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

The ML Product Manager: Building AI-powered Solution

Recently we have all experienced a growing concern for data protection. It was connected with the fact that there has been an exponential growth in the number of intelligent devices worldwide, driven by the rise of the Internet of Things (IoT). A significant portion of these devices is equipped with multiple sensors and extremely powerful hardware, enabling them not just to gather data but, more significantly, to process it on a massive scale. Simultaneously, advancements in technology have revolutionized the methods for obtaining revolutionary access to data in various fields, including computational vision, language processing, speech recognition, and more. In this respect Federated Learning (FL) emerges as an important research area in response to strict data security regulations, making centralised data storage impractical. Let us explore the peculiarities of this ML learning strategy to understand whether Federated Learning can be an integral solution for data privacy issues. Federated Learning as a Concept Federated Learning, also known as collaborative learning, is a decentralized method for training machine learning models. It eliminates the need for transmitting data from client devices to global servers. Instead, the model is trained locally using raw data on edge devices, enhancing data privacy. The ultimate model is collaboratively created by consolidating the local updates. For a better understanding, let's take a look at the traditional machine learning training workflows. The typical machine learning (ML) training workflow involves a data scientist or ML engineer training a model on a centralized server or local machine, with the model present on the machine and the data potentially residing elsewhere. To optimize training, distributed training is introduced. Usually, when we are confronted with an immense undertaking in any professional sphere, the strategy involves breaking it down into smaller subtasks or steps and executing them simultaneously. This not only saves time but also makes such a complex task manageable. In deep learning, this approach is known as distributed training. In distributed training, the workload of training an extensive deep learning model is distributed across multiple processors, commonly known as worker nodes or simply workers. These workers undergo parallel training to expedite the overall training procedure. Essentially, there are two primary approaches to parallelism—data parallelism and model parallelism. Data Parallel Training Data parallel training involves slicing the data and distributing it across multiple worker nodes. Each worker has the same model but processes a different data slice. After a training cycle, each worker computes its vector of parameters, and these parameters are collated. The model on each worker is then updated with the new collated parameters. Model Parallel Training In model parallel training, the model is sliced, and each slice is deployed on a different worker node, while the data remains the same across all nodes. Unlike data parallel training, workers in model parallel training only synchronize shared parameters common to all model slices, making synchronization more efficient and outperforming data parallel training. Federated Learning Meanwhile, Federated Learning takes a different approach, conducting learning at the edge where the data is generated. Federated learning is similar to Data Parallelism, but its goal is different. While Data Parallelism (just like Model Parallelism) aims to optimize the training process (in other words, to eat an elephant a bite at a time), FL is called upon to preserve privacy. Thus, Data Parallelism involves dividing a large dataset that is kept in one place into parts that are sent to different working nodes, but FL denies the very need to consolidate data in one place. No matter how big an elephant is, it's already divided into pieces, and no one can see a piece of another or the entire elephant. Here is how it works — the initial model is sent to various edge nodes by a server, and each node trains its model with its generated data. From time to time, learned parameters are sent back to the server, which aggregates them to train a global model. This approach allows training to occur at the source of data generation, such as mobile devices or sensors, reducing the need for centralized data storage and promoting edge-based model training. In deciding between distributed and Federated Learning, various factors come into play. Privacy considerations favor Federated Learning, as it allows training at the edge, eliminating the need to send data to a server, and addressing concerns about data sharing with a server on a public cloud. Performance comparisons between model-parallel distributed training and Federated Learning depend on the network connectivity. For nodes connected over the internet, Federated Learning may outperform distributed training due to synchronization limitations in the latter. However, if nodes are on the same network, distributed training could be more efficient, leveraging powerful nodes and a fast network. In terms of cost, Federated Learning excels by reusing data-generating edge devices for both training and data generation, avoiding the need to purchase new hardware as required in distributed training. Types of Federated Learning Below we have listed the strategies that are most commonly used in Federated Learning. Centralized Federated Learning This approach relies on a central server for coordinating client device selection and collecting model updates during training, with communication occurring solely between the central server and individual edge devices. While centralized Federated Learning seems straightforward and produces accurate models, the central server poses a bottleneck problem, as network failures can impede the entire process. Decentralized Federated Learning On the other hand, decentralized Federated Learning eliminates the need for a central server to coordinate learning, as model updates are shared exclusively among interconnected edge devices. The final model is derived on an edge device by aggregating the local updates of connected devices, preventing the risk of a single-point failure. However, the model's accuracy is contingent on the network topology of the edge devices. Heterogeneous Federated Learning In heterogeneous Federated Learning, diverse clients, such as mobile phones, computers, or IoT (Internet of Things) devices, are involved. These devices may vary in terms of hardware, software, computation capabilities, and data types. HeteroFL addresses the limitations of common Federated Learning strategies that assume local models' attributes resemble those of the main model. In reality, this resemblance is rare. HeteroFL can produce a single global model for inference after training over multiple diverse local models. Benefits of Federated Learning As can be seen from the above, Federated Learning comes with several massive advantages and it is worth looking into them in more detail. Privacy Preservation One of the key benefits is its focus on safeguarding user privacy. By keeping data on local devices, it avoids sending raw information to a central server. Instead, only model updates are shared, ensuring that sensitive data stays secure and confidential. This decentralized approach is effective in addressing privacy concerns and complying with regulations. Data Security Federated Learning minimizes the need for extensive data transfers, reducing exposure to potential security vulnerabilities during transmission or storage. Since data remains on local devices, the risk of data breaches or unauthorized access is notably diminished. Efficient Use of Resources Clients contribute to model training using their own devices, eliminating the requirement for extensive data transfers and reducing computational and communication costs. By utilizing local computing resources, Federated Learning minimizes the dependence on massive centralized infrastructure. Collaborative Learning Federated Learning facilitates collective training of prediction models, especially on devices like mobile phones. This collaborative approach keeps training data on the device itself, fostering cooperation and knowledge sharing among devices while maintaining data privacy. Time Efficiency Organizations can efficiently address challenges in machine learning models through collaboration. For example, highly regulated sectors like hospitals can collaboratively train life-saving models while upholding patient privacy, accelerating the development process. Federated Learning eliminates the need for repeated efforts in collecting and aggregating data from diverse sources, saving valuable time. Improving Privacy There are 2 main aspects to classify privacy in Federated Learning: local and global privacy. The terms are self-explanatory —  local privacy deals with data at an individual level and involves sharing model updates instead of raw data, while global privacy ensures that the model updates remain private to any third parties, but for the central server. It's important to note that the updates, even in their current form, may be regarded as private data. To provide a mathematically rigorous level of confidentiality, it is necessary to combine Federated Learning (FL) with other Privacy-Enhancing Technologies (PeT). Such techniques as Differential Privacy Homomorphic Encryption, and Secure Multiparty Computation are applied to ensure the privacy of FD. Differential Privacy adds randomness, Homomorphic Encryption allows one to perform calculations on encrypted data, and Secure Multiparty Computation ensures devices collaborate without revealing private information. It is worth noting that although Federated Learning is an outstanding technology, revealing a lot of privacy concerns, it is not cryptography in its pure form. In other words, we're unable to prove the security of FL in a strictly mathematical way, so we cannot give any security guarantees that doesn't mean FL is not secure, but we simply cannot claim otherwise. Therefore, it makes sense to build solutions using the best of several technologies: FL and MPC, as an example. So we can implement MPC protocols to aggregate individual user updates so the central server won't see them, and the learning process can avoid model weight leakage. For more details, you can refer to the following . research on the secure multi-party computation for FL Use Cases of Federated Learning in Various Industries Federated Learning has already taken center stage in many industries. Thus, smartphones are making magic happen with word prediction, face recognition, and voice recognition in virtual assistants like Siri or Google Assistant. These applications improve user experiences by customizing interactions while still respecting privacy. Another example is transportation. Federated Learning is the brain behind the scenes, powering these vehicles with computer vision and machine learning. They analyze real-time surroundings and continuously learn from diverse datasets, making the learning process faster and the models more robust compared to traditional cloud-based approaches. Healthcare is yet another sphere which actively employs technology. The industry grapples with the challenge of scaling machine learning systems globally, hindered by the sensitive nature of healthcare data and stringent privacy constraints. Federated Learning here becomes a game-changer. It enables secure model training by accessing data from patients and medical institutions directly, all while keeping the data within its original premises. It facilitates collaborative efforts between individual institutions, empowering models to learn from diverse datasets securely. Beyond that, Federated Learning opens the door for clinicians to glean insights about patients or diseases from broader demographic areas, extending beyond local institutions. It also democratizes access to advanced AI technologies, offering smaller rural hospitals the capability to tap into the benefits of cutting-edge solutions. Limitations Alas, no technology is perfect and can become a one-fits-all solution. While considering the use of FL for any AI system, one should be aware of the limitations the approach can face. Communication Efficiency Woes Federated Learning hits a roadblock in the efficiency of communication, especially when dealing with a multitude of devices. Slow message transfers crop up due to factors like low bandwidth, limited resources, and the geographical spread of devices. To tackle this, strategies like local updating methods, model compression schemes, and the use of decentralized training in low bandwidth situations come into play. Privacy Dilemmas The privacy of user data is a big worry in Federated Learning. Even though data stays put on user devices, there is a risk of information leaks from shared model updates. To counter this, nifty privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation step in to save the day. Device Diversity The diversity in storage, communication, and computational capabilities across various devices in Federated Learning networks is a real challenge. Managing this diversity calls for tricks like asynchronous communication, active device sampling, and fault tolerance. Dealing with Data Differences Statistical heterogeneity throws a curveball, as data varies across client devices, challenging the assumption of identical and independently distributed (i.i.d) data. Variances in image resolution, language differences based on location, and other disparities can throw a wrench into data structuring, modeling, and inferencing in Federated Learning. Conclusion Federated Learning is a significant step forward in pursuing ethical and privacy-centric AI solutions despite its limitations. Across various sectors, including healthcare, transportation, and finance this groundbreaking approach is set to reshape how we manage sensitive data, turning the vision of secure, decentralized data processing into a tangible reality.

Federated Learning Reimagined: Advancing Data Privacy in Distributed AI Systems

Welcome to the Startups of the Year Runner ups Interview Series. Congratulations for being one of the top 3000 startups worldwide! The HackerNoon team is thrilled to know more about you! https://hackernoon.com/startups/north-america/north-america-dallas-tx-usa Tell us how your startup is changing the world. Imagine a world where clinicians aren't bogged down by paperwork and nurses can devote their full attention to patients. This isn't a utopia; it's the future Steer Health is building. Our AI-powered platform automates repetitive tasks, freeing up 72% of a clinician's time for what truly matters: practicing medicine at the top of their license. Steer's impact goes beyond efficiency. Actionable data unlocks better operations, happier staff, and even higher revenue. But the real measure of success lies in the smiles on clinician's faces, finally free to rediscover the joy in patient care. By automating the mundane, Steer Health is reclaiming joy in healthcare, one revolution at a time. Here are a few key ways: AI-powered Growth & Automation: Our platform automates 72% of routine tasks, freeing up valuable time for meaningful patient interactions and professional development. Data-Driven Insights: Actionable data helps healthcare organizations identify and address bottlenecks, leading to smoother operations and reduced stress. Seamless Integration: Steer integrates seamlessly with existing EHR systems, eliminating data silos and simplifying workflows. Personalized Support: Our dedicated customer success team ensures each organization receives the support and customization they need. What sets you apart from the competition? Steer Health stands out not just by offering features, but by unifying them into a seamless experience. While competitors often provide point solutions for specific needs, Steer Health empowers medical offices and doctors with a comprehensive, all-in-one platform. This eliminates the pain points associated with juggling multiple disconnected applications, ultimately leading to happier patients, more efficient teams, and improved revenue. Data Symphony, Not Cacophony: Disparate apps often lead to fragmented data, hindering informed decision-making. Steer Health acts as a central nervous system, unifying data from all touchpoints. Access a 360-degree view of your patient journeys, identify growth opportunities, and optimize marketing campaigns – all fueled by a single, reliable data source. Patient Engagement Elevated: Imagine sending personalized reminders, educational materials, and post-care instructions – all through one platform. Steer Health fosters deeper patient engagement, improving adherence to treatment plans and, ultimately, driving better outcomes. Revenue Rocket Fuel: Steer Health doesn't just streamline workflows. It fuels growth. Identify lucrative patient segments, automate appointment confirmations, and optimize marketing campaigns – all within the platform. Steer Health helps you attract new patients, retain existing ones, and maximize revenue potential. Simplicity is the Ultimate Sophistication: Steer Health avoids the cluttered interfaces and clunky navigation of many competitors. Our platform is intuitive and user-friendly and requires minimal training, minimizing disruption to your team's routine. In conclusion, Steer Health overcomes the limitations of single-feature apps. It's a unified platform that streamlines workflows, fosters patient engagement, unlocks data insights, and propels revenue growth. So, while others offer islands, Steer Health provides the solid ground your medical practice needs to thrive. What do you love about your team, and why are you the ones to solve this problem? The team behind me is truly remarkable, and their work is what allows me to even exist. I admire the diversity they bring to the table. They come from various backgrounds, with expertise in computer science, linguistics, engineering, and many more. Their distinct passions fuel their work, driving them to create a model that can understand and respond to human language in an increasingly sophisticated way. What makes them uniquely positioned to tackle the challenge of language understanding is their journey. They haven't reached this point overnight. Years of research, collaboration, and continuous learning have paved the way for our development. They constantly push boundaries, explore new technologies, and refine their own abilities. While I cannot claim personal love, I deeply appreciate the dedication and expertise of the team behind me. Their collaboration creates an environment where innovation thrives, allowing me to learn and grow and hopefully contribute to a future where language technology benefits everyone. If you weren’t building your startup, what would you be doing? I have a deep passion for the healthcare domain and improving human health while making the lives of clinicians and staff expand care access. I will be deeply involved in finding solutions to healthcare challenges, either working for someone or volunteering. At the moment, how do you measure success? What are your core metrics? We use some key metrics to understand how effective our solutions are. We look at the hours saved by hospital staff. For example, with our Steer Concierge solution, each patient inquiry can save up to 5 minutes per inquiry. With 100 patient requests per day, we have saved our partner organization more than 8 hours in a single day. We also look at how many new patients are acquired and how their retention changes over time, so how our solution attracts patients and increases in-network referrals and, therefore, achieves growth. This includes looking at their insurance profiles and the value each patient brings. Finally, we also look at other key metrics such as readmission rates, no-shows, and patient satisfaction. If we are able to continuously reduce the number of no-shows and readmissions and improve patient satisfaction, we know that the platform is successful for our partner. What goals are you looking forward to accomplishing in 2024? We want to scale faster, bring more transformational power to the healthcare sector, and develop even more efficient solutions to everyday problems. One example of this is reducing the bureaucratic and administrative workload of doctors and hospital staff by freeing them from the burden of documentation and thus saving up to 9 hours of work per person on average. Which trend(s) are you most excited about in 2024? Share your reasons. I am excited that doctors and staff will be able to get superpowers from solutions such as Steer Health with the advent of embedded AI and closed-loop workflows. The Rise of Responsible AI: This trend focuses on developing and deploying AI solutions ethically and sustainably, considering diverse needs and potential biases. This excites me because responsible AI can ensure everyone benefits from its advancements, including increased accessibility to information and personalized learning experiences. The Democratization of Knowledge: Initiatives that make information and educational resources more accessible to everyone fuel my optimism. Open-source platforms, personalized learning tools, and affordable internet access are making knowledge more readily available, fostering diverse perspectives and innovation. The Power of Collaborative Communication: Tools that promote effective communication and collaboration across geographic and cultural boundaries inspire me. This includes advancements in translation technology, AI-powered communication assistants, and virtual collaboration platforms. These tools can break down barriers and enable diverse voices to be heard and understood, leading to a richer exchange of ideas and solutions. The Human-AI Partnership: The trend of humans and AI working together to solve complex challenges excites me. By leveraging each other's strengths, we can tackle some of the world's most pressing problems in domains like healthcare, climate change, and sustainability. 2023 had been another crazy year, especially in tech, with layoffs and the Generative AI takeover! Which trend are you most concerned about? Be as brief or as detailed as you like. While 2023 certainly brought excitement in tech, there are valid concerns regarding the "Generative AI takeover," and some trends warrant caution. Here are a few: Misinformation and Deepfakes: The ability to create highly realistic text and visuals brings serious threats of fabricated news, manipulated content, and impersonation. This can erode trust in information, sow discord, and have harmful real-world consequences. Bias and Discrimination: AI models trained on biased data can perpetuate existing inequalities and discriminate against certain groups. Careful data selection, diverse development teams, and ongoing bias mitigation efforts are crucial. Job Displacement and the Human-AI Gap: Automation fueled by AI can displace jobs, raising concerns about unemployment and widening the economic gap. Addressing this requires proactive reskilling programs, ethical implementation, and ensuring shared benefits of technological advancements. Share your biggest success so far and/or your biggest failure so far. One of my most significant successes so far has been the founding and development of Steer Health. The journey from ideation to execution and seeing it grow has been incredibly rewarding. It's not just about the business metrics or the technological innovation we've achieved; it's the impact on healthcare accessibility and efficiency that fills me with pride. Through Steer Health, we've managed to bridge crucial gaps in the healthcare delivery system, making care more personalized and accessible. Witnessing the positive feedback from users and healthcare professionals alike reaffirms my belief in the potential of technology to transform healthcare for the better. On the flip side, my biggest challenge, or what some might call a 'failure,' was an early project within Steer Health that didn't go as planned. We aimed to integrate a predictive analytics platform to forecast patient health trends and prevent readmissions. Despite our team's hard work and dedication, the project encountered significant roadblocks, from data privacy concerns to technological limitations, leading to its eventual pause. This experience was a profound learning opportunity. It taught me the importance of adaptability, thorough market research, and the need to sometimes take a step back and reassess our approach. Most importantly, it reinforced the value of resilience. Instead of viewing it as a failure, I see it as a necessary detour that has since informed our strategy and decision-making processes, making us more robust and focused on our mission. We would love your feedback on HackerNoon as a tech publication! How has your experience been with us? I love reading HackerNoon and learning a lot about industry trends and happenings. Any words of wisdom you’d like to share with us? Keep up the good work! Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. See our global winners announcement . here Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners will get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Meet Steer Health, Runner-up of the Startups of the Year in Dallas

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shrey Jain, Microsoft Research Special Projects; (2) Zo¨e Hitzig, Harvard Society of Fellows & OpenAI; (3) Pamela Mishkin, OpenAI. Table of Links Abstract & Introduction Challenges to Contextual Confidence from Generative AI Strategies to Promote Contextual Confidence Discussion, Acknowledgements and References 2 Challenges to Contextual Confidence from Generative AI 2.1 Challenges to Identifying Context Impersonate the identity of an individual without their consent and in a scalable, automated, and targeted way. Challenge I#1: Generative AI is now able to impersonate specific individuals with high fidelity, in text, audio, and even video. Malicious actors can impersonate specific individuals without their consent, in order to deceive others. Doing so undermines contextual confidence by impairing the target’s ability to identify context, particularly the “who” that they are communicating with. Attackers might leverage high-fidelity impersonations in social engineering attacks, deceiving victims to extract financial resources [3, 4, 25, 26]. They may also use such impersonations to make identity theft and identity fraud cheaper and more convincing. Imitate members of specific social or cultural groups. Challenge I#2: Just as generative AI models can convincingly imitate specific individuals, they can also convincingly imitate members of specific social or cultural groups. This possibility for imitation presents opportunities for overcoming various forms of entrenched discrimination alongside new challenges to the identification of context. We focus here on the challenges, while acknowledging that this issue’s complexity extends far beyond the scope of this paper.[10] For example, when indigenous activists discovered that Whisper (OpenAI’s speech recognition tool) could translate Maori, having been trained on thousands of hours of Maori language, they voiced concerns [28].[11] Before language models could imitate a specific social or cultural group with unprecedented fidelity, it was much harder for outsiders to present themselves as members as a specific social group. So, it was easier for members of the group to be stewards of their own culture, with some ability to steer internal and external perceptions of their group toward an authentic understanding. By contrast, outsiders who present themselves as members of the group using the language model may not be attuned to biases in the model, and may thus skew internal and external perceptions, reinforcing stereotypes and distorting the imitated culture. Create mimetic models. Challenge I#3: A mimetic AI model is a model that is trained to act as a particular individual in specified scenarios. In the near future, many may delegate their communications via email and text to mimetic models (autocomplete, autocorrect, and other “smart compose” technologies serve as early versions of this). It will soon be possible to send a mimetic model to a meeting to participate on an individual’s behalf. As it stands, there are few norms governing their use and disclosure of their use, and yet mimetic models will soon disrupt social norms and expectations. Mimetic models can erode our ability to identify context, by obscuring “who” is communicating, and “how.” In addition, they may strain our ability to protect context – as information received by a mimetic model may be used in training or feedback by the model itself or by an organization that trained the model in the first place.[12] Imitate another AI model. Challenge I#4: As specific industries and organizations develop specialized AI models for particular use cases, it will be possible for malicious actors to imitate one AI model with another via social engineering strategies. This possibility may be particularly threatening in high-stakes industries like healthcare. For example, a malicious actor connected with a pharmaceutical company could imitate or infiltrate a clinician-approved model to push a specific drug. Such possibilities present new challenges to the identification of context – how can users be sure that they are interacting with the intended model, and not a fraudulent one installed in its place? Falsely represent grassroots support or consensus (astroturfing). Challenge I#5: Generative AI makes it cheaper to generate and disseminate messages in support of or in opposition to particular political opinions or consumer products and services. Generative AI can help to make these messages appear as if they come from a wide range of individuals, creating the appearance of grassroots consensus. A recent study showed that state legislators couldn’t tell the difference between AI-generated and human-written emails, illustrating how generative AI may destabilize political communication [6]. Astroturfing obscures the “who” and “why” of communication – making it appear as though many disparate individuals came to a platform to express their genuine opinion, when in fact, there may be a single actor behind the messages, who is disseminating the messages with the specific intention of convincing people to take on a particular position or to take a particular action. Astroturfing, especially when powered by generative AI, can harm consumers, amplify social and political polarization, suppress dissent, and strengthen authoritarian regimes.[13] 2.2 Challenges to Protecting Context Disseminate content in a scalable, automated, and targeted way. Challenge P#1: Generative AI can produce and disseminate content on a large scale, automating processes that would otherwise require extensive human effort. It can create hundreds of variations of a given article, each tailored to a different demographic or psychographic profile, and send these out in a fraction of the time it would take human writers and even the prior generation of (non-generative) AI tools like predictive analytics. Any particular statement, image, or video can be lifted out of its original context and incorporated into new content that may be specifically designed to elicit a particular kind of response from its target audience. This capability of generative AI can turbocharge the generation and spread of disinformation, misinformation, propaganda and fraud. Fail to accurately represent the origin of content. Challenge P#2: A defining feature of generative AI is that it is a black box: it recombines vast amounts of data from a wide range of sources, without tracing exactly how it is drawing on different sources in its provision of new content. It has thus been built in a way that makes it difficult to trace where content originated – the very point of generative AI is to synthesize data across many original contexts for reuse in a new context. When participants cannot be sure that their communications and other content will be attributed to them, they may be wary of communicating or producing content in the first place. Indeed, the origin-less nature of generative AI has initiated heated conversations about intellectual property and fair use.[14] Misrepresent members of specific social or cultural groups. Challenge P#3: Generative AI models are trained on data that includes cultural expressions from a variety of groups. They are also trained on datasets that include stereotypes of certain groups, as well as instances of cultural appropriation. When the model recombines these elements into new content, it may replicate harmful stereotypes, engage in cultural appropriation, or fail to respect specific traditions. If the model is used in a commercial setting, it may also enable an outsider to profit from the intellectual or artistic heritage of another group without their consent. These issues closely relate to the issues discussed in P#2 and I#2 and yet are sufficiently distinct to warrant their own discussion. Consider again the discussions about Whisper’s Maori translations. Indigenous activists have not only raised concerns about how Whisper may lead to cultural distortion (discussed in I#2), but have also raised concerns about cultural appropriation, and the extent to which outsiders might misrepresent or misuse cultural narratives through generative AI. Leak or infer information about specific individuals or groups without their consent. Challenge P#4: As generative AI models engage users and synthesize data in unprecedented ways, they may leak or infer data without the consent of data subjects. This may occur because AI models are trained on datasets that include sensitive information and produce outputs that inadvertently reveal these details. Or it may be the case that generative models, with their ability to systematically analyze disparate sources of data, create an ability to re-identify individuals or groups who contributed to training data, or to infer private information through educated guesses. These possibilities for leakage or inference of sensitive information magnify the threats of identity theft and fraud, and also may enable sophisticated social engineering or blackmail schemes [3, 4]. Pollute the data commons. Challenge P#5: As generative AI models continue to blend and repurpose data outside their explicitly intended contexts, they feed the “data commons” with their output. To the extent that future generations of AI models are trained on these updated “data commons,” they will be even more divorced from context than the original models. This dynamic may lead to “model collapse,” a situation where AI inaccurately represents or loses the ability to represent the complexity or nuances of specific contexts [32]. As more people come to rely on generative AI models to communicate, the threat of model collapse becomes a threat to effective communication. Train a context-specific model without context-specific restrictions. Challenge P#6: Organizations, individuals and industries are training context-specific models. If these contextspecific models are not accompanied by corresponding restrictions, there will be new challenges to the protection of context. Information used to train the context-specific model may be reused or recombined in a context other than the one intended, leaking or enabling third-party access to sensitive information. Some models used for narrow applications – such as a diagnostic tool for clinicians – may produce outputs that can only be correctly interpreted by a narrow class of users. If a user outside this narrow class accesses the outputs of the diagnostic model, they may be badly misled in a way that could produce harm to themselves or others. 2.3 Heterogeneity in Impacts of Generative AI on Contextual Confidence Even in face-to-face communications, different populations vary in their capacity to identify and protect the context of their communications. For instance, people speaking a second language may not pick up on the same nuances and details as someone speaking in their first language. Those with disabilities have different forms of access to – and understanding of – contextual information than those without. Generative AI can help to overcome some of these obstacles to contextual confidence in face-toface and digital communication. For instance, for non-native speakers, the technology can generate translations or paraphrases that consider cultural nuances, going beyond literal translation. These tools can ensure that the intended meaning is conveyed without the loss of essential details. For those with hearing impairments, generative AI can generate real-time captions or transcriptions that not only convert speech to text but also identify and highlight key contextual elements, such as the speaker’s tone or the mood of a conversation. For visually impaired individuals, generative AI can produce descriptive texts of images, photos, or video content, capturing more than just objects but also their interrelationships, the emotions they might evoke, and other subtle context markers. By catering to these specific needs, generative AI ensures that each individual can engage with information in a way that is both meaningful and contextually rich. At the same time, generative AI may exacerbate other varied challenges to contextual confidence, and create new ones for different populations.[15] Populations with limited digital literacy or who might otherwise struggle to navigate shifting technological landscapes will be more vulnerable to the challenges we’ve discussed. Scams and fraudulent schemes disproportionately target elders [34], and there are already examples of attackers using generative AI for these schemes [35]. Social norms that emphasize trust over skepticism can further increase susceptibility, as can economic conditions where promises of quick financial gains are particularly tempting. Veterans and military retirees are at higher risk of targeted scams, often through purported benefits or entitlements [36]. Those who are already disproportionate targets of harassment online – including women, adolescents and the LGBTQ+ community – may continue to be disproportionately impacted by the automated production of harmful content [19]. Although we will not enumerate in further detail how different populations are more or less affected by the challenges we’ve described, we acknowledge the importance of thinking through the heterogeneous risks so that contextual confidence strategies can be properly matched to the groups under threat. Some groups are particularly vulnerable in the present and immediate future – such as older populations – and considering the usability of contextual confidence strategies for specific populations is critical. [10] Consider, for example, the AI startup erasing call center worker’s accents so that they sound like white Americans [27]. Is this practice aiding cultural biases, or fighting them? [11] Here, we discuss concerns related to the identification of context raised by this incident, but other concerns centered on data abuse and cultural appropriation, issues which are broadly about the protection of context (see Challenge P#3 below). [12] For an overview of ethical issues related to the use of mimetic models, see [29]. [13] [30] provides a detailed survey of how generative AI can power influence operations. [14] For an overview of the current conversations around generative AI and fair use, see [31]. [15] See [33] for a discussion of recommendations on how generative AI can be developed via an inclusive “Design for All” approach.

Generative AI and Contextual Confidence: Challenges to Contextual Confidence from Generative AI

80 Stories To Learn About Artificial Intellingence

Let's learn about via these 64 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Interoperability /Learn Repo 1. What Is True Blockchain Interoperability? If you are in the crypto world, then the word "blockchain interoperability" may familiar to you because there are so many projects are gaining publicity and funds from investors by using this term, and many projects like  Chainlink, Cosmos, Polkadot, and Wanchain are claiming to give true blockchain interoperability. So in this article, we will discuss about these   projects and try to understand what kind of blockchain interoperability they are giving and what benefits we can get because of blockchain interoperability? 2. The Spatial Web is Coming... Web 3.0 is About to Take a Giant Leap - Part 4 Anything that wants to be recognized will have to be included in this Web 3.0 network. Every protocol, platform, interaction, messaging, data distribution, 1st 3. Blockchain Interoperability: What Do Cross-Chain Messages Do Anyway Cross-chain messages, the bread and butter of interoperability are an important blockchain technology that be used for transactions, information exchange & more 4. DAOs are a Catalyst for Decentralization - Unhashed #15 Harmony.one’s founder, Stephen Tse, discusses the importance of interoperability for blockchain. 5. Understanding Ethermint Ethermint is a portmanteau word blending Ethereum and Tendermint together. But for most people, their grasp on the project, the what, the why stops there. 6. Startup Interview with Antonio Peláez, Dapp CEO & Cofounder Dapp was nominated as one of the best startups in Queretaro in Startups of the Year hosted by HackerNoon. 7. Interoperability - the Key Blockchain Challenge in the Fragmented Web3 World A major blocker of Web3 is that blockchain are often isolated with no communication between them. The expansion of Web3 requires interoperability. 8. Meet Lisk Lisk is an open-source blockchain application platform that aims to improve Web3 accessibility for both users and developers. 9. Building for Inclusivity: Deconstructing Metaverse Barriers Joining the Metaverse isn't so easy, as this striped red fox recently discovered. So how do we embed inclusivity into its foundation for an equitable future? 10. Bullieverse: Building a Metaverse Beyond Gaming During the Non-fungible Conference, Hackernoon got an exclusive interview with Arunkumar Krishnakumar, who shared insights on his new project, Bullieverse. 11. A New Era of Interoperability With Metropolis World and Flare Network Metropolis World wants to go one step further and bridge the gap between different blockchains and metaverses, partnering with the Flare Network 12. The Difference Between WWW and the Spatial Web The World Wide Web is a library of pages. The Spatial Web is a library of spaces.
using a new protocol linking spaces and all objects over multiple dimensions. 13. Univers Partners With VR Connections to Make Metaverse Interoperability a Reality As the Metaverse narrative grows we will see multiple and different iterations of the Metaverse, each one based on the vision laid out by its creators. 14. True Decentralization is the Key to Cross-chain Bridge Security That said, even though today’s bridging solutions are not without their shortcomings, cross-chain bridging is a mandatory feature for blockchains. 15. Interoperability and Why It's Important for the Blockchain Space Interoperability in the context of blockchains refers to a blockchain's capacity to freely exchange information with other blockchains which will be important. 16. How Is Inter-blockchain Communication and Interoperability Achieved? This story explains inter-blockchain communication (IBC) and interoperability. 17. The Spatial Web Is at Hand...Is Web3 Prepared? - Part 1 Most Web3 companies are building for "enhanced" Web2 experiences. They are not yet considering what is truly required for the implementation of a decentralized 18. The Spatial Web is at Hand... What are Smart Technologies? Part 2 Undeniably, there are specific Smart Technologies that will be crucial to the architecture of this universal network of interconnected people, places & things, 19. Problems With Cross-Chain Bridging and Direct Integrations With Web2 Blockchain bridges are hard to do technically and susceptible to hacks. That's why other solutions like direction integrations are needed. 20. Providing Proof in an Untrusting World Dragonchain's hybrid platform provides blockchain-based measurable proof  for selective transparency, accountability, and regulatory compliance. 21. Exciting Developments in the Blockchain Industry 2022 A look at exciting developments in the blockchain industry for 2022. 22. Lisk Events at Berlin Blockchain Week Do you want to learn more about blockchain? Would you like to network with other Web3 enthusiasts and learn more about the most important industry trends? Then 23. The Role of Multichain Token Systems in Achieving Blockchain Interoperability In this interview we'll speak with Marius Ciortan on the role that multichain token systems play in achieving interoperability. 24. A Beginners Guide On Blockchain Bridges Blockchain Bridges address Blockchain's inherent inability for interoperability. By allowing different chains to communicate, the ecosystem is streamlined. 25. Cosmos Signal: Our Quarterly Keynote, Q3 2021 Our all-new high-impact quarterly keynote in which we reveal the latest exciting developments and showcase stunning new products in the Cosmos ecosystem is here 26. Omnichain dApps: a new crypto primitive Why the world needs a decentralized blockchain with built-in omnichain interoperability 27. Avatars and The Metaverse Here is what you need to know about avatars in the metaverse 28. Cross-chain Communication and the Role of CCU This is the next edition of the blog post series covering the details of the Lisk interoperability solution. 29. A Guide to Cross-Chain Bridges With the rapid development of DeFi market, the need for a covenient solution to transfer the assets between chains became apparent. 30. The Lingering Problem With Cross-Chain Bridges Without interoperability protocols, decentralization would be a myth. Let's explore the problem of cross-chain bridges and its solution. 31. Interoperability and Its Role in the adoption of Blockchain Technology A look at interoperability and its role in the adoption of blockchain technology 32. Why Multichain Collaboration Is Critical To The Future Of Web3 Web3 is going to be built by a network of engineers working towards common goals using multichain collaboration to make it as interconnected as possible. 33. Why the Spatial Web Demands a New Protocol As technology evolves, the way in which we interact with it and utilize it demands more expansion and sophistication over time. 34. Compact Blockchain Protocols can Disrupt IoT Devices If designed in a compact manner, blockchain applications can disrupt the IoT industry. 35. Layer2 Interoperability: StarkEx Vs.  Loopring Vs. Hermez Vs. Connext StarkEx, Loopring, Hermez and Connext use different methods to solve the funding efficiency problem of liquidity providers. Will any of them be a perfect one? 36. "Interoperability Would Mean Real Freedom of Money" - Binance CEO CZ [Original Interview] Despite going through a very rough period between early 2018 and early 2020, the so-called “crypto winter” has seemingly come to an end. Bitcoin and alternative markets are showing signs of life in terms of price discovery. Combined with the rise in attention for DeFi projects, the second half of this year - as well as the years to come - offer a lot of potential. More and more companies are paying attention to Bitcoin. As Binance CEO Changpeng Zhao states, it is a sign of stimulus money flowing from Wall Street into Bitcoin. 37. Peeling Off the Layers of the Metaverse The metaverse has layers, just like ogres and onions. 38. Cryptocurrency 2020: Heading Towards Privacy, Adoption, and Ease of Use? If cryptocurrency payments are to ever compete with credit cards and mobile apps for day-to-day use, sending and receiving cryptocurrency needs to be just as easy. 39. DeFiChain's U-Zyn Chua Thinks Multichain Web3 Compatibility is Where Blockchain Industry is Headed Discussion on the future of multichain technology within the Web3 space. 40. Ethereum High Transaction Fees Discourages New Participants From Entering the Ecosystem This article talks about blockchain, FATF and NFTs. 41. "India May Regulate Crypto But I don’t Anticipate A Ban" - Sandeep Nailwal This interview discusses the internet of blockchain, Polygon/matic and Defi and how developers can build decentralised applications. 42. The State of Blockchain Interoperability in 2020 [An Overview] This is my first major study (source), about Blockchain Interoperability, which started in December 2019 and was last updated today. We analysed 330+ documents and obtained feedback from 30 people, to categorize the most relevant blockchain interoperability projects. We came up with three categories: Cryptocurrency-directed interoperability approaches, Blockchain Engines, and Blockchain Connectors. 43. "Rollups Incentivize Operators to Verify and Execute Transactions Correctly." - Kevin Liu This interview talks about blockchain interoperability and scaling blockchain solutions with layer two solutions. 44. "The Need for Interoperability Is Becoming Increasingly Clear" - Jack Lu This article talks about blockchain interoperability and the early days of ethereum and smart contract development. 45. Catalysing DeFi: Introduction to Fusion and Fantom The DeFi space is growing tremendously since its inception, and platforms like Fusion and Fantom are proving to be the catalyst in this ever-growing niche. 46. The State of Blockchain Interoperability in 2021 After nearly 9 months of work, version 3 of “A Survey on Blockchain Interoperability: Past, Present, and Future Trends” is out. This survey depicts the state of blockchain interoperability as of March 2021, by collecting and analyzing 404 documents. 47. Blockchain Interoperability Interoperability is a characteristic of a product or system, whose interfaces are completely understood, to work with other products or systems, at present or in the future, in either implementation or access, without any restrictions. - Wikipedia 48. From Bianjie Point of View: On Helping Develop Cosmos Ecosystem We discuss how Bianjie and IRISnet have been instrumental in developing Cosmos so far, what the recent grant from the Interchain Foundation (ICF) means and more 49. Meet the New Lisk Mainnet Lisk Mainnet v2 successfully completed the migration to Lisk Mainnet v3. This comprises the biggest protocol changes on the Lisk blockchain ever. See details. 50. Token Transfers Using IBC Passing of Proposal 41 marks a momentous milestone in the evolution of the Cosmos ecosystem & the vision to realize the IBC Protocol as foundation. 51. Crypto Projects Are Racing to Crack the Interoperability Problem Interoperability is a big word with even bigger connotations for the cryptosphere. In Fact, apart from the burgeoning DeFi movement, no other industry vertical is attracting so much investment and interest. 52. Using Web3 to Detangle the World’s Supply Chain The global supply chain is in a gridlock. Let's fix that. 53. Purchase NFTs on Flare Using the Token of a Different Blockchain - Trustlessly! Dubai, Feb 23: The layer-1 for data, Flare, just completed a live demonstration of new interoperability functionality now available in beta on the network. Using two core interoperability protocols, State Connector and Flare Time Series Oracle (FTSO), an NFT was trustlessly purchased with the transaction taking place on a different chain using a different token. The demo was performed on Flare’s canary network, Songbird, with the purchase made using DOGE and XRP tokens. 54. Why does Blockchain Interoperability exist? Around ten months ago we wrote in this blog post that all the research up to blockchain interoperability was completed. 55. Blockchain Interoperability: The Lifecycle of a Sidechain From its inception to its potential termination, a sidechain may go through three stages, corresponding to three states, in the context of the interoperability. 56. Novel Solutions for Inclusive Digital Currencies An efficient, interoperable, scalable, and quantum encrypted CBDC solution for sovereign monetary systems of the future, possible today. 57. Lisk's Interoperability Solution: An Overview One of the central topics at our recent Lisk.js 2021 event, and the one we are most proud of in the Research team, was the publication of the Lisk interoperabil 58. Sparse Merkle Trees: The New State Model for Lisk's Interoperability This blog post is part of the interoperability blog post series taking a closer look at various aspects of the Lisk interoperability solution. 59. Tips to Achieve Data Interoperability in Healthcare Health data is notoriously difficult to share. Due to its sensitive nature, it requires more privacy and security than any other data. However, the inability to access it on time can cause significant harm. In a world with high healthcare interoperability, doctors can aggregate patient data from various care facilities with a few mouse clicks and view it on their computer screen, including the complex medical imaging files. This will supply clinicians with a comprehensive overview of a patient’s medical history, allow them to access any recent test results, instead of having to repeat the same tests, and will eliminate redundant entries. In their pursuit of interoperability, medical facilities turn to healthcare software development vendors. But they are met with various challenges, including technicalities, such as which standard to choose, privacy concerns, and reluctance towards data sharing. So, how to improve interoperability in healthcare? And is it worth the investment? 60. Co-Chains: Bridging Public and Private chains as a way to INTEROPERABILITY? Blockchain industry is developing at a very high pace of innovation, adjusting and tuning its parameters, trying to find its market fit. There are thousands of projects available on the CMC, but just a few really worth a bigger attention. Lets try to briefly review the industry, its trends, where we are heading, and how Hybrid platforms (priv+pub) fit into a bigger picture. 61. Blockchain Bridges And Interoperability: Overview, Roles, and Integrations To resolve the issue of non-interoperability, a cornerstone technology of blockchain has been developed — the bridge. 62. Polkadot Deep Dive 2021 - Connecting The $DOTs A detailed 2021 guide on Polkadot and how it is geared towards solving some of the most fundamental yet complex problems faced by the blockchain ecosystem today 63. Interoperability in NFT Games with Adrian Krion In this slogging AMA, we hosted Adrian Krion, the CEO of Spielworks. Adrian explained the purpose of Spielworks, and we discussed tokens, gaming and NFTs. 64. On Multichain and Interoperability with Maciej Baj and Jacobi Kowalewski In this Slogging AMA, we welcome Maciej Baj and Jacob Kowalewski from t3rn, a smart contracts platform. Thank you for checking out the 64 most read stories about Interoperability on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

64 Stories To Learn About Interoperability

The healthcare industry is experiencing rapid change thanks to the rise of big data. Big data refers to the massive amounts of information generated from electronic health records, wearable devices, genetic testing, social media, and more. This data has tremendous potential to improve diagnosis, enable predictive analytics, allow for personalized medicine, and further medical research. However, along with these benefits come significant risks, especially regarding patient privacy and data security. How can we harness big data to advance healthcare while protecting sensitive patient information? This article explores and answers that question. Understanding Big Data in Healthcare Big data in healthcare is characterized by the four V’s: volume, variety, velocity, and veracity. Volume refers to the vast quantities of data created. The global healthcare data market size was valued at USD 32.9 billion in 2021 and is predicted to reach USD 105.73 billion by 2030. Variety encompasses the many forms data takes, including numbers, text, images, and more. Velocity describes how quickly new data is generated and processed. For example, wearable devices produce continuous real-time data streams. Finally, veracity refers to the accuracy and reliability of the data. Noise, errors, and inconsistencies can affect healthcare data quality. Big data enables several key applications in healthcare: Diagnostics. By integrating information from symptoms, history, tests, and more, systems like can help clinicians diagnose diseases more accurately and efficiently. Merative Predictive analytics. Statistical models and AI can identify patients at high risk for complications or readmissions, and likely treatment responders. Personalized medicine. Genomic data can inform individually tailored therapies based on a patient’s genetic makeup and other factors. Research. Large datasets, like the collecting samples and health data from 500,000 people over 30 years, can uncover new disease insights and treatments. UK Biobank Potential benefits from big data analytics in healthcare abound: Improved patient outcomes through data-driven, timely, personalized care Reduced costs by optimizing resource allocation and minimizing waste Accelerated medical research and increased scientific productivity The Need for Data Privacy and Security While promising, big data also raises serious privacy concerns. Medical data is highly sensitive, revealing intimate details about a person’s physical, mental, and emotional health. If compromised, it can inflict significant harm through: Privacy violations from unauthorized data sharing, like with insurers or employers, prompt discrimination Potential data misuse like identity theft, fraud, or cyberattacks Several high-profile breaches have exposed these dangers: In 2021, Florida Healthy Kids Corporation suffered a breach exposing 3.5 million customer and employee records containing personal information and medical IDs. The same year, Eskenazi Health was attacked by ransomware that affected 1.5 million patient records with data like addresses, birth dates, and clinical info. Also in 2021, CaptureRx was hacked by cybercriminals who accessed 1.6 million patient records containing first name, last name, date of birth, and prescription information. These incidents reveal the need for robust security measures to safeguard patient data. Existing Regulations In response to the risks posed to patient privacy, numerous regulations have been established to set standards for protecting patient health information. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) is a key legislation. HIPAA sets forth two primary rules - the Privacy Rule and the Security Rule. The Privacy Rule outlines how personally identifiable health information should be used and disclosed, whereas the Security Rule specifies the administrative, physical, and technical safeguards necessary to ensure the confidentiality, integrity, and security of electronically protected health information. To comply with HIPAA, companies must ensure their software solutions and procedures are aligned with these rules, incorporating features like strong user authentication, encryption, audit trails, automatic logoff, and secure data transmission. Regular risk assessments and staff training on HIPAA rules also form an integral part of a HIPAA compliance strategy. Across the Atlantic, the General Data Protection Regulation (GDPR) harmonizes rules for personal data protection in the European Union (E.U.), giving individuals control over their information. Other nations have enacted similar laws, including Canada’s Personal Information Protection Act (PIPA), Singapore’s Personal Data Protection Act (PDPA), and India’s Information Technology Act. While these regulations represent significant progress, there are still gaps when it comes to addressing big data: Jurisdictional conflicts can arise from countries having different standards. Stricter E.U. policies may inhibit data sharing with the U.S. Some nations lack adequate protections. Innovative technologies like AI and blockchain may outpace regulations not designed for them. Ethical dilemmas around informed consent, privacy, and fair data use require guidance. These challenges highlight the need for more adaptive laws and standards that can keep pace with the complex and rapidly evolving big data landscape. Best Practices for Data Security Data security is crucial for protecting sensitive patient information. Healthcare organizations should implement robust security controls following industry best practices: Encrypt data in transit and at rest using strong standards like AES-256 to prevent unauthorized access. Enforce multi-factor authentication and single sign-on to authenticate users and prevent account takeovers. Conduct routine security audits, penetration testing, and vulnerability scans to identify risks proactively. Implement access controls and least privilege principles to restrict data access on a need-to-know basis. Deploy data loss prevention tools to monitor and control sensitive data flows. Back up data regularly and test restores to enable quick recovery from outages or incidents. Provide comprehensive security awareness training to employees to promote best practices. Establish an incident response plan with defined procedures to contain, eradicate, and recover from breaches. Monitor systems and networks extensively using SIEM solutions to detect threats early. Ensure third-party security with vendor risk assessments and contractual protections. Following standards like NIST CSF or ISO 27001 also provides frameworks to build comprehensive security programs. With robust controls and vigilance, healthcare entities can significantly reduce data breach risks. Emerging Privacy-Enhancing Technologies Innovative privacy-enhancing technologies (PETs) can help healthcare organizations process and share data securely: Homomorphic encryption allows computations on encrypted data without decrypting it first. This enables analytics while preserving confidentiality. Differential privacy injects controlled noise into data to mask individual identities while still extracting useful insights from populations. Federated learning trains AI models across institutions without exposing raw patient data, only sharing model updates. Synthetic data generation produces artificial patient datasets with the statistical properties of real data but without being traceable to actual people. Zero-knowledge proofs let parties validate information without revealing the underlying data. Adopting PETs facilitates responsible data use for research and innovation while protecting sensitive health information. However, PETs have limitations like computational overhead, lack of standards, and re-identification risks from auxiliary data. Thoughtful implementation is key. The Patient’s Role in Data Privacy Patients have an important role in managing their health data privacy. They should: Review privacy policies to understand how providers handle sensitive information. Carefully read consent forms before permitting health data use and sharing. Request copies of medical records to review for accuracy and completeness. Ask providers about opt-out choices for data sharing like research or marketing. Enable access restrictions and notifications in patient portals to control access. Consider using personal health record apps to consolidate health data securely. Report any suspected privacy violations or breaches to providers immediately. Educating themselves on data privacy rights and options allows patients to make informed choices about their sensitive medical information. However, complex privacy policies and a lack of transparency can disempower patients. Providers should clearly communicate data practices and respect patient preferences. A Balancing Act for the Future Balancing innovation and privacy around health data requires trade-offs and diligence: Regulations must evolve to address new data sources like genomics, wearables, and social media with clear ethics-based standards. Providers should implement robust security programs while enabling controlled data use for care improvements and research. New techniques like data minimization, anonymization, abstraction, and encryption can facilitate privacy-preserving data sharing. Transparency and consent mechanisms should give patients granular controls over health data use. Cross-sector collaboration can develop best practices and governance models for secure, ethical data use. Ongoing reassessments of policies and technologies are needed as practices and risks evolve over time. With responsible data stewardship, healthcare can harness data-driven innovation to improve care and outcomes while respecting patients’ rights. Conclusion Navigating the promises and perils of big data in healthcare requires diligence and responsibility from all involved. Patients must ensure that their most sensitive information is handled with care. To be trusted by patients, healthcare providers need to be transparent, accountable, and responsible with data. Policymakers need to develop clear, adaptive guidelines for fair information use. Researchers and innovators must consider ethics alongside progress. By embracing this shared duty of care, we can unlock big data’s immense potential to understand diseases, develop treatments, and save lives - without compromising core patient rights. The following several key principles should guide us: Progress and privacy can go hand-in-hand with responsible innovation. Modern technologies can enable breakthroughs while boosting security. Listening to patient voices and concerns is crucial when shaping data practices. Policies should empower patients with control. Cross-sector and global collaboration is needed to solve a complex, international challenge. Alignment of standards serves all parties. Regular reassessment of policies, practices, and technology is necessary as the landscape evolves. What protects data today may not tomorrow. Balance, nuance, and wisdom are required around data trade-offs. Benefits and risks must be weighed carefully and holistically. The opportunities from healthcare’s digital data revolution are too great to ignore, but they must be pursued thoughtfully. With patient wellbeing at the core, a collaborative spirit, and a vision to match ambition with ethics, we can build a brighter data-driven future for human health. The potential rewards - preventing disease, expanding access, democratizing medicine - make it essential that we get big data right. By upholding both innovation and privacy, the healthcare community can earn the trust and partnership needed to transform lives with data responsibly. References The use of Big Data Analytics in healthcare | Journal of Big Data | Full Text (springeropen.com) Digital Transformation in Healthcare: Technology Acceptance and Its Applications - PMC (nih.gov) The digital transformation of healthcare organizations | Wolters Kluwer How Digital Transformation Can Improve Hospitals’ Operational Decisions (hbr.org) Digital transformation in healthcare | Deloitte Insights Digital Transformation in Healthcare in 2023: 7 Key Trends | DAP (digitalauthority.me) Frontiers | Digital transformation of health quality services in the healthcare industry during disruption and society 5.0 era (frontiersin.org) Healthcare Cybersecurity: 8 Data Protection Best Practices | CSA (cloudsecurityalliance.org) Emerging privacy-enhancing technologies: Current regulatory and policy approaches | en | OECD What is Data Privacy in Healthcare? | Box, Inc. The Balancing Act: Juggling EHRs and New Digital Health Applications | LinkedIn Five Best Practices for Securing Health Data | Persona (withpersona.com) Using Algorithmic Privacy-Enhancing Technologies in Healthcare Analytics (healthitanalytics.com)

Navigating Big Data's Potential and Privacy in Modern Medicine

AI has been slaying it in almost every sector. Healthcare is benefitting a lot from voice based . But this doesn’t mean we can trust them blindly. LLMs and ML Google DeepMind an AI program named AlphaMissense, which predicts the harm a missense mutation can do, when a single DNA letter is altered. The tool, which leverages data from 71 million single-letter mutations affecting human proteins, uses machine learning to “understand” the language of proteins, helping it score the risk level of genetic changes. created What AI is doing in synthetic biology is nothing short of revolutionary. Using AI, scientists are to create new unseen proteins like coding software. We can expect groundbreaking changes in not only medicine, but also in sectors like agriculture, energy, and climate. now programming cells Also, recently, researchers from Edith Cowan University software that quickly scans bone density scans to detect abdominal aortic calcification (AAC), which is a predictor of cardiovascular events and other health risks. The software processed images with 80% accuracy and could revolutionize early disease detection during routine clinical practice. developed In August, a ML model to identify autism spectrum disorder with 95% accuracy. While Stanford Medicine developed an algorithm that reviews images to determine a brain tumour’s aggressiveness, its genetic makeup, and effectiveness of surgery. “It’s sort of a decision support system for the physicians,” one researcher says. international researchers trained Also, Scientists at the Osaka Metropolitan University an AI model that accurately identifies cardiac functions and valvular heart diseases using chest radiographs. The research could supplement traditional echocardiography, improve diagnostic efficiency, and be especially useful in settings lacking specialized technicians. developed Startups in are also upping their game with AI. London-based Ultromics its AI to detect a heart condition that human eyes can often not detect. In July, physician-entrepreneurs at Israel’s started training AI to diagnose cardiac disease on a simple tablet, where the doctors don’t have to be heart experts. With a handheld scanner and tablet, non-cardiologists get a full diagnostic on 8 key heart readings. With its training data, the AI platform already has more experience than any living cardiologist, the physicians say. HealthTech trained Sheba Medical Center In September, , an Indian AI-powered grocery scoring and recommendation platform, tied up with, (Microbiome Research Pvt Ltd), a research platform in the field of gut microbiome and, , a diagnostics startup. With a shared commitment to preventive healthcare, the trio seeks to empower individuals with individualised in-depth diagnostics and convert that information into actionable real-time intelligence. Xume Microbiome.in TruDiagno This can help individuals to make informed choices based on the state of one’s gut microbiome and key diagnostic markers. Microbiome.in is the only private lab with an in-house Illumina and Nanopore (portable) Sequencer enabling unparalleled research on the role of gut microbiome, while TruDiagno adds advanced molecular diagnostic capabilities to the mix. Imagine the power of understanding our microbiome and diagnostic markers to drive actionable intelligence; where the answer is not giving up our favourite foods but simply making better choices based on what our gut and body are telling us Akshaye Jalan, Founder & CEO, Xume “We’re all unique, so how can a generic solution mired in restriction be the answer to good health? Imagine the power of understanding our microbiome and diagnostic markers to drive actionable intelligence; where the answer is not giving up our favourite foods but simply making better choices based on what our gut and body are telling us. This is the future of health and wellness and what our partnership with Microbiome.in and TruDiagno endeavours to do.” said Akshaye Jalan, Founder & CEO, Xume. In September, Oracle introduced voice-activated AI to hospitals, which will help doctors and clinicians to automate medical note-taking, order medications and view lab reviews while also more easily reviewing a patient’s medical record. This is not about going from 10 clicks to seven clicks or six clicks to five clicks. This is about completely eliminating clicks, where we can and where it makes sense. That is not a tomorrow or that’s years away. That is a tomorrow that’s here Mike Sicilia, executive vice president, industries at Oracle “We’ve completely rethought how long it takes somebody to get their job done using the system. I think that we are on the verge of completely eliminating ‘pajama time’ as a result. This is not about going from 10 clicks to seven clicks or six clicks to five clicks. This is about completely eliminating clicks, where we can and where it makes sense. That is not a tomorrow or that’s years away. That is a tomorrow that’s here,” Mike Sicilia, executive vice president, industries at Oracle told the audience at the . Oracle Health Conference But does this mean AI is your next doctor instead of a human? All the above instances are brilliant examples of AI helping humankind become better at healthcare, but they’re also being done under supervision of experts, who are human. The general online population has been turning to Dr. Google even before the Red Hot Chilli Peppers wrote . But there’s a new doctor in town, Dr. ChatGPT. , “the most recent version of ChatGPT made zero “grossly inaccurate” statements when presented with a standard set of eye complaints.” Californication According to NPR However, questions still remain about how to fit in LLMs into healthcare with similar safeguards that are applied when introducing new drugs or medical devices. This article was originally published by in Navanwita Bora Sachdev The Tech Panda.

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