The ceaseless churn of innovation continues to reshape our world at an ever-accelerating pace. In late 2024, several key technological domains are poised to redefine industries. They will alter the fabric of daily life. These changes will even reshape geopolitical landscapes in the coming years. Quantum computations quietly work behind the scenes, and infrastructure for extended reality applications is expanding. The landscape of technology is undergoing a shift that warrants close attention. This is not merely incremental progress but the potential foundation for the next major societal shifts.

What follows is an examination of some of the most pertinent technological trends emerging. These are not simply laboratory concepts but technologies gradually making their way into real-world applications. Their development trajectories suggest they will be pivotal in shaping the next decade. These technologies will impact everything from the mundane to the existential. The implications are vast. Understanding these trends is essential to navigate the complexities of our increasingly interconnected, technologically driven future.

Technology Trends

Augmented Reality (AR) Applications

Augmented reality applications, once a novelty, are now seeing a surge in adoption across various sectors. The technology, which overlays digital information onto the real world, is finding practical applications beyond entertainment. Currently, mobile devices remain the primary conduit for AR experiences. Smartphones and tablets come with advanced cameras and processing power. These features allow users to interact with digital content superimposed on their surroundings. For instance, users are already leveraging their phones to try on virtual furniture in their homes before purchasing, as seen in the IKEA Place app. Retailers like Sephora utilize AR filters that permit virtual makeup try-ons, enhancing the online shopping experience. Another widely known example of AR is Pokemon Go, a game that utilizes AR to allow players to “catch” virtual Pokemon in the real world.

The manufacturing and industrial sectors are also witnessing a steady integration of AR. Technicians are using AR headsets, such as the Microsoft HoloLens (2), to receive real-time data and guidance during complex assembly or repair tasks. This leads to increased efficiency and a reduction in errors. For example, Boeing uses AR to aid in the production of aircraft wire harnesses. Medical professionals are using AR in surgery and training. AR is being used to help surgeons see superimposed images on their patients, aiding with procedures.

Further into 2025, we see more companies announcing their plans. They are starting to develop software for the recently released Apple Vision Pro. This headset marks a substantial advancement in AR technology. The technology is improving the visual fidelity and interactivity of AR experiences. Expect to see more AR applications used with this device to improve customer experience with various retailers. There is also a lot of talk about Meta’s Quest (3) and how AR developers are starting to use it for more immersive games. As AR glasses become more powerful and user-friendly, they are anticipated to surpass mobile devices as the preferred medium for AR. The deployment of 5G and advancements in edge computing are also anticipated to fuel the growth of AR. Faster data transfer rates and reduced latency will enable more complex and responsive AR applications. AR-based navigation is on track to become increasingly sophisticated, providing users with real-time directions overlaid on their view of the real world.

It’s worth noting the growing investment in AR content creation tools. Platforms are emerging that allow users with minimal coding experience to design and deploy their own AR experiences. This democratization of AR content creation will likely lead to a surge in innovative applications across various domains. It’s an exciting time for augmented reality. The technology is steadily maturing from a niche interest. It is becoming a powerful tool with the potential to reshape various facets of our lives.

Virtual Reality (VR) Experiences,

As we conclude 2024, virtual reality (VR) experiences are an established, though still evolving, sector of the technology landscape. The most common application is in gaming, where headsets like the Meta Quest (3) offer a compelling level of immersion. Users can engage in complex worlds, interact with environments, and experience a sense of presence not possible with traditional flat-screen gaming. This device features a Qualcomm Snapdragon XR2 Gen (2) processor, and 8GB of RAM. This allows for detailed graphics and relatively smooth performance. The Quest (3) also employs ‘mixed reality’ passthrough, where cameras on the headset let users see a color representation of their real-world surroundings. This can be used for safety or for experiences where digital content overlays the real world.

Beyond gaming, VR is being used in training simulations. For example, Walmart utilizes VR to train employees in scenarios such as handling Black Friday crowds or learning to empathize with customers. The simulations provide a safe and repeatable environment for practicing skills that can then be applied in real-world situations. Other companies are using VR for product design and development. Architects and engineers can create virtual prototypes and walk through them, allowing them to identify design flaws or potential improvements early in the development process. This is exemplified by BMW’s use of VR in their design process, which enables them to test various configurations and make adjustments before committing to physical prototypes.

Looking to the near future, the integration of haptic feedback is poised to become more prevalent. Devices like the Teslasuit, a full-body haptic suit, are already available, though they remain relatively expensive and primarily targeted at enterprise applications. These suits use electrical muscle stimulation (EMS) and transcutaneous electrical nerve stimulation (TENS) to provide tactile sensations corresponding to in-VR events. Companies are also exploring the inclusion of olfactory feedback, using scent cartridges to enhance the realism of VR experiences.

Another area of development is in the reduction of headset size and weight. Lighter, more comfortable headsets will make extended use more appealing. Current high end headsets like the Varjo XR-(4) use displays that exceed 4k resolution per eye, with an advanced combination of focus and foveated rendering, where the resolution is concentrated where the user is looking. These technologies improve the quality, but the hardware is heavy, and the price tag makes this type of product exclusive. Improved optics, including advances in pancake lens technology, are contributing to smaller form factors. The Apple Vision Pro, released in early 2024, demonstrated a new standard in display quality and a sleek design, setting a benchmark for future headsets. Eye tracking is becoming a standard feature in higher-end VR headsets.

Mixed Reality (MR) Integrations

Mixed reality, a spectrum encompassing augmented and virtual realities, is steadily weaving its way into various facets of daily life and industrial applications. The technology overlays computer-generated images onto the real world or can fully immerse a user in a simulated environment. We are seeing a steady increase in use cases. Current iterations primarily involve advanced headsets that blend these realities.

One of the most prominent examples in late 2024 is the use of MR in remote collaboration. Platforms like Microsoft Mesh allow geographically dispersed teams to interact within shared virtual spaces, manipulating 3D models and data in real-time as if they were physically present together. This has become increasingly common in engineering and design fields. Architects, for instance, can walk clients through virtual building models superimposed onto actual construction sites. There are now multiple platforms that offer similar features to Microsoft Mesh.

In healthcare, mixed reality is beginning to see adoption in surgical training and planning. Surgeons are utilizing MR headsets to visualize complex anatomical structures in 3D, overlaid onto the patient during procedures. This provides crucial insights and enhances precision. Companies like Medivis offer FDA-cleared surgical visualization platforms. Manufacturing is also seeing the increasing application of MR for assembly guidance, maintenance, and quality control. Workers wearing MR headsets can receive step-by-step instructions and real-time feedback projected directly onto the machinery or products they are working with. Boeing, for instance, has reported a substantial increase in productivity when using MR for aircraft wiring assembly.

The near future of MR will see a shift towards more seamless and intuitive integrations. Lighter, less obtrusive headsets are under development, with companies like Apple and Meta investing heavily in this area. Apple are reportedly very close to releasing a new generation of lightweight MR glasses. These devices are set to make mixed reality experiences more accessible and practical for everyday use. There are also a significant number of start-ups working on lightweight MR glasses. Another development is the integration of AI for more dynamic and responsive MR environments. MR systems will be able to better understand user intent and the surrounding environment, allowing for more personalized and adaptive experiences.

We will also see enhanced gesture and voice controls, reducing the need for physical controllers. Meta has implemented hand tracking in some of their virtual reality headsets. This, along with advancements in eye-tracking technology, will enable users to interact with virtual content in a more natural way. These advancements mean MR is becoming more user-friendly and efficient.

Extended Reality (XR) Platforms

Extended Reality (XR) platforms, encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), are steadily moving beyond niche entertainment and into practical, everyday applications. The hardware is becoming more refined, with companies iterating on existing devices to improve comfort, visual fidelity, and processing power. Software development is following suit, creating experiences that are less about novelty and more about utility in a range of sectors.

Currently, Meta’s Quest headset represents the consumer VR market, offering a mixed-reality experience with improved passthrough capabilities, allowing users to see and interact with their real-world surroundings while wearing the headset. This is intended to make the device more useful for everyday tasks. Apple has also finally released their Apple Vision Pro which is a new, very high-end headset that has gained a large amount of traction. In the AR space, smartphones remain the primary access point, with apps like IKEA Place allowing users to visualize furniture in their homes before purchasing. Microsoft’s HoloLens continues to find traction in enterprise settings, particularly in manufacturing, healthcare, and design, where its hands-free holographic interface provides a way to overlay digital information onto the physical world.

In terms of what’s currently happening, the focus has shifted towards refining the user experience and expanding the ecosystem of applications. Developers are working on more intuitive interfaces, incorporating hand-tracking and eye-tracking technologies for more natural interaction. There’s a notable push towards creating collaborative XR environments, where multiple users can share the same virtual or augmented space, regardless of their physical location. This is particularly relevant in remote work scenarios, virtual training, and social interaction. For example, platforms like Spatial offer persistent virtual meeting rooms where teams can collaborate on 3D models and presentations as if they were in the same physical room.

There is a clear trend towards the integration of artificial intelligence with XR. AI algorithms are being used to enhance scene understanding, allowing XR devices to more accurately map and interpret the user’s environment. This leads to more realistic and interactive experiences. For example, AI can help identify objects in the real world and overlay relevant information or create virtual objects that interact realistically with the physical environment. We are also seeing the nascent stages of brain-computer interfaces being explored in conjunction with XR, with companies like Valve partnering with OpenBCI working on headwear for games and also for reading the emotions of the gamers.

Looking ahead, the next iterations of XR platforms will likely continue to blur the lines between the digital and physical worlds. We can expect to see smaller, lighter, and more powerful devices, potentially moving towards contact lens form factor in the long term. The development of more sophisticated sensors and AI-driven computer vision will enable more seamless integration of virtual content with the real world, making AR experiences more immersive and contextually aware. Enhanced 5G and edge computing infrastructure will help to offload processing from the devices, reducing their size and power consumption while enabling more complex and detailed XR experiences.

Artificial Intelligence (AI) Advancements

December 2024 finds artificial intelligence permeating numerous aspects of daily life and industry. The technology has matured from theoretical concepts to practical applications. Machine learning models are now routinely employed in healthcare diagnostics, financial modeling, and personalized education. For example, hospitals are widely using AI-driven systems to analyze medical images, such as MRI and CT scans, to assist in identifying conditions like tumors and fractures with high accuracy. Another example is in online learning, platforms like Khan Academy are deploying AI tutors that adapt to individual student learning styles, providing customized feedback and adjusting lesson plans in real-time.

Large Language Models (LLMs) are becoming increasingly sophisticated. These models, trained on vast amounts of text data, are now capable of generating human-quality text, translating languages with improved nuance, and even writing code. Businesses are leveraging LLMs to automate customer service interactions through chatbots that understand and respond to complex queries with a natural tone. Content creators are also using these models to assist in writing articles, marketing copy, and even scripts for videos. Tools like Github Copilot are assisting programmers by providing code completion and debugging suggestions based on its large data set of code.

Another development is the rise of multimodal AI, systems that can process and understand information from multiple data types simultaneously, such as text, images, and audio. These systems are being applied in areas such as autonomous vehicles, where they analyze data from various sensors to make driving decisions. For instance, companies like Tesla continue to refine their autopilot systems, integrating data from cameras, radar, and ultrasonic sensors to navigate complex road scenarios. This allows for features like adaptive cruise control and lane keeping, which are becoming standard in new vehicles.

Looking ahead, the focus is shifting toward developing more efficient and explainable AI. Researchers are working on techniques to reduce the computational resources needed to train and run large models, making them more accessible and sustainable. The new emphasis is to understand how AI models arrive at their decisions. This is particularly crucial in high-stakes applications like healthcare and finance, where transparency and accountability are paramount. Companies are investing in tools and methods to audit AI systems, providing insights into their inner workings and ensuring they adhere to ethical guidelines and regulatory requirements.

Researchers are working on embedding AI into edge devices such as smartphones and IoT sensors. This localized processing allows for real-time analysis of data without the need to rely on cloud servers, reducing latency and increasing data privacy. Imagine smart home devices that can process voice commands and sensor data locally to anticipate and respond to user needs instantly. In agriculture, sensors equipped with AI can analyze soil and weather conditions on-site, providing farmers with immediate feedback on irrigation and fertilization needs. This will allow for faster response times and better security.

Machine Learning (ML) Algorithms

Machine learning algorithms are currently integrated into numerous aspects of everyday life, from the mundane to the complex. One widely used application is in recommendation systems. Platforms such as Netflix and Amazon utilize algorithms to analyze user behavior and preferences, providing personalized content and product suggestions. These algorithms often employ collaborative filtering or content-based methods to predict user interests. Another prominent application is in fraud detection, where financial institutions use machine learning to identify anomalous transactions indicative of fraudulent activity, often employing anomaly detection algorithms. These models are trained on vast datasets of historical transaction data. Algorithms are also being used to analyze medical images such as X-rays and MRI scans to identify patterns indicative of disease. This often involves the use of convolutional neural networks, a type of deep learning algorithm specialized for image data. Some algorithms are also being used in the area of clinical trials. For example, some trials are exploring the use of AI to identify patients most likely to respond positively to a new drug.

Currently, there’s a notable trend towards developing more efficient and interpretable machine learning models. Researchers are focusing on techniques like federated learning, where models are trained across multiple decentralized devices holding local data samples, without exchanging the data itself. This addresses privacy concerns and reduces data transfer needs. Google, for instance, uses federated learning to improve the predictive text capabilities of its Gboard keyboard across millions of Android devices. The push for interpretability is driven by the need to understand why a model makes a particular decision, especially in critical applications like healthcare and finance. Techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are gaining traction, providing insights into the factors influencing model predictions. There is also increased research in the area of meta-learning. These algorithms learn to perform a task by leveraging knowledge gained from other tasks. For example, in 2024, Meta is using meta-learning to improve the speed at which new AI models can be trained.

Future of Machine Learning Algorithms

Looking ahead, the field of machine learning is moving towards the development of more robust and adaptable algorithms. One area of focus is on unsupervised and self-supervised learning, which reduce the reliance on large, labeled datasets that are often expensive and time-consuming to create. These techniques allow models to learn from unlabeled data by identifying patterns and structures autonomously. Another upcoming trend is the integration of machine learning with quantum computing. Quantum machine learning aims to harness the power of quantum computers to perform complex calculations that are intractable for classical computers. This could lead to the development of algorithms that can solve problems currently beyond reach, such as optimizing drug discovery processes or creating more accurate climate models. Another area is the development of machine learning algorithms that can learn from small datasets, referred to as “few-shot learning” or “one-shot learning.” The capability will make machine learning more accessible for tasks where data is scarce.

Deep Learning (DL) Networks

Deep learning networks are now firmly integrated into many facets of daily life, impacting everything from the way we communicate to the medical diagnoses we receive. One prevalent example is in natural language processing, where models like BERT and its successors power search engines, chatbots, and translation services. These models have the capacity to analyze and generate human-like text. They can comprehend context and nuance to a degree not previously seen in language processing algorithms. These models are continually being refined to increase their accuracy in interpreting complex language structures.

Another area where deep learning has made its presence felt is in computer vision. Convolutional Neural Networks (CNNs) are the backbone of many image and video analysis applications. Autonomous driving systems, for instance, rely on CNNs to identify objects, interpret traffic signals, and navigate roadways. In the medical field, CNNs are being utilized to analyze medical images like X-rays and MRIs. These algorithms can detect anomalies, assist in diagnoses, and even predict patient outcomes. These applications are being adopted with increasing frequency as they demonstrate the capability to reduce human error and improve efficiency.

The field of generative AI, powered largely by deep learning, is rapidly evolving. Generative Adversarial Networks (GANs) and diffusion models are now capable of producing remarkably realistic images, videos, and audio content. The company, OpenAI, has released DALL-E (3) and Sora, models that can create images and videos based on text descriptions. These models are used in creative industries for content generation, design prototyping, and even artistic expression. Their output quality is improving rapidly, blurring the lines between human-created and AI-generated content.

The trend of larger and more complex deep learning models is continuing. There is a move towards models with trillions of parameters, as seen in Google’s Pathways Language Model (2) (PaLM (2)), which has 540 billion parameters. These “foundation models” are trained on massive datasets and can be fine-tuned for a wide range of downstream tasks. This reduces the need to train specialized models from scratch for each application. Research is focused on making these models more efficient and accessible to those without access to the massive computing power they require.

In the near future, we can expect to see the further integration of deep learning into edge devices. This means that processing will increasingly happen on smartphones, wearables, and other Internet of Things (IoT) devices. This approach reduces latency, enhances privacy by keeping data local, and enables real-time applications. Companies like Qualcomm are actively developing specialized hardware and software to support on-device AI, including on their Snapdragon processors. Such advances will enable more sophisticated AI-powered features directly on consumer electronics, without relying on cloud servers.

Furthermore, the development of more robust and explainable AI is a key focus area. Researchers are working on techniques to make the decision-making processes of deep learning models more transparent and understandable. This is important for building trust and addressing ethical concerns surrounding the use of AI. Techniques like attention mechanisms and model visualization are becoming increasingly sophisticated. They offer insights into how these complex networks arrive at their conclusions. Methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) provide local explanations for individual predictions.

Finally, we are likely to witness progress in areas like multimodal learning, where models can process and integrate information from multiple data types simultaneously. This involves combining text, images, audio, and other data formats. This approach is vital for applications that require a holistic understanding of complex environments, such as advanced robotics and augmented reality systems. One example is Meta’s ImageBind, which can combine data from six modalities. There is also the expectation that deep learning models will contribute to solving complex scientific problems, such as drug discovery and climate modeling.

Natural Language Processing (NLP) Enhancements

Natural Language Processing (NLP), the field dedicated to enabling computers to understand, interpret, and generate human language, continues its rapid evolution. Current models excel at tasks that were once considered the sole domain of human intellect. For instance, large language models (LLMs) like Google’s Gemini family of models, the Mistral models from the company Mistral.ai, and OpenAI’s GPT-(4) are capable of generating coherent and contextually relevant text, translating languages with remarkable fluency, and even writing different kinds of creative content. These models demonstrate a nuanced grasp of language, often exceeding the capabilities of earlier iterations.

A prominent trend within NLP is the increasing focus on developing models with enhanced efficiency. This means achieving high performance with reduced computational resources and energy consumption. Efforts are directed towards optimizing model architectures and training methods, evidenced by the rise of models like Phi-(2) from Microsoft, a (2).(7)-billion-parameter model that outperforms models 25 times its size.

This trend is driven by the need for more sustainable AI, as well as the desire to deploy sophisticated NLP models on devices with limited processing power, such as smartphones and edge devices. As of December 2024, these smaller models are becoming more prevalent in applications where latency and cost are critical factors.

Multimodality, the ability of models to process and integrate information from various data types, including text, images, and audio, is also on the rise. Google’s Gemini, for example, can not only respond to text prompts but also analyze images and generate responses based on both textual and visual information.

This capability opens up possibilities for more intuitive and versatile human-computer interactions. Imagine an AI assistant that can “see” and understand the context of a real-world scene and respond accordingly. This is being deployed already, with companies making use of this technology to enhance customer experiences and streamline operations.

Looking ahead, we can anticipate further refinement of retrieval-augmented generation (RAG). This technique combines the power of LLMs with external knowledge bases, allowing them to access and incorporate up-to-date information into their responses. This will reduce the tendency of models to produce inaccurate or outdated information. As databases and retrieval mechanisms become more sophisticated, the reliability and trustworthiness of AI-generated content will further improve.

The ongoing development of specialized NLP models tailored for specific domains is also worth noting. We are seeing models designed explicitly for legal text analysis, scientific literature review, and medical diagnosis. These domain-specific models are trained on curated datasets, allowing them to achieve a higher level of accuracy and expertise in their respective fields. In the near future, these specialized models will become increasingly integrated into various industries, automating complex tasks and augmenting human capabilities.This will bring an exponential growth of capabilities in these areas.

Another area where progress is expected is in the area of causal reasoning in NLP models. While current models are adept at identifying correlations in data, they often struggle to understand cause-and-effect relationships. Researchers are actively working on techniques that will enable models to reason about the underlying causes of observed phenomena. This is a fundamental step towards creating truly intelligent systems that can not only answer questions but also explain their reasoning in a human-understandable way.

Computer Vision (CV) Systems

Computer vision (CV) systems, the ability of computers to “see” and interpret images and videos, are currently embedded in numerous aspects of daily life. Facial recognition technology unlocks smartphones and aids law enforcement in identifying individuals from surveillance footage. Automated image tagging systems organize vast online photo libraries, enabling users to search for images based on content rather than just keywords. In retail, CV powers self-checkout kiosks that automatically identify purchased items, eliminating the need for manual barcode scanning, and also allows store owners to monitor shopper behavior and optimize store layout.

Medical imaging analysis is another area experiencing CV integration. Algorithms now assist radiologists in detecting anomalies in X-rays, CT scans, and MRIs, such as identifying tumors and fractures with improved speed and accuracy. Automated screening of retinal images for diabetic retinopathy, a leading cause of blindness, is becoming increasingly common, enabling early detection and intervention. In agriculture, CV systems monitor crop health by analyzing images from drones and satellites, identifying areas affected by disease, pests, or water stress, allowing farmers to take targeted action.

In the automotive industry, CV is central to advanced driver-assistance systems (ADAS). These systems use cameras and other sensors to perceive the vehicle’s surroundings, enabling features like lane departure warnings, automatic emergency braking, and adaptive cruise control. Fully autonomous vehicles, currently being tested in various cities, rely on complex CV algorithms to understand road conditions, identify other vehicles and pedestrians, and make real-time driving decisions. The focus is now moving beyond simply detecting objects to predicting their behavior and trajectories. Waymo, for example, is operating a fully autonomous ride-hailing service in Phoenix, Arizona, and San Francisco, California, with its vehicles navigating complex urban environments.

Looking ahead, we can expect a continued evolution toward more robust and nuanced CV capabilities. Researchers are working on algorithms that can understand the context and relationships between objects in a scene, going beyond simple object recognition. This includes developing systems that can infer intent, anticipate actions, and even understand human emotions from facial expressions and body language. Work on 3D scene understanding, allowing the extraction of detailed, three-dimensional models from two-dimensional images or video feeds, is seeing progress, with implications for robotics, augmented reality, and virtual reality.

Furthermore, researchers are developing CV systems capable of handling adverse conditions, such as low light, fog, and rain. These systems are also becoming more energy-efficient, enabling their deployment on mobile devices with limited battery power. Progress is being made in developing new types of image sensors that can capture more information and operate in a wider range of conditions. For instance, quantum image sensors are being explored for their potential to capture images with higher sensitivity and dynamic range than traditional sensors.

Robotics Process Automation (RPA)

Robotic Process Automation (RPA) remains a vital part of enterprise automation strategies in December 2024. Companies are leveraging RPA to automate repetitive, rules-based tasks across various departments, including finance, human resources, and customer service. The current state of RPA involves sophisticated software “bots” that mimic human actions, interacting with applications, manipulating data, and triggering responses. A concrete example is in invoice processing, where RPA bots extract data from emailed invoices, validate the information against purchase orders, and enter it into accounting systems. Another example is employee onboarding. New employee information is automatically populated into human resources systems, creating profiles, assigning training, generating email accounts, and sending welcome messages. The bots are programmed to handle high-volume, repetitive actions with minimal human intervention, freeing up human workers for tasks that need higher-order thinking.

Currently, the RPA landscape is marked by an integration with other technologies, particularly Artificial Intelligence (AI) and Machine Learning (ML). This integration, often referred to as Intelligent Automation (IA), enables RPA systems to handle more complex processes that involve unstructured data and decision-making. For instance, using Natural Language Processing (NLP), a subset of AI, bots can now analyze the sentiment of customer emails and automatically route them to the appropriate department or agent. There is also a trend of process mining. Companies are beginning to make use of this to assess their processes for automation opportunities. Another example is in customer service chatbots. ML algorithms help bots learn from past interactions and improve their responses over time, creating a more nuanced customer service experience. Companies like UiPath, Automation Anywhere, and Blue Prism continue to refine their offerings in this space.

Looking ahead, the evolution of RPA will continue to be driven by advancements in AI and ML. “Hyperautomation” is emerging, a concept where entire end-to-end business processes are automated, combining RPA with more sophisticated AI tools, such as predictive analytics and computer vision. We can expect to see more autonomous decision-making capabilities in RPA systems, enabling them to adapt to changing conditions and handle exceptions without human intervention. Increased adoption of “citizen developers” will take place, with low-code/no-code platforms empowering business users to create their own automations. The use of APIs and microservices will allow RPA to better integrate with a wider array of systems, making it a more seamless part of the overall technology ecosystem. Increased use of RPA in areas like risk management and fraud detection, using machine learning to detect anomalies, will likely emerge.

Autonomous Vehicles (AVs)

The state of autonomous vehicles (AVs) in December 2024 reflects a period of measured progress rather than rapid transformation. Companies continue to refine their technology, focusing on specific operational domains rather than pursuing full, anywhere-anytime autonomy. Robotaxi services, one of the most visible applications of AV technology, are operational in select cities, primarily in the United States and China. Waymo, for example, operates a commercial ride-hailing service in parts of Phoenix, Arizona, and San Francisco, California, using vehicles equipped with its Waymo Driver system, generally considered Level (4) autonomy. Cruise, a General Motors subsidiary, also has a limited commercial service in San Francisco, although it recently faced operational setbacks, temporarily halting driverless operations nationwide following an incident in California. In China, companies like Baidu and AutoX are deploying similar services in cities like Beijing, Shenzhen, and Wuhan, navigating complex urban environments.

The technology underpinning these deployments relies heavily on a combination of sensors, including lidar, radar, and cameras, to perceive the environment. Sophisticated algorithms, often utilizing machine learning, process this sensory data to make driving decisions. These vehicles perform well in geofenced areas where they are extensively tested and mapped. However, they remain sensitive to unexpected situations, such as unusual weather conditions, complex traffic scenarios, and interactions with pedestrians and cyclists exhibiting unpredictable behavior. The focus in 2024 is largely on improving the robustness and reliability of these systems in their current operational domains. There are still legal barriers with governments being slow to adapt to these new technologies and implement regulations to facilitate the development of the technology.

Looking ahead, the coming years are likely to see an expansion of these limited deployments, both geographically and in terms of operational capabilities. Companies are likely to continue working on improving the perception and decision-making capabilities of AVs. An example of the future of AVs is the delivery of goods. Several companies, such as Nuro, are developing small, autonomous delivery vehicles designed for local goods transportation. These vehicles, which are often electric and operate at low speeds, are being tested in various cities for delivering groceries, packages, and other goods. ((5)) We can expect to see increasing investment in specific-use applications, such as last-mile delivery, trucking on highways, and public transportation in controlled environments. This might also include the integration of AV technology into existing public transit systems, perhaps with autonomous shuttles connecting underserved areas to major transit hubs.

Drone Delivery Systems

The world of aerial logistics is in a period of rapid evolution, with drone delivery systems at the forefront. These systems are no longer a futuristic concept, but a tangible reality in various parts of the globe. Companies like Wing, a subsidiary of Alphabet, and Amazon’s Prime Air are actively deploying drone fleets. Wing, for instance, operates commercially in parts of Australia, Finland, and the United States. In these locations, customers can order a range of goods, from groceries to medications, which are then delivered via autonomous drones. Similarly, Amazon has been testing and expanding its Prime Air service, with recent activities focused on scaling operations in select U.S. locations. A similar company, Zipline, are focused on medical delivery and has made over one million deliveries of medical supplies around the world, predominantly in Ghana and Rwanda.

The current state of drone delivery is characterized by a focus on regulatory approvals and technological refinements. The Federal Aviation Administration (FAA) in the United States and similar bodies globally are gradually developing frameworks for the safe integration of drones into airspace. This includes beyond visual line of sight (BVLOS) operations, which are crucial for the viability of drone delivery over longer distances. Technologically, advancements in battery life, drone stability, and autonomous navigation are continually being made. For example, the latest drones feature improved obstacle avoidance systems and more efficient energy consumption, extending their operational range. The weight that drones can carry are also increasing. For example, the latest version of the Flytrex drone can carry up to (6).(6) pounds.

Looking ahead, the next phase of drone delivery will likely involve greater integration with urban infrastructure and expanded service areas. This means cities may start to see dedicated drone ports or landing zones integrated into buildings or public spaces. The development of standardized communication protocols between drones and air traffic control systems is also on the horizon. Furthermore, research into drone swarms, where multiple drones coordinate to deliver larger payloads or cover wider areas, is gaining momentum. This could lead to more complex delivery networks, capable of handling a greater volume and variety of goods. The application of artificial intelligence to optimize delivery routes and improve safety measures is also expected to play a larger role in the near future. It can be expected that the amount of deliveries will grow exponentially in the next few years and become a more common way that products are distributed, especially medical supplies.

3D Printing/Additive Manufacturing

The realm of 3D printing, more formally known as additive manufacturing, continues to evolve, finding increased utility across various sectors. Industrial adoption has moved beyond prototyping and into the production of end-use parts. Materials science advancements are providing a broader range of printable substances, including metals, ceramics, and composites. For example, in the aerospace sector, companies like General Electric are utilizing 3D printing to manufacture fuel nozzles for jet engines. These printed nozzles are lighter and more durable than traditionally manufactured counterparts. The medical field is leveraging the technology to produce custom prosthetics, implants, and surgical tools, with companies such as Stryker producing patient-specific implants. ((1))

Another area seeing the technology become more common is the automotive sector. Companies such as Ford are printing parts on demand and custom parts for the luxury car market. ((2)) Personal fabrication remains a factor, with an expanding array of user-friendly, desktop 3D printers available to hobbyists and small businesses. These printers are increasingly capable of handling multiple materials and producing intricate designs. The latest printers are able to print materials that are more durable and in a wider range of colors, even transparent.

The current focus in the field centers on refining existing processes, increasing the speed and efficiency of printing, and expanding the range of usable materials. Bioprinting, the layer-by-layer deposition of biological materials to create tissue-like structures, is a particular area of interest. Scientists at institutions like the Wake Forest Institute for Regenerative Medicine are using bioprinting to create functional tissues and organs, such as skin grafts and organoids. ((3)) In other sectors, such as construction, the use of 3D printing to create homes has become more than just a novelty. The United Arab Emirates’ “Dubai 3D Printing Strategy” aims to construct 25% of its new buildings using 3D printing technology by 2030. ((4))

Looking to the near future, advancements in machine learning and artificial intelligence are expected to further enhance additive manufacturing processes. Expect developments that will incorporate AI algorithms that are capable of optimizing printing parameters in real-time, improving the quality and consistency of printed objects. In the last year, the precision of 3D printing has improved considerably, and in the coming years, the use of it in the medical field is anticipated to expand. One example is the work being conducted by researchers at Carnegie Mellon University, who are developing 3D printing methods for creating personalized heart tissue patches. ((5)) Integrating 3D printing with other emerging technologies, such as nanotechnology and robotics, will likely open new avenues for applications. One development could be micro-scale 3D printing for electronics. It is expected that in the coming years that the technology will become more affordable for smaller companies, leading to broader uptake across a range of industries.

4D Printing

This technology, an extension of 3D printing, creates objects that are designed to change their shape, properties, or functionality over time in response to a specific stimulus. Unlike 3D printing, which creates static, three-dimensional objects, 4D printing incorporates the element of time as a fourth dimension, hence the name. These pre-programmed transformations can include self-assembly, self-repair, or even the adaptation to different environmental conditions. 4D printing leverages principles of materials science, where materials are engineered at the nanoscale to respond to external stimuli such as heat, light, moisture, or pH changes, driving the desired transformations.

Currently, 4D printing is making notable progress in several areas. In biomedical engineering, researchers are developing 4D-printed stents that can expand within an artery to restore blood flow after implantation. For example, scientists at Rutgers University are developing 4D-printed vascular stents for use in pediatric patients which can change their shape and size over time as a child’s body develops ((1)). There is also work being done on 4D-printed drug delivery systems that release medication at specific times or locations in the body ((2)). These systems use hydrogels that swell or shrink in response to changes in body temperature or pH, releasing the embedded drug when triggered.

The aerospace industry is another sector exploring 4D printing. Engineers are developing materials that can change their aerodynamic properties in response to different flight conditions. An example is the development of morphing wings that adjust their shape based on air pressure and temperature, improving fuel efficiency and maneuverability. Airbus, for instance, has been researching the use of 4D-printed materials to create aircraft components that can adapt to changes in temperature and pressure during flight ((3)). This could lead to more energy-efficient and adaptable aircraft in the future.

Looking ahead, the next wave of developments in 4D printing is focused on improving material responsiveness and manufacturing precision. Scientists are working on new materials that can respond more quickly and predictably to stimuli, making 4D-printed objects more practical for real-world applications. We are also seeing a push towards multi-material 4D printing, where objects are made from several materials, each with different properties. This allows for more complex transformations and functionalities, such as objects that can both change shape and color.

One area of active research is the integration of electronic components into 4D-printed structures, creating smart materials that can respond to electronic signals. These materials could be used in soft robotics, creating robots that can adapt to their environment and perform complex tasks. Researchers at the University of Colorado Boulder have been working on 4D-printed soft actuators that can be used in soft robotics. These actuators are made from liquid crystal elastomers that change shape when exposed to heat or electricity ((4)). This technology could lead to the development of more adaptable and versatile robots that can perform a wider range of tasks in dynamic environments.

Another area of focus is the development of more sophisticated software tools for designing 4D-printed objects. These tools will need to account for the complex interactions between materials and stimuli, as well as the dynamic changes that occur over time. This will enable designers to create more intricate and functional 4D-printed objects, expanding the possibilities of this technology across various industries.

Internet of Things (IoT)

The Internet of Things (IoT) continues to weave itself into the fabric of daily life, albeit often in ways invisible to the average consumer. Billions of devices, from industrial sensors to home appliances, are now connected, exchanging data at an ever-increasing rate. This connectivity is creating a massive influx of data, that is feeding the machine-learning algorithms that drive automation and optimization across diverse sectors. This data is allowing the optimization of complex system, and allowing humans to extract further value.

In manufacturing, IoT sensors monitor equipment performance in real-time, predicting maintenance needs and minimizing downtime. For example, General Electric uses sensors on its jet engines to track performance metrics during flights, allowing for proactive maintenance and improved fuel efficiency. This has allowed GE to use a subscription model for some of their products. Some farmers are now using IoT devices to monitor the health of their crops and livestock, with data coming from small devices in the field. Farmers can then make data-driven decisions that improve yield.

Smart home adoption continues to grow in the middle of the decade, with a noticeable shift towards interoperability. Matter, a unified smart home standard backed by major tech companies like Amazon, Apple, and Google, has finally gained traction. Consumers can now purchase smart lights, thermostats, and locks from different brands, knowing they will seamlessly integrate into a single ecosystem, controlled through a central hub or smartphone app. The previous issue of incompatibility is now being overcome.

In urban environments, cities are leveraging IoT to enhance infrastructure management. Trash collection routes are optimized based on fill levels detected by sensors in bins, leading to reduced fuel consumption and emissions. Smart streetlights adjust their brightness based on ambient light and pedestrian traffic, saving energy and improving public safety. Public transport companies are now using IOT data to optimize traffic flow. These optimizations are helping cities lower costs.

Looking ahead, the convergence of IoT with edge computing is becoming increasingly important. Processing data closer to the source, rather than sending it all to the cloud, reduces latency and bandwidth requirements. This is crucial for applications requiring real-time responses, such as autonomous vehicles and remote surgery. Edge computing will make these activities safer and more reliable, as the speed of the decision-making will be quicker.

Another trend gaining momentum is the integration of digital twins with IoT. A digital twin is a virtual replica of a physical asset, system, or process, continuously updated with real-time data from IoT sensors. This allows for simulations and predictive analysis, enabling optimization and risk mitigation. For example, the city of Singapore has created a virtual replica of the entire island, using it to model and optimize urban planning and traffic flow. Such a model can also help in planning for weather emergencies.

The development of 5G and now 6G networks is providing the necessary bandwidth and low latency to support the growing demands of IoT deployments. Faster connectivity enables more complex applications, such as real-time video analysis from surveillance cameras and remote control of industrial robots. The rollout of these advanced networks is expected to accelerate IoT adoption across various sectors. The bandwidth and low latency of 6G are going to make new applications possible.

Finally, the use of blockchain technology is beginning to emerge as a solution for enhancing security and trust in IoT ecosystems. By creating immutable records of data transactions between devices, blockchain can prevent tampering and ensure data integrity. This is particularly relevant in applications like supply chain management and healthcare, where data security is paramount. The new methods of encryption will allow IOT devices to keep data private and safe.

Edge Computing

Edge computing is now an established part of the technological landscape, moving beyond initial hype and into practical deployment across various sectors. Instead of relying solely on centralized cloud servers for data processing, edge computing brings computation and data storage closer to the source of data generation. This shift is driven by the massive proliferation of Internet of Things (IoT) devices and the increasing demand for real-time data analysis. This reduces latency and bandwidth constraints associated with constant cloud communication.

Currently, industries are using edge computing to enhance operational efficiency and create new services. For example, in manufacturing, sensors on machinery collect data that is processed locally by edge devices to monitor performance and predict maintenance needs in real time. This allows for immediate adjustments and prevents costly downtime, moving beyond scheduled maintenance that was not responsive to real time conditions. Another example can be seen in retail. Edge computing powers in-store analytics, enabling personalized offers and optimized inventory management based on real-time customer behavior data, all analyzed locally. In the UK, Tesco has implemented edge to streamline operations at their distribution centers, automating much of the work involved in picking and packing. This also allows them to optimize their supply chain and reduce food waste, a key concern for the grocery giant. [(1)]

In the automotive sector, vehicles are becoming increasingly sophisticated computing platforms. Edge computing facilitates advanced driver-assistance systems (ADAS) that process data from multiple sensors to provide features like adaptive cruise control and lane keeping assistance, without the need to communicate constantly with the cloud. Tesla, for instance, uses edge computations within its vehicles to enable Autopilot features. The vehicle’s onboard computers process data from cameras, radar, and ultrasonic sensors in real-time to make driving decisions, such as steering, braking, and accelerating. The onboard computers also perform continuous updates to the autopilot software based on the massive volume of data processed locally. This allows vehicles to become safer over time, as they learn from their own data, and that shared by other vehicles. [(2)]

However, challenges persist in edge computing development. Security is a primary concern, as the distributed nature of edge networks increases potential attack surfaces. Moreover, managing the growing number of diverse edge devices and ensuring seamless interoperability between different platforms are ongoing efforts. Companies like Microsoft and Amazon have developed Edge tools to streamline their development. Microsoft Azure IoT Edge is now able to deploy AI modules directly to edge devices, allowing for real-time analytics and reducing the amount of data transmitted to the cloud. Amazon Web Services (AWS) has also improved its AWS IoT Greengrass, which extends AWS services to edge devices, making it possible for devices to act locally on the data they generate, while still using the cloud for management and storage. [(3), (4)]

Looking ahead, we can expect an evolution in the complexity and capabilities of edge devices. We are starting to see more powerful processors optimized for edge deployments and enhanced integration with 5G networks. These technologies will further enable sophisticated applications like autonomous navigation for drones and collaborative robots. The development of specialized AI chips tailored for edge processing will drive more intelligent and autonomous edge devices capable of complex decision-making without cloud connectivity. A number of companies, such as Hailo and Blaize, are already producing these chips and gaining considerable traction in the market, particularly in the automotive and industrial automation sectors, allowing for further expansion in these fields. [(5), (6)]

Furthermore, the convergence of edge computing with federated learning, a machine learning technique that trains algorithms across multiple decentralized edge devices or servers holding local data samples without exchanging them, is becoming increasingly relevant. This approach not only enhances privacy and data security but also allows for more efficient use of bandwidth. This means that data remains on local devices, and only the model updates are shared with the central server. We are seeing considerable use of this approach within healthcare, where patient data privacy is paramount. It is predicted to accelerate in the coming months.

Quantum Computing

Quantum computing, once a theoretical concept, has steadily progressed from academic research into a tangible, though still nascent, technology. As of December 2024, the field is characterized by machines with a growing number of qubits, the fundamental units of quantum information. These machines, however, still grapple with error rates that limit their computational capabilities. IBM, for instance, continues to lead the race in superconducting quantum processors. Their latest machines have processors with (1),121 qubits. While this machine was demonstrated in 2023, as of December 2024, it is still the largest known quantum processor. It has been shown to perform simulations beyond the reach of classical supercomputers. However, these remain largely specialized tasks. While the qubit count is the largest, the error rates are high on this machine. A different IBM machine, with only 133 qubits, is shown to have lower error rates. Researchers are focusing on error mitigation and fault-tolerant quantum computation techniques to address the limitations of current hardware. ((1))

Currently, quantum computers are predominantly accessible through cloud platforms. Companies like Amazon with their Braket service, Microsoft with Azure Quantum, and Google with their Quantum AI platform have made their quantum hardware available to researchers and developers worldwide. This democratization of access has fostered a growing community exploring potential applications. Quantum algorithms for specific tasks, such as materials science, drug discovery, and financial modeling, are being actively investigated. For example, in materials science, quantum simulations are being used to model the behavior of novel materials, potentially leading to the design of new catalysts or high-temperature superconductors. Financial institutions are exploring quantum algorithms for portfolio optimization and risk analysis. These real-world applications, are still largely experimental. ((2))

Alongside superconducting qubits, other qubit modalities are being pursued. Companies like IonQ and Quantinuum are developing trapped-ion quantum computers, which have demonstrated longer coherence times (the duration a qubit can maintain its quantum state). Photonic quantum computing, using photons as qubits, is also being explored by companies such as PsiQuantum and Xanadu. The goal with each of these is to address the challenge of error rates. While each system has shown promise, no single platform has emerged as dominant. They all struggle with similar challenges. The field continues to see heavy investment from both public and private sectors. The U.S. National Quantum Initiative Act and the European Quantum Flagship program continue to provide substantial funding for research and development. ((3))

In the next few years, quantum computers are expected to incrementally improve in terms of qubit count and, crucially, error rates. Fault-tolerant quantum computation, where errors are corrected as they occur, remains a central goal. It is possible that in the next five years that it will become reality. Companies are expected to produce a fault tolerant machine. Researchers are also actively working on quantum error correction codes. Hybrid quantum-classical algorithms, which leverage the strengths of both classical and quantum computers, are likely to see wider adoption. These algorithms are currently the best method to mitigate error rates. These allow for the execution of more complex quantum computations on near-term quantum devices. ((4))

The ecosystem around quantum computing is also expected to mature. Quantum software development kits are becoming more sophisticated, and specialized quantum programming languages are emerging. Efforts are underway to develop quantum algorithms tailored to specific industry needs. This means we will have access to more tools to work on these machines. Collaboration between academia, industry, and government will continue to be crucial in driving progress. The development of a quantum-ready workforce, through education and training programs, is also being prioritized. A workforce that can understand these machines will be needed to continue improving them. ((5))

Blockchain Applications Beyond Cryptocurrency

Blockchain technology, once solely associated with the volatile world of Bitcoin, is gradually permeating various sectors of the global economy. Its core function, providing a secure and transparent ledger, is proving applicable well beyond digital currencies. Today, the landscape is dotted with practical applications that are reshaping industries, and hinting at more profound changes to come.

In the supply chain realm, blockchain is being used to track goods with unparalleled precision. Walmart, for instance, employs a blockchain system to trace the provenance of its food products, allowing for rapid identification of contaminated items and streamlining recalls. This ensures greater consumer safety and minimizes waste. Similarly, shipping giant Maersk, in collaboration with IBM, has developed TradeLens, a platform that digitizes and shares shipping documentation on a blockchain. This reduces paperwork, speeds up customs processes, and increases transparency across the supply chain for all involved parties. ((1), (2))

Healthcare is also beginning to harness the power of distributed ledgers. Medicalchain utilizes blockchain to give patients secure control over their health records, allowing them to grant access to healthcare providers as needed. This approach enhances data privacy and ensures that patients are in control of their personal information. Meanwhile, pharmaceutical companies are exploring blockchain to track drugs through the supply chain, combating counterfeiting and improving the integrity of drug delivery. They can verify each step of the process. ((3), (4))

Digital identity is emerging as another area where blockchain is showing promise. The Estonian government, a pioneer in digital governance, has been using blockchain technology for years to secure citizen data and enable secure digital voting. This year, initiatives like the Self-Sovereign Identity (SSI) movement gained further momentum, with various pilot projects demonstrating how individuals can manage their digital identities on a blockchain, eliminating reliance on centralized identity providers. With this tech, you have one verified and secure identity, instead of multiple logins on the web. ((5))

Looking ahead, we can expect further integration of blockchain into existing systems. Decentralized finance (DeFi) is evolving, and moving beyond cryptocurrency lending and borrowing. It is showing potential to democratize access to financial services. ((6)) In addition, the concept of the “metaverse,” a persistent, shared virtual world, is gaining traction, with blockchain poised to play a critical role in managing digital assets and ensuring interoperability between different virtual environments. NFTs, or non-fungible tokens, which utilize blockchain, are the likely candidate for ownership verification within the metaverse. ((7))

The application of blockchain for the creation of Decentralized Autonomous Organizations (DAOs) is gaining more momentum in 2024. These internet-native entities have no central leadership, and decisions are made from the bottom-up. These organizations are governed by a set of rules encoded on a blockchain, and enforced by its members. This will allow organizations to be created and exist in a manner not yet seen in history.

Cybersecurity Enhancements (AI-driven)

The evolving landscape of cybersecurity in December 2024 is increasingly defined by the integration of artificial intelligence. Traditional rule-based systems are proving inadequate against the growing sophistication of cyber threats. AI, particularly machine learning algorithms, offers a dynamic alternative. These algorithms are now deployed to analyze vast datasets of network traffic, user behavior, and malware signatures, identifying subtle anomalies that might indicate an attack in progress, even from zero-day exploits.

A prominent example of AI-driven threat detection is the widespread use of User and Entity Behavior Analytics (UEBA) platforms. Companies like Microsoft and Google are actively incorporating UEBA into their security offerings, such as Microsoft Defender for Endpoint and Google Chronicle. These platforms leverage machine learning to establish behavioral baselines for individual users and devices. Deviations from these established norms, such as an employee suddenly accessing sensitive data from an unusual location, trigger alerts, allowing security teams to investigate and respond rapidly. These are being incorporated into the standard security offering for these companies, becoming almost ubiquitous.

Another area where AI is making its mark is in malware analysis. Companies like Palo Alto Networks and CrowdStrike utilize machine learning models to dissect the code of potential malware. By analyzing structural patterns, code similarities to known malware families, and predicted behavior in sandboxed environments, these systems can quickly classify new files as malicious or benign. This accelerates the process of identifying and blocking new malware variants. The new versions can often mutate to bypass traditional signature-based detection systems.

The integration of generative AI tools, based on large language models, is a significant development that is happening currently. These tools are being used to automate the creation of security reports, threat intelligence summaries, and even responses to security incidents. For example, IBM Security QRadar Advisor with Watson can now provide analysts with natural language explanations of complex security events, helping them understand the context and potential impact of an incident more quickly.

Looking ahead to the near future, by early 2025, we can expect to see the rise of AI-powered security automation and orchestration. This will involve the use of AI to automatically respond to certain types of cyber threats without human intervention. For instance, if an AI system detects a phishing attack, it might automatically block the malicious email, quarantine the affected user’s account, and initiate a password reset, all while notifying the security team. This will free up human analysts to focus on more complex and strategic tasks, making the overall security posture more robust and adaptable. This is still a work in progress as it is reliant on human trust in the AI system.

Further on, there is considerable research into the use of AI for vulnerability prediction and management. By analyzing code repositories and software configurations, AI systems could identify potential vulnerabilities before they are exploited. This proactive approach to security would shift the focus from reactive incident response to preemptive threat mitigation. This is beginning to be deployed by companies like Synopsys who are offering this service on their Polaris platform, amongst others.

Biometric Authentication Advancements

Biometric authentication is currently experiencing a period of rapid evolution, driven by increased demand for secure and convenient user experiences. The technology, which uses unique biological characteristics to verify identity, is now commonplace in various sectors, from smartphones to airports. Facial recognition, perhaps the most widely recognized form, is currently used for device unlocking, payment authorization, and access control in buildings. The technology relies on algorithms that analyze facial features like the distance between the eyes or the shape of the chin. Current research focuses on improving accuracy and reducing vulnerability to spoofing attacks that use photos or videos.

Fingerprint scanning remains a dominant method, particularly in mobile devices and banking applications. These systems typically capture a detailed image of the fingerprint’s ridges and valleys, creating a unique digital template for comparison. Advances in this area include ultrasonic sensors that can read fingerprints through thicker materials and are less susceptible to dirt or moisture. Another active area is behavioral biometrics, which analyzes patterns like typing speed or gait to authenticate users.

Voice recognition technology is finding increasing use in call centers and virtual assistants, enabling hands-free authentication. The systems analyze characteristics like pitch, tone, and rhythm to verify a speaker’s identity. Research is improving accuracy in noisy environments and developing methods to detect synthesized or manipulated voice recordings. Iris scanning, known for its high accuracy, is gaining traction in high-security environments like border control and data centers. This method captures the intricate patterns of the iris, which are unique to each individual. ([(3)] Daugman, J. (2004). How iris recognition works. IEEE transactions on circuits and systems for video technology, 14((1)), 21-30.)

The coming years will see the emergence of multimodal biometric systems that combine multiple authentication methods for enhanced security and reliability. This approach can leverage the strengths of each modality, mitigating individual weaknesses. For example, a system might combine facial recognition with voice analysis to ensure robust identification. Another key trend is the development of continuous authentication methods that monitor user behavior throughout a session, providing an added layer of security. There is ongoing research into DNA biometrics which offers near certain accuracy, however concerns over ethics and privacy are still to be fully addressed. This remains an area of academic interest, rather than industrial application. ([(4)] Mordini, E., & Massari, S. (2008). Body, biometrics and identity. Bioethics, 22(9), 488-498.)

Additionally, advancements in artificial intelligence and machine learning will continue to refine biometric algorithms, improving accuracy, speed, and resilience to spoofing attempts. Research into more sophisticated sensors and algorithms, like those capable of detecting liveness or subtle physiological signals, will become more prevalent. For example, vein recognition technology, which analyzes the unique pattern of veins beneath the skin, is being developed for use in various applications. The integration of biometrics with blockchain technology is also being explored, aiming to enhance data security and privacy by decentralizing the storage and management of biometric information.

Personalized Medicine (AI-driven diagnostics)

Personalized medicine, also referred to as precision medicine, is an evolving field aiming to tailor medical treatment to the individual characteristics of each patient. It departs from the one-size-fits-all approach, leveraging our expanding understanding of the human genome, environment, and lifestyle to inform diagnoses and treatment plans. This approach relies heavily on data from genomics, proteomics, metabolomics, and other -omics technologies.

The current state of personalized medicine in December 2024 finds widespread use of pharmacogenomics, the study of how genes affect a person’s response to drugs. For instance, genetic testing is now routinely employed to guide the prescription of warfarin, a common blood thinner, optimizing dosage to minimize bleeding risks. Similarly, oncologists utilize genetic profiles of tumors to select targeted therapies. There are many approved therapies that target the protein product of the HER2 gene in breast and gastric cancers. Many targeted cancer therapies are now available including drugs targeting the proteins KRAS and BRAF, which are commonly altered in different cancers. In the realm of rare diseases, gene therapies for conditions like spinal muscular atrophy and hemophilia are showing improved patient outcomes. The CRISPR-based gene-editing tool, exagamglogene autotemcel (Casgevy) has been approved for the treatment of sickle cell disease and beta-thalassemia.

Diagnostics in personalized medicine have advanced considerably. Liquid biopsies, which analyze DNA fragments shed by tumors into the bloodstream, are enabling earlier cancer detection and monitoring of treatment response. Companies like Guardant Health and GRAIL offer commercial tests that analyze circulating tumor DNA. Wearable devices are generating vast amounts of physiological data, including heart rate, activity levels, and sleep patterns. This data can be integrated with electronic health records, providing a more holistic view of patient health and aiding in the development of personalized wellness plans. Apple has released its latest smartwatch, which can track an expanded set of vital signs, including continuous glucose monitoring.

The near future of personalized medicine will likely see a continued growth in the application of artificial intelligence (AI) and machine learning. AI algorithms can analyze complex datasets from various sources, identifying patterns and making predictions about disease risk and treatment efficacy. This allows physicians to move from reactive to proactive medicine. AI is being used to refine drug development, as demonstrated by Insilico Medicine and others, which used AI to design a new drug that reached human clinical trials in record time. Moreover, the integration of multi-omics data will enable a deeper understanding of individual biology. This can lead to the development of polygenic risk scores for common diseases like diabetes and heart disease. These scores combine the effects of multiple genetic variants to predict an individual’s risk. We can expect more sophisticated digital health platforms that integrate data from wearables, electronic health records,

Nanotechnology in Medicine

Nanotechnology in medicine is currently demonstrating tangible advancements in diagnostics and therapeutics. In cancer treatment, for instance, nanoparticles are already being employed to deliver chemotherapy drugs directly to tumor cells. This targeted approach minimizes damage to healthy tissues. Gold nanoparticles, coated with specific antibodies, bind to cancer cells and can be heated with lasers to destroy the cells from within, a method known as photothermal therapy. This technique is in active use for the treatment of certain types of skin cancer, including melanoma. Companies such as Nanospectra Biosciences are commercializing this type of therapy, with their product AuroLase currently in use for the treatment of head and neck cancers.

Another active area is the development of nanosensors for rapid disease diagnosis. These tiny sensors can detect minute changes in the body, such as the presence of specific biomarkers indicative of a disease. For example, researchers at MIT have developed nanosensors that can be implanted under the skin to monitor glucose levels continuously. These devices could make it simpler for diabetes patients to manage the disease, replacing the usual method of finger-prick tests. In other areas, research is being undertaken using nanoparticles as contrast agents in medical imaging to improve the quality and detail of MRI and CT scans, helping in the early detection of diseases like Alzheimer’s.

Looking to the near future, we can anticipate the expansion of nanomedicine into more complex treatments and diagnostics. Clinical trials are currently underway using nanoparticles to deliver gene therapy directly to target cells in vivo, potentially providing a cure for genetic disorders. Another exciting prospect is the development of “nanorobots” – microscopic devices capable of performing tasks like clearing blocked arteries or delivering drugs to precise locations within the body. While still largely in the experimental stage, these nanorobots represent a move towards more autonomous and personalized medical interventions.

We are also likely to see an increase in the use of nanoparticles for regenerative medicine. Researchers are exploring the use of nanomaterials as scaffolds for tissue engineering, providing a structure for new tissue growth. For example, scientists at Northwestern University are developing nanofiber scaffolds that mimic the natural extracellular matrix, promoting the regeneration of bone and cartilage. This technology could revolutionize the treatment of injuries and degenerative diseases affecting tissues like bone, cartilage, and nerves.

Synthetic Biology

Synthetic biology, a field merging engineering principles with biological systems, is currently enabling practical applications across diverse sectors. In medicine, engineered cells are being deployed in living therapies. For example, CAR T-cell therapy, which involves genetically modifying a patient’s own immune cells to target cancer cells, is a reality for certain blood cancers. Companies are now focused on extending this approach to solid tumors, a far greater challenge. The modification of cells outside the body and infusion of these is a reality. The field is also exploring in vivo gene editing, where genetic modifications are made directly within a patient’s body, with clinical trials underway for diseases like sickle cell anemia using CRISPR-Cas9 technology.

Environmental applications are also taking hold. Microbes are being engineered to biodegrade pollutants, offering a potential solution for environmental remediation. Companies are developing microbes that can break down plastics, and others that can capture and convert carbon dioxide. On the agricultural front, nitrogen-fixing bacteria are being modified to enhance their efficiency, reducing the need for synthetic fertilizers that contribute to water pollution. These technologies are being deployed in real-world field trials.

In the realm of materials, bio-manufacturing is gaining traction. Spider silk, known for its strength and elasticity, is being produced by engineered yeast, paving the way for new textiles and materials with enhanced properties. Other companies are using microbes to produce sustainable alternatives to petroleum-based chemicals, including biofuels and bioplastics. The production of complex chemicals from yeast and other engineered organisms is commercially viable.

Looking ahead, the convergence of artificial intelligence (AI) and synthetic biology is poised to accelerate the design-build-test-learn cycle. Machine learning algorithms are being used to analyze vast datasets of genomic information, predicting the effects of genetic modifications and optimizing the design of biological systems. This AI-driven approach is streamlining the process of engineering organisms for specific tasks, reducing the time and resources required for development. There are many software tools available that allow researchers to quickly design and test genetic systems in silico.

Furthermore, advances in DNA synthesis and automation are enabling the construction of increasingly complex genetic circuits. High-throughput robotic platforms are automating laboratory workflows, allowing for the rapid prototyping and testing of engineered organisms. This combination of AI-powered design and automated experimentation is driving us closer to complex applications. This ability to program cells and organisms is making the field move faster than before. It is likely that these tools will soon be used to create new medicines and materials at a previously unseen rate.

Brain-Computer Interfaces (BCIs)

Brain-computer interfaces are a growing area of research and development, with applications emerging in both the medical and consumer markets. Currently, BCIs are primarily focused on restoring lost function. Individuals with paralysis, for instance, can use BCIs to control prosthetic limbs or computer cursors, bypassing damaged neural pathways. These systems typically rely on electrodes implanted directly into the brain, which record the electrical activity of neurons. The recorded signals are then decoded by algorithms that translate the neural patterns into commands for external devices. Synchron, an endovascular BCI company, has its device in human trials, a first for this type of BCI. The Synchron device is inserted via the jugular vein and then threaded up to the motor cortex, avoiding invasive open brain surgery. In December 2024, the company is reporting positive results with patients using the device to control digital devices with their thoughts, allowing them to text, email and browse the internet.

Non-invasive BCIs, using technologies like electroencephalography (EEG), are also seeing advancement. EEG-based systems place electrodes on the scalp to measure brain activity. These systems have lower signal resolution than implanted devices, but offer a less invasive, more accessible approach. Companies like NextMind and Neurable have developed consumer-grade EEG headsets that allow users to control simple digital interfaces and games with their brainwaves. The focus of these devices has shifted from gaming to more practical applications, such as hands-free control of smart home devices. Currently, the NextMind headset can control digital games or devices with high accuracy, although the lag between thought and action is noticeable.

Looking ahead, researchers are working to improve the bandwidth and precision of both invasive and non-invasive BCIs. For example, in October 2024 the University of California, San Francisco, reported that its Neuralink implant allowed a paraplegic patient to walk using a digital avatar in a virtual environment. In 2024, the University of Washington team is focused on developing a new generation of optogenetic interfaces that will improve the biocompatibility of implanted devices, reducing the risk of immune reactions and improving long-term stability. These advancements will contribute to more natural and seamless control of external devices, with researchers in Switzerland developing a system that allows two people to communicate telepathically via connected BCIs. In addition, non-invasive BCIs are expected to benefit from improved signal processing algorithms, and advanced machine learning techniques, enabling more sophisticated control and broader applications, including mental health monitoring and personalized neurofeedback therapies.

Neuromorphic Computing

Neuromorphic computing, inspired by the intricate architecture of the human brain, seeks to emulate the brain’s functionality for enhanced computational efficiency. Instead of relying on traditional binary logic and separate processing and memory units, neuromorphic systems integrate computation and memory using artificial neurons and synapses. This paradigm shift offers the potential for significantly reduced power consumption and faster processing speeds, particularly for tasks involving pattern recognition and data analysis. Currently, these systems are moving beyond theoretical models and initial prototypes and finding applications across various sectors.

One example of the current state of the art is Intel’s Loihi (2) chip. This research chip uses a (1) million-neuron architecture. It can be used in complex neural network models. Loihi (2) demonstrates improved energy efficiency compared to traditional computing architectures, particularly in applications like real-time object detection and natural language processing. Another example is IBM’s TrueNorth chip, which contains billion transistors organized into (4),096 neurosynaptic cores, with one million programmable neurons and 256 million programmable synapses. These chips were used in 2019 to create a spiking neural network that was then integrated into a robot. The robot could solve navigation problems in a small maze.

Researchers are actively exploring new materials and device architectures to enhance neuromorphic systems further. Memristors, for instance, are electronic devices that can mimic the behavior of biological synapses, offering the possibility of building denser and more efficient neuromorphic chips. A research group at the University of Massachusetts Amherst in 2023 developed a new type of memristor based on protein nanowires, potentially leading to biocompatible neuromorphic devices. Their research demonstrated that these protein-based memristors can efficiently process information and have the potential to be integrated with biological systems. They tested these devices for sensing applications.

Looking ahead, neuromorphic computing is poised to move towards more complex and larger-scale systems. Integrated systems combining neuromorphic hardware with advanced algorithms are being developed to tackle more sophisticated tasks. For example, researchers at the University of Sydney are building neuromorphic chips based on silver nanowires. The system was developed with the University of California, Los Angeles and Hokkaido University. It is currently being applied to pattern recognition in video, for facial identification, and to create ‘intelligent’ robots. These applications rely on the development of new methods to train and optimize neuromorphic networks.

There are still hurdles to be overcome with this technology. Memristors are still in the early phases of research. They have only recently started to be commercially used. Intel’s Loihi (2) has only recently become available for purchase. There are still no large scale robots, as mentioned above, that are available for purchase. Even more, the methods for creating algorithms for this technology are not well known, and require deep research and development. While the future is certainly bright for this technology, its current uses are still in the beginning phases.

Sustainable Energy Solutions (advanced solar, wind, etc.)

The pursuit of sustainable energy solutions remains a dominant theme in global technological development. In December 2024, solar energy continues to be a front-runner. Perovskite solar cells, a newer class of photovoltaic material, are no longer just confined to laboratories. These cells are emerging in commercial applications, offering higher efficiency rates and lower production costs compared to traditional silicon-based panels. For instance, companies are integrating flexible perovskite cells into building facades and even electronic devices, essentially turning entire structures into energy harvesters. Oxford PV is set to launch a pilot line by early 2025 in Germany.

Wind energy is experiencing its own wave of innovation. Offshore wind farms are becoming larger and venturing farther from coastlines. Floating wind turbine platforms are allowing for the harnessing of wind resources in deeper waters, previously inaccessible to fixed-bottom turbines. Companies like Equinor are deploying floating wind farms off the coasts of Scotland and Norway, tapping into strong, consistent winds. These platforms are tethered to the seabed with mooring lines, allowing them to pivot and capture wind from any direction. These floating farms are also being developed off the coast of California, with the first auction for floating offshore wind licenses completed in 2022.

Advancements are being seen in concentrated solar power (CSP) technologies. This method uses mirrors to concentrate sunlight onto a receiver, which then generates heat used to produce electricity. Newer iterations are incorporating molten salt as a heat storage medium, allowing for energy dispatch even when the sun is not shining. Projects like the Noor Ouarzazate solar complex in Morocco exemplify this trend, providing power well into the evening hours.

Looking ahead, the integration of advanced energy storage systems will be vital. Solid-state batteries, offering higher energy density and improved safety compared to lithium-ion batteries, are nearing wider commercialization. Companies like QuantumScape are partnering with automotive manufacturers to incorporate these batteries into electric vehicles, which will have a dual use. They can also act as grid-scale storage, absorbing excess renewable energy during peak production and releasing it during times of high demand, helping to stabilize the grid. [(4)] Furthermore, research into alternative storage methods, like liquid air energy storage (LAES), is gaining traction. LAES technology stores energy by liquefying air and then expanding it to drive a turbine, is currently being explored by several companies. The first commercial-scale plant was commissioned in the UK by Highview Power.

Smart Grid Technologies

The integration of advanced digital technologies into electrical grids, commonly referred to as “smart grids,” continues to reshape energy delivery and consumption. Utilities are increasingly deploying sensors, advanced metering infrastructure (AMI), and communication networks to enhance grid visibility and control. This allows for real-time monitoring of electricity flow, facilitating quicker identification and resolution of outages, and improving overall grid resilience. For example, Pacific Gas and Electric in California utilizes a network of thousands of sensors and automated switches to reroute power during wildfires or extreme weather events, minimizing disruptions.

Currently, many smart grid initiatives are centered on optimizing the integration of renewable energy sources. The intermittent nature of solar and wind power requires sophisticated grid management strategies. Utilities are implementing advanced forecasting models, using machine learning algorithms, to predict renewable energy generation. Companies like Duke Energy in the Carolinas are using these models, combined with battery storage systems, to smooth out fluctuations in solar power output and maintain a stable energy supply.

Another focus is on demand-side management programs. Utilities are leveraging AMI data to understand consumer energy usage patterns. They are offering time-of-use pricing structures and incentive programs that encourage consumers to shift their energy consumption away from peak hours. For instance, in Texas, utility companies are offering reduced rates or bill credits, as seen with the time-of-use programs available in Austin, to consumers who use energy during off-peak times, reducing strain on the grid during periods of high demand.

Data analytics is also playing a larger role. Utilities are using the vast amounts of data generated by smart grid systems to optimize operations. They are developing predictive maintenance models to identify potential equipment failures before they occur, reducing downtime and maintenance costs. For example, some utilities in Europe are using machine learning to analyze transformer data to predict when maintenance will be needed, extending the life of these critical assets.

Looking ahead, we can expect to see the continued expansion of decentralized energy resources, including rooftop solar, energy storage, and electric vehicles. Utilities are exploring the use of blockchain technology to facilitate peer-to-peer energy trading among these distributed resources, enabling a more localized and resilient energy system. Pilot projects in Australia, for example, are already testing blockchain-based platforms for managing energy transactions between homes with solar panels.

Further development of advanced communication protocols like 5G will also be important. These will enable faster and more reliable data transfer, crucial for managing the increasingly complex and data-intensive smart grid. Utilities will rely on this enhanced communication to support real-time grid optimization and the integration of a growing number of connected devices.

Finally, the use of artificial intelligence in grid management will likely become more sophisticated. AI algorithms will be used to automate decision-making in areas like voltage control, power flow optimization, and fault detection. For example, there is expected to be a greater push to use AI to adjust settings on equipment to maintain optimal voltage levels across the grid.

Space Tourism

The pursuit of making space accessible to the everyday person, a concept once relegated to science fiction, is steadily transitioning into reality. Private companies are leading this charge, with several having already achieved milestones that were previously the exclusive domain of government-funded space agencies. The current state of space tourism is focused on suborbital flights, offering passengers a few minutes of weightlessness and a view of Earth from the edge of space.

Blue Origin, founded by Jeff Bezos, is a key player in this burgeoning field. Its New Shepard rocket system has successfully flown numerous crewed missions, including a flight carrying the original Captain Kirk, William Shatner. These flights, lasting approximately 11 minutes, reach an altitude of around 100 kilometers, crossing the internationally recognized boundary of space, the Kármán line. Passengers experience the thrill of launch, a brief period of microgravity, and views of the curvature of the Earth before a gentle parachute landing back on terra firma.

Virgin Galactic, spearheaded by Richard Branson, is another prominent company in the suborbital tourism market. Their spaceplane, VSS Unity, is carried to an altitude of approximately 15 kilometers by a mothership before being released. The spaceplane then ignites its rocket motor, propelling passengers to an altitude exceeding 80 kilometers. Virgin Galactic has successfully completed several crewed test flights, and paying customers are expected to begin flying in the coming year. There is a waitlist of several hundred passengers on the company website.

While suborbital flights are the primary focus currently, orbital tourism is also beginning to take shape. Axiom Space, a Houston-based company, is developing a commercial space station that will eventually be attached to the International Space Station (ISS). Axiom has already arranged private astronaut missions to the ISS using SpaceX’s Crew Dragon spacecraft. These missions, lasting approximately ten days, allow private citizens to experience life in orbit and conduct research alongside professional astronauts.

The future of space tourism points towards longer stays in orbit and potentially even lunar tourism. SpaceX, with its ambitious Starship program, is aiming to make both of these possibilities a reality. Starship is designed to be a fully reusable launch vehicle capable of carrying large numbers of passengers and cargo to orbit, the Moon, and ultimately Mars. While still in development, Starship has already undergone several successful test flights, and a lunar flyby mission with a private crew is projected to happen by the end of 2024. ((4)) The ‘dearMoon’ mission, funded by Japanese billionaire Yusaku Maezawa, will take a crew of artists and creators around the Moon.

Further down the line, companies like Orbital Assembly Corporation are working on concepts for rotating space stations that would create artificial gravity, allowing for more comfortable and extended stays in space. Their Voyager Station design is aimed at accommodating tourists and researchers in a space hotel environment. These projects are still in the early stages of development but showcase the long-term vision for making space a destination accessible to a wider range of people.

Hypersonic Travel

Hypersonic travel, defined as motion at speeds of Mach (5) and above, is currently an area of intense focus for military applications, with nations around the globe developing hypersonic weapons. The United States, for example, has several active programs including the Air Force’s AGM-183A Air-launched Rapid Response Weapon (ARRW), and the Navy’s Conventional Prompt Strike (CPS) system. The ARRW is a boost-glide system, launched from an aircraft like the B-52 bomber, which then glides unpowered at hypersonic speeds to its target. Tests of the ARRW have been conducted. It is expected to enter service soon. The CPS is designed to be launched from submarines and surface ships and is a hypersonic boost-glide vehicle capable of striking targets anywhere in the world within an hour of launch. It is currently under development and testing.

China has also made rapid advances in this field. Their DF-ZF hypersonic glide vehicle, launched atop a DF-17 ballistic missile, is believed to be operational. This system is designed to maneuver during its hypersonic flight, making it challenging to intercept with existing missile defense systems. They are thought to have conducted numerous tests of this system, demonstrating its capability to travel at speeds exceeding Mach (5). Russia, similarly, has the Avangard hypersonic glide vehicle, which they claim is already deployed. Avangard is launched on an intercontinental ballistic missile and is reported to reach speeds of up to Mach 27. They also have the 3M22 Zircon, a ship-launched hypersonic cruise missile with a range of over 1000 kilometers and a maximum speed of Mach 9.

In the civilian sector, work is being done to examine the possibilities of commercial hypersonic flight. The EU-funded STRATOFLY project envisions a Mach ((8)) civil transport capable of antipodal travel. They are exploring the integration of technologies such as air-breathing ramjets and scramjets to achieve this. These engines use the aircraft’s forward motion to compress incoming air for combustion, allowing for sustained hypersonic flight. A subscale demonstrator, HEXAFLY-INT, has been tested in the wind tunnel and the flight environment with initial test flights taking place in 2023. The program looks forward to expanding the flight envelope into sustained hypersonic regimes over the coming years.

The next few years will likely see a continued increase in hypersonic weapons testing and deployment. Nations are looking to refine guidance systems for increased accuracy and maneuverability. In addition, the development of countermeasures against hypersonic threats is a high priority, with research into directed energy weapons and advanced interceptor systems. For commercial hypersonic flight, major advances are expected in materials science to handle the extreme heat generated during sustained hypersonic flight. Research on propulsion systems will also be crucial. However,

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Top Technology Trends Of The Future