytes collected daily, and much of that data being unstructured, it’s nearly impossible for a human to analyze data as precisely and efficiently as AI. 

SOME EXAMPLES OF AI IMPLEMENTATION:

Google’s AI-Powered Predictions:  Using anonymized location data from smartphones, Google Maps (Maps) can analyze the speed of movement of traffic at any given time. Maps can more easily incorporate user-reported traffic incidents like construction and accidents.

Commercial Flights Use an AI Autopilot:  AI autopilots in commercial airlines is a  surprisingly early use of AI technology that dates as far back as 1914, depending on how loosely you define autopilot. The New York Times reports that the average flight of a Boeing plane involves only seven minutes of human-steered flight, which is typically reserved only for takeoff and landing.

Smart Email Categorization:  Gmail uses a similar approach to categorize your emails into primary, social, and promotion inboxes, as well as labeling emails as important. Every time you mark an email as important, Gmail learns.

Robo-readers:  Essay grading is very labor-intensive, which has encouraged researchers and companies to build essay-grading AIs.

Voice-to-Text:  A standard feature on smartphones today is voice-to-text. By pressing a button or saying a particular phrase (“Ok Google”, for example), you can start speaking and your phone converts the audio into text.

The creation of a machine with human-level intelligence that can be applied to any task is the Holy Grail for many AI researchers. AI has long been the muse of dystopian science fiction, in which super-intelligent robots overrun humanity, but experts agree it’s not something we need to worry about anytime soon.   The days aren’t too far when your refrigerator might know more about your diet and suggest you the best food to eat. Or your chatbot like Alexa or Siri might even become your professional assistant. With the breakneck speed of innovative advancements happening in the world of AI, the applications of the technology are becoming limitless.

CONCLUSIONS:  “I definitely fall into the camp of thinking of AI as augmenting human capability and capacity.”  – Satya Nadella, CEO of Microsoft.

AI is now a continuum:  Assisted intelligence is where some machine judgment system based on predefined rules replaces many of the repetitive and standardized tasks done by humans. Augmented intelligence is where humans and machines feedback and learn from each other. Autonomous intelligence is where some adaptive and continuous machine system takes over in some cases. The decision to allow autonomous intelligence to take over certain tasks always depends on the economics of the situation.

The idea of AI floating around in consumer culture is an idea generated in myth and movies. When we interact with an AI system, the feeling that this scheme is truly intelligent may strike one initially. We keep looking for the soul of the machine and find nothing but repetition and half-understood word patterns.  What we can say with certainty is that handy device that will follow verbal commands and do things for us. These systems may be able to learn how we give it commands when we tediously train them or may be able to learn by repetition.. The rest is advertising and myth. Artificial intelligence is principally a matter of human perception.

The myths surrounding AI may have come out of the symbolic logic demonstrations in the 1960s. In 1963, a computer system called "Logic Theorist" was able to use the rules of symbolic logic to discover proofs to theorems in symbolic logic. A system called "DENDRAL" (now called an "expert system") was able to mechanize aspects of scientific reasoning used in organic chemistry. Another program called "MYCIN" (also now called an "expert system") was able to diagnose infectious diseases interactively.

Current Perspective:  There is something flawed with the quest for AI. There are a lot of interesting theories and experiments underway, and some undoubtedly will achieve commercial success. However, the question remains, "Where are the intelligent machines like the HAL 9000?" Where are the intelligent machines which can do us harm, machines whose power comes from their having their point of view?

The deep learning applications in artificial Intelligence are Fraud Detection; CRMS ( Customer Relationship Management System) ;  Computer Vision;  Supercomputers;  Natural Language Processing and  Advertising

Whether we’re attempting to read our emails, get driving directions, or find music or movie recommendations, AI can help us in every aspect of our life.

Artificial intelligence (AI) can be utilized to solve a variety of problems in the future like environmental issues, including climate change and natural disaster prediction. Many dangerous jobs like collecting radioactive waste will be taken over by AI in the future.  Some of the advantages of AI:-  Automation of machines;  Smart Decision Making;  Medical Advances;  Solving Complex Problems;  Efficient;  Minimizing Errors. 

According to Statista, revenue from the artificial intelligence (AI) software market worldwide is expected to reach 126 billion dollars by 2025.  As per Gartner, 37% of organizations have implemented AI in some form.  According to Servion Global Solutions, by 2025, 95% of customer interactions will be powered by AI.     More than 9 out of 10 (91%) top businesses report having an ongoing investment in AI, as noted in a New Vantage, 2022 research.

According to a McKinsey survey, high-performing companies attribute most of their profits to their integration and use of AI.   A Forbes report discusses that 2022 could see the “collective shift away from point solutions toward holistic platforms that offer a suite of business solutions.”

 In simple terms, Artificial Intelligence is made up of the phrases Artificial and Intelligence, with Artificial referring to “man-made” and Intelligence referring to “thinking power,” so AI refers to “a man-made thinking power”. To sum up, Artificial Intelligence is prominent in people’s life. Artificial intelligence was developed during the turn of the twenty-first century, significantly expanding the application of technology in a variety of fields.

Next-generation forecasting relies on AI capabilities, such as Machine Learning (ML) forecasting algorithms, to streamline and optimize demand forecasting processes. Planners can take vast amounts of structured and unstructured data and let AI/ML algorithms connect the data nodes and edges to discover patterns and relationships in ways that a traditional forecasting system could never do. This automation helps planners to make faster, better decisions

Beyond the buzzwords and the technology complexities, organizations are struggling to understand what AI means for their industry and how they can start their journey. We must go deeper into explaining the meaning and relevance of AI for your business. You will have to learn how to apply AI thinking across enterprise functions—including disruptive technologies such as IoT, Blockchain, and cloud—and transform your organization.    In order to maximize AI benefits, nine steps are suggested : -

Encourage greater data access for researchers without compromising users’ personal privacy,

Invest more government funding in unclassified AI research,

Promote new models of digital education and AI workforce development so employees have the skills needed in the 21st-century economy,

Create a federal AI advisory committee to make policy recommendations,

Engage with state and local officials so they enact effective policies,

Regulate broad AI principles rather than specific algorithms,

Take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,

Maintain mechanisms for human oversight and control, and

Penalize malicious AI behavior and promote cyber security.

 

REFERENCES :

1. Artificial Intelligence: A Modern Approach, authors Stuart Russel and Peter Norvig

2. Michigan State University’s Arend Hintze

3. 10 Wonderful Examples Of Using Artificial Intelligence (AI) For Good, by Bernard Marr,   Jun 22, 2020,12:19am EDT. 

4. 100+ AI Use Cases & Applications: In-Depth Guide,    FEBRUARY 16, 2018.

5.  Hardware for artificial intelligence, Wikipedia.

6. Artificial intelligence: 3 trends to watch in 2023, Yishay Carmiel; January 9, 2023. The enterprisers project  

7. AI Forecasting,  Oracle Cloud Infrastructure (OCI).

8. AI Applications: Top 14 Artificial Intelligence Applications in 2023, Avijeet Biswal; Jan. 2023

9. 10 Applications of Artificial Intelligence (AI) in Business,  Bhumika DuttaJul 16, 2021,  Anlytic steps.

10. How To Learn AI From Scratch [2023 Guide], Sakshi Gupta, March 2022; Spring Board

11. Artificial intelligence in healthcare, Wikipedia.