Tag Archives: AI

Powering Tomorrow: How AI Is Impacting Our National Grid

National Grid

In the world of energy, Virtual Power Plants (VPP) are poised to revolutionize the traditional energy market. With the integration of Machine Learning (ML) technology, VPPs are able to analyze data in real time and make intelligent decisions that will ensure efficient energy distribution while reducing costs. In this blog post, we’ll explore the effects of Machine Learning in Virtual Power Plants and dive into examples of companies that are already adopting this new technology.

As the demand for electricity continues to increase, traditional power plants are struggling to keep up. With aging infrastructure and a growing focus on renewable energy, it has become increasingly challenging to meet the demands of consumers while maintaining reliability and affordability. This is where Virtual Power Plants powered by Machine Learning come in. With ML algorithms, VPPs are able to predict energy production and consumption patterns, allowing for more accurate and efficient energy distribution. In addition, ML can also optimize the use of renewable energy sources, such as solar panels or wind turbines, by predicting when they will produce the most power.

Power Plant

Improved Reliability

Since VPPs are designed to work with multiple sources of renewable energy, the smart algorithms will ensure that the energy is distributed evenly, and the system can respond to any issues. With real-time data analysis, any occurrence of a failing energy supply can quickly be identified and addressed. With the integration of Machine Learning, VPPs can predict when the energy supply will fall short and make necessary changes automatically. This level of reliability is crucial for the stability of the energy grid and ensures a consistent supply of power to consumers.

Enhanced Efficiency

Virtual Power Plants improve energy distribution efficiency, which is particularly useful for peak times or sudden surges in power demand. ML will monitor real-time energy demand and supply, and make corrections to power distribution to ensure that the system remains in balance and there are no overloads or outages. With the use of ML, VPPs can optimize energy distribution processes while reducing energy wastage and preventing unnecessary energy costs.

Flexibility

As we pointed out earlier, Virtual Power Plants enabled with Machine Learning capabilities are highly responsive and have shown to be adaptable to changing energy demands. The intelligent system can monitor demand changes, weather patterns, and other factors and make adjustments accordingly. By predicting the energy needed the VPP can send the correct amount of energy exactly when and where it’s required. This kind of adaptability ensures that resources are not wasted, and the infrastructure can be utilized to its maximum potential.

Cost Reductions

Cost Reduction

By optimizing energy distribution, the system will reduce the number of fossil fuel-based power plants required to produce energy, resulting in reduced CO2 emissions and costs. By predicting the amount of renewable energy supply available and ensuring it is used efficiently, enables VPPs to operate on a significantly lower budget. By utilizing ML algorithms, VPPs are capable of not only predicting energy production and consumption patterns but also optimizing the use of renewable resources. This optimization occurs when the ML algorithm forecasts the periods of maximum energy output from renewable sources like solar panels and wind turbines. By harnessing energy during these peak periods, VPPs can store and distribute power when the demand is high, thereby reducing reliance on costly non-renewable sources.

The Impacts!

Machine Learning is making significant strides in shaping Virtual Power Plants (VPPs). Here are some ways in which Machine Learning is effecting change:

Predictive Analytics: Machine Learning algorithms work to analyze historical and real-time data, predicting energy demand, supply fluctuations, and market conditions. This foresight allows VPPs to optimize energy production and distribution in advance, ensuring efficiency.

Optimized Resource Allocation: Machine Learning empowers VPPs to dynamically allocate energy resources based on real-time demand. This includes the effective management of renewable energy sources, storage systems, and traditional power generation for maximum utilization.

Demand Response Optimization: Machine Learning is ramping up the ability of VPPs to take part in demand response programs. By recognizing patterns in energy consumption, the system can proactively adjust energy usage during peak times or low-demand periods, contributing to grid stability.

Fault Detection and Diagnostics: With Machine Learning algorithms, anomalies and faults in the energy system can be detected, allowing swift identification and resolution of issues, thereby improving the reliability of VPPs.

Market Participation Strategies: Machine Learning aids VPPs in developing sophisticated energy trading strategies. It analyzes market trends, pricing, and regulatory changes, enabling VPPs to make informed decisions and thereby maximizing revenue while minimizing costs.

Grid Balancing: VPPs leverage Machine Learning to balance energy supply and demand in real time. This is crucial for maintaining grid stability, particularly as the proportion of intermittent renewable energy sources increases.

Energy Storage Optimization: Machine Learning optimizes the use of energy storage systems within VPPs, determining the most effective times to store and release energy, which enhances storage solution efficiency.  Additionally, ML algorithms can also predict battery degradation and optimize maintenance schedules.

Cybersecurity: Machine Learning plays a critical role in enhancing the cybersecurity of VPPs. It continuously monitors for unusual patterns or potential threats, providing a robust line of defense. In the ever-evolving world of technology, the partnership between Machine Learning and VPPs is proving to be a game-changer.

Challenges and Opportunities 

Virtual Grid

As with any technological advancements this transition comes with its own set of difficulties. For instance, the management and security of the massive amounts of data generated from various energy sources is a significant challenge. Privacy becomes a crucial concern and necessitates robust cybersecurity measures. Furthermore, the complexity involved in executing Machine Learning algorithms requires a skilled workforce, and ongoing training becomes indispensable to harness the full potential of these technologies.

However, amid these challenges, there are several noteworthy opportunities. Machine Learning brings predictive analytics to the table, offering the possibility to optimize energy production and consumption, which leads to increased efficiency. VPPs, coordinating distributed energy resources, open the door to more resilient and decentralized energy systems. The integration of renewable energy sources is a substantial opportunity, promoting sustainability while reducing environmental impact.

Machine Learning also optimizes energy trading strategies within VPPs, paving the way for novel economic models and revenue streams for energy producers. In essence, while data management, security, and skill requirements present challenges, the amalgamation of Machine Learning and VPPs offers a promising opportunity to revolutionize energy systems. It holds the potential to make these systems more efficient, sustainable, and responsive to the evolving demands of the future.

Companies Using Machine Learning in Virtual Power Plants

Virtual Power Plant

Kraftwerke: The world’s largest open market for power and flexibility. The company has been a leader in the integration of Machine Learning techniques in energy management systems. By using ML algorithms in their VPPs, they can accurately forecast energy demand and produce a balance between energy supply and demand in real time.

AutoGrid: Offering flexibility management solutions to optimize distributed energy resources (DERs), hence improving grid reliability. Enbala, now a part of Generac, has also adopted Machine Learning for its distributed energy platform, concentrating on enhancing the performance of DERs within VPPs.

Siemens: Has been involved in projects that incorporate Machine Learning into VPPs, aiming to boost the efficiency and flexibility of power systems through advanced analytics. Similarly, Doosan GridTech harnesses machine learning and advanced controls to optimize the performance of distributed energy resources, focusing on improving the reliability and efficiency of VPPs.

Advanced Microgrid Solutions (AMS): Has implemented Machine Learning algorithms to fine-tune the operations of energy storage systems within VPPs. Their platform is designed to provide grid services and maximize the value of DERs. ABB, a pioneer in power and automation technologies, has delved into Machine Learning applications in VPP management and control, with solutions concentrating on grid integration and optimization of renewable energy sources.

General Electric (GE): A multinational conglomerate, is also involved in projects that apply Machine Learning for the optimization and control of DERs within VPPs, bringing their vast industry knowledge to the table.

Future Possibilities

National Grid

Looking ahead, the fusion of Machine Learning and Virtual Power Plants (VPPs) is poised to revolutionize the global energy landscape. The predictive analytics capabilities of Machine Learning hint at a future where energy systems are highly adaptive and able to forecast demand patterns accurately and proactively. The potential for VPPs, supercharged by Machine Learning algorithms, points towards a future where energy grids are fully optimized and decentralized.

The integration of renewable energy sources, enhanced by advanced Machine Learning technologies, promises a future where sustainable energy production is standard practice, not an exception. The refinement of energy trading strategies within VPPs could herald a new era of economic models, fostering innovative revenue generation avenues for energy producers.

As these technologies continue to mature and evolve, the future of energy looks dynamic and resilient, with Machine Learning and VPPs serving as key pivots in delivering efficiency, sustainability, and adaptability. Together, they are set to cater to the ever-changing demands of the global energy landscape, heralding an era of unprecedented progress and potential.

In conclusion, Machine Learning is driving the development of Virtual Power Plants, and the integration of ML technology in VPPs will lead to an effective, efficient, and sustainable energy system. The benefits of Machine Learning in VPPs are numerous, and the use of intelligent algorithms will ensure that the energy is distributed evenly, reduce energy costs, and enable the VPP to adapt to changing energy market demands. With its promising potential to increase reliability, reduce costs, and lower CO2 emissions, Machine Learning in Virtual Power Plants is indeed the future of energy operations.

 

Unleashing Tomorrow: The Resonance of Power in Hyper-Automation’s Symphony of Machine Learning

RPA

The field of technology continues to evolve every year, and businesses are forced to keep up with the changes to stay relevant. Our past few blogs have been focused on the advancements of machine learning and its effects on various industries. In this blog, we will explore the powerful effects of machine learning in hyper-automation and how it is revolutionizing commerce. 

What exactly is hyper-automation? Hyper-automation, involves the integration of multiple technologies to automate workflow, decision-making, and analysis. When these two technologies are combined, their effects are incredibly powerful, enhancing efficiency, accuracy, and productivity across various industries. Machine learning and hyper-automation have a significant impact on various aspects of society, economy, and technology. Hyper-automation allows for the automation of routine tasks, freeing up valuable time for organizations. This efficiency is further improved by machine learning, which continuously optimizes processes based on data insights.A compelling benefit of hyper-automation is cost reduction. Hyper-automation reduces labor costs and minimizes errors, leading to substantial cost savings for businesses. Machine learning algorithms bolster this effect with predictive analytics that optimize resource utilization and prevent costly issues before they occur.

In addition to these operational impacts, machine learning and hyper-automation offer considerable potential for innovation acceleration. Machine learning automates complex tasks, allowing organizations to focus their energy on more creative and strategic aspects. This freedom can lead to the development of new products, services, and even entirely new business models. Furthermore, machine learning algorithms analyze vast datasets to provide valuable insights, enhancing decision-making capabilities. When coupled with the swift execution capability of hyper-automation, this results in a substantial boost to overall organizational agility.

However, machine learning and hyper-automation do not only bring about operational and strategic shifts. They also have a profound effect on the job landscape and societal norms. While automation may displace certain jobs, particularly those that consist of routine and repetitive tasks, it simultaneously creates new opportunities in fields such as AI development, data analysis, and system maintenance. Moreover, data security, privacy challenges, increased complexity, and interconnectedness of systems are all critical areas that need attention as these technologies continue to evolve.

The Transformative Impact of Machine Learning and Hyper-automation

Artificial Intelligence

The combination of machine learning and hyper-automation is a match made in tech heaven, a powerful duo that is revolutionizing the way organizations function. By deploying algorithms that analyze past and current data, this integration streamlines processes, automates repetitive tasks, and liberates employees’ valuable time, thereby enhancing productivity and efficiency within the organization.

In the rapid-paced world of business where every second counts, harnessing the power of machine learning and hyper-automation tools offers a strategic edge. It refines decision-making processes by swiftly processing gargantuan volumes of data, mitigating human error, and fostering informed data-driven choices.

Moreover, there’s a secret sauce that machine learning brings to the hyper-automation table – a significant elevation of customer experience. It does this by scrutinizing data to zero in on patterns and preferences, enabling businesses to add a personal touch to their interactions. This custom-tailored approach leads to heightened customer satisfaction, fostering loyalty, and ensuring retention, creating a win-win for all involved.

As we traverse further into the era of digital transformation, the speed and precision of machine learning algorithms stand as a crucial pillar, contributing to improved efficiency and productivity. The blend of machine learning and hyper-automation not only amplifies decision-making accuracy but also keeps costs in check. It achieves this by automating tasks, optimizing resource allocation, and keeping errors to a minimum, thus paving the way for overall business optimization. The resonance of power in this symphony of technological integration is indeed unleashing tomorrow, today.

Examples of Companies Making Use of Hyper-Automation and Machine Learning

Automation

Netflix: The popular streaming service provider, uses machine learning algorithms to personalize recommendations for its users. Based on their previous viewing habits, Netflix algorithms suggest the next series or movie to watch. Hyper-automation also harmonizes their production, workflow, and decision-making process.

Amazon: Amazon has revolutionized the retail industry by integrating machine learning and hyper-automation into its operations. From personalized product recommendations to streamlining their supply chain management, these technologies have enabled Amazon to achieve cost savings, improve efficiency, and enhance customer experience.

Rally Health: Rally uses machine learning algorithms to analyze data and identify the health habits of patients. Through this technology, Rally assists doctors in predicting their patient’s future health risks, which allows them to take preventative measures. This not only improves the overall health of patients but also reduces healthcare costs. By automating certain processes, Rally can provide personalized care to each individual, leading to improved outcomes and a more efficient healthcare system.

Orange Bank: Orange Bank in France offers100% digital banking, giving their customers real-time personal finance insights. They employ machine learning algorithms to provide automated financial advice and other services to users. This not only enhances customer experience but also saves time and resources for both the bank and its customers.

Future Possibilities

RBA & Hyper Automation

The future of machine learning and hyper-automation indeed holds exciting prospects. The integration of these technologies will likely give rise to a world of Autonomous everything. From self-driving vehicles and drones to fully automated manufacturing processes, autonomy could become commonplace across various industries, revolutionizing how we live and work.

In the healthcare sector, machine learning could fortify personalized solutions, predict diseases, customize treatments, and significantly improve diagnostics. Meanwhile, hyper-automation could streamline administrative tasks, empowering healthcare professionals to dedicate more time to patient care and less on tedious paperwork.

Our cities could become smarter with the application of machine learning algorithms and hyper-automation. These technologies can optimize city functions such as traffic management, waste disposal, and energy consumption, resulting in urban environments that are not only more sustainable and efficient but also more livable.

The education sector stands to be revolutionized with personalized learning experiences shaped by machine learning. Hyper-automation could manage administrative tasks, freeing up educators to concentrate on providing tailored and interactive teaching methods.  Furthermore, these technologies could enable a more comprehensive evaluation process that considers individual learning styles and progress.

Finally, the evolution of machine learning could bring about highly intelligent personal assistants. These advanced aides will understand context, learn personal preferences, and perform complex tasks. Coupled with hyper-automation, the execution of tasks will be seamless, enhancing our day-to-day activities and making life easier. The future of machine learning and hyper-automation is inspiring and holds the potential to substantially transform various aspects of our lives.

Technological Innovations

Business Automation

The future landscape where machine learning and hyper-automation converge promises a multitude of benefits and transformative shifts across various sectors. As we look ahead, we can envision several key developments and their potential impacts on our world.

Enhanced Decision-Making: Machine learning algorithms are set to become even more sophisticated, offering invaluable support to organizations in making high-accuracy, data-driven decisions with unprecedented speed. When complemented by hyper-automation, the execution of these decisions will become seamlessly automated, improving operational efficiency and giving organizations a competitive edge.

Autonomous Systems: The advancements in both machine learning and automation technologies are paving the way for an era dominated by autonomous systems. From self-driving vehicles and automated manufacturing processes to smart cities, these innovations have the potential to make operations safer, more efficient, and sustainable.

Reduced Cognitive Load: A significant advantage that emerges from the intersection of machine learning and hyper-automation is the reduction of cognitive load on employees. By augmenting routine tasks and decision-making processes with automated systems, these technologies liberate the workforce from mundane and repetitive duties. This freedom allows professionals to direct their cognitive resources toward creative problem-solving and strategic planning.

Predictive Maintenance: The blend of machine learning and hyper-automation promises to refine predictive maintenance in industries like manufacturing and aviation, reducing downtime, extending equipment lifespan, and enhancing safety.

Healthcare Innovations: Machine learning and hyper-automation will play an instrumental role in healthcare, aiding in everything from disease diagnosis to the customization of treatment plans. This could lead to improved healthcare outcomes and increased efficiency in healthcare systems.

Data Security: As cyber threats evolve, machine learning will be essential in identifying and mitigating security breaches, with automation enabling real-time responses, thereby enhancing overall cybersecurity.

Supply Chain Optimization: Machine learning could enable organizations to optimize their supply chains by predicting demand, eliminating inefficiencies, and ensuring timely deliveries. Hyper-automation would allow for real-time adjustments in response to changing conditions.

Efficient Resource Management: In energy and resource-intensive industries, machine learning and hyper-automation could optimize resource consumption, leading to reductions in waste and environmental impact.

The future of hyper-automation, coupled with machine learning, will continue to revolutionize decision-making processes and improve organizational efficiency, accuracy, and productivity. With more and more businesses opting for a digital-first approach, it’s essential to stay ahead of the game by incorporating hyper-automation, machine learning, and other emerging technologies. It’s an exciting time to be leading technological innovation because the potential impact is limitless. As a technology thought leader, we look forward to seeing how hyper-automation and related technologies change the way companies work.

 

Machine Learning Unlocks Quantum Potential: A Paradigm-Shifting Partnership

Three Dimensional Qubit

In the modern world, technology has revolutionized the way we work, carry out our tasks, and interact with one another. These technological transformations have come into existence due to the application of various scientific discoveries and computing power advancements. In recent years, Machine Learning and Quantum Computing have both evolved to become game-changers, taking their place in the revolutionary field of computer science. This blog will discuss the effects of machine learning on Quantum Computing, and how the models and algorithms derived in machine learning can be applied to enhance the power of quantum computing.

Machine learning has been a hot topic in the world of computer science, with its ability to analyze and make predictions from vast amounts of data. This has led to significant advancements in various fields such as healthcare, finance, and transportation. On the other hand, quantum computing has sparked excitement with its potential to solve complex problems that are impossible for traditional computers.

The Impact of Machine Learning on Quantum Computing

Machine learning and quantum computing are two powerful technologies that have the potential to complement each other. The combination of these two fields can create a cutting-edge technology that can solve some of the most complex problems known to humankind. One of the key areas where machine learning has shown its impact on quantum computing is in the optimization of quantum algorithms.

Quantum computers are known for their ability to process large amounts of data in a fraction of the time it would take traditional computers. However, implementing quantum algorithms can be challenging due to the complexity involved. This is where machine learning comes into play. By using machine learning models and algorithms, scientists and researchers can optimize these quantum algorithms to work more efficiently and accurately. This not only saves time and resources but also improves the overall performance of quantum computers.

Another area where machine learning has shown its potential in enhancing quantum computing is in error correction. As with any technology, errors are inevitable. In quantum computing, these errors can significantly impact the accuracy and reliability of calculations. By utilizing machine learning techniques, researchers have been able to develop algorithms that can detect and correct errors in quantum systems. This has greatly improved the stability and efficiency of quantum computers, making them more viable for practical use.

Difference between a Bit and Qubit

Exactly How is Machine Learning Impacting Quantum Computing?

Quantum computing, on the other hand, is a unique form of computing that employs quantum-mechanical phenomena such as superposition and entanglement to manipulate information. Unlike classical computers, where information is represented in bits (0s and 1s), quantum computers use qubits to represent information. This allows them to handle and process multiple calculations simultaneously, making them incredibly powerful.

The integration of machine learning with quantum computing has opened new avenues for the development of more sophisticated algorithms and models that can solve complex problems. Machine learning techniques such as neural networks and deep learning are being applied to quantum computing, allowing for enhanced data processing and analysis. This has led to a better understanding and utilization of quantum properties, resulting in improved performance and accuracy in solving complex problems. The potential of this partnership is immense, and it has the potential to shape the future of computing.

Neural Network

Challenges and Opportunities

While the partnership between machine learning and quantum computing offers many opportunities, there are also some challenges that need to be addressed. One major challenge is the limited availability of quantum hardware. Quantum computers are still in their early stages of development, and only a few companies and research institutions have access to them. This can hinder the progress of using machine learning techniques in quantum computing.

Additionally, there is a shortage of experts who possess both machine learning and quantum computing knowledge. Both fields require a deep understanding of complex mathematical concepts, making it challenging to find individuals with expertise in both areas. As such, there is a need for more interdisciplinary training and collaboration between these fields to bridge this gap.

Machine Learning and Quantum Computing Effects

Machine learning and quantum computing have significant positive effects when used together. Machine learning can help quantum computing to identify, react, and handle large volumes of data quickly and efficiently. Both technologies rely on deep mathematical connections, and when combined, they can improve the precision and accuracy of quantum computations. This will enable quantum computers to solve complex problems much quicker than before. Additionally, machine learning can help in reducing the sensitivity of quantum computers to errors and noise, which are common in these systems. This will lead to improved stability and reliability of quantum computers, making them more practical for solving real-world problems.

Quantum Circuit

Moreover, the integration of machine learning with quantum computing can also aid in the development of new quantum algorithms. These algorithms can be used in various applications such as optimization problems, simulation, and machine learning. The combination of these two technologies has the potential to transform various fields, including finance, drug discovery, and climate modeling.

Some Examples of Companies using Machine Learning for Quantum Computing

Several companies use machine learning and quantum computing to improve their processes and services such as: IBM, Google, Microsoft, Rigetti and Anyon Systems.

IBM: IBM Quantum is at the forefront of research and development in quantum machine learning algorithms. They’ve launched the Qiskit Machine Learning library, enabling users to implement quantum machine learning models on IBM’s quantum computers.

Google: Known for its Quantum AI lab, has been exploring the acceleration of machine learning tasks using quantum processors, particularly in the development of quantum neural networks.

Rigetti: Rigetti has been actively using quantum computers for machine learning applications. They offer the Quantum Machine Learning (QML) toolkit, which implements machine learning algorithms on quantum hardware.

Microsoft: Microsoft has been actively researching quantum machine learning and has integrated quantum computing capabilities into their Azure cloud platform, providing resources for quantum machine learning research.

Anyon Systems: Anyon Systems, a quantum software company, explores the application of quantum computing to machine learning and optimization problems, providing software tools for quantum machine learning research.

It’s worth noting that the field of quantum computing is rapidly evolving, and new companies and developments are emerging continually.

Future Possibilities

Quantum Mechanics and Drug Discovery

The combination of machine learning and quantum computing holds immense potential for the future. As both technologies continue to advance and evolve, their integration will lead to groundbreaking innovations in fields such as drug discovery, finance, materials science, and more. With the ability to process vast amounts of data quickly and efficiently, quantum computers powered by machine learning will revolutionize problem-solving and decision-making processes. This will have a profound impact on various industries, leading to the development of new products and services that were previously unimaginable.

Here are some future possibilities and effects of the synergy between machine learning and quantum computing:

Faster Optimization: Quantum computers excel at solving optimization problems, which are prevalent in machine learning. They can significantly speed up tasks like hyperparameter tuning, portfolio optimization, and feature selection, making machine-learning models more efficient and accurate.

Quantum Machine Learning Models: Quantum machine learning algorithms may become a reality, utilizing the inherent properties of quantum systems to create novel models capable of solving complex problems.

Improved Data Processing: Quantum computing can enhance data preprocessing tasks like dimensionality reduction, clustering, and pattern recognition. Quantum algorithms can efficiently handle large datasets, potentially reducing the need for extensive data cleaning and preparation.

Enhanced AI Training: Quantum computers could expedite the training of deep learning models, which is a computationally intensive task. This could lead to faster model training and the ability to tackle more complex neural network architectures.

Quantum Data Analysis: Quantum computing can facilitate the analysis of quantum data, which is generated by quantum sensors and experiments. Quantum machine learning can help in extracting meaningful insights from this data, leading to advancements in physics, chemistry, and materials science.

Drug Discovery and Material Science: Quantum computing combined with machine learning can accelerate drug discovery and materials research. Quantum simulations can accurately model molecular structures and properties, leading to the development of new drugs and materials.

Quantum-Assisted AI Services: Cloud providers may offer quantum-assisted AI services, allowing businesses and researchers to harness the power of quantum computing for machine learning tasks via the cloud, similar to how cloud-based GPUs are used today.

Improved Security: Quantum machine learning can contribute to enhancing cybersecurity by developing more robust encryption and security protocols. Quantum-resistant encryption algorithms are being explored to safeguard data against quantum attacks.

It’s important to note that the full realization of these possibilities depends on advancements in both quantum hardware and quantum algorithms, as well as the integration of quantum computing into existing machine learning workflows. While quantum computing is a promising technology, it is still in its early stages, and practical applications may take several years to become widespread.

Additional Benefits of Machine Learning on Quantum Computing

With machine learning, quantum computing can quickly recognize patterns and anomalies, which can lead to improvements in supply chain logistics and customer service. Additionally, it has the potential to aid breakthrough research in cancer treatments and other scientific issues that currently require significant amounts of time and effort. Using machine learning with quantum computing could generate the solutions more efficiently. Moreover, as quantum computers continue to scale, the applications and potential benefits will only increase. It’s an exciting time for both fields, and the future possibilities are limitless. Combining these two technologies will pave the way for groundbreaking discoveries and advancements that will shape our society in unimaginable ways.

Qubit

Machine Learning has led to significant improvements in many sectors, and in recent years, Quantum Computing has begun to change how various industries process and analyze data. The effects of machine learning on Quantum Computing can enhance computing efficiency and precision and lead to groundbreaking research. As we continue to explore the possibilities of machine learning and quantum computing, the future is looking increasingly bright for the integration of these two innovative technologies. The application of machine learning to quantum computing has the potential to transform how we conduct research, and it is exciting to think about what changes will come about in the not-too-distant future. The possibilities are endless, and the integration of these two fields is just beginning. We can only imagine the advancements that will be made through this synergy and eagerly await what’s to come. So, it is essential to continue learning about both machine learning and quantum computing, staying updated on new developments, and exploring potential applications in various industries. By doing so, we can fully embrace and harness the power of machine learning and quantum computing, leading to a more advanced and innovative future. So, let’s keep learning and exploring the possibilities together!

In conclusion, machine learning and quantum computing are powerful technologies on their own, but when combined, their potential becomes even greater. As we continue to make advancements in both fields, it is crucial to explore and embrace the possibilities of their integration.

Unleashing the Transformative Potential of Augmented Reality in Robotics

AR in Robotics

The integration of augmented reality (AR) and robotics has brought about countless benefits and transformed many industries. This integration of AR in robotics has proven to be a game-changer since the technology is becoming increasingly prevalent in various sectors. For instance, robots can now recognize objects in a 3D environment, allowing them to manipulate objects more effectively than ever before. This means that robots can perform tasks that would have been impossible for them to do previously.

In this blog post, we will explore the powerful impact of augmented reality in robotics and how it has become the forefront of innovation. We will dive into the effects of augmented reality technology on the robotics industry, including new developments, and increased efficiency.

Increased Efficiency

Using AR, robots can identify, locate and sort objects quickly and accurately, resulting in an improvement in performance and overall productivity. For instance, AR technology used in manufacturing has enabled robots to minimize errors in assembly lines. The robots can recognize a product and its details and perform assigning tasks with precision and accuracy. This minimizes errors, and the time spent on the task and thus increasing overall productivity outcomes. Below are some examples of how AR is further shaping the field of robotics:

Augmented Reality

Robot Programming:

AR can simplify the programming of robots by overlaying intuitive graphical interfaces onto the robot’s workspace. This allows operators to teach robots tasks by physically demonstrating them, reducing the need for complex coding and making it accessible to non-programmers.

Maintenance and Troubleshooting:

When robots require maintenance or encounter issues, technicians can use AR to access digital manuals, schematics, and step-by-step repair guides overlaid on the physical robot. This speeds up troubleshooting and maintenance, reducing downtime.

Training and Simulation:

AR-based training simulators provide a safe and cost-effective way to train robot operators. Trainees can interact with virtual robots and practice tasks in a simulated environment, which helps them become proficient in operating and maintaining actual robots more quickly.

Remote Operation and Monitoring:

AR allows operators to remotely control and monitor robots from a distance. This is particularly useful in scenarios where robots are deployed in hazardous or inaccessible environments, such as deep-sea exploration or space missions.

Augmented Reality

Quality Control and Inspection:

Robots equipped with AR technology can perform high-precision inspections and quality control tasks. AR overlays real-time data and images onto the robot’s vision, helping it identify defects, measure tolerances, and make real-time adjustments to improve product quality.

Inventory Management:

In warehouses and manufacturing facilities, AR-equipped robots can efficiently manage inventory. They use AR to recognize and locate items, helping in the organization, picking, and restocking of products.

Teleoperation for Complex Tasks:

For tasks that require human judgment and dexterity, AR can assist teleoperators in controlling robots remotely. The operator can see through the robot’s cameras, receive additional information, and manipulate objects in the robot’s environment, such as defusing bombs or performing delicate surgical procedures.

Robotics Research and Development:

Researchers and engineers working on robotics projects can use AR to visualize 3D models, simulations, and data overlays during the design and development phases. This aids in testing and refining robotic algorithms and mechanics.

Robot Fleet Management:

Augmented Reality

Companies with fleets of robots can employ AR to monitor and manage the entire fleet efficiently. Real-time data and performance metrics can be displayed through AR interfaces, helping organizations optimize robot usage and maintenance schedules.

Top Companies that Utilize Augmented Reality in Robotics

AR technology is widely adopted by companies worldwide to boost sales in their robotics systems. Notable players in this arena include Northrop Grumman, General Motors, and Ford Motor Company. Within the automotive industry, reliance on robotic systems is significant, and the integration of AR technology has yielded enhanced efficiency and reduced operating costs. Moreover, experts anticipate that AR technology could slash training time by up to 50% while boosting productivity by 30%.

These are a few instances of companies that employ augmented reality (AR) in the field of robotics:

  • iRobot: iRobot, the maker of the popular Roomba vacuum cleaner robots, has incorporated AR into its mobile app. Users can use the app to visualize cleaning maps and see where their Roomba has cleaned, providing a more informative and interactive cleaning experience.
  • Universal Robots: Universal Robots, a leading manufacturer of collaborative robots (cobots), offers an AR interface that allows users to program and control their robots easily. The interface simplifies the setup process and enables users to teach the robot by simply moving it through the desired motions.
  • Vuforia (PTC): PTC’s Vuforia platform is used in various industries, including robotics. Companies like PTC provide AR tools and solutions to create interactive maintenance guides, remote support, and training applications for robotic systems.
  • KUKA: KUKA, a global supplier of industrial robots, offers the KUKA SmartPAD, which incorporates AR features. The SmartPAD provides a user-friendly interface for controlling and programming KUKA robots, making it easier for operators to work with the robots.
  • RealWear: RealWear produces AR-enabled wearable devices, such as the HMT-1 and HMT-1Z1, which are designed for hands-free industrial use. These devices are used in robotics applications for remote support, maintenance, and inspections.
  • Ubimax: Ubimax offers AR solutions for enterprise applications, including those in robotics. Their solutions provide hands-free access to critical information, making it easier for technicians to perform maintenance and repairs on robotic systems.
  • Vicarious Surgical: Vicarious Surgical is developing a surgical robot that incorporates AR technology. Surgeons wear AR headsets during procedures, allowing them to see inside the patient’s body in real-time through the robot’s camera and control the robot’s movements with precision.

Collaborative Robotics

Collaborative robots, also known as cobots, are rapidly gaining traction across various industries. By leveraging augmented reality (AR), human workers can effortlessly command and interact with cobots, leading to improved tracking and precision. This collaborative synergy brings forth a multitude of advantages, such as error identification and prompt issue resolution. Consequently, this approach streamlines and optimizes manufacturing processes, ushering in enhanced efficiency and productivity.

Examples of Augmented Reality (AR) in Collaborative Robotics

Assembly and Manufacturing Assistance:

AR can provide assembly line workers with real-time guidance and visual cues when working alongside cobots. Workers wearing AR glasses can see overlays of where components should be placed, reducing errors and increasing assembly speed.

Quality Control:

In manufacturing, AR can be used to display quality control criteria and inspection instructions directly on a worker’s AR device. Cobots can assist by presenting parts for inspection, and any defects can be highlighted in real-time, improving product quality.

Collaborative Maintenance:

During maintenance or repair tasks, AR can provide technicians with visual instructions and information about the robot’s components. Cobots can assist in holding or positioning parts while the technician follows AR-guided maintenance procedures.

Training and Skill Transfer:

AR can facilitate the training of workers in cobot operation and programming. Trainees can learn how to interact with and program cobots through interactive AR simulations and tutorials, reducing the learning curve.

Safety Enhancements:

AR can display safety information and warnings to both human workers and cobots. For example, it can highlight no-go zones for the cobot, ensuring that it avoids contact with workers, or provide real-time feedback on human-robot proximity.

Collaborative Inspection:

In industries like aerospace or automotive manufacturing, workers can use AR to inspect large components such as aircraft wings or car bodies. AR overlays can guide cobots in holding inspection tools or cameras in the correct positions for thorough examinations.

Material Handling:

AR can optimize material handling processes by showing workers and cobots the most efficient paths for transporting materials. It can also provide real-time information about inventory levels and restocking requirements.

Dynamic Task Assignment:

AR systems can dynamically assign tasks to human workers and cobots based on real-time factors like workload, proximity, and skill levels. This ensures efficient task allocation and minimizes downtime.

Collaborative Training Environments:

AR can create shared training environments where human workers and cobots can practice collaborative tasks safely. This fosters better teamwork and communication between humans and robots.

Multi-robot Collaboration:

AR can help orchestrate the collaboration of multiple cobots and human workers in complex tasks. It can provide a centralized interface for monitoring, controlling, and coordinating the actions of multiple robots.

Data Visualization

AR can display real-time data and analytics related to cobot performance, production rates, and quality metrics, allowing workers to make informed decisions and adjustments. These are just some of the ways that AR can be used to optimize collaborative robotics applications. By taking advantage of AR-enabled solutions, companies can improve efficiency in their operations and reduce downtime. With its ability to facilitate human-robot collaboration and enhance safety protocols, AR is an invaluable tool for unlocking the potential of cobots in industrial use cases.

Augmented reality (AR) technology is the cornerstone of robotics development. It seamlessly brings together various elements, resulting in an enhanced human-robot interaction. By integrating AR into robotics, efficiency is increased, and errors are eliminated. Successful examples of AR integration in robotic systems serve as proof of the substantial benefits it brings to diverse industries, including manufacturing, healthcare, automotive, and entertainment. The challenge for businesses now lies in identifying the significant opportunities that this technology offers and harnessing them for optimal benefits.

Stay Ahead of Your Competition with the Top Digital Marketing Trends of 2022

In an era of rapid technological acceleration, every year brings new avenues to market services and methods to boost sales. While the metaverse lurks on the horizon, it’s still in the developmental stage. Meanwhile, the current digital marketing landscape has evolved significantly within the past few years. Software developers and business owners must keep up on the latest trends in order to ensure that they don’t fall behind their competitors.

Here are some of the biggest trends in digital marketing today:

PERSONALIZATION

Success in digital marketing is increasingly dependent on how companies collect data and leverage it toward personalized ads. Studies show personalization can deliver five to eight times the ROI on marketing spend.

Personalization at its most basic level entails targeting users based on their demographic or location. For example, Guinness created a hyper-localized ad campaign which incorporated a unique Facebook ad for every Guinness venue in the UK and Ireland. Over 30,000 localized video ads for over 2,500 bars were updated dynamically based on the rugby matches playing at a given time.

Personalization relies on three tenets: data discovery, automated decision-making, and content distribution. Major corporations like Amazon leverage more extensive data with automated decision-making dictated by robust AI algorithms. Netflix’s complex viewing algorithms determine what users may like to view next based on their past viewing habits. The result is not only improved user experience, but a more personal relationship with the brand.

SOCIAL COMMERCE

Projections from Accenture show social commerce will reach $1.2 trillion globally by 2025—about 300% faster than traditional ecommerce. Gen Z and Millennials will be the biggest spenders, accounting for 62% of social revenue by 2025. Platforms are working behind the scenes to improve customer experience by creating payment methods without leaving social media apps. Two major social platforms to watch are TikTok and Youtube.

TikTok usage has risen rapidly and reached 1 billion users and counting. Engagement has been titanic with users in the United States spending up to 850 hours per month on the app. It was the top earning non-gaming app in 2021 with $110 million spent by users and its potential will only grow as influencers earn huge amounts through sponsorship deals. TikTok is not just for Gen Z, it’s a rapidly growing network and brands are taking advantage by offering influencers huge amounts of money for branded content.

As brands move their investment in traditional TV models toward streaming, one platform which stands to benefit is Youtube. Global revenue for the video streaming channel soared to $29 billion, a 46% increase from 2020. Youtube is beginning to attract more traditional TV advertisers and consequentially, their ad business is nearly matching Netflix in revenue. While revenue is ascending, there remains significant headroom for major brands to up their investment in Youtube advertising as traditional cable models phase out.

IN-GAME ADVERTISING

Just over 50% of global revenue in the gaming industry is driven by mobile games. With gaming reaching a growth rate higher than all other entertainment industries, brands are looking to in-game advertising as a way of reaching a larger audience.

The gaming demographic has recently reached a 50-50 split between men and women. Contrary to most preconceptions, in-game advertising will help you reach a wider audience of both men and women. In-game advertising not only reaches a wider audience, it makes it easy to track click-throughs and analytics. Extensive analytics enable brands to collect very precise data about their customers and foster a deeper understanding of their habits.

Playable ads have arisen as a major hallmark for brands to market their games. Playable ads are interactive and encourage the user to try a snippet of functionality from the game. Check out the examples in the video below by Vungle.

CONCLUSION

Brands need to move as fast as the times if they hope to stay on the forefront of their industry. In the era of big data, the bigger your brand, the more possibilities digital marketing entails. As AI becomes more accessible, businesses of all sizes are wise to take advantage of the digital landscape and find ways to offer a more personal experience for their customers.

How Chatbots Make Healthcare More Efficient

In the mid 1960s, Joseph Weizenbaum of the MIT Artificial Intelligence Laboratory created ELIZA, an early natural language processing computer program and the first chatbot therapist. While ELIZA did not change therapy forever, it was a major step forward and one of the first programs capable of taking the Turing Test. Researchers were surprised by the amount of people who attributed human-like feelings to the computer’s responses.

Fast-forward 50 years later, advancements in artificial intelligence and natural language processing enable chatbots to become useful in a number of scenarios. Interest in chatbots has increased by 500% in the past 10 years and the market size is expected reach $1.3 billion by 2025.

Chatbots are becoming commonplace in marketing, customer service, real estate, finance, and more. Healthcare is one of the top 5 industries where chatbots are expected to make an impact. This week, we explore why chatbots appeal to help healthcare providers run a more efficient operation.

SCALABILITY

Chatbots can interact with a large number of users instantly. Their scalability equips them to handle logistical problems with ease. For example, chatbots can make mundane tasks such as scheduling easier by asking basic questions to understand a user’s health issues, matching them with doctors based on available time slots, and integrating with both doctor and patient calendars to create an appointment.

At the onset of the pandemic, Intermountain Healthcare was receiving an overload of inquiries from people who were afraid they may have contracted Covid-19. In order to facilitate the inquiries, Intermountain added extra staff and a dedicated line to their call center, but it wasn’t enough. Ultimately, they turned to artificial intelligence in the form of Scout, a conversational chatbot made by Gyant, to facilitate a basic coronavirus screening which determined if patients were eligible to get tested at a time when the number of tests were limited.

Ultimately, Scout only had to ask very basic questions—but it handled the bevy of inquiries with ease. Chatbots have proved themselves to be particularly useful for understaffed healthcare providers. As they employ AI to learn from previous interactions, they will become more sophisticated which will enable them to take on more robust tasks.

ACCESS

Visiting a doctor can be challenging due to the considerable amount of time it takes to commute. Working people and those without access to reliable transport may prevent them from taking on the hassle of the trip. Chatbots and telehealth in general provide a straightforward solution to these issues, enabling patients to receive insight as to whether an in-person consultation will be necessary.

While chatbots cannot provide medical insight and prognoses, they are effective in collecting and encouraging an awareness of basic data, such as anxiety and weight changes. They can help effectively triage patients through preliminary stages using automated queries and store information which doctors can later reference with ease. Their ability to proliferate information and handle questions will only increase as natural language processing improves.

A PERSONALIZED APPROACH — TO AN EXTENT

Chatbot therapists have come a long way since ELIZA. Developments in NLP, machine learning, and more enable chatbots to deliver helpful, personalized responses to user messages. Chatbots like Woebot are trained to employ cognitive-behavioral therapy (CBT) to aid patients suffering from emotional distress by offering prompts and exercises for reflection. The anonymity of chatbots can help encourage patients to provide more candid answers unafraid of human judgment.

However, chatbots have yet to achieve one of the most important features a medical provider should have: empathy. Each individual is different, some may be scared away by formal talk and prefer casual conversation while for others, formality may be of the utmost importance. Given the delicacy of health matters, a lack of human sensitivity is a major flaw.

While chatbots can help manage a number of logistical tasks to make life easier for patients and providers, their application will be limited until they can gauge people’s tone and understand context. If recent advances in NLP and AI serve any indication, that time is soon to come.

How the Internet of Behaviors Will Shape the Future of Digital Marketing

In the digital age, businesses need to leverage every possible platform and cutting-edge technology in order to get a leg up on the competition. We’ve covered the Internet of Things extensively on the Mystic Media blog, but a new and related tech trend is making waves. This trend is called the Internet of Behaviors and according to Gartner, about 40% of people will have their behavior tracked by the IoB globally by 2023.

WHAT IS THE IOB?

Internet of Behavior, or the IoB, exists at the intersection of technology, data analytics, and behavioral science. The IoB leverages data collected from a variety of sources, including online activities, social media, wearable devices, commercial transactions and IoT devices, in order to deliver insights related to consumers and purchasing behavior.

With devices more interconnected than ever, the IoB tracks, gathers, combines and interprets massive data sets so that businesses can better understand their consumers. Businesses leverage analysis from the IoB to offer more personalized marketing with the goal of influencing consumer decision making.

HOW DOES IT WORK?

Traditionally, a car insurance company would analyze a customer’s driving history in order to determine if they are a good or bad driver. However, in today’s digital age, they might take it a step further and analyze social media profiles in order to “predict” whether a customer is a safe driver. Imagine what insights they could gather from a user’s Google search history or Amazon purchases? Access to large datasets enables large companies to create psychographic profiles and gather an enhanced understanding of their customer base.

Businesses can use the IoB for more than just purchasing decisions. UX designers can leverage insights to deliver more effective customer experiences. Large companies such as Ford are designing autonomous vehicles that change based on the city, modulating behavior based on vehicle traffic, pedestrians, bicycles and more.

GBKSOFT created a mobile application that collects data from wearable devices in order to help golfers improve their skills. The application records each golf ball hit, including the stroke, force, trajectory and angle, and delivers visual recommendations to improve their swing and technique. Insights gathered through data are translated into behavioral trends that are then converted into recommendations to improve the user’s game.

The IoB is all about collecting data that can be translated into behavior which helps companies understand consumer tendencies and translate them into meaningful actions.

CONCERNS

While there is quite a bit of enthusiasm surrounding the potential impact of the IoB for B2C companies, a number of legal concerns come with it. A New York Times article, written by Harvard Business School emeritus professor Shoshana Zuboff, warns of the age of surveillance capitalism where tech behemoths surveil humans with the intent to control their behavior.

Due to the speed at which technology and the ability to collect data has proliferated, privacy and data security are under-regulated and major concerns for consumers. For example, Facebook was applying facial recognition scans in advance of the 2016 election without user’s consent. Cambridge Analytica’s use of psychoanalytic profiles has been the subject of much derision. Momentum for data privacy regulation is growing and since the IoB hinges on the ability for companies to collect and market data, forthcoming regulations could inhibit its impact.

CONCLUSION

Despite regulatory concerns, the IoB is a sector that we expect to see grow over time. As the IoT generates big data and AI evolves to learn how to parse through and analyze it, it’s only natural that companies will take the next step to leverage analysis to enhance their understanding of their customers’ behaviors and use it to their advantage. The IoB is where that next step will take place.

LiDAR: The Next Revolutionary Technology and What You Need to Know

In an era of rapid technological growth, certain technologies, such as artificial intelligence and the internet of things, have received mass adoption and become household names. One up-and-coming technology that has the potential to reach that level of adoption is LiDAR.

WHAT IS LIDAR?

LiDAR, or light detection and ranging, is a popular remote sensing method for measuring the exact distance of an object on the earth’s surface. Initially used in the 1960s, LiDAR has gradually received increasing adoption, particularly after the creation of GPS in the 1980s. It became a common technology for deriving precise geospatial measurements.

LiDAR requires three components: the scanner, laser, and GPS receiver. The scanner sends a pulsed laser to the GPS receiver to calculate an object’s variable distances from the earth surface. The laser emits light which travels to the ground and reflects off things like buildings, tree branches and more. The reflected light energy then returns to the LiDAR sensor where the associated information is recorded. In combination with photodetector and optics, it allows for an ultra-precise distance detection and topographical data.

WHY IS LIDAR IMPORTANT?

As we covered in our rundown of the iPhone 12, new iOS devices come equipped with a brand new LiDAR scanner. LiDAR now enters the hands of consumers who have Apple’s new generation of devices, enabling enhanced functionality and major opportunities for app developers. The proliferation of LiDAR signals toward the technology finding mass adoption and household name status.

There are two different types of LiDAR systems: Terrestrial and Airborne. Airborne LiDAR are installed on drones or helicopters for deriving an exact measurement of distance, while Terrestrial LiDAR systems are installed on moving vehicles to collect pinpoints. Terrestrial LiDAR systems are often used to monitor highways and have been employed by autonomous cars for years, while airborne LiDAR are commonly used in environmental applications and gathering topographical data.

With the future in mind, here are the top LiDAR trends to look out for moving forward:

SUPERCHARGING APPLE DEVICES

LiDAR enhances the camera on Apple devices significantly. Auto-focus is quicker and more effective on those devices. Moreover, it supercharges AR applications by greatly enhancing the speed and quality of a camera’s ability to track the location of people as well as place objects.

One of the major apps that received a functionality boost from LiDAR is Apple’s free Measure app, which can measure distance, dimensions, and even whether an object is level. The measurements determined by the app are significantly more accurate with the new LiDAR scanner, capable of replacing physical rulers, tape measures, and spirit levels.

Microsoft’s Seeing AI application is designed for the visually impaired to navigate their environment, however, LiDAR takes it to the next level. In conjunction with artificial intelligence, LiDAR enables the application to read text, identify products and colors, and describe people, scenes, and objects that appear in the viewfinder.

BIG INVESTMENTS BY AUTOMOTIVE COMPANIES

LiDAR plays a major role in autonomous vehicles, relying on a terrestrial LiDAR system to help them self-navigate. In 2018, reports suggest that the automotive segment acquired a business share of 90 percent. With self-driving cars inching toward mass adoption, expect to see major investments in LiDAR by automotive companies in 2021 and beyond.

As automotive companies look to make major investments in LiDAR, including Volkswagen’s recent investment in Aeva, many LiDAR companies are competing to create the go-to LiDAR system for automotive companies. Check out this great article by Wired detailing the potential for this bubble to burst.

LIDAR DRIVING ENVIRONMENTAL APPLICATIONS

Beyond commercial applications and the automotive industry, LiDAR is gradually seeing increased adoption for geoscience applications. The environmental segment of the LiDAR market is anticipated to grow at a CAGR of 32% through 2025. LiDAR is vital to geoscience applications for creating accurate and high-quality 3D data to study ecosystems of various wildlife species.

One of the main environmental uses of LiDAR is for soliciting topographic information on landscapes. Topographic LiDAR is expected to see a growth rate of over 25% over the coming years. These systems can see through forest canopy to produce accurate 3D models of landscapes necessary to create contours, digital terrain models, digital surface models and more.

CONCLUSION

In March 2020, after the first LiDAR scanner became available in the iPad Pro, The Verge put it perfectly when they said that the new LiDAR sensor is an AR hardware solution in search of software. While LiDAR has gradually found increasing usage, it is still a powerful new technology with burgeoning commercial usage. Enterprising app developers are looking for new ways to use it to empower consumers and businesses alike.

For supplementary viewing on the inner workings of the technology, check out this great introduction below, courtesy of Neon Science.

How AI Fuels a Game-Changing Technology in Geospatial 2.0

Geospatial technology describes a broad range of modern tools which enable the geographic mapping and analysis of Earth and human societies. Since the 19th century, geospatial technology has evolved as aerial photography and eventually satellite imaging revolutionized cartography and mapmaking.

Contemporary society now employs geospatial technology in a vast array of applications, from commercial satellite imaging, to GPS, to Geographic Information Systems (GIS) and Internet Mapping Technologies like Google Earth. The geospatial analytics market is currently valued between $35 and $40 billion with the market projected to hit $86 billion by 2023.

GEOSPATIAL 1.0 VS. 2.0

geospatial

Geospatial technology has been in phase 1.0 for centuries; however, the boon of artificial intelligence and the IoT has made Geospatial 2.0 a reality. Geospatial 1.0 offers valuable information for analysts to view, analyze, and download geospatial data streams. Geospatial 2.0 takes it to the next level–harnessing artificial intelligence to not only collect data, but to process, model, analyze and make decisions based on the analysis.

When empowered by artificial intelligence, geospatial 2.0 technology has the potential to revolutionize a number of verticals. Savvy application developers and government agencies in particular have rushed to the forefront of creating cutting edge solutions with the technology.

PLATFORM AS A SERVICE (PaaS) SOLUTIONS

Effective geospatial 2.0 solutions require a deep vertical-specific knowledge of client needs, which has lagged behind the technical capabilities of the platform. The bulk of currently available geospatial 2.0 technologies are offered as “one-size-fits-all” Platform as a Service (PaaS) solutions. The challenge for PaaS providers is that they need to serve a wide collection of use cases, harmonizing data from multiple sensors together while enabling users to simply understand and address the many different insights which can be gleaned from the data.

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In precision agriculture, FarmShots offers precise, frequent imagery to farmers along with meaningful analysis of field variability, damage extent, and the effects of applications through time.

Mayday

In the disaster management field, Mayday offers a centralized artificial intelligence platform with real-time disaster information. Another geospatial 2.0 application Cloud to Street uses a mix of AI and satellites to track floods in near real-time, offering extremely valuable information to both insurance companies and municipalities.

SUSTAINABILITY

The growing complexity of environmental concerns have led to a number of applications of geospatial 2.0 technology to help create a safer, more sustainable world. For example, geospatial technology can measure carbon sequestration, tree density, green cover, carbon credit & tree age. It can provide vulnerability assessment surveys in disaster-prone areas. It can also help urban planners and governments plan and implement community mapping and equitable housing. Geospatial 2.0 can analyze a confluence of factors and create actionable insight toward analyzing and honing our environmental practices.

As geospatial 1.0 models are upgraded to geospatial 2.0, expect to see more robust solutions incorporating AI-powered analytics. A survey of working professionals conducted by Geospatial World found that geospatial technology will likely make the biggest impact in the climate and environment field.

CONCLUSION

Geospatial 2.0 platforms are very expensive to employ and require quite a bit of development.  The technology offers great potential to increase revenue and efficiency for a number of verticals. In addition, it may be a key technology to help cut down our carbon footprint and create a safer, more sustainable world..

AIoT: How the Intersection of AI and IoT Will Drive Innovation for Decades to Come

We have covered the evolution of the Internet of Things (IoT) and Artificial Intelligence (AI) over the years as they have gained prominence. IoT devices collect a massive amount of data. Cisco projects by the end of 2021, IoT devices will collect over 800 zettabytes of data per year. Meanwhile, AI algorithms can parse through big data and teach themselves to analyze and identify patterns to make predictions. Both technologies enable a seemingly endless amount of applications retained a massive impact on many industry verticals.

What happens when you merge them? The result is aptly named the AIoT (Artificial Intelligence of Things) and it will take IoT devices to the next level.

WHAT IS AIOT?

AIoT is any system that integrates AI technologies with IoT infrastructure, enhancing efficiency, human-machine interactions, data management and analytics.

IoT enables devices to collect, store, and analyze big data. Device operators and field engineers typically control devices. AI enhances IoT’s existing systems, enabling them to take the next step to determine and take the appropriate action based on the analysis of the data.

By embedding AI into infrastructure components, including programs, chipsets, and edge computing, AIoT enables intelligent, connected systems to learn, self-correct and self-diagnose potential issues.

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One common example comes in the surveillance field. Surveillance camera can be used as an image sensor, sending every frame to an IoT system which analyzes the feed for certain objects. AI can analyze the frame and only send frames when it detects a specific object—significantly speeding up the process while reducing the amount of data generated since irrelevant frames are excluded.

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While AIoT will no doubt find a variety of applications across industries, the three segments we expect to see the most impact on are wearables, smart cities, and retail.

WEARABLES

Wearable-IoT-Devices

The global wearable device market is estimated to hit more than $87 billion by 2022. AI applications on wearable devices such as smartwatches pose a number of potential applications, particularly in the healthtech sector.

Researchers in Taiwan have been studying the potential for an AIoT wearable system for electrocardiogram (ECG) analysis and cardiac disease detection. The system would integrate a wearable IoT-based system with an AI platform for cardiac disease detection. The wearable collects real-time health data and stores it in a cloud where an AI algorithm detects disease with an average of 94% accuracy. Currently, Apple Watch Series 4 or later includes an ECG app which captures symptoms of irregular, rapid or skipped heartbeats.

Although this device is still in development, we expect to see more coming out of the wearables segment as 5G enables more robust cloud-based processing power, taking the pressure off the devices themselves.

SMART CITIES

We’ve previously explored the future of smart cities in our blog series A Smarter World. With cities eager to invest in improving public safety, transport, and energy efficiency, AIoT will drive innovation in the smart city space.

There are a number of potential applications for AIoT in smart cities. AIoT’s ability to analyze data and act opens up a number of possibilities for optimizing energy consumption for IoT systems. Smart streetlights and energy grids can analyze data to reduce wasted energy without inconveniencing citizens.

Some smart cities have already adopted AIoT applications in the transportation space. New Delhi, which boasts some of the worst traffic in the world, features an Intelligent Transport Management System (ITMS) which makes real-time dynamic decisions on traffic flows to accelerate traffic.

RETAIL

AIoT has the potential to enhance the retail shopping experience with digital augmentation. The same smart cameras we referenced earlier are being used to detect shoplifters. Walmart recently confirmed it has installed smart security cameras in over 1,000 stores.

smart-shopping-cart

One of the big innovations for AIoT involves smart shopping carts. Grocery stores in both Canada and the United States are experimenting with high-tech shopping carts, including one from Caper which uses image recognition and built-in sensors to determine what a person puts into the shopping cart.

The potential for smart shopping carts is vast—these carts will be able to inform customers of deals and promotion, recommend products based on their buying decisions, enable them to view an itemized list of their current purchases, and incorporate indoor navigation to lead them to their desired items.

A smart shopping cart company called IMAGR recently raised $14 million in a pre-Series A funding round, pointing toward a bright future for smart shopping carts.

CONCLUSION

AIoT represents the intersection of AI, IoT, 5G, and big data. 5G enables the cloud processing power for IoT devices to employ AI algorithms to analyze big data to determine and enact action items. These technologies are all relatively young, and as they continue to grow, they will empower innovators to build a smarter future for our world.