Tag Archives: AR

Smart Cities: Transforming Urban Living for a Sustainable Future

Smart City

The Internet of Behaviors (IoB) is transforming how we interact with technology and data. By leveraging insights from user behaviors, IoB aims to enhance user experiences, streamline operations, and drive innovation across various sectors. Among its most promising applications is within the realm of smart cities. IoB smart cities are designed to create more efficient, sustainable, and livable urban environments by integrating IoT devices, data analytics, and behavioral insights. In this blog, we’ll delve into what makes IoB smart cities a blueprint for the future. 

The Rise of Smart Cities

The concept of smart cities revolves around creating environments that are not only technologically advanced but also focused on improving the everyday experiences of their inhabitants. At the core, smart cities utilize a network of interconnected devices and systems—often referred to as the Internet of Things (IoT) —to gather real-time data. This extensive data collection enables city planners and administrators to make informed decisions on urban planning and management.

For instance, smart transportation systems can reduce traffic congestion and improve public transit efficiency by analyzing patterns and predicting travel demands. Similarly, smart grids help optimize energy consumption, leading to more sustainable power usage and reduced environmental impact. Waste management systems benefit from sensors that monitor bin levels and dynamically adjust collection routes, ensuring efficient resource use and cleaner urban spaces. Public services such as street lighting, emergency response, and even healthcare are enhanced through data-driven approaches that adapt to the needs of residents.

Overall, the vision of a smart city is to seamlessly integrate technology and data into the urban fabric, creating a responsive, adaptive, and holistic environment that prioritizes the well-being of its citizens while managing resources judiciously.

The Importance of IoB Smart Cities

Internet Of Behaviors

The importance of IoB smart cities cannot be overstated. They represent a critical step towards creating urban environments that are not only more efficient and sustainable but also more responsive to the needs and behaviors of their inhabitants. By harnessing the power of data, IoB smart cities can optimize resource allocation, enhance public services, and foster economic growth, ultimately improving the quality of life for their residents. As we continue to explore the potential of IoB, smart cities will undoubtedly play a significant role in shaping our future. So, it is crucial to monitor and regulate their development carefully. IoB smart cities can pave the way toward a smarter, more connected, and sustainable future with proper planning and consideration for ethical and privacy concerns. 

As we continue to innovate and expand our understanding of IoB, the potential for smart cities will only continue to grow. By embracing the principles of sustainability, efficiency, and citizen-centric design, IoB smart cities have the power to create a better world for all. With careful planning and consideration for ethical and privacy concerns, these cities can pave the way toward a smarter, more connected, and sustainable future.

Benefits of IoB Smart Cities

One of the primary advantages of IoB smart cities is their contribution to sustainability and the environment. Utilizing green energy solutions such as solar panels and wind turbines, these cities can significantly reduce their carbon footprint. Additionally, advanced waste management systems powered by IoB technologies enable efficient sorting and recycling processes, leading to reduced landfill usage. Air quality monitoring devices provide real-time data, helping to mitigate pollution and protect public health. Water conservation efforts are also enhanced through smart metering and leak detection technologies, ensuring sustainable water usage. Overall, IoB smart cities promote sustainable living and contribute to a healthier environment.

Urban Mobility

IOB Smart City

Urban mobility is another area where IoB smart cities excel. Autonomous vehicles, guided by IoB data, promise to reduce traffic congestion and lower accident rates, offering a safer and more efficient mode of transport. Innovations in public transport include real-time scheduling and route optimization based on passenger behavior and preferences. Bike-sharing programs, powered by IoB, provide a flexible and eco-friendly alternative to traditional transportation methods. Advanced traffic management systems use data analytics to adjust traffic signals dynamically, reducing wait times and improving overall traffic flow. These advancements contribute to a more streamlined and efficient urban mobility experience.

Public Safety and Security

Smart Governance

Public safety and security are paramount in IoB smart cities. Modern surveillance systems equipped with facial recognition and behavior analysis can preemptively identify and address potential security threats. Emergency response technologies, such as connected fire alarms and medical alert systems, ensure rapid assistance during crises. Cybersecurity measures are crucial to protect the vast amounts of data generated and stored within IoB ecosystems, safeguarding both infrastructure and citizen information from malicious attacks. Through these technological advancements, IoB smart cities create a safer and more secure environment for their citizens.

Healthcare

In terms of healthcare and well-being, IoB plays a transformative role. Telemedicine services allow for remote consultations, making healthcare more accessible, especially in underserved areas. Smart hospitals utilize IoB technologies for patient monitoring, resource management, and operational efficiency, improving the overall standard of care. Wearable health monitoring systems enable individuals to track their vital signs and receive personalized health advice. Public health data collected through IoB devices aid in the early detection and management of disease outbreaks, promoting a healthier community. These advancements in healthcare and well-being, powered by IoB, contribute to a better quality of life for citizens.

Smart Governance

iOB governance

Smart governance is another cornerstone of IoB smart cities. E-governance platforms facilitate seamless interaction between citizens and government bodies, improving service delivery and citizen satisfaction. Data-driven decision-making ensures that policies and initiatives are based on accurate and timely information, enhancing their efficacy. Citizen services, such as online portals for utilities and permits, simplify processes and reduce administrative burdens. Open data initiatives enable citizens to access and analyze government data, fostering transparency and accountability. Through these digital advancements, IoB smart cities promote efficient, transparent, and responsive governance.

Connectivity in IoB Communities

Smart City Grid

Communities within IoB smart cities are connected through a seamless network of IoT devices and data platforms. This interconnectedness enables real-time communication and collaboration across various sectors. For example, smart grids dynamically balance energy supply and demand, while smart street lighting adjusts based on pedestrian and vehicular traffic patterns. Public transport systems communicate with traffic management centers to optimize routes and schedules, ensuring efficient and reliable service. These connected communities also foster citizen engagement and participation through social media platforms and community forums, allowing for input and feedback on various initiatives. The connectivity in IoB communities promotes a sense of belonging and enables collaboration towards shared goals, ultimately creating a more cohesive society.

Furthermore, IoB technologies facilitate community engagement. Residents can provide feedback and report issues through mobile apps, contributing to continuous improvement and fostering a sense of ownership and participation. Social media platforms and online forums enable the sharing of experiences and ideas, strengthening community bonds.

Cons of IoB Smart Cities

Despite their numerous benefits, IoB smart cities have challenges. The reliance on extensive data collection raises significant privacy concerns. Ensuring that personal information is securely stored and ethically used is paramount to maintaining public trust. Additionally, the high cost of implementation and maintenance can be a barrier for some municipalities. Integrating diverse technologies requires substantial investment in infrastructure and ongoing operational costs. 

There is also the risk of technological obsolescence. As new technologies emerge, existing systems may become outdated quickly, necessitating continuous upgrades and investments. Interoperability issues arise when integrating multiple IoT devices and platforms, potentially leading to inefficiencies and increased complexity. Finally, the need for standardization and regulations for IoB technologies can create compliance challenges and hinder widespread adoption. Policymakers and industry leaders need to address these concerns and establish guidelines to ensure the responsible implementation of IoB in smart cities.

The Future of IoB Smart Cities

Machine Learning

Looking ahead, the potential of IoB smart cities is immense. Future developments may include even more sophisticated AI and machine learning algorithms capable of predicting and responding to urban challenges with unprecedented accuracy. Enhanced connectivity through 5G networks will provide faster and more reliable communication between devices, enabling more complex and integrated solutions. As the IoB ecosystem continues to expand, it will also create new job opportunities in fields such as data science and cyber security. However, careful consideration must be given to data privacy and security as these technologies continue to evolve. As technology continues to shape the world around us, IoB smart cities will play a crucial role in how we design and manage our urban environments. By leveraging the power of data, connectivity, and citizen engagement, these cities have the potential to become more sustainable, efficient, and responsive

We can also expect advancements in autonomous systems, from self-driving cars to drone-based delivery services, further revolutionizing urban mobility and logistics. The integration of virtual and augmented reality technologies may offer new ways for citizens to interact with their environment, from virtual city tours to augmented reality navigation aids. The possibilities are endless, and as we continue to push the boundaries of what is possible with IoB, smart cities will continue to evolve and shape the world around us. Municipalities, policymakers, and industry leaders need to work together to ensure the responsible and ethical development of IoB smart cities for the betterment of society as a whole. With careful planning, collaboration, and consideration for all stakeholders, we can create a future where technology enhances our lives and makes our cities smarter, more connected, and more sustainable than ever before.

Conclusion

IoB smart cities represent a promising vision for the future of urban living. By integrating IoT devices, data analytics, and behavioral insights, these cities can achieve greater efficiency, sustainability, and quality of life for their residents. However, addressing privacy, cost, and technological obsolescence challenges is crucial for realizing their full potential.

As we continue to explore the possibilities of IoB, the next frontier lies in its application to behavioral analysis, which will be the focus of our upcoming blog. In our next blog, we will dive deeper into how IoB revolutionizes behavioral analysis, offering new insights into human behavior and driving innovations across various sectors.

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.

 

Unleashing Molecular Magic: Machine Learning is Transforming Chemical Alchemy

machine learning and chemical alchemy

With the growing need for computational power and analytical accuracy in various industries, machine learning has created a paradigm shift in the way we process, analyze, and make decisions based on vast amounts of data. In the chemical industry specifically, machine learning has brought about tremendous benefits in the development of new chemical compounds, formulations, and processes.

Machine learning has the potential to outperform traditional methods of chemical development by learning from existing data, predicting results for new scenarios, and continuously improving through iterative processes. This accelerates discovery and reduces the cost of the development of new chemicals. Moreover, machine learning algorithms can process large amounts of data in a fraction of the time it would take for humans, allowing for quicker and more efficient decision-making.

One of the main applications of machine learning in chemical development is in the prediction and optimization of chemical properties. By analyzing large datasets of existing compounds and their properties, machine learning algorithms can identify patterns and relationships between different chemical structures and their corresponding properties. This enables researchers to predict the properties of new compounds with a high degree of accuracy, saving time and resources in the development process.

Additionally, machine learning can also aid in the design of new chemical reactions. By analyzing existing reaction data, algorithms can suggest modifications or substitutions to improve yields and reduce side reactions. This not only speeds up the research and development process but also helps in creating more efficient and sustainable chemical processes.

The Influence of Machine Learning on Chemical Development

3D chemical compound

Revolutionizing Prediction Models

Machine learning has become the vanguard of chemical prediction, breaking free from the shackles of costly and time-consuming experiments. With its cutting-edge algorithms and statistical prowess, it empowers scientists to foresee the chemical properties of compounds and materials swiftly and with unparalleled precision. The result is a renaissance of novel chemical products, previously unfathomable in their complexity. Chemists today are pushing the boundaries of optimization, attaining levels of efficiency and cost-effectiveness that were once mere dreams.

Optimizing Formulations

Machine learning algorithms are the conductors in the orchestra of chemical formulations. They masterfully direct a symphony composed from enormous data sets, creating a melodious blend of chemical innovation. The fruits of this symphonic concoction are nothing short of miraculous: self-repairing polymers, solvents capable of capturing carbon, and robust coatings that stand undeterred in the face of extreme temperatures. These advancements are pushing the very limits of what we thought possible in material innovation.

Productivity Unleashed

Machine learning algorithms act as unwavering workhorses in the field of chemical development, taking the reins of the grueling tasks, and setting free scientists to ascend on the stratosphere of innovation and strategic planning. Consequently, productivity takes flight, as research and development luxuriate in the newly discovered realm of efficiency.

Unlocking New Frontiers

Machine learning’s discerning eye sifts through the avalanche of data to unearth novel chemical applications. It is the compass guiding companies towards uncharted territories, introducing groundbreaking products that cater to the overlooked needs of customers. For instance, consider Zymergen’s alchemy, where machine learning births industrial microorganisms without a touch of conventional chemistry, birthing materials that span the realms of industry, health, personal care, and agriculture.

The Elixir of Drug Discovery

cryptic chemistry set of elixirs

Streamlining the quest for drug discovery through machine learning possesses the oracle’s vision to predict the properties of chemical compounds, simplifying the arduous journey of identifying drug candidates. This leads to a lighter financial burden and a quicker pace, promising remedies and innovations at an unprecedented pace.

Designing Remarkable Materials

In the workshop of materials, machine learning is the virtuoso. Whether it be supercharged batteries, alchemical catalysts, or avant-garde polymers, it crafts tailor-made materials with properties precisely as desired.

Conjuring Chemical Reactions

Witness the sorcerer’s apprentice in the laboratory, where the alchemy of machine learning unfolds. Behold as the algorithm predicts the outcome of chemical reactions with uncanny precision and insight. Aided by its insights, chemists cast fewer experiments into the cauldron, preserving precious time and resources.

Guardian of Safety and Compliance

The ever-vigilant guardian of chemical processes, machine learning algorithms scrutinize copious data to ensure compliance with safety regulations and environmental standards. They ward off accidents and violations, ensuring a sanctuary of safety and integrity.

Healthcare

In the realm of healthcare, machine learning adorns the fabric of personalized medicine, customizing treatments to fit the intricate patterns of an individual’s genetic makeup and medical journey. It ushers in a utopia of healthcare, where the remedy is as exceptional as the patient themselves.

Challenges and Opportunities

Human face with DNA strand

While the benefits of machine learning are manifold, the journey toward fully

 implementing it into the chemical industry is not devoid of challenges. Machine learning, as efficient as it is, craves large, quality datasets – a requirement that can be daunting in an industry where data can sometimes be scarce or inconsistent in quality.

Moreover, the complexity of ML models can sometimes obscure their interpretation, a crucial aspect when it comes to understanding chemical processes and ensuring transparency. This opacity extends to the realm of regulatory compliance, where clarity is non-negotiable. Ethical concerns also arise when machine learning is applied in personalized medicine and drug discovery, particularly around issues of privacy, consent, and data security.

Ensuring that ML models can generalize to different chemical contexts is another challenge, essential for their broad applicability and reliability. However, these challenges, while formidable, are critical to unlocking the full potential of machine learning in the chemical industry. Addressing them will be instrumental in realizing the transformative impact of machine learning in chemical development, paving the way for a more efficient, sustainable, and innovative future for the industry.

Companies that leverage machine learning in chemical development

ExxonMobil: ExxonMobil is utilizing machine learning to improve its chemical manufacturing processes, making them more efficient and sustainable. They have also partnered with MIT’s Energy Initiative to advance research in the energy sector.

Novartis: Pharmaceutical giant Novartis has adopted machine learning for drug discovery, assisting in identifying potential treatments for various diseases. They have also collaborated with Google to develop an AI-based drug discovery platform.

BASF: German chemical company BASF is harnessing machine learning for predictive maintenance, reducing downtime and improving process efficiency. They have also established partnerships with various start-ups and research institutions to explore new applications of ML in the chemical industry.

Merck: Merck & Co is deploying machine learning across its research pipeline in the Boron complex, hoping to develop new drugs at a faster and more effective rate. It is evolving machine learning technology to understand and predict molecular noise.

Zymergen: Zymergen is a startup that integrates machine learning, software engineering, and robotic automation to identify, evaluate, and optimize microbial strains for omics applications. They create innovative materials for industrial, health, personal care, and agriculture quality products.

IBM: IBM’s AI research division is making substantial strides in the intersection of machine learning and chemistry. This global technology powerhouse is exploring ways to harness the power of AI in diverse areas of chemistry, including but not limited to drug discovery, materials science, and chemical optimization. Their innovative work is helping to expedite the design and synthesis of new molecules, opening up fresh possibilities in the world of chemistry.

Schrodinger: Schrodinger is at the forefront of incorporating machine learning into chemical research. With a keen focus on accelerating the pace of drug discovery and materials research, the company presents a range of software solutions that leverage machine learning to simulate and predict molecular properties. Their platform deftly combines computational capabilities with deep scientific insights, allowing researchers to delve deeper into the world of molecules and their myriad potentials.

Future Possibilities

Using VR to create new chemical compounds

The prospective applications of machine learning in the field of chemical development are vast and exciting. Here’s a glimpse into the directions where machine learning is poised to make substantial contributions:

Innovative Material Discoveries: Thanks to ML algorithms, we could see unprecedented materials with specially tailored properties, stirring innovations in domains like energy storage, catalysis, and sophisticated materials.

Environmental Considerations: ML technology could assist in making chemical processes greener by reducing waste, decreasing emissions, and ensuring adherence to strict environmental policies.

Robotics Integration: The fusion of machine learning with robotics could lead to self-governing experimentation, with robots executing experiments round-the-clock and using AI to assess outcomes and make informed decisions.

Energy Efficiency and Sustainability: ML can aid in the development of sustainable energy solutions and greener chemistry, which is essential for climate change mitigation.

Quantum Computing Integration: The amalgamation of quantum computing with machine learning could push the boundaries of solving intricate chemical problems by simulating quantum systems more effectively.

Enhanced Research Collaboration: Machine learning could facilitate faster analysis and sharing of research findings and data on a global scale, fostering worldwide collaboration among institutions.

alchemy set

In summary, the future trajectory of machine learning in chemical development points towards significant strides in research, innovation, and sustainability, steering the chemical industry towards heightened efficiency and environmental responsibility. As more companies and institutions explore the potential of machine learning, we can expect to see even more groundbreaking applications in this dynamic field. With its transformative capabilities, machine learning is paving the way for a brighter future for chemistry and all its allied industries.

Machine learning has emerged as a powerful tool in chemical development, providing significant benefits to the industry’s efficiency, accuracy, and innovation. The integration of machine learning and chemical development has opened up unprecedented possibilities that could revolutionize the world. In conclusion, it’s not a matter of whether machine learning will shape the chemistry industry but more of when and how it would shape the industry. Companies that do not integrate machine learning into their chemical processes risk being left behind in what is sure to become a volatile and dynamic market.

With its potential for groundbreaking advancements and significant contributions to sustainability, it’s clear that machine learning has a bright future in the world of chemistry. So, buckle up and get ready to witness the unprecedented transformation of the chemical industry with machine learning at its core.

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.

The Future of Professional Sports: Augmented Reality

Augmented Reality In Sports

The world of professional sports has always been at the forefront of utilizing cutting-edge technologies to enhance the experience of fans and improve team performance. One of the most exciting emerging technologies in this space is augmented reality (AR), which has the potential to revolutionize the way sports are played and viewed. Augmented reality involves overlaying digital information and images onto the real world, in real-time, through a device like a smartphone or AR headset. In this blog post, we will explore the technical side of AR in professional sports, including examples of teams that are already using AR to gain a competitive edge on the field.

Player Training and Performance Analysis

AR is already being used by professional sports teams to train and analyze player performance. For example, some basketball teams are using AR technology to track the shooting accuracy of their players during practice sessions. By overlaying digital targets and data onto a real basketball court, players can see how accurate their shots are and adjust their techniques accordingly. In football, AR is being used to simulate game scenarios and support off-field training for players. Coaches can use apps like NFL Game Theory to create plays and test them out in a digital environment. This allows players to become more familiar with different scenarios and improve their decision-making skills.

Enhancing Fan Engagement and Experience

In terms of fan engagement and experience, AR has opened up exciting new possibilities for professional sports teams. Manchester City FC, for instance, launched an AR app called “CityVR” in 2019 that allowed fans to explore their Etihad Stadium in 360 degrees, access exclusive content, and engage with the team in a fresh, immersive way. Similarly, the NBA’s Golden State Warriors used AR to improve fan engagement by bringing fans closer to the team’s pre-game rituals and player interactions through an official mobile app.

Several NFL teams, like the Tampa Bay Buccaneers and Baltimore Ravens, have also harnessed the power of AR to bring team mascots or famous players into fans’ surroundings through their mobile apps. Meanwhile, FC Barcelona enabled fans to interact with live AR stats, player statistics, and take a virtual tour of the iconic Camp Nou Stadium via their “Barça Live” AR app.

The Los Angeles Dodgers took the AR experience to the next level by providing AR glasses to fans during their games, overlaying real-time player statistics and information onto their view of the field. The San Francisco 49ers have also utilized AR in player training, developing a VR/AR-based program called “VRtize” to enhance game scenario understanding and decision-making among players.

The New York Yankees used AR to create interactive experiences for fans such as virtual tours of Yankee Stadium, while Formula 1 infused the fan experience with AR, enabling access to live data, track positions, and driver information during races via their F1 AR app. Various NHL and MLB teams have similarly leveraged AR to engage fans with initiatives like the Minnesota Wild’s AR app for photos with virtual players and the Boston Red Sox’s AR-based scavenger hunt within Fenway Park.

These diverse examples demonstrate how professional sports teams are leveraging augmented reality to connect with fans, enhance player performance, and create unique, interactive experiences both inside and outside the stadium. As AR technology continues to evolve, it is slated to play a significant role in shaping the future of sports entertainment.

Virtual Advertising

AR also provides a new way for teams to monetize their advertising real estate. Virtual advertising involves overlaying digital advertisements onto the real-world environment. This has the potential to revolutionize the way teams approach sponsorship deals, as they can now sell virtual ad space rather than relying solely on traditional advertising methods. For example, during an NFL game, virtual advertisements could be overlaid onto the field, visible to TV viewers but not to fans in the stadium.

AR-Enhanced Stadiums

Looking to the future, we can expect to see more stadiums and arenas incorporate AR technology directly into their architecture. For example, the forthcoming home stadium for the Golden State Warriors will include AR screens in its luxury suites, giving fans a more immersive experience during games. The Australian National Rugby League is also preparing to rollout AR technology in its stadiums, with the goal of enhancing the viewing experience for fans.

Challenges and Limitations

While AR has the potential to revolutionize professional sports, there are still challenges and limitations that must be overcome. One of the biggest issues is the cost and complexity of implementing AR technology. It requires significant investment in both hardware and software, as well as the expertise to develop and maintain AR applications. There are also concerns around data privacy and security, as AR applications often collect sensitive personal information.

In conclusion, augmented reality has the potential to significantly impact the world of professional sports, providing players with new training and analysis tools, fans with a more immersive viewing experience, and teams with new sources of revenue. However, there are still challenges and limitations that need to be overcome before AR becomes mainstream in this space. The good news is that we are already seeing some examples of teams successfully implementing AR, and as the technology becomes more advanced and accessible, we can expect to see even more exciting applications emerging. As always, staying ahead of the curve and embracing new technologies will be critical for maintaining a competitive edge in professional sports.

How 5G Is Unlocking the Hidden Potential of Mobile Gaming

5G is the fastest growing mobile generation of all time. There are 236 active 5G subscriptions with 3 billion 5G subscriptions anticipated by 2025. The vastly improved speed will have a transformative impact on a number of technologies—especially mobile gaming.

The game industry generates over $175 billion in revenue. Mobile gamers constitute over 2.6 billion mobile gamers worldwide. Mobile gaming is big business and 5G stands to unlock even more hidden potential in a variety of ways.

SAY GOODBYE TO LATENCY

Imagine thousands of players simultaneously playing Call of Duty on mobile devices with no lag and professional-level speed. Mobile operators like Ericsson are pushing the 5G revolution everyday to make these scenarios a reality.

In competitive gaming, latency can make or break a game. Online gamers can easily become frustrated and churn if latency ruins their experience. With speeds up to 10Gbps, 5G delivers 10 times the speed of 4G LTE. Response times as low as 5 milliseconds will virtually eliminate lag between input and response. As 5G becomes more reliable, it will hold true even for massive online games with a large number of competitors.

In a recent survey conducted by OpenSignal on the best networks for gaming experiences, mmWave 5G led the way—ahead of private wi-fi networks.

OFFLOADING PROCESSING

For IoT developers, 5G’s ability to bolster cloud computing poses major opportunities. 5G enables simple devices and sensors to complete complex tasks by offloading major processing duties to the cloud.

Mobile gaming can similarly benefit from the raw speed of 5G. Higher bandwidth and lower response times open up the possibility of offloading key processes. Games will be able to handle more arduous rendering duties remotely, producing a higher quality stream. With the cloud carrying the workload, the quality and age of the user’s device would matter significantly less provided the user has a 5G data plan. Gamers will still be able to expect the same high-quality experiences regardless of whether they have the latest smartphone.

AR AND VR BOON

Massive increases in streaming speed and cloud processing power will enable AR and VR developers to take their projects to the next level. Expect to see bigger, more detailed and immersive virtual worlds. For AR and VR apps to realize their potential, data needs to be rendered at the moment of decision-making. Lag and latency take users out of what’s intended to be the pinnacle of immersive gaming. The speeds of 5G will trigger a momentous evolution for AR and VR gaming.

CAN MOBILE GAMING OVERTAKE CONSOLES?

Smartphones provide game developers with the broadest platform and userbase. The bolstered processing power provided by smartphones with 5G will enable mobile devices to provide graphics on par with consoles. However, before mobile gaming can dethrone console gaming in terms of popularity, there are a few roadblocks.

While some major console games are available on mobile devices, the vast majority are not. Gamers are not at the point where they expect the same games on their mobile devices. Until game developers can effectively transfer major franchises to mobile devices, console gaming will remain king.

One of the major obstacles facing mobile gaming is the controller. iOS and Android touch screens simply cannot match the precision of playing on an Xbox Series X or PS5. Gamepads like the Backbone One latch onto smart devices to enable more fluid controls, but they also take away the mobile aspect of games since they are arduous to carry. Furthermore, they require every game to map inputs to each controller’s layout. The controller poses a simple but major problem and until it’s addressed, console gaming will provide more reliable experiences.

How Business Owners Can Leverage the Metaverse to Turn a Profit

As many have heard, Facebook’s highly publicized rebranding as Meta in 2021 signaled their long-term expansion from social media into VR, the metaverse, and more. In the process, they launched the metaverse into a household name technology. The $500 million virtual real estate boom which proceeded only further hammered the point home—as far as Meta is concerned, the metaverse is the future. With VR and AR technologies developing at an astounding rate, businesses are entering the metaverse intent on generating revenue while the technology remains in its nascent stage.

The metaverse is here to stay—the question for business owners is: how can one take advantage of it? There is a long list of apps that use AR features to enhance the shopping experience. Sephora’s Virtual Artist app enables users to view how makeup will look before trying it on. The Ikea Place application enables users to view how furniture will look in their house before they place them.

The big question is: should you as a business owner delve into the metaverse? And if so, how can you leverage a metaverse presence to generate commerce? In this week’s blog, we present a few ideas to consider when making your decision.

REAL ESTATE

The first step to a metaverse presence is the acquisition of a virtual space for the business. Luckily, metaverse real estate has proven to be an extraordinarily profitable investment in and of itself. Armando Aguilar told Coindesk recently that the price of his three-bedroom, two-bathroom home outside New York City has appreciated two and a half times, while his metaverse property surged by 1,400% during that same time.

When purchasing real estate within the metaverse, consider which platform you’d like to see your company grow in. There’s a long list of options, from Sandbox to Decentraland, each with their own pros and cons.

When choosing a platform, consider which platform will provide the most short-term gains for your customers. Where do you anticipate long-term value? Investing wisely will lead to revenue as the price of virtual property rises.

For a list of the top metaverse platforms, check out XR Today’s list of the top metaverse platforms to watch in 2022.

EXPAND YOUR PHYSICAL PRESENCE

The metaverse presents an opportunity for companies to translate their brand into a virtual experience. Similar to the early aughts of the internet, companies which fail to capitalize on the new channel may lose money to companies that adopted the metaverse faster.

The metaverse differs from the internet in that it enables businesses to replicate three dimensional, physical spaces. This is a huge leap as we are physical beings and crave physical experiences.

While nothing can replace the physical retail experience, a great deal of commerce is generated by monetizing convenience. Delivery services like Amazon and Seamless generate billions of dollars in revenue by offering a variety of products to their customers through their digital presence, cutting out the need to stray from the home.

The metaverse capitalizes on our desire for physical experiences while enabling us to access them from the comfort of our couch. Rather than recreate their websites, businesses need to think about how they can create a physical experience for their customers.

Five years down the line, businesses will likely require a team of consultants and developers to build the metaverse experience. Check out the video below showing how Walmart created a physical shopping experience in the metaverse.

NFTs AND THE METAVERSE

Commerce in the metaverse will incorporate cryptocurrency and NFTs. NFTs, or nonfungible tokens, are unique digital files which function as certificates of ownership verified by the blockchain. They can be associated with any number of things—from sneakers, to songs and works of art. Although the market shows volatility similar to cryptocurrency, NFT value can appreciate by quite a bit.

NFTs represent a new buying channel that’s crucial to the metaverse. Creating exclusive experiences in the metaverse will draw your customers in.

Offering exclusive NFTs is a way to both create value and boost engagement with your customers. Adidas recently took advantage of this, generating over $23 million in ethereum in their first NFT drop.

Consider incorporating NFT’s alongside physical product drops. For example, offer an NFT to customers who enter the metaverse to make early pre-orders of future releases.

The metaverse is an opportunity to drive engagement and create value by offering personalized, exclusive experiences to your customers. NFTs are the perfect method to achieve this.

CONCLUSION

The metaverse poses a business opportunity for enterprises unafraid of innovation. What kind of experience can you deliver to your customers in the metaverse that you cannot in any other realm? Businesses which manage to answer that question will be rewarded with long-term profit.

Learn How Google Bests ARKit with Android’s ARCore

Previously, we covered the strengths of ARKit 4 in our blog Learn How Apple Tightened Their Grip on the AR Market with the Release of ARKit 4. This week, we will explore all that Android’s ARCore has to offer.

All signs point toward continued growth in the Augmented Reality space. As the latest generations of devices are equipped with enhanced hardware and camera features, applications employing AR have seen increasing adoption. While ARCore represents a breakthrough for the Android platform, it is not Google’s first endeavor into building an AR platform.

HISTORY OF GOOGLE AR

In summer 2014, Google launched their first AR platform Project Tango.

Project Tango received consistent updates, but never achieved mass adoption. Tango’s functionality was limited to three devices which could run it, including the Lenovo Phab 2 Pro which ultimately suffered from numerous issues. While it was ahead of its time, it didn’t receive the level of hype ARKit did. In March 2018, Google announced that it will no longer support Project Tango and that the tech titan will be continuing AR Development with ARCore.

ARCORE

ARCore uses three main technologies to integrate virtual content with the world through the camera:

  • Motion tracking
  • Environmental understanding
  • Light estimation

It tracks the position of the device as it moves and gradually builds its own understanding of the real world. As of now, ARCore is available for development on the following devices:

ARCORE VS. ARKIT

ARCore and ARKit have quite a bit in common. They are both compatible with Unity. They both feature a similar level of capability for sensing changes in lighting and accessing motion sensors. When it comes to mapping, ARCore is ahead of ARKit. ARCore has access to a larger dataset which boosts both the speed and quality of mapping achieved through the collection of 3D environmental information. ARKit cannot store as much local condition data and information. ARCore can also support cross-platform development—meaning you can build ARCore applications for iOS devices, while ARKit is exclusively compatible with iOS devices.

The main cons of ARCore in relation to ARKit mainly have to do with their adoption. In 2019, ARKit was on 650 million devices while there were only 400 million ARCore-enabled devices. ARKit yields 4,000+ results on GitHub while ARCore only contains 1,400+. Ultimately, iOS devices are superior to software-driven Android devices—particularly given the TrueDepth Camera—meaning that AR applications will run better on iOS devices regardless of what platform they are on.

OVERALL

It is safe to say that ARCore is the more robust platform for AR development; however, ARKit is the most popular and most widely usable AR platform. We recommend spending time determining the exact level of usability you need, as well as the demographics of your target audience.

For supplementary reading, check out this great rundown of the best ARCore apps of 2021 from Tom’s Guide.

Learn More About Triggering Augmented Reality Experiences with AR Markers

We expect a continued increase in the utilization of AR in 2021. The iPhone 12 contains LiDAR technology, which enables the use of ARKit 4, greatly enhancing the possibilities for developers. When creating an AR application, developers must consider a variety of methods for triggering the experience and answer several questions before determining what approach will best facilitate the creation of a digital world for their users. For example, what content will be displayed? Where will this content be placed, and in what context will the user see it?

Markerless AR can best be used when the user needs to control the placement of the AR object. For example, the IKEA Place app allows the user to place furniture in their home to see how it fits.

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Location-based AR roots an AR experience to a physical space in the world, as we explored previously in our blog Learn How Apple Tightened Their Hold on the AR Market with the Release of ARKit 4. ARKit 4 introduces Location Anchors, which enable developers to set virtual content in specific geographic coordinates (latitude, longitude, and altitude). To provide more accuracy than location alone, location anchors also use the device’s camera to capture landmarks and match them with a localization map downloaded from Apple Maps. Location anchors greatly enhance the potential for location-based AR; however, the possibilities are limited within the 50 cities which Apple has enabled them.

Marker-based AR remains the most popular method among app developers. When an application needs to know precisely what the user is looking at, accept no substitute. In marker-based AR, 3D AR models are generated using a specific marker, which triggers the display of virtual information. There are a variety of AR markers that can trigger this information, each with its own pros and cons. Below, please find our rundown of the most popular types of AR markers.

FRAMEMARKERS

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The most popular AR marker is a framemarker, or border marker. It’s usually a 2D image printed on a piece of paper with a prominent border. During the tracking phase, the device will search for the exterior border in order to determine the real marker within.

Framemarkers are similar to QR Codes in that they are codes printed on images that require handheld devices to scan, however, they trigger AR experiences, whereas QR codes redirect the user to a web page. Framemarkers are a straightforward and effective solution.

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Framemarkers are particularly popular in advertising applications. Absolut Vodka’s Absolute Truth application enabled users to scan a framemarker on a label of their bottle to generate a slew of more information, including recipes and ads.

GameDevDad on Youtube offers a full tutorial of how to create framemarkers from scratch using Vuforia Augmented Reality SDK below.

 

NFT MARKERS

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NFT, or Natural Feature Tracking, enable camera’s to trigger an AR experience without borders. The camera will take an image, such as the one above, and distill down it’s visual properties as below.

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The result of processing the features can generate AR, as below.

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The quality and stability of these can oscillate based on the framework employed. For this reason, they are less frequently used than border markers, but function as a more visually subtle alternative. A scavenger hunt or a game employing AR might hide key information in NFT markers.

Treasury Wine Estates Living Wine Labels app, displayed above, tracks the natural features of the labels of wine bottles to create an AR experience which tells the story of their products.

OBJECT MARKERS

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The  toy car above has been converted into an object data field using Vuforia Object Scanner.

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Advancements in technology have enabled mobile devices to solve the issue of SLAM (simultaneous localization and mapping). The device camera can extract information in-real time, and use it to place a virtual object in it. In some frameworks, objects can become 3D-markers. Vuforia Object Scanner is one such framework, creating object data files that can be used in applications for targets. Virtual Reality Pop offers a great rundown on the best object recognition frameworks for AR.

RFID TAGS

Although RFID Tags are primarily used for short distance wireless communication and contact free payment, they can be used to trigger local-based virtual information.

While RFID Tags are not  widely employed, several researchers have written articles about the potential usages for RFID and AR. Researchers at the ARATLab at the National University of Singapore have combined augmented reality and RFID for the assembly of objects with embedded RFID tags, showing people how to properly assemble the parts, as demonstrated in the video below.

SPEECH MARKERS

Speech can also be used as a non-visual AR marker. The most common application for this would be for AR glasses or a smart windshield that displays information through the screen requested by the user via vocal commands.

CONCLUSION

Think like a user—it’s a staple coda for app developers and no less relevant in crafting AR experiences. Each AR trigger offers unique pros and cons. We hope this has helped you decide what is best equipped for your application.

In our next article, we will explore the innovation at the heart of AIoT, the intersection of AI and the Internet of Things.

Learn How Apple Tightened Their Hold on the AR Market with the Release of ARKit 4

Since the explosive launch of Pokemon Go, AR technologies have vastly improved. Our review of the iPhone 12 concluded that as Apple continues to optimize its hardware, AR will become more prominent in both applications and marketing.

At the 2020 WWDC in June, Apple announced ARKit 4, their latest iteration of the famed augmented reality platform. ARKit 4 features some vast improvements that help Apple tighten their hold on the AR market.

LOCATION ANCHORS

ARKit 4 introduces location anchors, which allow developers to set virtual content in specific geographic coordinates (latitude, longitude, and altitude). When rebuilding the data backend for Apple Maps, Apple collected camera and 3D LiDAR data from city streets across the globe. ARKit downloads the virtual map surrounding your device from the cloud and matches it with the device’s feed to determine your location. The kicker is: all processing happens using machine learning within the device, so your camera feed stays put.

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Devices with an A12 chip or later, can run Geo-tracking; however, location anchors require Apple to have mapped the area previously. As of now, they are supported in over 50 cities in the U.S. As the availability of compatible devices increases and Apple continues to expand its mapping project, location anchors will find increased usage.

DEPTH API

ARKit’s new Depth API harnesses the LiDAR scanner available on iPad Pro and iPhone 12 devices to introduce advanced scene understanding and enhanced pixel depth information in AR applications. When combined with 3D mesh data derived from Scene Geometry, which creates a 3D matrix of readings of the environment, the Depth API vastly improves virtual object occlusion features. The result is the instant placement of digital objects and seamless blending with their physical surroundings.

FACE TRACKING

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Face tracking has found an exceptional application in Memojis, which enables fun AR experiences for devices with a TrueDepth camera. ARKit 4 expands support to devices without a camera that has at least an A12. TrueDepth cameras can now leverage ARKit 4 to track up to three faces at once, providing many fun potential applications for Memojis.

VIDEO MATERIALS WITH REALITYKIT

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ARKit 4 also brings with it RealityKit, which adds support for applying video textures and materials to AR experiences. For example, developers will be able to place a virtual television on a wall, complete with realistic attributes, including light emission, texture roughness, and even audio. Consequentially, AR developers can develop even more immersive and realistic experiences for their users.

CONCLUSION

iOS and Android are competing for supremacy when it comes to AR development. While the two companies’ goals and research overlap, Apple has a major leg up on Google in its massive base of high-end devices and its ability to imbue them with the necessary structure sensors like TrueDepth and LiDAR.

ARKit has been the biggest AR development platform since it hit the market in 2017. ARKit 4 provides the technical capabilities tools for innovators and creative thinkers to build a new world of virtual integration.