Tag Archives: Development

Unleashing Molecular Magic: Machine Learning is Transforming Chemical Alchemy

November 14, 2023Artificial Intelligence & Machine Learning, Augmented Reality, Healthcarealchemy, AR, Chemical Alchemy, chemistry, Development, Machine Learning, ML, Technologyadmin

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

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

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

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

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.

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.

Machine Learning Unlocks Quantum Potential: A Paradigm-Shifting Partnership

November 1, 2023Artificial Intelligence & Machine LearningAI, Artificial Intelligence & Machine Learning, Development, ML, Smart, Software, Technologyadmin

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.

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.

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.

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

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.

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.

How 5G Is Unlocking the Hidden Potential of Mobile Gaming

May 25, 2022Android Development, Gaming & Monetization Strategies, iOS Development5G, Android, AR, Cloud, Console, Development, Game, IOS, IoT, Mobile, Network, Processing, Reality, Virtual, VRadmin

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.

Mystic Media Announced as a 2022 Local Excellence Award Winner by UpCity!

May 17, 2022Android Development, Digital Marketing, iOS DevelopmentApp, B2B, Development, Digital, Marketing, Mobile, Software, UpCityadmin

For more than a decade, UpCity’s mission has been—and continues to be—to help businesses find B2B service providers they can trust. The UpCity Recommendability Rating was developed to determine a service provider’s credibility and recommendability, giving UpCity the confidence to recommend them to the more than 2 million businesses that visit their site.

Each year, UpCity analyzes and scores more than 70,000 service providers based on their UpCity Recommendability Rating and acknowledges the top national and local providers with an UpCity Excellence Award. The results are in, and we won!

We are proud to announce that the Mystic Media team has been recognized as one of the top B2B service providers of 2022 in the Salt Lake City area by UpCity!

Joe Banks, SVP of Engineering at UpCity, had this to say about Mystic Media:

“The team at Mystic Media brings decades of combined experience and quality that helps them stay ahead of the curve in all things digital. We are proud to recognize them with a 2022 Local Excellence Award. Congratulations!” —Joe Banks, SVP of Engineering, UpCity

This recognition has been driven in large part by our 4.9-star review rating on UpCity. Here are a few of our favorite pieces of feedback we’ve received from our incredible customers:

- - “This was a joint effort where we developed the hardware interface between our controller and Mystic Media developed the iOS app. Consequently, there was a lot of information exchange and testing during the process. The basic user interface was completed by Mystic Media in very short order. The rest of the development was the implementation of the various inputs and outputs. The interface retrieves and passes to the door controller via an RS485 communication port. The commands the app sends to the controller via the interface are in hexadecimal. I was very pleased with the speed and efficiency of the development and the ‘can do’ attitude of Mystic Media. They are very professional, respectful, and easy to work with. I would use them again.” – Carl Goodman, June 2021

- - “We hired Mystic for a large and complex project and are very happy with our experience. We were well out of our element with only an idea and a rough one at that. Their creative and knowledgeable team took our idea and pulled us into a process that was efficient and truly felt like a partnership. They care and want to help us see our vision through. We are currently very close to completion and I look forward to them being a valuable part of our journey forward!” – Russell Taylor, June 2021

Throughout the changes our industry has seen, the one thing that never gets old is seeing our clients succeed. We are so grateful for the collaboration opportunities we’ve had over the years and are honored to receive this recognition.

Learn more about the UpCity Excellence Awards.

How Apple & Google Are Enhancing Battery Life and What We as App Developers Can Do to Help

March 7, 2022Android Development, iOS DevelopmentAndroid, Apple, Application, Battery, Developer, Development, Device, Google, Graphene, Guide, Hardware, IOS, Lithium, Optimization, Software, Trend, Wake Lockadmin

In 1799, Italian physicist Alessandro Volta created the first electrical battery, disproving the theory that electricity could only be created by human beings. Fast forward 250 years, brands like Duracell and Energizer popularized alkaline batteries—which are effective, inexpensive and soon become the key to powering household devices. In 1991, Sony released the first commercial rechargeable lithium-ion battery. Although lithium-ion batteries have come a long way since the 90s, to this day they power most smartphones and many other modern devices.

While batteries have come a long way, so have the capabilities of the devices which need them. For consumers, battery life is one of the most important features when purchasing hardware. Applications which drain a device’s battery are less likely to retain their users. Software developers are wise to understand the latest trends in battery optimization in order to build more efficient and user-friendly applications.

HARDWARE

Lithium-ion batteries remain the most prevalent battery technology, but a new technology lies on the horizon. Graphene batteries are similar to traditional batteries, however, the composition of one or both electrodes differ. Graphene batteries increase electrode density and lead to faster cycle times as well as the ability to improve a battery’s lifespan. Samsung is allegedly developing a smartphone powered by a graphene battery that could fully charge its device within 30 minutes. Although the technology is thinner, lighter, and more efficient, production of pure graphene batteries can be incredibly expensive, which may inhibit its proliferation in the short-term.

Hardware companies are also coming up with less technologically innovative solutions to improve battery life. Many companies are simply attempting to cram larger batteries into devices. A more elegant solution is the inclusion of multiple batteries. The OnePlus 9 has a dual cell battery. Employing multiple smaller batteries means both batteries charge faster than a single cell battery.

SOFTWARE

Apple and Google are eager to please their end-users by employing techniques to help optimize battery life. In addition, they take care to keep app developers updated with the latest techniques via their respective developer sites.

Android 11 includes a feature that allows users to freeze apps when they are cached to prevent their execution. Android 10 introduced a “SystemHealthManager” that resets battery usage statistics whenever the device is unplugged, after a device is fully charged or goes from being mostly empty to mostly charged—what the OS considers a “Major charging event”.

Apple has a better track record of consuming less battery than Android. iOS 13 and later introduced Optimized Battery Charging, enabling iPhones to learn from your daily charging routine to improve battery lifespan. The new feature prevents iPhones from charging up to 100% to reduce the amount of time the battery remains fully charged. On-site machine learning then ensures that your battery is fully charged by the time the user wakes up based on their daily routines.

Apple also offers a comprehensive graph for users to understand how much battery is being used by which apps, off screen and on screen, under the Battery tab of each devices Settings.

WHAT APPLICATION DEVELOPERS CAN DO

App developers see a 73% churn rate within the first 90 days of downloading an app, leaving very little room for errors or negative factors like battery drainage. There are a number of techniques application developers can employ in their design to reduce and optimize battery-intensive processes.

It’s vital to review each respective app store’s battery saving standards. Both Android and Apple offer a variety of simple yet vital tips for reducing battery drain—such as limiting the frequency that an app asks for a device’s location and inter-app broadcasting.

One of the most important tips is to reduce the frequency of network refreshes. Identify redundant operations and cut them out. For instance, can downloaded data be cached rather than using the radio repeatedly to re-download it? Are there tasks that can be deferred by the app until the device is charging? Backing up data to the cloud can consume a lot of battery on a task that is not always time sensitive.

Wake locks keep the phone’s screen on when using an app. There was a time where wake locks were frequently employed—but now it is frowned upon. Use wake locks only when absolutely necessary—if at all.

CONCLUSION

Software developers need to be attentive to battery drain throughout the process of building their application. This begins at conception, through programming, all the way into a robust testing process to identify potential battery drainage pitfalls. Attention to the details of battery optimization will lead to better, more user-friendly applications.

Part 3: Techniques to Keep Users Coming Back & Increase Retention

February 8, 2022Android Development, Gaming & Monetization Strategies, iOS DevelopmentApplication, Content, Design, Development, Feature, Follow, Friend, Gamification, Notification, Push, Retention, Social, Software, Updateadmin

How Gamification Can Boost Retention on Any App Part 3: Techniques to Keep Users Coming Back & Increase Retention

The Mystic Media Blog is currently endeavoring on a 3 part series on how gamification mechanics can boost retention on any app—not just gaming apps but utility apps, business apps and more. In this third entry, we explore additional techniques to keep users coming back and increase retention.

Your users have downloaded your app and are acclimated with its features. You’ve perfected your core loop to ensure users can complete meaningful actions in the app on a daily basis. Now the question becomes—how can you retain ongoing usage? The average cost to acquire a mobile app user is $4, yet retention rates can quickly drop from there. Statistics show that a 5% increase in retention can boost profitability by up to 75%.

There are a variety of techniques employed by mobile games that app developers can use in their non-gaming apps to keep users engaged long after the application ends.

INVEST IN THE FUTURE

An optimized application development process requires thinking about how your product can evolve beyond the initial release. Often this is due to schedule and budgetary constraints. It is natural in any creative endeavor to have more ideas than time and money to complete them. However, thinking long-term can be an advantage. New features entice users to continue using the application after download and to allow push notifications for fear of missing out on updates.

Mobile games often have to confront this since the amount of content they offer is finite—a certain amount of levels, achievements, and unlockables which can be completed. Games can offer additional modes and levels to entice users to come back. Similarly, non-gaming apps can offer new content—such as informative blogs, new features, and new product lines.

During the development process, plan out multiple phases and deliver new features and content updates on a regular basis. If you have a blog, host it on your application and keep users coming back for content updates.

IMPLEMENT SOCIAL FEATURES

Game developers know that “Socializers”, or users who thrive on social interaction, constitute one of the most important Bartle Types. Social features are crucial not only to retaining interest and daily usage of an application, but as a marketing technique to encourage users to engage with one another and spread the word. Once your userbase is established, implementing social features will increase engagement.

Consider implementing the following social features in phase 2 of your application:

Customizable user profiles: Enabling usernames, profile pictures, bios and other user customization features help users feel more connected to the app vis a vis their profile.
Rewarded social sharing: Encourage users to spread the love by rewarding them with discounts and reward points when they share to social media.
Likes and comments on products: Implementing comments and likes not only gives users another avenue for engagement, it creates a platform for automated push notifications that will likely result in more daily opens.
Follow and friend other users: Allowing users to connect can result in meaningful social relationships which will increase their connection with your application.
Rewarded actions: Encourage users to complete an action for the first time by offering them some kind of reward.

PUSH NOTIFICATIONS STRATEGY

Push notifications are integral to every app developers’ retention strategy. They are the most effective vessel for delivering timely reminders and relevant notifications about new features on applications. Users can disallow push notifications at any time, so developers need to pick their spots or risk losing one of their most prized tools.

When developing your push notification strategy, consider the following:

Timing: Rather than sending push notifications all at once, target users based on their time zone. Make sure the timing of your notifications makes sense based on the message.
Personalization: Optimize UI by tracking app usage data and leveraging it for personalized push notifications. Personalize push notifications based on a user’s behavior such as their purchase history to help build app loyalty and keep notifications relevant.
Prudence: If you bombard users with irrelevant notifications, the decision to unsubscribe to push notifications becomes easy. Exercise restraint when sending push notifications and only send valuable information and reminders.

Users are always looking for value and discount—which is why delivery and transportation applications often use push notifications to send discount codes. Shopping apps can also send push notifications which notify users when they have items left in their cart—a timely prompt to finish the purchase can directly lead to revenue.

KEEP INNOVATING

The app development process does not have to end with an apps initial release into app stores. Rolling out new features to maintain engagement with your audience and bolster your application will result in improved retention.

Part 2: Optimize Onboarding with Gamification

January 19, 2022Android Development, Gaming & Monetization Strategies, iOS DevelopmentAndroid, App, Application, Business, Design, Development, Experience, Gamification, Gaming, Interface, IOS, Mechanics, Mobile, Onboarding, Registration, Software, Tutorial, UI, User, UXadmin

How Gamification Can Boost Retention on Any App Part 2: Optimize Onboarding with Gamification

The Mystic Media Blog is currently endeavoring on a 3 part series on how gamification mechanics can boost retention on any app—not just gaming apps but utility apps, business apps and more. In this second entry, we explore how to refine and gamify your onboarding process to keep customers coming back.

ONBOARDING

Your app has been downloaded—a hard-fought battle in and of itself—but the war isn’t over; the onboarding process has just begun.

App onboarding is the first point of contact a user has within an application. It’s one of the most crucial parts of the user experience. Situating users in your application is the first step to ensuring they come back. Twenty-five percent of apps are only opened once after being downloaded. Many apps simply do not make it simple enough for users to understand the value and get the hang of the application—step one in your retention process.

Here are the top tips for smooth onboarding:

MINIMIZE REGISTRATION

A prolonged registration process can turn off new users. Users do not always have time to fill out extensive forms and can quickly become resentful of the pacing of your app. Keep registration to a minimum, minimize required fields, and get users going faster.

We recommend enabling user registration altogether with “Continue as Guest” functionality. Games typically employ this and it enables users to get hands on with the application before they undergo the tenuous account creation process. Hook them with your app, then let them handle the administrative aspects later. Account creation with Google, Facebook, or Twitter can also save quite a bit of time.

Gamification is all about rewarding the user. Offer users an incentive to create their account to positively reinforce the process and you will see more accounts created. If they haven’t created an account, make sure to send prompts to remind them of what the reward they are missing out on. As we detailed in our last entry, FOMO is a powerful force in gamification.

TUTORIAL BEST PRACTICES

When a user enters your application for the first time, they generally need a helping hand to understand how to use it. Many games incorporate interactive tutorials to guide the user through functionality—and business apps are wise to use it as well. However, an ineffective tutorial will only be a detriment to your application.

Pacing is key. A long tutorial will not only bog the onboarding process down, too much information will likely go in and out of the user’s brain. Space your tutorial out and break it into different sections introducing key mechanics as they become relevant. On-the-go tutorials like the four-screen carousel below by Wavely help acclimate users quickly and easily.

And don’t forget to offer a reward! Offer users some kind of reward or positive reinforcement upon completing tutorials to encourage them to continue using the application.

AVOID DEAD ENDS AND EMPTY STATES

An empty state is a place in an application that isn’t populated with any information. For example, favorites, order history, accomplishments, etc.—these pages require usage in order to be populated for information. New users will see these pages and become confused or discouraged. Many applications will offer self-evident statement such as “No Favorites Selected”. Or, in the case of UberEats below, no message is displayed.

It’s confusing and discouraging for users to see these statements. Avoid discouraging your users by offering more information, for example: “Save your favorite restaurants and find them here.” Check out Twitter’s exemplary message for users who’ve yet to favorite a tweet below.

CONCLUSION

Onboarding is the first and most crucial step to building a relationship with your userbase. One of the major things business apps can learn from gaming apps is that time is of the essence when it comes to capturing a user’s attention. Keep it short, punchy, and to the point.

How Gamification Can Boost Retention on Any App Part 1: The Starbucks Test and Appointment Mechanics

December 7, 2021Android Development, Gaming & Monetization Strategies, iOS DevelopmentAndroid, App, Appointment, Badge, Bonus, Business, Currency, Development, Discount, Gamification, Gaming, IOS, Login, Mechanics, Mobile, Monetization, Retention, Rewards, Software, Starbucks, System, Testadmin

When creating any application, retention is a vital part of ensuring users are engaged and continuing to use the app months and years after downloading. Mobile games have been on the forefront of developing advanced retention mechanics, but regardless of whether your app is a game or serves an entirely different purpose, retention is the key to enriching your app’s relationship with its customers and engraining usage into the user’s life.

With that in mind, the Mystic Media Blog will endeavor on a 3 part series on how gamification mechanics can boost retention on any app—not just gaming apps but utility apps, business apps and more. In this first entry, we will explore how appointment mechanics can boost engagement and the importance of passing the Starbucks Test.

THE STARBUCKS TEST

Can your users have a meaningful experience with your application in the time it takes for a barista to make a caramel machiatto—roughly two minutes? That’s the challenge that Nicolas Lovell and other app developers put forth known as The Starbucks Test. We’ve covered it previously in several of our blogs on Gaming and Monetization strategies.

The goal of the Starbucks Test is to encourage users to use the application at any point in their day. When an application becomes a regular part of a user’s life, it puts game developers in the catbird seat to extract revenue through ads and/or in-app purchases. For business apps, it means users are going to be familiar with the app’s interface and ready to use it when they need it most.

With the firm goal of adding features which will pass the Starbucks Test in mind, the question becomes: What can your app offer users within 2 minutes?

The answers vary wildly depending on the application. Some mobile games are designed for the user to complete a full core loop within the Starbucks Test, while others offer side tasks which can be swiftly completed. Shopping applications can prompt users to view new products and time-sensitive price updates while business apps can offer short informative articles or reward users for completing their profile on the app. The key is to not only put forth an action that can be completed swiftly, but to offer users something in exchange for the user’s actions.

CREATE A REWARD SYSTEM

In order to gamify an app, you need to not only create tasks but incentivize the user to complete them by offering some kind of reward. For games, this is easy as almost all games incorporate some kind of level or point system. For non-gaming apps, you have to get creative. Consider these three common rewards:

Discounts: Offer a direct discount for services, products, or in-app purchases in exchange for completing an action in the application. Discounts that are time-sensitive will add urgency and create FOMO (Fear of Missing Out). Many apps offer some type of discount in exchange for providing reviews on app stores.
In-App Currency: Offer an in-app currency reward for completing an action in the application. For example, loyalty points help keep customers engaged and incentivize them to shop with you. The Starbucks app employs a profuse gamification strategy using it’s loyalty point program on their mobile app.
Badges: Some apps employ badge systems to great effect. For example, Alamo Drafthouse patrons receive credit toward a badge system every time they visit the theater. Badges give users the feeling of status and strengthen a user’s identification with brands while incentivizing them to continue using the app to attain new badges.

APPOINTMENT MECHANICS

Once you have a rewards system, the time comes to create tasks that can be completed on a daily basis without infringing on the user’s time. Many mobile games use appointment mechanics—a concept that can extend beyond the gaming sphere. Appointment mechanics are time-based incentives that reward users and create FOMO. If the user does not complete the action in time, they miss out on a reward.

Often appointment mechanics refresh after a specific time interval—hourly, daily, weekly, etc. One commonly used appointment mechanic is a daily login bonus. All the user has to do is login to receive a reward—be it loyalty points or in-app currency. Take it a step further, and you can offer consecutive daily bonuses which accrue as the user completes daily logins. Consecutive daily bonuses can be applied to any action—for example, rewarding users with double loyalty points for using your app to order a product or service on consecutive days.

Mindfulness apps like Headspace take appointment mechanics to the next level by asking the user when they’d like to be reminded to use the app on a daily basis. These regularly refreshing mechanics keep a fresh reward in front of users at all times.

In our next entry, we will explore optimizing the onboarding process to foster long-term relationships with your users.

The Top In-App Purchase Tactics for 2022

October 13, 2021Android Development, Gaming & Monetization Strategies, iOS DevelopmentAd, Anchoring, Android, App, Apple, Behavioral, Bundle, Development, Economics, Google, IAP, IOS, Psychology, Purchase, Sales, Subscription, UIadmin

According to Sensor Tower, consumers spent $111 billion on in-app purchases, subscriptions, and premium apps in 2020 on the Apple App Store and Google Play Store. How can your app take advantage to maximize revenue? Every app is different and begets a unique answer to the all important question: What’s the best way to monetize?

App Figures recently published a study which showed only 5.9% of Apple App Store apps are paid, compared to a paltry 3.7% on Google Play. Thus, the freemium model reigns supreme—according to app sales statistics, 48.2% of all mobile app revenue derives from in-app purchases.

When creating an in-app purchase ecosystem, many psychological and practical considerations must be evaluated. Below, please find the best practices for setting in-app purchase prices in 2022.

BEHAVIORAL ECONOMICS

Behavioral economics is a method of economic analysis that applies psychological insights into human behavior to explain economic decision-making. Creating an in-app purchase ecosystem begins with understanding and introducing the psychological factors which incentivize users to make purchases. For example, the $0.99 pricing model banks on users perceiving items that cost $1.99 to be closer to a $1 price point than $2. Reducing whole dollar prices by one cent is a psychological tactic proven to be effective for both in-app purchases and beyond.

Another psychological pricing tactic is to remove the dollar sign or local currency symbol from the IAP storefront and employ a purchasable in-app currency required to purchase IAPs. By removing the association with real money, users see the value of each option on a lower stakes scale. Furthermore, in-app currencies can play a major role in your retention strategy.

ANCHORING

Anchoring is a cognitive bias where users privilege an initial piece of information when making purchasing decisions. Generally, this applies to prices—app developers create a first price point as an anchoring reference, then slash it to provide users with value. For example, an in-app purchase might be advertised at $4.99, then slashed to $1.99 (60% off) for a daily deal. When users see the value in relation to the initial price point, they become more incentivized to buy.

Anchoring also relates to the presentation of pricing. We have all seen bundles and subscriptions present their value in relation to higher pricing tiers. For example, an annual subscription that’s $20/year, but advertised as a $36 value in relation to a monthly subscription price of $2.99/month. In order for your users to understand the value of a purchase, you have to hammer the point home through UI design.

OPTIMIZE YOUR UI

UI is very important when it comes to presenting your in-app purchases. A well-designed monetization strategy can be made moot by insufficient UI design. Users should always be 1-2 taps away from the IAP storefront where they can make purchases. The prices and discounts of each pricing option should be clearly delineated on the storefront.

Furthermore, make sure you are putting your best foot forward with how you present your prices. Anchoring increases the appeal of in-app purchases, but in order for the user to understand the deal, you have to highlight the value in your UI design by advertising it front and center in your IAP UI.

OFFER A VARIETY OF CHOICES

There are a number of IAPs trending across apps. In order to target the widest variety of potential buyers, we recommend offering a variety of options. Here are a few commonly employed options:

BUNDLES: Offer your IAPs either à la carte or as a bundle for a discount. Users are always more inclined to make a bigger purchase when they understand they are receiving an increased value.
AD FREE: Offer an ad-free experience to your users. This is one of the more common tactics and die-hard users will often be willing to pay to get rid of the ad experience.
SPECIAL OFFERS: Limited-time offers with major discounts are far more likely to attract user attention. Special offers create a feeling of scarcity as well as instill the feeling of urgency. Consider employing holiday specials and sending personalized push notifications to promote them.
MYSTERY BOX: Many apps offer mystery boxes—bundles often offered for cheap that contain a random assortment of IAPs. Users may elect to take a chance and purchase in hopes of receiving a major reward.

While offering users a variety of choices for IAPs is key, having too many choices can cause analysis paralysis and be stultifying to users. Analysis paralysis is when users are hesitant to make an in-app purchase because they’ve been given too many options. Restrict your IAPs to the most appealing options to make decisions easy for your users.

TESTING IS KEY

As with any component of app development, testing is the key to understanding your audience and refining your techniques. We recommend testing your app with a random user group and taking their feedback as well as having them fill out a questionnaire. A/B Testing, or split-run testing, consists of testing two different user groups with two different app experiences. A/B testing enables app developers to see how users react to different experiences and to evaluate what tactics are most user-effective.

There are many tactics to help incentivize users to make that big step and invest capital in an app. Savvy developers innovate every day—stay tuned on the latest trends to keep your in-app purchase strategy on the cutting edge.

Cloud-Powered Microdroid Expands Possibilities for Android App Developers

July 6, 2021Android Development, Internet of ThingsAndroid, App, Application, Blockchain, Cloud, Computing, Developer, Development, Embedded, Flutter, Gaming, IoT, Linux, Microdroidadmin

Android developers have a lot to look forward to in 2021, 2022, and beyond. Blockchain may decentralize how Android apps are developed, Flutter will see increased adoption for cross-platform development, and we expect big strides in AR and VR for the platform. Among the top trends in Android development, one potential innovation has caught the attention of savvy app developers: Microdroid.

Android developers and blogs were astir earlier this year when Google engineer Jiyong Park announced via the Android Open Source Project that they are working on a new, minimal Android-based Linux image called Microdroid.

Details about the project are scant, but it’s widely believed that Microdroid will essentially be a lighter version of the Android system image designed to function on virtual machines. Google is preparing for a world in which even smartphone OS’s require a stripped-down version that can be run through the cloud.

Working from a truncated Linux, Microdroid will pull the system image from the device (tablet or phone), creating a simulated environment accessible from any remote device. It has the ability to enable a world in which users can access Google Play and any Android app using any device.

What does this mean for developers?

Microdroid will open up new possibilities for Android apps in embedded and IoT spaces which require potentially automated management and a contained virtual machine which can mitigate security risks. Cloud gaming, cloud computing—even smartphones with all features stored in the cloud—are possible. Although we will have to wait and see what big plans Google has for Microdroid and how Android developers capitalize on it, at this juncture, it’s looking like the shift to the cloud may entail major changes in how we interact with our devices. App developers are keen to keep their eyes and heads in the cloud.

Although no timeline for release has been revealed yet, we expect more on Microdroid with the announcement of Android 12.