Introduction to Artificial Intelligence (AI)

Talking of advancements, from Abacus to Super-Computers, the world has come a long way. The world, say a hundred years ago was highly dependent on manual execution. Simple tasks such as arithmetic operations too were long and tedious. Having realized this difficulty, various technologies capable of executing complex calculations were introduced.
These technologies grew rapidly and soon the world saw it’s potential. Calculations have now been quicker and more accurate. Such technologies saw a great implementation in various fields including research and development defiance, healthcare, business, etc.
But however efficient these machines were, there was always a lack of “Intelligence”. Computers may be reliable, accurate and a gazillion times faster than a human but they were all but “dumb machines”. Artificial Intelligence is a concept far superior to any other concept and aims to make machines able to learn and respond on their own.
Though the term Artificial Intelligence has been around for more than 5 decades.

    it was not until 2 decades before that the world started realizing its huge potential. Artificial Intelligence has a plethora of applications in areas such as Natural Language Processing, Simulations, Robotics and Speech Recognition to name a few.While the potential of Artificial Intelligence and its applications has been realized, but due to the complexities involved, the advancements in this field, as of now, is only restricted to the development of Weak Artificial Intelligence Systems also known as Narrow Artificial Intelligence Systems.There has been a steady development in the field of Artificial Intelligence and the growth is exponential. Today, Artificial Intelligence is everywhere. From Google to Facebook and Shopping to Learning, Artificial Intelligence is at the forefront.There are many technologies in existence today that have a direct or indirect application of Artificial Intelligence.

    In 2023 Top10 Artificial Intelligence (AI) Technologies

    1.    Natural Language Generation


    Popularly known as “Language Production” among Psycholinguists, Natural Language Generation is a procedure that aims to transform any structured data into a natural language. In layman terms, natural language generation can be thought of as a process that converts thoughts into words.

    For example, when a child looks at a butterfly flying in a garden, he may think of it in various ways. Those thoughts may be called ideas. But when the child describes his thought process in his natural language (mother tongue), this process may be termed as Natural Language Generation.

    Natural Language Generation, otherwise known as NLG, is a software process driven by artificial intelligence that produces natural written or spoken language from structured and unstructured data. It helps computers to feed back to users in human language that they can comprehend, rather than in a way a computer might.For example, NLG can be used after analyzing customer input (such as commands to voice assistants, queries to chatbots, calls to help centers or feedback on survey forms) to respond in a personalized, easily-understood way. This makes human-seeming responses from voice assistants and chatbots possible.

    It can also be used for transforming numerical data input and other complex data into reports that we can easily understand. For example, NLG might be used to generate financial reports or weather updates automatically.

    2.    Natural Language Understanding



    Natural Language Understanding is the opposite of Natural Language Generation. This procedure is more inclined towards the interpretation of Natural Language.
    In the example above, if the child is told about the butterfly rather than shown, he may interpret the data given to him in a variety of ways. Based on that interpretation, the boy will make a picture of a butterfly flying in a garden. If the interpretation was correct, then one may infer that the procedure (Natural Language Understanding) was successful.

    Natural Language Understanding (NLU) is the ability of a computer to understand human language. You can use it for many applications, such as chatbots, voice assistants, and automated translation services. The most basic form of NLU is parsing, which takes text written in natural language and converts it into a structured format that computers can understand. For example, the words "hello world" would be converted into their respective parts of speech (nouns and verbs), while "I am hungry" would be split into two sentences: "I am" and "hungry." Parsing is only one part of NLU; other tasks include sentiment analysis, entity recognition, and semantic role labeling. Natural language processing is the process of turning human-readable text into computer-readable data. It's used in everything from online search engines to chatbots that can understand our questions and give us answers based on what we've typed.

    3.    Speech Recognition



    As the name suggests, Speech Recognition is a technology that uses Artificial Intelligence to convert human speech into a computer-accessible format. The process is very helpful and acts as a bridge in human-computer interaction.
    Using Speech Recognition technology, the computer can understand human speech in several natural languages. This further enables the computer to have a faster and smoother interaction with humans.
    For example, let’s say that the child in the first example was asked, “How are you?” during a normal human to human interaction. When the child listens to the human speech sample, he processes the sample according to the data (knowledge) already present in his brain.
    The child draws necessary inferences and finally comes up with an idea about what the sample is about. This way, the child can understand the meaning of the speech sample and respond accordingly.

    4.    Machine Learning



    Machine Learning is yet another useful technology in the Artificial Intelligence domain. This technology is focussed on training a machine (computer) to learn and think on its own. Machine Learning typically uses many complex algorithms for training the machine.
    During the process, the machine is given a set of categorized or uncategorized training data pertaining to a specific or a general domain. The machine then analyses the data, draws inferences and stores them for future use.
    When the machine encounters any other sample data of the domain it has already learned, it uses the stored inferences to draw necessary conclusions and give an appropriate response.
    For example, let’s say that the child in the first example was shown a collection of toys. The child interacts (using his senses like touch, see, etc.) with the training data (toys) and learns about the toys’ properties. These properties can be anything from size, colour, shape, etc. of the toys.
    Based on his observations the child stores the inferences and uses them to distinguish between any other toys that he may have any future encounters with. Thus, it can be concluded that the child has learned.

    Every day, most of us use the internet in some form or another. Whether it be checking the news, looking for new dinner recipes, or planning your next vacation, a search engine probably plays a significant part in your internet exploring. The most notable and popular search engine is Google. The Google search engine is a prime example of an A.I. system that works to find personalized and specific information that it thinks you would like to see.The internet is a powerful search engine composed of millions of recourses that allows us to find the exact information we are looking for. Google Search learns about your habits, preferences, locations, and much more in order to give you information that it thinks you will like best. The machine learning aspect of Google A.I. comprises reading and analyzing your search history, the amount of time spent on webpages, geographical location points, and much more. Once assessed, the A.I. has an excellent understanding of who you are and how to make your experience with the search engine enjoyable and easy to use.

     

    5.    Virtual Agents



    Virtual Agents are a manifestation of a technology which aims to create an effective but digital impersonation of humans. Quite popular in the customer care domain, Virtual Agents use the combination of Artificial Intelligence programming, Machine Learning, Natural Language Processing, etc. to understand the customer and his grievances.
    A clear understanding by the Virtual Agents is subject to the complexity and technologies used in the creation of the agent. These systems are nowadays highly used through a variety of applications such as chatbots, affiliate systems, etc. These systems are capable of interacting with humans in a humane way.
    In the above-mentioned examples, if the child is considered a Virtual Agent and is made to interact with unknown participants, the child will use a combination of his already learned knowledge, language processing and other necessary “tools” to understand the participant.
    Once the interaction is complete, the child will derive inferences based on the interaction and be able to address the queries posed by the participant effectively.     A virtual agent, a specialized software agent that interacts with humans in a human-agent team

    Virtual assistant

    Intelligent agent, in artificial intelligence

    Dialogue system

    Pedagogical agent

    Chatbot, a software robot

    6.    Expert Systems



    In the context of Artificial Intelligence, Expert Systems are computer systems that utilize a pre-stored knowledge base and mimic the decision-making ability of humans. These complex systems utilize reasoning ability and the predefined ‘if-then’ rules.
    Contrary to conventional procedural code based machines, Expert Systems are highly efficient in solving complex problems. Extending the above examples a bit further, the child, based on his pre-existing knowledge base and inference deriving capability is capable of analyzing problems and suggest methods to solve them.
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    Facts for a knowledge base must be acquired from human experts through interviews and observations. This knowledge is then usually represented in the form of “if-then” rules (production rules): “If some condition is true, then the following inference can be made (or some action taken).” The knowledge base of a major expert system includes thousands of rules. A probability factor is often attached to the conclusion of each production rule and to the ultimate recommendation, because the conclusion is not a certainty. For example, a system for the diagnosis of eye diseases might indicate, based on information supplied to it, a 90 percent probability that a person has glaucoma, and it might also list conclusions with lower probabilities. An expert system may display the sequence of rules through which it arrived at its conclusion; tracing this flow helps the user to appraise the credibility of its recommendation and is useful as a learning tool for students. Capabilities of Expert Systems

    The expert systems are capable of −

    Advising

    Instructing and assisting human in decision making

    Demonstrating

    Deriving a solution

    Diagnosing

    Explaining

    Interpreting input

    Predicting results

    Justifying the conclusion

    Suggesting alternative options to a problem

    7.    Decision Management



    Modern Decision Management Systems highly rely on Artificial Intelligence abilities in interpreting and converting data into predictive models. These models, in the long run, help an organization to take necessary and effective decisions.
    These systems are widely used in a vast number of enterprise-level applications. Such applications provide automated decision-making capabilities to any person or organization using it.
    If the child in the above example is considered as a Decision Management System, based on the knowledge set and reasoning abilities, he shall be able to manage his decisions effectively. If the child is given access to a certain behavioural data of say 10 people, then the child will be able to make near-accurate predictions. Such predictions will govern the decisions the child will make to address the problem at hand.

    Decision management, also known as enterprise decision management (EDM) or business decision management (BDM) entails all aspects of designing, building and managing the automated decision-making systems that an organization uses to manage its interactions with customers, employees and suppliers. Computerization has changed the way organizations are approaching their decision-making because it requires that they automate more decisions, to handle response times and unattended operation required by computerization, and because it has enabled "information-based decisions" – decisions based on analysis of historical behavioral data, prior decisions, and their outcomes.

    8.    Deep Learning



    Deep Learning is a special subset of Machine Learning based on Artificial Neural Networks. During the process, learning is carried out at different levels where each level is capable of transforming the input data set into composite and abstract representations.
    The term “deep” in this context refers to the number of levels of data transformation carried out by the computer system. The technology finds its applications in a variety of domains such as Computer Vision, News Aggregation (sentiment-based), development of efficient chatbots, automated translations, rich customer experience, etc.
    For the sake of a simpler example, if the child in the above examples carries out learning restricted to only a single level, then the output (response) may not be specific to the problem but general. Learning at a deeper level helps the child in understanding the problem better. Hence it can be inferred that deeper the learning is, more accurate is the response.

    9.    Robotic Process Automation



    Artificial Intelligence is also heavily used at industrial levels to automate various processes. While manual robotics is capable of completing the job, it lacks the necessary automation required to complete the task without human intervention.
    Such automated systems help in larger domains where it is not feasible to employ humans. If the child, in the above examples, is considered a Robot without intelligence, he shall be dependent on others to carry out his chores.
    While he may still be able to complete his work, he would not be able to do it all by himself. Intelligence enables him to work independently without having to rely on any external interventions.     Robotic process automation (RPA) is a form of business process automation technology based on metaphorical software robots (bots) or on artificial intelligence (AI)/digital workers.[1] It is sometimes referred to as software robotics (not to be confused with robot software).In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back end system using internal application programming interfaces (APIs) or dedicated scripting language. In contrast, RPA systems develop the action list by watching the user perform that task in the application's graphical user interface (GUI), and then perform the automation by repeating those tasks directly in the GUI. This can lower the barrier to the use of automation in products that might not otherwise feature APIs for this purpose.

    RPA tools have strong technical similarities to graphical user interface testing tools. These tools also automate interactions with the GUI, and often do so by repeating a set of demonstration actions performed by a user. RPA tools differ from such systems in that they allow data to be handled in and between multiple applications, for instance, receiving email containing an invoice, extracting the data, and then typing that into a bookkeeping system.

    10.  Text Analytics



    Text Analytics can be defined as an analysis of text structure. Artificially Intelligent Systems use text analytics to interpret and learn the structure, meaning, and intentions of text they may come across.
    Such systems find their applications in security and fraud detection systems. An Artificial Intelligence enabled system can distinguish between any two types of text samples without any human intervention. This independence makes such a system effective, efficient and faster than its human counterparts.
    The child’s intelligence, in the above examples, will also be able to make him capable of distinguishing between the handwritings written by his family members.
    To summarize, Artificial Intelligence finds a variety of applications in various fields. In all the examples mentioned above, the child was able to tackle all the problems independently because he was intelligent and was not dependent on external instructions but relied on his own inferences.     Text analysis is the process of using computer systems to read and understand human-written text for business insights. Text analysis software can independently classify, sort, and extract information from text to identify patterns, relationships, sentiments, and other actionable knowledge.

     

    Conclusion

    Being highly advanced and capable of solving very complex problems, Artificial Intelligence is the key to the future. Various industries and organizations today, are making extensive use of Artificial Intelligence to fulfil the requirements that were once considered very difficult to meet.
    Modern research has suggested growth in the Artificial Intelligence domain at the rate of 36.6 % and shall be worth $190.60 billion by the year 2025.
    While all the artificial intelligence technologies are expecting a massive growth, Deep Learning is expected to grow the highest in terms of the Compound Annual Growth Rate (CAGR).
    In terms of market share, Artificial Intelligence based software has been forecasted to hold the largest market share. While in terms of geographical area, Asia Pacific is the top contender in terms of the highest Compound Annual Growth Rate (CAGR) and North America is to hold the largest market share.
    In a span of just around two decades, Artificial Intelligence has made an exemplary mark on today’s Information Technology industry. It has further provided an impressive set of tools and applications having a wider range in various domains.
    Artificial Intelligence has changed the understanding of the world regarding the power of reasoning and methods of problem-solving. Additionally, it has also enlightened us about the complexity of human intelligence.
    While some people may perceive Artificial Intelligence as a threat to human existence, responsible and limited use will help humans and technology to co-exist together. Such a co-existence, together, will help in reshaping the very reality we live in and change the face of this world entirely.
    For those who are interested in pursuing a career in Artificial Intelligence, you can take up an artificial intelligence online course and upskill today. 



    Artificial Intelligence (AI) is a rapidly evolving field with many new and emerging technologies. Here are some of the latest AI technologies according to the search results there are as follows.

    1.Natural language generation: AI technology that can produce human-like language

    2.Speech recognition: AI technology that can recognize and interpret human speech

    3.Virtual agents: AI technology that can interact with humans in a natural way

    4.Decision management: AI technology that can help businesses make better decisions

    5.Biometrics: AI technology that can identify individuals based on their unique physical characteristics

     6.Natural language understanding: AI technology that can understand human language and respond appropriately

    7.Machine learning: AI technology that can learn from data and improve its performance over time

    8.Biosensor technology: AI technology that allows devices to be operated solely through thought

    9.Resilient artificial muscles: AI technology that enables insect-scale aerial robots to recover flight performance after suffering severe damage

    10.ChatGPT: AI-powered chatbot that can answer questions, generate essays, and write scientific papers from a short prompt

    11.AI in data security: AI technology that can help protect data from cyber threats

    12.AI in the automotive industry: AI technology that can help improve the efficiency and safety of vehicles

    13.AI in healthcare: AI technology that can help diagnose diseases and develop personalized treatment plans

    14.AI in eCommerce: AI technology that can help improve customer experience and increase sales

    15.AI in education: AI technology that can help personalize learning and improve student outcomes

    16.AI in finance: AI technology that can help detect fraud and improve investment decisions

    17.AI in robotics: AI technology that can help improve the performance and capabilities of robots

    18.AI in simulations: AI technology that can help simulate complex systems and predict outcomes

     With the increasing demand for sustainable products, companies are now looking for ways to reduce their carbon footprint. One way to achieve this is by using renewable energy sources such as solar and wind power. By investing in these technologies, companies can not only reduce their environmental impact but also save on energy costs in the long run. Additionally, implementing sustainable practices throughout the supply chain can also have a positive impact on the environment. This includes using eco-friendly packaging materials and reducing waste through recycling and composting. As consumers become more conscious of their impact on the environment, companies that prioritize sustainability will have a competitive advantage in the market. Artificial Intelligence (AI) is a diverse field with numerous technologies. Some of the latest AI technologies include natural language generation, speech recognition, virtual agents, decision management, biometrics, natural language understanding, machine learning, biosensor technology, resilient artificial muscles, ChatGPT, AI in data security, AI in the automotive industry, AI in healthcare, AI in eCommerce, AI in education, AI in finance,

    AI in robotics, and AI in simulations. These technologies have the potential to improve various industries, including healthcare, finance, and education, and make our lives easier and more efficient.

    FAQ 

    What is an example of a natural language?

    A natural language is a human language, such as English or Standard Mandarin, as opposed to a constructed language, an artificial language, a machine language, or the language of formal logic.

    What is an example of a natural language?

    Natural language generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narratives from a data set.

    What does virtual agent do?

    A virtual agent is a type of software that uses natural language processing (NLP) to understand customer intent and search inquiries. They support the human employees in customer service departments by answering basic questions and performing standard procedures.

    What are examples of intelligent virtual agents?

    Siri, Alexa, Cortana, and Google Assistant are three well-known examples of AI assistants that many developers integrate into their applications.

    What type of agent is virtual agent?

    A virtual agent is a contact centre capability that uses a virtual character, created through computer generation, animation and artificial intelligence (AI), to serve customers via chatbot functionality.

    What is expert system examples?

    A computer program that simulates the judgment of a human expert is known as an expert system in ai. A few examples of an expert system are DENDRAL, a molecular structure prediction tool for chemical analysis. Another example of an expert system that predicts the kind and extent of lung cancer is PXDES.

    What is the difference between AI and expert system?

    Definition: Artificial Intelligence – Artificial intelligence is the deception of human intelligence processes by machines, especially computer systems. Expert system – Expert systems are computer programs that simulate the human expert thought process to solve complex decision problems in a specific domain.

    Which language is used in expert system?

    Procedural languages include C++ and Java. These are relatively general-purpose languages that can be used in many different programming situations, e.g. expert systems, Microsoft Windows programs and company-specific applications.

    What is the future of expert systems?

    A further integration of expert systems into the mainstream of IS operations can be expected. In- creasingly, expert systems shells will be written in conventional programming languages, and the applications will be implemented on standard computing equipment.

    What is an example of decision management?

    In management, decision making is about acting in a way that meets organizational goals and objectives. For example, a business manager may decide to invest in marketing to attract new customers.

    What are the 5 importance of decision-making?

    The five functions of management- planning, staffing, organising, directing and controlling are incomplete without the decision-making process. Every step of these functions is successful only on the basis of effective decision-making.

    Why is decision most important?

    Importance of decision making Our decisions affect people. Nearly every decision we make will affect different people in one way or another. It's important to be aware of the influence our decisions will have, and understand what the "human cost" will be. The decisions we make demonstrate our values.

    What is deep learning and examples?

    Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.

    What is machine learning vs deep learning?

    Machine learning and deep learning are both types of AI. In short, machine learning is AI that can automatically adapt with minimal human interference. Deep learning is a subset of machine learning that uses artificial neural networks to mimic the learning process of the human brain.

    Where is deep learning mostly used today?

    Top 25 Deep Learning Applications Used Across Industries. Deep Learning Applications. Virtual Assistants. Virtual Assistants are cloud-based applications that understand natural language voice commands and complete tasks for the user. ... Chatbots. Healthcare. Entertainment. News Aggregation and Fake News Detection. Composing Music. Image Coloring.

    What is a real life example of RPA?

    For example:Credit Card Applications Bots are fundamental to most credit card applications in the modern world. RPA can be programmed to gather information such as documents and credit and background checks. The software can also decide whether the individual's application is a success and they can receive the card.

    What is also known as text analytics?

    Text mining (also referred to as text analytics) is an artificial intelligence (AI) technology that uses natural language processing (NLP) to transform the free (unstructured) text in documents and databases into normalized, structured data suitable for analysis or to drive machine learning (ML) algorithms.

    What are the benefits of text analytics?

    The Benefits of Text Analytics Work with Verbatim Comments in Many Types of Media or Language. Improve Experiences for Customers, Employees, and Other Stakeholders. Increase Your Company's Revenue. Gain Better Control Over Your Costs. Boost Efficiency of Working with Unstructured Data. Make More Data-driven Decisions.