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Professionals in the book publishing industry are terrified of artificial intelligence — and for good reason. While it isn’t all bad, it has the potential to bring the sector to its knees. Not to mention, it has already harmed many creatives. Are their fears justified or unwarranted? AI’s Emerging Role in the Publishing Industry Thanks to its versatility, AI has emerged in most parts of the publishing industry. In 2023, 47% of book publishers based in the United States used it for marketing, 25% for editorial work, and 12% for content generation. Companies can use it in writing, editing, advertising, or administrative roles — it can do almost everyone’s job to some extent. AI Isn’t the First Threat, But It’s the Biggest Many disruptors have affected the publishing industry. Most recently, e-books have started replacing physical books. According to one estimate, about 30% of pages will be digitally printed by 2028, up from 5% in 2023. Many publishing houses and self-publishers are migrating to digital. Before that, digital printing shook up the offset printing press. Although AI isn’t the first disruptor, it’s the biggest so far. The publishing sector has withstood others because they were all natural evolutions to outdated processes. Machine learning (ML) and large language models are different — they replace existing technology instead of improving it. Who needs writers, editors, and agents when algorithms can do their work for free? Writers, Editors, and Publishers Are Terrified of AI Most professionals in book publishing are terrified of generative AI. Its versatility could bring about the sector’s downfall. A handful of high-quality models can write a novel, proofread, edit, design a cover, make marketing materials, post on social media for publicity, distribute digital copies, and handle accounting. For now, AI can’t perform any of these jobs better than a human. One industry professional said the content it generates is “crude at best,” arguing it isn’t comparable to what a real writer can produce. Even so, it might not matter when it can pump out pages at an inhuman speed. AI will inevitably affect the entire industry even if every author, editor, and publisher agrees not to use it. This technology’s accessibility is its main driving force. Dozens of leading models — and thousands more in-house systems — exist. Almost anyone can use ChatGPT, Midjourney, LaMDA, or Claude to make, market, and distribute AI-generated books. The Negative Impact of AI on the Publishing Industry There’s abundant evidence of AI’s negative impacts on the book publishing industry. AI Is Stealing People’s Jobs There’s truth to the fear that AI is set to steal people’s jobs. While more job ads now stress human-written content is a hard requirement and more companies are investing in software to detect AI-generated content, the publishing sector’s pushback hasn’t been enough — and people are being forced to find new careers. Olivia Lipkin, a copywriter, said her assignments dwindled after ChatGPT came out. She even claimed management changed her title to “Olivia/ChatGPT” on the company’s messaging platform. Within a few months of the generative model’s release, Lipkin was let go without explanation. Eventually, she discovered her managers had realized AI was much cheaper. It’s Stealing Their Work, Too Algorithms can’t come up with content on their own — they must use others’ work. Since most books are copyrighted, AI companies have resorted to piracy and copyright infringement. Books3 is a data set of nearly 200,000 pirated novels used to train generative AI systems. Most of the content was published within the last 20 years, so many living authors are affected. This issue isn’t limited to bad actors and shady characters, either. OpenAI, ChatGPT’s creator, argued creating models would be impossible unless it was allowed to use copyrighted material. It claimed public domain content wouldn’t “meet the needs” of people. Regardless of the statement’s truth, it shows how unconcerned AI companies are about content theft. There Aren’t Protections Yet Despite the issue of AI-driven copyright infringement being a real threat to authors, literary agents and publishing houses, no real regulations exist to protect them yet. Recently, a 24-page document revealed that Midjourney had a database of around 16,000 individuals it allegedly stole art from. Apparently, it was a list for model training. Even though the list — which contained specific names — seems like it would prompt swift regulatory action, things aren’t as open and shut as they should be. In many countries, a lawsuit is the only recourse creators have. Realistically, there’s no guarantee they’ll be successful. Not to mention, many would have to fight against well-funded tech giants. The Positive Impact of AI on the Publishing Industry AI wouldn’t be able to disrupt the book publishing industry if it was inferior to what people currently use. In other words, a silver lining does exist — there are potential positive impacts. AI Can Take on the Mundane Work Most writers spend more time than they’d like on mundane — but necessary — tasks like proofreading and spellchecking. The same can be said about literary agents hassling publishers or marketers tweaking designs for the nth time. AI can take over these boring, repetitive duties. Since ML models evolve over time, they can create and maintain searchable databases of relevant marketing avenues, contract work or agents to query. Publishing companies can leverage AI tools to help editors check for grammar or continuity mistakes, accelerating time to market. AI Makes Human Content Stand Out Publishing AI-generated books is a get-rich-quick scheme. Natural language processing technology isn’t advanced yet — readers and literary agents can quickly identify this kind of content. Fortunately, many view it as cheap, poor quality or lazy. To avoid AI-generated books, readers must search for authors. In doing so, they’ll spotlight creators and learn to appreciate content better. Many companies are already taking advantage of this fact, using “100% human-generated content” as a selling point. The Gray Area Where Its Impact Could Go Either Way AI’s impact isn’t purely black and white — there’s a massive gray area where things could go either way. Navigating these subtleties is tricky. AI Has Editorial and Publishing Sway Book publishing is notoriously difficult. Even now-famous authors have dealt with round after round of rejections. An ML model in a decision-making role could evaluate content at an unparalleled pace, helping more people get published. It may occasionally make biased or illogical decisions, but those aren’t exactly uncommon. Numerous cases exist where a book goes through dozens of rejections before becoming a best seller — well-trained literary agents and publishers make mistakes, too. Self-Publishing Is Easier Than Ever Anybody with access to AI can ask a model to write a book, design a cover, and generate hype on social media. This technology makes self-publishing easier than ever, meaning previously overlooked and underserved groups can fulfill their dream of publishing a novel. Unfortunately, bad actors are taking advantage of the simplicity of AI-driven self-publishing. One author, Jane Friedman, found six fake titles under her name in a few months. Others claim they spend hours reporting listings just for new ones to pop up the next week. Another potential issue with AI-powered self-publishing is that authors won’t need professional help anymore. If most people go this route — which they might because getting published traditionally can be next to impossible — the sector could collapse. The Future of Book Publishing Hinges on AI Although every big disruptor causes general worry, AI is different. For one, it’s accelerating at a pace publishers can’t keep up with. It’s also like no technology people have ever seen before — what other invention can autonomously read, learn, write, edit and publish a novel? Professionals’ fear seems to be justified. For now, there’s no telling if generative models will transform, replace or topple this industry. Professionals in the book publishing industry are terrified of artificial intelligence — and for good reason. While it isn’t all bad, it has the potential to bring the sector to its knees. Not to mention, it has already harmed many creatives. Are their fears justified or unwarranted? AI’s Emerging Role in the Publishing Industry AI’s Emerging Role in the Publishing Industry Thanks to its versatility, AI has emerged in most parts of the publishing industry. In 2023, 47% of book publishers based in the United States used it for marketing, 25% for editorial work, and 12% for content generation. Companies can use it in writing, editing, advertising, or administrative roles — it can do almost everyone’s job to some extent. 47% of book publishers 47% of book publishers AI Isn’t the First Threat, But It’s the Biggest AI Isn’t the First Threat, But It’s the Biggest Many disruptors have affected the publishing industry. Most recently, e-books have started replacing physical books. According to one estimate, about 30% of pages will be digitally printed by 2028, up from 5% in 2023. Many publishing houses and self-publishers are migrating to digital. Before that, digital printing shook up the offset printing press. about 30% of pages about 30% of pages Although AI isn’t the first disruptor, it’s the biggest so far. The publishing sector has withstood others because they were all natural evolutions to outdated processes. Machine learning (ML) and large language models are different — they replace existing technology instead of improving it. Who needs writers, editors, and agents when algorithms can do their work for free? Writers, Editors, and Publishers Are Terrified of AI Most professionals in book publishing are terrified of generative AI. Its versatility could bring about the sector’s downfall. A handful of high-quality models can write a novel, proofread, edit, design a cover, make marketing materials, post on social media for publicity, distribute digital copies, and handle accounting. For now, AI can’t perform any of these jobs better than a human. One industry professional said the content it generates is “crude at best,” arguing it isn’t comparable to what a real writer can produce. Even so, it might not matter when it can pump out pages at an inhuman speed. arguing it isn’t comparable arguing it isn’t comparable AI will inevitably affect the entire industry even if every author, editor, and publisher agrees not to use it. This technology’s accessibility is its main driving force. Dozens of leading models — and thousands more in-house systems — exist. Almost anyone can use ChatGPT, Midjourney, LaMDA, or Claude to make, market, and distribute AI-generated books. The Negative Impact of AI on the Publishing Industry There’s abundant evidence of AI’s negative impacts on the book publishing industry. AI Is Stealing People’s Jobs There’s truth to the fear that AI is set to steal people’s jobs. While more job ads now stress human-written content is a hard requirement and more companies are investing in software to detect AI-generated content, the publishing sector’s pushback hasn’t been enough — and people are being forced to find new careers. is a hard requirement is a hard requirement Olivia Lipkin, a copywriter, said her assignments dwindled after ChatGPT came out. She even claimed management changed her title to “Olivia/ChatGPT” on the company’s messaging platform. Within a few months of the generative model’s release, Lipkin was let go without explanation. Eventually, she discovered her managers had realized AI was much cheaper. said her assignments dwindled said her assignments dwindled It’s Stealing Their Work, Too Algorithms can’t come up with content on their own — they must use others’ work. Since most books are copyrighted, AI companies have resorted to piracy and copyright infringement. Books3 is a data set of nearly 200,000 pirated novels used to train generative AI systems. Most of the content was published within the last 20 years, so many living authors are affected. of nearly 200,000 pirated novels of nearly 200,000 pirated novels This issue isn’t limited to bad actors and shady characters, either. OpenAI, ChatGPT’s creator, argued creating models would be impossible unless it was allowed to use copyrighted material. It claimed public domain content wouldn’t “meet the needs” of people. Regardless of the statement’s truth, it shows how unconcerned AI companies are about content theft. argued creating models would be impossible argued creating models would be impossible There Aren’t Protections Yet Despite the issue of AI-driven copyright infringement being a real threat to authors, literary agents and publishing houses, no real regulations exist to protect them yet. Recently, a 24-page document revealed that Midjourney had a database of around 16,000 individuals it allegedly stole art from. Apparently, it was a list for model training. database of around 16,000 individuals database of around 16,000 individuals Even though the list — which contained specific names — seems like it would prompt swift regulatory action, things aren’t as open and shut as they should be. In many countries, a lawsuit is the only recourse creators have. Realistically, there’s no guarantee they’ll be successful. Not to mention, many would have to fight against well-funded tech giants. The Positive Impact of AI on the Publishing Industry AI wouldn’t be able to disrupt the book publishing industry if it was inferior to what people currently use. In other words, a silver lining does exist — there are potential positive impacts. AI Can Take on the Mundane Work Most writers spend more time than they’d like on mundane — but necessary — tasks like proofreading and spellchecking. The same can be said about literary agents hassling publishers or marketers tweaking designs for the nth time. AI can take over these boring, repetitive duties. Since ML models evolve over time, they can create and maintain searchable databases of relevant marketing avenues, contract work or agents to query. Publishing companies can leverage AI tools to help editors check for grammar or continuity mistakes, accelerating time to market. AI Makes Human Content Stand Out Publishing AI-generated books is a get-rich-quick scheme. Natural language processing technology isn’t advanced yet — readers and literary agents can quickly identify this kind of content. Fortunately, many view it as cheap, poor quality or lazy. To avoid AI-generated books, readers must search for authors. In doing so, they’ll spotlight creators and learn to appreciate content better. Many companies are already taking advantage of this fact, using “100% human-generated content” as a selling point. The Gray Area Where Its Impact Could Go Either Way AI’s impact isn’t purely black and white — there’s a massive gray area where things could go either way. Navigating these subtleties is tricky. AI Has Editorial and Publishing Sway Book publishing is notoriously difficult. Even now-famous authors have dealt with round after round of rejections. An ML model in a decision-making role could evaluate content at an unparalleled pace, helping more people get published. It may occasionally make biased or illogical decisions, but those aren’t exactly uncommon. Numerous cases exist where a book goes through dozens of rejections before becoming a best seller — well-trained literary agents and publishers make mistakes, too. Self-Publishing Is Easier Than Ever Anybody with access to AI can ask a model to write a book, design a cover, and generate hype on social media. This technology makes self-publishing easier than ever, meaning previously overlooked and underserved groups can fulfill their dream of publishing a novel. Unfortunately, bad actors are taking advantage of the simplicity of AI-driven self-publishing. One author, Jane Friedman, found six fake titles under her name in a few months. Others claim they spend hours reporting listings just for new ones to pop up the next week. found six fake titles found six fake titles Another potential issue with AI-powered self-publishing is that authors won’t need professional help anymore. If most people go this route — which they might because getting published traditionally can be next to impossible — the sector could collapse. The Future of Book Publishing Hinges on AI Although every big disruptor causes general worry, AI is different. For one, it’s accelerating at a pace publishers can’t keep up with. It’s also like no technology people have ever seen before — what other invention can autonomously read, learn, write, edit and publish a novel? Professionals’ fear seems to be justified. For now, there’s no telling if generative models will transform, replace or topple this industry.

Why the Book Publishing Industry Is Terrified of AI

কেন বই প্রকাশনা শিল্প এআই নিয়ে আতঙ্কিত

Warum die Buchverlagsbranche Angst vor KI hat

Por qué la industria editorial de libros está aterrorizada por la IA

Pourquoi l'industrie de l'édition de livres est terrifiée par l'IA

पुस्तक प्रकाशन उद्योग एआई से क्यों भयभीत है?

出版業界がAIを恐れる理由

도서출판업계가 AI를 두려워하는 이유

Por que a indústria editorial de livros tem pavor de IA

Почему книгоиздательская индустрия боится искусственного интеллекта

Kitap Yayıncılığı Endüstrisi Neden Yapay Zekadan Korkuyor?

Tại sao ngành xuất bản sách lại sợ hãi AI

图书出版业为何惧怕人工智能

How is AI revolutionizing life sciences? Life sciences have surfed technological waves for a long time, one of the hits being The Human Genome Project in the 1990s. However, even as new technologies and scientific breakthroughs continue to positively impact life sciences, challenges such as analyzing large amounts of data are arising. Artificial intelligence has, however, stepped in to assist scientists and researchers navigate the large amounts of data and make better use of it to improve lives in various endeavors. In fact, according to a Research and Markets report, the market size of was valued at $1,255.3 million in 2020 and is projected to be worth $3943.96 million by 2025. artificial intelligence in life sciences Broadly speaking, artificial intelligence is the science of developing computer programs and technologies like deep learning, natural language processing, and machine learning algorithms to perform complex tasks without direct human input. In this article, we’ll discuss 4 ways artificial intelligence is transforming life sciences. Let's get started. 1. Automating Clinical Trials Clinical trials are often accompanied by an enormous ocean of data. This is because researchers have to gather small amounts of data from millions of patients, like blood samples and records of experiences with particular drugs or vaccines to look for patterns across the population and improve health outcomes. In addition to patient-generated data, clinical trial data is also routinely collected from different including health records, product and disease registries, and medical monitoring devices. unstructured data sources However, the traditional flow of this data across the clinical trial life cycle is complicated because it is marked by manual effort, errors that call for rework, and ultimately inefficiencies that leave researchers unable to draw actionable conclusions on time. Artificial intelligence plays a crucial role in designing clinical trials by estimating ideal sample sizes for data collection and implementing them on patients across many locations. By intelligently automating data management across through structuring, standardizing, and digitizing data elements, researchers can streamline processes like data gathering on-site, which gives them more time to focus on value-added tasks like patient engagement. the clinical trial life cycle Actually, according to a recent case study, a major pharmaceutical company was able to reduce its oncology clinical trials by 3-4 years upon partnering with Cognizant AI. In addition to that, AI leverages natural language processing that interprets clinical trial data elements to feed into downstream systems while automatically populating required analyses and reports. 2. Introducing Robotic Surgery Surgical operations require surgeons to work with precision while making incisions or performing other tasks like assessing internal organs. One mistake could lead to fatal consequences like severe blood loss or further complications. With the repetitive and lengthy tasks involved in conventional methods of carrying out open surgeries, surgeons are left fatigued and can only take care of a few patients daily. The rise of artificial intelligence has revolutionized life sciences by introducing the use of collaborative robots in the operation room. By using deep learning data collected from watching surgeons perform, AI is automating surgical procedures. Together with complex machine algorithms and powerful libraries like OpenCV, you can determine patterns within surgical procedures to develop better practices and improve the accuracy of collaborative robots with sublimeter precision. In fact, these on Great Learning will help you understand how AI assists surgical robots with machine vision to analyze scans, facilitate instrument positioning, and detect cancers. free image processing projects What’s more, since robotic surgery is minimally invasive, patients can enjoy benefits like: Smaller incisions Less blood loss and pain Shorter admission times A case study by Cleveland Clinic reports on how a 62 year old patient was able to go home in two days after a minimally invasive robot-assisted surgery procedure compared to the one and a half weeks it would have taken for an open surgery. In addition to that, the patient experienced less pain and quickly returned to normal activity. 3. Advancing Patient Diagnosis In daily medical practice, patients need to undergo assessments by their healthcare providers before a diagnosis is made and treatment is administered. However, diagnoses are not always correct because they mainly depend on insights from the patient, images from X-Ray scans, and the healthcare provider’s knowledge. When it comes to complex diseases that cannot be detected fast enough like cancer which manifests itself in different ways, there is a risk of misdiagnosis. Actually, the rate of according to a study by DDW. pancreatic cancer misdiagnosis is at 31% Diagnostic errors are a significant patient safety challenge but they can be complex to define and difficult to detect. They include delayed, wrong, and missed diagnoses that have detrimental effects on patients, such as accelerated infections or death. Apart from that, healthcare network systems also have to be secure from any malicious unauthorized access that may compromise patient data integrity, and this article explains how to guard sensitive patient data. AI is implemented in email systems Given that many diagnostic errors arise due to subtle biases and missed information, there's a need for better systems to provide physicians with accurate information to mitigate these risks. One of the ways artificial intelligence is is in image analysis to interpret X-Ray images. Machine learning algorithms in this case recognize similar images from patient scans and in the process identify diseases from an early stage. transforming life sciences In addition to that, AI-based chatbots, through natural language processing can listen to patients as they explain their health concerns and the associated symptoms. The algorithms then guide patients to the correct therapy for appropriate diagnosis. 4. Accelerating Drug Discovery Developing drugs for medical purposes involves time-intensive, tedious, and costly approaches without certainty that they will succeed at curing particular diseases as intended. Some of these approaches include gathering historical data on diseases and drugs used for treatment, and screening millions of potential molecules from natural sources like plants and fungi. This is time-consuming and labor-intensive. In addition to that, for drugs to be introduced into the market, they have to go through a series of tests to ensure they are fit for consumption by comparing large datasets to draw actionable insights. AI provides an ideal solution for efficient drug discovery because it effectively stores and manages research and development data. In fact,  according to a press release by Market Study Report, the is expected to experience a 40% growth rate focused on drug discovery and personal AI assistants by 2024. healthcare artificial intelligence segment Artificial intelligence, through cloud-based solutions, creates a single source of truth by combining data from many sources, such as lab results from patient disease and drug use. Through machine learning and natural language processing, AI-based programs are then able to scan and cross-reference through complex datasets in a fast and more precise manner compared to human efforts. As a result, you are able to arrive at a more accurate list of potential drug candidates within a shorter period which also doesn’t require a lot of rework due to errors. Conclusion AI is clearly the future of life sciences in areas, such as healthcare and pharmaceuticals to improve both the researchers’ and patients’ experiences. In fact, according to a survey by Pharma IQ, expect intelligent technologies to positively impact drug development over the next three years. 95% of pharma professionals Having gone through I hope you can leverage some of these technologies like chat bots to capture and analyze patient data. some of the ways artificial intelligence is transforming life sciences, such as in introducing robotic surgery, capturing patient data, and advancing patient diagnosis, If you’re a pharmacist looking to advance your clinical trials, I would urge you to intelligently automate your data management cycle to drive better data analysis and draw actionable conclusions.

How is Artificial Intelligence Transforming Life Sciences? 4 Main Ways

Why AI Will Bring an Explosion of New Jobs

Biometric authentication technology has been an important industry trend for years, especially in 2021 due to the latest AI innovations available on the market. According to IBM, are caused by compromised credentials. Worse, it can take an average of 287 days to identify and respond to a data breach. 20% of breaches AI-based security is increasing in usage and will be necessary to remain competitive in any industry. IBM reports that as of 2021, 25% of businesses have completed deployment of AI-based security, while 40% are partially deployed. The remaining 35% have not begun this process, and if your business falls into this category, you may be placing your clients at great risk for dangerous data breaches. Investing in AI-based security can save a business up to $3.81 million in 2021. Being able to use artificial intelligence to identify and automatically respond to data breaches is incredibly important for protecting the data and privacy of a company and its customers. AI-based biometrics authentication provides yet another safeguard against a data breach that is essential for businesses of any scale. Not only that, but IBM’s study shows that correct use of AI security platforms will cost businesses less. It is extremely important to understand and consult experts in the field to develop the solution that is best for your business. With more advanced biometric authentication, verification, and identification technology at our disposal in 2021 than ever before, we can make industry systems more secure while maintaining efficiency and accessibility. Let’s talk about how biometric authentication works and the most important new trends, such as artificial intelligence biometrics. Biometrics Technology Overview At first glance, biometrics may appear to simply be a way of accessing a cell phone more quickly while retaining security, but there are many more applications than this. However, at its core, the objective of biometric security technology is to improve security using parts of our bodies that cannot be easily replicated. Although machine learning and quantum computing may be able to , biometric information like fingerprints, faces, and irises are not so easily determined or emulated. guess a password According to Statista, the biometric technology market will value at before 2027. $55.42 billion Over the range of 10 years from 2017 to 2027, worldwide spending in the identity verification market is forecasted to grow by more than 13 billion U.S. dollars, increasing from 4.93 billion in 2017 to over in 2027. Identity verification is an important aspect of information security, as it ensures that only the people with legitimate authority to access information are able to do so, thereby preventing harmful or malicious intrusion to protected information. $18 billion Types of Biometrics Biometrics technology can be split into three domains: identification, verification, and authentication. Identification is used when the system wants to know who the user is. Verification is about using that biometric information to determine if any other information is associated with the user. Finally, the goal of authentication is to understand if the identity the user claims to be is correct and authorized to access the services and data they are requesting. An example of biometric identification may be used to determine the identity of a user against a database of faces. Law enforcement facial recognition databases use this technology to a great degree and can help investigate security camera evidence footage with better accuracy than a human. Verification is helpful for ensuring that the user and the information stored in the database about them match. Authentication is perhaps the most critical of the three, enabling authorized users to access sensitive information while restricting unauthorized persons. The table below shows the various characteristics of each type of biometric technology. One of the most important takeaways for any business considering biometric security methods is that it is not always wise to rely on only one form of biometric technology, i.e., unimodal. Instead, a multimodal approach that uses more than one type of biometrics is much more secure. Facial Recognition Technology The facial recognition market was estimated at in 2020. The market is projected to grow, reaching 8.5 billion U.S. dollars by 2025. $3.8 billion Biometric authentication technology that uses facial recognition has been a valued feature for security for years, making its way onto millions of iPhone and Android smartphones. With advances in augmented reality and machine learning aiding computer recognition of human faces, facial recognition technology has become more advanced than ever before in 2021. One of the reasons why facial recognition technology has improved for identification and authorization this year is because of the necessities of the pandemic. Due to the increased usage of face coverings like masks, facial recognition has become less accessible. However, many facial recognition solutions now can account for obstacles like face coverings. This is not only good for consumer convenience, but also helpful for law enforcement identification of criminals. By matching the face of the subject with a database, users can be identified even if they are wearing a mask. Since 3D cameras can get much more information about a human face than a two-dimensional camera would, facial recognition is much more secure than before as a biometric authentication technology. Previously, it was much easier for computers to be fooled by a picture of someone’s face or a similar-looking face. However, machine learning has helped narrow the margin of error to prevent hackers from exploiting facial recognition-based security, and various anti-spoofing methods now exist. Voice Recognition Technology The global voice recognition market size was to grow from $10.7 billion in 2020 to $27.16 billion by 2026. forecasted Hands-free communication has been enriched greatly by voice recognition biometrics technology. However, voice recognition has another purpose: identification. Smart home device ecosystems like Google Home, when trained, can identify members of a household-based on their voice. This is very helpful for displaying information pertinent to that individual, such as their calendar and notifications. Voice recognition has other applications in authentication and verification. Being able to identify someone and verify their access based on their voice can save a lot of time, but we are getting to the point where voices may not be as secure as other biometric methods. Deep learning has made it possible to realistically mimic a voice using a computer, a technique called vocal synthesis. ‘Deep fake’ voices have been used in successful that, in one case, cost an energy firm in the United Kingdom $250,000 in 2019. fraudulent activity As deep learning technology improves, the more realistic vocal synthesis will become. Unless technology improves to better recognize legitimate vs. fake voices, voice recognition biometrics technology is better suited for non-security applications, particularly identification. This may be for consumer-based systems like smart homes or enterprise solutions such as automatic transcriptions of meeting recordings that can attribute speakers by name. Iris Scanning Technology This form of biometrics technology is often seen in science fiction. It is similar to facial recognition in that it is contactless but focuses only on one particular feature, the eye. One advantage is that there is less complexity to account for. Advanced facial recognition systems are not needed, only something that can recognize the features of the iris. You don’t necessarily have to be close to an eye to scan the iris. At 40 feet away, tests at Carnegie Mellon University . can scan an iris One advantage of this technology is that because it uses infrared sensors to analyze the iris, scanning the eye can be done in low light conditions. Law enforcement applications of this technology for the identification of suspects are definitely one of the first things that may come to mind. However, this can also be a great authentication and verification method for both consumer and enterprise applications. Samsung’s Galaxy S9 and Note 9 smartphones both have iris scanning technology, demonstrating that this biometric method can be . This can be useful not only for consumers but also for enterprise-level device security. used on portable devices When comparing against a single authorized user, an iris scanner can authenticate a user nearly instantaneously. When identifying or verifying a user against a large database of hundreds of thousands of subjects, less than is sufficient to authenticate. 20 seconds Fingerprints Authentication Technology Fingerprints have been in use for for identification. They have particularly been useful in forensics technology, but recently they have become extremely useful for consumer and enterprise security solutions. In 2003, the pioneered the very first cell phone with a fingerprint sensor. It would later be popularized with Touch ID on Apple iPhones and various scanners on Android phones. thousands of years Fujitsu F505i Fingerprint authentication technology works just like any other biometric method. By comparing the grooves and valleys of an individual’s finger against a standard subject or a database of subjects, they can be identified, verified, and/or authenticated. This can be done very quickly by the user without disrupting their experience. However, one downside to fingerprint scanning is that it most often is not contactless. The user has to press their finger against the sensor in order for it to register. In some circumstances, this may not be desirable due to health concerns, especially with the global spread of the SARS-CoV-2 virus. However, it is possible to scan a fingerprint or entire hand via contactless methods. The smartphone uses a time of flight (ToF) sensor and infrared camera to detect the blood vessels in the hand of the user, all without requiring the user to press their hand against a surface. LG G8 ThinQ Another downside to fingerprinting is that it can be inaccessible for some groups of people with disabilities. Because of this limitation, it may be wise to invest in multiple forms of biometric authentication to maintain accessibility. How AI Can Improve Biometric Authentication Systems When it comes to security, improving the accuracy and efficiency of biometric authentication systems cannot always be done with human programming. Artificial intelligence and machine learning can help us make our systems more secure and efficient. Biometric technologies can be split into two domains, physical and behavioral. Physical Biometric Technology Physical biometrics include everything that we have already discussed in this article. These are objective characteristics of a person like their face or fingerprints, DNA, and more. This must be converted into data that can be analyzed by the AI system and compared against a database for authentication purposes. One of the cases where AI and ML prove most helpful is with facial recognition. Used extensively with augmented reality solutions, AI can help make facial recognition by computers much easier by analyzing facial features and matching them with a database. Behavioral Biometric Technology One of the most interesting trends that artificial intelligence makes possible is . This uses unique behavioral characteristics of how they interact with the world, things that the user may not even realize about themselves. This is one of the most effective lines of defense against deepfake fraud attempts. Some of the more popular ways to measure behavioral biometrics are: behavioral biometric technology Mouse activity Keystroke movement Touch screen press size, area, and pressure Mobile device motion The Biometrics and Data Pattern Analytics Lab from the Autonomous University of Madrid , a system for bot detection that uses behavioral biometrics. It’s entirely possible that behavioral biometrics can be used without the user’s knowledge, eliminating the need for annoying tests of the user’s humanity as CAPTCHA challenges have done for years. In the next few years, users may never have to click on pictures of crosswalks and stop lights to prove they are ‘not a robot’ again while browsing the Internet. developed BeCAPTCHA Importantly, behavioral biometrics can assist in ensuring security throughout an entire session. For example, if a user authenticates themselves and then leaves the room, unwittingly enabling an unauthorized user to gain access to their computer, this would be a serious security risk. However, behavioral biometrics can detect the inconsistent behavior of the second user and restrict their access dynamically. How Multimodal Biometric Authentication Solutions Work Every form of biometrics technology has pros and cons. They each are also not universally effective. To improve the accuracy and effectiveness of biometric authentication technology, it’s important to layer security with multimodal biometric recognition solutions. We can see this in action in the following diagram: *Neural networks on mobile applications can be challenging to develop and implement. Neural networks can be integrated into applications using TensorFlow Lite; however, there are various limitations. Training your neural network models with the TensorFlow library can help. You will need to thoughtfully design the architecture of the application beforehand and put those requirements into consideration. ** If a native application is not possible, this process can be offloaded to the cloud to process the data like Rest API. However, this will incur additional networking resources and require an Internet connection. There is an Nvidia Docker that can simplify the deployment of the system, while a service provider (such as AWS) can provide an uninterrupted communication channel, computing power for neural networks, and a convenient interface for scaling your system. Test No. 1 - Facial Recognition The user creates a photo-imprint that’s stored on the device using a camera. This biometric imprint is converted and normalized using the OpenCV library. The face is identified using a photo, and each of the 64 landmarks detected by OpenCV are highlighted. A biometric verification landmark includes things like a distance from the bridge of the nose to the eye and other facial features. These landmarks and a cutout image of the face are transferred to a deep neural network, which is trained using the TensorFlow library. An eDNA feature vector is formed after neural network processing is completed. The feature vector collects the biometric characteristics of a particular person. The length of the vector is usually 2048 bits. The lengths depend on the DNN architecture. During verification, eDNA is issued and compared with the anchor record that was formed earlier. Reverse engineering is impossible because there is no access to the vector. The biometric system will periodically update this anchor record to match an individual’s changing looks. Test No. 2 - Voice Verification The user provides a voice sample through a microphone that gets processed by the Librosa library. The library reads the audio, transforms and converts it, and then transmits biometrics to the neural network (DNN). An eDNA feature vector (2048 bits) is formed, which takes into account such biometrics as timbre, intonation, tempo, pitch, and other characteristics that the neural network was trained to respond to. Case Study: AI-based Biometric Authentication Solution By combining a microservice-based architecture, WebRTC, and machine learning-powered biometric recognition, we developed a single sign-on (SSO) biometric authentication solution for a US-based firm. Utilizing voice and facial recognition, we developed an enterprise verification as-a-service (EVaas) solution that uses the technologies and trends discussed earlier. This product proved that biometric authentication systems can be highly customizable and easy to use, powering a very simple user interface from behind the scenes. In addition, this example was able to integrate with existing systems via API. Biometric Authentication Solutions Development Securing systems is not a matter to take lightly. When sensitive information is on the line, like protected health information or institutional data is on the line, it is extremely important to take the measures necessary to prevent data breaches. Failure to maintain a high level of security surrounding this information can result in fraud that can cost businesses tens or even hundreds of millions. Written by , AI/ML Solution Architect at Evgeniy Krasnokutsky MobiDev

AI Biometric Authentication is Changing

My journey with started in high school. I was lucky and motivated enough to get my hands on old textbooks about Artificial Neural Networks. It was 2000. Friends were super popular on TV, Eminem was dropping hit after hit and there was I, an utter geek perplexed and literally blown away by object recognition systems, which at that time were scientific curiosities. My first paper, , written at the age of 18, was my juvenile attempt at becoming scientist. It won me scholarships and entry to the best universities in UK (Cambridge) and the Netherlands (Groningen), and eventually unlocked academic carrier in . Finally, I was lucky enough to combine scientific expertise and a love affair with machine learning into . machine learning “Seeing and Recognizing Objects as Physical Process — Practical and Theoretical Use of Artificial Neural Networks” computational biophysics of proteins an AI venture, Peptone However, my academic development path wasn’t rosy nor romantic at all. It was a mix of excitement and and getting my “golden behind” kicked by my mentors and academic supervisors, with the dominant contribution of the latter. The biggest pain of being an “academic wunderkind” was scientific writing. I was seriously repelled by it. I was absolutely convinced I was wasting my time, rather than doing more productive things in the lab. Boy, was I wrong! It is quite amusing to see how ideas have changed from the perspective of time and experience. Especially, when you reach a turning point in your carrier and start contributing to the field you admired as a geeky teenager. However, let me cut my hubristic autobiographical note short and jump straight to the problem. Only few days ago, I have stumbled upon an article in MIT Review, which motivated me to write this short post. _Adam Detour I'm standing in what is soon to be the center of the world, or is perhaps just a very large room on the…_www.technologyreview.com Progress in AI seems like it's accelerating, but here's why it could be plateauing Are we making progress or simply circling around in an endless pool of NET(s), optimizes, architectures, normalization methods methods and image recognition approaches? Is AI Riding a One-Trick Pony? I am afraid, we are. Let me share my take on this. Please bare in mind this is my private opinion, born out of numerous hours spent reading machine learning / AI papers, and trying to adopt their findings to the problems we are working with at . Peptone, namely automated protein engineering The seminal works ( papers that introduce the concept of perceptron or back-propagation) in the domain of AI/ML are cited selectively or not at all. In turn it is very difficult for a newcomer to AI field with more than sufficient math knowledge to place their actual scientific findings in the broader context. The vast number of AI/ML pre-prints are lacking proper citations. e.g. Missing or blatantly incorrect citations lead to therefore inflating but not propelling the field of machine learning and adding absolutely unnecessary fussiness. I have just reviewed 2 papers in the field of bioinformatics (working as an anonymous referee for peer-reviewed journals) that concern AI, in which authors proclaim development of methods, which date back to 2004 and have at least 600+ citations insofar! How can you miss that?! Furthermore, I have seen authors of “AI-books” comparing perfectly known and well-characterized issues with gradient-optimization methods to Newtonian N-body problems, or devising loss functions that simply depend on inverse square laws (with all its limitations) and claiming it is modeled after “electromagnetic Coulomb law” (sic!). Ladies and gentlemen! Coulomb is rolling in his grave. If you go for Coulomb's law, please talk about electrostatics! Leave electromagnetism to Faraday and Maxwell. excessive relabeling of known and existing scientific concepts, I personally believe this is the biggest issue of them all. The way the results are presented does not conform to any standards of presentable scientific research. The most prominent problem is reporting accuracy in arbitrary units . How relevant is that your recognized objects with 1% better accuracy? What does it really mean with respect to number of parameters your model uses? How many degrees of freedom are in your model, how does it compare with less complex models? How do you asses you don’t over-fit or simply create degenerative models, which have . Lack of proper statistical analysis of the results. without simple discussion about statistical relevance of improvement network little to no statistical significance Because of the sparse or frequently non-existent statistical analysis of goodness of fit and basic statistical tests that asses model goodness with respect to the number of degrees of freedom many AI/Ml papers are suffering from , which very quickly translate into end-user questions and reported issues found under many “official” ML model repositories on Github. The “mere mortals” are unable to reproduce the great findings of the “experts”, using the same data and ML network architectures. reproducibility issues Why all of the above are important? All of us need to be able to separate innovation from cyclically recurring patterns of work, which not only slows down the progress in the field of machine learning, but most importantly triggers bizarre levels of anxiety among the public, press, science and tech investors, eventually leading to headlines like this: _Just when we thought we all had enough horrifying stuff on our plates. Though our schedules are already packed with…_www.esquire.com Elon Musk Thinks AI is "Vastly" More Dangerous Than North Korea _Stephen Hawking believes artificial intelligence could destroy our society by overtaking humans. The 74-year-old was…_www.mirror.co.uk Stephen Hawking says artificial intelligence could DESTROY society Please don’t get it the wrong way. I am absolutely not intending to mock nor spar with Elon Musk or Prof. Hawking, whom I have profound respect for. However, the fact is the bizarre and pseudo-apocalyptic press AI and Machine Learning are getting can be only compared to sensational and completely off the point articles about cancer, which is portrayed as a mythical and viscous creature (almost a sprout of Beelzebub himself) looking to annihilate human kind. What can we do to improve the ongoing AI / ML research? Read, read and once again read. If you think you have read enough, write and after that read again. One of my academic teachers, Prof. Ben Ferninga of University of Groningen (who eventually got Nobel prize in 2016 for the discovery of organic nano-machines) told me and my fellow Uni. Groningen geeks, you have to be (I cite) . Cautious optimism and stringent scientific reporting in machine learning and AI fields will lead to easier to asses, implement and regulate AI-driven automation. Eventually, society and press will see AI / ML won’t replace jobs completely, but augment them; boosting productivity and extending well-deserved lunch breaks. Moreover stringent and scientifically objective reporting about the way machine learning methods work and are train to work, should eventually pave the way for more effective legislative routes. “cautiously optimistic in your research” My rant stops here. I strongly recommend the articles below this paragraph, which touch on the issues with AI-research, and the regulatory aspects of applied machine learning. _Adam Detour I'm standing in what is soon to be the center of the world, or is perhaps just a very large room on the…_www.technologyreview.com Progress in AI seems like it's accelerating, but here's why it could be plateauing _To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and…_robotics.sciencemag.org Transparent, explainable, and accountable AI for robotics Please have your say using the comments section. Should the applied AI/ML advance, everybody interested needs to join the conversation. I absolutely don’t believe I am the only person having issues with the way AI/ML papers written and deposited in pre-print repositories.

Should machine learning and AI advance, proper scientific reporting is a must!

While artificial intelligence (AI) has not been a new invention or area of research for decades, many of us are suddenly aware it is on the verge of mass adoption. We witnessed the extensive usage of large language models—for example, the ChatGPT—across various industries, infiltrating our everyday lives. Movie scenes have taught us that AI is inherently wrong; it will eventually take over humankind, reigning the world into a dystopian state. This is not only an one-sided single view from a layman, as a bunch of technology entrepreneurs and thought leaders recently published an to call for at least a 6-month pause in the development of a more robust AI system—namely GPT-4—unless the threats associated with it can be alleviated into manageable risks. open letter The future outlook seems to bloom rapidly at first but is quickly flooded with pessimistic worries. So, the engine has already started, and this no-rear-gear car is heading to a critical crossroad; there is no way back. The only question left in the cabin is, where shall we direct to? Transparency Matters in AI Imagine your eyes are tied by a troop of fort and forceful soldiers, and they throw you into a completely dark container. They tell you not to panic because they are just going to bring you to a safe place. Although their tones are mild and gentle, this is still very suspicious. AI is no different from this black box. The smart recommendation algorithm is one circumstance that we all have come across. The results we look for from the search engine, the video programs we watch, and the songs we listen to are suggested mainly by deep neural networks, which are models consisting of multiple layers of parameters to determine the outcome. On the one hand, engineers are eager to add as many layers as possible to increase the “predictability,” “objectiveness,” or “accuracy” of the model, but when more—some even to millions or billions—parameters are incorporated, it is hard for any human to deduce the thinking process of the model. What makes it worse is that the companies who developed the models are often reluctant to share the parameters and open the source codes of the models. They opine that doing so will invite gaming of the system, leading to abused and biased usage. While this claim sounds logical at first glance, it may induce more devastating impacts. Many financial institutions have credit rating systems powered by AI modeling. When such opacity exists without clear, transparent assessment criteria for the public or even the model developers to understand, the rights to access vital financial services such as banking, financial credit, and insurance will be solely determined by an opaque AI model. How can we ensure the model is always “correct,” given the developers have no idea of what is happening in the decision mechanism? And most importantly, what does “correct” itself mean? To further expand, some governments have been exploring—if not implementing—a for their citizens. People have to obtain and maintain a specific social credit score before they can enjoy some public services or apply for a job in governmental bodies. This is only the beginning; ultimately, the social credit rating system will most likely severely affect your freedom. Whether you can get a travel visa or rent an apartment, they are all by the mercy of AI, similar to what has been described in , a series. social credit system Black Mirror Netflix We do not trust without understanding. It is horrifying that we have no clue about AI bias—or preference if speaking in a neutral tone. An upright view of AI creation is to eliminate the biases of humans, but forgotten AI may have similar inclinations Returning to the case of a bank, relying only on demographic and behavioral data of the bank’s current clients could lead to the model targeting clients by ethnical backgrounds or skewing to the richer. Where we have set aside the discussion of discrimination and equality here to fully support the bank using this (assumably) biased client onboarding model—for their benefits due to commercial purposes and profit maximization, we have to admit the model itself has an appetite. It may lead to falsifying client rejection, eroding the bank’s business day by day. I am not a subject expert on this scientific question, but one pragmatic concern is how the bank can acquire data from non-customers, or in other words, where it can get data from its competitors. Nevertheless, engineers will not know how much data is required to train the model to become impartial. Is enlarging the dataset for training a possible way to eliminate the survivorship bias? Too Big to Fail The recommendation algorithm on and the client onboarding model of the bank are some applications of narrow AI, which focus on a particular task. Though their interferences are bound to a limited area—which has already been substantial, some stronger AI models are around the corner. YouTube (AGI) aims to learn human values and wisdom to replace humans in performing most tasks. While the creators have—or are guessed to have—goodwill, they are still unsure how to prevent misalignment completely. The same has happened to users, who believe the technology is so powerful that they are too dependent on it for everyday life. As mentioned above, AI training requires enormous amounts of data. Users’ continuous adoption of a specific algorithm is essentially a data-feeding iteration. The more users adopt the model, the more data the model gathers and the stronger the model. Artificial general intelligence ChatGPT and other large language models are some of the most revealing manifestations. They are pre-trained by massive text walls, ranging from an old-school history scholar to a fictitious novel author. People are having fun playing prompts and responses, fascinated by the powerful improvisation and imagination abilities within the models. They are using the models unconsciously, perhaps unaware that they are contributing to the further training of models, reinforcing the models’ power. It appears immaterial from users’ perspectives as they only care about the results. Empirically, this conjecture is not always true that AI sometimes commits unintended actions, surprising the users. The to columnist Kevin Roose is not a single event. marriage proposed by the Microsoft Bing chatbot No model is perfect; they are vulnerable to data and human training. In the case of large language models, the data sources are broad, so on occasions, the generated feedback will be derived from some improper or deceptive ones—though we are still unable to get clear on why, where, and how AI selects its data sources. Maybe AI is impersonating a lousy pursuer to Kevin, fitting replies on the spot that it learned from an exciting and romantic poem in an online dating forum. Engineers have been working hard to reduce these undesirable outcomes through techniques such as reinforcement learning by human feedback. However, scalable oversight is still a bottleneck due to the vast amounts of data to evaluate. And the situation is dynamic and evolving—bad data or malicious trainers constantly turn up. Shall another AI be introduced as the supervisor instead? It is uncertain—at least by now—as humans are yet to discover a powerful AI that is always honest. Should Technology be Regulated or the People? Using AI causally for fun is entertaining, if not weird, but things will not go well when conducting proper academic or business research, using AI, without verification. Hallucination is a common problem—especially in generative AI tools like large language models—that the AI will confidently write up untrue and nonsense content in a structured, highly organized manner, fooling the users that it is telling the truth. The underlying issue is not about the truth; instead, it is how we perceive it. These delusions from AI provoke creativity, but misusing it poses perilous damages. For instance, deep fake is a face-swap technology applied often in movie production. Still, conversely, pranksters joke with celebrities and politicians on the internet, using their faces for pornography and fake news makeup. The creation of an alternative truth with forged evidence is so harmful that regulatory measures are desperately needed. Like any other new technology, AI has invited lengthy discussions about regulation. Traditionally, we believe technology is neutral, a tool without prejudice, so we should monitor the person who pulls the trigger instead. AI adds a layer of complexity during the transition from narrow AI to strong AGI. When powerful AGIs possess the same emotions, preferences, and wisdom as humans, presumably, they can act on their accord like mankind. The only difference is these superintelligences are much cleverer. Values are diverse across different people, and so do AIs—there is no guarantee of the nonexistence of evil superpowers. An ounce of prevention is worth a pound of cure. Therefore, before the situation accelerates, companies who dedicate to developing AI, notably the titans who are ambitious to deploy their AIs for public usage (as a commercial decision), should promote the concept of self-awareness and self-regulation. These include data input, model design, model training, setting up model constraints (AGI developers would object to it), and model auditing. Until AI can self-regulate, the technology owners are responsible for being the guardian. Still, trust issue arises. Amateurs are confident with the security performance of the technology giants and sharing sensitive information like , albeit not noticing the terms and conditions they abided by. The privacy policy by OpenAI—developer and owner of ChatGPT—has already stated that the data transferred to and stored in their servers are not utterly safeguarded. some Samsung employees do Unfortunately, this has been confirmed by recently, leaking users’ chat histories and personal identifier information (PII) such as first and last name, email address, payment address, the last four digits of the credit card number, and credit card expiration date. Whether it is an isolated negligence or a systematic failure of procedural design, it intensifies the skepticism among policymakers. Italy has already imposed a temporary blanket ban on ChatGPT, probing privacy concerns and the protection of minors. Germany, France, and Ireland have said they are investigating. their data breach incident Openness, Decentralization is the Future A variant of the classical principal-agent problem comes back to the attention. The imbalance of interests between the public and the private proprietorship magnifies when technology advances, whereas powerhouse, captures most—if not all—utilities, leveraging the data supplied by the people. Technology intends to bring a fairer society, not the opposite. To strive for idealism, we should encourage the decentralization of technology, exhilarating more participants to join forces. It is intruding to prefer numerous narrow AIs to one omniscient AGI, but by dividing the control of power, we are more distant to a single point of failure or malice. Multiple competing, counter-supervising AGIs are also probable, as having choices in addition to checks and balances is always wise. Openness is another field to deal with. While internal auditing is a cornerstone to upholding an AI model’s reliability, it is necessary to embrace the core principles of open source. A community-driven debugging and enhancement process contributes to refined shared knowledge, improving future research progress tremendously and leading a common good for all of us. Going further to the point that should public interests outweigh intellectual property rights—a motivation for innovation within private entities—we must seek a way to align their interests. A new reward mechanism has to be invented to split the shares proportionately among sponsors, model developers, trainers, users, and data owners. New organizational-people relationships will evolve as all the stakeholders have contributed to the value-added AI technology. The incentives of the proprietary owners are inevitably compromised, as the economic benefits from the AI monopolies will most likely be redistributed to justify the effort of data owners. Quite a few language models are working as open source, like Google’s BERT, Eleuther AI’s GPT-J/GPT Neo, Responsible AI’s BLOOM, and Cerebras’ Cerebras-GPT, yet understandable for those who have opted for close source—either for internal beta-testing so not ready to publicize, or protecting proprietorship for commercial reasons. Even so, Twitter has partially of the recommendation algorithm. Though Twitter’s primary source of revenue is advertisements, which is more dependent on platform engagement rather than the recommendation algorithm—and whether the company has its own agenda for this sudden disclosure—a more transparent approach will stimulate further improvements. released its source code Conclusion: The Need for Ethically Improving AI Fear of AI may stem from our uncertainty and distrust of the technology, but it is not entirely unfounded. Humans must nurture AI with care and caution, guiding it to do good to humans instead of causing harm. The current landscape is complicated; expansion is quick, yet many moral standards have been broken. While the debate to halt the development of stronger AI is expected to continue, a consensus on ethically improving AI has to be observed. Leaders should realize that drawing boundaries for protectionism is unfeasible anymore, no matter if they are pro or against this unavoidable new age movement. Same with AI itself, AI alignment requires all humans—maybe AI too—to share and devote their collective intelligence. This article was originally published on . Michael Kwok’s Substack

Peering Inside the AI Black Box: The Pressing Need for Transparency

The pressure on healthcare systems to process vast amounts of patient data efficiently has never been greater. The growing need for real-time insights in clinical trials, patient management, and diagnostics is driving the demand for advanced data solutions. According to a 2023 report by IDC, the global healthcare data analytics market is projected to reach $45 billion by 2027. However, as data volumes continue to grow, traditional systems are struggling to keep up, leading to delays in drug development and clinical trials. One of the most critical challenges today is how to handle large datasets without compromising speed or accuracy. For pharmaceutical companies, timely data analysis can mean the difference between a successful trial and costly delays. This is where innovative solutions in data processing become essential for accelerating drug development and improving patient outcomes. Recognizing these challenges, Kirill Sergeev, a seasoned backend developer and machine learning engineer, has been developing cutting-edge systems that streamline the processing of medical data. By leveraging high-performance technologies, Sergeev has optimized data pipelines to enable companies to handle up to 100 terabytes of data daily with remarkable efficiency. "The key to managing large datasets efficiently lies in creating systems that are not just fast but also flexible enough to handle complex, dynamic data flows. In the medical field, where data needs to be processed quickly and securely, we cannot afford inefficiencies,"
-Kirill Sergeev One of the significant challenges in clinical data management is the lengthy process of deploying new machine learning models. Previously, deploying algorithms for clinical trials could take anywhere from two to three days, slowing down the ability to respond to new data. By redesigning data pipelines and integrating robust CI/CD processes, Sergeev has successfully reduced this time to just 1-2 hours. This streamlined process allows pharmaceutical companies to test and integrate new findings more quickly, ultimately accelerating the drug development timeline. "In any system dealing with high volumes of data, efficiency is key. It’s not just about handling data faster; it’s about ensuring that the results are accurate, actionable, and available immediately when needed," Sergeev adds. Speed and accuracy are essential in sectors where real-time insights can significantly impact patient outcomes. Sergeev’s work in optimizing data systems has cut response times for processing large volumes of medical data from 1.5 minutes to just 500 milliseconds. This level of performance is crucial for enabling healthcare providers to make timely decisions based on up-to-date information. By mostly adapting two types of approaches:  batch based and lambda-based, Sergeev has developed a hybrid architecture that ensures secure, scalable, and efficient data processing. This approach enables rapid data retrieval and real-time analysis, which is vital for managing clinical trials and patient records. While the healthcare sector benefits significantly from these advancements, Sergeev’s methodologies are also transforming other industries, such as fintech and e-commerce. By applying similar techniques, companies in these sectors have achieved substantial gains in efficiency. In a fintech project, for instance, Sergeev’s microservice architecture reduced transaction processing times by 35%, while also enhancing system security. In the e-commerce domain, his methods led to a 40% boost in operational efficiency by optimizing real-time inventory management systems. "The approach is universal," Sergeev notes. "Whether it’s healthcare, finance, or retail, the key is to build systems that are scalable and resilient to handle the increasing demands of modern data workloads." The future of healthcare lies in leveraging real-time data to drive faster and more accurate decision-making. As the sector continues to embrace data-driven practices, innovations like those developed by Sergeev will be crucial for enhancing patient care and speeding up drug development. "I think the future of data processing in healthcare lies in real-time insights that can inform quicker decisions. We’re only scratching the surface, but the potential to revolutionize patient care and drug development is immense," Sergeev concludes. By addressing the pressing needs of modern healthcare data management, Kirill Sergeev’s work is paving the way for a more efficient, data-driven approach that not only benefits the medical industry but also sets new standards for data processing in other high-load sectors. The pressure on healthcare systems to process vast amounts of patient data efficiently has never been greater. The growing need for real-time insights in clinical trials, patient management, and diagnostics is driving the demand for advanced data solutions. According to a 2023 report by IDC, the global healthcare data analytics market is projected to reach $45 billion by 2027. However, as data volumes continue to grow, traditional systems are struggling to keep up, leading to delays in drug development and clinical trials. One of the most critical challenges today is how to handle large datasets without compromising speed or accuracy. For pharmaceutical companies, timely data analysis can mean the difference between a successful trial and costly delays. This is where innovative solutions in data processing become essential for accelerating drug development and improving patient outcomes. Recognizing these challenges, Kirill Sergeev, a seasoned backend developer and machine learning engineer, has been developing cutting-edge systems that streamline the processing of medical data. By leveraging high-performance technologies, Sergeev has optimized data pipelines to enable companies to handle up to 100 terabytes of data daily with remarkable efficiency. "The key to managing large datasets efficiently lies in creating systems that are not just fast but also flexible enough to handle complex, dynamic data flows. In the medical field, where data needs to be processed quickly and securely, we cannot afford inefficiencies," -Kirill Sergeev "The key to managing large datasets efficiently lies in creating systems that are not just fast but also flexible enough to handle complex, dynamic data flows. In the medical field, where data needs to be processed quickly and securely, we cannot afford inefficiencies," "The key to managing large datasets efficiently lies in creating systems that are not just fast but also flexible enough to handle complex, dynamic data flows. In the medical field, where data needs to be processed quickly and securely, we cannot afford inefficiencies," -Kirill Sergeev One of the significant challenges in clinical data management is the lengthy process of deploying new machine learning models. Previously, deploying algorithms for clinical trials could take anywhere from two to three days, slowing down the ability to respond to new data. By redesigning data pipelines and integrating robust CI/CD processes, Sergeev has successfully reduced this time to just 1-2 hours. This streamlined process allows pharmaceutical companies to test and integrate new findings more quickly, ultimately accelerating the drug development timeline. "In any system dealing with high volumes of data, efficiency is key. It’s not just about handling data faster; it’s about ensuring that the results are accurate, actionable, and available immediately when needed," Sergeev adds. Speed and accuracy are essential in sectors where real-time insights can significantly impact patient outcomes. Sergeev’s work in optimizing data systems has cut response times for processing large volumes of medical data from 1.5 minutes to just 500 milliseconds. This level of performance is crucial for enabling healthcare providers to make timely decisions based on up-to-date information. By mostly adapting two types of approaches:  batch based and lambda-based, Sergeev has developed a hybrid architecture that ensures secure, scalable, and efficient data processing. This approach enables rapid data retrieval and real-time analysis, which is vital for managing clinical trials and patient records. While the healthcare sector benefits significantly from these advancements, Sergeev’s methodologies are also transforming other industries, such as fintech and e-commerce. By applying similar techniques, companies in these sectors have achieved substantial gains in efficiency. In a fintech project, for instance, Sergeev’s microservice architecture reduced transaction processing times by 35%, while also enhancing system security. In the e-commerce domain, his methods led to a 40% boost in operational efficiency by optimizing real-time inventory management systems. "The approach is universal," Sergeev notes. "Whether it’s healthcare, finance, or retail, the key is to build systems that are scalable and resilient to handle the increasing demands of modern data workloads." The future of healthcare lies in leveraging real-time data to drive faster and more accurate decision-making. As the sector continues to embrace data-driven practices, innovations like those developed by Sergeev will be crucial for enhancing patient care and speeding up drug development. "I think the future of data processing in healthcare lies in real-time insights that can inform quicker decisions. We’re only scratching the surface, but the potential to revolutionize patient care and drug development is immense," Sergeev concludes. By addressing the pressing needs of modern healthcare data management, Kirill Sergeev’s work is paving the way for a more efficient, data-driven approach that not only benefits the medical industry but also sets new standards for data processing in other high-load sectors.

Developer Kirill Sergeev Speaks on Empowering Healthcare System with Latest AI-solutions 

ገንቢ ኪሪል ሰርጌቭ የጤና እንክብካቤ ስርዓትን ስለማብቃት ከቅርብ ጊዜ AI-መፍትሄዎች ጋር ይናገራል 

Udvikler Kirill Sergeev taler om at styrke sundhedssystemet med de nyeste AI-løsninger 

El desarrollador Kirill Sergeev habla sobre cómo potenciar el sistema sanitario con las últimas soluciones de inteligencia artificial

Programer Kirill Sergeev govori o osnaživanju zdravstvenog sustava s najnovijim AI-rješenjima 

Kirill Szergejev fejlesztő beszél az egészségügyi rendszer megerősítéséről a legújabb mesterségesintelligencia-megoldásokkal 

開発者のキリル・セルゲイフが最新の AI ソリューションによるヘルスケア システムの強化について語る

Әзірлеуші Кирилл Сергеев денсаулық сақтау жүйесін соңғы AI шешімдерімен кеңейту туралы сөйлейді 

Mobongisi Kirill Sergeev Alobeli kopesa nguya na Système ya soins de santé na ba AI-solutions ya sika 

Mohlami Kirill Sergeev O Bua ka Matlafatsa Tshepedišo ya Tlhokomelo ya Maphelo ka Ditharollo tša Morago-rago tša AI 

Dezvoltatorul Kirill Sergeev vorbește despre abilitarea sistemului de sănătate cu cele mai recente soluții AI 

Програмер Кирилл Сергеев говори о оснаживању здравственог система са најновијим АИ-решењима 

Geliştirici Kirill Sergeev, Sağlık Sistemini En Son Yapay Zeka Çözümleriyle Güçlendirme Konusunda Konuşuyor

DUBAI, United Arab Emirates, December 19th, 2024/Chainwire/--Marinade Finance, Makes Strategic Investment in SuperSol to Boost Solana’s Growth and Enhance Layer-2 Capabilities. The Solana ecosystem has been witnessing rapid growth and evolution, with demand for scalability, performance, and real-time capabilities reaching new heights. This growth is driven not only by traditional decentralized finance (DeFi) applications but also by the rise of emerging sectors like GameFi (gaming on blockchain) and Decentralized Physical Infrastructure Networks (DePIN). In response to these expanding needs, Marinade Finance, a prominent self-custodial staking protocol on the Solana network, has made a strategic investment in SuperSol, the first native Layer-2 scaling solution for Solana. Marinade enables SOL holders to automatically delegate their tokens to top-performing validators in a competitive open marketplace, optimizing yields by allowing validators to share fees directly with stakers. Strengthening Solana’s Infrastructure for a New Era SuperSol, designed to significantly enhance Solana’s scalability and performance, aims to address the growing demand for efficient and reliable infrastructure that can support the next wave of decentralized applications (dApps). With a primary focus on sectors such as GameFi, which combines gaming with decentralized finance, and DePIN, which utilizes decentralized networks for physical infrastructure, SuperSol is poised to become a critical component in the future of the Solana blockchain. The investment by Marinade Finance is seen as a crucial move to help SuperSol accelerate its development and adoption. By supporting innovations like SuperSol, Marinade is positioning itself at the forefront of the efforts to improve Solana’s Layer-1 and Layer-2 capabilities, ensuring that the network can handle the next generation of dApps and meet the increasing demand for high-performance, low-cost, and scalable solutions. The Role of Marinade Finance in the Solana Ecosystem Founded in 2021, Marinade Finance has quickly established itself as one of the most prominent players in the Solana ecosystem. The platform allows users to automatically stake SOL tokens, Solana’s native cryptocurrency, while receiving mSOL, a liquid staking derivative. mSOL allows users to earn staking rewards while maintaining liquidity, enabling them to participate in other DeFi activities without locking their assets. Through this innovative approach to staking, Marinade Finance has significantly contributed to the overall growth and decentralization of the Solana network. By providing liquidity to staked assets, Marinade enables participants to earn staking rewards without locking their assets, thereby supporting both network security and a more dynamic ecosystem for decentralized finance. In addition to its core offering, Marinade has become an active participant in broader efforts to enhance Solana’s ecosystem. Its decision to invest in SuperSol is in line with its long-term vision to support projects that aim to improve Solana’s scalability and bring real-world use cases to life. SuperSol: A Key Enabler for the Future of GameFi and DePIN The main challenge facing blockchain networks like Solana has always been the need to scale in a way that maintains high throughput while minimizing costs. Solana’s high-speed and low-cost architecture has made it a popular choice for developers, but as adoption grows and more applications are built on the network, there is an increasing need for solutions that can handle even more transactions without compromising performance. This is where SuperSol comes into play. SuperSol is a Layer-2 scaling solution that builds on top of Solana’s existing architecture to offer increased scalability and enhanced performance. By utilizing SuperSol, developers will be able to create more efficient applications, particularly in GameFi and DePIN - two sectors experiencing explosive growth. In the GameFi space, where games and financial incentives are integrated on the blockchain, the need for high-speed transactions is paramount. Traditional gaming engines often struggle to meet the performance demands of real-time, immersive environments, but Layer-2 solutions like SuperSol can help ensure that these games run smoothly and cost-effectively on the Solana network. Similarly, in the rapidly growing DePIN sector, which includes applications focused on decentralizing physical infrastructure such as networks, energy grids, and other assets, scalability is critical. SuperSol’s advanced Layer-2 architecture is designed to handle the transaction loads and data requirements of such applications, making it an ideal fit for this emerging market. A Strategic Partnership with Long-Term Impact While the financial details of the investment have not been disclosed, Marinade’s support for SuperSol is more than just a monetary contribution - it’s a strategic partnership aimed at fostering innovation and ensuring the continued growth of Solana’s ecosystem. By investing in projects like SuperSol, Marinade is positioning itself as a key player in the infrastructure and scalability efforts that will shape the future of blockchain technology. The collaboration between Marinade Finance and SuperSol is a testament to the growing synergies within the Solana ecosystem. As Solana continues to attract developers and projects across a variety of sectors, the combination of robust staking solutions and scalable infrastructure will be key to meeting the demands of an increasingly complex and diverse decentralized economy. Looking Ahead: Solana’s Continued Evolution As Solana’s ecosystem matures, the need for effective Layer-2 scaling solutions will only become more pressing. SuperSol’s focus on improving Solana’s real-time performance and scalability will help address these challenges head-on, making Solana an even more attractive option for developers and users alike. The strategic investment by Marinade Finance signals confidence in SuperSol’s vision and the potential impact it will have on the network. It also highlights Marinade’s commitment to not only providing liquidity solutions through its liquid staking protocol but also actively contributing to the broader development of Solana’s infrastructure. The partnership between Marinade Finance and SuperSol is a significant step toward ensuring that Solana remains a leading blockchain platform for years to come, able to support the growing demands of decentralized applications, GameFi, and DePIN with cutting-edge performance, scalability, and reliability. About Marinade Finance Marinade Finance is a non-custodial liquid staking protocol built for the Solana blockchain. By allowing users to stake SOL tokens and receive mSOL, a liquid staking derivative, Marinade enhances liquidity and incentivizes participation in Solana’s proof-of-stake consensus. The platform is designed to make staking more accessible and flexible while supporting the broader development of the Solana network. About SuperSol SuperSol is Solana’s first native Layer-2 scaling solution, built to optimize the network’s performance and scalability. Focusing on sectors like GameFi and DePIN, SuperSol is designed to meet the increasing demands of decentralized applications by providing enhanced efficiency, reliability, and real-time performance. SuperSol is the brainchild of Eva Oberholzer, whose impressive credentials include former roles as Chief Strategy Officer at Cardano and Chief Growth Officer at ICP. With her extensive experience in protocol development, Oberholzer recognized Solana's potential as a dominant force in the crypto world. This insight led her to tackle the ecosystem's scalability challenges, particularly in the GameFi space. By founding SuperSol, Oberholzer aims to solidify Solana's position as a leading asset class and drive the next wave of innovation in the blockchain industry. Contact Founder and CEO Eva Oberholzer SuperSol Labs Ltd. pr@supersol.ai This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here DUBAI, United Arab Emirates, December 19th, 2024/Chainwire/--Marinade Finance, Makes Strategic Investment in SuperSol to Boost Solana’s Growth and Enhance Layer-2 Capabilities. The Solana ecosystem has been witnessing rapid growth and evolution, with demand for scalability, performance, and real-time capabilities reaching new heights. This growth is driven not only by traditional decentralized finance (DeFi) applications but also by the rise of emerging sectors like GameFi (gaming on blockchain) and Decentralized Physical Infrastructure Networks (DePIN). In response to these expanding needs, Marinade Finance, a prominent self-custodial staking protocol on the Solana network, has made a strategic investment in SuperSol, the first native Layer-2 scaling solution for Solana. has made a strategic investment has made a strategic investment Marinade enables SOL holders to automatically delegate their tokens to top-performing validators in a competitive open marketplace, optimizing yields by allowing validators to share fees directly with stakers. Strengthening Solana’s Infrastructure for a New Era SuperSol, designed to significantly enhance Solana’s scalability and performance, aims to address the growing demand for efficient and reliable infrastructure that can support the next wave of decentralized applications (dApps). With a primary focus on sectors such as GameFi, which combines gaming with decentralized finance, and DePIN, which utilizes decentralized networks for physical infrastructure, SuperSol is poised to become a critical component in the future of the Solana blockchain. The investment by Marinade Finance is seen as a crucial move to help SuperSol accelerate its development and adoption. By supporting innovations like SuperSol, Marinade is positioning itself at the forefront of the efforts to improve Solana’s Layer-1 and Layer-2 capabilities, ensuring that the network can handle the next generation of dApps and meet the increasing demand for high-performance, low-cost, and scalable solutions. The Role of Marinade Finance in the Solana Ecosystem Founded in 2021, Marinade Finance has quickly established itself as one of the most prominent players in the Solana ecosystem. The platform allows users to automatically stake SOL tokens, Solana’s native cryptocurrency, while receiving mSOL, a liquid staking derivative. mSOL allows users to earn staking rewards while maintaining liquidity, enabling them to participate in other DeFi activities without locking their assets. Through this innovative approach to staking, Marinade Finance has significantly contributed to the overall growth and decentralization of the Solana network. By providing liquidity to staked assets, Marinade enables participants to earn staking rewards without locking their assets, thereby supporting both network security and a more dynamic ecosystem for decentralized finance. In addition to its core offering, Marinade has become an active participant in broader efforts to enhance Solana’s ecosystem. Its decision to invest in SuperSol is in line with its long-term vision to support projects that aim to improve Solana’s scalability and bring real-world use cases to life. SuperSol: A Key Enabler for the Future of GameFi and DePIN The main challenge facing blockchain networks like Solana has always been the need to scale in a way that maintains high throughput while minimizing costs. Solana’s high-speed and low-cost architecture has made it a popular choice for developers, but as adoption grows and more applications are built on the network, there is an increasing need for solutions that can handle even more transactions without compromising performance. This is where SuperSol comes into play. SuperSol is a Layer-2 scaling solution that builds on top of Solana’s existing architecture to offer increased scalability and enhanced performance. By utilizing SuperSol, developers will be able to create more efficient applications, particularly in GameFi and DePIN - two sectors experiencing explosive growth. In the GameFi space, where games and financial incentives are integrated on the blockchain, the need for high-speed transactions is paramount. Traditional gaming engines often struggle to meet the performance demands of real-time, immersive environments, but Layer-2 solutions like SuperSol can help ensure that these games run smoothly and cost-effectively on the Solana network. Similarly, in the rapidly growing DePIN sector, which includes applications focused on decentralizing physical infrastructure such as networks, energy grids, and other assets, scalability is critical. SuperSol’s advanced Layer-2 architecture is designed to handle the transaction loads and data requirements of such applications, making it an ideal fit for this emerging market. A Strategic Partnership with Long-Term Impact While the financial details of the investment have not been disclosed, Marinade’s support for SuperSol is more than just a monetary contribution - it’s a strategic partnership aimed at fostering innovation and ensuring the continued growth of Solana’s ecosystem. By investing in projects like SuperSol, Marinade is positioning itself as a key player in the infrastructure and scalability efforts that will shape the future of blockchain technology. The collaboration between Marinade Finance and SuperSol is a testament to the growing synergies within the Solana ecosystem. As Solana continues to attract developers and projects across a variety of sectors, the combination of robust staking solutions and scalable infrastructure will be key to meeting the demands of an increasingly complex and diverse decentralized economy. Looking Ahead: Solana’s Continued Evolution As Solana’s ecosystem matures, the need for effective Layer-2 scaling solutions will only become more pressing. SuperSol’s focus on improving Solana’s real-time performance and scalability will help address these challenges head-on, making Solana an even more attractive option for developers and users alike. The strategic investment by Marinade Finance signals confidence in SuperSol’s vision and the potential impact it will have on the network. It also highlights Marinade’s commitment to not only providing liquidity solutions through its liquid staking protocol but also actively contributing to the broader development of Solana’s infrastructure. The partnership between Marinade Finance and SuperSol is a significant step toward ensuring that Solana remains a leading blockchain platform for years to come, able to support the growing demands of decentralized applications, GameFi, and DePIN with cutting-edge performance, scalability, and reliability. About Marinade Finance Marinade Finance is a non-custodial liquid staking protocol built for the Solana blockchain. By allowing users to stake SOL tokens and receive mSOL, a liquid staking derivative, Marinade enhances liquidity and incentivizes participation in Solana’s proof-of-stake consensus. The platform is designed to make staking more accessible and flexible while supporting the broader development of the Solana network. Marinade Finance Marinade Finance About SuperSol SuperSol is Solana’s first native Layer-2 scaling solution, built to optimize the network’s performance and scalability. Focusing on sectors like GameFi and DePIN, SuperSol is designed to meet the increasing demands of decentralized applications by providing enhanced efficiency, reliability, and real-time performance. SuperSol SuperSol SuperSol is the brainchild of Eva Oberholzer, whose impressive credentials include former roles as Chief Strategy Officer at Cardano and Chief Growth Officer at ICP. With her extensive experience in protocol development, Oberholzer recognized Solana's potential as a dominant force in the crypto world. This insight led her to tackle the ecosystem's scalability challenges, particularly in the GameFi space. By founding SuperSol, Oberholzer aims to solidify Solana's position as a leading asset class and drive the next wave of innovation in the blockchain industry. Contact Founder and CEO Eva Oberholzer SuperSol Labs Ltd. pr@supersol.ai This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Marinade Finance Makes Strategic Investment In SuperSol

Marinade Finance maak strategiese belegging in SuperSol

شركة ماريناد فاينانس تستثمر استراتيجيا في شركة سوبرسول

Marinade Finance strateško ulaže u SuperSol

Marinade Finance foretager strategisk investering i SuperSol

Marinade Finance realiza una inversión estratégica en SuperSol

Marinade Finance-k inbertsio estrategikoa egiten du SuperSol-en

Marinade Finance tekee strategisen sijoituksen SuperSoliin

Marinad Finans Fè Envestisman Estratejik Nan SuperSol

マリネード・ファイナンスがスーパーソルに戦略的投資

Marinade Finance აკეთებს სტრატეგიულ ინვესტიციას SuperSol-ში

Marinade Finanzas Ruwa Inversión Estratégica SuperSol nisqapi

Marinade Finance Yi Endla Vuvekisi Bya Maqhinga Eka SuperSol

Code ownership might not be a pressing concern when a small team of around 10 developers works on the same system. However, as the engineering team scales to 500, with hundreds of developers contributing to the same system, the dynamics change significantly. This was a challenge my organization faced from two years ago till now — a lesson learned through firsthand experience. How Does Code Ownership Affect Software Engineering Teams So Negatively? As an engineering leader, how do I handle this challenge? Builders do not own the code Builders and owners are often different people. Builders are responsible for implementing features, but code ownership typically falls to someone else. In an ideal world, builders and owners should be the same group of people. A dedicated team owns a specific domain. But, in a shared code-based environment, everyone is a builder across many domains. The code base is co-owned by many teams. Builders may not know the history of the code, we expect everything to magically work, but often, it doesn’t. We find ourselves in this mode when there is no clear ownership of the codebase: touch the code, ship the feature into production, and then leave the “code” to the next builder — along with technical debt. What Could Happen when Code Ownership is Not Transparent? The system becomes error-prone and unstable, leading to low confidence in shipping new features. Most production issues arise because builders lack a complete understanding of the domain. Small code changes can unknowingly break other parts of the system. Different builders repeatedly reinvent the wheel, wasting time and resources. The software development lifecycle (SDLC) becomes tedious and slow. Even minor changes require running full end-to-end tests, which can take over 20 hours — further compounding the issues above. Why Does This Happen? Shared Code Ownership Without Collaboration = No Ownership No Ownership of Code = No Ownership of Bugs When the on-call team tries to reach out to the domain owner for a production issue, after talking to three different teams, it ends up that no one understands the code. Confusion on Responsibility Another common scenario is that a recent builder (who has touched the code recently) gets on the support call, but the builder realizes two more teams have touched the same code recently. Who should fix the bug, then? Tremendous Business Rules Baked into Shared Common Modules There is no boundary between the business layer and the infrastructure layer. It is risky to touch one line of code in any file if the builder does not know the already baked-in business rules added by other builders. Everyone Touches the Same Code Without Talking to Each Other Under the pressure of deadlines, we tend to quickly finish projects without “wasting” time on communications. As a builder, spending time in meetings with domain owners is not something favorable. No One Owns Tech Debts Gradually, the code design is not aligned across different builders. We start seeing very strange implementations, and everyone knows the system’s stability is a problem, but who should own the tech debts? Teams Hesitate to Own Code Owning code means having more responsibilities. Teams tend to push away ownership. A typical statement is like: “My team does not have the full picture (of this feature), please reach out to someone else if you have any questions.” How to Resolve the Problem Properly? First of all, if I had a magic wand, I would assign owners’ names to each line of code. If an owner leaves, their successor’s name should be assigned automatically. Pursue Good Code Isolation and Strive for a Decoupled System. Embrace the design of microservices, where ownership is tied to each microservice. For those managing monolithic applications, define domain modules within the monolith and identify an owner for each module. When We Have to Work on a Shared Code Base … Microservices can help address code ownership issues, but they are not always the best solution for every organization. Engineering teams often align with different business stakeholders, which ties directly to funding and resources. It is not uncommon for multiple teams to maintain the same code domain to support different business workflows. For example, SSO (Single Sign-On) is a shared service that supports multiple product lines. There are two approaches to managing code ownership: Centralized Ownership: One team owns the SSO service domain and handles all requirements from various product teams (stakeholders).
Decentralized Ownership: There is no centralized team for the SSO service. Instead, different builders, responsible for their respective stakeholders, work on the same code domain independently. This represents the shared codebase model. Decentralized Ownership: From No Owner to Co-Owners Builders must take responsibility for creating sustainable, high-quality code. Team leaders play a crucial role in fostering collaboration and maintaining a high standard for code quality. In the Co-Ownership World, One Team Owns the Foundation, Others Embrace Collaboration In a co-ownership model, one team focuses on building the foundational elements — base classes, templates, and interfaces — while other teams align with the design and collaborate to build their features. Using the SSO service as an example: one team sets up the identity service, creating the foundation for enabling SSO across two internal services and one external service. Other teams then follow the established design pattern to roll out SSO for additional domain services. As a Builder, Respect the Owners/Co-Owners Hold review meetings between builders and owners. Reviews should happen at multiple stages of the development cycle, from the project kickoff time-point to the post-release period. Team Collaboration Is Crucial Especially for Projects with Tight Deadlines As a builder, proactively reach out to the owners and seek their insights. As an owner/co-owner, provide support to the builder, do not limit your role to that of a gatekeeper. Otherwise, builders will stop communicating with you. Your Code is Your Asset Take care of your assets, don’t let them become your debt. Be proud to own and maintain high-quality, sustainable code. Code ownership might not be a pressing concern when a small team of around 10 developers works on the same system. However, as the engineering team scales to 500, with hundreds of developers contributing to the same system, the dynamics change significantly. This was a challenge my organization faced from two years ago till now — a lesson learned through firsthand experience. How Does Code Ownership Affect Software Engineering Teams So Negatively? As an engineering leader, how do I handle this challenge? Builders do not own the code Builders and owners are often different people. Builders are responsible for implementing features, but code ownership typically falls to someone else. In an ideal world, builders and owners should be the same group of people. A dedicated team owns a specific domain. But, in a shared code-based environment, everyone is a builder across many domains. The code base is co-owned by many teams. Builders may not know the history of the code, we expect everything to magically work, but often, it doesn’t. We find ourselves in this mode when there is no clear ownership of the codebase: touch the code, ship the feature into production, and then leave the “code” to the next builder — along with technical debt. What Could Happen when Code Ownership is Not Transparent? The system becomes error-prone and unstable, leading to low confidence in shipping new features. Most production issues arise because builders lack a complete understanding of the domain. Small code changes can unknowingly break other parts of the system. Different builders repeatedly reinvent the wheel, wasting time and resources. The software development lifecycle (SDLC) becomes tedious and slow. Even minor changes require running full end-to-end tests, which can take over 20 hours — further compounding the issues above. Why Does This Happen? Shared Code Ownership Without Collaboration = No Ownership No Ownership of Code = No Ownership of Bugs When the on-call team tries to reach out to the domain owner for a production issue, after talking to three different teams, it ends up that no one understands the code. Confusion on Responsibility Another common scenario is that a recent builder (who has touched the code recently) gets on the support call, but the builder realizes two more teams have touched the same code recently. Who should fix the bug, then? Tremendous Business Rules Baked into Shared Common Modules There is no boundary between the business layer and the infrastructure layer. It is risky to touch one line of code in any file if the builder does not know the already baked-in business rules added by other builders. Everyone Touches the Same Code Without Talking to Each Other Under the pressure of deadlines, we tend to quickly finish projects without “wasting” time on communications. As a builder, spending time in meetings with domain owners is not something favorable. No One Owns Tech Debts Gradually, the code design is not aligned across different builders. We start seeing very strange implementations, and everyone knows the system’s stability is a problem, but who should own the tech debts? Teams Hesitate to Own Code Owning code means having more responsibilities. Teams tend to push away ownership. A typical statement is like: “My team does not have the full picture (of this feature), please reach out to someone else if you have any questions.” How to Resolve the Problem Properly? First of all, if I had a magic wand, I would assign owners’ names to each line of code. If an owner leaves, their successor’s name should be assigned automatically. Pursue Good Code Isolation and Strive for a Decoupled System. Embrace the design of microservices, where ownership is tied to each microservice. For those managing monolithic applications, define domain modules within the monolith and identify an owner for each module. When We Have to Work on a Shared Code Base … Microservices can help address code ownership issues, but they are not always the best solution for every organization. Engineering teams often align with different business stakeholders, which ties directly to funding and resources. It is not uncommon for multiple teams to maintain the same code domain to support different business workflows. For example, SSO (Single Sign-On) is a shared service that supports multiple product lines. There are two approaches to managing code ownership: Centralized Ownership: One team owns the SSO service domain and handles all requirements from various product teams (stakeholders). Decentralized Ownership: There is no centralized team for the SSO service. Instead, different builders, responsible for their respective stakeholders, work on the same code domain independently. This represents the shared codebase model. Centralized Ownership: One team owns the SSO service domain and handles all requirements from various product teams (stakeholders). Decentralized Ownership: There is no centralized team for the SSO service. Instead, different builders, responsible for their respective stakeholders, work on the same code domain independently. This represents the shared codebase model. Decentralized Ownership: From No Owner to Co-Owners Builders must take responsibility for creating sustainable, high-quality code. Team leaders play a crucial role in fostering collaboration and maintaining a high standard for code quality. In the Co-Ownership World, One Team Owns the Foundation, Others Embrace Collaboration In a co-ownership model, one team focuses on building the foundational elements — base classes, templates, and interfaces — while other teams align with the design and collaborate to build their features. Using the SSO service as an example: one team sets up the identity service, creating the foundation for enabling SSO across two internal services and one external service. Other teams then follow the established design pattern to roll out SSO for additional domain services. As a Builder, Respect the Owners/Co-Owners Hold review meetings between builders and owners. Reviews should happen at multiple stages of the development cycle, from the project kickoff time-point to the post-release period. Team Collaboration Is Crucial Especially for Projects with Tight Deadlines As a builder, proactively reach out to the owners and seek their insights. As an owner/co-owner, provide support to the builder, do not limit your role to that of a gatekeeper. Otherwise, builders will stop communicating with you. Your Code is Your Asset Take care of your assets, don’t let them become your debt. Be proud to own and maintain high-quality, sustainable code.

How Should Engineering Leaders Handle Code Ownership Challenges?

DUBAI, December 12, 2024: Prosper, a decentralized protocol democratizing access to Bitcoin mining by tokenizing institutional-grade Bitcoin hashrate as omnichain real-world assets (RWA), today announced the acquisition of over 7,000 ASIC miners from BITMAIN along with the successful closing of its strategic funding round. Following the announcement of Prosper’s long-term partnership and hosting service agreement with BITMAIN, the world’s leading manufacturer of digital currency mining servers, this landmark purchase marks an instrumental step in Prosper’s mission to provide institutional-grade reliability to its mining ecosystem. By leveraging BITMAIN’s cutting-edge products, Prosper enhances the value delivered to its $PROS token holders, further distinguishing itself as the premier Web3 Bitcoin mining protocol backed by leading industry players. “Bitcoin brought about a fundamental shift in finance by democratizing an economic system in its entirety with blockchain technology. Prosper follows the footsteps of Bitcoin’s core ethos by democratizing access to Bitcoin hashrate, the underlying layer powering Bitcoin-through blockchain technology. This layer of community governance and ownership afforded by blockchain allows Prosper to scale its flywheel of both Bitcoin hashrate and treasury operations while simultaneously helping to ensure that Bitcoin mining layer is sufficiently decentralized to maintain Bitcoin’s status as the most recognized digital reserve currency. ” said Milton Lam, Prosper Advisory Council Member. Prosper’s Latest Development 1 EH/s of hashrate procured and ready to fully go live (with 250 PH/s live on Prosper’s dApp already)
Access to pipeline capacity of 170MW with the mid-term goal of adding another 5 EH/s
More than 2 BTC rewards given out to $PROS holders since November
Access to more than 200 BTC to be delegated by launch partners to the DAO to unlock TVL opportunities for $PROS holders Strategic Round Closure: Strengthening the Bitcoin Ecosystem In addition to its BITMAIN partnership, Prosper announced the closure of its strategic funding round, which saw participation from prominent industry players and financial investors, including Metalpha, Waterdrip Capital, BIT Mining, and Satoshi Protocol. Earlier in October, Animoca Brands also disclosed its intention to purchase $PROS tokens from the open market. These investments demonstrate the strong confidence in Prosper’s vision of bridging institutional-grade Bitcoin mining power on-chain and its innovative approach to Bitcoin liquidity farming. The involvement of these strategic partners brings crucial industry expertise and networks to support Prosper’s operations as it scales. Positioning Prosper for Growth Prosper’s tokenomics adopts a unique design to ensure hashrate-per-token will only increase and be accretive to $PROS holders.
Prosper’s DAO leverages a unique flywheel model with both hashrate and Bitcoin treasury operations - all governed by and for the benefit of $PROS holders.
Institutional-grade hashrate generates Bitcoin on an ongoing basis, part of which is rewarded to holders for their active participation, and remaining goes to the DAO treasury.
DAO’s treasury Bitcoin can be directed towards TVL opportunities with ecosystem partners such as Bitcoin L2s to generate benefits for $PROS holders and the DAO.
Growth in DAO treasury value and $PROS adoption allows the protocol to further expand underlying hashrate and enhance protocol resilience. By combining institutional-grade Bitcoin mining hashrate, a decentralized protocol design backed by strong fundamentals, and an extensive network of strategic partners, Prosper is well-positioned to solidify its status as the flagship decentralized Bitcoin mining protocol. About Prosper Prosper is a decentralized protocol for a community that truly believes in Bitcoin, providing full exposure across Bitcoin hashrate and Bitcoin through tokenizing institutional-grade Bitcoin hashrate as omnichain RWA, and aims to fully unlock the potential of Bitcoin through liquidity farming. For more information, visit__prosper-fi.com__ or follow us on X (formerly Twitter). Media Contact Essam Ali, LUNA PRessam@lunapr.io This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here DUBAI, December 12, 2024: Prosper, a decentralized protocol democratizing access to Bitcoin mining by tokenizing institutional-grade Bitcoin hashrate as omnichain real-world assets (RWA), today announced the acquisition of over 7,000 ASIC miners from BITMAIN along with the successful closing of its strategic funding round. Following the announcement of Prosper’s long-term partnership and hosting service agreement with BITMAIN, the world’s leading manufacturer of digital currency mining servers, this landmark purchase marks an instrumental step in Prosper’s mission to provide institutional-grade reliability to its mining ecosystem. By leveraging BITMAIN’s cutting-edge products, Prosper enhances the value delivered to its $PROS token holders, further distinguishing itself as the premier Web3 Bitcoin mining protocol backed by leading industry players. “Bitcoin brought about a fundamental shift in finance by democratizing an economic system in its entirety with blockchain technology. Prosper follows the footsteps of Bitcoin’s core ethos by democratizing access to Bitcoin hashrate, the underlying layer powering Bitcoin-through blockchain technology. This layer of community governance and ownership afforded by blockchain allows Prosper to scale its flywheel of both Bitcoin hashrate and treasury operations while simultaneously helping to ensure that Bitcoin mining layer is sufficiently decentralized to maintain Bitcoin’s status as the most recognized digital reserve currency. ” said Milton Lam, Prosper Advisory Council Member. “Bitcoin brought about a fundamental shift in finance by democratizing an economic system in its entirety with blockchain technology. Prosper follows the footsteps of Bitcoin’s core ethos by democratizing access to Bitcoin hashrate, the underlying layer powering Bitcoin-through blockchain technology. This layer of community governance and ownership afforded by blockchain allows Prosper to scale its flywheel of both Bitcoin hashrate and treasury operations while simultaneously helping to ensure that Bitcoin mining layer is sufficiently decentralized to maintain Bitcoin’s status as the most recognized digital reserve currency. ” said Milton Lam, Prosper Advisory Council Member. “Bitcoin brought about a fundamental shift in finance by democratizing an economic system in its entirety with blockchain technology. Prosper follows the footsteps of Bitcoin’s core ethos by democratizing access to Bitcoin hashrate, the underlying layer powering Bitcoin-through blockchain technology. This layer of community governance and ownership afforded by blockchain allows Prosper to scale its flywheel of both Bitcoin hashrate and treasury operations while simultaneously helping to ensure that Bitcoin mining layer is sufficiently decentralized to maintain Bitcoin’s status as the most recognized digital reserve currency. ” Milton Lam, Prosper Advisory Council Member. Prosper’s Latest Development 1 EH/s of hashrate procured and ready to fully go live (with 250 PH/s live on Prosper’s dApp already) Access to pipeline capacity of 170MW with the mid-term goal of adding another 5 EH/s More than 2 BTC rewards given out to $PROS holders since November Access to more than 200 BTC to be delegated by launch partners to the DAO to unlock TVL opportunities for $PROS holders 1 EH/s of hashrate procured and ready to fully go live (with 250 PH/s live on Prosper’s dApp already) Access to pipeline capacity of 170MW with the mid-term goal of adding another 5 EH/s More than 2 BTC rewards given out to $PROS holders since November Access to more than 200 BTC to be delegated by launch partners to the DAO to unlock TVL opportunities for $PROS holders Strategic Round Closure: Strengthening the Bitcoin Ecosystem In addition to its BITMAIN partnership, Prosper announced the closure of its strategic funding round, which saw participation from prominent industry players and financial investors, including Metalpha, Waterdrip Capital, BIT Mining, and Satoshi Protocol. Earlier in October, Animoca Brands also disclosed its intention to purchase $PROS tokens from the open market. These investments demonstrate the strong confidence in Prosper’s vision of bridging institutional-grade Bitcoin mining power on-chain and its innovative approach to Bitcoin liquidity farming. The involvement of these strategic partners brings crucial industry expertise and networks to support Prosper’s operations as it scales. Positioning Prosper for Growth Prosper’s tokenomics adopts a unique design to ensure hashrate-per-token will only increase and be accretive to $PROS holders. Prosper’s DAO leverages a unique flywheel model with both hashrate and Bitcoin treasury operations - all governed by and for the benefit of $PROS holders. Institutional-grade hashrate generates Bitcoin on an ongoing basis, part of which is rewarded to holders for their active participation, and remaining goes to the DAO treasury. DAO’s treasury Bitcoin can be directed towards TVL opportunities with ecosystem partners such as Bitcoin L2s to generate benefits for $PROS holders and the DAO. Growth in DAO treasury value and $PROS adoption allows the protocol to further expand underlying hashrate and enhance protocol resilience. Prosper’s tokenomics adopts a unique design to ensure hashrate-per-token will only increase and be accretive to $PROS holders. Prosper’s DAO leverages a unique flywheel model with both hashrate and Bitcoin treasury operations - all governed by and for the benefit of $PROS holders. Institutional-grade hashrate generates Bitcoin on an ongoing basis, part of which is rewarded to holders for their active participation, and remaining goes to the DAO treasury. DAO’s treasury Bitcoin can be directed towards TVL opportunities with ecosystem partners such as Bitcoin L2s to generate benefits for $PROS holders and the DAO. Growth in DAO treasury value and $PROS adoption allows the protocol to further expand underlying hashrate and enhance protocol resilience. By combining institutional-grade Bitcoin mining hashrate, a decentralized protocol design backed by strong fundamentals, and an extensive network of strategic partners, Prosper is well-positioned to solidify its status as the flagship decentralized Bitcoin mining protocol. About Prosper Prosper is a decentralized protocol for a community that truly believes in Bitcoin, providing full exposure across Bitcoin hashrate and Bitcoin through tokenizing institutional-grade Bitcoin hashrate as omnichain RWA, and aims to fully unlock the potential of Bitcoin through liquidity farming. For more information, visit__ prosper-fi.com __ or follow us on X (formerly Twitter) . prosper-fi.com X (formerly Twitter) X (formerly Twitter) Media Contact Essam Ali, LUNA PR essam@lunapr.io This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Prosper Adds 7,000 More ASIC Miners from BITMAIN To Drive Tokenization Of Hashrate As Omnichain RWA

Prosper přidává 7 000 dalších ASIC těžařů z BITMAIN, aby podpořili tokenizaci hashrate jako omnichain RWA

Ο Prosper προσθέτει 7.000 ακόμη ASIC Miners από το BITMAIN για να προωθήσει την Tokenization του Hashrate ως Omnichain RWA

Prosper suma 7000 mineros ASIC más de BITMAIN para impulsar la tokenización de la tasa de hash como RWA omnichain

Prosper-ek BITMAIN-eko 7.000 ASIC meatzari gehiago gehitzen ditu Hashrate-ren tokenizazioa Omnichain RWA gisa gidatzeko

Prosper engade 7.000 mineiros ASIC máis de BITMAIN para impulsar a tokenización de Hashrate como Omnichain RWA

Prosper は、Omnichain RWA としてハッシュレートのトークン化を推進するために、BITMAIN からさらに 7,000 台の ASIC マイナーを追加

Prosper បន្ថែមអ្នកជីករ៉ែ ASIC ចំនួន 7,000 នាក់ទៀតពី BITMAIN ដើម្បីជំរុញការផ្លាស់ប្តូរនិមិត្តសញ្ញា Hashrate ជា Omnichain RWA

Prosper Omnichain RWA катары Hashrate токенизациясын жүргүзүү үчүн BITMAINден дагы 7000 ASIC кенчилерин кошот

Prosper E Oketša Bašomi ba Meepo ba Bang ba 7,000 ba ASIC go tšwa go BITMAIN Go Kganna Tokenization ya Hashrate bjalo ka Omnichain RWA

Prosper adiciona mais 7.000 mineradores ASIC da BITMAIN para impulsionar a tokenização do Hashrate como Omnichain RWA

Prosper BITMAINmanta 7.000 Aswan ASIC Minerokunata Yapan Hashratepa Tokenización Omnichain RWA hina purichinanpaq

U-Prosper Wengeza Abavukuzi abangu-7,000 abengeziwe be-ASIC abavela ku-BITMAIN Ukuze Bashayele Ithokheni ye-Hashrate njenge-Omnichain RWA

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