By producing dynamic, real-time reproductions of actual systems, assets, or processes, digital twins are transforming entire sectors. These virtual models, which are driven by cutting-edge AI and IoT, give enterprises unparalleled accuracy in monitoring, forecasting, and optimising operations. The potential of digital twins is expected to grow rapidly over the next 10 years due to both the intricacy of real-world problems and technology developments. Here is a look at the developments, trends, and industry influence influencing digital twins going forward.
Emerging trends in Digital Twins:
The predictive power of digital twins is being improved through their integration with AI and machine learning. Digital twins can simulate different situations, optimise resource allocation, and predict equipment breakdowns thanks to AI-driven insights. As companies emphasise real-time decision-making and predictive maintenance to reduce expenses and downtime, this trend will gain traction.
Digital twins have historically been employed in manufacturing and aerospace, but they are currently becoming more popular in sectors including urban planning, healthcare, and retail. Digital twins, for instance, are being used by healthcare professionals to model patient outcomes and customise treatments, while smart cities are employing them to plan infrastructure development, optimise traffic flow, and lower energy use.
Richer, more detailed data is being made available to digital twins by the proliferation of IoT devices. This enhances operational efficiency and decision-making by allowing for a more thorough and accurate representation of the physical equivalent. The extensive use of IoT will considerably expand the possibilities of digital twins throughout the course of the following ten years.
Digital twins will be essential for maximising resource utilisation, cutting waste, and accomplishing environmental objectives as sustainability gains prominence. To lessen carbon footprints, they can monitor emissions, model energy-efficient building designs, and streamline logistics networks.
Innovations driving the future
In order to facilitate immersive, real-time cooperation, future digital twins will integrate virtual reality (VR) and augmented reality (AR). By interacting with the digital twin concurrently, teams from all over the world may make decisions more quickly and accurately.
By processing data closer to its source, edge computing usage is anticipated to improve digital twin efficiency. Faster decision-making is made possible by this decreased latency, especially in time-sensitive applications like smart factories and driverless cars.
AI developments are creating self-learning digital twins that get better with time. These twins will automatically improve their models and adjust to shifting circumstances, increasing their resilience and dependability.
Industry impact
Digital twins are used in manufacturing to improve product quality, forecast maintenance requirements, and optimise production processes. They streamline inventory management, cut down on delays, and enhance logistics planning in supply chains.
By modelling traffic patterns, energy consumption, and infrastructure requirements, digital twins are revolutionising urban planning. They enhance the standard of living for citizens while allowing cities to expand sustainably.
By facilitating individualised treatment regimens and enhancing the effectiveness of medical devices, digital twins are transforming patient care in the healthcare industry.
Digital twins will play a crucial role in sectors looking to improve decision-making, streamline operations, and meet sustainability targets over the course of the next ten years. Businesses that use digital twins will have a competitive edge as these technologies advance, opening up new avenues for efficiency and creativity. Entopy is dedicated to spearheading this change by offering state-of-the-art digital twin solutions that enable businesses to prosper in a complex, data-driven environment.
Understanding the many facets and uses of artificial intelligence (AI) has become crucial as it develops further. At Entopy, we use a variety of AI techniques to turn complicated, disparate data into useful intelligence that supports vital systems in a range of settings. However, not all artificial intelligence (AI) is made equal. It can range from simple automation to complicated systems that imitate human intelligence. The many forms of AI and their contributions to the future of data-driven decision-making are examined here.
The simplest type of AI is represented by reactive machines, which are made to do particular tasks in response to instantaneous inputs. They lack the capacity to learn or change over time because they are unable to retain or apply prior experiences to guide their actions in the future. IBM’s Deep Blue chess-playing computer is a classic example; it could analyse moves and react appropriately without foreseeing future events. Reactive machines are helpful in real-world applications for straightforward, repetitive jobs like scanning, sorting, and basic data analysis. They provide a basis for process automation for enterprises, especially in environments with good data consistency and minimal decision-making complexity.
Limited Memory AI is a sophisticated type of AI that can learn from past data to enhance responses in the future. This category includes the majority of machine learning (ML) applications today, such as self-driving cars, which make safe navigation decisions based on historical observations (such as traffic patterns and barriers). Limited Memory AI is especially useful in dynamic contexts because it can adjust to shifting circumstances while continuously improving performance. This kind of AI is used by Entopy to assist operational intelligence and predictive analytics, enabling businesses to make defensible decisions based on historical insights and real-time data.
A more sophisticated idea is Theory of Mind AI, which aims to comprehend and interpret human feelings, convictions, and intentions. This kind of AI, which is still mostly in the research and development phase, has the potential to revolutionise industries like healthcare, education, and customer service since it can identify and react to the mental states of people. Theory of Mind AI has the potential to improve human-AI interactions by understanding and predicting user demands, resulting in more contextually aware and personalised experiences. Even though Theory of Mind AI is not yet widely used, it is a crucial area for future developments, especially in fields that depend on human interaction.
The most sophisticated and theoretical type of AI is self-aware AI, in which robots are sentient and conscious. This kind of AI would have its own mind, feelings, and needs in addition to being able to comprehend human emotions. The search for self-aware AI presents ethical questions and possible societal repercussions, even if it is currently the focus of science fiction. Understanding the ramifications of this technology as it develops will be essential, particularly with regard to safety, governance, and the interactions between humans and AI.
In addition to these categories, AI can be further separated into two categories: General AI and Narrow AI. Weak AI, sometimes referred to as narrow AI, is made to do particular tasks, including language translation or facial recognition. It is integrated into commonplace gadgets, like virtual assistants and smartphones, and controls the majority of today’s applications. On the other hand, general artificial intelligence (AI), a theoretical and unrealised type of AI, would have human-like cognitive capacities and be able to comprehend, learn, and use intelligence in a variety of tasks. Narrow AI, which focusses on data integration, predictive modelling, and operational intelligence to provide targeted, highly impactful insights, is the main application of Entopy.
Businesses may properly utilise AI’s potential as it develops if they have a solid understanding of its types and applications. At Entopy, we’re dedicated to leveraging cutting-edge AI to streamline intricate data environments and promote predictive and operational intelligence across industries. We promote resilience in vital infrastructure, maximise efficiency, and facilitate wiser decision-making by utilising the advantages of many forms of artificial intelligence. AI has a bright future ahead of it, and as it develops, the potential to completely change an industry only increases.
Operational efficiency has emerged as the industry standard in a world that is becoming more and more data-driven. Making sense of large, divergent data sources is essential to remaining competitive and efficient in a variety of fields, including infrastructure management, urban planning, logistics, and transportation. By fusing the digital and physical worlds, Entopy is leading the way in this field and providing organisations all over the world with operational intelligence that is revolutionary.
Digital twins, which are highly accurate, real-time digital copies of actual assets, systems, or surroundings, are at the heart of Entopy’s goal. With the help of these digital twins, businesses can see and examine intricate processes in a dynamic, data-rich manner. Digital twins can be used to seamlessly monitor, simulate, and optimise everything from the complex traffic flow in a smart city to the movements of vessels in a busy port. Entopy’s creative methodology guarantees that information isn’t merely gathered and saved; rather, it is converted into significant, useful insights that enable more intelligent choices.
What makes Entopy unique in the world of digital twins, then? It all comes down to foresight and integration. More than just static models, Entopy’s AI-powered digital twins are predictive engines that can foresee interruptions, spot bottlenecks, and proactively streamline procedures. Consider the marine sector, where ports may incur substantial time and resource costs due to vessel congestion. By simulating and forecasting the effects of weather variations, vessel arrivals, and resource allocation, Entopy’s digital twins can provide solutions that increase productivity and reduce delays.
Entopy’s digital twin technology has a lot to offer urban settings as well. Managing resources like infrastructure, public transit, and traffic flow becomes increasingly difficult as cities grow smarter and more connected. Councils and urban planners may better control traffic, simulate various situations, and distribute resources with the help of Entopy’s digital twins. Predictive intelligence of this type promotes sustainable growth and development while also improving the urban experience for locals.
Digital twins are also used in public sector initiatives where waste reduction and efficiency maximisation are essential. Entopy’s solutions can be used by governments and municipalities to improve emergency response planning, environmental projects, and infrastructure investments. Authorities can respond more quickly and make better plans by foreseeing any problems before they materialise, protecting communities’ safety and well-being.
However, Entopy’s digital twins’ true strength is found in their capacity for learning and development. Over time, these models get smarter and more accurate as they are fed more data. For long-term operational planning and strategy, this ongoing learning loop is crucial. AI-driven suggestions might help businesses better understand their operations, streamline supply chains, or even redesign warehouse layouts. Like the data itself, the possibilities are endless.
Entopy is more than just an AI business; it is a trailblazer in using digital twins to make a difference in the real world. Entopy helps organisations understand their environments and alter them in ways that promote efficiency, sustainability, and creativity by bridging the gap between data and operational intelligence. Entopy’s strategy for utilising digital twins guarantees that the future is both controllable and full of opportunities as sectors continue to struggle with ever-increasing complexity.
Around the world, governments and local authorities are facing an increasing amount of difficulty due to traffic congestion. Bottlenecks have a detrimental effect on the economy and standard of living by causing delays, increasing fuel consumption, and decreasing the effectiveness of road networks. Governments require sophisticated, data-driven solutions to efficiently monitor, forecast, and control traffic flow in order to meet these problems. This is where the AI-powered solutions from Entopy are useful. Entopy assists governments in identifying traffic bottlenecks and optimising road networks for more seamless and effective transportation systems by utilising AI and real-time data.
Understanding traffic bottlenecks
Road capacity restrictions, accidents, construction, and higher demand during peak hours are some of the common causes of traffic bottlenecks. These problems make it difficult for traffic to move freely by causing congestion, delays, and slowdowns. Governments have traditionally addressed these issues with static traffic models and manual monitoring, but these methods are frequently reactive, slow, and unable to anticipate future disruptions.
A more dynamic, predictive method is provided by Entopy, which analyses massive datasets from multiple sources, including weather reports, GPS data, traffic cameras, and road sensors, using AI and machine learning. Entopy can forecast the locations of bottlenecks and offer practical solutions to alleviate them by continuously analysing real-time data.
Predicting and preventing traffic conditions
The capacity of Entopy’s AI to forecast traffic patterns and congestion in advance is one of its main advantages. Entopy’s method creates predictive models that assist governments in anticipating potential areas of congestion by examining past traffic patterns and present circumstances. Through lane closures, real-time signal timing adjustments, or the use of smart traffic systems to give cars alternate routes, this enables authorities to proactively control traffic.
For instance, using past data, weather, and the time of day, Entopy’s AI can forecast peak traffic hours. Local authorities can optimise traffic flow and avert slowdowns before they happen by modifying traffic lights based on real-time recommendations. Everyone’s road safety is increased, delays are decreased, and the total burden on road networks is lessened thanks to this predictive intelligence.
Optimising road networks with real-time data
Entopy assists governments in optimising their road networks for long-term efficiency in addition to resolving acute traffic bottlenecks. Entopy builds a virtual model of the road network using Digital Twin technology, which enables local governments to test infrastructure modifications, model various traffic situations, and predict the effects of new developments. Governments can use this simulation capability to inform data-driven decisions on new intersections, road extensions, and traffic management tactics.
For example, Entopy’s Digital Twin can model how a new road may impact local traffic patterns prior to construction. This knowledge enables planners to enhance connectivity, optimise the road’s design, and guarantee that the new infrastructure meets present and future traffic demands.
Real-time response to disruptions
Significant traffic disruptions can be caused by accidents, construction projects, and unforeseen circumstances. Entopy’s software provides real-time data on these incidents by integrating with current traffic control systems. Entopy’s AI system can assess the situation and recommend the best course of action in the event of a disruption, including rerouting traffic, blocking specific lanes, or sending out emergency response teams.
Governments can respond more swiftly and efficiently by automating these reactions, which lessens the influence of these occurrences on traffic flow. This guarantees that there are fewer delays for drivers and that the road system as a whole runs more smoothly.
The benefits of Entopy’s AI solutions for traffic management
Reduced Congestion: By reducing delays and enhancing traffic flow generally, Entopy’s predictive models assist governments in proactively addressing congestion.
Optimised Road Usage: Entopy offers insights into how road networks can be used more effectively, resulting in more effective infrastructure, by evaluating real-time data.
Enhanced Decision-Making: Governments may make more informed judgements regarding road extensions and traffic control tactics by using Digital Twin simulations and predictive analytics.
Real-Time Response: By enabling prompt, automatic reactions to unforeseen traffic disturbances, Entopy’s AI enhances road safety and shortens the duration of traffic jams.
Sustainability and Cost Savings: Fuel consumption, pollution, and government and road user costs are all decreased when traffic congestion is reduced, and road usage is optimised.
In a time of increasing urbanisation and road network demand, governments want cutting-edge techniques to alleviate traffic jams and improve infrastructure. The AI-powered solutions from Entopy provide a thorough, data-driven strategy for traffic management, congestion reduction, and increased route efficiency. Entopy gives governments the ability to design more intelligent and resilient transport systems that benefit both the environment and drivers by utilising real-time data analysis and predictive intelligence.
The use of AI in traffic management will become more and more crucial as cities continue to expand, and Entopy is setting the standard for assisting governments in overcoming this difficult obstacle.
The need for smarter, more effective urban planning and administration is growing as cities continue to expand and change. Councils face many issues as a result of the fast urbanisation of the city, such as infrastructure development, traffic congestion, and sustainability objectives. Many cities are using AI-driven technologies to streamline their operations and decision-making procedures in order to manage these complications. Leading this movement is Entopy, which offers cutting-edge AI technology to strengthen smart cities and assist councils in improving urban administration and planning.
The Role of AI in Smart Cities
AI is an effective tool that, through the analysis of enormous volumes of real-time data, helps cities run more smoothly. AI may offer useful information on everything from resource usage to traffic patterns, assisting councils in making better decisions, enhancing public services, and cutting expenses. Smart cities may become more resilient, flexible, and sustainable by putting AI-powered solutions into practice.
The AI platform from Entopy is made to address the intricate problems that modern cities face. Our technology creates a single, real-time picture of urban dynamics by combining data from various sources, including environmental sensors, public services, transportation systems, and more. This makes it possible for councils to keep an eye on the infrastructure of the city, forecast trends for the future, and deal with possible problems before they get out of hand.
Enhancing traffic management
Traffic management is one of the more obvious uses of Entopy’s AI for smart cities. In cities, traffic congestion is a recurring problem that causes delays, higher emissions, and a worse standard of living for locals. By examining data from traffic lights, vehicle movement, and road networks, Entopy’s AI-driven solutions offer real-time traffic insights. Councils are able to increase road safety, minimise bottlenecks, and optimise traffic flow as a result.
For instance, in order to reduce congestion, our AI can forecast periods of high traffic and modify traffic signals accordingly. Furthermore, Entopy’s platform may suggest other routes to vehicles in the event of accidents or road construction, cutting down on delays and enhancing traffic efficiency overall. As a result, local companies and citizens alike gain from a more efficient and secure transport system.
Smart resource allocation
Resources used in urban management are diverse and include everything from energy use to the provision of municipal services like waste disposal and street lighting. The AI platform from Entopy assists councils in making the most use of these resources by forecasting demand and offering data-driven suggestions for effective distribution. For example, our platform can recommend strategies to lower use during peak hours, resulting in cost savings and more sustainable energy practices, by analysing patterns of energy usage.
Moreover, AI can assist in planning and executing public services more effectively. Councils can cut costs and enhance service delivery by forecasting the peak times and locations for services like garbage collection. In addition to helping locals, this promotes environmental sustainability by using fewer resources than necessary.
Supporting long-term urban planning
Beyond daily tasks, artificial intelligence (AI) is a useful instrument for long-term urban planning. Entopy’s Digital Twin technology allows councils to generate virtual duplicates of the city surroundings, enabling them to model and plan for future developments. These models shed light on potential effects on the city from environmental shifts, population increase, and new infrastructure developments. To ensure sustainable urban growth, councils can evaluate potential hazards, test various scenarios, and make data-driven decisions.
For instance, authorities can assess the effects on traffic, resource allocation, and public services using Entopy’s Digital Twin prior to constructing new housing projects or transport hubs. This foresight ensures that cities grow in a balanced, well-planned manner and helps to minimise disturbances.
Why it matters
The demand on councils to deliver effective, long-lasting services will only grow as cities grow. With real-time data integration, predictive intelligence, and resource management that is more intelligent, Entopy’s AI-powered solutions help cities stay ahead of these difficulties. By integrating AI, councils may develop more liveable, sustainable communities that suit the requirements of their rising populations.
In summary, Entopy’s AI technology is essential to enabling smart cities to prosper in a world that is getting more complicated. Our solutions give councils the power to make well-informed, data-driven decisions that improve the quality of life for locals and promote sustainable urban growth, from traffic management to long-term urban planning.
Data availability or accessibility remains a critical challenge in the delivery of data-driven or Artificial Intelligence (AI) based solutions. This challenge is particularly prominent in ecosystem environments where data sensitivities prevent the sharing of data or in emerging environments where infrastructure and systems are in the process of being implemented.
At Entopy, we have been progressing research to address some of the challenges associated with data accessibility, developing methods to produce accurate synthetic tabular datasets enabling us to expand datasets with synthetic data to overcome the ‘lack of data’ problem that can prevent successful implementation of our AI-enabled Digital Twin software.
This article, written by Toby Mills and Shahin Shemshian, discusses Entopy’s recent progress through the development of an AI model capable of generating highly accurate synthetic tabular datasets, helping to overcome data availability challenges within multistakeholder, real-world operational environments, maximising the efficacy and reducing the time for deployment of its AI-enabled Digital Twin software.
*Entopy’s research and development in this area is contributing to an Academic Research Paper that is expected to be put forward in early 2025 in partnership with the University of Essex and for this reason, this blog will not go into specific details nor share actual results.
The challenge
Data is critical to driving the envisaged wave of transformation discussed by industry and government. But in many cases, a lack of data (or unavailability of data) prevents solutions from being mobilised. This is particularly prominent where multiple organisations are involved in an operation or ecosystem and therefore, data must be contributed by many independent stakeholders, a dynamic true of most real-world operational environments where AI and Digital Twin technologies have the potential to deliver the most profound impacts.
Entopy has developed technology that enables data to be shared between organisations whilst ensuring the privacy and security of that data. Its software has been used in large, ecosystem contexts with highly sensitive data being shared in real-time to support the automation of processes and the identification of key events/alerts.
Entopy’s AI micromodel technology delivers the same capabilities (amongst others) when trying to derive predictive and simulative intelligence across multistakeholder, real-world operational environments, leveraging a distributed network of smaller but more focused AI models, integrating into an overall Digital Twin with the outputs from many models orchestrated together with real-time data to deliver highly effective intelligence across many contexts.
However, it is in the context of delivering predictive intelligence that we have identified additional significant barriers to data availability. This is due to the amount of data needed to deliver effective probabilistic models, therefore requiring large amounts of historical data to be shared. In this context, it is not just ensuring that permissions on data shared can be controlled at a granular level (again, amongst other things) but also that there is enough data available and that only relevant data is requested. To make this simple, we have listed a few of the challenges we have seen below (these are from an Entopy perspective based on what we are seeing and what affects us the most – there will likely be others):
Generating accurate synthetic tabular data with Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN) have been used for a long time to generate synthetic data. They are typically used to generate images or text and have been widely successful. The idea is simple really: to have two competing Artificial Neural Networks (ANN), with one generating ‘fake’ data (the Generator) and the other being fed both real data and fake data and detecting real from fake (the Detector). As the models compete, the Generator model becomes better, delivering more effective fake data in its attempt to beat the Detector to a point that the fake data is indistinguishable from the real data.
In the context of the challenge Entopy is trying to overcome, we wanted to use GAN as a method to generate synthetic data but instead of generating text or images, we wanted to generate tabular data to help train and improve our operational AI models.
There is a BIG difference. The accuracy of images and text is subjective. There’s no right or wrong, only the audience’s perception. But with tabular data, it is binary. Bad tabular data fed into an operational AI micromodel will lead to a very badly performing operational AI micromodel. In partnership with the University of Essex, Entopy’s research has focused on progressing concepts in the domain of synthetic data generation using GAN to deliver effective methods for generating accurate synthetic tabular data.
A specific business need was presented which supported the research activity. A customer wanted to achieve highly accurate predictive intelligence across a specific aspect of its multi-faceted operation (which involves several stakeholders contributing within the overall ecosystem). The area of the operation had a seasonal aspect but due to various system upgrades, there were only 12 months of historical operational data available.
Using the actual data, Entopy was able to achieve a modest AI micromodel performance of ~70% but it was clear that to achieve better model performance and ensure confidence in the operational AI micromodels deployed, more data was needed.
The results from Entopy’s research are models capable of delivering accurate synthetic tabular data by learning from the available real data to it. By statistically analysing and checking the data pattern of both the real and generated datasets, we can conclude that both datasets show the same characteristics. Also, training different ML models on both datasets shows a very similar performance. On the other hand, the generated data must be plausible based on the reality. For instance, if you’re generating data about car speeds, the generated value must only be positive and limited. This analysis shows how ‘real’ the generated data is.
The model was used to expand the original dataset by multiple times and used to train operational AI micromodels, achieving a much-improved model accuracy.
Further research to overcome known challenges
Alongside the GAN research, Entopy has been progressing research in the domain to understand the feature importance of target datasets. This research is progressing concepts in reinforcement learning and its primary use to accelerate the development and deployment of operational AI micromodels.
Entopy has developed effective reinforcement learning algorithms that are deployed today within the operational AI micromodel context, providing predictive intelligence for certain problem types within target environments. However, this research looks to use reinforcement learning as a ‘back-end’ tool, helping Entopy’s, delivery teams mobilise effective operational AI models more quickly, reducing the problem/evaluation process through automation.
However, looking forward, the ability to effectively understand datasets and identify feature importance could be a useful mechanism to overcome certain sensitivity challenges associated with AI-enabled Digital Twin deployments in multistakeholder environments.
What does this mean for Entopy and its customers?
This breakthrough innovation means that Entopy can deploy its software in areas where others can’t, enabling Entopy to overcome data availability challenges through the generation of highly accurate synthetic tabular data to support the mobilisation of highly effective operational AI micromodels.
This is a prominent challenge in Entopy’s strategic focus areas of critical infrastructure, where there is a mix of new and legacy systems with an accelerating ambition to upgrade legacy systems and a distinct lack of available data, which also involve ecosystems of partners causing high sensitivity around data and the sharing thereof.
Furthermore, given the global pressures on critical infrastructure, changes to increase capacity are inevitable. Synthetic data will be a key tool in simulating the impact of future changes and the impact on operational aspects and the ecosystem.