Tag Archives: Data

Bridging Biology and Technology: The New Frontier in Drug Discovery and Development

Artificial Intelligence & Machine Learning, Healthcare Tech, , , , , , , ,

Futuristic landscape

In the world of biotech and bioinformatics, the phrases “drug discovery” and “drug development” are often heard. These processes are the backbone of new treatments, potentially saving millions of lives. This blog is part of a series focused on exploring the multifaceted world of biotech and bioinformatics. We will unravel the complexities of drug discovery and development, offering you enriching insights and a profound understanding of this captivating field that holds the promise of transforming healthcare as we know it.

Introduction to Drug Discovery and Development

Drug discovery and development begin with the critical task of identifying potential drug candidates, which sets the foundation for the entire process. This initial stage typically involves high-throughput screening of compound libraries to find molecules that exhibit the desired biological activity against a specific target. Once promising candidates are identified, the pathway progresses through rigorous phases of preclinical and clinical trials, ensuring not only efficacy but also safety for human use.

It’s important to note that this journey is lengthy and fraught with challenges, as it requires collaboration across various scientific disciplines, including biology for understanding disease mechanisms, chemistry for synthesizing and optimizing compounds, and computer science for data analysis and modeling predictions. For engineers and technology executives, grasping the intricacies of these stages is vital. This knowledge can foster innovation and streamline efforts to tackle the inefficiencies that often plague the drug development pipeline. As we delve deeper, we will examine each of these stages in detail, elucidating how they interconnect and contribute to bringing a new drug to market successfully.

Changes in Medical Care

Recent breakthroughs in speeding up the process of developing new drugs.

In this insightful video, BBC StoryWorks explores the transformative role of artificial intelligence (AI) in the field of drug discovery. By leveraging machine learning algorithms and vast datasets, researchers can uncover new patterns and insights that significantly speed up the identification of potential drug candidates.

The Initial Stages of Drug Discovery

Colorful pills in a jar

The initial step in drug discovery involves identifying biological targets linked to a disease, such as proteins or genes that are vital to disease progression. Bioinformatics tools, including the Protein Data Bank (PDB) for 3D protein structures and BLAST for homologous sequence identification, play a crucial role in this phase. Additionally, resources like KEGG offer insights into metabolic pathways, while Cytoscape aids in visualizing biomolecular interaction networks. Once targets are confirmed, high-throughput screening tests thousands of compounds for biological activity, facilitated by advanced robotics and data analysis software like Tecan Freedom EVO and Panorama. Following this, the lead optimization phase occurs, where scientists alter the chemical structure of candidates to enhance efficacy and minimize side effects, using computational chemistry and molecular modeling to assess the impact of these modifications.

Preclinical Development

Before a drug candidate moves to clinical trials, it undergoes rigorous in vitro (test tube) and in vivo (animal) testing. These studies assess the drug’s safety, efficacy, and pharmacokinetics (how the drug is absorbed, distributed, metabolized, and excreted in the body). Engineers play a crucial role in developing and maintaining the sophisticated equipment used in these tests. Toxicology studies are also conducted during preclinical development to evaluate the potential adverse effects of the drug. Bioinformatics tools help analyze the data collected from these studies, aiding in the identification of any toxicological concerns that could halt further development. REACH (Registration, Evaluation, Authorisation, and Restriction of Chemicals) plays a pivotal role in managing chemical safety data and ensuring regulatory compliance throughout the drug development process. Alongside this, SAS (Statistical Analysis System) provides advanced analytics, multivariate analysis, business intelligence, and data management capabilities, which are vital for interpreting the complex datasets generated during research. Once preclinical studies are complete, a detailed dossier is prepared and submitted to regulatory agencies like the FDA, EMA, and EFSA. This dossier includes all preclinical data and outlines the proposed plan for clinical trials. Obtaining regulatory approval is a significant milestone, paving the way for human testing.

Clinical Development

Scientist holding a vaccine

Phase I trials are the first stage of human testing, involving a small group of healthy volunteers. The primary goal is to assess the drug’s safety and determine the appropriate dosage. Engineers and technology executives must ensure that data collection and analysis systems are robust and compliant with regulatory standards. Phase II trials involve a larger group of patients who have the disease the drug is intended to treat. These trials aim to evaluate the drug’s efficacy and further assess its safety. Bioinformatics tools are used to analyze clinical data, helping researchers identify trends and make informed decisions. Phase III trials are the final stage of clinical testing before a drug can be approved for market. These large-scale studies involve thousands of patients and provide comprehensive data on the drug’s efficacy, safety, and overall benefit-risk profile. Advanced data management systems are essential for handling the vast amounts of information generated during these trials.

Post-Approval and Market Launch

After successful Phase III trials, the drug developer submits a New Drug Application (NDA) to regulatory agencies for approval. Once approved, the drug can be marketed, with engineers and technology executives ensuring that manufacturing processes are scalable and compliant with Good Manufacturing Practices (GMP). Ongoing monitoring is essential for maintaining the drug’s safety and efficacy post-approval through post-marketing surveillance. This involves gathering and analysing data from real-world usage to identify potential long-term side effects or rare adverse events. Key bioinformatics tools, such as the FDA’s Sentinel Initiative and WHO’s VigiBase, play crucial roles in tracking safety signals. Continuous improvement and lifecycle management are vital, as they involve refining manufacturing processes and exploring new uses for the drug, with engineers driving these necessary innovations.

Pros and Cons

Molecule structure

Pros of Drug Discovery and Development

Personalized medicine represents a paradigm shift in how treatments are developed and delivered, moving away from a one-size-fits-all approach to more customized therapies. By leveraging advancements in biotechnology and bioinformatics, researchers can now analyze an individual’s genetic profile to identify specific biomarkers associated with diseases. This knowledge enables the design of targeted therapies that are more effective with potentially fewer side effects, as they specifically address the underlying mechanisms of a patient’s condition.

For instance, in oncology, treatments can be tailored to target mutations found in a patient’s cancer cells, resulting in more successful outcomes than traditional chemotherapy, which often affects healthy cells as well. Moreover, this approach reduces the trial-and-error method of prescribing, enabling clinicians to choose the most effective medication from the outset. As research continues to uncover more genetic connections to diseases, the scope of personalized medicine is expected to expand, offering hope for innovative treatments for a broader range of conditions previously deemed untreatable.

Cons of Drug Discovery and Development

Drug discovery and development are time-consuming and expensive, with the average cost of bringing a new drug to market estimated at over $2.6 billion. Additionally, the failure rate is high, with only a small percentage of drug candidates making it through to market approval.

Moreover, the lengthy timeline required for drug discovery and development can span over a decade, often delaying access to new therapies for patients in need. This extensive period includes not only preclinical and clinical trials but also rigorous regulatory scrutiny that ensures the drug’s safety and efficacy. Such delays can hinder innovation and frustrate researchers and patients alike.
Additionally, the high financial burden associated with drug development often pressures companies to prioritize projects with potentially higher financial returns, which may lead to underfunding of research into less profitable but important conditions. This profit-driven approach can result in significant gaps in treatment availability, particularly for rare diseases or conditions affecting smaller patient populations. The inherently uncertain nature of the process—combined with potential regulatory obstacles and the need for substantial investment—adds to the challenges faced by drug developers in bringing effective therapeutics to market.

Cost Efficiency in Drug Development

Microscope

Despite these challenges, there are ways to improve cost efficiency in drug development. Leveraging advanced bioinformatics tools can streamline target identification and lead optimization, reducing the time and resources required for these stages. Additionally, adopting flexible manufacturing systems and continuous improvement practices can lower production costs and increase overall efficiency.

Companies can adopt several strategies to enhance cost efficiency in drug development. A crucial approach is integrating artificial intelligence (AI) and machine learning (ML) technologies to expedite the drug discovery process by analyzing large datasets and effectively predicting compound behavior. This reduces the reliance on trial-and-error methods. Another key strategy is applying adaptive trial designs in clinical research, allowing for modifications based on interim results to utilize resources more efficiently and increase the likelihood of success. Establishing strategic partnerships with academic institutions and biotech firms can also facilitate resource sharing and innovation, reducing costs.

Furthermore, implementing robust project management, including data analytics for real-time tracking, can identify and address bottlenecks early, optimizing resources. Finally, fostering a culture of innovation encourages continuous improvement and cross-disciplinary collaboration, enhancing operational efficiency and ensuring timely access to new therapeutics for patients.

Innovative Companies in Drug Discovery and Development

Scientists in a lab

Several companies are in charge of transforming drug discovery and development through the integration of advanced technologies and innovative strategies. Moderna, known for its groundbreaking mRNA vaccine technology, has effectively leveraged artificial intelligence to streamline the drug development process, significantly accelerating timelines from concept to clinical trials. Their approach exemplifies how biotech firms can utilize modern computational tools to enhance efficiency and responsiveness in therapeutic development.

Amgen is another notable player, actively employing adaptive trial designs in their clinical research to optimize resource allocation and improve chances of success. Their commitment to innovation and collaboration with academic institutions has fostered an environment ripe for discovering new treatments for complex diseases.

Additionally, Gilead Sciences has made headway in personalized medicine by developing targeted therapies that address specific patient populations. Their focus on utilizing sophisticated data analytics has allowed them to identify promising drug candidates and streamline their research and development processes.

Finally, Roche is at the forefront of integrating big data and AI in oncology, constantly refining their approaches based on real-world evidence and insights. This commitment not only brings therapies to market more efficiently but also ensures they are tailored to the unique needs of patients.

Conclusion

Drug discovery and development are at the heart of modern healthcare, offering immense potential to transform lives and address unmet medical needs. The intricate processes involved in bringing new therapeutics to the market require a deep understanding of scientific principles and a keen awareness of regulatory frameworks and market dynamics.

As we look towards the future, pushing the boundaries of what is possible in drug development is crucial. Engaging with cutting-edge technologies, such as artificial intelligence and machine learning, can enhance our ability to predict outcomes and streamline the development pipeline, thereby reducing costs and accelerating time to market. Moreover, the emphasis on personalized medicine is set to revolutionize therapeutic approaches, making treatments not only more effective but also more aligned with patients’ unique genetic makeups.

Stay tuned for the next installment in our blog series, where we will delve into the fascinating world of biopharmaceutical production. This exploration will provide valuable insights into the sophisticated mechanisms that underpin the production of life-saving biologics, highlighting the critical role this sector plays in advancing healthcare.

From Data to Decisions: Edge AI Empowering IoT Innovations and Smart Sensors

Artificial Intelligence & Machine Learning, , , , , , , , , ,

Cons

Throughout this blog series on Edge AI, we have touched upon various fascinating applications, including Edge AI in autonomous vehicles and Edge AI in consumer electronics. In autonomous vehicles, edge AI plays a pivotal role in enabling real-time decision-making and improving the overall safety and efficiency of transportation systems. Meanwhile, in consumer electronics, edge AI enhances user experiences by providing smart, responsive features in everyday devices such as smartphones, smart home systems, and wearable technology.

Lastly, in the rapidly evolving landscape of technology, Edge AI is paving new ways to harness the power of IoT (Internet of Things) devices and smart sensors. These advancements are not just buzzwords but fundamental shifts that promise to enhance efficiency, improve data management, and offer unprecedented insights. This blog will explore the effects of Edge AI on IoT devices and smart sensors, providing insights into its current applications, benefits, and future potential. By the end, you’ll have a comprehensive understanding of how Edge AI can revolutionize your business operations.

Smart Sensors Explained

This RealPars video explores the transformative role of Smart Sensors in Industry 4.0’s Smart Factory framework

It traces the evolution from the First Industrial Revolution to today’s IoT-driven Smart Factories, highlighting how Smart Sensors surpass traditional sensors with advanced features like data conversion, digital processing, and cloud communication. Discover how these intelligent devices are revolutionizing manufacturing, enhancing efficiency, and driving innovation.

The Intersection of Edge AI and IoT

Real time

Enhancing Real-Time Data Processing

One of the most significant benefits of Edge AI is its ability to process data in real-time. Traditional IoT systems often rely on cloud-based servers to analyze data, which can result in delays and increased latency. Edge AI mitigates these issues by enabling IoT devices to process and analyze data locally. This real-time processing capability is crucial for applications requiring immediate responses, such as autonomous vehicles or industrial automation.

For example, consider a manufacturing plant equipped with smart sensors to monitor machinery performance. With Edge AI, any anomalies in the data can be detected and addressed instantly, preventing potential breakdowns and costly downtime.

Improving Bandwidth Efficiency

Bandwidth efficiency is another critical advantage of Edge AI on IoT devices. Sending vast amounts of raw data to the cloud for processing can strain network resources and incur significant costs. By processing data locally, Edge AI reduces the amount of data that needs to be transmitted, thus optimizing bandwidth usage.

Imagine a smart city project where thousands of sensors collect data on traffic, weather, and public safety. Edge AI can filter and preprocess this data locally, sending only the most relevant information to the central server. This approach not only conserves bandwidth but also ensures faster and more efficient decision-making.

Enhancing Security and Privacy

Security

Security and privacy are paramount concerns in the age of data-driven technologies. Edge AI offers enhanced security by minimizing the need to transfer sensitive data over the network. Localized data processing reduces the risk of data breaches and unauthorized access, making it a more secure option for businesses dealing with sensitive information.

For instance, healthcare facilities using IoT devices to monitor patient vitals can benefit from Edge AI. By processing data locally, patient information remains within the facility’s secure network, reducing the risk of data breaches and ensuring compliance with privacy regulations.

Take, for example, a hospital equipped with smart beds that monitor patient heart rates, blood pressure, and oxygen levels. With Edge AI, these smart beds can analyze data in real-time and alert medical staff to any abnormalities immediately, thereby enhancing patient care and response times.

Another example is remote patient monitoring systems used in home healthcare setups. Edge AI can process data from wearable devices, such as glucose monitors or digital stethoscopes, ensuring that sensitive health information is analyzed on the device itself before only the necessary summarized data is sent to healthcare providers. This not only preserves the patient’s privacy but also ensures timely intervention when needed.

Pros of Edge AI on IoT Devices and Smart Sensors

Operational Costs

Reduced Latency

One of the most significant advantages of Edge AI is its ability to reduce latency. By processing data closer to the source, Edge AI eliminates the delays associated with transmitting data to and from cloud servers. This reduced latency is crucial for applications requiring real-time decision-making, such as autonomous vehicles or industrial automation.
Consider a smart factory where machines are equipped with sensors to monitor their performance. With Edge AI, any anomalies in the data can be detected and addressed instantly, preventing potential breakdowns and costly downtime.

In an automated warehouse where robotic systems manage inventory, Edge AI can be used to process data from various sensors in real time. If a sensor detects an obstruction in the robot’s path, Edge AI can immediately reroute the robot, avoiding potential collisions and maintaining a smooth flow of operations. This instant decision-making capability minimizes interruptions and maximizes operational efficiency, showcasing how Edge AI significantly benefits environments that rely on the timely processing of critical data.

Improved Bandwidth Efficiency

Another positive aspect of Edge AI is its ability to enhance bandwidth efficiency. By processing data locally, Edge AI minimizes the volume of data transmitted to central servers. This is particularly advantageous for data-intensive applications, such as video surveillance or smart city monitoring. For instance, in a smart city, Edge AI can process video feeds from traffic cameras locally and only send relevant alerts or summarized data, significantly reducing network load and transmission costs.

Enhanced Resilience and Reliability

Edge AI enhances system resilience and reliability by ensuring critical functions can operate even without network connectivity. For instance, in autonomous vehicles, edge computing allows real-time decision-making even in regions with poor internet connections. Similarly, in industrial automation, machines can perform essential operations independently of cloud-based systems. This decentralized approach ensures that even in the event of network failures, Edge AI devices maintain functionality and consistent performance.

Cons of Edge AI on IoT Devices and Smart Sensors

Cons

Initial Setup Costs

One of the primary challenges of implementing Edge AI is the initial setup cost. Deploying Edge AI infrastructure requires significant investment in hardware, software, and skilled personnel. For small and medium-sized businesses, these costs can be a barrier to adoption.

However, it’s important to consider the long-term benefits and potential cost savings associated with Edge AI. Businesses that invest in Edge AI can achieve significant returns through improved efficiency, reduced operational costs, and enhanced decision-making capabilities.

Limited Processing Power

Another potential drawback of Edge AI is the limited processing power of edge devices. Unlike cloud servers, edge devices may have limited computational resources, which can impact their ability to handle complex AI algorithms.

Businesses must carefully evaluate their specific use cases and determine whether Edge AI devices have the necessary processing power to meet their needs. In some cases, a hybrid approach that combines edge and cloud processing may be the most effective solution.

Data Management Challenges

Data Management

Edge AI also presents data management challenges for businesses. With data being processed and stored on various edge devices, managing and maintaining this data can be complex and time-consuming. This issue is further compounded by the sheer volume of data generated by IoT devices, making it challenging to extract meaningful insights.

To address this challenge, businesses must have robust data management strategies in place, including implementing efficient data storage solutions and leveraging advanced analytics tools to make sense of large datasets. Overall, while there are challenges associated with Edge AI on IoT devices, its numerous benefits make it a valuable tool for businesses looking to utilize real processing and improve decision-making capabilities.

Maintenance and Management

Maintaining and managing Edge AI infrastructure can be challenging, especially for businesses with limited IT resources. Edge devices require regular updates, monitoring, and maintenance to ensure optimal performance and security. Businesses can partner with managed service providers (MSPs) that specialize in Edge AI deployment and management. MSPs can provide the expertise and support needed to maintain a robust and secure Edge AI infrastructure.

Future Plans and Developments

Future

Advancements in Edge AI Hardware

The future of Edge AI is bright, with ongoing advancements in hardware technology. Next-generation edge devices will feature more powerful processors, enhanced memory capabilities, and improved energy efficiency. These advancements will enable businesses to deploy even more sophisticated AI algorithms at the edge.
For example, companies like NVIDIA and Intel are developing cutting-edge processors specifically designed for Edge AI applications. These processors will enable faster and more efficient data processing, opening up new possibilities for IoT and smart sensor applications.

Integration with 5G Networks

5G

The rollout of 5G networks will significantly impact the adoption of Edge AI. With its ultra-low latency and high-speed data transmission capabilities, 5G will enhance the performance of Edge AI applications, enabling real-time decision-making and data processing on a larger scale.

Industries such as autonomous driving, smart cities, and industrial automation will benefit greatly from the combination of 5G and Edge AI. The synergy between these technologies will drive innovation and transform the way businesses operate. Overall, the future of Edge AI looks promising, with endless possibilities for improving efficiency, security, and decision-making capabilities in various industries. As hardware technology continues to advance and more businesses adopt Edge AI solutions, we can expect to see even greater developments and advancements in this field.

Expansion of Edge AI Use Cases

As Edge AI technology continues to evolve, we can expect to see an expansion of use cases across various industries. From healthcare and agriculture to manufacturing and retail, businesses will find new and innovative ways to leverage Edge AI to improve efficiency, enhance customer experiences, and drive growth.
For instance, in agriculture, Edge AI-powered drones can monitor crop health in real time, enabling farmers to make data-driven decisions and optimize their yields. In retail, smart shelves equipped with Edge AI can track inventory levels and automatically reorder products, reducing stock outs and improving customer satisfaction. The possibilities are endless, and the future of Edge AI is full of exciting potential. One example of a company that is in charge of creating Edge AI-powered drones for agriculture is DroneDeploy. DroneDeploy offers innovative solutions that enable farmers to monitor crop health with precision and efficiency.

Conclusion

As we conclude our Edge AI blog series, we hope you have gained valuable insights into the benefits, challenges, and future developments associated with this transformative technology. From understanding its impact on various industries to exploring its innovation potential, Edge AI represents a significant advancement in the way we process and utilize data.

Edge AI is revolutionizing the way businesses leverage IoT devices and smart sensors. By enabling real-time data processing, optimizing bandwidth usage, and enhancing security, Edge AI offers significant benefits for businesses across various industries. However, it’s essential to consider the initial setup costs, limited processing power, and maintenance challenges associated with Edge AI.

Looking ahead, advancements in Edge AI hardware, integration with 5G networks, and the expansion of use cases will drive the continued growth and adoption of this technology. For CEOs, technology executives, and business owners, staying informed about Edge AI developments and exploring its potential applications can provide a competitive advantage in today’s tech-driven world. Stay tuned for more in-depth explorations of the latest trends and technologies shaping our world.

Apple’s iPhone 16 Revolutionizes Business Tech – A Comprehensive Review

Artificial Intelligence & Machine Learning, iOS Development, , , , , , , , ,

iPhone 16 and 16Pro

With every new iPhone release, Apple sets new standards, and the iPhone 16 is no exception. This latest marvel is packed with features that promise to transform how business leaders, tech executives, and influencers operate. In this blog post, we’ll explore the groundbreaking features of the iPhone 16, discuss its pros and cons, and take a sneak peek at what the future holds for Apple’s flagship product.

A Leap in Performance

With the iPhone 16 and 16 Pro, Apple is changing the way we think about smartphone photography. The new camera control buttons provide users with enhanced tactile feedback and more precise control over camera functions. These buttons make taking professional-quality photos and videos easier than ever, allowing users to focus on capturing the perfect moment with just a simple touch. Let’s dive into the details and see how these innovative features are set to transform the photography and videography landscape.

Cutting-Edge Processor

The iPhone 16 is powered by the A18 Bionic chip, delivering outstanding performance for seamless multitasking and productivity. Its advanced architecture boosts processing speeds while enhancing energy efficiency, ideal for long working hours. For tech leaders, the A18 effortlessly handles complex applications and data tasks, ensuring efficient operations and swift decision-making. With enhanced AI capabilities, businesses can leverage machine learning for predictive analytics and security, solidifying the iPhone 16 as an essential innovation tool in the corporate landscape.

Apple Intelligence Integration

Apple Intelligence

Apple Intelligence, an evolution of Siri with significantly upgraded AI and machine learning capabilities, is one of the standout features of the iPhone 16. This next-generation assistant is designed to become an indispensable business tool, offering seamless integration with various corporate applications and services. With enhanced natural language processing, Apple Intelligence understands and interprets complex commands and queries more accurately, providing precise and relevant responses.
For business leaders, Apple Intelligence can schedule and manage meetings, send emails, and compile reports simply through voice commands, reducing the need for manual input and minimizing errors. The AI’s advanced predictive analytics can forecast market trends and customer behaviors, delivering actionable insights to drive strategic planning. In customer service, Apple Intelligence can automate responses to common queries, improving response times and customer satisfaction.

Superior Battery Life

Battery life is crucial for business professionals on the go, and the iPhone 16 excels in this area, offering up to 27 hours of playback time for the iPhone 16 ProMax and 22 hours for the standard iPhone 16. This impressive battery life allows CEOs and business owners to stay connected without the constant search for an outlet.

Additionally, the iPhone 16 features fast charging capabilities, enabling quick top-ups during meetings or calls, ensuring users maintain productivity throughout the day. With smart energy-saving features that adapt to user habits, this device meets the demanding needs of busy professionals, alleviating the stress of low battery warnings in critical situations.

Enhanced Camera Ergonomics and Precision

The iPhone 16 and 16 Pro models revolutionize smartphone photography with new physical camera control buttons. These buttons are designed for optimal ergonomics, providing enhanced tactile feedback and precise control, similar to professional camera equipment. Integrated seamlessly with the iPhone 16’s photographic capabilities, they enable smooth navigation between modes, exposure adjustments, and zoom functionality, ensuring high-quality image capture. For professional photographers and content creators, the ability to make nuanced adjustments quickly is invaluable, enhancing the ability to capture fleeting moments without on-screen menu navigation. Constructed from high-quality materials and based on user feedback, these buttons cater to both amateur and professional users alike, setting a new standard for smartphone photography and accessibility to professional-quality imagery.

Redefined Display Technology

Display

ProMotion XDR Display

The iPhone 16’s ProMotion XDR display for Pro models revolutionizes user interaction with its impressive refresh rate of up to 120Hz, resulting in smoother scrolling and sharper visuals. This advanced technology enhances the visual appeal of apps and videos while dynamically adapting to conserve battery life. With HDR support, users can enjoy a wider color gamut and improved contrast for more lifelike imagery. For professionals who rely on their devices for impactful presentations, the clarity and vibrancy of the display ensure that key messages resonate with audiences.

True Tone and HDR10+ Support

True Tone technology and HDR10+ support greatly improve the iPhone 16’s display by enhancing color accuracy and dynamic range. True Tone automatically adjusts the white balance to suit ambient lighting, ensuring a natural viewing experience that is crucial for creative professionals who need precise color representation. This allows artists and designers to work confidently, knowing their edits will reflect accurately on other displays. HDR10+ further elevates the visual experience with improved contrast and brightness, rendering photos and videos more vibrant and lifelike. For businesses, this means showcasing products with remarkable clarity, boosting marketing efforts and customer engagement, making the iPhone 16 an essential tool for effective communication and brand presentation.

Eye Comfort Mode

Eye Comfort Mode effectively reduces blue light emissions, minimizing eye strain for professionals who spend long hours on their devices. By adjusting the display’s color temperature in low-light conditions, it creates a warmer viewing experience that alleviates discomfort. Excessive blue light exposure can disrupt sleep and lead to digital eye strain, including symptoms like dryness and irritation. With Eye Comfort Mode, users can work late into the night or tackle early morning tasks without adverse effects, promoting visual well-being and maintaining productivity.

Security and Privacy

Privacy

Advanced Face ID

Security is essential in business, and the iPhone 16 elevates this with Advanced Face ID, a facial recognition system that uses infrared sensors and machine learning for precise authentication. Unlike fingerprint scanners, it captures intricate facial details and functions well in various lighting conditions for swift access. This technology safeguards sensitive data with advanced anti-spoofing features and allows support for multiple user profiles, which is perfect for shared devices. With an error rate of just one in one million, Advanced Face ID enhances security while seamlessly integrating with secure payment systems, making it a vital resource for business leaders focused on safety and efficiency.

Secure Enclave

The Secure Enclave in the iPhone 16 is crucial for protecting user privacy and data integrity. It securely stores biometric data—like fingerprints and facial recognition—along with encryption keys, isolating this sensitive information from the main operating system to reduce exposure risks. This chip enables business executives to confidently store sensitive data while adhering to security standards. It performs cryptographic operations without exposing the underlying data, shielding against malware and unauthorized access. With its support for secure boot and device encryption, the Secure Enclave ensures device integrity from the outset, making it vital for compliance with regulations such as GDPR and HIPAA, thus fostering trust with clients.

Privacy-Focused Features

Apple’s dedication to user privacy shines through features like Mail Privacy Protection and App Tracking Transparency. Mail Privacy Protection enables users to conceal their IP addresses and keeps email open statuses hidden from senders, prompting marketers to rethink engagement metrics. Meanwhile, App Tracking Transparency requires apps to seek explicit user permission for tracking activity, allowing individuals to control their shared data.

Connectivity and Communication

iPhone 16

5G Capabilities

The iPhone 16 features advanced 5G capabilities, providing faster download and upload speeds, reduced latency, and improved connectivity. This enhancement leads to seamless video conferencing and rapid file sharing, which is crucial for business owners and tech leaders. With speeds over 1 Gbps, users can enjoy high-definition streaming and quick access to cloud applications. The low latency significantly improves virtual meetings and collaboration, ensuring productivity for remote and global teams.

Wi-Fi 6E Support

Wi-Fi 6E support enhances wireless connections by utilizing the 6 GHz spectrum, which alleviates congestion found in the traditional 2.4 GHz and 5 GHz bands. This expanded bandwidth is vital in crowded environments like conferences and corporate offices, enabling multiple devices to connect simultaneously without speed loss. For technology executives, it means uninterrupted connectivity during meetings and seamless access to cloud services, promoting efficiency. Additionally, improved latency and capacity allow teams to collaborate in real time through video conferencing and shared digital workspaces, making Wi-Fi 6E an essential asset for organizations embracing hybrid work models.

Enhanced Audio Quality

Enhanced audio quality is achieved through spatial audio support and advanced microphone technology, providing an exceptional listening experience on the iPhone 16. Spatial audio creates a surround sound effect, making video calls feel more interactive and lifelike, which is particularly useful for CEOs conveying complex ideas without distractions. The improved microphone isolates the speaker’s voice while minimizing background noise, ensuring crystal-clear calls.

Business-Centric Features

Dedicated Business Mode

The iPhone 16 features a dedicated Business Mode designed to enhance professional productivity. This mode prioritizes work notifications and allows users to customize settings, focusing on essential apps while minimizing distractions. With enhanced Do Not Disturb options, personal notifications can be silenced during work hours, and users can set different profiles for various environments, such as meetings or focused work.

Seamless Integration with Apple Ecosystem

Seamless integration with the Apple ecosystem—including MacBooks, iPads, and Apple Watches—facilitates smooth transitions for business professionals using the iPhone 16. Users can employ features like Handoff to start a task on one device and effortlessly continue on another, such as finishing an email or sharing documents via AirDrop. This continuity allows access to the same files and applications across devices, enhancing collaboration with shared calendars, notes, and reminders. Such interconnectedness boosts productivity and ensures crucial information is readily accessible, empowering professionals to make informed decisions and respond swiftly to challenges.

Robust App Store for Business

The App Store offers a wide range of business applications, from project management to financial software, all optimized for the iPhone 16. Business owners can easily find tools tailored to their needs, like CRM systems and collaboration apps. Regular updates provide access to the latest features, and seamless integration with Apple’s ecosystem ensures efficient data sharing. Flexible in-app purchases and subscription models allow businesses to adjust their software usage as they scale. This extensive selection of apps helps professionals streamline operations and drive growth effectively.

Pros and Cons of the iPhone 16

A18 Bionic Chip

Pros

The iPhone 16 delivers exceptional performance with the A18 Bionic chip, ensuring rapid efficiency and smooth multitasking, perfect for business professionals. Its ProMotion XDR display provides vibrant visuals and smooth scrolling, enhancing productivity for presentations and creative tasks. With advanced security features like Face ID and the Secure Enclave, users can trust that their sensitive data is well-protected. Connectivity is robust, thanks to 5G and Wi-Fi 6E support, facilitating fast video conferencing and quick file sharing. Moreover, Apple prioritizes user privacy with tools such as Mail Privacy Protection, empowering users to safeguard their information effectively.

Cons

Despite its many advantages, the iPhone 16 comes with a high price point, which may be a barrier for some consumers. The premium cost could prevent potential buyers from accessing its advanced features and capabilities. Additionally, limited customization options within Apple’s closed ecosystem can be a drawback for those accustomed to more flexibility offered by Android devices. This can leave some users feeling restricted in how they personalize their devices. Lastly, there is a learning curve associated with adapting to the new features and interface of the iPhone 16. Some users may find it challenging to navigate these changes, which could hinder their overall experience with the device.

Future Plans for the Next iPhone

Future

Continuous Innovation

Apple is renowned for its unwavering commitment to continuous innovation, and the future iPhone models will undoubtedly expand upon the advancements introduced with the iPhone 16. Anticipate the emergence of even more powerful processors that leverage cutting-edge semiconductor technology, providing unparalleled performance and efficiency for demanding applications. Enhanced AI capabilities are on the horizon as well, with machine learning algorithms becoming more sophisticated, enabling features such as predictive text, advanced photo editing, and superior personal assistant functionalities.

Augmented Reality (AR) Integration

Augmented Reality (AR) is poised to be a key feature in future iPhone models, significantly enhancing user experiences in both personal and professional settings. Apple’s ongoing investment in AR technologies highlights its commitment to this innovation. Upcoming iPhones are expected to feature advanced AR capabilities, including better object recognition and realistic virtual overlays, which could transform industries with immersive shopping experiences, virtual try-ons, and interactive training sessions involving 3D models.

Sustainability Efforts

Apple is dedicated to reducing its environmental impact, and future iPhones will likely incorporate more sustainable materials and energy-efficient technologies. The company’s commitment to sustainability extends beyond product design; it encompasses the entire lifecycle of its devices, from sourcing raw materials to manufacturing, transportation, and eventual recycling. For instance, Apple aims to use 100% recycled aluminum in the enclosures of its products, which significantly reduces the demand for newly mined metals and minimizes carbon emissions associated with extraction processes.

Conclusion

The iPhone 16 stands as a monumental leap in business technology, providing unmatched performance, robust security, and superior connectivity. For business professionals, tech executives, and influencers alike, it is an indispensable tool that fuels productivity and sparks innovation. As we peer into the future, Apple’s unwavering dedication to innovation promises even more groundbreaking advancements. Be sure to stay tuned for the final blog in our BioTech series, where we will explore exciting developments in medical diagnostics and imaging.

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

Digital Marketing, , , , , , , , , , , , , , , , , , ,

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

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

PERSONALIZATION

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

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

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

SOCIAL COMMERCE

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

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

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

IN-GAME ADVERTISING

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

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

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

CONCLUSION

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

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

Artificial Intelligence & Machine Learning, Digital Marketing, Internet of Things, , , , , , , , , , , , , , , ,

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

WHAT IS THE IOB?

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

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

HOW DOES IT WORK?

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

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

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

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

CONCERNS

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

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

CONCLUSION

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

How Bluetooth Became the Gold Standard of Wireless Audio Technology

Internet of Things, Radio Streaming, , , , , , , , , , , , , , , ,

Bluetooth technology has established itself over the years as the premiere wireless audio technology and a staple of every smartphone user’s daily mobile experience. From wireless headphones, to speakers, to keyboards, gaming controllers, IoT devices, and instant hotspots—Bluetooth is used for a growing variety of functions every year.

While Bluetooth is now a household name, the path to popularity was built over the course of over 20 years.

CONCEPTION

In 1994, Dr. Jaap Haartsen—an electrical engineer working for Ericsson’s Mobile Terminal Division in Lund—was tasked with creating an indoor wireless communication system for short-range radio connections. He ultimately created the Bluetooth protocol. Named after the renowned Viking king who united Denmark and Norway in 958 AD, the Bluetooth protocol was designed to replace RS-232 telecommunication cables using short range UHF radio waves between 2.4 and 2.485 GHz.

In 1998, he helped create the Bluetooth Special Interest Group, driving the standardization of the Bluetooth radio interface and obtaining worldwide regulatory approval for Bluetooth technology. To this day, Bluetooth SIG publishes and promotes the Bluetooth standard as well as revisions.

BLUETOOTH REACHES CONSUMERS

In 1999, Ericsson introduced the first major Bluetooth product for consumers in the form of a hands-free mobile headset. The headset won the “Best of Show Technology” award at COMDEX and was equipped with Bluetooth 1.0.

Each iteration of Bluetooth has three main distinguishing factors:

  • Range
  • Data speed
  • Power consumption

The strength of these factors is determined by both the modulation scheme and data packet employed. As you might imagine, Bluetooth 1.0 was far slower than the Bluetooth we’ve become accustomed to in 2021. Data speeds capped at 1Mbps with a range up to 10 meters. While we use Bluetooth to listen to audio on a regular basis today, it was hardly equipped to handle music and primarily designed for wireless voice calls.

THE BLUETOOTH EVOLUTION

The Bluetooth we currently enjoy in 2021 is version 5. Over the years, Bluetooth’s range, data speed, and power consumption have increased dramatically.

In 2004, Bluetooth 2.0 focused on enhancing the data rate, pushing from 0.7Mbps in version 1 to 1-3Mbps while increasing range from 10m to 30m. Bluetooth 3.0 increased speeds in 2009, allowing up to 24Mbps.

In 2011, Bluetooth 4.0 introduced a major innovation in BLE (Bluetooth Low Energy). BLE is an alternate Bluetooth segment designed for very low power operation. It enables major flexibility to build products that meet the unique connectivity requirements of their market. BLE is tailored toward burst-like communications, remaining in sleep mode before and after the connection initiates. The decreased power consumption takes IoT devices like industrial monitoring sensors, blood pressure monitoring, and Fitbit devices to the next level. These devices can employ BLE to run at 1Mbps at very low power consumption rates. In addition to lowering the power consumption, Bluetooth 4.0 doubles the typical maximum range from 30m in Bluetooth 3.0 to 60m.

BLUETOOTH 5

Bluetooth 5 is the latest version of the technology. Bluetooth 5 doubles the bandwidth by doubling the speed of transmission. In addition, it quadruples the typical max range, bringing it up to 240m. Bluetooth 5 also introduces Bluetooth Low Energy audio, which enables one device to share audio with multiple other devices.

CONCLUSION

Bluetooth is a game-changing technology which stands to revolutionize more than just audio. IoT devices, health tech, and more stand to improve as the Bluetooth SIG continues to upgrade the protocol. After thirty years of improvement, the possibilities remain vast for savvy developers to take advantage of the latest Bluetooth protocols to build futuristic wireless technologies.

HL7 Protocol Enhances Medical Data Transmissions–But Is It Secure?

Healthcare Tech, , , , , , , , , , ,

In our last blog, we examined how DICOM became the standard format for transmitting files in medical imaging technology. As software developers, we frequently find ourselves working in the medical technology field navigating new formats and devices which require specialized attention.

This week, we will jump into one of the standards all medical technology developers should understand: the HL7 protocol.

The HL7 protocol is a set of international standards for the transfer of clinical and administrative data between hospital information systems. It refers to a number of flexible standards, guidelines, and methodologies by which various healthcare systems communicate with each other. HL7 connects a family of technologies, providing a universal framework for the interoperability of healthcare data and software.

Founded in 1987, Health Level Seven International (HL7) is a non-profit, ANSI-accredited standards developing organization that manages updates of the HL7 protocol. With over 1,600 members from over 50 countries, HL7 International represents brain trust incorporating the expertise of healthcare providers, government stakeholders, payers, pharmaceutical companies, vendors/suppliers, and consulting firms.

HL7 has primary and secondary standards. The primary standards are the most popular and integral for system integrations, interoperability, and compliance. Primary standards include the following:

  • Version 2.x Messaging Standard–an interoperability specification for health and medical transactions
  • Version 3 Messaging Standard–an interoperability specification for health and medical transactions
  • Clinical Document Architecture (CDA)–an exchange model for clinical documents, based on HL7 Version 3
  • Continuity of Care Document (CCD)–a US specification for the exchange of medical summaries, based on CDA.
  • Structured Product Labeling (SPL)–the published information that accompanies a medicine based on HL7 Version 3
  • Clinical Context Object Workgroup (CCOW)–an interoperability specification for the visual integration of user applications

While HL7 may enjoy employment worldwide, it’s also the subject of controversy due to underlying security issues. Researchers from the University of California conducted an experiment to simulate an HL7 cyber attack in 2019, which revealed a number of encryption and authentication vulnerabilities. By simulating a main-in-the-middle (MITM) attack, the experiment proved a bad actor could potentially modify medical lab results, which may result in any number of catastrophic medical miscues—from misdiagnosis to prescription of ineffective medications and more.

As software developers, we advise employing advanced security technology to protect patient data. Medical professionals are urged to consider the following additional safety protocols:

  • A strictly enforced password policy with multi-factor authentication
  • Third-party applications which offer encrypted and authenticated messaging
  • Network segmentation, virtual LAN, and firewall controls

While HL7 provides unparalleled interoperability for health care data, it does not provide ample security given the level of sensitivity of medical data—transmissions are unauthenticated and unvalidated and subject to security vulnerabilities. Additional security measures can help medical providers retain that interoperability across systems while protecting themselves and their patients from having their data exploited.

HOW DICOM BECAME THE STANDARD IN MEDICAL IMAGING TECHNOLOGY

Android Development, iOS Development, , , , , , , , , , , , , , , , ,

Building applications for medical technology projects often requires extra attention from software developers. From adhering to security and privacy standards to learning new technologies and working with specialized file formats—developers coming in fresh must do a fair amount of due diligence to get acclimated in the space. Passing sensitive information between systems requires adherence to extra security measures—standards like HIPAA (Health Insurance Portability and Accountability Act) are designed to protect the security of health information.

When dealing with medical images and data, one international standard rises above the rest: DICOM. There are hundreds of thousands of medical imaging devices in use—and DICOM has emerged as the most widely used healthcare messaging standards and file formats in the world. Billions of DICOM images are currently employed for clinical care.

What is DICOM?

DICOM stands for Digital Imaging and Communications in Medicine. It’s the international file format and communications standard for medical images and related information, implemented in nearly every radiology, cardiology, imaging, and radiotherapy devices such as X-rays, CT scans, MRI, ultrasound, and more. It’s also finding increasing adoption in fields such as ophthalmology and dentistry.

DICOM groups information into data sets. Similar to how JPEGs often include embedded tags to identify or describe the image, DICOM files include patient ID to ensure that the image retains the necessary identification and is never separated from it. The bulk of images are single frames, but the attribute can also contain multiple frames, allowing for storage of Cineloops.

The History of DICOM

DICOM was developed by the American College of Radiology (ACR) and the National Electrical Manufacturer’s Association (NEMA) in the 1980s. Technologies such as CT scans and other advanced imaging technologies made it evident that computing would play an increasingly major role in the future of clinical work. The ACR and NEMA sought a standard method for transferring images and associated information between devices from different vendors.

The first standard covering point-to-point image communication was created in 1985 and initially titled ACR-NEMA 300. A second version was subsequently released in 1988, finding increased adoption among vendors. The first large-scale deployment of ACR-NEMA 300 was in 1992 by the U.S. Army and Air Force. In 1993, the third iteration of the standard was released—and it was officially named DICOM. While the latest version of DICOM is still 3.0, it has received constant maintenance and updates since 1993.

Why Is DICOM Important?

DICOM enables the interoperability of systems used to manage workflows as well as produce, store, share, display, query, process, retrieve and print medical images. By conforming to a common standard, DICOM enables medical professionals to share data between thousands of different medical imaging devices across the world. Physicians use DICOM to access images and reports to diagnose and interpret information from any number of devices.

DICOM creates a universal format for physicians to access medical imaging files, enabling high-performance review whenever images are viewed. In addition, it ensures that patient and image-specific information is properly stored by employing an internal tag system.

DICOM has few disadvantages. Some pathologists perceive the header tags to be a major flaw. Some tags are optional, while others are mandatory. The additional tags can lead to inconsistency or incorrect data. It also makes DICOM files 5% larger than their .tiff counterparts.

The Future

The future of DICOM remains bright. While no file format or communications standard is perfect, DICOM offers unparalleled cross-vendor interoperability. Any application developer working in the medical technology field would be wise to take the time to comprehensively understand it in order to optimize their projects.

How Artificial Intuition Will Pave the Way for the Future of AI

Artificial Intelligence & Machine Learning, , , , , , , , , , , ,

Artificial intelligence is one of the most powerful technologies in history, and a sector defined by rapid growth. While numerous major advances in AI have occurred over the past decade, in order for AI to be truly intelligent, it must learn to think on its own when faced with unfamiliar situations to predict both positive and negative potential outcomes.

One of the major gifts of human consciousness is intuition. Intuition differs from other cognitive processes because it has more to do with a gut feeling than intellectually driven decision-making. AI researchers around the globe have long thought that artificial intuition was impossible, but now major tech titans like Google, Amazon, and IBM are all working to develop solutions and incorporate it into their operational flow.

WHAT IS ARTIFICIAL INTUITION?

ADy2QfDipAoaDjWjQ4zRq

Descriptive analytics inform the user of what happened, while diagnostic analytics address why it happened. Artificial intuition can be described as “predictive analytics,” an attempt to determine what may happen in the future based on what occurred in the past.

For example, Ronald Coifman, Phillips Professor of Mathematics at Yale University, and an innovator in the AI space, used artificial intuition to analyze millions of bank accounts in different countries to identify $1 billion worth of nominal money transfers that funded a well-known terrorist group.

Coifman deemed “computational intuition” the more accurate term for artificial intuition, since it analyzes relationships in data instead of merely analyzing data values. His team creates algorithms which identify previously undetected patterns, such as cybercrime. Artificial intuition has made waves in the financial services sector where global banks are increasingly using it to detect sophisticated financial cybercrime schemes, including: money laundering, fraud, and ATM hacking.

ALPHAGO

One of the major insights into artificial intuition was born out of Google’s DeepMind research in which a super computer used AI, called AlphaGo, to become a master in playing GO, an ancient Chinese board game that requires intuitive thinking as part of its strategy. AlphaGo evolved to beat the best human players in the world. Researchers then created a successor called AlphaGo Zero which defeated AlphaGo, developing its own strategy based on intuitive thinking. Within three days, AlphaGo Zero beat the 18—time world champion Lee Se-dol, 100 games to nil. After 40 days, it won 90% of matches against AlphaGo, making it arguably the best Go player in history at the time.

AlphaGo Zero represents a major advancement in the field of Reinforcement Learning or “Self Learning,” a subset of Deep Learning which is a subset of Machine Learning. Reinforcement learning uses advanced neural networks to leverage data into making decisions. AlphaGo Zero achieved “Self Play Reinforcement Learning,” playing Go millions of times without human intervention, creating a neural network of “artificial knowledge” reinforced by a sequence of actions that had both consequences and inception. AlphaGo Zero created knowledge itself from a blank slate without the constraints of human expertise.

ENHANCING RATHER THAN REPLACING HUMAN INTUITION

The goal of artificial intuition is not to replace human instinct, but as an additional tool to help improve performance. Rather than giving machines a mind of their own, these techniques enable them to acquire knowledge without proof or conscious reasoning, and identify opportunities or potential disasters, for seasoned analysts who will ultimately make decisions.

Many potential applications remain in development for Artificial Intuition. We expect to see autonomous cars harness it, processing vast amounts of data and coming to intuitive decisions designed to keep humans safe. Although its ultimate effects remain to be seen, many researchers anticipate Artificial Intuition will be the future of AI.

Maximize Profits with the Top Freemium Tactics of 2020

Android Development, Gaming & Monetization Strategies, iOS Development, , , , , , , , , , , , , , ,

The global gaming market is estimated at $152 billion, with 45% derived from mobile games. The mobile game market is constantly evolving, new tactics and even platforms, like Apple Arcade, are being introduced. As a mobile game developer, being dynamic and staying up on the latest trends is of the utmost importance. Staying on top of these trends will help make a more engaging and profitable mobile game.

Keeping this in mind, below are the top freemium tactics of 2020:

RETENTION IS (STILL) KING

Mobile game developers must remember that freemium games begin and end with a good retention strategy that keeps users engaged.

Daily Tasks: Set-up daily tasks that pass the Starbucks Test. One of them can be opening the app on a daily basis. These should be fairly simple to complete and offer a reward, encouraging users to integrate gameplay into their daily lives.

Rewards Pack on User Birthdays: Give users some kind of bonus on their birthday to enrich their personal relationship with the game.

Challenge Dormant Users: After 3 days, give users a special, temporary challenge to reengage them with the app. Temporary promotions can be an effective way to instill a sense of urgency in the call-to-action and trigger users to open the app.

Promotion Before Subscription/Free Trial Ends: Tempt the user to sign-up or to extend their subscription by offering a temporary promotion 24-48 hours before their free trial/subscription ends.

When it comes to measuring retention, check out the model retention rates below from The Tool (Performance-Based Mobile ASO):

  • Day 1 Retention – 40%
  • Day 7 Retention – 20%
  • Day 28 Retention – 10%

Retention can also be tracked hourly instead of daily where Day 1 Retention will be the percentage of users who returned within 24-48 hours from the install. Here’s how it might look in analytics systems such as devtodev (via The Tool):

Retention-Analytics

OUTSTREAM VIDEO ADS

Outstream Video is a new type of video ad unit, referred to sometimes as “native video”, designed for targeting mobile users.

Outstream Video ads do not require placement within a Youtube video. They play with the sound off on mobile screens when more than 70% of the ad is visible. The user can tap the ad to turn the sound on and restart the video from the beginning, or they can continue scrolling. When less than 70% of the ad is visible, the video pauses.

Advertisers such as the Hong Kong tourism board have had great success using Outstream Video ads, delivering 30% incremental reach with a 40% lower cost per completed video and 85% lower CPM.

REWARDED ADS PAY OFF

When it comes to monetizing a mobile game through advertising, rewarded ads remain at the top of the food chain. A recent survey of app publishers asked what their most successful monetization method was. Rewarded Video Ads won with 75% of the vote.

By offering users some kind of in-game reward, such as an extra life, a bonus item, or a new avatar, app developers can improve UI and engagement while encouraging ad views without bothering the user. Rewarded ads remain the ad unit with the highest earning potential.

LOOT BOXES

A loot box is a randomized box of in-game prizes. Users pay for an in-app purchase, but there is no guarantee of whether it will contain gold or pennies, the user has to make the decision to purchase in exchange a random reward. While this tactic is somewhat controversial in Europe where Belgium and the Netherlands have cracked down and labeled it gambling, it remains a popular tactic. Loot boxes are particularly effective for  Whales, wealthy mobile game users who will readily pay to improve their performance in the game.

SELL YOUR DATA

The collection and sale of data is a massive industry. If your app offers the technical means to collect user-generated data such as geolocation, it may be worth it to acquire user consent to license that data.

Applications like Waze & Foursquare receive community-generated data from their users and effectively leverage it to monetize their applications. Waze licenses data to businesses placing location-based ads, whereas Foursquare licenses point of interest geolocation data to Google & Apple for their first party GPS apps Apple Maps & Google Maps.

CONCLUSION

It is important to keep in mind that monetization is the icing on the cake—without an engaging game that hooks users, there will be nothing to monetize. However, making key decisions in the development process with the monetization strategy in mind will *literally* pay off in the long run.

Check out our previous blogs on Mobile Game Monetization for an overview of the fundamentals.