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What is Artificial Intelligence (AI): Types, Benefits, Tools

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20th Sep, 2023
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    What is Artificial Intelligence (AI): Types, Benefits, Tools

    Artificial Intelligence (AI) is now a very popular term worldwide. It's an evolving technology that trains machines to think, act or react like humans. Hence, everybody needs to know exactly what AI is, how AI works and its main tools and applications. On a basic level, it is well-known that machines are programmed to mimic human actions, speak and interact like humans, and work tirelessly. However, it is important to know the types of AI, the main subfields like Machine Learning, Deep Learning, and Natural Language Processing (NLP) and its applications in various domains and the progress made up until now. This article dwells on all the above areas and tries to make the reader well aware of the crucial aspects of AI. 

    What is Artificial Intelligence (AI)? 

    Artificial Intelligence, or AI, is a technology that allows a computer program to learn, reason, and act on its own. It's a machine-to-machine emulation of human intellect that makes them do things that humans normally do. In simplest terms, AI refers to a device's ability to function similarly to human intelligence and perform tasks that humans would perform.  

    AI improves the way we think and the way we explore new horizons. AI boosts our ability to imagine and explore new possibilities. Data Science course can help you break into the IT business by teaching you the fundamentals of AI, features, and how to use it. It will help you attain the knowledge and curate your own set of skills.  

    Different Types of Artificial Intelligence 

    There are three main types of AI, depending on their capabilities. Let us have a look at them. 

    1. Weak or Narrow Artificial Intelligence

    Weak AI focuses only on one task. It has certain limitations. Thus, Weak AI cannot perform beyond its limits. It performs only one task at a time. 

    2. Strong or General Artificial Intelligence

    Strong AI is capable of understanding and learning the tasks that any human being can do. It can work beyond its limitations to understand, learn, and act accordingly. 

    3. Super Artificial Intelligence

    As the name suggests, Super AI surpasses human intelligence and can perform any task better than any human would have performed. (It is still a concept that will sooner or later come into being.)  

    Artificial intelligence's benefits and drawbacks are a heated issue of debate. There is no doubt in saying that Artificial Intelligence plays an essential part in the development and growth of humans. Of course, there are many advantages and some disadvantages of Artificial Intelligence. A common mistake can lead to disruption in that growth. Below are several benefits of AI. 

    Why Do We Need Artificial Intelligence (AI)?

    Artificial intelligence is the basis for all computer learning exercises and is the main asset to solving all complex decision-making in daily life. It has remarkable capabilities, which makes it important, as discussed below: 

    1. Helps Machines to Learn from Experience: As we know, repetitive attempts through iterative steps like the number of epochs, learning rates changing weights, and biases, try to improve model performance. Similarly, the model learns from previous experience through unsupervised learning and reinforcement learning. Better-performing models of AI thus assist in making correct predictions which is crucial for businesses. 
    2. Performs High-Volume Tasks Easily : High volume tasks like sorting, assembly of parts by robots plus analyzing large data, or performing complex mathematical calculations at high speed and accuracy are easily done by AI. 
    3. To Adapt Itself toProgressive Learning: AI's Deep Learning neural networks can help design multi-layer architecture with varying parameters that can be used for progressive learning like gaming performance, chess competition, or self-learning in recommendation engines. 
    4. Make Data More Meaningful and Resourceful: Every data contains vast and varied information, and it is difficult to find all useful information manually from it. The extraction tools can find more information from the data, which gives better insights required for businesses. Machine Learning and Deep Learning tools of AI make this possible. Online bootcamp data science courses can help you grasp relevant Data Science and concept of artificial intelligence. 
    5. Need for Hazardous Tasks: Humans face the risk to life on many tasks like waste separation, recycling jobs, explosives search operations, etc. Robots programmed for such tasks can handle these jobs obviously without risk. 
    6. Need to do Error-free Jobs Without Breaks and Emotions: Humans develop fatigue, hence cannot work continuously and are prone to emotions, also likely to make errors in their jobs. Machines programmed through AI can work 24×7 without a break and with precision and accuracy. 
    7. To Assist in Fraud Detection and Crimes: Tasks in Computer vision, Natural language processing, and optical character recognition include facial recognition techniques and document analysis that can help fraud detection and assist in criminal investigations for faster and more accurate disposal. 

    How Does Artificial Intelligence (AI) Work? 

    Python and R are two commonly used programming languages used for AI systems.  

    To understand how AI actually works, we must first agree that it is not only programming with some algorithms but a blend of different processes and technologies forming a special system. Hence, we have to understand the following steps: 

    1. What AI Does?

    AI builds a full-fledged computer system that develops capabilities to think like humans and take specific problem-solving actions 

    2. How Does It Achieve the Above?

    To achieve the above task, AI incorporates a host of units or sub-fields like: 

    • Machine Learning: This is a subset of AI in which models are trained to solve specific problems of classification, regression and better their performance. These models highlight the patterns, trends, and probabilities predictions that help in decision-making. 
    • Deep Learning: This is a subset of machine learning which utilizes neural networks of various architectures to go deeper for analyzing the input and forms an important asset of computer vision (CV) and natural language processing (NLP). The neural networks based on the working of the human brain processes different parameters that can fine-tune the process of analysis and prediction. 
    • Natural Language Processing (NLP): This is a very important unit of AI that enables the computer to understand human language, be it text or speech. This further helps to understand human behavior and trains the machine to mimic human speech, tasks, and action. 
    • Computer Vision (CV): This is another important application of AI wherein images are studied for detection and separation. It has wide applications where study and analysis of images can be done faster and more accurately than human beings like in impersonation cases, money laundering, and disease identification in humans and crops. 

    3. Resources

    AI uses the abovementioned processes for solving problems and improvements. Last but not least, to achieve what AI aims for, it needs the support of the following factors: 

    1. Big Data: The larger the dataset, the better the solution for the problem. Hence, the first important requirement is adequate and proper data collected through IoT devices from machines and humans.  
    2. Graphical Processing Unit (GPU): Computation goes deeper and becomes complex and large, requiring proper hardware of required capacity. This facilitates going for millions of repetitions or iterations during the model's training to get accurate results.  
    3. Algorithms: These are the backbones of training and developing suitable models appropriate for specific applications. The host of algorithms already developed for training the models goes a long way in arriving at the best solutions. The research community and IT giants are striving relentlessly in pursuit of yet competent algorithms that will be needed in the future for further development of the current status of AI. 
    4. Application Programming Interface (API): They form the bridge between two identities, and by providing AI functions to software programs, they improve the capabilities in better identification of trends and patterns of data.

    AI Use Cases and Benefits  

    AI has several important applications in human life, and several use cases can be sighted. A few of them are listed in the following section: 

    1. Finance Sector

    AI tools have been developed that help detect and prevent fraudulent transactions before they occur. These tools provide more accurate assessment methods than traditional methods like credit scores and customer purchase and sales history. Banks, Lending houses, and Insurance companies greatly benefit; therefore, almost all of these have in-house AI systems. 

    2. Manufacturing Industries

    These adopt AI in inventory management, production planning, quality management, and logistics management to effectively utilize manpower, machinery, and materials. Timely deliveries, prevention of breakdown, and customer satisfaction result in higher revenues and prevents losses.  

    3. Healthcare

    Developments and manufacturing of drugs, patient monitoring, surgical operations by robots, and record maintenance of history are some applications where AI is suitably used. The predictions of diseases with the best possible accuracy help physicians identify malignant or benign cancer cases and the extent of damage to human organs in accidents through image analysis. 

    4. Retail Sector

    AI is used by retail stores in managing their inventories to properly cater demands of customers throughout the year. Recent development can be observed when the program advises the customer online about forgotten items or similar types of other articles' recommendations. Reviews and feedback data help to increase sales and customer satisfaction through AI. 

    Applications of Artificial Intelligence  

    1. Applications of Artificial Intelligence in Agriculture

    Food grain yield depends on the soil and seed quality, moisture absorption, and weather conditions. Previous records about these give requisite data about suitable crops to be taken on a particular land. Machine learning models, deep learning models for prediction, and image classification together can give proper support for disease-free higher output. Application of robots while preparing the soil, irrigation, and crop cutting all such operations can be done at higher speeds than by human labor. 

    2. Applications of Artificial Intelligence in Automobiles

    Top vehicle manufacturers have practiced the use of AI for quite some time. Efforts are being made to use software programs to automate all actions a human can take while driving as perfectly as possible. The main tasks being tried by AI are avoiding obstacles, preventing collisions and accidents, reading signals properly, and observing road safety regulations in the absence of a driver. The electronic control unit, sensors, cameras, Graphical processing unit, and cloud services are being developed and utilized daily for higher efficiency to achieve near perfection. 

    3. Applications of Artificial Intelligence in customer interaction by Chatbots 

    Businesses can improve from customer feedback by answering their common queries. Hence it is common to find chat boats or assistants asking, "May I help you?" when you visit an online e-commerce website. The chat boat has a standard set of questions that it can recognize through NLP and give set answers to them. Most of the queries are answered to customer satisfaction all day long, and for some unknown query, it directs the query to the company executive. A lot of time and manpower are saved while business opportunities increase. Insurance and real estate companies regularly use this option. 

    4. Applications of Artificial Intelligence in Ecommerce

    Few application areas of artificial intelligence can be highlighted in this sector where AI has become very popular. 

    • Recommendation Engines: It is based on customers' habits and preferences AI-powered engines can recommend further movies, similar household products, songs, clothes, etc. 
    • Use of reviews: collected reviews can help companies improve their products and services. Fake reviews can be identified by AI, while displaying authentic reviews help to attract more customers. 
    • Use of survey data by AI: The data collected through surveys help businesses understand customer behavior and further expectations to launch new or improved products and market share. 

    5. Applications of Artificial Intelligence in Finance

    Providing loans and accepting deposits from individuals, organizations, and industries are common transactions with all finance-related companies. With the increase in huge volumes, humans can't tackle such tasks involving large amounts and many customers without errors. Artificial intelligence and statistical tools together can not only handle big data speedily with accuracy but, at the same time, prevent fraudulent transactions by analyzing trends and patterns. 

    6. Applications of Artificial Intelligence in Healthcare

    Artificial Intelligence has several applications in the healthcare domain. Deep learning algorithms of computer vision can build models to identify benign or malignant tumors, cancerous cells, fracture severity, etc. Complicated surgeries, assisted by robots, are performed. Accurate diagnosis is possible by applying prediction models with available data. Sonography and electrocardiography are used to find any deformities or life-threatening conditions accurately. New drugs are also being discovered with past data, patient response, and intelligent analysis. Hospitals can use AI for administration and patient and Resource Management. 

    7. Applications of Artificial Intelligence in Human Resource

    The use of AI in human resource employment is not new now. Resumes submitted are scanned for requisite skills for a particular job position. Since many applications are received for any position, shortlisting manually is time-consuming and costly. Hence companies use automatic scanners which reject unsuitable applications and forward only those which fulfill most of the required qualifications and skill sets. Creating sample tests and their evaluation is also done in earlier stages for shortlisting. For employed staff, performance evaluation is also possible through AI. 

    8. Applications of Artificial Intelligence in Daily Life

    Artificial Intelligence is influencing our daily life in interesting ways. A few of these are listed below. 

    • Household appliances: The automatic washing machines, star rated refrigerators have inbuilt AI-powered applications for comfortable operations with the least human involvement. TV and electric appliances can be timed and switched on and off with programs built on AI. 
    • Health Monitoring Devices: Smartwatches manufactured by various companies help individuals to monitor health parameters like oxygen saturation, sleep patterns, calories burnt on exercise, etc. Graphical information in visualization format is also displayed. 
    • Housing Internal Arrangements: one can try out various optimum combinations and convenient locations of household furniture, appliances, furnishings, etc, with AI tools very easily. 
    • Email Facilities: Emails have become the most common communication mode for individuals and organizations in daily interaction. Shifting unknown or unwanted emails to spam section is a special feature. 

    9. Applications of Artificial intelligence in Navigation

    AI can be applied to satellite images to identify geographical entities such as terrain, water bodies, vegetation, etc. It can also be applied to images containing street views of different cities for navigation purposes. This helps develop GPS technology providing users with accurate, timely, and detailed information during their travel. The application uses Convolutional Neural Network (CNN) and Graph Neural Network to improve the navigation experience by automatically recognizing the number of lanes and road types behind road barriers. 

    10. Applications of Artificial Intelligence in Social Media

    People use four very popular social media platforms for interaction, sharing information, and similar activities made possible by AI. They are: 

    • Facebook: Facebook heavily uses AI, especially, DeepText, for interpreting conversations and automatically translating postings from multiple languages. AI also shows relevant recommendations for connections and posts based on the user profile and previous history. 
    • Instagram: Here, AI analyses the user account for previous likes and dislikes and accounts followed by the user to determine the relevant images and videos to be displayed in the user feed for a better user experience.  
    • LinkedIn: People share their profiles, achievements, interesting happenings, and new developments in engineering and science fields. Job information shared is very useful for recruiters as well as applicants in a great way. Global mutual connections are developed, and data collected is used for beneficial purposes. LinkedIn strives to develop better machine learning techniques for improving customer and user satisfaction. 
    • Twitter: Here, AI is used for expressing people's own opinions about different happenings which are shared. However, analyzing the contents, objectionable matters, wrong propaganda, and rumors such things are usually filtered out while tweets by state agencies in the public interest are shared. It has a huge number of users. 

    There are still many more applications of AI, like manufacturing, materials management, travel and tourism, logistics and supply chain management, etc.  

    AI Tools and Frameworks 

    1. Scikit Learn

    Scikit-learn (Sklearn) is a Python open-source package based on NumPy, SciPy, and Matplotlib. It is Python's most efficient and robust machine learning package. It offers a set of effective tools for machine learning and statistical modeling, such as classification, regression, clustering, and dimensionality reduction, via a Python interface. 

    2. Tensorflow

    TensorFlow is a well-known software library or framework created by the Google team which combines computational algebra and optimization techniques to make it simple to calculate numerous mathematical statements. TensorFlow is a very popular deep learning framework that can train and run deep neural networks for a variety of deep learning projects, such as handwritten digit classification, image recognition, word embedding, etc. 

    3. Theano

    Theano is a Python library built on NumPy that allows for rapid numerical calculation on the CPU or GPU. It is an essential Python deep learning toolkit that can be used to build deep learning models directly or through wrapper libraries that simplify the process. 

    4. Caffe

    The Caffe (Convolutional Architecture for Fast Feature Embedding) deep learning framework was created at the Berkeley Vision and Learning Center (BVLC). Caffe makes it simple to experiment with deep learning. It is developed in C++ and has Python and Matlab bindings. It supports GPU and is, therefore, well suited for deep neural network production environments. It also works with CPU-based kernel libraries like NVIDIA, CUDA Deep Neural Network Library (cuDNN), and Intel Math Kernel Library (Intel MKL). 

    5. MxNet

    Apache MXNet is a powerful open-source deep learning software framework that helps developers develop, train and deploy Deep Learning models. It allows you to trade computation time for memory using a feature dubbed "forgetful background," which is very useful for recurrent networks with extremely long sequences. Its popularity comes from its simple, easy-to-use support for multi-GPU and multi-machine training. 

    6. Keras

    Keras is a Python-based open-source deep learning framework. Keras supports various platforms by running on top of open-source machine libraries such as TensorFlow, Theano, or Cognitive Toolkit (CNTK). It is a user-friendly high-level framework that hides the backend processing and enables us to easily build a neural network model that works on both the CPU and the GPU. 

    7. PyTorch

    PyTorch is an open-source deep learning framework first launched and developed by Facebook's AI Research division (FAIR). The PyTorch library is popularly used for deep learning solutions, and its applications range from machine learning to natural language processing and computer vision. They are also used in various software architectures like Uber's Pyro, Tesla Autopilot, Hugging Face Transformers, PyTorch Lightning, etc. 

    8. CNTK

    Microsoft Cognitive Toolbox, sometimes known as CNTK, is an open-source deep-learning toolkit that describes neural networks as a series of computational steps using a directed graph. It is intended to support commercial-grade datasets and algorithms. This library improves the maintenance of separating computation networks by providing machine learning methods and model descriptions. It can also construct, train, and execute a variety of deep neural networks. 

    Artificial Intelligence: 4 Important Parts of the Development 

    The purpose of AI is to copy human activities and reproduce them to near perfection. This particular requirement divides AI into four stages of development. They're namely: 

    1. Reactive AI 
    2. Limited Memory 
    3. Theory of Mind, and  
    4. Self-Awareness.  

    The first two are reasonably developed, but the next two are being attempted, but how much time it will take nor the percentage of achievement cannot be predicted right now. We will elaborate on these four stages in the following paragraphs. 

    1. Reactive AI

    It is a segment where AI trains machines to respond to situations as programmed in a similar fashion every time. Here it cannot learn anything by itself but responds to the input provided. It doesn't have the capability to respond to past or future events. "Deep Blue," the supercomputer of the 1980s developed by IBM, defeated Russian chess champion Garry Kasparov is an example of this Artificial Intelligence.  

    Siri, the Apple bot, is a slightly advanced version of this AI. Since this AI has limited responding power to reactions, it fails to match human reaction capability, which is much higher. Therefore, there is much scope for improvement in this AI. 

    2. Limited Memory AI

    This is the second stage of earlier AI and has tried to improve upon previous capabilities. It accepts the learning data and tries to improve its performance. Machine learning and deep learning models work in similar ways and hence are obvious tools of this AI.  

    Each model is improved further by making it learn in-depth based on its earlier performance drawbacks. Thus, this limited type of memory AI can be used to solve complex tasks of classification and prediction as it can respond to incoming data. Self-driving cars use this limited memory AI for driving safely on roads. As the name suggests, the memory is limited and cannot perceive long and old inputs. 

    3. Theory of Mind AI

    As per psychology terms, this is a capability to read the minds of others and its own, including the emotions, beliefs, desires, knowledge, etc. When developed, this AI can give machines the capacity to think like humans, whereby they can perceive emotions and feelings, remember them, then behave like humans and interact with others.  

    However, humans have a wide range of frequently changing emotions like fear, surprise, joy, etc., and act differently in such situations, which are not very easy for machines to learn. Progress has been achieved to some extent in the form of humanoid robots like "Sofia," who can recognize faces and interact with similar facial expressions.  

    4. Self-Awareness AI

    This is the most advanced stage of predicted AI. Here the machines are supposed to be developed so that they become aware of their own mind in terms of developing different emotions, behavior, and knowledge. Consciousness realizes the potential of awareness of one's capability, which is important in this case. 

    Through its programming mode, AI can develop self-awareness in robots that otherwise are mere machines, and when self-awareness is created, the robots develop consciousness and act as per their own feelings. This is expected in this AI, which is definitely hard to achieve, being complex but not impossible in the coming decades. The only consideration is that it should be for the benefit of humans and not otherwise.      

    Future of AI

    Whenever we think about the future of any important activity or event around us, we have to take cognizance of many relevant stages and affecting components of this activity like: its present status, what are constituent elements, and what impact presently this activity has on society, and how it will progress in future with consequent effects on human life. Obviously, this consideration is crucial for AI as it occupies a significant part of human life and society at large. 

    1. Present Status and Impacting Elements

    With rapid development in computerization, the presently available hardware, software programs, devices, and huge computing speeds all have helped AI to become the centre stage of research to better human life. Machine learning and deep learning models built under AI can analyse big data to give meaningful insights for progress.  

    The sensors' capacity and camera images have good capabilities at present but are likely to improve in the future. Continuous efforts by research communities and technical universities are contributing to building better technology and algorithms for this purpose.  

    This is evident from topics of AI in current Ph.D. programs, as well as a significant number of patents getting registered daily. Big IT giants like Google, Microsoft, and IBM, along with business icons like Amazon, and Apple, are spending billions on updating and deploying AI capabilities. 

    2. Current Impact of AI

    It is readily seen in our lives. Industries are using it for inventory management, production, quality monitoring, and maintenance by manipulating and analysing relevant data. The Healthcare domain has been using it to develop new drugs, identify the onset of diseases and their progress as well as help in preventing the diseases.  

    Educational Institutes are using AI power to launch new courses in demand. In fact, it's rare to find any sector where AI is not currently being used. Some are in the initial phase, while some are at quite advanced stages in their adoption, like security in finance transactions, disaster management, aviation, and space research, to name a few.  

    Although much is achieved, hunger is not dying. Robots are doing routine human tasks like sorting parts, cleaning premises, and assembling components, while computer vision applications are used for image classification, face recognition, reading texts, etc. Still, human intelligence and perception power are yet to be achieved near perfection. Definitely, the efforts are ongoing at a fast pace and volume in that direction.  

    3. Artificial Intelligence Future Possibilities

    AI has numerous advantages and few disadvantages, as we have discussed earlier, based on the present scenario. In the coming days, decade by decade, things are going to change in all directions.  

    AI will empower machines and devices to better their performance, trying to mimic human activities and hopefully in a positive and beneficial direction. However, due to the downsides of ai, there is definite fear as to whether this will lead to the destruction of human life and the environment if wrongly used.  

    Manual Intelligence has shown its darker side of deployment in terms of harmful activities like fraud, thefts, and hacking which can be extremely dangerous if attempted on a scale with the power of AI.  

    Today AI is created or deployed by humans, but one can imagine what will happen if AI develops itself without human intervention. Another obvious possibility is unemployment if humans get replaced in most places of work by robots. 

    On the one hand, you need to have extremely intelligent robots in complex manufacturing and critical surgical operations for precision and accuracy, but totally replacing humans in all tasks could prove dangerous. There is a need to train more and more people in this technology but also provide options for alternate jobs in case of necessity. Thus, the future of AI is definitely promising, but deployment needs to be ensured for human welfare and safety in totality. Check out KnowledgeHut’s Data Science with Python online course to learn core concepts of programming and ai projects. 

    Conclusion

    Summing up, it is evident that AI has occupied a good portion of human life. All needs of humans from food, medicine, and shelter are manufactured and supplied by various businesses.  

    Competition for survival and growth has made it mandatory to adopt modern technologies in all operations of businesses. Data is a primary source for analyzing demands and resulting resource management.  

    AI systems have proved beyond doubt that they can fulfil such tasks by making the best decisions based on accumulated relevant data. The technology, software, and hardware have developed to such an extent that many complex tasks are better handled by AI than humans.  

    Although the role of humans cannot be eliminated, it is to be agreed that AI with a positive intent for the benefit of human life needs to be accepted. AI will probably reach our demanding expectations in the coming decades. 

    Frequently Asked Questions (FAQs)

    1Why is AI important?

    AI is important because it helps to solve many complex problems like fraud detection, image recognition, crime prevention, etc., easily and faultlessly. 

    2What are four types of AI?

    AI is divided into four types or stages: 

    1. Reactive AI 
    2. Limited Memory 
    3. Theory of Mind  
    4. Self-Awareness 

    Good progress has been made in the first two, and attempts are being made to accomplish the third and fourth. 

    3Will AI create unemployment?

    No, some sectors may face unemployment problems, but at the same time, equal opportunities will evolve through AI's progress.

    4Is Machine Learning and Deep Learning different from AI?

    No, they are subsets of AI on which its entire progress and achievement depend.

    Profile

    Devashree Madhugiri

    Author

    Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms.
    She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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