AI and Augmented Reality: How They Work Together and Why They Matter

Artificial intelligence (AI) and augmented reality (AR) are two of the most exciting and disruptive technologies of the 21st century. They have the potential to transform various domains, such as education, entertainment, health, and industry, by creating immersive and interactive digital experiences that enhance the physical world.

But what are AI and AR, and how do they work together? In this blog post, we will explore the definitions, applications, and benefits of AI and AR, and how they complement each other to create more realistic and engaging AR experiences.

What is AI?

AI is a broad term that encompasses various techniques and applications that enable machines to perform tasks that normally require human intelligence, such as learning, reasoning, and decision making. AI can be divided into two categories: narrow AI and general AI.

• Narrow AI refers to AI systems that are designed to perform specific tasks, such as image recognition, natural language processing, or chess playing. These systems use data and algorithms to learn from patterns and make decisions based on predefined rules and objectives. Examples of narrow AI include Google’s search engine, Amazon’s Alexa, and IBM’s Watson.
• General AI refers to AI systems that can perform any intellectual task that a human can, such as understanding, reasoning, and creating. These systems use data and algorithms to learn from their own experiences and goals, and adapt to new situations and challenges. Examples of general AI include HAL 9000 from 2001: A Space Odyssey, Skynet from The Terminator, and Samantha from Her.

What is AR?

AR is a technology that overlays digital information, such as images, text, or sound, on the physical environment, creating a mixed reality that enhances the user’s perception and interaction. AR can be experienced through various devices, such as smartphones, tablets, glasses, or headsets, that use cameras, sensors, and displays to capture, process, and present the AR content.

AR can be classified into three types: marker-based AR, markerless AR, and projection-based AR.

• Marker-based AR refers to AR systems that use physical markers, such as QR codes, barcodes, or images, to trigger the display of digital content. The markers are scanned by the device’s camera, and the AR content is aligned and rendered on the screen according to the marker’s position and orientation. Examples of marker-based AR include Snapchat lenses, Pokemon Go, and IKEA Place.
• Markerless AR refers to AR systems that use the device’s sensors, such as GPS, gyroscopes, accelerometers, and compasses, to track the user’s location, movement, and direction, and display digital content accordingly. The AR content is not dependent on any specific marker, but rather on the user’s context and environment. Examples of markerless AR include Google Maps, Waze, and Star Walk.
• Projection-based AR refers to AR systems that use projectors, lasers, or holograms to project digital content directly onto the physical environment, creating a seamless and realistic illusion. The projected content can be interactive, responsive, or dynamic, depending on the user’s input and feedback. Examples of projection-based AR include Microsoft’s HoloLens, Magic Leap, and Disney’s Haunted Mansion.

How do AI and AR work together?

AI and AR are closely related technologies that can work together to create more advanced and immersive AR experiences. AI can enhance AR in various ways, such as:

• Data analysis and pattern recognition: AI can use large amounts of data from various sources, such as medical records, genetic tests, brain scans, wearable devices, and online platforms, to analyze and identify patterns and correlations that can provide useful and relevant information for the AR content. For example, AI can use facial expressions, voice tones, and speech patterns to detect signs of depression, anxiety, or schizophrenia, and display appropriate AR content to help the user cope or seek help¹²³.
• Prediction and classification: AI can use data-driven models and algorithms to predict and classify the user’s behavior, preferences, and needs, and tailor the AR content accordingly. For example, AI can use natural language processing to analyze the content and sentiment of text messages, social media posts, or online reviews, and display AR content that matches the user’s mood, interest, or intention⁴.
• Personalization and recommendation: AI can use data-driven insights and feedback to personalize and recommend the best AR content and devices for each user, based on their characteristics, goals, and context. For example, AI can use reinforcement learning to adapt and optimize the delivery and content of online screening tests, questionnaires, or chatbots, to enhance the user’s engagement and accuracy⁵.

AI can also help overcome some of the challenges and limitations of AR, such as:

• Computational complexity and latency: AR systems require high computational power and speed to process and render the AR content in real time, which can be challenging for mobile devices and wireless networks. AI can help reduce the computational complexity and latency of AR systems by using techniques such as edge computing, cloud computing, and compression, which can distribute, offload, or optimize the AR tasks among different devices and servers.
• User interface and interaction: AR systems require intuitive and natural user interfaces and interactions, which can be difficult to design and implement for complex and dynamic AR content. AI can help improve the user interface and interaction of AR systems by using techniques such as speech recognition, gesture recognition, and eye tracking, which can enable the user to communicate and control the AR content using voice, body, or gaze.
• User experience and satisfaction: AR systems require high user experience and satisfaction, which can be influenced by factors such as realism, immersion, and comfort of the AR content and devices. AI can help enhance the user experience and satisfaction of AR systems by using techniques such as computer graphics, computer vision, and machine learning, which can improve the quality, accuracy, and adaptability of the AR content and devices.

Why do AI and AR matter?

AI and AR are not only technological innovations, but also societal transformations. They have the potential to create positive impacts and benefits for various domains, such as:

• Education: AI and AR can create engaging and interactive learning environments that can enhance the student’s motivation, attention, and retention. AI and AR can also provide personalized and adaptive learning content and feedback that can suit the student’s level, pace, and style. Examples of AI and AR in education include Google Expeditions, Quiver, and Mondly.
• Entertainment: AI and AR can create immersive and interactive entertainment experiences that can enhance the user’s enjoyment, creativity, and socialization. AI and AR can also provide personalized and adaptive entertainment content and recommendations that can suit the user’s taste, mood, and context. Examples of AI and AR in entertainment include Netflix, Spotify, and TikTok.
• Health: AI and AR can create supportive and empowering health environments that can enhance the patient’s diagnosis, treatment, and recovery. AI and AR can also provide personalized and adaptive health content and feedback that can suit the patient’s condition, goal, and progress. Examples of AI and AR in health include BlueSkeye AI, Mindstrong, and Woebot .
• Industry: AI and AR can create efficient and effective industrial environments that can enhance the worker’s productivity, safety, and collaboration. AI and AR can also provide personalized and adaptive industrial content and feedback that can suit the worker’s task, skill, and situation. Examples of AI and AR in industry include Volkswagen MARTA, IKEA Place, and Microsoft Dynamics 365 .

AI and AR are not only beneficial, but also challenging and risky. They also pose various ethical, social, and legal issues, such as:

• Data quality and privacy: AI and AR rely on large amounts of data to function and improve, but the data may not be accurate, representative, or secure. The data may also be sensitive, personal, or confidential, raising issues of privacy, consent, and ownership.
• Ethical and social implications: AI and AR may have unintended or unforeseen consequences on the individuals and society, affecting their rights, values, and interests. The AR content may be inaccurate, misleading, or harmful, causing distress, stigma, or discrimination. The AI decisions may be biased, unfair, or unaccountable, causing injustice, inequality, or distrust.
• Human-AI-AR interaction and collaboration: AI and AR may change the way humans interact and collaborate with each other and with the machines, affecting their roles, responsibilities, and relationships. The AR content may be automated, delegated, or augmented by AI, affecting the human agency, autonomy, and identity. The AI decisions may be mediated, facilitated, or supported by AR, affecting the human communication, feedback, and engagement.

To address these issues, some of the possible solutions and recommendations are:

• Data governance and regulation: Data quality and privacy can be ensured by establishing and enforcing standards, guidelines, and regulations for the collection, storage, and use of data, such as the General Data Protection Regulation (GDPR) in the European Union. Data governance and regulation can also involve the participation and empowerment of the data subjects, such as the users, the developers, and the public, in the decision making and oversight of the data practices.
• Ethical and social awareness and education: Ethical and social implications can be anticipated and mitigated by raising and promoting the awareness and education of the ethical and social aspects of AI and AR, such as the ethical principles, frameworks, and codes of conduct for the design, development, and deployment of AI and AR, such as the IEEE Ethically Aligned Design.