/PRNewswire/ -- Tencent Cloud, the cloud business of the global leading technology company Tencent, has announced a strategic partnership with the S.M.A.R.T...

Tencent Cloud Partners with S.M.A.R.T Entrepreneurship Club to build smart businesses with AI Digital Human solution

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In this exclusive interview, Twin Protocol CEO Stacey Engle shares insights on combining AI and blockchain technology for digital twin solutions. She discusses the platform's development strategy, privacy measures, and vision for democratizing access to digital twin technology through the TWIN token ecosystem. Ishan Pandey: Hi Stacey Engle, welcome to our 'Behind the Startup' series. Twin Protocol sits at the intersection of AI and blockchain technology. What inspired you to combine these technologies for digital twin solutions? Stacey Engle: The inspiration to combine AI and blockchain for Twin Protocol came from recognizing the transformative potential of these technologies, and our amazing team that has the capability to bring them to life. I've spent my career helping people unlock their potential, and after advising multiple AI and blockchain leaders and companies, it was clear that combining the technologies was a true differentiator here. Twin Protocol's mission is to better the world through knowledge and wisdom sharing. We see this as a new horizon in interpersonal communication, personal legacy, and organizational continuity, where your AI twin becomes a real time participant in your life versus merely a reflection. This leap in humans communicating with AI and AIs communicating with other AIs is as transformative as the creation of the telephone and the internet. Ishan Pandey: You've been a strong advocate for ethical AI use. How does Twin Protocol balance innovation with responsible AI development, particularly in the context of digital twins? Stacey Engle: Twin Protocol balances innovation with responsible AI development by ensuring data integrity and user sovereignty. We use blockchain technology to provide immutable records of data usage, preventing unauthorized access, and ensuring transparency. Our AI Twins are used only with the user's explicit permission, safeguarding digital identities. We empower individuals and organizations to train digital twins in our AI and blockchain ecosystem, offering 24/7 access to mentorship and insights. Ishan Pandey: Data privacy is a crucial concern in both AI and blockchain. Could you explain Twin Protocol's approach to ensuring secure, private digital interactions while maintaining functionality? Stacey Engle: Twin Protocol prioritizes data privacy by implementing robust encryption standards, ensuring only authorized users can access or modify AI Twins. It leverages advanced blockchain technology to provide a decentralized, tamper-proof system where all transactions and interactions are securely recorded. The platform is designed to eliminate central points of failure, enhancing privacy and security. Users have control over their data, with the ability to revoke access at any time. Twin Protocol also adheres to strict privacy policies and ethical guidelines, fostering a safe, inclusive, and respectful digital environment. This comprehensive approach ensures secure, private digital interactions while maintaining functionality. Ishan Pandey: Your vision extends beyond technology to include digital education and accessibility. How does Twin Protocol's utility token ecosystem support these broader societal goals? Stacey Engle: Twin Protocol's utility token, TWIN, supports digital education and accessibility by enabling users to unlock advanced features and more storage for their AI Twins. It also facilitates secure transactions on our marketplace, making it easier to access personalized mentorship and consultation services. This tokenized ecosystem democratizes access to knowledge, fostering a culture of perpetual learning. It bridges generations of knowledge, turning data into a timeless contribution to the world's knowledge treasury. Thus, it aligns with our broader societal goals of promoting digital education and accessibility. Ishan Pandey: As a female leader in tech, you've been vocal about diversity and inclusivity. How do these values influence Twin Protocol's development and community-building strategies? Stacey Engle: Diversity and inclusivity are integral to Twin Protocol's ethos. We believe that everyone should have access to knowledge and the opportunity to contribute to collective wisdom. It is critical at this stage of technology development that people from all walks of life contribute to the training data as well as the implementation opportunities. We also encourage community involvement in our development process, fostering a diverse range of perspectives and ideas. This inclusive approach not only enriches our ecosystem but also aligns with our mission to empower individuals and organizations through our AI and blockchain ecosystem. Ishan Pandey: The digital twin space is rapidly evolving. What unique challenges have you encountered in creating personalized digital interactions, and how has Twin Protocol addressed them? Stacey Engle: Creating personalized digital interactions presents challenges like ensuring privacy, scalability, and legacy preservation. Twin Protocol addresses these by creating a digital twin that encapsulates user expertise, allowing real-time interactions in any language. It also ensures data safety and privacy, while preserving knowledge for perpetual accessibility, thus preventing legacy loss. Ishan Pandey: Looking ahead, Stacey Engle, what developments in AI and blockchain excite you the most, and how is Twin Protocol positioning itself to embrace these future opportunities while maintaining its ethical stance? Stacey Engle: The convergence of AI and blockchain in creating secure, personalized digital interactions excites me. Twin Protocol is positioning itself by decentralizing the network, enhancing privacy, and fostering a community-driven environment. We're democratizing access to technology and information, encouraging innovation, and making advanced AI and blockchain benefits simple and fun. We want everyone to participate in a way that supports and feels good. We are forging partnerships every day to easily bring tools and knowledge to the broader public. Ishan Pandey: Thank you for your time and insights, Stacey! Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR In this exclusive interview, Twin Protocol CEO Stacey Engle shares insights on combining AI and blockchain technology for digital twin solutions. She discusses the platform's development strategy, privacy measures, and vision for democratizing access to digital twin technology through the TWIN token ecosystem. Ishan Pandey: Hi Stacey Engle, welcome to our 'Behind the Startup' series. Twin Protocol sits at the intersection of AI and blockchain technology. What inspired you to combine these technologies for digital twin solutions? Ishan Pandey: Stacey Engle: The inspiration to combine AI and blockchain for Twin Protocol came from recognizing the transformative potential of these technologies, and our amazing team that has the capability to bring them to life. Stacey Engle: I've spent my career helping people unlock their potential, and after advising multiple AI and blockchain leaders and companies, it was clear that combining the technologies was a true differentiator here. Twin Protocol's mission is to better the world through knowledge and wisdom sharing. We see this as a new horizon in interpersonal communication, personal legacy, and organizational continuity, where your AI twin becomes a real time participant in your life versus merely a reflection. This leap in humans communicating with AI and AIs communicating with other AIs is as transformative as the creation of the telephone and the internet. Ishan Pandey: You've been a strong advocate for ethical AI use. How does Twin Protocol balance innovation with responsible AI development, particularly in the context of digital twins? Ishan Pandey: Stacey Engle: Twin Protocol balances innovation with responsible AI development by ensuring data integrity and user sovereignty. We use blockchain technology to provide immutable records of data usage, preventing unauthorized access, and ensuring transparency. Our AI Twins are used only with the user's explicit permission, safeguarding digital identities. We empower individuals and organizations to train digital twins in our AI and blockchain ecosystem, offering 24/7 access to mentorship and insights. Stacey Engle: Ishan Pandey: Data privacy is a crucial concern in both AI and blockchain. Could you explain Twin Protocol's approach to ensuring secure, private digital interactions while maintaining functionality? Ishan Pandey: Stacey Engle: Twin Protocol prioritizes data privacy by implementing robust encryption standards, ensuring only authorized users can access or modify AI Twins. It leverages advanced blockchain technology to provide a decentralized, tamper-proof system where all transactions and interactions are securely recorded. The platform is designed to eliminate central points of failure, enhancing privacy and security. Users have control over their data, with the ability to revoke access at any time. Twin Protocol also adheres to strict privacy policies and ethical guidelines, fostering a safe, inclusive, and respectful digital environment. This comprehensive approach ensures secure, private digital interactions while maintaining functionality. Stacey Engle: Ishan Pandey: Your vision extends beyond technology to include digital education and accessibility. How does Twin Protocol's utility token ecosystem support these broader societal goals? Ishan Pandey: Stacey Engle: Twin Protocol's utility token, TWIN, supports digital education and accessibility by enabling users to unlock advanced features and more storage for their AI Twins. It also facilitates secure transactions on our marketplace, making it easier to access personalized mentorship and consultation services. This tokenized ecosystem democratizes access to knowledge, fostering a culture of perpetual learning. It bridges generations of knowledge, turning data into a timeless contribution to the world's knowledge treasury. Thus, it aligns with our broader societal goals of promoting digital education and accessibility. Stacey Engle: Ishan Pandey: As a female leader in tech, you've been vocal about diversity and inclusivity. How do these values influence Twin Protocol's development and community-building strategies? Ishan Pandey: Stacey Engle: Diversity and inclusivity are integral to Twin Protocol's ethos. We believe that everyone should have access to knowledge and the opportunity to contribute to collective wisdom. It is critical at this stage of technology development that people from all walks of life contribute to the training data as well as the implementation opportunities. Stacey Engle: We also encourage community involvement in our development process, fostering a diverse range of perspectives and ideas. This inclusive approach not only enriches our ecosystem but also aligns with our mission to empower individuals and organizations through our AI and blockchain ecosystem. Ishan Pandey: The digital twin space is rapidly evolving. What unique challenges have you encountered in creating personalized digital interactions, and how has Twin Protocol addressed them? Ishan Pandey: Stacey Engle: Creating personalized digital interactions presents challenges like ensuring privacy, scalability, and legacy preservation. Twin Protocol addresses these by creating a digital twin that encapsulates user expertise, allowing real-time interactions in any language. It also ensures data safety and privacy, while preserving knowledge for perpetual accessibility, thus preventing legacy loss. Stacey Engle: Ishan Pandey: Looking ahead, Stacey Engle, what developments in AI and blockchain excite you the most, and how is Twin Protocol positioning itself to embrace these future opportunities while maintaining its ethical stance? Ishan Pandey: Stacey Engle: The convergence of AI and blockchain in creating secure, personalized digital interactions excites me. Twin Protocol is positioning itself by decentralizing the network, enhancing privacy, and fostering a community-driven environment. We're democratizing access to technology and information, encouraging innovation, and making advanced AI and blockchain benefits simple and fun. Stacey Engle: We want everyone to participate in a way that supports and feels good. We are forging partnerships every day to easily bring tools and knowledge to the broader public. Ishan Pandey: Thank you for your time and insights, Stacey! Ishan Pandey: Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Twin Protocol CEO Stacey Engle on Building AI Digital Twins with Blockchain Technology

Surveying the Evolution and Future Trajectory of Generative AI - Generative AI Research Taxonomy

AI and machine learning have changed how people communicate, learn, create, and consume content. With AI entering more and more facets of everyday life, some worry about losing the human element; however, experts in the field of AI are working to not only make AI work for you but also to use it to connect people better than ever before. Jin Tang is a software engineer whose work has informed how AI functions in today's internet. With her extensive knowledge and background in machine learning, Tang has helped create AI-driven tools that have transformed the world's digital content, improving user and creator experience along the way. Jin Tang's First Inspiration for Better Life Through Tech Jin Tang's interest in tech was initially inspired by her father, who taught himself software development. Watching her father work exposed her to a world full of potential for creativity. Tang started her tech journey by interning at a leading tech company in China, where she worked on their smartphone assistant product (similar to Siri) and saw that AI could transform how digital content works for its users. Observing the digital assistant's capabilities, she realized her work was more than a fun and convenient product; it had the potential to help people with disabilities by better enabling them to connect with others through their smartphone assistant. For drivers, it could make navigation safer. It could even help children by serving as a portal to learning more about the world around them. Inspired by these revelations, Tang embarked on a more extensive journey in tech to build products that really benefit people. Tang attended Boston University, where she immersed herself in Mathematics and Computer Science. Ever curious about the possibilities of new technologies, she continued her education at Yale University and earned her master's degree. After graduating, Tang was hired at a major tech conglomerate, where her skills were used in developing new technologies that could reshape how people use social media and produce digital content. How AI-Driven Personalization Drives User Engagement Jin Tang has used AI to transform digital content by focusing on engagement. When she began her work in AI for social media, she focused on improving the user experience. The key to this proved to be personalization. Machine learning was also crucial for producing digital content tailored to each individual's preferences. Tang’s development of an interest-based, machine learning-driven feed significantly increased user engagement, leading to a feed session time increase of 1.3%. This demonstrated that AI resonated with users when applied in this format; rather than alienating users, the personalized spaces that AI could create made a better online experience. Empowering Digital Creators with Machine Learning Tools Jin Tang's work focused not only on those who consume content but also on those who create it. She found that AI and machine learning could positively transform the content creator experience. Initially, Tang needed help to identify features that would suit creators. She saw that creators have distinct needs as opposed to the users that followed their content. Through extensive research and close collaboration with experts, she developed machine learning tools that could empower digital creators. These tools focused on allowing creators to understand their audience better and produce content that would draw in more significant numbers. Tang built a personalized feed for influencers. The AI-powered tool led to a 10% increase in followers and a 2.2% increase in feed session time. Through machine learning, creators could more effectively connect with their audience and grow their digital presence. AI as a Tool for Enhancing Marketing Performance Beyond users and content creators, AI and machine learning tools can be scaled up to assist businesses and digital marketers. Analyzing data and personalizing strategies for success allows for marketing that reaches broader audiences, potentially transforming how companies operate. Jin Tang's development of professional tools for small businesses has helped empower 6 million entrepreneurs to understand and improve their marketing performance. During the pandemic, many small businesses saw their sales plummet. AI-driven strategies helped business owners to adapt and find new strategies for survival. Staying ahead of trends is essential in a digital marketplace that continues to grow and evolve. AI-driven tools can help businesses remain adaptable and competitive. The Future of Digital Content Through AI Innovation Jin Tang believes that the future of digital content lies in its ability to adapt to users' interests. Driven by ongoing advancements in AI and machine learning, technology has changed the way the world functions. As these technologies continue to develop, they have the potential to unlock better user experiences through more personalized and engaging content. With AI, social media can grow more responsive to its users' interests, creating a more dynamic experience. Jin Tang's Commitment to a Better Tech World Through AI Through her work in AI, Jin Tang hopes to continue the trend of better online experiences with personalization and user-focused content. She is also committed to further integrating generative AI into digital products. Tang has also empowered future innovators in tech. She has mentored four interns, using her personalized approach to tailor their projects to their interests and skills. This mentorship helped them sharpen their tech skills and deepen their understanding of the industry.As a result, Tang proudly saw all her mentees receive return offers after completing their internships. The Transformative Properties of AI and Machine Learning Jin Tang's expertise and work in the tech industry have demonstrated how AI and machine learning can transform today's digital content. By better connecting people to the world through their smartphones, creating more personalized, user-driven spaces, empowering content creators with tools that help them better understand their audiences, assisting businesses with their market performance, and developing social media experiences that are more responsive to users' interests, these new tools have paved the way for a better world through technology. Those wishing to see more of Jin Tang's work can follow her LinkedIn page. As she continues her work on AI-driven digital tools, she wants to remind users and tech experts of her favorite motto by Steve Jobs: "Stay foolish, stay hungry." AI and machine learning have changed how people communicate, learn, create, and consume content. With AI entering more and more facets of everyday life, some worry about losing the human element; however, experts in the field of AI are working to not only make AI work for you but also to use it to connect people better than ever before. Jin Tang is a software engineer whose work has informed how AI functions in today's internet. With her extensive knowledge and background in machine learning, Tang has helped create AI-driven tools that have transformed the world's digital content, improving user and creator experience along the way. Jin Tang's First Inspiration for Better Life Through Tech Jin Tang's interest in tech was initially inspired by her father, who taught himself software development. Watching her father work exposed her to a world full of potential for creativity. Tang started her tech journey by interning at a leading tech company in China, where she worked on their smartphone assistant product (similar to Siri) and saw that AI could transform how digital content works for its users. Observing the digital assistant's capabilities, she realized her work was more than a fun and convenient product; it had the potential to help people with disabilities by better enabling them to connect with others through their smartphone assistant. For drivers, it could make navigation safer. It could even help children by serving as a portal to learning more about the world around them. Inspired by these revelations, Tang embarked on a more extensive journey in tech to build products that really benefit people. Tang attended Boston University, where she immersed herself in Mathematics and Computer Science. Ever curious about the possibilities of new technologies, she continued her education at Yale University and earned her master's degree. After graduating, Tang was hired at a major tech conglomerate, where her skills were used in developing new technologies that could reshape how people use social media and produce digital content. How AI-Driven Personalization Drives User Engagement Jin Tang has used AI to transform digital content by focusing on engagement. When she began her work in AI for social media, she focused on improving the user experience. The key to this proved to be personalization. Machine learning was also crucial for producing digital content tailored to each individual's preferences. Tang’s development of an interest-based, machine learning-driven feed significantly increased user engagement, leading to a feed session time increase of 1.3%. This demonstrated that AI resonated with users when applied in this format; rather than alienating users, the personalized spaces that AI could create made a better online experience. Empowering Digital Creators with Machine Learning Tools Jin Tang's work focused not only on those who consume content but also on those who create it. She found that AI and machine learning could positively transform the content creator experience. Initially, Tang needed help to identify features that would suit creators. She saw that creators have distinct needs as opposed to the users that followed their content. Through extensive research and close collaboration with experts, she developed machine learning tools that could empower digital creators. These tools focused on allowing creators to understand their audience better and produce content that would draw in more significant numbers. Tang built a personalized feed for influencers. The AI-powered tool led to a 10% increase in followers and a 2.2% increase in feed session time. Through machine learning, creators could more effectively connect with their audience and grow their digital presence. AI as a Tool for Enhancing Marketing Performance Beyond users and content creators, AI and machine learning tools can be scaled up to assist businesses and digital marketers. Analyzing data and personalizing strategies for success allows for marketing that reaches broader audiences, potentially transforming how companies operate. Jin Tang's development of professional tools for small businesses has helped empower 6 million entrepreneurs to understand and improve their marketing performance. During the pandemic, many small businesses saw their sales plummet. AI-driven strategies helped business owners to adapt and find new strategies for survival. Staying ahead of trends is essential in a digital marketplace that continues to grow and evolve. AI-driven tools can help businesses remain adaptable and competitive. The Future of Digital Content Through AI Innovation Jin Tang believes that the future of digital content lies in its ability to adapt to users' interests. Driven by ongoing advancements in AI and machine learning, technology has changed the way the world functions. As these technologies continue to develop, they have the potential to unlock better user experiences through more personalized and engaging content. With AI, social media can grow more responsive to its users' interests, creating a more dynamic experience. Jin Tang's Commitment to a Better Tech World Through AI Jin Tang's Commitment to a Better Tech World Through AI Through her work in AI, Jin Tang hopes to continue the trend of better online experiences with personalization and user-focused content. She is also committed to further integrating generative AI into digital products. Tang has also empowered future innovators in tech. She has mentored four interns, using her personalized approach to tailor their projects to their interests and skills. This mentorship helped them sharpen their tech skills and deepen their understanding of the industry.As a result, Tang proudly saw all her mentees receive return offers after completing their internships. The Transformative Properties of AI and Machine Learning Jin Tang's expertise and work in the tech industry have demonstrated how AI and machine learning can transform today's digital content. By better connecting people to the world through their smartphones, creating more personalized, user-driven spaces, empowering content creators with tools that help them better understand their audiences, assisting businesses with their market performance, and developing social media experiences that are more responsive to users' interests, these new tools have paved the way for a better world through technology. Those wishing to see more of Jin Tang's work can follow her LinkedIn page. As she continues her work on AI-driven digital tools, she wants to remind users and tech experts of her favorite motto by Steve Jobs: "Stay foolish, stay hungry." LinkedIn LinkedIn

5 Ways AI and Machine Learning Are Transforming Digital Content, According to Jin Tang

5 τρόποι με τους οποίους η τεχνητή νοημοσύνη και η μηχανική μάθηση μεταμορφώνουν το ψηφιακό περιεχόμενο, σύμφωνα με τον Jin Tang

Cinco formas en las que la IA y el aprendizaje automático están transformando el contenido digital, según Jin Tang

जिन टैंग के अनुसार, 5 तरीके जिनसे AI और मशीन लर्निंग डिजिटल कंटेंट को बदल रहे हैं

AIと機械学習がデジタルコンテンツを変革する5つの方法（ジン・タン氏）

Джин Танның айтуынша, AI және машиналық оқыту цифрлық мазмұнды түрлендірудің 5 жолы

5 ວິທີ AI ແລະການຮຽນຮູ້ເຄື່ອງຈັກກໍາລັງຫັນປ່ຽນເນື້ອຫາດິຈິຕອນ, ອີງຕາມການ Jin Tang

5 sposobów, w jaki sztuczna inteligencja i uczenie maszynowe zmieniają treści cyfrowe, według Jin Tanga

5 spôsobov, ako AI a strojové učenie transformujú digitálny obsah, podľa Jin Tanga

5 Nzira AI uye Muchina Kudzidza Ari Kushandura Digital Zvemukati, Maererano naJin Tang

ஜின் டாங்கின் கூற்றுப்படி, AI மற்றும் இயந்திர கற்றல் டிஜிட்டல் உள்ளடக்கத்தை மாற்றும் 5 வழிகள்

Jin Tangyň pikiriçe, AI we maşyn öwrenmegiň sanly mazmuny üýtgetmeginiň 5 usuly

5 Paraan na Binabago ng AI at Machine Learning ang Digital Content, Ayon kay Jin Tang

Pioneering Legal Frameworks for the Next Generation of Web3

Baanbrekende regsraamwerke vir die volgende generasie van Web3

Web3-ün növbəti nəsli üçün qabaqcıl hüquqi çərçivələr

Marcos jurídicos pioneros para la próxima generación de la Web3

Marcos legais pioneiros para a próxima xeración de Web3

次世代のWeb3のための先駆的な法的枠組み

Web3 кийинки муун үчүн пионердик укуктук негиздери

ບຸກເບີກໂຄງຮ່າງກົດໝາຍສຳລັບ Web3 ລຸ້ນຕໍ່ໄປ

Novatorisks tiesiskais regulējums nākamās paaudzes Web3

Bobulamadibogo Ditlhako tša Molao tša Moloko wo o Latelago wa Web3

د Web3 راتلونکي نسل لپاره مخکښ قانوني چوکاټونه

Асосҳои ҳуқуқӣ барои насли ояндаи Web3

Pioneering Legal Frameworks para sa Susunod na Henerasyon ng Web3

TORTOLA, British Virgin Islands, August 2nd, 2024/Chainwire/--Sharpe AI, an AI-powered crypto super-app for traders, has announced the listing of its token $SAI on Gate.io followed by more exchanges starting at 1 pm UTC, 5th August, 2024. The token will be available in the trading pair USDT, setting the stage for a new chapter in AI-powered crypto trading. The anticipation is soaring following the blockbuster success of $SAI's Initial DEX Offering (IDO) this past April. The IDO shattered all previous records, achieving an astounding 300x oversubscription. With over 120,000 participants bringing in $576 million in assets, it became one of the biggest IDO in history—a true milestone moment for the crypto space. Key Highlights Sharpe AI has 25+ live products with over 150,000 users and $500+ million in platform volume
Received grants from Google, Microsoft, and IBM
Backed by industry leaders including Animoca Brands, GBV Capital, Morningstar Ventures, Maven Capital, MetaBros, Contango Digital, Ape Terminal, AIT Protocol
$SAI IDO on Ape Terminal was 300x oversubscribed, making it one of the biggest IDO in history with over 120k participants connecting $576M in assets
$SAI is listed on Aevo Pre-markets, a spot reserved for only the most anticipated projects in the space, and has smashed every record for daily volumes
Received CME license (the only crypto company that has received it)
First Bittensor ecosystem project, using the AIT subnet to mine $TAO
Ranked #1 on Product Hunt globally for all AI and Crypto categories
Sharpe is backed by Contango Digital (main investor of Bittensor) $SAI Token Utility $SAI is the native utility and governance token underpinning the Sharpe AI ecosystem. The token is carefully designed to align incentives between Sharpe AI, token holders, and the broader ecosystem. It provides economic incentives for participants, funds future development and growth initiatives through a community treasury model, and enables decentralized governance over the platform. $SAI token holders will enjoy a range of benefits within the Sharpe AI ecosystem: Discounts: Staking $SAI unlocks discounts on trading products
Airdrops: Potential airdrops of project tokens launching through Sharpe Launchpad and Sharpe Perps
Governance rights: Ability to vote on crucial platform decisions
Staking rewards: Additional benefits including fee rebates and early access to new features. Sharpe AI aims to create a token that encourages long-term participation through: A buyback and burn mechanism to reduce token supply and reward token holders over time
Long-term incentives through rewards and discounts via staking
Governance participation for token holders Part of the profits generated from all products will be used to repurchase $SAI from the open market through a series of repurchasing programs, such as TWAP orders. Tokens bought will be burned, deflating supply over time. As part of the rollout of staking, the buyback & burn mechanism will split into buybacks that will be allocated towards both burns and staking rewards — allowing stakers to accrue additional platform ownership over time. $SAI Listing Details Date: August 5th, 2024
Time: 1 PM UTC
Exchanges: Gate.io, Uniswap.
Pair: SAI/USDT The platform is notably the first Web3 project recognized by tech giants Google, Microsoft, IBM and OpenAI. Backed by industry-leading investors Animoca Brands, GBV Capital, MorningStar Ventures,  Maven Capital, MetaBros, Contango Digital, Ape Terminal, and AIT Protocol. Sharpe AI stands out as a leader in the AI x Crypto space. As one of the first projects in the Bittensor ecosystem leveraging the AIT subnet to mine $TAO. This advantage drives its rapid growth, establishing Sharpe AI as the largest AI super app currently boasting a live platform with 25+ live products. "The listing of $SAI marks a pivotal moment for Sharpe AI and the broader AI-crypto ecosystem," said Rishabh Narang, CEO of Sharpe AI. "We're excited to bring our vision of intelligent, accessible crypto trading to a global audience." Current Sharpe AI Integrations and PaPartnerships Aethir, Ionet, Bittensor, Lido, Aevo, Hyperliquid, Arbitrum, Aioz, Paradigm, Orderly Network, Paradex, 0x, Eesee, TradingView, Li.Fi, LogX, AIT Protocol, Ape Terminal, Aave, HyperGPT, Paraswap, Galxe, Arbitrum, Mantle, OpenOcean, and more. About Sharpe AI Sharpe AI is pioneering the future of crypto interface with its AI-powered superapp. With a focus on intelligence, tracking, and investing in digital assets. Sharpe is now the largest crypto super-app, boasting over 150,000 users and more than $500M in volume. Sharpe AI has a vision of creating a vertically integrated moat by combining data intelligence, trading products and DeFi execution on leading chains, culminating in a comprehensive platform unparalleled in the industry. The platform is rapidly expanding its capabilities, having recently acquired Brownian (specializing in full-stack AI intelligence) and launched HiveIntelligence (building decentralized AI superintelligence network). Key Products: Sharpe Perps: An omnichain decentralized orderbook perpetual DEX
Sharpe DEX: A leading decentralized exchange aggregator
Sharpe Bridge: A platform for seamless crypto swapping and bridging across multiple blockchains
Sharpe NFT Marketplace: A hybrid NFT marketplace aggregator with 20+ top marketplaces
Sharpe Terminal: A Bloomberg-like terminal for crypto, consolidating news, governance, research, and various market data. Looking ahead, Sharpe AI plans to expand its AI capabilities, integrate with additional blockchain networks, and launch new products aimed at institutional investors. Sharpe AI is also exploring partnerships with traditional finance institutions to bridge the gap between conventional and crypto markets. For more information about Sharpe AI and the upcoming $SAI token sale, users can visit Sharpe AI's official website or join their community on Twitter, Telegram, and Discord. For media inquiries, users can contact: Email: team@sharpe.ai Contact CEO Rishabh Narang Sharpe Labs rishabh@sharpe.ai This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. TORTOLA, British Virgin Islands, August 2nd, 2024/Chainwire/--Sharpe AI, an AI-powered crypto super-app for traders, has announced the listing of its token $SAI on Gate.io followed by more exchanges starting at 1 pm UTC, 5th August, 2024. The token will be available in the trading pair USDT, setting the stage for a new chapter in AI-powered crypto trading. The anticipation is soaring following the blockbuster success of $SAI's Initial DEX Offering (IDO) this past April. The IDO shattered all previous records, achieving an astounding 300x oversubscription. With over 120,000 participants bringing in $576 million in assets, it became one of the biggest IDO in history —a true milestone moment for the crypto space. biggest IDO in history biggest IDO in history Key Highlights Sharpe AI has 25+ live products with over 150,000 users and $500+ million in platform volume Received grants from Google, Microsoft, and IBM Backed by industry leaders including Animoca Brands, GBV Capital, Morningstar Ventures, Maven Capital, MetaBros, Contango Digital, Ape Terminal, AIT Protocol $SAI IDO on Ape Terminal was 300x oversubscribed, making it one of the biggest IDO in history with over 120k participants connecting $576M in assets $SAI is listed on Aevo Pre-markets, a spot reserved for only the most anticipated projects in the space, and has smashed every record for daily volumes Received CME license (the only crypto company that has received it) First Bittensor ecosystem project, using the AIT subnet to mine $TAO Ranked #1 on Product Hunt globally for all AI and Crypto categories Sharpe is backed by Contango Digital (main investor of Bittensor) Sharpe AI has 25+ live products with over 150,000 users and $500+ million in platform volume Received grants from Google, Microsoft, and IBM Backed by industry leaders including Animoca Brands, GBV Capital, Morningstar Ventures, Maven Capital, MetaBros, Contango Digital, Ape Terminal, AIT Protocol $SAI IDO on Ape Terminal was 300x oversubscribed, making it one of the biggest IDO in history with over 120k participants connecting $576M in assets $SAI is listed on Aevo Pre-markets, a spot reserved for only the most anticipated projects in the space, and has smashed every record for daily volumes Received CME license (the only crypto company that has received it) First Bittensor ecosystem project, using the AIT subnet to mine $TAO Ranked #1 on Product Hunt globally for all AI and Crypto categories Sharpe is backed by Contango Digital (main investor of Bittensor) $SAI Token Utility $SAI is the native utility and governance token underpinning the Sharpe AI ecosystem. The token is carefully designed to align incentives between Sharpe AI, token holders, and the broader ecosystem. It provides economic incentives for participants, funds future development and growth initiatives through a community treasury model, and enables decentralized governance over the platform. $SAI token holders will enjoy a range of benefits within the Sharpe AI ecosystem: Discounts: Staking $SAI unlocks discounts on trading products Airdrops: Potential airdrops of project tokens launching through Sharpe Launchpad and Sharpe Perps Governance rights: Ability to vote on crucial platform decisions Staking rewards: Additional benefits including fee rebates and early access to new features. Discounts: Staking $SAI unlocks discounts on trading products Airdrops: Potential airdrops of project tokens launching through Sharpe Launchpad and Sharpe Perps Governance rights: Ability to vote on crucial platform decisions Staking rewards: Additional benefits including fee rebates and early access to new features. Sharpe AI aims to create a token that encourages long-term participation through: A buyback and burn mechanism to reduce token supply and reward token holders over time Long-term incentives through rewards and discounts via staking Governance participation for token holders A buyback and burn mechanism to reduce token supply and reward token holders over time Long-term incentives through rewards and discounts via staking Governance participation for token holders Part of the profits generated from all products will be used to repurchase $SAI from the open market through a series of repurchasing programs, such as TWAP orders. Tokens bought will be burned, deflating supply over time. As part of the rollout of staking, the buyback & burn mechanism will split into buybacks that will be allocated towards both burns and staking rewards — allowing stakers to accrue additional platform ownership over time. $SAI Listing Details Date: August 5th, 2024 Time: 1 PM UTC Exchanges: Gate.io, Uniswap. Pair: SAI/USDT Date: August 5th, 2024 Time: 1 PM UTC Exchanges: Gate.io , Uniswap. Gate.io Gate.io Pair: SAI/USDT The platform is notably the first Web3 project recognized by tech giants Google, Microsoft, IBM and OpenAI. Backed by industry-leading investors Animoca Brands , GBV Capital , MorningStar Ventures , Maven Capital , MetaBros , Contango Digital , Ape Terminal , and AIT Protocol . Animoca Brands Animoca Brands GBV Capital GBV Capital MorningStar Ventures MorningStar Ventures Maven Capital Maven Capital MetaBros MetaBros Contango Digital Contango Digital Ape Terminal Ape Terminal AIT Protocol AIT Protocol Sharpe AI stands out as a leader in the AI x Crypto space. As one of the first projects in the Bittensor ecosystem leveraging the AIT subnet to mine $TAO. Bittensor Bittensor This advantage drives its rapid growth, establishing Sharpe AI as the largest AI super app currently boasting a live platform with 25+ live products. largest largest "The listing of $SAI marks a pivotal moment for Sharpe AI and the broader AI-crypto ecosystem," said Rishabh Narang, CEO of Sharpe AI. "We're excited to bring our vision of intelligent, accessible crypto trading to a global audience." "The listing of $SAI marks a pivotal moment for Sharpe AI and the broader AI-crypto ecosystem," said Rishabh Narang , CEO of Sharpe AI. "We're excited to bring our vision of intelligent, accessible crypto trading to a global audience." Rishabh Narang Rishabh Narang Current Sharpe AI Integrations and PaPartnerships Aethir, Ionet, Bittensor, Lido, Aevo, Hyperliquid, Arbitrum, Aioz, Paradigm, Orderly Network, Paradex, 0x, Eesee, TradingView, Li.Fi, LogX, AIT Protocol, Ape Terminal, Aave, HyperGPT, Paraswap, Galxe, Arbitrum, Mantle, OpenOcean, and more. About Sharpe AI Sharpe AI is pioneering the future of crypto interface with its AI-powered superapp. With a focus on intelligence, tracking, and investing in digital assets. Sharpe is now the largest crypto super-app, boasting over 150,000 users and more than $500M in volume. Sharpe AI has a vision of creating a vertically integrated moat by combining data intelligence, trading products and DeFi execution on leading chains, culminating in a comprehensive platform unparalleled in the industry. The platform is rapidly expanding its capabilities, having recently acquired Brownian (specializing in full-stack AI intelligence) and launched HiveIntelligence (building decentralized AI superintelligence network). Brownian Brownian HiveIntelligence HiveIntelligence Key Products: Sharpe Perps: An omnichain decentralized orderbook perpetual DEX Sharpe DEX: A leading decentralized exchange aggregator Sharpe Bridge: A platform for seamless crypto swapping and bridging across multiple blockchains Sharpe NFT Marketplace: A hybrid NFT marketplace aggregator with 20+ top marketplaces Sharpe Terminal: A Bloomberg-like terminal for crypto, consolidating news, governance, research, and various market data. Sharpe Perps: An omnichain decentralized orderbook perpetual DEX Sharpe DEX: A leading decentralized exchange aggregator Sharpe Bridge: A platform for seamless crypto swapping and bridging across multiple blockchains Sharpe NFT Marketplace: A hybrid NFT marketplace aggregator with 20+ top marketplaces Sharpe Terminal: A Bloomberg-like terminal for crypto, consolidating news, governance, research, and various market data. Looking ahead, Sharpe AI plans to expand its AI capabilities, integrate with additional blockchain networks, and launch new products aimed at institutional investors. Sharpe AI is also exploring partnerships with traditional finance institutions to bridge the gap between conventional and crypto markets. For more information about Sharpe AI and the upcoming $SAI token sale, users can visit Sharpe AI's official website or join their community on Twitter , Telegram, and Discord . Sharpe AI's official website Sharpe AI's official website Twitter Twitter Telegram, Telegram, Discord Discord For media inquiries, users can contact: Email: team@sharpe.ai Contact CEO Rishabh Narang Sharpe Labs rishabh@sharpe.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

Sharpe AI Announces $SAI Token Listing On Gate.i

Sharpe AI Gate.i-এ $SAI টোকেন তালিকা ঘোষণা করেছে৷

Sharpe AI kündigt Notierung des $SAI-Tokens auf Gate.i an

Sharpe AI anuncia el listado de tokens $SAI en Gate.i

Sharpe AI annonce la cotation des jetons $ SAI sur Gate.i

शार्प एआई ने Gate.i पर $SAI टोकन लिस्टिंग की घोषणा की

Sharpe AI、Gate.i への $SAI トークン上場を発表

Sharpe AI, Gate.i에 $SAI 토큰 상장 발표

Sharpe AI anuncia listagem de token $ SAI no Gate.i

Sharpe AI объявляет о листинге токена $SAI на Gate.i

Sharpe AI, Gate.i'de $SAI Token Listelemesini Duyurdu

Sharpe AI công bố danh sách token $SAI trên Gate.i

Sharpe AI 宣布在 Gate.i 上推出 $SAI 代币

The Spatial Web Protocol: Ushering in a New Era of Internet and AI Integration

Staying One Step Ahead: How Fintechs and Financial Institutions Can Outsmart Fraudsters

Да останем една крачка напред: Как финтех компаниите и финансовите институции могат да надхитрят измамниците

Rimanere un passo avanti: come le fintech e le istituzioni finanziarie possono superare in astuzia i truffatori

Een stap voor blijven: hoe fintechs en financiële instellingen fraudeurs te slim af kunnen zijn

Rămâi cu un pas înainte: cum Fintech-urile și instituțiile financiare pot depăși fraudatorii

Pananatiling Isang Hakbang: Paano Magagawa ng mga Fintech at Mga Institusyong Pananalapi ang mga Manloloko

Бути на крок попереду: як фінтех-компанії та фінансові установи можуть перехитрити шахраїв

The role of a site reliability engineer (SRE) is not to be understated. With the recent wave of developments in cloud computing and artificial intelligence (AI), scalability, security, and the actual reliability of these technologies can come into question. One tenured senior site reliability engineer is Chaitanya Kanth Tummalachervu, who has provided his services across sectors ranging from medical insurance, finance, retail, the automobile industry, and even video communications. In these fields, Tummalachervu’s principal focus lies in DevOps – or developer operations – where he supports teams of coders as they work to create, support, and protect AI-powered products for businesses and consumers alike. DevOps: How Automation, Security, and Deployment Are Perfected The inner workings of a developer team can vary greatly between projects. While small, agile teams may not run into issues, more complex workplaces or large-scale projects can often be mired in clutter - both digitally and physically. Site reliability engineers, such as Tummalachervu himself, help reduce the strain on these teams by creating systems tailored to the needs of each operation. He says, “The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7.” Central to his work, the field of digital artificial intelligence products can be as demanding as it is expansive. With the complexity of advanced neural networks, operations can be bogged down by miscommunication, collapsed dependencies, and delays in the development pipeline. Even worse, unsecured systems of such importance are also a prime target for malicious actors, furthering the burden on the teams supporting it. “It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams,” explains Tummalachervu. Indeed, his focus on software-driven automation has reportedly increased developer productivity by 40%, with incident response time also seeing a 20% reduction as well. Expertise Delivered in Contemporary AI Platforms Of particular note is Tummalachervu’s current project, where his integration of strategic team collaboration and choice software systems has secured a premiere AI-based video streaming platform. Here, a combination of software development and IT team coordination is employed to enable update releases in a timely and secure manner. Based on cloud infrastructure accessible to the public, Tummalachervu and his team contributed through his implementation of “DevSecOps” - a sub-field of DevOps focused on ensuring full security throughout the development process. “We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date,” says Tummalachervu. In addition to security and team coordination, Tummalachervu has also been commended for his utilization of Infrastructure as Code (IAC). A modern strategy involving the use of code to support computing infrastructure rather than manual tweaks and settings adjustments, IAC is seeing widespread adoption across many enterprise-level systems. It is most effective when deployed in cloud environments, as IAC-enabling tools such as Terraform, Helm, and Cloudformation are wielded by Tummalachervu to great effect. “I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” elaborates Tummalachervu. “With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use.” Prior to working on said cloud-based infrastructure itself, Tummalachervu was instrumental in migrating systems and data therein, keeping his signature focus on strategy throughout each project. “One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams,” he says. Beyond the Clouds: Contributions and Teachings In addition to his technical expertise, Tummalachervu is highly regarded in the IT community for his extensive body of work, which has been published in renowned journals throughout the academic and technology spheres. His contributions to these communities extend beyond publications; he has also conducted numerous faculty development programs, directly benefiting nearly 150 PhD scholars, professors, university students, and colleagues. “Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles,” Tummalachervu says. “I’ve even been asked to judge at a few hackathons and international award ceremonies.” Central to the digital world, site reliability engineers like Chaitanya Tummalachervu act as pillars for what will become the future of technology: cloud-based environments. Secured, stabilized, and maintained by SREs and their teams, the future can rest easy knowing that the global infrastructure is supported by these skilled engineers, armed with nothing but best practices and sheer determination. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. The role of a site reliability engineer (SRE) is not to be understated. With the recent wave of developments in cloud computing and artificial intelligence (AI), scalability, security, and the actual reliability of these technologies can come into question. One tenured senior site reliability engineer is Chaitanya Kanth Tummalachervu , who has provided his services across sectors ranging from medical insurance, finance, retail, the automobile industry, and even video communications. In these fields, Tummalachervu’s principal focus lies in DevOps – or developer operations – where he supports teams of coders as they work to create, support, and protect AI-powered products for businesses and consumers alike. Chaitanya Kanth Tummalachervu Chaitanya Kanth Tummalachervu DevOps: How Automation, Security, and Deployment Are Perfected The inner workings of a developer team can vary greatly between projects. While small, agile teams may not run into issues, more complex workplaces or large-scale projects can often be mired in clutter - both digitally and physically. Site reliability engineers, such as Tummalachervu himself, help reduce the strain on these teams by creating systems tailored to the needs of each operation. He says, “ The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7. ” The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7. Central to his work, the field of digital artificial intelligence products can be as demanding as it is expansive. With the complexity of advanced neural networks, operations can be bogged down by miscommunication, collapsed dependencies, and delays in the development pipeline. Even worse, unsecured systems of such importance are also a prime target for malicious actors, furthering the burden on the teams supporting it. “ It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams, ” explains Tummalachervu. Indeed, his focus on software-driven automation has reportedly increased developer productivity by 40%, with incident response time also seeing a 20% reduction as well. It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams, Expertise Delivered in Contemporary AI Platforms Of particular note is Tummalachervu’s current project, where his integration of strategic team collaboration and choice software systems has secured a premiere AI-based video streaming platform. Here, a combination of software development and IT team coordination is employed to enable update releases in a timely and secure manner. Based on cloud infrastructure accessible to the public, Tummalachervu and his team contributed through his implementation of “DevSecOps” - a sub-field of DevOps focused on ensuring full security throughout the development process. “ We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date, ” says Tummalachervu. We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date, In addition to security and team coordination, Tummalachervu has also been commended for his utilization of Infrastructure as Code (IAC). A modern strategy involving the use of code to support computing infrastructure rather than manual tweaks and settings adjustments, IAC is seeing widespread adoption across many enterprise-level systems. It is most effective when deployed in cloud environments, as IAC-enabling tools such as Terraform, Helm, and Cloudformation are wielded by Tummalachervu to great effect. “ I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” elaborates Tummalachervu. “ With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use. ” I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use. Prior to working on said cloud-based infrastructure itself, Tummalachervu was instrumental in migrating systems and data therein, keeping his signature focus on strategy throughout each project. “ One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams, ” he says. One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams, Beyond the Clouds: Contributions and Teachings In addition to his technical expertise, Tummalachervu is highly regarded in the IT community for his extensive body of work, which has been published in renowned journals throughout the academic and technology spheres. His contributions to these communities extend beyond publications; he has also conducted numerous faculty development programs, directly benefiting nearly 150 PhD scholars, professors, university students, and colleagues. “ Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles, ” Tummalachervu says. “ I’ve even been asked to judge at a few hackathons and international award ceremonies. ” Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles, I’ve even been asked to judge at a few hackathons and international award ceremonies. Central to the digital world, site reliability engineers like Chaitanya Tummalachervu act as pillars for what will become the future of technology: cloud-based environments. Secured, stabilized, and maintained by SREs and their teams, the future can rest easy knowing that the global infrastructure is supported by these skilled engineers, armed with nothing but best practices and sheer determination. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here . here

Cloud Expert Chaitanya Kanth Tummalachervu Uses Complex DevOps and Cloud Architecture on AI Products

The cloud has transformed businesses’ operations, offering unparalleled scalability, agility, and cost-efficiency. But with this digital transformation comes a heightened responsibility to secure vast amounts of data and applications across complex cloud environments. Security Operations teams (SecOps) are on the front lines of this ongoing battle, and as cyber threats evolve in sophistication, traditional security methods are often stretched thin. However, this is where AI steps in as a powerful game-changer. By leveraging AI’s analytical prowess and automation capabilities, SecOps teams can significantly bolster their cloud security posture. And with 69% of IT professionals believing that AI will be necessary to respond to future cyber attacks, the urgency to implement AI into SecOps has never been more vital. In this article, we’ll delve into the intersection of AI and SecOps, discovering the practical steps for successful AI implementation in SecOps frameworks, how AI augments a business’s security capabilities, and the tangible benefits it brings. Let’s get started. Practical steps for AI implementation AI offers SecOps many benefits that lead to enhanced cloud security, but knowing how and where to start can be unclear for CISOs. In fact, Darktrace’s State of AI Cybersecurity research report found that “only 26% of security professionals report a full understanding of the different types of AI in use within security products.” So, here’s a breakdown of practical steps to leverage AI for a more robust SecOps environment and enhanced cloud security: Identify AI-powered opportunities The first step is pinpointing areas within your SecOps workflow where AI can provide the most value. Look for repetitive tasks, data analysis bottlenecks, or security blind spots where AI can augment human capabilities. For instance, imagine your SecOps team spends significant time sifting through security logs from various cloud resources, searching for anomalies—which is slow and error-prone. By implementing an AI-powered log analysis tool, you can automate the initial filtering of logs and identify potential threats based on predefined rules and historical data. This frees up your analysts to focus on investigating the truly high-risk events flagged by the AI, improving overall efficiency. Evaluate AI security tools Next, explore the security landscape and research AI-powered tools that align with your specific cloud environment and security needs. Consider factors like scalability to handle future growth, ease of integration to minimize disruption, and the tool’s effectiveness in threat detection and response. One popular example includes Microsoft Azure Sentinal, a cloud-native security information and event management tool that leverages machine learning (ML) for advanced threat hunting and security analysis. This caters to companies with a well-established security posture, as Sentinel’s capabilities extend beyond fundamental log analysis. Skilled security analysts are needed to interpret its insights and investigate potential incidents effectively. Another option is AWS GuardDuty, which also leverages ML and analyzes security logs to pinpoint suspicious activity. This automated approach suits companies with expanding cloud environments or limited security resources. It doesn’t require a large SecOps team or extensive threat-hunting expertise, making it easier to manage. Pilot and integrate for success Don’t jump straight into full-scale deployment. Begin with a pilot project to test and evaluate your chosen AI SecOps solution in a controlled environment. This allows you to assess its effectiveness and identify any integration challenges before fully integrating it into your existing security infrastructure. Upskilling the human firewall: AI automates tasks and streamlines processes, but human expertise remains irreplaceable. Invest in training your SecOps team to leverage AI effectively. Empower them for tasks like threat hunting and incident response, and use AI insights to improve your overall security posture continuously. By combining human expertise with AI’s capabilities, you can create a robust and comprehensive security strategy for your cloud environment. How AI supercharges SecOps tools SecOps teams are inundated with a constant stream of security alerts, making it challenging to distinguish genuine threats from false positives. This strains resources and slows down response times to attacks. Manual alert triage costs US organizations $3.3 billion annually. To help counter this, SecOps teams can leverage AI, which is now being integrated into various SecOps tools such as user and entity behavior analytics, endpoint detection and response, network traffic analysis, and threat intelligence platforms. The AI models within SecOps tools work by collecting vast amounts of data from various sources within an organization’s IT infrastructure. This data can include network traffic logs, system configurations, user activities, and threat intelligence feeds. Once the data is collected, AI algorithms process and analyze the data to identify patterns, anomalies, and potential security threats. In particular, AI excels at spotting unusual activity. It can analyze data points like user login attempts and file access patterns to identify deviations from established baselines. These deviations could indicate potential security incidents. The Cost of a Data Breach Report 2023 found that companies using extensive security AI and automation tools contained breaches 108 days faster than those who did not. Furthermore, AI can be trained on historical data about known threats. This allows it to identify characteristics associated with specific cyberattacks and use those models to flag similar activity in real-time, making it an ideal fit for threat modeling. Optimized resource allocation and faster incident response: The transformative benefits of AI-powered SecOps AI and its keen analytical eye can be applied to various instances within SecOps, but one of the key advantages is optimizing resource allocation. While SecOps teams traditionally rely on standardization, security orchestration, automation, and response platforms, along with meticulous tracking for resource allocation, these methods can struggle with the ever-increasing volume and complexity of security data. However, the analytical skills of AI go beyond traditional methods. AI can analyze vast amounts of security data, using ML for threat prioritization and resource mapping, optimizing resource allocation in a way that conventional methods simply cannot. Moreover, companies already utilizing standardization and automation are well-positioned to leverage AI with well-defined processes and data. These existing practices serve as stepping stones for implementing AI-powered SecOps solutions. AI can automate many time-consuming and repetitive tasks currently handled by SecOps personnel, including log analysis for anomaly detection, vulnerability scanning, and prioritization. Automating these tasks helps reduce human error and burnout while freeing up valuable time for SecOps professionals to focus on more strategic initiatives like threat hunting, incident response planning and improvement, and security architecture review and optimization. Response times to security incidents can also be significantly improved with help from AI. Traditional SecOp methods often rely on manual analysis of security logs and alerts, leading to delays in identifying threats. AI can analyze vast amounts of data in real-time, identifying anomalies and suspicious activity much faster than humans. By prioritizing these threats based on severity and potential impact, AI ensures that SecOps teams focus on the most critical incidents first. AI can even go one step further as AI-powered SecOps solutions can automate initial response actions for low-level threats. This includes isolating compromised systems, blocking malicious traffic, or initiating basic containment measures. Automating these initial steps saves valuable time for SecOps personnel, allowing them to focus on investigating and remediating high-priority threats. Wrapping up The future of cloud security is undoubtedly intertwined with AI. By automating routine tasks and augmenting human capabilities, AI empowers security teams to stay one step ahead of adversaries. The journey towards AI-powered SecOps is not just a destination but an ongoing evolution towards greater resilience, agility, and trust in the digital age. Sashank Purighalla, Founder and CEO of BOS Framework The cloud has transformed businesses’ operations, offering unparalleled scalability, agility, and cost-efficiency. But with this digital transformation comes a heightened responsibility to secure vast amounts of data and applications across complex cloud environments. Security Operations teams (SecOps) are on the front lines of this ongoing battle, and as cyber threats evolve in sophistication, traditional security methods are often stretched thin. The cloud However, this is where AI steps in as a powerful game-changer. By leveraging AI’s analytical prowess and automation capabilities, SecOps teams can significantly bolster their cloud security posture. And with 69% of IT professionals believing that AI will be necessary to respond to future cyber attacks, the urgency to implement AI into SecOps has never been more vital. 69% In this article, we’ll delve into the intersection of AI and SecOps, discovering the practical steps for successful AI implementation in SecOps frameworks, how AI augments a business’s security capabilities, and the tangible benefits it brings. Let’s get started. Practical steps for AI implementation AI offers SecOps many benefits that lead to enhanced cloud security, but knowing how and where to start can be unclear for CISOs. In fact, Darktrace’s State of AI Cybersecurity research report found that “only 26% of security professionals report a full understanding of the different types of AI in use within security products.” 26% So, here’s a breakdown of practical steps to leverage AI for a more robust SecOps environment and enhanced cloud security: So, here’s a breakdown of practical steps to leverage AI for a more robust SecOps environment and enhanced cloud security: Identify AI-powered opportunities Identify AI-powered opportunities The first step is pinpointing areas within your SecOps workflow where AI can provide the most value. Look for repetitive tasks, data analysis bottlenecks, or security blind spots where AI can augment human capabilities. For instance, imagine your SecOps team spends significant time sifting through security logs from various cloud resources, searching for anomalies—which is slow and error-prone. By implementing an AI-powered log analysis tool, you can automate the initial filtering of logs and identify potential threats based on predefined rules and historical data. This frees up your analysts to focus on investigating the truly high-risk events flagged by the AI, improving overall efficiency. Evaluate AI security tools Evaluate AI security tools Next, explore the security landscape and research AI-powered tools that align with your specific cloud environment and security needs. Consider factors like scalability to handle future growth, ease of integration to minimize disruption, and the tool’s effectiveness in threat detection and response. One popular example includes Microsoft Azure Sentinal, a cloud-native security information and event management tool that leverages machine learning (ML) for advanced threat hunting and security analysis. This caters to companies with a well-established security posture, as Sentinel’s capabilities extend beyond fundamental log analysis. Skilled security analysts are needed to interpret its insights and investigate potential incidents effectively. Another option is AWS GuardDuty, which also leverages ML and analyzes security logs to pinpoint suspicious activity. This automated approach suits companies with expanding cloud environments or limited security resources. It doesn’t require a large SecOps team or extensive threat-hunting expertise, making it easier to manage. Pilot and integrate for success Pilot and integrate for success Don’t jump straight into full-scale deployment. Begin with a pilot project to test and evaluate your chosen AI SecOps solution in a controlled environment. This allows you to assess its effectiveness and identify any integration challenges before fully integrating it into your existing security infrastructure. Upskilling the human firewall: AI automates tasks and streamlines processes, but human expertise remains irreplaceable. Invest in training your SecOps team to leverage AI effectively. Empower them for tasks like threat hunting and incident response, and use AI insights to improve your overall security posture continuously. By combining human expertise with AI’s capabilities, you can create a robust and comprehensive security strategy for your cloud environment. Upskilling the human firewall: How AI supercharges SecOps tools SecOps teams are inundated with a constant stream of security alerts, making it challenging to distinguish genuine threats from false positives. This strains resources and slows down response times to attacks. Manual alert triage costs US organizations $3.3 billion annually. $3.3 billion To help counter this, SecOps teams can leverage AI, which is now being integrated into various SecOps tools such as user and entity behavior analytics, endpoint detection and response, network traffic analysis, and threat intelligence platforms. The AI models within SecOps tools work by collecting vast amounts of data from various sources within an organization’s IT infrastructure. This data can include network traffic logs, system configurations, user activities, and threat intelligence feeds. Once the data is collected, AI algorithms process and analyze the data to identify patterns, anomalies, and potential security threats. In particular, AI excels at spotting unusual activity. It can analyze data points like user login attempts and file access patterns to identify deviations from established baselines. These deviations could indicate potential security incidents. The Cost of a Data Breach Report 2023 found that companies using extensive security AI and automation tools contained breaches 108 days faster than those who did not. 108 days faster Furthermore, AI can be trained on historical data about known threats. This allows it to identify characteristics associated with specific cyberattacks and use those models to flag similar activity in real-time, making it an ideal fit for threat modeling. Optimized resource allocation and faster incident response: The transformative benefits of AI-powered SecOps AI and its keen analytical eye can be applied to various instances within SecOps, but one of the key advantages is optimizing resource allocation. While SecOps teams traditionally rely on standardization, security orchestration, automation, and response platforms, along with meticulous tracking for resource allocation, these methods can struggle with the ever-increasing volume and complexity of security data. However, the analytical skills of AI go beyond traditional methods. AI can analyze vast amounts of security data, using ML for threat prioritization and resource mapping, optimizing resource allocation in a way that conventional methods simply cannot. Moreover, companies already utilizing standardization and automation are well-positioned to leverage AI with well-defined processes and data. These existing practices serve as stepping stones for implementing AI-powered SecOps solutions. AI can automate many time-consuming and repetitive tasks currently handled by SecOps personnel, including log analysis for anomaly detection, vulnerability scanning, and prioritization. Automating these tasks helps reduce human error and burnout while freeing up valuable time for SecOps professionals to focus on more strategic initiatives like threat hunting, incident response planning and improvement, and security architecture review and optimization. Response times to security incidents can also be significantly improved with help from AI. Traditional SecOp methods often rely on manual analysis of security logs and alerts, leading to delays in identifying threats. AI can analyze vast amounts of data in real-time, identifying anomalies and suspicious activity much faster than humans. By prioritizing these threats based on severity and potential impact, AI ensures that SecOps teams focus on the most critical incidents first. Response times to security incidents AI can even go one step further as AI-powered SecOps solutions can automate initial response actions for low-level threats. This includes isolating compromised systems, blocking malicious traffic, or initiating basic containment measures. Automating these initial steps saves valuable time for SecOps personnel, allowing them to focus on investigating and remediating high-priority threats. Wrapping up The future of cloud security is undoubtedly intertwined with AI. By automating routine tasks and augmenting human capabilities, AI empowers security teams to stay one step ahead of adversaries. The journey towards AI-powered SecOps is not just a destination but an ongoing evolution towards greater resilience, agility, and trust in the digital age. Sashank Purighalla, Founder and CEO of BOS Framework Sashank Purighalla, Founder and CEO of BOS Framework Sashank Purighalla , Founder and CEO of BOS Framework Sashank Purighalla BOS Framework

AI in SecOps: Transforming Cloud Security with Advanced Threat Detection

One fascinating concept that has emerged in recent years is the idea of digital twins — computational models that aim to replicate objects, processes, or even entire ecosystems. Such replicas can mimic the real-world objects they model, be used for simulations, and make predictions about their behavior. A digital twin of a city, for example, can be applied for capturing traffic, energy consumption, and more, all driven by AI algorithms that analyze sensory data and predict potential issues. These models are also constantly learning and adjusting themselves based on real-time data feeds. Human Digital Twins As technology progresses, allowing us to collect increasing amounts of data and develop increasingly complex models, there is a growing effort to create digital twins that simulate living beings, especially humans. The applications of human digital twins can revolutionize many industries. For example, in medicine, researchers are exploring the use of digital twins for patients, creating personalized models that factor in medical history, genetics, and lifestyle to treat patients based on all of their personal information. This tackles one of the major flaws in today's medical treatment – doctors can't consider all the massive amounts of information each individual holds when deciding upon treatment. Digital twins make it possible. Imagine a surgeon feeding a patient's data into an AI-powered digital twin of their heart, allowing them to practice a complex procedure on a digital replica of the patient's heart before the actual surgery – a glimpse into the future of personalized medicine. Another application of this technology can be applied in the world of human resources. AI-powered digital replicas of employees can capture their skills, career aspirations, and even personality traits. AI could use these digital twins, for example, in the recruitment process of employees, analyzing data to discover the strengths and potential of candidates. Additionally, they could be used for personalized training and development plans. By analyzing an employee's digital twin, AI could recommend courses, mentorships, or even jobs that best suit their skills and career goals. Ethical Concerns All of this sounds good on paper, but this topic raises many ethical questions that are at the core of AI technology. AI algorithms used to build digital twins rely heavily on the data they're trained on. This raises questions like which data is collected? To what extent? Is the data collected with consent? How will this data be used? For example – Is it acceptable to collect social network data like Instagram or personal forums to evaluate candidates for a job? While some of this data might be available to collect easily, the collection of more personal information raises many privacy concerns. Challenges in Replicating Humans In addition, creating digital twins for people is much more challenging than for other systems due to the complexities of human behavior and cognition. People don't always make optimal choices, and may use heuristics, or make choices that are "good enough." Their decisions are influenced by context and change over time. People have subjective experiences, intentions, and emotions that are difficult to quantify or measure. Their goals change, and their behavior is influenced by emotions. People act in social contexts with norms and expectations, playing different roles. They have self-image, empathy, and may act selflessly. This complexity is difficult to capture, and evaluating and giving verdicts on people based on such models is difficult, to say the least. Biased data collection can create prejudices in hiring practices or performance evaluations. The way AI models are built today makes it difficult to understand how they arrive at certain conclusions about an employee. Conclusion To conclude, AI-powered digital human twins have the potential to set the stage for hyper-personalized experiences in many aspects, from medicine to entertainment. The ethical issues raised have to be considered. Furthermore, humans are complicated creatures. It is quite a challenge to accurately replicate individuals through digital twins. Only time will tell how this field will evolve. References: https://arxiv.org/pdf/2208.04289?embedable=true https://www.bimplus.co.uk/digital-twin-adoption-success-lies-in-understanding-the-ethics?embedable=true One fascinating concept that has emerged in recent years is the idea of digital twins — computational models that aim to replicate objects, processes, or even entire ecosystems. Such replicas can mimic the real-world objects they model, be used for simulations, and make predictions about their behavior. A digital twin of a city, for example, can be applied for capturing traffic, energy consumption, and more, all driven by AI algorithms that analyze sensory data and predict potential issues. These models are also constantly learning and adjusting themselves based on real-time data feeds. Human Digital Twins Human Digital Twins As technology progresses, allowing us to collect increasing amounts of data and develop increasingly complex models, there is a growing effort to create digital twins that simulate living beings, especially humans. The applications of human digital twins can revolutionize many industries. For example, in medicine, researchers are exploring the use of digital twins for patients, creating personalized models that factor in medical history, genetics, and lifestyle to treat patients based on all of their personal information. This tackles one of the major flaws in today's medical treatment – doctors can't consider all the massive amounts of information each individual holds when deciding upon treatment. Digital twins make it possible. Imagine a surgeon feeding a patient's data into an AI-powered digital twin of their heart, allowing them to practice a complex procedure on a digital replica of the patient's heart before the actual surgery – a glimpse into the future of personalized medicine. Another application of this technology can be applied in the world of human resources. AI-powered digital replicas of employees can capture their skills, career aspirations, and even personality traits. AI could use these digital twins, for example, in the recruitment process of employees, analyzing data to discover the strengths and potential of candidates. Additionally, they could be used for personalized training and development plans. By analyzing an employee's digital twin, AI could recommend courses, mentorships, or even jobs that best suit their skills and career goals. Ethical Concerns Ethical Concerns All of this sounds good on paper, but this topic raises many ethical questions that are at the core of AI technology. AI algorithms used to build digital twins rely heavily on the data they're trained on. This raises questions like which data is collected? To what extent? Is the data collected with consent? How will this data be used? For example – Is it acceptable to collect social network data like Instagram or personal forums to evaluate candidates for a job? While some of this data might be available to collect easily, the collection of more personal information raises many privacy concerns. Challenges in Replicating Humans Challenges in Replicating Humans In addition, creating digital twins for people is much more challenging than for other systems due to the complexities of human behavior and cognition. People don't always make optimal choices, and may use heuristics, or make choices that are "good enough." Their decisions are influenced by context and change over time. People have subjective experiences, intentions, and emotions that are difficult to quantify or measure. Their goals change, and their behavior is influenced by emotions. People act in social contexts with norms and expectations, playing different roles. They have self-image, empathy, and may act selflessly. This complexity is difficult to capture, and evaluating and giving verdicts on people based on such models is difficult, to say the least. Biased data collection can create prejudices in hiring practices or performance evaluations. The way AI models are built today makes it difficult to understand how they arrive at certain conclusions about an employee. Conclusion Conclusion To conclude, AI-powered digital human twins have the potential to set the stage for hyper-personalized experiences in many aspects, from medicine to entertainment. The ethical issues raised have to be considered. Furthermore, humans are complicated creatures. It is quite a challenge to accurately replicate individuals through digital twins. Only time will tell how this field will evolve. References: https://arxiv.org/pdf/2208.04289?embedable=true https://arxiv.org/pdf/2208.04289?embedable=true https://www.bimplus.co.uk/digital-twin-adoption-success-lies-in-understanding-the-ethics?embedable=true https://www.bimplus.co.uk/digital-twin-adoption-success-lies-in-understanding-the-ethics?embedable=true

The Black Mirror? A Future of AI-Powered Digital Twins

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