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The intersection of machine learning (ML) and product management is a rapidly evolving field. As businesses realize the power of AI-powered solutions across industries like advertising, finance, and healthcare, product managers who understand ML are becoming indispensable. How can product managers build successful careers in this exciting space, grab the unique opportunities ML provides for product innovation, and overcome the challenges of responsibly developing and deploying AI products? I spoke to Gleb Sinev, an ML product manager with experience at companies like Surfingbird, Handl, and Mantika.ai - a startup that is developing software for the early detection of lung cancer through computer vision technology - who offered valuable insights into the ML product manager's journey. Here's what we can learn from his extensive career. Adaptability, Diversity, and Understanding The global ML market was valued at $19.20 billion in 2022 and is . That’s a phenomenal rate of growth and offers an incredible opportunity, but it also shows that ML touches a significant number of business sectors and potential solutions. Sinev suggests that new ML product managers shouldn’t box themselves in by becoming specialists in only one of them until they’ve found their passion. expected to grow from $26.03 billion in 2023 to $225.91 billion by 2030 “Technologies and markets evolve rapidly,” Sinev said. “It is critical to stay updated on ML and associated advancements, such as computer vision, to remain competitive. Because ML can address challenges across industries, from ad personalization to lung cancer detection, product managers should be open to exploring a wide range of use cases.” Once an ML product manager has been involved in various applications, a passion for one or two of them will arise. Sinev’s advice is never to stop learning about the foundational aspects of ML, but once you have a clear interest in one sector, go as deep as you can to advance your career and come out as a leader in that.“ A foundational understanding of ML is vital at all times, but good product managers benefit even more when they deepen their expertise in specific domains like healthcare or finance once they understand this is the area they love the most,” Sinev said. “This also helps us keep the industry safe. As stewards of powerful technology, ML product managers must ensure data privacy, algorithmic fairness, and transparency, and a deep understanding of the requirements and regulations in one sector is easier to manage than staying broad.” Harnessing ML to Disrupt Industries ML's impact on product innovation is undeniable. It has changed how we understand massivamounts of information and allowed us to serve personalized products, services, music, movies, and messaging to everyone with a smartphone or a laptop. Sinev embarked on his professional journey as a product manager at Surfingbird, an AdTech company housing two distinct platforms: the consumer-centric Surfingbird, which provided personalized "read-more" content, and Relap.io, a B2B native advertising platform. Later, Sinev joined the startup Dbrain, now rebranded as Handl, as a product and project manager focusing on ML and Computer Vision. Handl later graduated from Y Combinator W20. Their primary product, DOCR, a SaaS solution for financial institutions, simplifies document information extraction and conversion into editable formats. Under Sinev's leadership, DOCR saw significant success, continually improving its capabilities, particularly in text recognition. Sinev also spearheaded the development of a data annotation platform, streamlining AI training data labeling across various modalities. He played a key role in product development, team management, and investor and client relations. “Our platform has been applied in diverse sectors, from food delivery quality control with Dodo Pizza to cattle health monitoring in agriculture, and has been embraced by industry leaders like Nestle, AMG, and Ligolab,” says Sinev. Importantly, AI excels at analyzing vast medical datasets, from CT scans to genomic data. Tools like Mantika.ai are pioneering ML's role as a vital tool for doctors, potentially saving lives. As an illustration, algorithms can up to two days in advance. A significant tool in this field is Google Cloud's "Target and Lead Identification Suite," which was introduced last year. This tool assists companies in forecasting and comprehending protein structures, which is essential for drug design and personalized medicine. predict the onset of sepsis Medical imaging analysis, such as CT scans, MRI, and X-rays, has seen significant advancements thanks to ML and AI. These technologies have demonstrated exceptional proficiency in image interpretation, reaching a level comparable to that of human experts. Notably, ML algorithms have achieved an . impressive 87.0% sensitivity and 92.5% specificity Sinev’s experience as a lead product manager at Mantika.ai centered around creating a machine learning-based healthcare product from scratch. “Our team devised a product that could identify potential early-stage lung cancer by flagging suspicious areas in CT scans,” Sinev said. “The product's successful deployment in several clinics reinforced Sinev’s belief in the potential of machine learning technologies for early cancer detection.” Sinev emphasizes the precision of machine learning in detecting early diseases like cancer, which enhances decision-making in clinical settings. This, in turn, has a significant impact on patient care and treatment planning. Overcoming Obstacles to ML Adoption Despite its potential, ML in product development faces challenges. Trust is paramount, especially in healthcare. Patients and other end-users are understandably cautious about AI's role in this field. Extensive testing, transparency, and human oversight are non-negotiable. But trust is crucial for products in any industry as consumers are more savvy than ever, especially after revelations from whistleblowers who explained how our social media data is being used to manipulate us. This is why ML product managers must stay up-to-date on country-wide, state-wide, and local regulations and laws. However, those regulations could be holding the industry back. “Outdated regulations sometimes hinder innovation,” Sinev said. “Product managers must work closely with stakeholders and policymakers to develop ethical and practical frameworks for AI deployment and ML applications.” ML's reliance on data can amplify existing biases, leading to discriminatory outcomes. A robust bias mitigation strategy is crucial. that an AI tool, trained on medical images such as X-rays and CT scans, had unexpectedly acquired the ability to identify patients' self-reported race. This capability emerged despite the tool being trained solely to aid clinicians in diagnosing patient images. A recent study discovered “The potential misuse of this technology, which can identify patients' race even when their physician cannot, raises concerns about the possibility of future abuse or inadvertently directing subpar care toward communities of color without detection or intervention, so we all have to play a part in ensuring lack of bias,” Sinev said. The Future of ML Product Managers ML's role in product development will only expand. Product managers should not only understand ML's technical underpinnings but also drive these considerations. “While powerful, ML shouldn't be treated as a cure-all,” Sinev said. “Products that integrate ML with the nuanced understanding of human users have the strongest chance of success.” Product managers should also, according to Sinev, be able to create systems that explain themselves to help eradicate errors, bias, and other problems. “Increasingly, stakeholders will demand more clarity on how ML systems reach decisions, and not just run with the decisions they make,” Sinev said. “Product managers must collaborate with developers on explainability techniques.” From social impact to unintended consequences, ML product managers have a broad responsibility. A strong ethical focus is not just good practice, but crucial for long-term success. According to Sinev, product managers who embrace ML can build exceptional products that solve critical problems innovatively. By remaining adaptable, prioritizing ethics, and fostering collaboration, these professionals will shape the future of countless industries.

The ML Product Manager: Building AI-powered Solution

Ingleside, 1st November 1917

Picturing a world where we can “upgrade” ourselves with cybernetic enhancements is starting to look more realistic by the day. Except not in a fantastical, Hollywood trope kind of way, but  instead with regards to hacking our brains and bodies to work in unfathomable ways. As we stand on the precipice of the next technological revolution, the rise of augmented humanity is blurring the boundaries between science fiction and reality. In this article, we'll look at the ways human augmentation is set to impact us and our jobs, and how it challenges what we know about life. Let's explore the trajectory of what it means to be human in a progressively technological world. Content Overview The Essence of Human Augmentation Is Augmentation a New Technology? Will Augmented Humanity Disrupt Societies and Businesses? Challenges and Problems of Augmented Humanity What Does It Take to Have a Successful 21st Century Career? Conclusion The Essence of Human Augmentation The concept of augmented humanity is about enhancing a person’s capacity and productivity by expanding or altering typical human abilities. In the coming Biotech Age, humans will no longer be the means to operate the machine. The two will work in synergy, ushering in an era where individuals and machines redefine the meaning of being human. And while we’re all cautiously curious about where this will lead us, many are excited to live out their Cyberpunk 2077 fantasies in real life. Imagine how we can improve our physical traits, intelligence, and social interactions with future tech. The potential for superhuman capabilities or improved quality of life lies ahead. Working and living with cyberware tools where your body is your interface will be the way to remain competitive and innovative in a rapidly changing technological landscape. **Is Augmentation a New Technology? \ Human augmentation is nowhere near a new concept. The idea of maximizing human abilities and functions through various means, including technology, has been explored for centuries. Take the use of eyeglasses, for example. Improving your vision is a form of augmentation that dates back to the 13th century. However, the scope and sophistication of these technologies have significantly evolved. Nowadays, human-computer interaction (HCI) has led to far more complex and powerful augmentation methods. These encompass a wide range of enhancements, such as exoskeletons, 3D bio-printed prosthetics, brain-computer interfaces (BCIs), and genetic engineering. While gadgets serve specific functions (for instance, smartphones for communication), augmentation physically alters or expands human abilities. Will Augmented Humanity Disrupt Societies and Businesses? Here’s some food for thought: Where is the threshold between human augmentation and human replacement? This is not just a futuristic dream anymore—we’re on the cusp of transforming societies and businesses as we know them. As the lines between biology and technology blur, people feel that robotics and artificial intelligence (AI) threaten the value of their work. There’s no telling how much industries and communities will change due to the fusion of man and machine. However, we already have telltale signs of how it will play off in certain fields. The augmented workforce is already a reality We’re not all transforming our physical bodies with cybernetic enhancements yet. However, many jobs are already heavily reliant on robotics and bioengineering to make decisions and execute tasks. Take the logistics industry, for instance, where companies like Amazon have revolutionized warehouse operations with robotics. These collaborative robots, or cobots, work alongside employees, handling repetitive or physically demanding labor. On the other hand, healthcare institutions are already helping patients with biologically inspired engineering solutions. This is a fantastic step toward a more inclusive and accessible world. People in need can receive mobility aids with bionic prosthetic devices or implants that repair damaged brain-muscle connections. These are just a couple of examples, but enough to illustrate that we are already living in a world where humans and machines coexist and complement each other. Automation and augmentation are our present and future means of enhancing job efficiency and saving lives. The next step is the physical merging between the two. **The convergence of biology, technology, and society \ , Elon Musk's brain implant startup, was called the PRIME Study. The company received FDA clearance for human trials after raising $280 million in funding. Musk has been working on this technology for several years, inspired by science fiction authors. Neuralink recently approved to begin its first human clinical trial The merging of biology and technology is a controversial topic, but we can’t ignore its many benefits. Body augmentation has the potential to revolutionize healthcare, enabling more precise surgeries, faster diagnoses, and personalized treatment plans. For Musk and others with ambitions of the same caliber, it’s been an uphill battle to get testing approval. Neuralink faced scrutiny after a monkey died during testing in 2022. We’ll wait for years to establish proper regulations before brain implants become widely available. The impact on industries and businesses Employees are increasingly becoming overseers of technology, focusing on complex decision-making, creative solutions, and customer interactions. While some view this as a positive development, others express fears of job displacement. The perceived threat comes from machines that are potentially more productive and, in some scenarios, on the brink of surpassing human intelligence—a concept known as the technological singularity. But automation is not eliminating jobs—it's reshaping them. Rather than succumbing to anxiety, we should focus on staying informed and preparing to take on new, dynamic roles. Take the advice of the great stoic philosopher Seneca, who once said, “It's ruinous for the soul to be anxious about the future and miserable in advance of misery.” This industrial transformation raises new challenges related to human-machine interactions, decision-making authority, and fairness. Therefore, we’ll see a lot of changes in laws and regulations to protect the fragile balance between humans and machines in the workforce. Rethinking learning and development In the 1995 movie , Keanu Reeves plays a character who gets gigabytes of important data slotted into his head. Johnny Mnemonic The plot is set in 2021, yet we still can’t download all of our favorite books or songs straight into our brains. However, augmentation tech can be a phenomenal tool for educational purposes. Teachers can modernize the classroom with augmented reality (AR) for interactive learning experiences. Ever heard of the method of loci? It’s a mnemonic technique that involves associating information with specific locations or places to help with memory recall and retention. AR makes the application of this technique much easier. Teachers can project a whole and visualize instructional content, promoting student engagement, faster learning, and real-time feedback. virtual environment Lastly, augmentation in the classroom can ensure inclusivity by providing visual and auditory support for students with learning difficulties. Challenges and Problems of Augmented Humanity One thing’s for sure: You can’t move the needle of innovation if you’re primarily motivated by fear. The tech revolution is led by those who are most daring: the risk-takers, visionaries, and pioneers who push boundaries. However, the general population is apprehensive about life-altering technology with unforeseen consequences. And with recent cautionary tales, such as the OceanGate Titan accident, many are shaking their heads at the irresponsible hubris of inventors who disregard human safety. Do the potential benefits of human augmentation outweigh the risks? Here are some of the primary concerns when it comes to these advanced ideas: Socioeconomic disparities and inequality: As of today, bionic solutions like bioprinted prosthetics are too expensive for the average person. There's a natural concern that these technologies may exacerbate existing socioeconomic inequalities. If that doesn’t change, access to cybernetic enhancements would be limited to those with financial means, potentially creating a division between augmented and non-augmented individuals. Regulatory and safety risks: Many are skeptical of inserting strange new devices into their bodies. Some conspiracy theories even speculate about mind control from brain microchips. The development and use of augmentation tech require robust regulatory frameworks to ensure safety and effectiveness. Issues like the monkey's death in Neuralink testing highlight the importance of rigorous safety measures. Existential questions: What does it mean to be human? Are we unique creations of the universe or nothing but highly intelligent mammals? What does it mean to be human if we can alter our biology beyond recognition? As technology changes the status quo of human potential, many ethical and existential questions arise with no unanimous answer. Psychological and societal impacts: Bodily augmentation could lead to unforeseen and even irreversible consequences. We still don’t know exactly what growing up with social media does to a child, let alone living with a chip in the head. The rapid integration of such technologies could disrupt established norms in ways that are challenging to predict. What Does It Take to Have a Successful 21st Century Career? Many are saying that AI will be the printing press of the 21st century. An invention that is so groundbreaking it will reshape society. In the not-so-distant future, our workplaces will be even more intertwined with technology. We’re very far off from an era where humans are obsolete—that’s a future no one is looking forward to. But how does one stay ahead of the curve in these dynamic times? Let’s dive into some skills and strategies for businesses and employees to transition into an augmented work environment. Skills and competencies required for future careers The best way to thrive in a tech-driven world is by working harmoniously side by side with intelligent machines. Excellence requires us to cultivate a diverse set of skills and competencies, the forefront of which would be digital literacy. Aside from universally applied human skills, we’ll all have to be a bit more flexible too. Traits like the ability to collaborate, communicate, and take responsibility will never stop being essential. In fact, industries like customer service are already leveraging advanced call center technology to enhance their efficiency and customer interactions, with key executive assistant skills at the core of their exercise. has become an important tool for businesses aiming to provide seamless customer experiences. It not only enhances efficiency but also empowers customer service representatives with real-time insights and data-driven solutions. Embracing this technology is not just an option; it's a necessity for companies striving to meet the growing demands of a digitally connected world. AI customer service But knowing how to adapt to the demands of an augmented workforce will be a major factor in our job skillset. **Preparing the workforce for the future with training and lifelong learning \ Something we can't ignore is the role of education in preparing people for a future with augmented careers. We can expect a rising demand for teachers and instructors in all fields related to human enhancement and artificial intelligence. Training the workforce to fit into this new narrative means redesigning education systems. New subjects of study will emerge with a greater emphasis on practical tech skills, including proficiency in managing and analyzing data from vast sources, such as a . data warehouse Lastly, in the upcoming years, more than ever, we’ll need to be proactive with our upskilling. Gaining vital knowledge won’t end with formal education. Instead, fostering a mindset of lifelong learning would be essential to remaining agile and valuable in the evolving job market. Conclusion Human augmentation stands at the forefront of innovation, promising to reshape our world. While it offers remarkable possibilities for transforming work and human-machine interaction, it demands a thoughtful balance between advancement and ethical considerations. We should all maintain a collective understanding that the point of augmenting humanity is about supplementing our abilities, not replacing them. We’re heading toward a bionic world and hold the power to shape a path for a more harmonious future. Let's seize the opportunities and address the concerns to make this evolution a benefit for all.

From Sci-Fi to Reality: The Dawn of Augmented Humanity

Sci-Fi থেকে বাস্তবে: বর্ধিত মানবতার ভোর

Von Science-Fiction zur Realität: Der Beginn der erweiterten Menschheit

De la ciencia ficción a la realidad: el amanecer de la humanidad aumentada

De la science-fiction à la réalité : l'aube de l'humanité augmentée

विज्ञान-कल्पना से वास्तविकता तक: संवर्धित मानवता की सुबह

SF から現実へ: 拡張人類の夜明け

공상과학에서 현실까지: 증강 인류의 여명

Da ficção científica à realidade: o alvorecer da humanidade aumentada

От научной фантастики к реальности: рассвет дополненного человечества

Bilim Kurgudan Gerçeğe: Artırılmış İnsanlığın Şafağı

Từ khoa học viễn tưởng đến hiện thực: Bình minh của nhân loại tăng cường

从科幻到现实：增强人类的黎明

254 Stories To Learn About Healthcare

In the world of cybersecurity, dumpster diving may not be the first thing that comes to mind when discussing potential threats. While organizations invest heavily in firewalls, antivirus software, and intrusion detection systems, they often overlook the very real and often underestimated risks associated with physical security breaches. Dumpster diving, a practice where cybercriminals scavenge through discarded documents and digital hardware, can yield a treasure trove of sensitive information that could be exploited for malicious purposes. This article explores the concept of dumpster diving in cybersecurity, its potential consequences, and ways to protect your organization against this often-neglected threat. The Origin of Dumpster Diving in Cybersecurity The concept of "Dumpster Diving" in the context of cybersecurity has its origins in the broader practice of physical dumpster diving, which predates the digital age. In this context of cybersecurity, dumpster diving is a tactic employed by malicious actors to obtain sensitive information, especially in a physical form, from discarded materials. While the term "dumpster diving" is often used in a cybersecurity context, it's essentially an extension of the traditional practice adapted to exploit vulnerabilities in an organization's physical security. Unlike the traditional dumpster diving which refers to the act of searching through trash or discarded items for valuables, often pursued by individuals looking for discarded goods, food, or potentially valuable items other than data on documents. The digital age and the increased use of electronic devices have expanded the scope of dumpster diving in cybersecurity. Cybercriminals may target discarded hard drives, laptops, USB drives, or other digital storage media to retrieve valuable data, such as confidential documents, passwords, or sensitive business information. The practice of dumpster diving in cybersecurity underscores the importance of holistic security measures. It serves as a reminder that information security isn't limited to digital safeguards but also encompasses physical security considerations. As a result, organizations must address this often underestimated threat by implementing policies and practices that protect sensitive data not only in the digital realm but in the physical world as well. What is Dumpster Diving in Cybersecurity? To concisely define Dumpster diving in Cybersecurity; It is a form of information harvesting where cybercriminals search through physical trash bins, recycling containers, or even digital trash folders for discarded documents or hardware. This practice involves both low-tech and high-tech methods to recover data. Types of Data Targeted by Dumpster Diving Threat Actors Dumpster diving can yield a variety of sensitive data, including printed documents, old hard drives, discarded laptops, USB drives, and other storage devices. Cybercriminals are looking for anything that might contain valuable information, such as confidential documents, passwords, intellectual property, or personally identifiable information (PII). Organizations mostly affected by Dumpster Diving Threat Actors Organizations across various industries and sectors can be affected by dumpster diving threat actors. Dumpster diving is not limited to a specific type of organization, as malicious actors may target any entity that handles sensitive or valuable information. It's essential for organizations in these and other sectors to recognize the potential risks of dumpster diving and take proactive measures to secure sensitive information. This includes implementing robust physical security measures, educating employees, and maintaining data disposal policies to mitigate the threat posed by dumpster diving threat actors. Some notable examples of organizations mostly affected: Large corporations and businesses often handle a vast amount of sensitive information, including intellectual property, financial records, and customer data. Dumpster diving can be a threat to these organizations, especially when disposing of outdated documents or electronic equipment. Corporations and Businesses: Banks, credit unions, and financial organizations deal with highly sensitive financial and personal information. Dumpster diving can result in the theft of customer data, account details, and financial records. Financial Institutions: Healthcare organizations store patient records, medical histories, and personal health information. Dumpster diving can lead to privacy breaches, identity theft, or medical fraud. Healthcare Providers: Government agencies handle classified and sensitive information. Dumpster diving can compromise national security or reveal confidential government operations. Government Agencies: Schools, colleges, and universities maintain records of students, faculty, and academic research. Dumpster diving can expose personal information or valuable research data. Educational Institutions: Law firms often handle confidential client information, legal documents, and case files. Dumpster diving can compromise attorney-client privilege and sensitive legal matters. Legal Firms: Retail businesses process customer payment information and may have customer databases. Dumpster diving can lead to the theft of customer data and payment details. Retailers: Organizations involved in research and development may possess proprietary information and trade secrets. Dumpster diving can result in the theft of valuable intellectual property. Research and Development Companies: Technology firms often handle sensitive information related to product designs, software code, and patents. Dumpster diving can compromise innovation and product development. Technology Companies: Nonprofits may store donor information, financial records, or grant proposals that contain sensitive details. Dumpster diving can affect their reputation and donor trust. Nonprofit Organizations: Various professional services, such as accounting, engineering, and consulting firms, maintain confidential client data. Dumpster diving can lead to the exposure of client information and business strategies. Professional Services Providers: Manufacturing companies may have confidential manufacturing processes, supply chain information, or product specifications. Dumpster diving can jeopardize their competitive edge. Manufacturers: Media and entertainment organizations create content, including scripts, storyboards, and unpublished work. Dumpster diving can result in unauthorized access to creative assets. Media and Entertainment Companies: Facilities related to critical infrastructure, such as energy, water, and transportation, may have security vulnerabilities exposed through dumpster diving. Critical Infrastructure: How to identify Dumpster Diving threats actors and vectors Identifying potential dumpster diving threat actors and vectors in the context of cybersecurity involves understanding the methods, motivations, and profiles of individuals who may engage in this activity. Recognizing dumpster diving threats and actors requires a proactive approach that combines physical security measures, employee training, and ongoing vigilance. By understanding the motivations and tactics of potential threat actors and implementing robust security practices, organizations can better protect themselves from this often underestimated cybersecurity risk. The key steps to help you recognize these threats include: Start by understanding why someone might engage in dumpster diving for malicious purposes. Common motivations include identity theft, corporate espionage, financial gain, or gathering competitive intelligence. Understanding Motivations: Identify potential threat actors based on their motivations. For example, a disgruntled former employee may seek revenge or financial gain through dumpster diving, while corporate spies from competitors might be after valuable business secrets. Profile Threat Actors: Assess your organization's physical security vulnerabilities. Look for weak points in your disposal process, such as unsecured dumpsters, improper disposal of sensitive documents, or lack of employee awareness regarding data disposal. Recognizing Vulnerabilities: Train employees to recognize the importance of proper document and data disposal. Make them aware of the risks associated with dumpster diving and encourage a culture of security within the organization. Employee Training: Invest in physical security measures such as locked dumpsters, surveillance cameras, and access controls to prevent unauthorized access to disposal areas. Implementing Security Measures: Implement data destruction policies that include shredding sensitive documents before disposal and physically destroying electronic storage devices to make data recovery difficult. Secure Data Destruction: Conduct periodic audits of disposal areas to check for signs of tampering or unauthorized access. These audits can help detect and prevent potential threats. Regular Audits: Stay informed about known incidents related to dumpster diving in your industry or region. Threat intelligence can provide insights into the tactics used by malicious actors. Threat Intelligence: Develop an incident response plan that includes procedures for handling potential dumpster diving incidents. This plan should outline steps to take if sensitive information is suspected to have been compromised. Incident Response Plan: In the event of a dumpster diving incident, collaborate with law enforcement agencies and provide them with any information or evidence that could assist in identifying and apprehending the threat actors. Collaborate with Law Enforcement: Encourage employees to report any suspicious activities or potential breaches related to dumpster diving. They can be valuable sources of information in identifying threats. Employee Reporting: Some threat actors may attempt to sell or trade stolen data on the dark web or underground forums. Consider monitoring these channels for any information related to your organization. Monitoring Dark Web and Online Forums: Consequences of Dumpster Diving The consequences of Dumpster Diving are quite vast; however, this article will narrow to the followings: One of the most immediate risks associated with dumpster diving is identity theft. Cybercriminals can use the information they find to impersonate individuals, opening fraudulent accounts or committing financial fraud. Identity Theft and Fraud: In the corporate world, dumpster diving can result in the theft of intellectual property, business strategies, and confidential client information. Competitors may exploit this data to gain a competitive advantage. Corporate Espionage: Dumpster diving can be an entry point for larger-scale data breaches. By piecing together bits of information gathered from trash, attackers can construct a more comprehensive view of an organization's security weaknesses. Data Breaches: Methods used by organization to mitigate/curb Dumpster Diving threats Dumpster diving is a real and often underestimated threat in the realm of cybersecurity. While organizations invest heavily in digital security measures, they must not overlook the potential consequences of physical security breaches. Mitigating and curbing dumpster diving threats involves a combination of proactive implementation of strict data disposal policies, educating employees, and enhancing of physical security measures, employee training, and established policies and procedures. By implementing these measures, organizations can significantly reduce the risks associated with dumpster diving and protect sensitive information from falling into the wrong hands. This proactive approach to physical security complements digital security measures in safeguarding an organization's valuable data. Hence, this will further address this often neglected threat and viable businesses can fortify their overall cybersecurity posture and reduce the potential for data breaches and identity theft. The underlisted methods are used by organizations to mitigate dumpster diving threats: Implement a secure document disposal policy that includes shredding sensitive documents before disposal. Ensure that employees are aware of the importance of proper document disposal. Secure Document Disposal: Encrypt sensitive data on electronic storage devices to make it unreadable if the device is found. Additionally, physically destroy old hard drives and storage devices before disposal. Data Encryption: Enhance physical security around disposal areas. Use locked dumpsters or containers that are not easily accessible to outsiders. Consider installing security cameras to monitor the disposal area. Physical Security Measures: Restrict access to areas where dumpsters or trash bins are located. Use access controls, such as key card systems or locked gates, to limit unauthorized entry. Access Control: Educate employees about the risks of dumpster diving and the importance of proper data disposal. Regularly conduct security awareness training to ensure all staff members are informed. Employee Training: Establish clear policies for document retention and disposal. Ensure that documents are only kept for as long as necessary and are properly disposed of when they reach the end of their useful life. Document Retention Policies: Conduct periodic audits of disposal areas to check for signs of tampering or unauthorized access. This can help detect and prevent potential threats. Regular Audits: Consider outsourcing the shredding of sensitive documents to professional shredding services. These services often provide secure containers and a chain of custody for documents until they are shredded. Physical Shredding Services: Use locks on dumpsters to prevent easy access. Locks can be a simple yet effective physical deterrent to potential dumpster divers. Dumpster Locks: Maintain an inventory of sensitive data within the organization and classify it based on its sensitivity. This helps in identifying what needs extra protection and proper disposal. Data Inventory and Classification: Develop an incident response plan specifically for dealing with dumpster diving incidents. This should include steps to take if sensitive information is suspected to have been compromised. Incident Response Plan: In the event of a dumpster diving incident, collaborate with local law enforcement agencies and provide them with any information or evidence that could assist in identifying and apprehending the threat actors. Collaboration with Law Enforcement: Ensure employees maintain clean workspaces and do not leave sensitive documents or electronic devices unattended. Implementing a Clean Desk Policy: Stay informed about known incidents related to dumpster diving in your industry or region. Threat intelligence can provide insights into the tactics used by malicious actors. Threat Intelligence and Monitoring: Final Thoughts In conclusion, it is apposite to note that in an era dominated by digital threats and cybersecurity challenges, it is easy to overlook the physical aspects of security. Dumpster diving, a seemingly outdated practice, remains a potent threat that, when underestimated, can expose individuals and organizations to significant risks. The potential consequences of a successful dumpster diving attack include identity theft, corporate espionage, data breaches, and financial fraud. As such, organizations must take proactive measures to safeguard their sensitive information and protect their interests. These precautions outlined in this article are vital in mitigating the dumpster diving threat. Secure document disposal, data encryption, physical security measures, access control, employee training, and incident response planning are all essential components of a comprehensive strategy to counter this risk. By incorporating these precautions into their security protocols, organizations can significantly reduce the likelihood of sensitive data falling into the wrong hands. Dumpster diving has served as a stark reminder that cybersecurity is not limited to the digital realm. The boundary between the physical and digital worlds is often blurred, making it crucial for organizations to fortify both aspects of their security posture. Neglecting physical security vulnerabilities can expose an organization to devastating consequences, especially when dealing with confidential data, proprietary information, and personally identifiable information. This menace remains a tangible and persistent threat in the world of cybersecurity. The recognition of its risks, understanding potential threat actors and vectors, and implementing precautionary measures, organizations can take substantial steps toward protecting their sensitive information and further create a comprehensive defense against the multifaceted landscape of modern security threats. In the ongoing battle for data security, vigilance and preparedness are the keys to success, and dumpster diving is a threat that no organization can afford to overlook.

Dumpster Diving in Cybersecurity: A Deep Dive into a Neglected Threat

To unlock the main character’s theurgy moves in P3Reload, you have to have certain personas unlocked. So, it’s important to keep expanding the compendium. After explaining what they are and how to use them, the game gives you 1 to start with, but 6 more can be unlocked. Here’s how: Jack Brothers Personas you need: Jack o’Lanter, Jack Frost Its ability: “Deals medium Almighty damage to all foes. High chance of inflicting Down.” King and I Personas you need: Black Frost, King Frost Its ability: “Heavy Ice damage to all foes, ignoring resistances. Medium chance of Freeze.” Best Friends Personas you need: Decarabia, Forneus Its ability: “Next physical/magic attack will deal more than double damage for 1 ally.” Scarlet Havoc Personas you need: Siegfried, Mithras Its ability: “Deals severe Slash damage to all foes, ignoring resistances.” Trickster Personas you need: Susano-o, Loki Its ability: “Massive Almighty damage to all foes. High chance of inflicting an ailment.” Armageddon Personas you need: Helel, Satan Its ability: “Ultimate Almighty damage to all foes, but reduce’s user’s HP and SP to 1.” Are the Theurgy Special Moves Necessary? Necessary? No, you can get by without unlocking them or even using them. But if you’re struggling to get through Tartarus, especially the Gatekeepers, the Theurgy moves really come in handy. Regardless of the boss, its health, or how high a level it is, the Theurgy can put a sizable dent into their life bar. On top of the Gatekeepers, they work really well on the Monad bosses. Those bosses aren’t particularly hard but when you have to fight 3 back to back, having some extra firepower doesn’t hurt. And if you’re worried about these Theurgy moves making the experience too easy for you, you can just not use them. That’s the great thing about them. Which Theurgy Special Move Is the Best? The one that I pulled out the most was Scarlet Havoc. It really came in clutch when I wanted to steamroll Gatekeeper after Gatekeeper, and it’s legitimately one of the best if not the best in the game. However, the most powerful by far is Armageddon. It does 9,999 damage to all enemies. Which sounds great, until you realize it essentially wipes out your character’s HP and SP; it completely reduces both to 1. This isn’t really that big of a deal if you have a healer on your team, and you can regain SP if you have an invigorate ability, but it can be risky if you’re careless. So, while the other special moves aren’t bad, for my buck, Scarlet Havoc and Armageddon are the two best Theurgy Special Moves in Persona 3 Reload. Read More: The MCU Phase 4 Movies in Order The MCU Phase 3 Movies in Order The Apollo Justice Trilogy: The 5 Best Cases Feature image source

How to Unlock All the MC's Theurgy Super Moves in Persona 3 Reload

পারসোনা 3 রিলোড-এ সমস্ত MC এর Theurgy সুপার মুভগুলি কীভাবে আনলক করবেন

So schalten Sie alle Theurgy-Supermoves des MC in Persona 3 Reload frei

Cómo desbloquear todos los súper movimientos de teurgia del MC en Persona 3 Reload

Comment débloquer tous les super mouvements de théurgie du MC dans Persona 3 Reload

पर्सोना 3 रीलोड में एमसी के सभी थर्गी सुपर मूव्स को कैसे अनलॉक करें

ペルソナ 3 リロードですべての MC のテウルギー スーパー ムーブのロックを解除する方法

페르소나 3 리로드에서 모든 MC의 Theurgy 슈퍼 무브를 잠금 해제하는 방법

Como desbloquear todos os super movimentos de teurgia do MC no Persona 3 Reload

Как разблокировать все суперприемы теургии MC в Persona 3 Reload

Persona 3 Reload'da MC'nin Tüm Theurgy Süper Hareketlerinin Kilidini Açma

Cách mở khóa tất cả các chiêu thức siêu phàm của MC trong Persona 3 Tải lại

如何解锁《女神异闻录 3：重装上阵》中 MC 的所有神术超级动作

Authors:
(1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions;
(2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine;
(3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions;
(4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions;
(5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Table of Links Introduction Methods Results and Discussions Conclusion & References Introduction The integration and exchange of health data across diverse platforms and systems remain challenging due to the absence of standardized formats and a shared semantic understanding. This challenge becomes more significant when critical health information is embedded in unstructured data rather than well-organized structured formats. Standardizing unstructured health data, such as clinical notes, into FHIR resources can alleviate ambiguity across different health providers and, therefore, improve interoperability. However, it is by no means an easy task. Previous studies 1,2 have attempted to transform clinical notes into FHIR resources using a combination of natural language processing and machine learning tools through multi-step processes involving clinical named entity recognition, terminology coding, mathematical calculations, structural formatting, and human calibrations. However, these approaches require additional human effort to consolidate the results from multiple tools and have achieved only moderate performances, with F1 scores ranging from 0.7 to 0.9 in different elements. To this end, we intend to harness Large Language Models (LLMs) to directly generate FHIR-formatted resources from free-text input. The utilization of LLMs is expected to simplify the previously multi-step processes, enhance the efficiency and accuracy of automatic FHIR resource generation, and ultimately improve health data interoperability. Methods Data Annotation To the best of our knowledge, there is no largely publicly available dataset in the FHIR standard that is generated from contextual data. Therefore, we have chosen to annotate a dataset containing both free-text input and structured output in FHIR formats. The free-text input was derived from the discharge summaries of the MIMICIII datase. 3 Thanks to the 2018 n2c2 medication extraction challenge 4 , which essentially involves named entity recognition tasks, elements in medication statements have been identified. Our annotations built upon these n2c2 annotations and standardized the free text into multiple clinical terminology coding systems, such as NDC, RxNorm, and SNOMED. We organized the contexts and codes into FHIR medicationStatement resources. The converted FHIR resources underwent validation by the official FHIR validator (https://validator.fhir.org/) to ensure compliance with FHIR standards, including structure, datatype, code sets, display names, and more. These validated results were considered the gold standard transformation results and could be used to test against the LLMs. No ethical concerns exist regarding data usage, as both the MIMIC and n2c2 datasets are publicly available to authorized users. Large Language Model We used OpenAI's GPT-4 model as the LLM for FHIR format transformation. We used five separate prompts to instruct the LLM to transform input free text into medication (including medicationCode, strength, and form), route, schedule, dosage, and, reason, respectively. All prompts adhered to a template with the following strucuture: task instructions, expected output FHIR templates in .JSON format, 4-5 conversion examples, a comprehensive list of codes from which the model can make selections, and then the input text. As there was no finetuning or domain-specific adaptation in our experiments, we initially had the LLM generate a small subset (N=100). Then, we manually reviewed the discrepancies between the LLM-generated FHIR output and our human annotations. Common mistakes were identified and used to refine the prompts. It's important to note that we did not have the access to the whole lists of NDC, RxNorm, and SNOMED Medication codes for drug names, as well as SNOMED Finding codes for reasons. Additionally, even if we had such comprehensive lists, they would have exceeded the token limits for LLMs. Thus, we did not task LLMs with coding these entities; instead, we instructed them to identify the contexts mentioned in the input text. For other elements, e.g. drug routes and forms, numbering in the hundreds, we allowed LLMs to directly code them. When evaluating the LLM-generated output, our primary criterion was the exact match rate, which necessitates precise alignment with human annotations in all aspects, including codes, structures, and more. Additionally, we reported precision, recall, and F1 scores for specific element occurrences. We accessed the GPT-4 APIs through the Azure OpenAI service, aligning with responsible use guidelines for MIMIC data. The specific model we used was 'gpt-4-32k' in its '2023-05-15' version. Each text input was individually transformed into a MedicationStatement resource. To optimize efficiency, we made multiple asynchronous API calls. Results and Discussions The results of annotation and FHIR generation are presented in Table 1. In summary, we annotated 3,671 medication resources, covering over 625 distinct medications and associated with 354 reasons. The Large Language Model (LLM) achieved an impressive accuracy rate of over 90% and an F1 score exceeding 0.96 across all elements. In prior studies, F1 scores reached 0.750 in timing.repeat, 0.878 in timing.route, and 0.899 in timing dosage. 1 The LLM improved these F1 scores by at least 8%. It's worth noting that the previous studies used a smaller private dataset, did not employ the strictest evaluation metrics like exact match rate, skipped terminology coding, and required extensive training. On further investigation, we were also impressed by the high accuracy in terminology coding (which essentially involves a classification task with more than 100 classes), mathematical conversion (e.g., inferring a duration of 10 days when the input mentions 'TID, dispense 30 tablets'), format conformity (with less than a 0.3% chance that the results cannot be interpreted in .JSON format), and cardinality (the LLM can handle both 1:N and 1:1 relationships). The accuracy of the output is highly dependent on the instruction prompts used. Based on our extensive trials and errors, we have the following recommendations: i) provide diverse conversion examples that encompass a wide range of heterogeneous edge cases; ii) use strong language, such as “MUST”, to ensure that the output adheres to the expected formats and rults; iii) continuously update and refine the prompts by reviewing results from a small subset, which can help identify common mistakes and enhance overall accuracy; iv) be cautious about out-of-vocabulary codings. LLMs may attempt to cater users by inventing codes that do not exist when they cannot find a close match. Conclusion In this study, we provided the foundations of leveraging LLMs to enhance health data interoperability by transforming free-text input into the FHIR resources. Future studies will aim to build upon these successes by extending the generation to additional FHIR resources and comparing the performance of various LLM models. Reference 1. Hong N, Wen A, Shen F, Sohn S, Liu S, Liu H, Jiang G. Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Summits on Translational Science Proceedings. 2018;2018:74. 2. Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, Jiang G. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA open. 2019 Dec;2(4):570-9. 3. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific data. 2016 May 24;3(1):1-9. 4. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 2020 Jan;27(1):3-12. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions; (2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine; (3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions; (4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions; (5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Authors: Authors: (1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions; (2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine; (3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions; (4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions; (5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Table of Links Introduction Introduction Methods Methods Results and Discussions Results and Discussions Conclusion & References Conclusion & References Introduction The integration and exchange of health data across diverse platforms and systems remain challenging due to the absence of standardized formats and a shared semantic understanding. This challenge becomes more significant when critical health information is embedded in unstructured data rather than well-organized structured formats. Standardizing unstructured health data, such as clinical notes, into FHIR resources can alleviate ambiguity across different health providers and, therefore, improve interoperability. However, it is by no means an easy task. Previous studies 1,2 have attempted to transform clinical notes into FHIR resources using a combination of natural language processing and machine learning tools through multi-step processes involving clinical named entity recognition, terminology coding, mathematical calculations, structural formatting, and human calibrations. However, these approaches require additional human effort to consolidate the results from multiple tools and have achieved only moderate performances, with F1 scores ranging from 0.7 to 0.9 in different elements. To this end, we intend to harness Large Language Models (LLMs) to directly generate FHIR-formatted resources from free-text input. The utilization of LLMs is expected to simplify the previously multi-step processes, enhance the efficiency and accuracy of automatic FHIR resource generation, and ultimately improve health data interoperability. Methods Data Annotation To the best of our knowledge, there is no largely publicly available dataset in the FHIR standard that is generated from contextual data. Therefore, we have chosen to annotate a dataset containing both free-text input and structured output in FHIR formats. The free-text input was derived from the discharge summaries of the MIMICIII datase. 3 Thanks to the 2018 n2c2 medication extraction challenge 4 , which essentially involves named entity recognition tasks, elements in medication statements have been identified. Our annotations built upon these n2c2 annotations and standardized the free text into multiple clinical terminology coding systems, such as NDC, RxNorm, and SNOMED. We organized the contexts and codes into FHIR medicationStatement resources. The converted FHIR resources underwent validation by the official FHIR validator (https://validator.fhir.org/) to ensure compliance with FHIR standards, including structure, datatype, code sets, display names, and more. These validated results were considered the gold standard transformation results and could be used to test against the LLMs. No ethical concerns exist regarding data usage, as both the MIMIC and n2c2 datasets are publicly available to authorized users. Data Annotation Large Language Model We used OpenAI's GPT-4 model as the LLM for FHIR format transformation. We used five separate prompts to instruct the LLM to transform input free text into medication (including medicationCode, strength, and form), route, schedule, dosage, and, reason, respectively. All prompts adhered to a template with the following strucuture: task instructions, expected output FHIR templates in .JSON format, 4-5 conversion examples, a comprehensive list of codes from which the model can make selections, and then the input text. As there was no finetuning or domain-specific adaptation in our experiments, we initially had the LLM generate a small subset (N=100). Then, we manually reviewed the discrepancies between the LLM-generated FHIR output and our human annotations. Common mistakes were identified and used to refine the prompts. It's important to note that we did not have the access to the whole lists of NDC, RxNorm, and SNOMED Medication codes for drug names, as well as SNOMED Finding codes for reasons. Additionally, even if we had such comprehensive lists, they would have exceeded the token limits for LLMs. Thus, we did not task LLMs with coding these entities; instead, we instructed them to identify the contexts mentioned in the input text. For other elements, e.g. drug routes and forms, numbering in the hundreds, we allowed LLMs to directly code them. When evaluating the LLM-generated output, our primary criterion was the exact match rate, which necessitates precise alignment with human annotations in all aspects, including codes, structures, and more. Additionally, we reported precision, recall, and F1 scores for specific element occurrences. We accessed the GPT-4 APIs through the Azure OpenAI service, aligning with responsible use guidelines for MIMIC data. The specific model we used was 'gpt-4-32k' in its '2023-05-15' version. Each text input was individually transformed into a MedicationStatement resource. To optimize efficiency, we made multiple asynchronous API calls. Large Language Model Results and Discussions The results of annotation and FHIR generation are presented in Table 1. In summary, we annotated 3,671 medication resources, covering over 625 distinct medications and associated with 354 reasons. The Large Language Model (LLM) achieved an impressive accuracy rate of over 90% and an F1 score exceeding 0.96 across all elements. In prior studies, F1 scores reached 0.750 in timing.repeat, 0.878 in timing.route, and 0.899 in timing dosage. 1 The LLM improved these F1 scores by at least 8%. It's worth noting that the previous studies used a smaller private dataset, did not employ the strictest evaluation metrics like exact match rate, skipped terminology coding, and required extensive training. On further investigation, we were also impressed by the high accuracy in terminology coding (which essentially involves a classification task with more than 100 classes), mathematical conversion (e.g., inferring a duration of 10 days when the input mentions 'TID, dispense 30 tablets'), format conformity (with less than a 0.3% chance that the results cannot be interpreted in .JSON format), and cardinality (the LLM can handle both 1:N and 1:1 relationships). The accuracy of the output is highly dependent on the instruction prompts used. Based on our extensive trials and errors, we have the following recommendations: i) provide diverse conversion examples that encompass a wide range of heterogeneous edge cases; ii) use strong language, such as “MUST”, to ensure that the output adheres to the expected formats and rults; iii) continuously update and refine the prompts by reviewing results from a small subset, which can help identify common mistakes and enhance overall accuracy; iv) be cautious about out-of-vocabulary codings. LLMs may attempt to cater users by inventing codes that do not exist when they cannot find a close match. Conclusion In this study, we provided the foundations of leveraging LLMs to enhance health data interoperability by transforming free-text input into the FHIR resources. Future studies will aim to build upon these successes by extending the generation to additional FHIR resources and comparing the performance of various LLM models. Reference 1. Hong N, Wen A, Shen F, Sohn S, Liu S, Liu H, Jiang G. Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Summits on Translational Science Proceedings. 2018;2018:74. 2. Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, Jiang G. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA open. 2019 Dec;2(4):570-9. 3. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific data. 2016 May 24;3(1):1-9. 4. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 2020 Jan;27(1):3-12. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license.

Enhancing Health Data Interoperability with Large Language Models: A FHIR Study

বৃহৎ ভাষার মডেলগুলির সাথে স্বাস্থ্য ডেটা আন্তঃক্রিয়াশীলতা বৃদ্ধি করা: একটি এফএইচআইআর স্টাডি

Verbesserung der Interoperabilität von Gesundheitsdaten mit großen Sprachmodellen: Eine FHIR-Studie

Mejora de la interoperabilidad de los datos sanitarios con modelos de lenguaje grandes: un estudio del FHIR

Améliorer l'interopérabilité des données de santé avec de grands modèles linguistiques : une étude FHIR

बड़े भाषा मॉडल के साथ स्वास्थ्य डेटा इंटरऑपरेबिलिटी को बढ़ाना: एक FHIR अध्ययन

大規模言語モデルによる医療データの相互運用性の強化: FHIR 調査

대규모 언어 모델을 통한 건강 데이터 상호 운용성 향상: FHIR 연구

Melhorando a interoperabilidade de dados de saúde com grandes modelos de linguagem: um estudo FHIR

Повышение совместимости медицинских данных с помощью больших языковых моделей: исследование FHIR

Büyük Dil Modelleriyle Sağlık Verilerinin Birlikte Çalışabilirliğini Artırma: Bir FHIR Çalışması

Nâng cao khả năng tương tác dữ liệu sức khỏe với các mô hình ngôn ngữ lớn: Nghiên cứu của FHIR

利用大型语言模型增强健康数据互操作性：FHIR 研究

The quest for love can often feel like navigating a labyrinth of endless options and fleeting connections. The rise of dating apps has transformed the modern dating scene, offering both unprecedented opportunities and daunting challenges. Online dating is riddled with pitfalls that can impact users' mental health and overall well-being. , the company behind over 70 online dating and social discovery sites and apps, conducted a curious survey shedding light on the evolving dynamics of digital romance. Their findings reveal a deep-seated desire among users for mindfulness, healthier connections, and personal growth. Social Discovery Group Data speaks volumes, indicating a notable shift in the behavior of Gen Z, the vanguards of digital innovation. A staggering 46% of Gen Zers utilize apps to combat loneliness, compared to just 15% of millennials and 9% of Gen X. But what drives this shift towards prioritizing mental health on dating apps? The answer lies in the profound impact of technology on our social lives. As users grapple with the complexities of modern dating, they seek solace in platforms that offer more than just superficial connections. They yearn for healing tips and strategies for healthy communication, recognizing that mental health is paramount in fostering meaningful relationships. “Technology was supposed to connect people, but, in fact, it has increased our loneliness. Screens, in some way, have separated us from each other because connection doesn't necessarily mean attention, empathy, or love,” said Dmitry Volkov, Ph.D, Founder of Social Discovery Group. “However, I would like to argue that technology can indeed be beneficial and make our social lives different and better. I believe AI is one of those significant ideas that can serve as a cure for loneliness, providing support and companionship, and enhancing the overall well-being of people in an increasingly digitized world.” Challenges Users Face in Modern Dating and Their Impact on Mental Health Navigating the modern dating scene can be a minefield of challenges, often leading to feelings of loneliness, anxiety, and diminished self-esteem. Ghosting, a common occurrence where communication abruptly ceases without explanation, leaves individuals feeling rejected and questioning their worth. Similarly, breadcrumbing, characterized by sporadic flirtatious messages or attention, fosters confusion and insecurity instead of genuine connections. These behaviors often leave users emotionally drained and unsatisfied, exacerbating mental health struggles. Online dating is not without its darker side, with nearly of women experiencing harassment, ranging from offensive name-calling to unsolicited explicit images. Such pervasive harassment not only threatens women's safety but also worsens mental health, contributing to anxiety, depression, and low self-esteem. 75% By embracing platforms that prioritize mindfulness, healthier connections, and personal growth, we can cultivate a more compassionate and supportive online dating community. Why People Are Motivated to Prioritize Mental Health Amidst the trials of modern dating, users are increasingly recognizing the importance of prioritizing mental health and wellbeing. In a world of superficiality and instant gratification, they crave deeper, more meaningful connections that nourish the soul. Kate Kubrick, a marketing expert from SDG, notes, Users seek genuine connections that resonate on a deeper level, transcending surface-level interactions. "Today's dating trends reflect a societal shift towards mindfulness and authenticity." How Dating Apps Enhance Features to Safeguard Mental Health Dating apps have come a long way from simply connecting people based on interests. Nowadays, they're evolving to meet users' changing needs, with a strong emphasis on mental health and wellbeing. Modern users demand more than just basic matchmaking. They seek features that cater to specific needs, ensuring that every individual finds their ideal space for communication and meeting new people while prioritizing mental health. Social Discovery Group recognizes this demand and is constantly innovating to meet it. Let’s explore some notable examples. Niche applications like , an SDG app for individuals aged 45 and above, gather similar audiences in a safe space free from ageism. These communities offer a sense of belonging and understanding, fostering genuine connections. Building Communities. DateMyAge SDG incorporates game mechanics into its apps to make the experience more engaging and enjoyable. Features like interactive games help users stay distracted while waiting for a response from a match. For instance, introduces a team-centric game revolving around Haikus--Japanese-style poems--allowing users to showcase their creativity and personality. Gamification. Dil Mil SDG leverages artificial intelligence (AI) to enhance the user experience. By incorporating AI into algorithms, the apps create personalized search feeds tailored to each user's preferences. This ensures that users are presented with relevant matches, increasing the likelihood of meaningful connections. AI Integration. To maintain a safe and positive environment, SDG verifies users and implements AI moderation to prevent unwanted content. This proactive approach helps to create a respectful and welcoming community where users can interact without fear of harassment or abuse. User Verification and AI Moderation. Mental Health and Queer Community For queer individuals, dating apps can be a vital resource in a world that may not always be accepting or inclusive. These platforms provide a safe space for LGBTQ+ individuals to explore their identity, connect with others, and form supportive communities. However, queer individuals also face unique challenges in the dating world, including discrimination, fetishization, and erasure. It is crucial for dating apps to prioritize inclusivity and diversity to ensure that all users feel seen, heard, and valued. One of the examples of how SDG works within the queer community is the app. Designed to bring new joy and freedom into users' lives, this fabulous gay dating app offers a vibrant platform where like-minded individuals can easily strike up conversations and forge meaningful connections in a welcoming and inclusive environment. GuysOnly Mental Health and AI Artificial intelligence (AI) has emerged as a game-changer in the realm of online dating, offering users personalized support and guidance. AI-driven features, such as chatbots and behavior analysis algorithms, are revolutionizing how we approach dating and relationships. By leveraging AI technologies, dating apps can better understand users' preferences, anticipate their needs, and facilitate meaningful connections. Whether it's boosting confidence, providing validation, or offering coping strategies for loneliness, AI has the potential to transform the online dating experience for the better. Take, for example, conversational AI apps. These platforms assist users in increasing confidence, navigating complex relationships, and reducing anxiety around sexuality. A significant 38% of men have used AI to verbalize their needs and desires, while 42% credit AI with helping them navigate arguments or disagreements in their real-life relationships. Moreover, the benefits of AI extend beyond individual users to society as a whole. By providing a sense of companionship and decreasing feelings of loneliness, AI has become a powerful tool in combating the loneliness epidemic. In fact, 71% of singles who choose not to meet their matches in person report that virtual messaging alone has alleviated feelings of isolation. As dating apps continue to evolve, so too do our expectations and aspirations. No longer satisfied with mere "matching," users are demanding platforms that prioritize their mental health and well-being. By embracing mindfulness, authenticity, and inclusivity, dating apps have the power to foster meaningful connections that enrich our lives and nourish our souls. actively works to prioritize our mental health and embrace the transformative potential of technology to create a world where love and well-being go hand in hand. Social Discovery Group

How Dating Apps Shift Focus from Simple “Matching” to Mental Health

কীভাবে ডেটিং অ্যাপগুলি সাধারণ "ম্যাচিং" থেকে মানসিক স্বাস্থ্যের দিকে মনোনিবেশ করে

Wie Dating-Apps den Fokus vom einfachen „Matching“ auf die psychische Gesundheit verlagern

Cómo las aplicaciones de citas cambian el enfoque de la simple "combinación" a la salud mental

Comment les applications de rencontres déplacent l’attention du simple « matching » vers la santé mentale

कैसे डेटिंग ऐप्स फोकस को साधारण "मैचिंग" से हटाकर मानसिक स्वास्थ्य पर केंद्रित करते हैं

マッチングアプリはいかにして単純な「マッチング」からメンタルヘルスに焦点を移すか

데이트 앱이 단순한 "매칭"에서 정신 건강으로 초점을 전환하는 방법

Como os aplicativos de namoro mudam o foco da simples “combinação” para a saúde mental

Как приложения для знакомств смещают фокус с простого «совпадения» на психическое здоровье

Flört Uygulamaları Odağı Basit "Eşleştirmeden" Akıl Sağlığına Nasıl Kaydırıyor?

Cách các ứng dụng hẹn hò chuyển trọng tâm từ “kết hợp” đơn giản sang sức khỏe tâm thần

约会应用程序如何将焦点从简单的“匹配”转移到心理健康

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