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How Are AI and IoT Changing the Landscape?

8 Helpful Everyday Examples of Artificial Intelligence

Artificial intelligence (AI) enables machines and programs to analyze data and detect patterns, allowing machines to make decisions using real-time data. AI is said to unlock the true power of IoT and is a necessary part of many automated systems.

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Artificial Intelligence

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Technology

Connectivity technologies, Bluetooth or LPWAN for example, enable devices to communicate and exchange data through the Internet. Connectivity technologies vary in what they bring to individual IoT solutions and devices, each with its advantages and drawbacks.

Connectivity

Blockchain is a distributed ledger technology that allows records to be stored globally across many participating computers, ensuring that it cannot be forged or altered without changing all subsequent blocks. Blockchain technology can enable IoT devices to transfer and store data in a tamper-proof format.

Blockchain

New technologies mean new standards and regulations. Make sure you're staying up-to-date and complying with the latest.

Standards & Regulations

Research and development activities by companies, organizations, governments, and people to develop new IoT products and services and improve existing ones.

Research and Development

Augmented reality (AR) combines a user's view of the real-world with a computer-generated image, often to provide contextual information. Through the integration of IoT, AR systems can request and relay data in real time.

Augmented Reality

Cloud software leverages data or programs over the Internet, enabling programs to utilize remote systems or components. Through cloud computing, IoT deployments can utilize resources remotely to simplify on-site installation and maintenance.

Cloud Software

Device management is a vital part of an IoT solution's installation and continued maintenance. Device management is the process by which devices are added to a network, monitored, and maintained. This process includes provisioning, configuration, diagnostics and regular software maintenance and updates.

Device Management

Edge computing allows data to be processed at the source, rather than the cloud. Edge computing is especially useful in situations where Internet access is intermittent or where latency is a concern.

Edge Computing

Hardware components are integral to the success of any IoT deployment or service. Hardware components cover a vast array of devices that can be used to gather, process, store and transmit data. What devices or sensors you need are highly dependent on the goals of your IoT system and Applications.

Hardware

Integration services help you get the most out of your existing infrastructure while upgrading with new IoT solutions or devices. IoT integration helps to lower the cost of integrating new IoT devices by allowing you to utilize existing infrastructure, rather than replace it.

Integration Services

Location technologies are vital to any IoT applications that require monitoring the location of assets or people. Which location technology is best for your solution depends on your needs - your access to the Internet and power, the range at which you need to track assets, and more.

Location Technology

Machine learning (ML) is an application of artificial intelligence that enables systems to learn and improve from experience without human intervention. The goal of machine learning is to create systems that can access and learn from data without explicit programming.

Machine Learning

Mixed reality (MR) uses components of both virtual reality and augmented reality to create environments where both real and virtual objects coexist. Through integration with IoT, MR systems are able to access real-time data and connect with other devices to create more immersive and robust systems.

Mixed Reality

Understanding network standards and protocols is vital to choosing the connectivity technology to power your IoT solution or device. Some of the common IoT protocols to know are BLE, Cellular Connectivity, LPWANs, Mesh protocols, and RFID - each has its advantages and disadvantages and which one to use will be determined by your particular Applications.

Network and Protocols

Virtual reality (VR) immerses users in a virtual environment and enables them to interact with it, typically, in a physical way. Applications integrating IoT and VR include simulating control of something in the physical world, like a remote surgical robot, or enabling users to connect remotely in a virtual space.

Virtual Reality

Voice interaction, or voice user interfaces (VUI), enables a user to control a machine via voice commands. VUI enables touchless control of devices, even from a short distance away, and is a common control method for IoT devices in the smart home. Specifically via voice assistants like Google Assistant or Alexa.

Voice Interaction

Data analytics is the result of the process of data analysis in which raw data is inspected, cleaned, transformed, and processed into more useful and organized formats. The goal is to find useful and new correlations and information that can inform tactical and strategic decision-making.

 

Data Analytics

Cybersecurity is the software side of the protection of computer systems and networks from bad actors looking to commit cybercrimes and data leakage like information disclosure. It is often used to prevent interruptions of service and also hardware, software, and data theft or damage.

Cybersecurity

Digital transformation is the adoption of digital technology to digitize non-digital products, services, or operations. The goal is to increase value through innovation, invention, customer experience, and efficiency.

Digital Transformation

Mobile technology refers to portable devices like smartphones and tablets that are connected through wireless networks and allow users to communicate, access information, and use various applications from almost anywhere.

Mobile

Embedded systems are networked or otherwise connected computer systems made up of processors, memory, and input/output peripheral devices that have a dedicated function within a larger mechanical or electronic system.

Embedded Systems

IoT platforms are software suites designed to manage or control IoT systems, either on-premise or from the cloud. They often incorporate a dashboard for viewing analytics, network status, and sensor and device activity. An IoT platform can also include functions that allow remote control and management of these endpoints, gateways, and networks. As of 2021, there are about 700 IoT Platforms on the market, depending on criteria.

IoT Platforms

Low-code development is a way to make software without needing to write lots of code. You can use a platform with easy-to-use visual tools and drag-and-drop features instead of complicated programming languages.

Low Code Software

Electric vehicles (EVs) are cars that run on electric motors using electricity instead of gasoline or diesel. IoT can help EV owners remotely monitor and control their cars through apps or web interfaces. IoT can also enable communication between EVs, charging stations, and energy grids so that EV owners can easily find charging for their cars.

Electric Vehicles

Computer vision relies on a combination of algorithms and artificial intelligence to process information like shapes, colors, and textures to understand what is before it.

Computer Vision

AIoT, or Artificial Intelligence of Things, is a developing term for the layering of Artificial Intelligence and other automated tools for statistical analysis onto Internet of Things infrastructure. The combination of these two important aspects of Digital Transformation is expected to lead to better execution and growth of IoT systems and projects. It is also tied to improved process automation and human-machine interactions.

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AIoT

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New to AI

Generative AI refers to a type of artificial intelligence that is designed to create or generate new content, such as images, text, music, and even videos.

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Generative AI

The creative application of artificial intelligence (AI) algorithms and machine learning techniques to generate, modify, or interpret artistic works. AI art can involve the use of neural networks, deep learning, and generative models to create visual art, music, literature, and other forms of creative expression.

AI Art

Generative design is an innovative approach to product and architectural design that leverages the power of algorithms and artificial intelligence to generate design options based on a set of defined parameters and constraints. Rather than relying solely on human intuition and creativity, generative design systems explore a vast design space, often producing novel and optimized solutions.

Generative Design

Deepfakes are a type of synthetic media created using deep learning and artificial intelligence techniques. They involve the manipulation of existing images, videos, or audio recordings to depict something that never actually happened. Deepfakes can convincingly alter the appearance, voice, or actions of individuals, often with the intent to deceive or mislead viewers.

Deepfakes

Neural networks are a type of machine learning algorithm inspired by the structure and function of the human brain. A neural network consists of interconnected nodes (neurons) that are organized in layers. Each neuron receives input from other neurons and applies a nonlinear transformation to the input before passing it on to the next layer.

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Neural Networks

Convolutional Neural Networks (CNNs) are a class of deep neural networks designed for processing structured grid data, primarily images and videos. They have had a profound impact on computer vision tasks, enabling machines to understand and interpret visual information. CNNs are characterized by their use of convolutional layers, pooling layers, and fully connected layers.

Convolutional Neural Networks (CNNs)

News, updates, and developments in AI and tech.

AI News

The framework and guidelines instituted by governments and international bodies to ensure the ethical, safe, and responsible development and deployment of artificial intelligence. This encompasses standards for transparency, fairness, privacy, and accountability, aiming to mitigate potential risks and abuses.

AI Regulation

AI Law refers to the legal and ethical considerations and regulations surrounding the development, deployment, and use of artificial intelligence (AI) and machine learning technologies. AI law encompasses a wide range of legal issues and challenges arising from the growing influence of AI in various industries and domains.

AI Law

A form of artificial intelligence that possesses the ability to understand, learn, and perform any intellectual task that a human being can. Unlike narrow AI, which is designed for specific tasks, AGI exhibits versatile problem-solving and adaptive learning across diverse domains.

Artificial General Intelligence (AGI)

An advanced subset of artificial intelligence (AI) that aims to emulate human thought processes, reasoning, and problem-solving abilities. Cognitive AI systems are designed to understand, interpret, and learn from data in a way that allows them to make informed decisions and adapt to new situations. They often incorporate elements of natural language processing, machine learning, and deep learning to achieve human-like cognitive capabilities.

Cognitive AI

AI hardware refers to specialized hardware components and systems designed to accelerate the processing of artificial intelligence (AI) workloads. These hardware solutions are optimized for the unique computational demands of AI tasks, including machine learning, deep learning, and neural network inference and training.

AI Hardware

A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to accelerate the processing of images and videos in a computer system. Originally developed for rendering graphics and enhancing the visual experience in video games and multimedia applications, GPUs have evolved to become essential components in a wide range of computational tasks, including artificial intelligence, scientific simulations, and data processing.

GPUs

A Tensor Processing Unit (TPU) is a custom-designed application-specific integrated circuit (ASIC) developed by Google for accelerating machine learning and artificial intelligence workloads. TPUs are optimized to efficiently perform operations on tensors, which are multi-dimensional arrays commonly used in neural network computations.

Artificial Intelligence

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