What is AI? A beginner’s guide to understanding AI

AI is subtly becoming a vital part of our everyday life, from virtual assistants like Siri and Google Assistant to self-driving cars and ride-sharing apps. It has made businesses smarter and more efficient by automating and optimizing various processes, including data analysis and customer service. With the help of AI subsets, especially Machine Learning and deep learning, AI is paving the way for developing more innovative and intelligent solutions that can learn from a huge number of data, identify patterns, and make predictions with exceptional accuracy.

Even with its widespread use, many people still lack a thorough understanding of artificial intelligence and its workings. This article aims to give a comprehensive guide on how to understand AI and explore its daily impact on our lives. This article is a must-read for anyone looking to understand AI better or a business owner exploring its possibilities.

• What is Artificial Intelligence?
• Types of Artificial Intelligence
• Subfields of Artificial Intelligence
• Working mechanism of AI and its fascinating connection with human brain
• Learning algorithms used to make machines artificially intelligent
• Key components of AI application architecture
• The drivers behind AI adoption
• Use cases and applications of AI across industries
• Advantages of AI

What is Artificial Intelligence?

Artificial Intelligence (AI) is a broad branch of computer science that aims to create systems that can function intelligently and independently, just like humans. It can imitate human-like intelligence processes, like learning, reasoning, and self-correction.

Systems developed using AI are designed to process information, learn from experience and can adapt to changing circumstances. Specific AI applications include machine vision, speech recognition, and expert systems.

After understanding artificial intelligence, you need to know how it works and the components that make machines work intelligently.

Types of Artificial Intelligence

Artificial Intelligence (AI) can be broadly classified into two main categories based on capabilities and functionalities. Each category encompasses different types of AI systems with distinct characteristics and applications.

Based on capabilities

1. Narrow AI (Weak AI)

Narrow AI, also known as Weak AI, refers to systems designed and trained to perform specific tasks or a narrow range of tasks. These systems excel in their designated functions but lack the broad cognitive abilities and understanding of human intelligence.

Characteristics of narrow AI:

• Specialized capabilities: Highly specialized and focused on performing specific functions or solving particular problems.
• Limited scope: Cannot generalize knowledge or skills to other domains outside their designated tasks.
• Lack of consciousness: Operates based on predefined algorithms and data inputs without understanding the context or implications of its actions.

Examples: Virtual personal assistants like Siri and Alexa, recommendation systems, image recognition software, chatbots, and autonomous vehicles.

2. General AI (Strong AI)

General AI, also known as Strong AI or Artificial General Intelligence (AGI), refers to systems that replicate human-like cognitive abilities and understanding across a wide range of tasks and domains. Unlike Narrow AI, General AI aims to simulate human intelligence comprehensively.

Characteristics of general AI:

• Human-like cognitive abilities: Can understand, learn, and apply knowledge across various tasks and domains.
• Adaptability: Able to generalize knowledge and skills to new situations, tasks, and environments.
• Consciousness: Theorized to possess self-awareness and consciousness, though achieving this level of intelligence remains a theoretical challenge.

Examples: General AI remains largely theoretical, with no concrete examples yet achieved. Development in this area continues to be a subject of ongoing research.

3. Super Intelligent AI

Super Intelligent AI, also known as Artificial Superintelligence (ASI), refers to systems that surpass human intelligence in virtually every aspect. This type of AI represents the highest level of AI capabilities and poses significant implications for society and the future of humanity.

Characteristics of Super intelligent AI:

• Cognitive superiority: Outperforms humans in terms of cognitive abilities, including problem-solving, creativity, and strategic planning.
• Rapid learning and adaptation: Capable of acquiring and processing information at an unprecedented speed and scale, leading to rapid advancements in various fields.
• Ethical and existential risks: Raises concerns about its potential impact on society, including risks related to control, alignment with human values, and existential threats.

Examples: Super Intelligent AI remains theoretical, with no concrete examples currently existing. Researchers are actively exploring its implications and challenges.

Based on functionalities

1. Reactive machines

Reactive machines are the most basic type of AI and are purely reactive. They do not form memories or use past experiences to inform decisions.

Characteristics of reactive machines:

• No memory storage: Perceive the world and act on it without storing memories or past experiences.

Examples: IBM’s Deep Blue chess-playing supercomputer, which beat grandmaster Garry Kasparov in 1997 by choosing optimal chess moves without considering past moves.

2. Limited memory

Limited memory machines can look into the past to some extent, using memories to inform decisions.

Characteristics of limited memory:

• Observational capabilities: Can observe and learn from past data to make informed decisions.

Examples: Self-driving cars, which observe other cars’ speed and directions and act accordingly.

3. Theory of mind

Theory of mind AI can understand that people have beliefs, emotions, and expectations, and it possesses some understanding of these mental states.

Characteristics of Theory of mind:

• Emotional understanding: Can think emotionally and respond with emotions.

Examples: Advanced prototypes like Sophia, though research in this area is still ongoing.

4. Self-awareness

Self-aware AI represents the pinnacle of AI development, with machines possessing consciousness and self-awareness.

Characteristics of self-awareness:

• Sense of self: Have a sense of “I” or “me” and can predict others’ feelings.

Examples: No such machines currently exist, and their invention would be a significant milestone in AI.

Understanding these types of AI helps in comprehending the current state and future potential of artificial intelligence technologies.

Subfields of Artificial Intelligence

AI has several subfields, the prominent of which are:

Machine Learning (ML)

This subfield of AI aims to develop algorithms that enable machines to learn from data without explicit programming. Due to the rise of big data and the need to process and make sense of vast amounts of information, machine learning has become increasingly important in recent years. ML, in itself, has multiple subfields, like deep learning and supervised and unsupervised learning.

Natural Language Processing (NLP)

Natural language processing aims to teach machines to understand and generate human language. Speech recognition, language translation, and text analysis are some methods used in NLP.

Expert Systems

In contrast to conventional procedural code, expert systems solve problems in a specific domain by reasoning about knowledge, primarily represented as ‘if-then’ rules. It refers to machines mimicking a human expert’s decision-making intelligence.

Robotics

As a field of study, robotics focuses on designing, developing, and programming robots that interact with the external environment, just like humans. The field of robotics brings together knowledge from mechanical, electrical, and computer engineering.

Computer Vision

Computer vision aims to teach machines to interpret and understand visual data. It includes tasks such as object recognition, facial recognition, and image and video analysis.

Neural Networks

A neural network is an algorithm inspired by the arrangement and function of the human brain. In areas such as image recognition and speech recognition, these models are used to model complex relationships between inputs and outputs.

Now that we know about Artificial Intelligence and its subfields let us understand how they are related to the human brain.

Working mechanism of AI and its fascinating connection with human brain

Understanding artificial intelligence is not enough until you know how it works. But don’t worry; we’ve got your back.

Artificial intelligence tries to replicate how the human brain works, and one way it does that is through neural networks. If we use more complex networks to learn complicated things, that’s called deep learning. There are different types of deep learning, and they use fundamentally different techniques to replicate what the human brain does. For example, convolutional neural networks (CNN) are used to recognize objects in a scene, and this is how computer vision works for object recognition in AI.

To teach machines how to learn, we use machine learning. We feed machines lots of data to see patterns and make predictions based on what it has learned. Machines can learn in many more aspects than humans, like even a hundred or thousands, enabling them to look at many high-dimensional data and determine patterns. We can use these machine learning techniques to do two things: classify or predict. If you assign new customers to a group based on some information about customers, you’re classifying them. If you predict whether they’re likely to defect to a competitor based on data, you’re making a prediction.

AI development uses certain learning algorithms to make machines smarter. We can create better AI systems by understanding how different learning algorithms perform.

Learning algorithms used to make machines Artificially Intelligent

Although numerous learning algorithms exist to make machines highly intelligent, the prominent ones are the following:

• • Supervised Learning

If you train an algorithm with a labeled dataset, which means the data already contains the right answers, then it’s called supervised learning. For instance, when you train a machine to recognize your friends by name, you must identify them for the computer. In supervised learning, once the data is fed to the algorithm, it is trained to determine the connection between the input features and its correct output. After training the algorithm, it can make predictions based on new data. Examples of supervised learning include image recognition, spam filtering and speech recognition.

• • Unsupervised Learning

If you train an algorithm with data where you want the machine to figure out the patterns in the data, then it’s unsupervised learning. For example, you could feed the data about celestial objects in the universe and expect the machine to create patterns in that data by itself. In this type of learning, the dataset used is unlabeled, meaning it has no predefined labels or categories. Once fed with the data, the algorithm must find patterns or structures independently. Unsupervised learning applications include clustering and anomaly detection.

• • Reinforcement Learning

If you give any algorithm a goal and expect the machine, through trial and error, to achieve that goal, then it’s called reinforcement learning. A robot’s attempt to climb over the wall until it succeeds is an example of that. In this, the algorithms interact with an environment and receive feedback as rewards or punishments regarding their behavior. The algorithm uses this feedback to improve its decision-making process.

Key components of AI application architecture

Artificial intelligence (AI) application architecture is essential for developing and deploying effective AI solutions. It consists of four core layers, each serving a specific purpose and utilizing distinct technologies. Here’s a detailed look at these key components:

Layer 1: Data layer

The data layer forms the foundation of AI applications, focusing on data preparation and management. It encompasses various technologies, including machine learning (ML), natural language processing (NLP), and image recognition. This layer is responsible for ensuring the availability and quality of data, which is crucial for training AI models. The data layer includes the necessary hardware infrastructure, given the significant computational resources required for modern algorithms. This can often be accessed as a fully managed service from third-party cloud providers, ensuring scalability and reliability.

Layer 2: ML frameworks and algorithm layer

This layer consists of machine learning frameworks that are developed through collaboration between engineers and data scientists. These frameworks cater to specific business use cases, providing developers with prebuilt functions and classes to simplify the construction and training of models. Popular frameworks such as TensorFlow, PyTorch, and scikit-learn are integral to this layer, offering essential functionalities that facilitate the development of robust AI models.

Layer 3: Model layer

At the model layer, application developers implement and train the AI model using the data and algorithms from the previous layers. This layer is pivotal for the AI system’s decision-making capabilities and includes several key components:

• Model structure: This determines the model’s architecture, including layers, neurons, and activation functions. Depending on the problem being addressed, developers might select from various architectures, such as feedforward neural networks or convolutional neural networks (CNNs).
• Model parameters and functions: The learned values, such as neural network weights and biases, are critical for making predictions. A loss function evaluates the model’s performance, aiming to minimize the difference between predicted and actual outputs.
• Optimizer: This component adjusts model parameters to reduce the loss function effectively. Different optimizers, such as gradient descent and the Adaptive Gradient Algorithm (AdaGrad), serve unique purposes and help enhance model performance.

Layer 4: Application layer

The application layer is the customer-facing part of AI architecture, where end users interact with the AI system. This layer allows users to request specific tasks, generate information, provide insights, or make data-driven decisions. The application layer is crucial in delivering a seamless user experience and ensuring that AI solutions are accessible and actionable.

The drivers behind AI adoption

Several key factors are driving the widespread adoption of AI today:

1. Affordable high-performance computing

Affordable, high-performance computing capability is now readily available, primarily due to the proliferation of cloud computing. The abundance of commodity computing power in the cloud enables easy access to affordable, high-performance computing power. Previously, the only available computing environments for AI were non-cloud-based and prohibitively expensive. This shift to cloud-based computing has significantly lowered the barrier to entry for AI adoption.

2. Large volumes of data for training

AI systems require vast amounts of data to train effectively and make accurate predictions. The availability of large volumes of data, coupled with the ease of data labeling and affordable storage, has enabled more robust algorithm building and training. The ability to process both structured and unstructured data has further accelerated the development and deployment of AI applications.

3. Competitive advantage through applied AI

Enterprises are increasingly recognizing the competitive advantage of applying AI insights to business objectives. AI-driven solutions, such as targeted recommendations, help businesses make better decisions faster. The features and capabilities of AI can lead to lower costs, reduced risks, faster time to market, and enhanced operational efficiency. As a result, AI has become a business-wide priority for many organizations.

4. Proliferation of data and maturity of cloud innovations

AI adoption is growing rapidly due to the proliferation of data and the maturity of cloud processing and computing power. Companies now have access to an unprecedented amount of data, including previously untapped “dark data.” These vast data resources fuel the growth of AI, enabling more sophisticated and accurate AI models.

5. Business value and innovation

AI is increasingly seen as a critical source of business value and innovation. Automation enabled by AI cuts costs and enhances consistency, speed, and scalability of business processes. For instance, some Accenture clients have reported time savings of up to 70 percent through AI-driven automation. Moreover, AI drives growth by providing actionable insights and enabling organizations to scale their operations effectively. Companies that successfully scale AI see a threefold return on their AI investments compared to those still in the pilot stage. This potential for significant return on investment makes AI a compelling proposition for businesses.

6. Agility and competitive advantage

AI is not just about efficiency; it also enhances agility and competitive advantage. AI applications powered by machine learning and deep learning can learn from data in near real-time, analyzing new information from multiple sources and adapting accordingly. This ability to self-learn and self-optimize enables AI to continually generate business benefits. AI helps businesses adapt quickly to changing conditions, providing constant insights to drive innovation and maintain a competitive edge.

7. Strategic priority and survival

For many organizations, scaling AI has become a strategic priority. Three out of four C-suite executives believe that if they don’t scale AI within the next five years, they risk going out of business entirely. This high-stakes environment underscores the critical importance of AI adoption for achieving growth objectives and sustaining competitive advantage in a rapidly evolving market.

The convergence of affordable computing power, vast data availability, and the strategic imperative to harness AI’s potential drive rapid adoption across industries. As organizations continue to leverage AI for efficiency, innovation, and competitive advantage, its transformative impact on business and society will only intensify.

Use cases and applications of AI across industries

AI has rapidly evolved to become a transformative force across various industries. Its ability to analyze vast amounts of data, learn from patterns, and make informed decisions has opened up new avenues for innovation and efficiency. Here, we explore some of the most impactful use cases and applications of AI across various sectors.

1. Healthcare

• Medical imaging and diagnostics: AI algorithms can analyze medical images such as X-rays, MRIs, and CT scans with high accuracy, aiding in the early detection and diagnosis of diseases.
• Personalized treatments: AI helps tailor treatments to individual patients by analyzing genetic information, lifestyle factors, and other relevant data.
• Virtual health assistants: AI-powered chatbots and virtual assistants provide 24/7 patient support, answering queries and offering medical advice.
• Administrative workflow: Automating routine administrative tasks, such as patient scheduling and billing, streamlines operations and reduces human error.

2. Finance

• Fraud detection: AI systems detect fraudulent activities by analyzing transaction patterns and identifying anomalies in real time.
• Customer service: AI-powered chatbots provide instant support for banking inquiries, account management, and financial advice.
• Risk assessment: Machine learning models assess credit risk and insurance claims, improving decision-making processes.

3. Retail

• Personalized shopping experiences: AI analyzes customer data to offer personalized product recommendations, enhancing the shopping experience and boosting sales.
• Inventory management: AI-driven systems predict demand trends, optimizing stock levels and reducing overstock or stockouts.
• Pricing optimization: Dynamic pricing algorithms adjust prices based on market conditions, competitor pricing, and consumer demand.
• Customer service: Chatbots and virtual assistants handle customer inquiries, returns, and complaints, enhancing the overall shopping experience.

4. Manufacturing

• Predictive maintenance: AI systems predict equipment failures before they occur, reducing downtime and maintenance costs.
• Quality control: AI-powered vision systems inspect products in real-time, identifying defects and ensuring high-quality standards.
• Supply chain optimization: AI analyzes supply chain data to optimize logistics, reduce costs, and improve efficiency.

5. Logistics

• Route optimization: AI algorithms calculate the most efficient routes for delivery vehicles, reducing fuel consumption and delivery times.
• Fleet management: AI systems monitor vehicle health, driver behavior, and fuel usage to optimize fleet operations.
• Demand forecasting: AI predicts future product demand, helping businesses plan their logistics and inventory management to meet customer expectations.

6. Hospitality

• Booking systems: AI enhances booking platforms by offering personalized recommendations and seamless booking experiences.
• Customer service: AI chatbots provide instant assistance to guests, answering queries and making reservations.
• Revenue management: AI analyzes booking patterns and market trends to optimize pricing strategies.
• Personalized guest experiences: AI tailors services based on guest preferences, enhancing customer satisfaction and loyalty.
• Operational efficiency: AI streamlines hotel operations, from front desk management to housekeeping schedules.

7. Education

• Personalized learning: AI tailors educational content to individual students, addressing their unique learning needs and paces.
• Student support: AI chatbots offer 24/7 support to students, answering questions and providing study resources.
• Administrative automation: AI streamlines administrative tasks, such as grading, scheduling, and student enrollment.
• Learning analytics: AI analyzes student data to identify learning patterns and areas for improvement, guiding instructional strategies.

8. Insurance

• Claims processing: AI automates claims processing, reducing turnaround times and improving accuracy.
• Risk assessment: AI models evaluate risk factors and calculate premiums, enhancing underwriting efficiency.
• Fraud detection: AI detects suspicious claims and activities, mitigating fraud risks.
• Customer service: Chatbots provide instant support for policy inquiries, claims status updates, and coverage information.

9. Content generation and media

• Automated content creation: AI generates news articles, reports, and marketing content, streamlining content production.
• Personalized recommendations: AI curates personalized content recommendations for users, enhancing engagement.
• Audience analysis: AI analyzes audience behavior and preferences, optimizing content strategies and ad targeting.
• Interactive experiences: AI-driven chatbots and virtual assistants engage users through interactive content and real-time responses.
• Social media management: AI-powered tools manage social media accounts, schedule posts, and engage with followers to enhance brand presence.

10. Real estate

• Property valuation: AI analyzes various data points, such as location, property features, and market trends, to provide accurate property valuations, aiding buyers, sellers, and real estate agents in making informed decisions.
• Customer insights: AI helps real estate agents understand client preferences and behavior, enabling more personalized services.
• Smart property management: AI systems optimize property management by automating tasks such as maintenance scheduling, rent collection, and tenant communication, improving efficiency and tenant satisfaction.
• Tenant screening: AI tools assess tenant applications by analyzing credit scores, rental history, and other relevant data, ensuring reliable and responsible tenants.
• Customized property recommendations: AI-powered recommendation engines suggest properties based on a buyer’s preferences, budget, and other criteria, improving the property search experience.

11. Legal

• Document review and analysis: AI-powered tools automate the review and analysis of legal documents, contracts, and case files, significantly reducing the time and effort required and improving accuracy.
• Legal research: AI systems streamline legal research by quickly finding relevant case laws, statutes, and legal precedents, allowing lawyers to focus on higher-level analysis and strategy.
• Contract management: AI automates the drafting, review, and management of contracts, ensuring compliance with regulations and reducing the risk of errors.
• E-discovery: AI tools efficiently handle e-discovery by sorting through large volumes of electronic documents to identify relevant information for litigation, saving time and resources.
• Chatbots for legal assistance: AI-powered chatbots provide instant legal assistance and information to clients, improving accessibility and customer service.

AI is transforming industries by offering innovative solutions to complex challenges. From enhancing customer experiences to optimizing operational efficiencies, AI’s applications are vast and varied. As AI technology advances, its impact across industries is set to grow, driving progress and transformation in unprecedented ways.

Advantages of AI

AI offers a wide range of advantages that enhance operational efficiency, decision-making, and customer satisfaction across various industries. Here are some key benefits:

Solving complex problems

AI leverages machine learning (ML) and deep learning to solve intricate problems with human-like intelligence. It processes large volumes of information, detects patterns, identifies relevant data, and provides solutions. This capability is valuable in fields such as fraud detection, medical diagnosis, and business analytics. By automating complex analytical tasks, AI helps organizations make better-informed decisions and tackle challenges more efficiently.

Automating business processes

AI operates 24/7 without performance degradation, automating repetitive and tedious tasks accurately and without errors. This allows human employees to focus on more strategic aspects of the business, ultimately reducing workloads and streamlining operations. By taking over mundane tasks, AI enhances productivity and allows businesses to allocate their human resources more effectively.

Making smarter decisions

AI’s ability to analyze vast datasets quickly enables it to identify trends and provide data-driven guidance. By forecasting future outcomes, AI can suggest optimal courses of action, enhancing decision-making processes. AI minimizes human error, making it ideal for data-intensive and complex decision-making processes. The speed and accuracy of AI-driven analysis empower businesses to make timely and precise decisions, driving better outcomes.

Improving customer service

AI-powered bots and virtual assistants offer guidance and support to many people simultaneously, enhancing customer service reach. This scalability allows businesses to handle a higher volume of inquiries and provide consistent support without increasing headcount. AI-driven tools ensure customers receive timely and accurate responses, improving engagement. AI continuously learns from past experiences and provides 360-degree customer insights for hyper-personalization, efficiently routing help desk queries to enhance satisfaction and retention. AI-driven customer service tools enable businesses to deliver high-quality, personalized experiences, fostering stronger customer relationships and driving growth.

Providing relevant recommendations

AI analyzes customer interests and behaviors to provide more personalized and relevant recommendations, improving customer engagement. By understanding individual preferences, AI helps businesses tailor their offerings to meet customer needs more effectively. Personalized recommendations enhance the customer experience, leading to increased satisfaction and loyalty.

Ensuring end-to-end efficiency

AI enhances analytics and resource utilization across an organization, reducing costs, automating complex processes, and predicting maintenance needs to minimize downtime. By optimizing operations and resource allocation, AI drives significant efficiency gains and cost savings. The predictive capabilities of AI also help businesses anticipate issues before they arise, ensuring smoother operations.

Offering infinite availability

AI operates without time constraints, offering continuous service when deployed in the cloud. This uninterrupted availability ensures that AI can handle tasks and provide support at any time, meeting the demands of a global, always-on market. The flexibility of AI systems to operate around the clock enhances business continuity and service levels.

Accelerating research and development

AI accelerates breakthroughs in research by quickly analyzing vast datasets, aiding in predictive modeling. By speeding up the R&D process, AI helps organizations bring innovations to market faster and more efficiently. The ability of AI to process and interpret large volumes of data drives scientific and technological advancements.

Reducing human error

AI eliminates manual errors in various tasks through consistent and precise algorithms. By following standardized processes, AI ensures accuracy and reliability in data processing, analytics, and other operations. Reducing human error leads to higher-quality outputs and greater trust in the results produced by AI systems.

These points highlight the comprehensive benefits AI brings to businesses, enabling them to operate more efficiently, make smarter decisions, and improve customer experiences.

Endnote

As we have seen, artificial intelligence has evolved rapidly since its inception, and there are no limits to its potential. It can reconstruct businesses and unimaginably shape the future by using its innovative features like natural language processing, machine learning, image processing, and robotic process automation. By adopting the right approach, AI development can revolutionize industries and transform how we live and work. With AI on the verge of reshaping our world for years to come, it is clear that we are undertaking an exciting journey of discovery and innovation.

LeewayHertz’s AI development team is proficient in developing profit-driven AI solutions. Get in touch with us to develop your own custom AI solution for your business today.