Qured

qured

— Today, Qredo is thrilled to announce its – a cross-chain primitive that will allow Web3 developers to generate, sign, and broadcast data and transactions between different blockchains. Qredo is asking developers for their feedback to inform and shape QSign's future development. 27 April 2023 technology preview of QSign Using Qredo's distributed multi-party computation technology, QSign establishes an unprecedented level of security for cross-chain communication standards by reducing the many risks involved in managing centrally stored private keys. Web3 developers using QSign will be able to interact with assets and applications across different blockchains, connect to other blockchains and control numerous assets. They will be able to safely and reliably unlock market liquidity on chains, build secure cross-chain applications, and achieve open, permissionless blockchain interoperability. QSign can help to realize the full potential of blockchain interoperability, enabling cross-chain use cases such as distributed bridges, interchain AMMs, lending protocols, and cross-chain smart contract wallets. Using QSign, developers will be able to programmatically generate cryptocurrency addresses on different blockchains, and then remotely control that address from their contract, generating and broadcasting signed transactions to move funds or interact with other contracts. A new crypto primitive, QSign stands alongside emergent blockchain tech developments such as Account Abstraction and will be easily integrated into smart contracts with ready interfaces. We've created a dedicated channel, #QSign, in the , and we encourage developers to ask questions, give feedback and help us shape the roadmap for QSign together. Qredo Discord server Further information can be found on the and in the . Qredo website Qredo developer documentation QSign is perfectly aligned with our mission at Qredo to bring secure, reliable self-custody to the entire blockchain ecosystem. We invite developers to test our technology preview, share insights and use cases, and actively contribute to shaping this ground-breaking new crypto primitive. – Gabriele Farei, Chief Product Officer at Qredo About Qredo Qredo is a leading provider of institutional-grade digital asset self-custody solutions. The company's distributed multi-party computing (dMPC) technology provides a secure and regulated environment for users to store, transfer, and manage their digital assets. Press Contact: pr@qredo.com Also published . here

Qredo Announces QSign Technology Preview: New Cross-Chain Crypto Primitive

Qredo anuncia la vista previa de la tecnología QSign: nueva criptoprimitiva de cadena cruzada

Qredo annonce un aperçu de la technologie QSign : nouvelle crypto primitive inter-chaînes

Qredo ने QSign टेक्नोलॉजी प्रीव्यू की घोषणा की: नई क्रॉस-चेन क्रिप्टो प्रिमिटिव

Qredo が QSign テクノロジー プレビューを発表: 新しいクロスチェーン暗号プリミティブ

Qredo anuncia prévia da tecnologia QSign: novo cripto primitivo de cadeia cruzada

Qredo công bố bản xem trước công nghệ QSign: Tiền điện tử chuỗi chéo mới

Qredo 宣布 QSign 技术预览：新的跨链加密原语

**SINGAPORE, Singapore, September 13th, 2024/Chainwire/--**SynFutures, the leading DeFi derivatives protocol, today announced the second edition of Borderless Summit , the first crypto event series dedicated to bridging East and West and offering global perspectives on some of the most timely topics in Web3. The summit will be held at Sofitel Singapore City Centre on September 17, 2024, during TOKEN2049 Singapore and explore the evolution of the Web3 user experience. With a diverse lineup of speakers from Base, Dragonfly Capital, 1inch, Founders Fund, Framework Ventures, Agora, and more leading Web3 organizations, Borderless Summit will offer a unique platform for diverse cross-border perspectives on trending narratives. The half-day summit will take place during the TOKEN2049 activities and feature panel discussions and a networking happy hour. Speakers include: ​​Sergej Kunz, Co-Founder at 1inch


Haseeb Qureshi, Managing Partner at Dragonfly Capital


Vance Spencer, Co-Founder at Framework Ventures


Nick Matthew, Investor at Standard Crypto


Eugene Ng, Co-Founder at DWF Labs/OpenEden


Eric Brown, Senior Developer Advocate at Base


Simon Baksys, VP of BD at LayerZero Labs


Mr. Block, Core Team at Puffer & Curve.fi


Joey Anthony, Head of Infrastructure, Monad


Jocy, Founding Partner, IOSG Ventures


Mark Lee, Partner and CMO at SynFutures


Ryan Kim, Founding Partner at Hashed


Joey Krug, Partner at Founders Fund


Nick van Eck, Co-Founder and CEO at Agora


Mike Silagadze, CEO at Ether.fi


Maarten Henskens, Head of Growth at Soneium


Shuyao Kong, Co-Founder at MegaETH


Paul Frambot, Co-Founder and CEO at Morpho Labs


Antonio Garcia-Martinez, CEO & Founder, Spindl


Irene Zhao of Asianmom


Chris Carapola, Co-Founder and CEO at Curvance


TN, Founder at Pendle


Jeff Liu, Co-Founder and CEO at Fuzzland Inc


Tigran Bolshoi, Co-founder, Kinetex Network


Jon Jones, Co-Founder & CBO at Supra


Fred Hsu, CEO & Co-Founder, D3


Uwe Cerron, Core Contributor, Convergence "We are thrilled to host Borderless Summit in Singapore during one of the biggest Web3 conferences of the year," said Rachel Lin, co-founder and CEO at SynFutures. “This event represents a unique opportunity to not only explore one of the biggest trends in Web3 but gather some of the most brilliant minds across the world together in one room.” Borderless Summit will be held on Tuesday, September, from 1:30 to 7:30 PM GMT+8. Registration will begin at 1:30 PM. For the full agenda, see the event page. The event is organized by SynFutures. Fuzzland is a Diamond Sponsor. D3, Kinetex Network, and Supra are Gold Sponsors, and zkLink, Zomma Protocol, and Yay! are Silver Sponsors. Media partners include ​Wu Blockchain, CoinPost, BlockBeats, Token Post, Xangle, Traders Guild, PANews, Beincrypto, Foresight News, Cryptonews, Moongate, Talkchain Malaysia, Cointelegram, Coindesk Indonesia, Cointelegraph Japan, The Coin Republic, CryptoWesearch, and Bitcoin Addict. Registration and Additional Information To register for Borderless Summit 2024 and for more details about the event, visit https://lu.ma/borderlesssummit2. About Borderless Summit Borderless Summit is the first crypto event series dedicated to bridging East and West. Designed to help founders and builders better navigate global markets, Borderless Summit offers a unique platform for diverse cross-border perspectives on trending narratives in Web3. For more information, go to https://lu.ma/borderlesssummit2. About SynFutures SynFutures is a decentralized perpetual futures protocol that facilitates open and transparent trading on any assets and listings instantly. The V3 Oyster AMM launched the industry's first-ever unified AMM and onchain order book model. Backers include Tier 1 Web3 institutional investors Pantera Capital, Polychain Capital, Susquehanna International Group (SIG), Dragonfly Capital, Standard Crypto, and Framework Ventures, and the team has extensive experience at global financial institutions, fintech companies and blockchain technology companies such as Alipay, Bitmain, Credit Suisse, Deutsche Bank, Matrixport, and Nomura Securities. Contact CMO Mark Lee SynFutures mark.lee@synfutures.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **SINGAPORE, Singapore, September 13th, 2024/Chainwire/--**SynFutures, the leading DeFi derivatives protocol, today announced the second edition of Borderless Summit , the first crypto event series dedicated to bridging East and West and offering global perspectives on some of the most timely topics in Web3. The summit will be held at Sofitel Singapore City Centre on September 17, 2024, during TOKEN2049 Singapore and explore the evolution of the Web3 user experience. With a diverse lineup of speakers from Base, Dragonfly Capital, 1inch, Founders Fund, Framework Ventures, Agora, and more leading Web3 organizations, Borderless Summit will offer a unique platform for diverse cross-border perspectives on trending narratives. The half-day summit will take place during the TOKEN2049 activities and feature panel discussions and a networking happy hour. Speakers include: ​​Sergej Kunz, Co-Founder at 1inch Haseeb Qureshi, Managing Partner at Dragonfly Capital Vance Spencer, Co-Founder at Framework Ventures Nick Matthew, Investor at Standard Crypto Eugene Ng, Co-Founder at DWF Labs/OpenEden Eric Brown, Senior Developer Advocate at Base Simon Baksys, VP of BD at LayerZero Labs Mr. Block, Core Team at Puffer & Curve.fi Joey Anthony, Head of Infrastructure, Monad Jocy, Founding Partner, IOSG Ventures Mark Lee, Partner and CMO at SynFutures Ryan Kim, Founding Partner at Hashed Joey Krug, Partner at Founders Fund Nick van Eck, Co-Founder and CEO at Agora Mike Silagadze, CEO at Ether.fi Maarten Henskens, Head of Growth at Soneium Shuyao Kong, Co-Founder at MegaETH Paul Frambot, Co-Founder and CEO at Morpho Labs Antonio Garcia-Martinez, CEO & Founder, Spindl Irene Zhao of Asianmom Chris Carapola, Co-Founder and CEO at Curvance TN, Founder at Pendle Jeff Liu, Co-Founder and CEO at Fuzzland Inc Tigran Bolshoi, Co-founder, Kinetex Network Jon Jones, Co-Founder & CBO at Supra Fred Hsu, CEO & Co-Founder, D3 Uwe Cerron, Core Contributor, Convergence ​​Sergej Kunz, Co-Founder at 1inch ​​Sergej Kunz, Co-Founder at 1inch Haseeb Qureshi, Managing Partner at Dragonfly Capital Haseeb Qureshi, Managing Partner at Dragonfly Capital Vance Spencer, Co-Founder at Framework Ventures Vance Spencer, Co-Founder at Framework Ventures Nick Matthew, Investor at Standard Crypto Nick Matthew, Investor at Standard Crypto Eugene Ng, Co-Founder at DWF Labs/OpenEden Eugene Ng, Co-Founder at DWF Labs/OpenEden Eric Brown, Senior Developer Advocate at Base Eric Brown, Senior Developer Advocate at Base Simon Baksys, VP of BD at LayerZero Labs Simon Baksys, VP of BD at LayerZero Labs Mr. Block, Core Team at Puffer & Curve.fi Mr. Block, Core Team at Puffer & Curve.fi Joey Anthony, Head of Infrastructure, Monad Joey Anthony, Head of Infrastructure, Monad Jocy, Founding Partner, IOSG Ventures Jocy, Founding Partner, IOSG Ventures Mark Lee, Partner and CMO at SynFutures Mark Lee, Partner and CMO at SynFutures Ryan Kim, Founding Partner at Hashed Ryan Kim, Founding Partner at Hashed Joey Krug, Partner at Founders Fund Joey Krug, Partner at Founders Fund Nick van Eck, Co-Founder and CEO at Agora Nick van Eck, Co-Founder and CEO at Agora Mike Silagadze, CEO at Ether.fi Mike Silagadze, CEO at Ether.fi Maarten Henskens, Head of Growth at Soneium Maarten Henskens, Head of Growth at Soneium Shuyao Kong, Co-Founder at MegaETH Shuyao Kong, Co-Founder at MegaETH Paul Frambot, Co-Founder and CEO at Morpho Labs Paul Frambot, Co-Founder and CEO at Morpho Labs Antonio Garcia-Martinez, CEO & Founder, Spindl Antonio Garcia-Martinez, CEO & Founder, Spindl Irene Zhao of Asianmom Irene Zhao of Asianmom Chris Carapola, Co-Founder and CEO at Curvance Chris Carapola, Co-Founder and CEO at Curvance TN, Founder at Pendle TN, Founder at Pendle Jeff Liu, Co-Founder and CEO at Fuzzland Inc Jeff Liu, Co-Founder and CEO at Fuzzland Inc Tigran Bolshoi, Co-founder, Kinetex Network Tigran Bolshoi, Co-founder, Kinetex Network Jon Jones, Co-Founder & CBO at Supra Jon Jones, Co-Founder & CBO at Supra Fred Hsu, CEO & Co-Founder, D3 Fred Hsu, CEO & Co-Founder, D3 Uwe Cerron, Core Contributor, Convergence Uwe Cerron, Core Contributor, Convergence "We are thrilled to host Borderless Summit in Singapore during one of the biggest Web3 conferences of the year," said Rachel Lin, co-founder and CEO at SynFutures. “This event represents a unique opportunity to not only explore one of the biggest trends in Web3 but gather some of the most brilliant minds across the world together in one room.” "We are thrilled to host Borderless Summit in Singapore during one of the biggest Web3 conferences of the year," said Rachel Lin, co-founder and CEO at SynFutures. “This event represents a unique opportunity to not only explore one of the biggest trends in Web3 but gather some of the most brilliant minds across the world together in one room.” Borderless Summit will be held on Tuesday, September, from 1:30 to 7:30 PM GMT+8. Registration will begin at 1:30 PM. For the full agenda, see the event page . event page event page The event is organized by SynFutures. Fuzzland is a Diamond Sponsor. D3, Kinetex Network, and Supra are Gold Sponsors, and zkLink, Zomma Protocol, and Yay! are Silver Sponsors. Media partners include ​Wu Blockchain, CoinPost, BlockBeats, Token Post, Xangle, Traders Guild, PANews, Beincrypto, Foresight News, Cryptonews, Moongate, Talkchain Malaysia, Cointelegram, Coindesk Indonesia, Cointelegraph Japan, The Coin Republic, CryptoWesearch, and Bitcoin Addict. Registration and Additional Information To register for Borderless Summit 2024 and for more details about the event, visit https://lu.ma/borderlesssummit2 . https://lu.ma/borderlesssummit2 https://lu.ma/borderlesssummit2 About Borderless Summit Borderless Summit is the first crypto event series dedicated to bridging East and West. Designed to help founders and builders better navigate global markets, Borderless Summit offers a unique platform for diverse cross-border perspectives on trending narratives in Web3. For more information, go to https://lu.ma/borderlesssummit2 . https://lu.ma/borderlesssummit2 https://lu.ma/borderlesssummit2 About SynFutures SynFutures is a decentralized perpetual futures protocol that facilitates open and transparent trading on any assets and listings instantly. The V3 Oyster AMM launched the industry's first-ever unified AMM and onchain order book model. Backers include Tier 1 Web3 institutional investors Pantera Capital, Polychain Capital, Susquehanna International Group (SIG), Dragonfly Capital, Standard Crypto, and Framework Ventures, and the team has extensive experience at global financial institutions, fintech companies and blockchain technology companies such as Alipay, Bitmain, Credit Suisse, Deutsche Bank, Matrixport, and Nomura Securities. Contact CMO Mark Lee SynFutures mark.lee@synfutures.com 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

Borderless Summit To Explore The Era Of Consumer-First Web3 During TOKEN2049 Singapore

قمة بلا حدود لاستكشاف عصر Web3 الذي يركز على المستهلك خلال TOKEN2049 سنغافورة

Borderless Summit za istraživanje ere Consumer-First Web3 tokom TOKEN2049 Singapur

La cumbre sin fronteras explorará la era de la Web3 centrada en el consumidor durante TOKEN2049 Singapur

Mugarik gabeko gailurra Kontsumitzaileen lehen web 3aren garaia aztertzeko TOKEN2049 Singapurren zehar

Անսահման գագաթնաժողով՝ ուսումնասիրելու սպառողների առաջին վեբի դարաշրջանը TOKEN2049 Սինգապուրի ժամանակ

ボーダーレスサミット、TOKEN2049シンガポールで消費者第一のWeb3時代を探る

Vovonana tsy misy sisintany hijerena ny vanim-potoanan'ny mpanjifa-voalohany Web3 mandritra ny TOKEN2049 Singapore

TOKEN2049 Сингапурын үеэр хэрэглэгчийн анхны вэб3-ын эрин үеийг судлах хил хязгааргүй дээд хэмжээний уулзалт

Cumbre Sin Fronteras Explorar La Era De Consumidor-Primero Web3 TOKEN2049 Singapur Kachkaptin

සිංගප්පූරුවේ TOKEN2049 අතරතුර පාරිභෝගික-පළමු Web3 යුගය ගවේෂණය කිරීමට දේශසීමා රහිත සමුළුව

Саміт без кордонів, щоб дослідити еру Consumer-First Web3 під час TOKEN2049 Сінгапур

TOKEN2049 سنگاپور کے دوران کنزیومر فرسٹ ویب 3 کے دور کو دریافت کرنے کے لیے بارڈر لیس سمٹ

ChatGPT in Classrooms: Opportunities, Challenges, and Ethical Considerations

Author:
(1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction History of Using AI in Education Research Methodology Literature Review Summary Conclusion and References 5. Summary The integration of generative AI models in education offer a wide range of potential benefits, including improved productivity, enhanced creativity, and personalized learning, providing timely feedback and support, and automating tasks (Farrokhnia et al., 2023; Cotton et al., 2023). The advantages of using generative AI models in education are summarised in Table 1. However, the adoption of generative AI models in education raises challenges that need to be tackled in order to guarantee a safe and responsible usage of such technologies (Su & Yang, 2023). These challenges are summarised in Table 2. Findings of this survey underscore the need for further research, regulatory policies, and pedagogical guidance to navigate issues related to academic integrity and equity in the era of AI enhanced learning (Chaudhry et al., 2023; Yan, 2023; Cotton et al., 2023). The irresponsible implementation of generative AI models in education have ethical and legal consequences (Chaudhry et al., 2023). Table 3 discusses potential ethical consideration for the adoption of Generative AI models in education. Whereas, Table 4 focuses on the legal implication. Governments around the world are developing regulations to address the ethical and legal challenges posed by generative AI models. For example, the ”European Union Artificial Intelligence Act (EU AI Act)” was proposed to promote the development and use of trustworthy AI, while also mitigating the risks associated with AI (Madiega, 2021). The Act does this by establishing a riskbased approach to AI regulation. AI systems are classified into four risk categories: ”unacceptable risk, high risk, limited risk, and minimal risk”. The EU AI Act includes the following provisions: (a) Risk Assessment: the AI Act mandates the assessment of risks associated with AI systems, ensuring that potential harms are identified and mitigated, (b) Transparency: transparency requirements are specified to enhance the explainability of AI systems and make their decisionmaking processes more understandable, the EU AI Act specified the following three transparency requirements when it comes to adopt generative AI technology: • Disclosure of AI Generation: The imperative for transparency in the context of generative AI pertains to the acknowledgment and notification that content has been produced by artificial intelligence rather than a human agent. Such disclosures serve to elucidate the nature of the content creation process, fostering user understanding and trust. • Mitigation of Illicit Content Generation: The design and implementation of generative AI models must encompass measures to forestall the generation of content that contravenes legal statutes. These measures are integral to ethical and legal compliance, involving content moderation and safety mechanisms to curtail the production of potentially harmful, offensive, or unlawful materials. • Publication of Summaries of Copyrighted Data Utilized in Training: In the sphere of AI model development, the utilization of copyrighted data engenders concerns pertaining to intellectual property rights. To address these concerns and exemplify transparency, developers may be encouraged to provide summaries or descriptions of the copyrighted data sources employed during training. This step showcases adherence to copyright laws while enhancing transparency regarding the model’s training data. and (c) Human Oversight: the AI Act emphasizes the importance of human oversight in AI systems to ensure responsible and ethical use. However, there are some limitations to the current regulatory landscape (Schuett, 2023). For example, the EU AI Act does not explicitly address the issue of generative AI models hallucination. Additionally, the EU AI Act focuses on the obligations of AI developers and users, rather than on the rights of individuals who may be affected by AI systems (Enqvist, 2023). Findings from literature also provide valuable insights into the determinants of intention to use generative AI models for educational purposes. The findings have important implications for the models developers, instructors, and universities as they work to accelerate the adoption of them in educational contexts. Table 5 discusses a set of recommendations for the different stakeholders fostering them to have a safe and responsible adoption of such technologies in education. The adoption or generative AI models in education requires a trust raising plan (Choudhury & Shamszare, 2023). In addition to addressing the forementioned challenges (See Table 2), there are a number of steps that can be done to build trust in generative AI, including: (1) Transparency: developers should be transparent about the limitations, biases, data sources, and potential risks of these models. (2) Explainability: developers should develop explainable AI methods to explain how their models generate outputs (Dosilovi ˇ c et al., 2018). (3) ´ Human-in-the-loop validation: human experts should be involved in the evaluation of AI-generated content to identify and rectify errors. This will help into solving other problems such as bias, and hallucination. (4) Accountability: developers and users of generative AI should be accountable for the consequences of their actions. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Author: Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction Abstract and Introduction History of Using AI in Education History of Using AI in Education Research Methodology Research Methodology Literature Review Literature Review Summary Summary Conclusion and References Conclusion and References 5. Summary The integration of generative AI models in education offer a wide range of potential benefits, including improved productivity, enhanced creativity, and personalized learning, providing timely feedback and support, and automating tasks (Farrokhnia et al., 2023; Cotton et al., 2023). The advantages of using generative AI models in education are summarised in Table 1. However, the adoption of generative AI models in education raises challenges that need to be tackled in order to guarantee a safe and responsible usage of such technologies (Su & Yang, 2023). These challenges are summarised in Table 2. Findings of this survey underscore the need for further research, regulatory policies, and pedagogical guidance to navigate issues related to academic integrity and equity in the era of AI enhanced learning (Chaudhry et al., 2023; Yan, 2023; Cotton et al., 2023). The irresponsible implementation of generative AI models in education have ethical and legal consequences (Chaudhry et al., 2023). Table 3 discusses potential ethical consideration for the adoption of Generative AI models in education. Whereas, Table 4 focuses on the legal implication. Governments around the world are developing regulations to address the ethical and legal challenges posed by generative AI models. For example, the ”European Union Artificial Intelligence Act (EU AI Act)” was proposed to promote the development and use of trustworthy AI, while also mitigating the risks associated with AI (Madiega, 2021). The Act does this by establishing a riskbased approach to AI regulation. AI systems are classified into four risk categories: ”unacceptable risk, high risk, limited risk, and minimal risk”. The EU AI Act includes the following provisions: (a) Risk Assessment: the AI Act mandates the assessment of risks associated with AI systems, ensuring that potential harms are identified and mitigated, (b) Transparency: transparency requirements are specified to enhance the explainability of AI systems and make their decisionmaking processes more understandable, the EU AI Act specified the following three transparency requirements when it comes to adopt generative AI technology: • Disclosure of AI Generation: The imperative for transparency in the context of generative AI pertains to the acknowledgment and notification that content has been produced by artificial intelligence rather than a human agent. Such disclosures serve to elucidate the nature of the content creation process, fostering user understanding and trust. Disclosure of AI Generation: • Mitigation of Illicit Content Generation: The design and implementation of generative AI models must encompass measures to forestall the generation of content that contravenes legal statutes. These measures are integral to ethical and legal compliance, involving content moderation and safety mechanisms to curtail the production of potentially harmful, offensive, or unlawful materials. • Mitigation of Illicit Content Generation: • Publication of Summaries of Copyrighted Data Utilized in Training: In the sphere of AI model development, the utilization of copyrighted data engenders concerns pertaining to intellectual property rights. To address these concerns and exemplify transparency, developers may be encouraged to provide summaries or descriptions of the copyrighted data sources employed during training. This step showcases adherence to copyright laws while enhancing transparency regarding the model’s training data. • Publication of Summaries of Copyrighted Data Utilized in Training: and (c) Human Oversight: the AI Act emphasizes the importance of human oversight in AI systems to ensure responsible and ethical use. However, there are some limitations to the current regulatory landscape (Schuett, 2023). For example, the EU AI Act does not explicitly address the issue of generative AI models hallucination. Additionally, the EU AI Act focuses on the obligations of AI developers and users, rather than on the rights of individuals who may be affected by AI systems (Enqvist, 2023). Findings from literature also provide valuable insights into the determinants of intention to use generative AI models for educational purposes. The findings have important implications for the models developers, instructors, and universities as they work to accelerate the adoption of them in educational contexts. Table 5 discusses a set of recommendations for the different stakeholders fostering them to have a safe and responsible adoption of such technologies in education. The adoption or generative AI models in education requires a trust raising plan (Choudhury & Shamszare, 2023). In addition to addressing the forementioned challenges (See Table 2), there are a number of steps that can be done to build trust in generative AI, including: (1) Transparency: developers should be transparent about the limitations, biases, data sources, and potential risks of these models. (2) Explainability: developers should develop explainable AI methods to explain how their models generate outputs (Dosilovi ˇ c et al., 2018). (3) ´ Human-in-the-loop validation: human experts should be involved in the evaluation of AI-generated content to identify and rectify errors. This will help into solving other problems such as bias, and hallucination. (4) Accountability: developers and users of generative AI should be accountable for the consequences of their actions. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Generative AI in Education: Benefits, Challenges, and Future Directions

Comprehensive Review of AI Chatbots and ChatGPT in Education

Author:
(1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction History of Using AI in Education Research Methodology Literature Review Summary Conclusion and References 3. Research Methodology This section discusses the research methodology we followed to develop search, filter, and select the related work on using generative AI models in general and ChatGPT in particular in different educational settings. 3.1. Search approach In this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) (Moher et al., 2009; Knobloch et al., 2011) statement was followed to select relevant articles. The search for articles started on June 25, 2023, until July 10. The version of ChatGPT discussed in the articles was the original release on November 30, 2022 (i.e. ChatGPT 3.5). The search keywords (”ChatGPT”, ”Education”, ”Generative AI”, ”Chatpot”) were used to search for related publications in different databases including ACM, IEEE Xplore, Scopus, and Web of Science. Articles that mentioned these keywords in their title, abstract, or keywords were selected. The publication period was filtered to be after 2022. Despite searching multiple databases, the number of relevant articles found was limited. To ensure a comprehensive search, Google Scholar was used to conduct a title search for the term ”ChatGPT” within the same search period. This additional search method allowed for the identification of relevant articles that may not have been captured in the initial database search. 3.2. Paper Selection Criteria In this review, academic articles published between November, 2022, and July, 2023, were included. The review included only advanced online publications published in journals indexed by Scopus as Q1 or Q2 journals. The focus of the review was on articles that discussed the application of generative AI models (mainly ChatGPT) in education, without any specific constraints on educational contexts. Identified literature reviews were used as background references and were excluded from the synthesis to avoid redundancy in references. Moreover, Only articles written in English were included in this rapid review, ensuring a consistent language focus for the analysis. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Author: Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction Abstract and Introduction History of Using AI in Education History of Using AI in Education Research Methodology Research Methodology Literature Review Literature Review Summary Summary Conclusion and References Conclusion and References 3. Research Methodology This section discusses the research methodology we followed to develop search, filter, and select the related work on using generative AI models in general and ChatGPT in particular in different educational settings. 3.1. Search approach 3.1. Search approach In this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) (Moher et al., 2009; Knobloch et al., 2011) statement was followed to select relevant articles. The search for articles started on June 25, 2023, until July 10. The version of ChatGPT discussed in the articles was the original release on November 30, 2022 (i.e. ChatGPT 3.5). The search keywords (”ChatGPT”, ”Education”, ”Generative AI”, ”Chatpot”) were used to search for related publications in different databases including ACM, IEEE Xplore, Scopus, and Web of Science. Articles that mentioned these keywords in their title, abstract, or keywords were selected. The publication period was filtered to be after 2022. Despite searching multiple databases, the number of relevant articles found was limited. To ensure a comprehensive search, Google Scholar was used to conduct a title search for the term ”ChatGPT” within the same search period. This additional search method allowed for the identification of relevant articles that may not have been captured in the initial database search. 3.2. Paper Selection Criteria 3.2. Paper Selection Criteria In this review, academic articles published between November, 2022, and July, 2023, were included. The review included only advanced online publications published in journals indexed by Scopus as Q1 or Q2 journals. The focus of the review was on articles that discussed the application of generative AI models (mainly ChatGPT) in education, without any specific constraints on educational contexts. Identified literature reviews were used as background references and were excluded from the synthesis to avoid redundancy in references. Moreover, Only articles written in English were included in this rapid review, ensuring a consistent language focus for the analysis. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Analyzing ChatGPT and Generative AI in Education

History of Using AI in Education

শিক্ষায় এআই ব্যবহারের ইতিহাস

Geschichte des Einsatzes von KI in der Bildung

Historia del uso de la IA en la educación

Histoire de l'utilisation de l'IA dans l'éducation

शिक्षा में एआई के उपयोग का इतिहास

教育におけるAI活用の歴史

AI를 교육에 활용한 역사

História do uso de IA na educação

История использования ИИ в образовании

Eğitimde Yapay Zeka Kullanımının Tarihçesi

Lịch sử sử dụng AI trong giáo dục

人工智能在教育领域的应用历史

Author:
(1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction History of Using AI in Education Research Methodology Literature Review Summary Conclusion and References Abstract The wide adoption and usage of generative artificial intelligence (AI) models, particularly ChatGPT, has sparked a surge in research exploring their potential applications in the educational landscape. This survey examines academic literature published between November, 2022, and July, 2023, specifically targeting high-impact research from Scopus-indexed Q1 and Q2 journals. This survey delves into the practical applications and implications of generative AI models across a diverse range of educational contexts. Through a comprehensive and rigorous evaluation of recent academic literature, this survey seeks to illuminate the evolving role of generative AI models, particularly ChatGPT, in education. By shedding light on the potential benefits, challenges, and emerging trends in this dynamic field, the survey endeavors to contribute to the understanding of the nexus between artificial intelligence and education. The findings of this review will empower educators, researchers, and policymakers to make informed decisions about the integration of AI technologies into learning environments. Keywords: Artificial Intelligence, Education, ChatGPT, Generative AI, Technology-enhanced Learning. 1. Introduction Generative artificial intelligence (AI) is developing rapidly with a great potential to revolutionize education. ChatGPT, a large language model (LLM), is a particularly promising generative AI model that can be applied in education. One of the key advantages of using generative AI in education is that it can help to personalize learning (Farrokhnia et al., 2023). Generative AI models can be used to create personalized learning materials, such as practice problems, study guides, and feedback, tailored for each student (Cotton et al., 2023). This can help students learn in a way that is tailored to their individual needs and interests and at their own pace. Another advantage of using generative AI in education is that it can provide students with feedback and support outside of the classroom. For example, generative AI can be used to create chatbots that can answer students’ questions and provide them with support (Keng-Boon Ooi & Wong, 2023). Using Chatbots in education can be especially helpful for students who are struggling with learning or who need support outside of class time. Finally, generative AI can be used to automate tasks such as grading and creating reports. This can free up teachers’ time to focus on more important tasks, such as interacting with students and providing them with personalized support. While generative AI has the potential to revolutionize education, there are also some challenges that need to be addressed. One challenge is ensuring the accuracy and reliability of generated content (Glaser, 2023). Generative AI models can produce realistic but inaccurate content. It is important to develop mechanisms to ensure the accuracy and reliability of generated content, especially when it is used for educational purposes. Another challenge is addressing ethical concerns. The integration of generative AI in education raises ethical concerns and challenges, such as the potential for cheating and plagiarism, and teacher displacement (Dwivedi et al., 2023) . It is important to develop ethical guidelines for the use of generative AI in education and to educate students about these guidelines (Susarla et al., 2023). By addressing the challenges and conducting further research, we can ensure that generative AI is used to improve learning for all students. In this survey paper, we will explain the history and evolution of generative AI models and their application in education (Section 2). Moreover, we will review the current state of research on the use of generative AI and ChatGPT in education (Section 4), and provide specific examples of how ChatGPT is being used in educational settings today (Section 4.2). We will also discuss the potential benefits and challenges of using generative AI in education (Section 4.3). We will conclude by discussing the survey findings and providing recommendations for future research (Section 5, 6). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Author: Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction Abstract and Introduction History of Using AI in Education History of Using AI in Education Research Methodology Research Methodology Literature Review Literature Review Summary Summary Conclusion and References Conclusion and References Abstract The wide adoption and usage of generative artificial intelligence (AI) models, particularly ChatGPT, has sparked a surge in research exploring their potential applications in the educational landscape. This survey examines academic literature published between November, 2022, and July, 2023, specifically targeting high-impact research from Scopus-indexed Q1 and Q2 journals. This survey delves into the practical applications and implications of generative AI models across a diverse range of educational contexts. Through a comprehensive and rigorous evaluation of recent academic literature, this survey seeks to illuminate the evolving role of generative AI models, particularly ChatGPT, in education. By shedding light on the potential benefits, challenges, and emerging trends in this dynamic field, the survey endeavors to contribute to the understanding of the nexus between artificial intelligence and education. The findings of this review will empower educators, researchers, and policymakers to make informed decisions about the integration of AI technologies into learning environments. Keywords: Artificial Intelligence, Education, ChatGPT, Generative AI, Technology-enhanced Learning. 1. Introduction Generative artificial intelligence (AI) is developing rapidly with a great potential to revolutionize education. ChatGPT, a large language model (LLM), is a particularly promising generative AI model that can be applied in education. One of the key advantages of using generative AI in education is that it can help to personalize learning (Farrokhnia et al., 2023). Generative AI models can be used to create personalized learning materials, such as practice problems, study guides, and feedback, tailored for each student (Cotton et al., 2023). This can help students learn in a way that is tailored to their individual needs and interests and at their own pace. Another advantage of using generative AI in education is that it can provide students with feedback and support outside of the classroom. For example, generative AI can be used to create chatbots that can answer students’ questions and provide them with support (Keng-Boon Ooi & Wong, 2023). Using Chatbots in education can be especially helpful for students who are struggling with learning or who need support outside of class time. Finally, generative AI can be used to automate tasks such as grading and creating reports. This can free up teachers’ time to focus on more important tasks, such as interacting with students and providing them with personalized support. While generative AI has the potential to revolutionize education, there are also some challenges that need to be addressed. One challenge is ensuring the accuracy and reliability of generated content (Glaser, 2023). Generative AI models can produce realistic but inaccurate content. It is important to develop mechanisms to ensure the accuracy and reliability of generated content, especially when it is used for educational purposes. Another challenge is addressing ethical concerns. The integration of generative AI in education raises ethical concerns and challenges, such as the potential for cheating and plagiarism, and teacher displacement (Dwivedi et al., 2023) . It is important to develop ethical guidelines for the use of generative AI in education and to educate students about these guidelines (Susarla et al., 2023). By addressing the challenges and conducting further research, we can ensure that generative AI is used to improve learning for all students. In this survey paper, we will explain the history and evolution of generative AI models and their application in education (Section 2). Moreover, we will review the current state of research on the use of generative AI and ChatGPT in education (Section 4), and provide specific examples of how ChatGPT is being used in educational settings today (Section 4.2). We will also discuss the potential benefits and challenges of using generative AI in education (Section 4.3). We will conclude by discussing the survey findings and providing recommendations for future research (Section 5, 6). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

ChatGPT and Beyond: The Generative AI Revolution in Education

**TOKYO, Japan, March 27th, 2024/Chainwire/--**Funds from the Pre-Series A round led by Dragonfly will further develop MyShell’s open-source models and agent platform for empowering AI creators. , a decentralized AI consumer layer, announced today that it has raised US$11 million in a Pre-Series A round, with participation from a cohort of high-profile investors, including Dragonfly, Delphi Ventures, Bankless Ventures, Maven11 Capital, Nascent, Nomad Capital, OKX Ventures, etc. MyShell Prominent angel investors, such as former Coinbase CTO Balaji Srinivasan, NEAR Protocol Co-Founder Illia Polosukhin, former Paradigm Investment Partner Casey K. Caruso, and ex-Parafi partner Santiago R. Santos also joined the round, which brought MyShell’s total amount raised to date to $16.6 million. "MyShell is poised to revolutionize the AI creator ecosystem,” MyShell CEO Ethan Sun said. “With this new funding, we're not just scaling our operations, we're catalyzing a movement towards a more open, collaborative, and democratic AI landscape." MyShell, an AI consumer layer that empowers users to build, share, and own AI agents, is home to over 1 million registered users and 50,000 creators. The company plans to use the new funding to add resources to develop its open-source foundational model; empower AI creators; AI assets trading platform; and further support the open-source community. In contrast to most dominant AI companies such as OpenAI, MyShell is committed to decentralization and open-source development. This focus has already facilitated the growth of several successful projects on the platform, including OpenVoice, which has earned over 15,000 stars on Github; MeloTTS, a pioneering open-source text-to-speech solution that closely mimics the human voice and supports a wide range of languages and accents; and Allice, the open-source agent framework for complicated AI development workflow. “The demand for personalized AI experiences has grown explosively in the last year, but right now, most of those experiences are centralized with gatekeepers,” said Dragonfly Managing Partner Haseeb Qureshi. “MyShell uses crypto and decentralization to put the power over the AI ecosystem back into the hands of users and allows open-source creators and communities to work together to create magical experiences and capture the fruits of their labor. There’s a lot of unfounded hype right now at the intersection of crypto and AI, but Ethan and the MyShell team have the technical chops with research backgrounds that allow them to execute their vision.” The funding reflects MyShell’s position as a pioneer in the AI consumer layer with its open-value network that deploys a revenue-share economic model. MyShell also provides unique toolkit features such as the “Workshop” where users build AI agents through prompts, and “Makerspace,” a no-code AI workflow builder for creators. The investment enables MyShell to deepen its commitment to lower the barrier for AI creation, fostering a community where openness and collaboration are paramount. By championing decentralization and supporting the creator economy, MyShell is not just building tools; it is nurturing an ecosystem where every voice has the power to shape the AI landscape. About MyShell MyShell is the AI consumer layer that empowers users to build, share, and own AI agents. It's Features include an interactive AI app store that stands on multiple open-source foundation models, and integrates other models into a robust model hub, offering a dynamic and engaging user experience. Contact Co-founder Ethan Sun MyShell ethan@myshell.ai This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

MyShell Raises $11 Million For Its Decentralized AI Consumer Layer

Let's learn about via these 53 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Data Scraping /Learn Repo 1. How I Successfully "Reverse-Engineered" ChatGPT to Create an Unofficial API Wrapper Scraping ChatGPT with Python 2. How POST Requests with Python Make Web Scraping Easier To scrape a website, it’s common to send GET requests, but it's useful to know how to send data. In this article, we'll see how to start with POST requests. 3. AutoScraper Introduction: Fast and Light Automatic Web Scraper for Python In the last few years, web scraping has been one of my day to day and frequently needed tasks. I was wondering if I can make it smart and automatic to save lots of time. So I made AutoScraper! 4. How To Scrape Google With Python Ever since Google Web Search API deprecation in 2011, I've been searching for an alternative. I need a way to get links from Google search into my Python script. So I made my own, and here is a quick guide on scraping Google searches with requests and Beautiful Soup. 5. My Journey Building a Scraper with Ruby Last week I finished my Ruby curriculum at Microverse. So I was ready to build my Capstone Project. Which is a solo project at the end of each of the Microverse technical curriculum sections. 6. How to Scrape NLP Datasets From Youtube Too lazy to scrape nlp data yourself? In this post, I’ll show you a quick way to scrape NLP datasets using Youtube and Python. 7. How to Scrape Data From Any Website With JavaScript Learn how to scrape the web using scripts written in node.js to automate scraping data off of the website and using it for whatever purpose. 8. Playwright Vs Selenium: Comparing the Two A brief comparison between Selenium and Playwright from a web scraping perspective. Which one is the most convenient to use? 9. How Web Scraping Helps Businesses Outperform Their Competition It’s safe to say that the amount of data available on the internet nowadays is practically limitless, with much of it no more than a few clicks away. However, gaining access to the information you need sometimes involves a lot of time, money, and effort. 10. Web Scraping Using Node.js While there are a few different libraries for scraping the web with Node.js, in this tutorial, i'll be using the puppeteer library. 11. Web Scraping con Python: Guía Paso a Paso La necesidad de extraer datos de sitios web está aumentando. Cuando realizamos proyectos relacionados con datos, como el monitoreo de precios, análisis de negocios o agregador de noticias, siempre tendremos que registrar los datos de los sitios web. Sin embargo, copiar y pegar datos línea por línea ha quedado desactualizado. En este artículo, le enseñaremos cómo convertirse en un "experto" en la extracción de datos de sitios web, que consiste en hacer web scraping con python. 12. America's Secret Pager Giant Early January 2022, I spontaneously bought a pager. I looked into the US pager market, and to my surprise... 13. 8 Browser Extensions for Scraping Google Maps like a Pro These extensions for scraping Google maps can be used for a number of purposes in various situations that can be either data collection or market research. 14. How Do I Build a LinkedIn Scraper For Free? Check out this step-by-step guide on how to build your own LinkedIn scraper for free! 15. What is Web Data Collection? Everything you need to know to automate, optimize and streamline the data collection process in your organization! 16. How To Scrape Amazon, Yelp and GitHub Profiles in 30 Seconds The most talented developers in the world can be found on GitHub. What if there was an easy, fast and free way to find, rank and recruit them? I'll show you exactly how to to this in less than a minute using free tools and a process that I've hacked together to vet top tech talent at BizPayO. 17. Web Scraping con Python: Guía Paso a Paso La necesidad de extraer datos de sitios web está aumentando. Cuando realizamos proyectos relacionados con datos, como el monitoreo de precios, análisis de negocios o agregador de noticias, siempre tendremos que registrar los datos de los sitios web. Sin embargo, copiar y pegar datos línea por línea ha quedado desactualizado. En este artículo, le enseñaremos cómo convertirse en un "experto" en la extracción de datos de sitios web, que consiste en hacer web scraping con python. 18. Web Crawling vs Scraping: What's the Difference Between Crawlers and Scrapers? Learn the fundamental distinctions between web crawling and web scraping, and determine which one is right for you. 19. Where Do I Find the Right Social Media Marketing Data? As a marketer, you probably know that social media marketing is part art, part science. 20. Scraping Google Search Console Backlinks Learn how to emulate a normal user request and scrape Google Search Console data using Python and Beautiful Soup. 21. How to Use Web Scraping to Empower Marketing Decisions Learn how to leverage web scraping in marketing. In this article, we unpack use cases and tips for getting started. 22. Scraping Glassdoor Job Data Glassdoor is one of the biggest job markets in the world but can be hard to scrape. In this article, we'll legally extract job data with Python & Beautiful Soup 23. Scraping Tweet Replies with Python and Tweepy Twitter API [A Step-by-Step Guide] A Quick Method To Extract Tweets and Replies For Free 24. Las 15 preguntas más frecuentes sobre Web Scraping Previously published at https://www.octoparse.es/blog/15-preguntas-frecuentes-sobre-web-scraping 25. A Step-by-Step Guide to Building a Football Data Scraper Scraping football data (soccer in the US) is a great way to build comprehensive datasets to help create stats dashboards.  Check out our football data scraper! 26. An Intro to No-Code Web Scraping Web scraping has broken the barriers of programming and can now be done in a much simpler and easier manner without using a single line of code. 27. How is Web Crawling Used in Data Science No-Code tools for collecting data for your Data Science project 28. How to Build a Web Crawler from Scratch How often have you wanted a piece of information and have turned to Google for a quick answer? Every piece of information that we need in our daily lives can be obtained from the internet. You can extract data from the web and use it to make the most effective business decisions. This makes web scraping and crawling a powerful tool. If you want to programmatically capture specific information from a website for further processing, you need to either build or use a web scraper or a web crawler. We aim to help you build a web crawler for your own customized use. 29. How to Develop a Price Comparison Tool in Python Online Shopping for various commodities is no more a luxury but has rather become a necessity now. Getting your desired product on your doorstep has made it easier for consumers to shop effortlessly. As a result, several niche e-commerce or generic shopping sites pop up every year. This trend is not limited to some specific region rather it’s a global phenomenon now, as more and more people are preferring online shopping over visiting outlets due to traffic congestions and ease of purchasing. This is why it’s predicted that by 2021, overall 15.5% of sales will be generated via online websites. 30. PHP Web Scraping Using Goutte When you talk about web scraping, PHP is the last thing most people think about. 31. How To Monitor a Forum for Keywords Using Python and AWS Lambda While building ScrapingBee I'm always checking different forums everyday to help people about web scraping related questions and engage with the community. 32. The Evolution of Big Data And Web Scraping As the CEO of a proxy service and data scraping solutions provider, I understand completely why global data breaches that appear on news headlines at times have given web scraping a terrible reputation and why so many people feel cynical about Big Data these days. 33. How To Create A Slick iOS Widget In JavaScript With a Scriptable app, it’s possible to create a native iOS widget even with basic JavaScript knowledge. 34. Scraping Amazon using Puppeteer and Browserless An easy tutorial showcasing the power of puppeteer and browserless. Scrape Amazon.com to gather prices of specific items automatically! 35. 3 Mejores Formas de Crawl Datos desde Website La necesidad de crawling datos web ha aumentado en los últimos años. Los datos crawled se pueden usar para evaluación o predicción en diferentes campos. Aquí, me gustaría hablar sobre 3 métodos que podemos adoptar para scrape datos desde un sitio web. 36. An Intro to Web Scraping: What it is and How to Start A quick introduction to web scraping, what it is, how it works, some pros and cons, and a few tools you can use to approach it 37. Data Scraping in Node.js 101 How to gather data without those pesky databases. 38. How to Scrape Bestbuy Products with Scrapezone SDK Welcome to the new way of scraping the web. In the following guide, we will scrape BestBuy product pages, without writing any parsers, using one simple library: Scrapezone SDK. 39. A Quick Primer on Data Scraping Suppose you want to get large amounts of information from a website as quickly as possible. How can this be done? 40. A Guide to Web Scraping With JavaScript and Node.js With the massive increase in the volume of data on the Internet, this technique is becoming increasingly beneficial in retrieving information from websites and applying them for various use cases. Typically, web data extraction involves making a request to the given web page, accessing its HTML code, and parsing that code to harvest some information. Since JavaScript is excellent at manipulating the DOM (Document Object Model) inside a web browser, creating data extraction scripts in Node.js can be extremely versatile. Hence, this tutorial focuses on javascript web scraping. 41. How to Extract Knowledge from Wikipedia, Data Science Style As Data Scientists, people tend to think what they do is developing and experimenting with sophisticated and complicated algorithms, and produce state of the art results. This is largely true. It is what a data scientist is mostly proud of and the most innovative and rewarding part. But what people usually don’t see is the sweat they go through to gather, process, and massage the data that leads to the great results. That’s why you can see SQL appears on most of the data scientist position requirements. 42. How to Scrape a Medium Publication: A Python Tutorial for Beginners A while ago I was trying to perform an analysis of a Medium publication for a personal project. But getting the data was a problem – scraping only the publication’s home page does not guarantee that you get all the data you want. 43. Scraping Data With Selenium: Upwork Series #2 Hi Devs! 44. How To Scrape Amazon Using Python Scrapy Library [Tutorial] Scrapy is an application framework for crawling web sites and extracting structured/unstructured data which can be used for a wide range of applications such as data mining, information processing or historical archival.As we all know, this is the age of “Data”. Data is everywhere, and every organisation wants to work with Data and take its business to a higher level. In this scenario Scrapy plays a vital role to provide Data to these organisations so that they can use it in wide range of applications. Scrapy is not only able to scrap data from websites, but it is able to scrap data from web services. 45. Scraping Amazon Reviews using Scrapy in Python [Tutorial] Are you looking for a method of scraping Amazon reviews and do not know where to begin with? In that case, you may find this blog very useful in scraping Amazon reviews. In this blog, we will discuss scraping amazon reviews using Scrapy in python. Web scraping is a simple means of collecting data from different websites, and Scrapy is a web crawling framework in python. 46. 5 Técnicas Anti-Scraping que Puedes Encontrar Con el advenimiento de los grandes datos, las personas comienzan a obtener datos de Internet para el análisis de datos con la ayuda de rastreadores web. Hay varias formas de hacer su propio rastreador: extensiones en los navegadores, codificación de python con Beautiful Soup o Scrapy, y también herramientas de extracción de datos como Octoparse. 47. How Can The Travel Industry Benefit From Data Scraping The travel industry is a major service sector in most countries these days. It is also a major employment and revenue provider. This demands a lot of constant innovation and maintenance. The travel industry is a dynamic industry where the needs and preferences of a customer change every moment. The market players in this field need to keep up with the trends in the industry, the choices of the customers and even on the details of their own historical performance to perform better as time progresses. Thus, as you would presume, the companies working in the travel sector need a lot of data from multiple sources and a pipeline to assess and use that data for insights and recommendations. 48. The A-Z of Web Scraping in 2020 [A How-To Guide] Web data extraction or web scraping in 2020 is the only way to get desired data if owners of a web site don't grant access to their users through API. 49. How to Web Scrape Using Python, Snscrape & HarperDB Learn how  to execute web scraping on Twitter using the snsscrape Python library and store scraped data automatically in database by using HarperDB. 50. Big Data: 70 Increíbles Fuentes de Datos Gratuitas que Debes Conocer para 2020 Por favor clic el artículo original：http://www.octoparse.es/blog/70-fuentes-de-datos-gratuitas-en-2020 51. Scraping with Selenium 101: The Big Hole on Data Scientists Toolset [Part 1] Usually forgotten in all Data Science masters and courses, Web Scraping is, in my honest opinion a basic tool in the Data Scientist toolset, as is the tool for getting and therefore using external data from your organization when public databases are not available. 52. Python for Data Science: How to Scrape Website Data via the Internet's Top 300 APIs In this post we are going to scrape websites to gather data via the  API World's top 300 APIs of year. The major reason of doing web scraping is it saves time and avoid manual data gathering and also allows you to have all the data in a structured form. 53. How To Build a First Strike OTM Call Options Watchlist from Cashtags wHAOR Today, We're going to build a script that scrapes Twitter to gather stock ticker symbols. We'll use those symbols to scrape yahoo finance for stock Options data. To ensure we can download all the Options data, we’ll make each web request with High Availability Onion Routing. In the end, we’ll do some Pandas magic to pull the first out of the money call contract for each symbol into the final watchlist. Thank you for checking out the 53 most read stories about Data Scraping on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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