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Difference Between IoT and AI: Which is Better?

20th Nov, 2023
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    Difference Between IoT and AI: Which is Better?

    As the tech space rapidly evolves, new trends and machines that assist humans are being developed. The size of the global industrial AI automation market is projected to grow to USD 441.7 billion. Many industries and domains are switching to AI automation to enhance work performance and results in several folds. As a result, employers look for candidates with the best AI certification to run automation and write algorithms.

    Another technology, the Internet of Things, has been widely used. Not only individually but in conjunction with AI as one of the primary data sources used to train AI automation systems. Both IoT and AI have grabbed the eyeballs and are buzzing about in the tech space. 

    Though both the terms are synonymously used, they are clearly distinct. Artificial intelligence (AI) focuses on giving computers an intelligence feature to make them as clever and intelligent as people. And the Internet of Things (IoT) aims to unite all electronic gadgets into a single, interconnected network. 

    Read on for better clarity on how AI and IoT work, how they differ, and their vitality in today’s world.

    What is IoT (Internet of Things)? 

    By definition, IoT is a collection of physical objects integrated with software, sensors, and other devices for communicating and sharing data with different systems and devices over the internet. All linked devices can transmit information through built-in intelligent technology, facilitating the creation of wearable technology, smart cities, and smart homes, among many other uses for intelligent gadgets.

    What is AI (Artificial Intelligence)? 

    On the other hand, The concept behind artificial intelligence (AI), a cutting-edge branch of computer science, is to build intelligent, perceptive computers that can act and respond much like people. AI aims to emulate human intelligence and behavior in machines so that they behave more empathetically. Businesses process enormous amounts of data and provide real-time results by utilizing AI.

    Artificial Intelligence vs Internet of Things

    Before diving into the details, let’s start by skimming through a few differences in how the technologies work. 

    ParametersArtificial IntelligenceInternet of Things
    DefinitionArtificial Intelligence (AI) emulates human intelligence in machines that are made to think and behave like people, such as comprehension, learning, and problem-solving.IoT is when, through the internet, a communication medium, the interacting devices in this ecosystem communicate data where codes are used to direct these devices to work during a specific occurrence.
    PurposeIoT functions like an infrastructure enabling global connectivity and communication with objects.AI aims to imitate human intelligence and conduct in machines so that they behave more compassionately.
    DependabilityGenerates a vast amount of data, much of which needs to be captured and some of which loses value in milliseconds.AI tools and systems can generate the same data with the least human involvement and lower dependence.
    ScalabilityIt has more scalability than AI.It has less scalability but can be implemented on IoT devices to increase scalability.
    CostsIoT costs relatively less but requires interconnected hardware components, such as controllers, LED displays, sensors, etc.AI costs more as it requires massive computations, and highly configurable system architectures are needed.
    Success ratesHas higher success rates (42%)Has relatively lower success rates (25%)
    Data RequirementData is the basis of AI.Unlike AI, the Internet of Things is the collection of moments from sensors, which collect, store, and retrieve data upon demand.

    Difference Between IoT and AI

    Now that you are brushed on how AI and IoT are different from each other, let's have a closer look.

    1. IoT vs AI: Purpose

    Though AI can be implemented alongside IoT, the purposes of both systems are distinct. 

    • IoT: IoT functions like a network that lets users connect and interact with objects from any global location. It is the interconnectedness of physical objects that can communicate with one another without the need for human intervention, such as sensors, actuators, and other essential electronics. The goal is to enable items to transmit and receive data via the Internet. For example, temperature sensors are the most commonly used IoTs. They record temperature variations and identify heat. Additionally, motion sensors use ultrasonic wave monitoring to identify movement and initiate an action that is wanted when those waves are disrupted. 
    • AI: Artificial Intelligence (AI) aims to imitate human intelligence and behavior in machines, hence promoting more compassionate behavior. The aim is to develop technology that facilitates human-machine collaboration. For instance, people frequently receive personalized recommendations from artificial intelligence based on past searches, purchases, and other online activities. 

    2. IoT vs AI: Dependability

    Dependability relates to human intervention, and IoT and AI have distinct dependability rates. 

    • IoT: IoT enables data to move between different physically connected devices, and AI aids in data interpretation. Through a vast network of linked devices, the Internet of Things (IoT) generates a vast amount of data, much of which is not even captured and some of which loses value in milliseconds. This necessitates a method for deriving insights from the data via intelligent analysis. As a result, IoT has higher dependability compared to AI systems.
    • AI: Compared to IoT, AI systems depend on humans, especially during the development and training phases. They may also require intervention to address adversarial attacks, model drift, and unintended consequences, which may be somewhat predictable.

    3. IoT vs AI: Scalability

    Regarding scalability, IoT takes the front seat for many reasons. 

    • IoT: The current cloud-based framework makes it easier to grow IoT initiatives. Numerous variables, such as pace and architectural design, might impact a project's scalability. However, scaling any IoT project developed with scalability in mind is simpler.
    • AI: The large number of variables makes scaling AI projects somewhat challenging. However, additional flexibility and modularity in the architecture facilitate more straightforward scalability. Inadequate data quantity can restrict the system's resilience and generalization, while poor data quality can result in erroneous, biased, or untrustworthy AI outputs.

    4. IoT vs AI: Costs

    The cost of AI systems lies on the higher end than that of IoT as it involves multiple, high configuration requirements to function. 

    • IoT: IoT projects often involve expenses for host servers (if applicable), hardware, wireless connectivity, and corresponding software development. The cost of IoT is lower than that of AI. The expense of purchasing specialized controllers is additionally decreased by the ability to control IoT devices using portable electronics like smartphones.
    • AI: In contrast, data collection, software development, model deployment, data lakes/warehouses, and model deployment are typically associated with expenditures associated with AI projects. Highly configurable system architectures are needed for AI to execute massive computations. Therefore, renting or leasing distant servers comes at a slightly higher cost.

    5. IoT vs AI: Success Rates

    Artificial intelligence projects often have a lower success rate than the Internet of Things.

    • IoT: Businesses can achieve success by having a thorough understanding of consumer behavior and decisions. With the Internet of Things, this is now feasible. Businesses can use IoT to collect, track, and analyze data from mobile, social media, video surveillance, and internet usage. IoT has simple and workable systems, making its success rates slightly higher than AI's.
    • AI: Compared to IoT, AI projects often have a lower success rate. According to an IDC poll, only 30% of the organizations claimed the highest success rates for AI. The failure rate for the remaining cases varied from 10% to 49%. Among many others, one of the leading causes of AI project failures is a need for more data (both high-quality and high-quantity).

    6. IoT vs AI: Data Requirements

    Data is the basis of AI, while IoT requires hardware systems, sensors, and minimum data to function. If you want to dive into the details of data requirements and management under each system, you can take up the best Data Science courses.

    • IoT: The foundation of the Internet of Things is the collection of moments from sensors, which collect, store, and retrieve data upon demand. Therefore, the more sensors used, the more influential the data collection.
    • AI: Artificial intelligence possesses the ability to comprehend patterns and actions. Massive amounts of data, including patterns, trends, and knowledge of human behavior, are needed for AI. To carry out tasks like data modeling and many more, the data utilized in AI must be preprocessed and relevant.

    IoT and AI: Which One Should You Choose?

    There’s no one answer to “IoT vs AI, which is better.” AI and IoT both have significant and promising prospects. Both individually and as a group. 

    Companies often use IoT because of its capability to accumulate real-time information when the primary goal is to collect real-time data from multiple devices or environments. Moreover, IoT is a preferred tool where continuous, remote monitoring of physical assets is required. 

    On the other hand, when companies already have enough data sources and want to jump into extracting insights and making predictions, they often benefit more from utilizing AI than IoT. AI can analyze historical data and generate actionable insights without additional IoT sensors.

    Thus, the choice between IoT and AI solely depends on what kind of problem you want to solve. Whether it concerns data generation and interpretation or human errors and low productivity. 

    However, nowadays, AIoT is the talk of the town. AIoT, short for Artificial Intelligence of Things, is a transformative concept that combines two. AIoT leverages the capabilities of AI to enhance the functionality and intelligence of IoT devices and networks. It allows these devices to gather, analyze, and act upon data in a more advanced and autonomous manner. 

    Final Thoughts 

    Artificial Intelligence and the Internet of Things are two separate but complementary ideas. AI systems can analyze, learn from, and automate tasks using data from IoT devices. They are concerned with analysis, interpretation, and decision-making, whereas IoT is more concerned with connectivity and automation.

    There are several distinctions and parallels between how IoT and AI operate. However, both have a significant impact, provided their potential is correctly utilized in the business process. Both the technologies come with their pros and cons. To understand the details of these systems and enhance your knowledge of automation, you can take up the KnowledgeHut best AI certification. It will help you understand the technologies better and equip you as an expert.

    Frequently Asked Questions (FAQs)

    1What is the difference between IoT and AI?

    AI is concerned with analysis, interpretation, and decision-making, whereas IoT is more concerned with connectivity and automation. AI systems can analyze, learn from, and automate tasks using data from IoT devices.

    2What is the future of AI in IoT?

    AI might further improve IoT by ultimately boosting productivity and streamlining procedures. Businesses can make well-informed decisions by using AI to analyze the data gathered from the sensor.

    3Is IoT better than robotics?

    Both IoT and robotics are evolving together under a system called IoRT. IoRT technology reduces human ignorance and errors during task performance while conserving human labor and energy.

    4Which language is best for IoT?

    Python. Because it is a scalable language that may be used for software development and small- and large-scale IoT applications. Because of the modular nature of this language, it's simple to divide a complicated program into smaller, easier-to-manage parts.


    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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