Your Guide to Emerging Technologies and Tech Trends in 2024

First, let’s explore how our interactions with people, places, content, and things evolve by blending online and offline experiences. Advances in AI, IoT, and other technologies are creating interconnected systems that make our lives easier and more efficient. From AI avatars and digital twins to multimodal interfaces and smart spaces, the Smart World is changing how we engage with everything around us.

AI avatars are digitally created personas that look and act like humans. They use advanced technologies like computer-generated imagery (CGI) and artificial intelligence to mimic human appearance, speech, and behavior. These avatars are transforming how we engage with technology and with each other.

Use cases:

Digital twins are digital copies of physical or virtual assets, processes, or organizations. They use technologies like IoT, AI, and machine learning to create a real-time digital representation that mirrors its real-world counterpart.

Use cases:

1. Industrial Manufacturing: Did you know that the market for digital twins is forecasted to be worth $73.5 billion by 2027? Technological giants like Siemens use digital twins to improve product design and production processes. They create virtual prototypes for extensive testing and optimization before physical production to shorten development times.
2. Smart Cities: The ASPERN project in Vienna, supported by Siemens Advanta, uses this technology to manage and optimize the city’s energy grid. This helps plan for energy demands and improve the infrastructure’s resilience.
3. Product Development: IBM employs digital twins to simulate and test products digitally, improving quality and reducing the time and cost associated with physical prototypes.

Future Scenarios

Healthcare: Personalized medicine and treatment planning could use patient-specific digital twins. Simulating drug effects before clinical trials might lead to safer medications.

Smart Cities and Urban Planning: Digital twins could simulate and optimize urban systems. Integration with IoT sensors might enable real-time monitoring of city infrastructure.

Next-Generation Transportation: They could also be used to optimize traffic flow and reduce congestion. They might support predictive maintenance for autonomous vehicles and simulate new mobility concepts before implementation.

Future Scenarios:

Healthcare: Doctors could benefit significantly from the technology in the future. During surgeries, they could navigate medical images using hand gestures and voice commands, maintaining sterility and efficiency.

Education: In classrooms, students could interact with educational materials through touchscreens, voice commands, and gestures, making learning more engaging and effective.

Retail: Shopping could become more interactive. Imagine using interactive kiosks and smart mirrors to explore products, receive personalized recommendations, and purchase using touch and voice commands.

Smart spaces are physical environments where humans and technology-enabled systems interact in increasingly connected and intelligent ecosystems. These spaces use a network of sensors, IoT devices, and intelligent infrastructure to collect and analyze data, improving the environment’s functionality and efficiency.

Use Cases:

1. Smart Offices: Smart offices integrate IoT devices and sensors to create a connected workplace. Employees can quickly locate meeting rooms, control their workspaces, and access real-time data for better decision-making.
2. Smart Campuses: Educational institutions use smart spaces to improve campus safety, automate administrative processes, and offer seamless connectivity to students and staff.
3. Smart Venues: Event spaces and stadiums use smart technologies to optimize resource usage, enhance visitor experiences, and improve crowd management.
4. Smart Healthcare Facilities: These spaces revolutionize healthcare with patient monitoring, staff duress systems, and medical equipment tracking.

Future Scenarios

Adaptive Urban Environments: Smart spaces could dynamically adjust to changing conditions and needs. For example, streets could automatically reconfigure to prioritize pedestrians or cyclists during off-peak hours, or public spaces could transform to accommodate different events or weather conditions.

Healthcare: Smart spaces could also create personalized environments that optimize individual health needs. This could include adjusting lighting, air quality, and ambient noise based on an individual’s health data.

Augmented Reality (AR) Overlays: Physical spaces could be enhanced with AR overlays, providing contextual information, navigation assistance, or interactive experiences tailored to each user. This could revolutionize areas like tourism, education, and retail.

Education: Educational spaces could dynamically adjust to match learning conditions with different subjects or individual student needs, including lighting, temperature, and even the physical configuration of the space.

This section describes the impact of generative AI on productivity across different sectors. This change involves using GenAI to streamline operations, optimize resources, and foster innovation. AI technology solutions allow businesses to operate more intelligently and effectively, significantly influencing how we approach work.

Model compression reduces the size of AI models, making it easier to deploy them on small devices or enhance the capacity of central systems. With the ever-growing amount of data and limited computing resources, compression is key to keeping legacy systems efficient and sustainable.

Why It’s Important: Computational resources are under strain. Model compression helps manage them more effectively by making AI models less demanding. This improves efficiency and reduces the environmental impact since smaller models consume less power.

How It’s Used: Compressed models are essential for various applications. On mobile devices, they enable features like real-time translation and augmented reality. Industrial settings allow real-time data analysis and decision-making on edge devices, which is vital for applications like predictive maintenance. In healthcare, compressed models support portable diagnostic tools.

Sustainability: As AI’s environmental footprint grows, model compression becomes essential. By reducing the energy consumption of AI models, compression techniques help lower the overall carbon footprint, making AI operations more environmentally friendly.

Autonomous unmanned aerial vehicles (drones) operate without human pilots. They are primarily used for asset inspection but are increasingly being considered for delivering small packages.

Why They’re Important: UAVs offer various advantages in commercial applications. For example, they can inspect infrastructure, such as power lines and pipelines. They are also undergoing testing for medical deliveries in challenging environments, ensuring prompt transport of medical supplies to remote or inaccessible areas.

How They’re Used: In logistics, companies such as DHL are exploring UAVs for package delivery, which could streamline operations and reduce delivery times. In healthcare, UAVs have demonstrated potential for delivering medical supplies, ensuring that critical items reach their destinations quickly and safely. Additionally, the mining industry uses drones to enhance safety and efficiency by inspecting hazardous areas without putting human workers at risk.

Generative AI refers to technologies that create new content, strategies, designs, and methods by learning from large datasets of source content. Over the last two years, GenAI has advanced significantly and is mainly used for creating text, summaries, prototypes, and more.

In contrast, discriminative AI has been used for a longer time based on statistical methods like logistic regression. It’s particularly well-suited for tasks where the goal is to make decisions or classifications based on input data. This makes it ideal for applications like spam detection.

Importance and Uses: Applications of generative AI are widespread.

• In marketing, it creates personalized ad copy, email campaigns, and social media posts.
• It assists in product development by generating concepts and 3D models.
• In data analysis, it summarizes reports and extracts key insights.
• Customer service benefits from AI-powered chatbots and virtual assistants providing personalized responses.
• In software development, it generates code and documentation and automates testing.
• Healthcare uses generative AI for drug discovery and personalized treatment plans.
• Financial services apply it to financial reports, risk assessments, and fraud detection.
• Education leverages it for personalized learning materials and instant feedback, while the legal industry uses it for drafting documents and summarizing case law.

GenAI-enabled virtual assistants represent a new generation of VAs that use large language models (LLMs) to provide advanced functionalities. Unlike traditional VAs, these advanced assistants handle more complex tasks and provide more accurate responses through text and voice interactions.

Importance and Uses: These VAs operate similarly to AI avatars but focus on text and voice interaction. Examples include Microsoft’s Copilot in Windows, which integrates with various applications to help users complete tasks more efficiently. They are also used in customer service, providing quick and accurate responses to customer inquiries.

Vision transformers (ViTs) are neural networks that analyze relationships between pixels in different sections of images. This improves tasks like image classification, object detection, and image generation. ViTs allow for more accurate and detailed visual analysis, which is essential in many applications.

Why are they important: Visual Transformers are very useful in fields like image recognition and autonomous driving. They operate by dividing an image into patches, embedding these patches, and then processing them using a transformer architecture.

Moreover, they help use resources more efficiently, which is important for the environment as it helps reduce the amount of energy needed for processing. For example, they can achieve high performance with fewer computational resources when pre-trained on large datasets and fine-tuned on specific tasks.

Applications: ViTs enable better monitoring and analysis of environments, which enhances safety and operational efficiency. For example, in a smart building, ViTs can manage energy consumption by detecting occupancy and adjusting lighting and temperature accordingly.

In healthcare, ViTs improve the accuracy of diagnostic tools by providing detailed analysis of medical images, such as X-rays and MRIs. While implemented in autonomous vehicles, they improve the detection and classification of objects on the road.

Intelligent applications are software programs that use AI techniques to learn from external and alternative data sources. They use AI, machine learning, and data analytics to provide users with personalized, adaptive, and context-aware experiences.

Importance: Intelligent applications automate complex tasks and provide insightful data analysis. By learning from large datasets, these applications help businesses streamline operations, improve customer interactions, and foster innovation. They allow companies to quickly implement advanced capabilities without extensive in-house development, saving time and resources.

Uses: These applications are used across various fields. Tools like Typeface analyze market trends and optimize campaigns, while cybersecurity applications like Abnormal help protect digital assets by detecting and preventing threats. In healthcare, IBM’s Watson aids in diagnostics and treatment recommendations, helping healthcare professionals deliver better care.

Synthetic data is artificially generated rather than collected from real-world events. It is created using algorithms and simulations to produce datasets that resemble real-world data without including sensitive or private information.

Importance: Synthetic data is essential for training AI models, testing new solutions, and creating simulations, especially when dealing with sensitive or confidential information.

As artificial intelligence becomes more common and data volumes grow, synthetic data provides a solution to adhere to stricter data privacy regulations and remove biases found in real-world data. Moreover, synthetic data can include images, which is beneficial for advancements in computer vision and other visual-based AI applications.

Uses: Synthetic data is used in various fields. Training AI models helps generate large datasets without data availability or privacy concerns. It also offers a secure means to experiment with new solutions. In simulations, such as those for autonomous driving or medical research, it enables testing and development under realistic conditions.

Self-supervised learning (SSL) is a machine learning approach where the model creates its labels from the data without relying on historically labeled data or human supervision. The model identifies patterns within the data to generate criteria, which it then uses and refines through further learning. This method is mainly used for processing images and text data in natural language processing (NLP) models.

Importance: Self-supervised learning is essential due to the massive amount of unstructured data. Gartner estimates that unstructured data accounts for 80 to 90 percent of all new enterprise data, growing three times faster than structured data. ITC research predicts that unstructured data will increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. These numbers show the need for efficient data processing methods like SSL, which can manage large volumes of unstructured data without extensive manual labeling.

Uses: SSL is widely used in both image and text data processing. Computer vision helps models understand and categorize images by learning from the data, improving tasks such as object detection and image classification without manually labeling datasets.

In natural language processing, SSL improves the understanding and generation of human language. By creating their own labels from large text datasets, models can learn language patterns and generate more accurate text predictions and translations.

By using self-supervised learning, businesses and researchers can more effectively process and analyze vast amounts of unstructured data. This method also supports the development of more sophisticated AI models capable of understanding complex patterns and making informed decisions based on large datasets.

As we look beyond 2024, it is clear that emerging technologies are poised to revolutionize various sectors and offer new avenues for growth and innovation. Remaining informed and adaptable to these trends will be essential for business leaders aiming to leverage these advancements effectively.

Don’t miss part 2 of our series, which discusses many more transformative technologies listed in Gartner’s comprehensive Tech Impact Radar. Explore how they will shape industries and what steps you can take to maximize their potential for your business success.