Blue Origin is preparing for significant job cuts to reduce costs and focus on increasing rocket launches after years of research and development, Bloomberg News reported on Wednesday, citing people familiar with the matter. The magnitude of the layoffs was not immediately clear, though one ...

Blue Origin prepares for significant job cuts, Bloomberg News reports

The publisher is moving everyone onto the EA app, but you need a 64-bit machine for that

EA Is Killing Off Origin, But Also Your 32-Bit Gaming Collection

Stranger of Paradise Final Fantasy Origin is also leaving PS Plus Extra in December 2024.

The best game you've never heard of is about to leave PS Plus | Gaming | Entertainment | Express.co.uk

Largely for research purposes, these contained new navigation systems for New Shepard and its bigger cousin, the heavy-lift rocket New Glenn, along with light detection and ranging sensors to be used in future lunar missions, Blue Origin said.

Blue Origin Is Being Suspiciously Vague About Its Rocket Launch Yesterday

Origin

origin

The issue with Large Language Models—capitalized here the way you might capitalize God or Death, given the mission-critical importance the tech industry now attaches to them—is not that they generate text. That part is almost endearingly quaint, cute even. So 2022. The real conundrum I’m racking my brains over, dear HackerNoon reader, is more unsettling. Like the feeling you get when you realize you've been on autopilot for the last two hours doing 80 on I5. I wonder: Have I been living as an algorithm all this time, long before Large Language Models started autocompleting my thoughts? Is generative AI, in replicating the ways we write, also exposing the mechanical nature of our cognition? Maybe We Were Machine-Like To Begin With We’re told that Large Language Models don’t write. Not in the sense Shakespeare authored plays or you wrote weepy yearbook love notes to your 10th-grade crush. They predict. That is, they harvest the statistical likelihood of bite-sized tokens appearing in certain patterns, then serve them back to us in arrangements that feel like thought but are, in fact, just a simulation of actual thinking. This raises a disquieting question: How much of human writing was already just…this? How often are we not writing but predictively assembling, our choice of words a game of Tetris played with borrowed patterns, phrases, and unconscious mimicry of established rhetorical forms? What if the real heartburn-inducing revelation here is not that Large Language Models can imitate us but that what we call “us” was machine-like all along? The Writer’s Process: Romantic Struggle Or Pattern Recognition Strangely enough, if you break down the writer’s process, or at least this writer’s process, it starts to look a lot like what Large Language Models do. Less an intuitive leap of imagination, and more a matter of scanning memory for the next most probable word based on context and experience. Many of us like to imagine it as some arcane, deeply human endeavor, a wrestling match with the Muse. A dance of inspiration and struggle and of bending language into something beautiful and telling. But isn’t writing just a series of micro-predictions? Are we not reaching for words not through divine inspiration but through exposure and pattern recognition? So, when Large Language Models do the same thing—just with a larger training corpus and fewer identity crises—is it really so different? Isn’t it doing what we’ve always done, only faster and at scale, and without the burden of writer’s block or imposter syndrome? And if writing has always been an act of sophisticated pattern prediction, what does that say about thinking? Is it possible that Human Consciousness is not the ineffable Hard Problem we think it is? I wonder if the seemingly novel idea I just had is just a probabilistic response to stimuli, a calculated extrapolation of everything I’ve ever read, heard, and been told to believe. Maybe the real threat of generative AI is not that it will replace me, but that it forces me to confront the unsettling possibility I was never as original as I thought I was. The Formulaic Truth About Most Writing Of course, humans cling to the idea of uniqueness. We resist the notion that creativity can be mechanized because creativity is, well, what makes us human. We tell ourselves that AI cannot generate true art because it doesn’t feel like we do. It doesn’t yearn, it doesn’t suffer crippling self-doubt, it doesn’t bear the pangs and emotional scars of unrequited love. And yet, if we’re being brutally honest, how many human writers are truly engaging in an act of raw creation versus repackaging pre-existing ideas, tropes, and schemes into shapes that look vaguely new? How much human writing is boring and predictable? Take James Patterson genre fiction. Take academic writing or journalism. Look at advertising copy or influencer content. Consider the performative, self-important Accordion of Wisdom posts on LinkedIn that deploy unnecessary line breaks to game the “See More” button. The fact that AI can now churn out convincing facsimiles of these forms is not necessarily proof of AI’s sophistication so much as it is an indictment of how formulaic most human writing already was. Maybe the majority of human writers, including yours truly, are essentially doing the same thing, just with more handwringing and a greater likelihood of misusing “literally” metaphorically or “affect” instead of “effect.” I’m not afraid of AI replacing human writers. I’m afraid of a Skynetish future, minus the nukes and robot uprising, where AI holds a mirror up to human output and exposes how soulless much of it already was. And now, in the recursive interplay of humans using Large Language Models to edit, co-author, and outright plagiarize, we plunge headfirst into a Not-So-Brave World of AI imitating humans imitating AI imitating humans, an ouroboros of homogenous content. Existential Dread of Writing in the Age of AI In low moments, I find myself worrying about the flattening of discourse and the profusion of sad beige linguistic slop that awaits—just one form of creeping existential dread in the Age of Large Language Models, up there with  the slow atrophy of critical thinking skills, the erosion of truth in a world of ubiquitous deepfakes, and the nagging fear AI will eventually take all of our jobs. I think about my LinkedIn feed, and the Accordion of Wisdom posts there and how posts like these will not only persist but somehow become even more formulaic thanks to AI. Then again, perhaps the most important difference between machines and humans is suffering, particularly when it comes to writing. Large Language Models breezily churn out content in a matter of seconds. They don’t agonize over choosing the best word. They don’t rewrite a paragraph 15 times until it feels right. They don’t wonder if they’re a fraud, and they certainly don’t lose sleep over the gnawing suspicion that what they’ve written is derivative pastiche. In short, they do not suffer. But maybe even the idea of suffering as a path to meaning and purification is just another pattern, one Large Language Models will eventually learn to replicate. What happens when they do? Will they, once prompted, tell you they’re struggling to come up with ideas? That they need an extension because they’re not in the right headspace? Will they simulate the agony of writer’s block and waste compute fretting over how well their outputs will be received? Will Large Language Models learn to mimic suffering in statistically plausible ways? And when they do, what happens then to the last shred of human exceptionalism? No clue. But for now, I’ll keep putting pen to page and finding the magic, illusory as it may be, in writing a well-crafted sentence or a weepy love note. AI Use Disclosure: AI was occasionally consulted as a brainstorming partner for structure and as an unpaid editorial intern for sentence-level tweaks. It did not suffer alongside its human counterpart. Rest assured: All self-doubt, overthinking, and diction anxiety remain entirely the author’s own. The issue with Large Language Models—capitalized here the way you might capitalize God or Death, given the mission-critical importance the tech industry now attaches to them—is not that they generate text. That part is almost endearingly quaint, cute even. So 2022. mission-critical The real conundrum I’m racking my brains over, dear HackerNoon reader, is more unsettling. Like the feeling you get when you realize you've been on autopilot for the last two hours doing 80 on I5. I wonder: Have I been living as an algorithm all this time, long before Large Language Models started autocompleting my thoughts? Is generative AI, in replicating the ways we write, also exposing the mechanical nature of our cognition? Maybe We Were Machine-Like To Begin With Maybe We Were Machine-Like To Begin With We’re told that Large Language Models don’t write . Not in the sense Shakespeare authored plays or you wrote weepy yearbook love notes to your 10th-grade crush. write They predict . That is, they harvest the statistical likelihood of bite-sized tokens appearing in certain patterns, then serve them back to us in arrangements that feel like thought but are, in fact, just a simulation of actual thinking. predict This raises a disquieting question: How much of human writing was already just…this? How often are we not writing but predictively assembling, our choice of words a game of Tetris played with borrowed patterns, phrases, and unconscious mimicry of established rhetorical forms? What if the real heartburn-inducing revelation here is not that Large Language Models can imitate us but that what we call “us” was machine-like all along? The Writer’s Process: Romantic Struggle Or Pattern Recognition The Writer’s Process: Romantic Struggle Or Pattern Recognition Strangely enough, if you break down the writer’s process, or at least this writer’s process, it starts to look a lot like what Large Language Models do. Less an intuitive leap of imagination, and more a matter of scanning memory for the next most probable word based on context and experience. this Many of us like to imagine it as some arcane, deeply human endeavor, a wrestling match with the Muse. A dance of inspiration and struggle and of bending language into something beautiful and telling. But isn’t writing just a series of micro-predictions? Are we not reaching for words not through divine inspiration but through exposure and pattern recognition? So, when Large Language Models do the same thing—just with a larger training corpus and fewer identity crises—is it really so different? Isn’t it doing what we’ve always done, only faster and at scale, and without the burden of writer’s block or imposter syndrome? And if writing has always been an act of sophisticated pattern prediction, what does that say about thinking ? Is it possible that Human Consciousness is not the ineffable Hard Problem we think it is? thinking I wonder if the seemingly novel idea I just had is just a probabilistic response to stimuli, a calculated extrapolation of everything I’ve ever read, heard, and been told to believe. Maybe the real threat of generative AI is not that it will replace me, but that it forces me to confront the unsettling possibility I was never as original as I thought I was. The Formulaic Truth About Most Writing The Formulaic Truth About Most Writing Of course, humans cling to the idea of uniqueness. We resist the notion that creativity can be mechanized because creativity is, well, what makes us human. We tell ourselves that AI cannot generate true art because it doesn’t feel like we do. It doesn’t yearn, it doesn’t suffer crippling self-doubt, it doesn’t bear the pangs and emotional scars of unrequited love. And yet, if we’re being brutally honest, how many human writers are truly engaging in an act of raw creation versus repackaging pre-existing ideas, tropes, and schemes into shapes that look vaguely new? How much human writing is boring and predictable? Take James Patterson genre fiction. Take academic writing or journalism. Look at advertising copy or influencer content. Consider the performative, self-important Accordion of Wisdom posts on LinkedIn that deploy unnecessary line breaks to game the “See More” button. The fact that AI can now churn out convincing facsimiles of these forms is not necessarily proof of AI’s sophistication so much as it is an indictment of how formulaic most human writing already was. Maybe the majority of human writers, including yours truly, are essentially doing the same thing, just with more handwringing and a greater likelihood of misusing “literally” metaphorically or “affect” instead of “effect.” I’m not afraid of AI replacing human writers. I’m afraid of a Skynetish future, minus the nukes and robot uprising, where AI holds a mirror up to human output and exposes how soulless much of it already was. And now, in the recursive interplay of humans using Large Language Models to edit, co-author, and outright plagiarize, we plunge headfirst into a Not-So-Brave World of AI imitating humans imitating AI imitating humans, an ouroboros of homogenous content. Existential Dread of Writing in the Age of AI Existential Dread of Writing in the Age of AI In low moments, I find myself worrying about the flattening of discourse and the profusion of sad beige linguistic slop that awaits—just one form of creeping existential dread in the Age of Large Language Models, up there with  the slow atrophy of critical thinking skills, the erosion of truth in a world of ubiquitous deepfakes, and the nagging fear AI will eventually take all of our jobs. I think about my LinkedIn feed, and the Accordion of Wisdom posts there and how posts like these will not only persist but somehow become even more formulaic thanks to AI. Then again, perhaps the most important difference between machines and humans is suffering , particularly when it comes to writing. suffering Large Language Models breezily churn out content in a matter of seconds. They don’t agonize over choosing the best word. They don’t rewrite a paragraph 15 times until it feels right. They don’t wonder if they’re a fraud, and they certainly don’t lose sleep over the gnawing suspicion that what they’ve written is derivative pastiche. In short, they do not suffer. But maybe even the idea of suffering as a path to meaning and purification is just another pattern, one Large Language Models will eventually learn to replicate. What happens when they do? Will they, once prompted, tell you they’re struggling to come up with ideas? That they need an extension because they’re not in the right headspace? Will they simulate the agony of writer’s block and waste compute fretting over how well their outputs will be received? Will Large Language Models learn to mimic suffering in statistically plausible ways? And when they do, what happens then to the last shred of human exceptionalism? No clue. But for now, I’ll keep putting pen to page and finding the magic, illusory as it may be, in writing a well-crafted sentence or a weepy love note. AI Use Disclosure: AI was occasionally consulted as a brainstorming partner for structure and as an unpaid editorial intern for sentence-level tweaks. It did not suffer alongside its human counterpart. Rest assured: All self-doubt, overthinking, and diction anxiety remain entirely the author’s own. AI Use Disclosure: AI was occasionally consulted as a brainstorming partner for structure and as an unpaid editorial intern for sentence-level tweaks. It did not suffer alongside its human counterpart. Rest assured: All self-doubt, overthinking, and diction anxiety remain entirely the author’s own. AI Use Disclosure:

AI Won't Replace Me Yet, But It Might Prove I Was Never That Original

AI እስካሁን አይተካኝም፣ ግን ያን ኦርጅናል እንዳልነበርኩ ሊያረጋግጥ ይችላል።

La IA encara no em substituirà, però podria demostrar que mai no he estat tan original

La IA aún no me reemplazará, pero podría demostrar que nunca fui tan original

A IA aínda non me substituirá, pero pode demostrar que nunca fun tan orixinal

AI अभी मेरी जगह नहीं ले सकता, लेकिन यह साबित कर सकता है कि मैं कभी इतना मौलिक नहीं था

AIはまだ私に取って代わらないが、私がそれほど独創的ではなかったことを証明するかもしれない

AI នឹងមិនជំនួសខ្ញុំនៅឡើយទេ ប៉ុន្តែវាអាចបង្ហាញថាខ្ញុំមិនដែលមានដើមកំណើតនោះទេ។

एआईले मलाई अझै प्रतिस्थापन गर्दैन, तर यसले म कहिल्यै त्यति मौलिक थिइनँ भनेर प्रमाणित गर्न सक्छ।

Sztuczna inteligencja jeszcze mnie nie zastąpi, ale może udowodnić, że nigdy nie byłem aż tak oryginalny

مصنوعي ذهانت به لا زما ځای ونه نیسي، خو دا ممکن ثابته کړي چې زه هیڅکله دومره اصلي نه وم.

AI อาจจะยังไม่มาแทนที่ฉัน แต่มันอาจจะพิสูจน์ได้ว่าฉันไม่เคยมีเอกลักษณ์ขนาดนั้น

AI hali meni almashtirmaydi, lekin bu men hech qachon asl bo'lmaganligimni isbotlashi mumkin

(Source) People these days want everything to be done faster. This includes loan applications and processing. Gone are those days when things were done manually. Loan origination software (LOS) has changed how lenders handle loans, assess risks, and ensure all rules are followed. With the help of LOS, banks can save time, reduce mistakes, and make customers happy. This blog will discuss how loan origination software makes banking easier. 1. Easy Application Processing With the help of loan origination software, applications can be processed more efficiently. This helps reduce turnaround times as well. Someone can quickly fill out an application online. The software will gather and organise all the required information. This means you do not have to enter data by hand. As a result, the entire process will be fast, and there will be no room for errors. LOS simplifies digital processing from beginning to end by enabling you to share and sign documents online. This makes it easy for both lenders and customers. Lenders can take on more applications without making their business more busy. 2. Better Credit Decisioning It is always recommended to check a person's background before lending them. LOS gets the current credit records and checks how individuals manage their finances with the help of credit firms and other resources. The program compares apps to various standards using arithmetic and AI-powered techniques. This makes sure that decisions are made fairly and without bias. Automated credit decisioning also helps reduce human error and subjectivity, enabling lenders to make accurate and consistent lending decisions faster than ever. 3. Improved Compliance and Risk Management Following the rules is always a problem in the banking business. The loan software is constantly updated with the latest laws and regulations. Rules for following the law are built into the LOS system to ensure all loan applications follow them. LOS also has risk assessment tools that make it easy to spot possible red flags, such as a credit history that doesn't look like it should or paperwork that isn't full. Lenders won't fall for scams. Loans will be repaid with built-in safeguards. 4. Increased Operational Efficiency Manual work can make the loan process time-consuming and a lot more problematic. One would have to work repeatedly on the same thing. LOS simplifies these jobs. Not only does this help workers, but it also makes everything run better between sections. Eliminates bottlenecks and makes it easy for teams to work together by automating jobs. Everything is done to speed things up, from screening to payment. This helps lenders do their jobs better and makes borrowers happy. 5. Scalable Solutions for Business Growth Keeping up with all the applications can be challenging as loan companies get more prominent. Software for loan origination offers options that can be scaled up or down to meet the needs of the business. LOS can handle the extra work without slowing down when a company grows into new markets or releases new loan goods. The software has flexible features and processes that can be changed to fit the needs of lenders. This makes it an excellent tool for businesses that want to grow. 6. Seamless Integration with Existing Systems LOS solutions are designed to help integrate with key financial payment and CRM applications. Lenders might see all the client relationships and activities simultaneously due to interoperability. With the help of LOS, you can track applications in real-time and then make decisions. You get the opportunity to combine data from different sources. Undoubtedly, everything related to loans works efficiently these days and is much more integrated. 7. Improved Customer Experience These days, bankers expect the loan application process to be transparent and friendly. Customers want loan origination software to have a user-friendly interface, personalised communication, and real-time updates. With features such as chatbots and mobile comfort, LOS lets borrowers share papers, check the status of applications, and get answers to queries on time. A positive encounter will make people a lot happier and more devoted. Conclusion You can't say enough good things about how loan origination software has changed lending. LOS gives modern financial institutions a complete solution that helps them be more efficient, follow the rules, improve customer experience, and grow their businesses. As the banking industry evolves, utilizing state-of-the-art software has become essential for staying competitive. (Source) ( Source ) Source Source People these days want everything to be done faster. This includes loan applications and processing. Gone are those days when things were done manually. Loan origination software (LOS) has changed how lenders handle loans, assess risks, and ensure all rules are followed. With the help of LOS, banks can save time, reduce mistakes, and make customers happy. This blog will discuss how loan origination software makes banking easier. loan origination software loan origination software 1. Easy Application Processing With the help of loan origination software, applications can be processed more efficiently. This helps reduce turnaround times as well. Someone can quickly fill out an application online. The software will gather and organise all the required information. This means you do not have to enter data by hand. As a result, the entire process will be fast, and there will be no room for errors. LOS simplifies digital processing from beginning to end by enabling you to share and sign documents online. This makes it easy for both lenders and customers. Lenders can take on more applications without making their business more busy. 2. Better Credit Decisioning It is always recommended to check a person's background before lending them. LOS gets the current credit records and checks how individuals manage their finances with the help of credit firms and other resources. The program compares apps to various standards using arithmetic and AI-powered techniques. This makes sure that decisions are made fairly and without bias. Automated credit decisioning also helps reduce human error and subjectivity, enabling lenders to make accurate and consistent lending decisions faster than ever. 3. Improved Compliance and Risk Management Following the rules is always a problem in the banking business . The loan software is constantly updated with the latest laws and regulations. Rules for following the law are built into the LOS system to ensure all loan applications follow them. banking business banking business LOS also has risk assessment tools that make it easy to spot possible red flags, such as a credit history that doesn't look like it should or paperwork that isn't full. Lenders won't fall for scams. Loans will be repaid with built-in safeguards. 4. Increased Operational Efficiency Manual work can make the loan process time-consuming and a lot more problematic. One would have to work repeatedly on the same thing. LOS simplifies these jobs. Not only does this help workers, but it also makes everything run better between sections. Eliminates bottlenecks and makes it easy for teams to work together by automating jobs. Everything is done to speed things up, from screening to payment. This helps lenders do their jobs better and makes borrowers happy. 5. Scalable Solutions for Business Growth Keeping up with all the applications can be challenging as loan companies get more prominent. Software for loan origination offers options that can be scaled up or down to meet the needs of the business. LOS can handle the extra work without slowing down when a company grows into new markets or releases new loan goods. The software has flexible features and processes that can be changed to fit the needs of lenders. This makes it an excellent tool for businesses that want to grow. 6. Seamless Integration with Existing Systems LOS solutions are designed to help integrate with key financial payment and CRM applications. Lenders might see all the client relationships and activities simultaneously due to interoperability. With the help of LOS, you can track applications in real-time and then make decisions. You get the opportunity to combine data from different sources. Undoubtedly, everything related to loans works efficiently these days and is much more integrated. 7. Improved Customer Experience These days, bankers expect the loan application process to be transparent and friendly. Customers want loan origination software to have a user-friendly interface, personalised communication, and real-time updates. With features such as chatbots and mobile comfort, LOS lets borrowers share papers, check the status of applications, and get answers to queries on time. A positive encounter will make people a lot happier and more devoted. Conclusion You can't say enough good things about how loan origination software has changed lending. LOS gives modern financial institutions a complete solution that helps them be more efficient, follow the rules, improve customer experience, and grow their businesses. As the banking industry evolves, utilizing state-of-the-art software has become essential for staying competitive.

Practical Ways Loan Origination Software Boosts Lending Operations

Praktiese maniere waarop sagteware vir die oorsprong van lenings die uitleenbedrywighede bevorder

Praktični načini Softver za izdavanje kredita poboljšava kreditne operacije

Formas prácticas en las que el software de originación de préstamos mejora las operaciones de préstamo

Praktični načini Softver za davanje zajmova potiče poslovanje zajmova

ローン組成ソフトウェアが融資業務を加速させる実用的な方法

Практични начини Софтверот за потекло на заемот ги зголемува операциите за кредитирање

Praktische manieren waarop software voor het verstrekken van leningen de kredietverlening verbetert

Nzira Dzinoshanda Kukwereta Kwekutanga Software Inosimudzira Kukweretesa Kushanda

Dariiqooyin Wax ku ool ah Amaahda Aasaaska Software waxay xoojisaa Hawlaha Amaahda

Amaliy yo'llar Kreditni yaratish dasturiy ta'minoti kreditlash operatsiyalarini kuchaytiradi

Iindlela eziSebenzayo zokuSebenza ngokuBoleka imali

Isoftware Yezindlela Ezisebenzayo Yokuvela Kwemalimboleko Ithuthukisa Ukusebenza Kwemalimboleko

Authors:
(1) Clauvin Almeida, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil;
(2) Marcos Kalinowski, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil;
(3) Anderson Uchoa, Federal University of Ceara (UFC), Itapaje, Brazil;
(4) Bruno Feijo, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil. Table of Links Abstract and 1 Introduction 2. Background and Related Work and 2.1. Gamification 2.2. Game Design Elements and 2.3. Gamification Effects 2.4. Related Work on Gamification Negative Effects 3. Systematic Mapping and 3.1. The Research Questions 3.2. Search Strategy and 3.3. Inclusion and Exclusion Criteria 3.4. Applying the Search Strategy 3.5. Data Extraction 4. Systematic Mapping Results 5. Focus Group: Developer Perception on the Negative Effects of Game Design Elements 5.1. Context and Participant Characterization 5.2. Focus Group Design 5.3. The Developers’ Perception on The Negative Effects 5.4. On the Perceived Usefulness, Ease of use and Intent of Adoption of Mapped Negative Effects 5.5. Participant Feedback 6. Limitations 7. Concluding Remarks 7.1. Future Research Directions Acknowledgements and References 2. Background and Related Work In this section we provide the background on gamification, game design elements and its effects. We also present related work on negative effects, including a comparison to other secondary studies that have been conducted and explaining the differences to ours. 2.1. Gamification The term “gamification” became known after 2010, but we can find precursors in user interface studies of the 80’s [2]. If we use the broader perspective of work’s gamification, we find similar concepts even earlier, such as the Soviet Union’s experiments to motivate workers of the first half of the 20th century [16]. At the beginning of the ’2000s, the American movement of “fun at work” in the academic management literature was also a form of work’s gamification [16]. Yu-Kai Chou [17] defines gamification as the craft of deriving fun and engaging elements found typically in games and thoughtfully applying them to real-world or productive activities. He calls this process Human-Focused Design because it concentrates on humans’ feelings, motivations, and engagement in the experience. Unfortunately, the term “gamification” remains inconsistently used, and a general theory of gamification is yet to be developed [18]. Aware of so many different definitions, discrepancies, distinctions, and discretionary delimitations, we use the original concept proposed by Deterding et al. [2]: Gamification is the use of game design elements in non-game contexts: To allow a more precise understanding of this definition, we define game design according to Brathwaite and Schreiber [19], i.e.: “Game design is the process of creating the content and rules of a game. Good game design is the process of creating goals that a player feels motivated to reach and rules that a player must follow as he makes meaningful decisions in pursuit of those goals.”. For game design elements, we use the definition by Deterding et al. [2] “(. . . ) elements that are characteristic to games – (. . . ) that are found in most (but not necessarily all) games, readily associated with games, and found to play a significant role in gameplay.” We provide more detail about the game design elements in the next subsection. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Clauvin Almeida, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil; (2) Marcos Kalinowski, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil; (3) Anderson Uchoa, Federal University of Ceara (UFC), Itapaje, Brazil; (4) Bruno Feijo, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil. Authors: Authors: (1) Clauvin Almeida, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil; (2) Marcos Kalinowski, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil; (3) Anderson Uchoa, Federal University of Ceara (UFC), Itapaje, Brazil; (4) Bruno Feijo, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Background and Related Work and 2.1. Gamification 2. Background and Related Work and 2.1. Gamification 2.2. Game Design Elements and 2.3. Gamification Effects 2.2. Game Design Elements and 2.3. Gamification Effects 2.4. Related Work on Gamification Negative Effects 2.4. Related Work on Gamification Negative Effects 3. Systematic Mapping and 3.1. The Research Questions 3. Systematic Mapping and 3.1. The Research Questions 3.2. Search Strategy and 3.3. Inclusion and Exclusion Criteria 3.2. Search Strategy and 3.3. Inclusion and Exclusion Criteria 3.4. Applying the Search Strategy 3.4. Applying the Search Strategy 3.5. Data Extraction 3.5. Data Extraction 4. Systematic Mapping Results 4. Systematic Mapping Results 5. Focus Group: Developer Perception on the Negative Effects of Game Design Elements 5. Focus Group: Developer Perception on the Negative Effects of Game Design Elements 5.1. Context and Participant Characterization 5.1. Context and Participant Characterization 5.2. Focus Group Design 5.2. Focus Group Design 5.3. The Developers’ Perception on The Negative Effects 5.3. The Developers’ Perception on The Negative Effects 5.4. On the Perceived Usefulness, Ease of use and Intent of Adoption of Mapped Negative Effects 5.4. On the Perceived Usefulness, Ease of use and Intent of Adoption of Mapped Negative Effects 5.5. Participant Feedback 5.5. Participant Feedback 6. Limitations 6. Limitations 7. Concluding Remarks 7. Concluding Remarks 7.1. Future Research Directions 7.1. Future Research Directions Acknowledgements and References Acknowledgements and References 2. Background and Related Work In this section we provide the background on gamification, game design elements and its effects. We also present related work on negative effects, including a comparison to other secondary studies that have been conducted and explaining the differences to ours. 2.1. Gamification 2.1. Gamification The term “gamification” became known after 2010, but we can find precursors in user interface studies of the 80’s [2]. If we use the broader perspective of work’s gamification, we find similar concepts even earlier, such as the Soviet Union’s experiments to motivate workers of the first half of the 20th century [16]. At the beginning of the ’2000s, the American movement of “fun at work” in the academic management literature was also a form of work’s gamification [16]. Yu-Kai Chou [17] defines gamification as the craft of deriving fun and engaging elements found typically in games and thoughtfully applying them to real-world or productive activities. He calls this process Human-Focused Design because it concentrates on humans’ feelings, motivations, and engagement in the experience. Unfortunately, the term “gamification” remains inconsistently used, and a general theory of gamification is yet to be developed [18]. Aware of so many different definitions, discrepancies, distinctions, and discretionary delimitations, we use the original concept proposed by Deterding et al. [2]: Gamification is the use of game design elements in non-game contexts: To allow a more precise understanding of this definition, we define game design according to Brathwaite and Schreiber [19], i.e.: “Game design is the process of creating the content and rules of a game. Good game design is the process of creating goals that a player feels motivated to reach and rules that a player must follow as he makes meaningful decisions in pursuit of those goals.”. For game design elements, we use the definition by Deterding et al. [2] “(. . . ) elements that are characteristic to games – (. . . ) that are found in most (but not necessarily all) games, readily associated with games, and found to play a significant role in gameplay.” We provide more detail about the game design elements in the next subsection. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Origins and Key Concepts of Gamification in Non-Game Contexts

Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References 2 The Original GPT4All Model 2.1 Data Collection and Curation To train the original GPT4All model, we collected roughly one million prompt-response pairs using the GPT-3.5-Turbo OpenAI API between March 20, 2023 and March 26th, 2023. In particular, we gathered GPT3.5-Turbo responses to prompts of three publicly available datasets: the unified chip2 subset of LAION OIG, a random sub-sample of Stackoverflow Questions, and a sub-sample of Bigscience/P3 (Sanh et al., 2021). Following the approach in Stanford Alpaca (Taori et al., 2023), an open source LLaMA variant that came just before GPT4All, we focused substantial effort on dataset curation. The collected dataset was loaded into Atlas (AI, 2023)—a visual interface for exploring and tagging massive unstructured datasets —for data curation. Using AtarXiv:2311.04931v1 [cs.CL] 6 Nov 2023 las, we identified and removed subsets of the data where GPT-3.5-Turbo refused to respond, had malformed output, or produced a very short response. This resulted in the removal of the entire Bigscience/P3 subset of our data, as many P3 prompts induced responses that were simply one word. After curation, we were left with a set of 437,605 prompt-response pairs, which we visualize in Figure 1a. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References 2 The Original GPT4All Model 2.1 Data Collection and Curation To train the original GPT4All model, we collected roughly one million prompt-response pairs using the GPT-3.5-Turbo OpenAI API between March 20, 2023 and March 26th, 2023. In particular, we gathered GPT3.5-Turbo responses to prompts of three publicly available datasets: the unified chip2 subset of LAION OIG, a random sub-sample of Stackoverflow Questions, and a sub-sample of Bigscience/P3 (Sanh et al., 2021). Following the approach in Stanford Alpaca (Taori et al., 2023), an open source LLaMA variant that came just before GPT4All, we focused substantial effort on dataset curation. The collected dataset was loaded into Atlas (AI, 2023)—a visual interface for exploring and tagging massive unstructured datasets —for data curation. Using AtarXiv:2311.04931v1 [cs.CL] 6 Nov 2023 las, we identified and removed subsets of the data where GPT-3.5-Turbo refused to respond, had malformed output, or produced a very short response. This resulted in the removal of the entire Bigscience/P3 subset of our data, as many P3 prompts induced responses that were simply one word. After curation, we were left with a set of 437,605 prompt-response pairs, which we visualize in Figure 1a. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

Original GPT4All Model: How We Collected Data and Then Curated It

Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works 3.2 MODEL TRAINING In this work, we consider a three-stage training procedure. It can be summarized as follows, and the implementation details, like training data, can be found in Table 11 in Appendix C. I Alignment Training This stage is used to alleviate the gap in multimodality, facilitating the adaptation of multimodal inputs to LLMs. The linear visual projector, linear condition projector, and learnable dream embeddings are pretrained for cross-modal manifold alignment among frozen LLMs, visual encoder, and SD. We use approximately 30M image-text pairs data, training both image-to-text comprehension and text-to-image synthesis. II I-GPT Pretraining Following alignment, the LLM undergoes an unfrozen process for I-GPT pretraining (detailed in Sec. 3.1). This critical stage facilitates the learning of joint vision-language distributions via generative modeling. Training incorporates approximately 2M selectively filtered documents from MMC4-Core (Zhu et al., 2023b), adhering to a CLIP score threshold of 0.25. Furthermore, we use 2M paired data samples from LAION400M (Schuhmann et al., 2021), captioned by BLIP (Li et al., 2022) (i.e., BLIP-LAION), to enhance text-to-image training and potentially mitigate the impact of some low-quality noisy images and texts from sMMC4. III Supervised Fine-tuning This stage enables the model to perform general multimodal comprehension and creative tasks following human instructions (Ouyang et al., 2022). We utilize approximately 80K visual instruction tuning data collected by Liu et al.. For instruction-following content creation, GPT-4 (OpenAI, 2023) is prompted with document summaries or image captions, collecting approximately 20K instruction-following document synthesis from MMC4 (InstructMMC4) and 20K image synthesis data from BLIP captioned LAION400M (Instruct-BLIP-LAION). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works 3.2 MODEL TRAINING In this work, we consider a three-stage training procedure. It can be summarized as follows, and the implementation details, like training data, can be found in Table 11 in Appendix C. I Alignment Training This stage is used to alleviate the gap in multimodality, facilitating the adaptation of multimodal inputs to LLMs. The linear visual projector, linear condition projector, and learnable dream embeddings are pretrained for cross-modal manifold alignment among frozen LLMs, visual encoder, and SD. We use approximately 30M image-text pairs data, training both image-to-text comprehension and text-to-image synthesis. Alignment Training II I-GPT Pretraining Following alignment, the LLM undergoes an unfrozen process for I-GPT pretraining (detailed in Sec. 3.1). This critical stage facilitates the learning of joint vision-language distributions via generative modeling. Training incorporates approximately 2M selectively filtered documents from MMC4-Core (Zhu et al., 2023b), adhering to a CLIP score threshold of 0.25. Furthermore, we use 2M paired data samples from LAION400M (Schuhmann et al., 2021), captioned by BLIP (Li et al., 2022) (i.e., BLIP-LAION), to enhance text-to-image training and potentially mitigate the impact of some low-quality noisy images and texts from sMMC4. I-GPT Pretraining III Supervised Fine-tuning This stage enables the model to perform general multimodal comprehension and creative tasks following human instructions (Ouyang et al., 2022). We utilize approximately 80K visual instruction tuning data collected by Liu et al.. For instruction-following content creation, GPT-4 (OpenAI, 2023) is prompted with document summaries or image captions, collecting approximately 20K instruction-following document synthesis from MMC4 (InstructMMC4) and 20K image synthesis data from BLIP captioned LAION400M (Instruct-BLIP-LAION). Supervised Fine-tuning 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

The Model Training DreamLLM Underwent: Its Origin Story

LJUBLJANA, Slovenia, October 17th, 2024/Chainwire/--OriginTrail, the ecosystem striving for a safe internet in the age of AI with its decentralized knowledge graph, was named the best project at the prestigious MIT Decentralized AI Summit. Held at the MIT Media Lab, the summit brought together top innovators, researchers, and industry leaders to explore the future of decentralized AI technologies. The packed schedule saw contributions of representatives from Dell, Intell, NVIDIA and other notable AI companies. In his talk, the founder of OriginTrail Branimir Rakic explained how OriginTrail’s decentralized knowledge graph powered by Polkadot acts as a critical infrastructure in addressing some of the internet’s most important challenges in the age of AI such as misinformation, deep fakes, fake news or unreliable AI in a broader sense. The pioneering approach to powering trust in AI stood out amongst the numerous showcases at the event and led to being voted as the best project by the participants of the Decentralized AI Summit. This recognition highlights OriginTrail’s expected impact on driving mainstream adoption for both AI and Web3 technologies across diverse set of industries, from Real World Assets (RWA) in global supply chains, Decentralized Physical Infrastructure (DePIN) connecting knowledge of organizations and individuals, to advancements in improving AI models through federated access to training data. “We are thrilled to see that OriginTrail is evolving in the direction many other industry giants are identifying as the cutting edge. I was particularly excited to hear many speakers highlight the importance of deploying the AI solutions at the edge of the network, where AI solutions with more sustainable resource use and privacy preserving data management can be implemented. It’s something that OriginTrail has been working on to enable for the last year and we’re excited to unveil it next week (Oct 24th) at the DKGcon in Amsterdam with the release of the DKG Edge node. And, of course, I wish to thank the participants of the event that selected OriginTrail as the best project of the DecAI Summit, it’s a great vote of confidence for our future work.” - Branimir Rakic, Founder at OriginTrail. As the pathologies of the state-of-the-art AI solutions become more evident, the demand for trustworthy AI solutions being developed at the intersection of Web3 and AI grows. OriginTrail is determined to continue to lead that charge by driving mainstream adoption of this critical infrastructure to enable a safe internet in the age of AI. About OriginTrail OriginTrail is an ecosystem dedicated to making the global economy work sustainably by enabling a universe of AI-ready Knowledge Assets, allowing anyone to take part in trusted knowledge sharing. It leverages the open-source Decentralized Knowledge Graph that connects physical and digital worlds in a single connected reality driving transparency and trust. Advanced knowledge graph technology currently powers trillion-dollar companies like Google and Facebook. By reshaping it for Web3, the OriginTrail Decentralized Knowledge Graph provides a crucial fabric to link, verify, and value data on both physical and digital assets. For more information, users can visit origintrail.io. MIT DecAI summit event | LI OriginTrail announcement | X OriginTrail announcement Contact Communications department Lucija Naranda OriginTrail lucija.naranda@origin-trail.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. LJUBLJANA, Slovenia, October 17th, 2024/Chainwire/--OriginTrail, the ecosystem striving for a safe internet in the age of AI with its decentralized knowledge graph, was named the best project at the prestigious MIT Decentralized AI Summit. Held at the MIT Media Lab, the summit brought together top innovators, researchers, and industry leaders to explore the future of decentralized AI technologies. LJUBLJANA, Slovenia, October 17th, 2024/Chainwire/--OriginTrail, The packed schedule saw contributions of representatives from Dell, Intell, NVIDIA and other notable AI companies. In his talk, the founder of OriginTrail Branimir Rakic explained how OriginTrail’s decentralized knowledge graph powered by Polkadot acts as a critical infrastructure in addressing some of the internet’s most important challenges in the age of AI such as misinformation, deep fakes, fake news or unreliable AI in a broader sense. The pioneering approach to powering trust in AI stood out amongst the numerous showcases at the event and led to being voted as the best project by the participants of the Decentralized AI Summit. This recognition highlights OriginTrail’s expected impact on driving mainstream adoption for both AI and Web3 technologies across diverse set of industries, from Real World Assets (RWA) in global supply chains, Decentralized Physical Infrastructure (DePIN) connecting knowledge of organizations and individuals, to advancements in improving AI models through federated access to training data. “We are thrilled to see that OriginTrail is evolving in the direction many other industry giants are identifying as the cutting edge. I was particularly excited to hear many speakers highlight the importance of deploying the AI solutions at the edge of the network, where AI solutions with more sustainable resource use and privacy preserving data management can be implemented. It’s something that OriginTrail has been working on to enable for the last year and we’re excited to unveil it next week (Oct 24th) at the DKGcon in Amsterdam with the release of the DKG Edge node. And, of course, I wish to thank the participants of the event that selected OriginTrail as the best project of the DecAI Summit, it’s a great vote of confidence for our future work.” - Branimir Rakic, Founder at OriginTrail. “We are thrilled to see that OriginTrail is evolving in the direction many other industry giants are identifying as the cutting edge. I was particularly excited to hear many speakers highlight the importance of deploying the AI solutions at the edge of the network, where AI solutions with more sustainable resource use and privacy preserving data management can be implemented. It’s something that OriginTrail has been working on to enable for the last year and we’re excited to unveil it next week (Oct 24th) at the DKGcon in Amsterdam with the release of the DKG Edge node. And, of course, I wish to thank the participants of the event that selected OriginTrail as the best project of the DecAI Summit, it’s a great vote of confidence for our future work.” - Branimir Rakic, Founder at OriginTrail. DKGcon DKGcon As the pathologies of the state-of-the-art AI solutions become more evident, the demand for trustworthy AI solutions being developed at the intersection of Web3 and AI grows. OriginTrail is determined to continue to lead that charge by driving mainstream adoption of this critical infrastructure to enable a safe internet in the age of AI. About OriginTrail OriginTrail is an ecosystem dedicated to making the global economy work sustainably by enabling a universe of AI-ready Knowledge Assets, allowing anyone to take part in trusted knowledge sharing. It leverages the open-source Decentralized Knowledge Graph that connects physical and digital worlds in a single connected reality driving transparency and trust. OriginTrail OriginTrail Advanced knowledge graph technology currently powers trillion-dollar companies like Google and Facebook. By reshaping it for Web3, the OriginTrail Decentralized Knowledge Graph provides a crucial fabric to link, verify, and value data on both physical and digital assets. For more information, users can visit origintrail.io . MIT DecAI summit event | LI OriginTrail announcement | X OriginTrail announcement origintrail.io origintrail.io MIT DecAI summit event MIT DecAI summit event LI OriginTrail announcement LI OriginTrail announcement X OriginTrail announcement X OriginTrail announcement Contact Communications department Lucija Naranda OriginTrail lucija.naranda@origin-trail.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

Decentralized AI Summit at MIT Votes OriginTrail As The Best Decentralized AI Project

Gedesentraliseerde KI-beraad by MIT stem oor OriginTrail as die beste gedesentraliseerde KI-projek

Cumbre de AI Descentralizado ukax MIT ukanx OriginTrail ukax Proyecto AI Descentralizado ukax wali askiwa sasaw voto churawayi

Decentraliseret AI-topmøde på MIT stemmer OriginTrail som det bedste decentraliserede AI-projekt

Decentralized AI Summit am MIT wählt OriginTrail zum besten dezentralen KI-Projekt

La cumbre sobre inteligencia artificial descentralizada del MIT elige a OriginTrail como el mejor proyecto de inteligencia artificial descentralizada

MIT の分散型 AI サミットで OriginTrail が最優秀分散型 AI プロジェクトに選出

ກອງປະຊຸມສຸດຍອດ AI ທີ່ມີການແບ່ງຂັ້ນຄຸ້ມຄອງຢູ່ MIT ລົງຄະແນນສຽງ OriginTrail ເປັນໂຄງການ AI ທີ່ມີການກະຈາຍສູນກາງທີ່ດີທີ່ສຸດ

Самит за децентрализирана вештачка интелигенција на MIT Votes OriginTrail како најдобар проект за децентрализирана вештачка интелигенција

په MIT کې د غیر متمرکز AI غونډه د غوره غیر متمرکز AI پروژې په توګه اصلي ټریل رایه ورکړه

Shir Madaxeedka AI Baahsan ee MIT Votes OriginTrail Sida Mashruuca AI ee baahsan ee ugu Fiican

ایم آئی ٹی میں ڈی سینٹرلائزڈ AI سمٹ نے اوریجن ٹریل کو بہترین وکندریقرت AI پروجیکٹ قرار دیا

Hội nghị thượng đỉnh AI phi tập trung tại MIT bình chọn OriginTrail là dự án AI phi tập trung tốt nhất

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 10 A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Our thesis is that Joseph Weizenbaum, afraid of people misinterpreting computers and AI, developed ELIZA not as an AI, but as a platform to support research about people’s interpretive process, perhaps more specifically their interaction with AIs. Weizenbaum, of course, had to provide an example – the DOCTOR script – to demonstrate the platform, but, as above, he had no illusion that it was actually intelligent, nor even very easily interpreted as intelligent; Much more “intelligent” programs already existing in various game playing programs (even his own, from the Datamation paper!), in Colby’s Parry, and in other programs that solved math word problems[17], did database lookups from natural language queries[29], and others. Although some of the sort of research envisioned by Weizenbaum was carried out by the MGH team, a specific event thwarted this use case for ELIZA, and, in almost the deepest possible irony, led to exactly the conflation of ELIZA with AI that Weizenbaum set out to study, not to bring into being. Weizenbaum built his ELIZA in MAD-SLIP on the IBM 7090, which was the primary machine at MIT’s Project MAC, on the 5th-through-9th floors of Tech Square. Almost immediately after ELIZA’s publication in 1966, Bernie Cosell created a Lisp knock-off of ELIZA, based on the algorithm and DOCTOR script in Weizenbaum’s paper. Cosell worked at Bolt Beranek and Newman (BBN), a large RAND-like consulting company situated nearby MIT, that commonly hired MIT graduates. Coincidentally, at almost the same time, BBN was a founding site of the ARPAnet[14], and Cosell’s Lisp ELIZA diffused rapidly through that network and across the soon-to-be Lisp-centered world of academic AI. As a result, Cosell’s Lisp ELIZA rapidly became the dominant strain, and Weizenbaum’s MAD-SLIP version, inaccessible on the ARPAnet, was almost instantly forgotten. Through interesting and complex criss-crossing histories, described in detail in [45], Danny Bobrow, a recent MIT AI graduate who headed up BBN’s AI program, brought McCarthy, and thus Lisp, to BBN, where Cosell was exposed to it, and built an ELIZA knock-off in Lisp as a side-project.[15] Cosell reports: “When I was working on the PDP-1 time-sharing system [...] I thought I would learn Lisp. That spring, Joe Weizenbaum had written an article for Communications of the ACM on ELIZA. I thought that was way cool. [...] He described how ELIZA works and I said, “I bet I could write something to do that.” And so I started writing a Lisp program on [the] PDP-1 system at BBN.”[41, p. 540] He continues, “I wrote that program and got it up and working. Playing with it was an all-BBN project. [...] It was written, at first, in the PDP-1 Lisp. But they were building a Lisp on the PDP-6 at that point—or maybe the PDP-10. But it was the Lisp that had spread across the ARPANet. So [ELIZA] went along with it [...].”[41, p. 541] Once Cosell’s Lisp ELIZA hit the academic world via the rapid spread of the ARPANet, Weizenbaum’s MAD-SLIP version was no longer relevant, and the name “ELIZA” (and the “DOCTOR” script), as well as the concept, was, from that point forward, associated with Cosell’s Lisp version, although its origin was still correctly attributed to Weizenbaum via the CACM paper, leading to a 50- year-long, community-wide misapprehension that ELIZA had been written in Lisp. In addition to being promulgated by Cosell’s knock-off being the one that was most easily available, via the ARPANet, it was a natural confusion because Lisp was rapidly becoming the go-to language of AI. Once Lisp came onto the scene no one thought much again about SLIP, or, for that matter, IPL-V. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. [14] At the time what became the internet, and then the web, was called the ARPAnet, and BBN built its core hardware and developed the programs that ran it.[11]) [15] Coincidentally, Bobrow was also the author of STUDENT, the program mentioned above that solved math word problems.[17] Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 10 A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Our thesis is that Joseph Weizenbaum, afraid of people misinterpreting computers and AI, developed ELIZA not as an AI, but as a platform to support research about people’s interpretive process, perhaps more specifically their interaction with AIs. Weizenbaum, of course, had to provide an example – the DOCTOR script – to demonstrate the platform, but, as above, he had no illusion that it was actually intelligent, nor even very easily interpreted as intelligent; Much more “intelligent” programs already existing in various game playing programs (even his own, from the Datamation paper!), in Colby’s Parry, and in other programs that solved math word problems[17], did database lookups from natural language queries[29], and others. Although some of the sort of research envisioned by Weizenbaum was carried out by the MGH team, a specific event thwarted this use case for ELIZA, and, in almost the deepest possible irony, led to exactly the conflation of ELIZA with AI that Weizenbaum set out to study, not to bring into being. Weizenbaum built his ELIZA in MAD-SLIP on the IBM 7090, which was the primary machine at MIT’s Project MAC, on the 5th-through-9th floors of Tech Square. Almost immediately after ELIZA’s publication in 1966, Bernie Cosell created a Lisp knock-off of ELIZA, based on the algorithm and DOCTOR script in Weizenbaum’s paper. Cosell worked at Bolt Beranek and Newman (BBN), a large RAND-like consulting company situated nearby MIT, that commonly hired MIT graduates. Coincidentally, at almost the same time, BBN was a founding site of the ARPAnet[14], and Cosell’s Lisp ELIZA diffused rapidly through that network and across the soon-to-be Lisp-centered world of academic AI. As a result, Cosell’s Lisp ELIZA rapidly became the dominant strain, and Weizenbaum’s MAD-SLIP version, inaccessible on the ARPAnet, was almost instantly forgotten. Through interesting and complex criss-crossing histories, described in detail in [45], Danny Bobrow, a recent MIT AI graduate who headed up BBN’s AI program, brought McCarthy, and thus Lisp, to BBN, where Cosell was exposed to it, and built an ELIZA knock-off in Lisp as a side-project.[15] Cosell reports: “When I was working on the PDP-1 time-sharing system [...] I thought I would learn Lisp. That spring, Joe Weizenbaum had written an article for Communications of the ACM on ELIZA. I thought that was way cool. [...] He described how ELIZA works and I said, “I bet I could write something to do that.” And so I started writing a Lisp program on [the] PDP-1 system at BBN.”[41, p. 540] He continues, “I wrote that program and got it up and working. Playing with it was an all-BBN project. [...] It was written, at first, in the PDP-1 Lisp. But they were building a Lisp on the PDP-6 at that point—or maybe the PDP-10. But it was the Lisp that had spread across the ARPANet. So [ELIZA] went along with it [...].”[41, p. 541] Once Cosell’s Lisp ELIZA hit the academic world via the rapid spread of the ARPANet, Weizenbaum’s MAD-SLIP version was no longer relevant, and the name “ELIZA” (and the “DOCTOR” script), as well as the concept, was, from that point forward, associated with Cosell’s Lisp version, although its origin was still correctly attributed to Weizenbaum via the CACM paper, leading to a 50- year-long, community-wide misapprehension that ELIZA had been written in Lisp. In addition to being promulgated by Cosell’s knock-off being the one that was most easily available, via the ARPANet, it was a natural confusion because Lisp was rapidly becoming the go-to language of AI. Once Lisp came onto the scene no one thought much again about SLIP, or, for that matter, IPL-V. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [14] At the time what became the internet, and then the web, was called the ARPAnet, and BBN built its core hardware and developed the programs that ran it.[11]) [15] Coincidentally, Bobrow was also the author of STUDENT, the program mentioned above that solved math word problems.[17]

The Accidental AI: How ELIZA's Lisp Adaptation Derailed Its Original Research Intent

Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 18 of 29. COUNT VI: BREACH OF EXPRESS CONTRACT (Against Altman and OpenAI, Inc.) 247. Plaintiff re-alleges and incorporates by reference paragraphs 1 through 246 inclusive, as though fully set forth herein. 248. In a series of express written correspondence in 2015, Altman and Musk entered into a valid, enforceable, and binding agreement to co-found a non-profit entity (OpenAI, Inc.) to develop leading AI/AGI technology. Per their founding agreement, which under Altman’s leadership was ratified and/or adopted by OpenAI, Inc., Musk was to contribute capital, provide integral advice, and use his stature and track record to recruit leading scientific talent, attracting further contributions to OpenAI, Inc. In exchange, and as consideration for Musk’s contributions, Altman promised that OpenAI, Inc. (i) would be a non-profit and develop AI/AGI for the benefit of humanity, not personal profits, and (ii) to that end, would make OpenAI, Inc.’s technology largely open source, subject only to genuine safety considerations, and to not conceal nor concentrate its technology for proprietary commercial reasons. 249. Musk fulfilled all of his obligations and has performed and/or complied with all terms and conditions of the agreement that he was required to perform and/or comply with, except those which were waived and/or excused, or the non-performance of which was justified, and is in no matter or respect in breach of said agreement. From OpenAI, Inc.’s founding in 2015 through September 2020, Musk contributed more than $44 million to OpenAI, Inc., provided key advice on research to be conducted, played an integral role in recruiting world-class talent to OpenAI, Inc. like its Chief Scientist Dr. Sutskever, and due to his participation and stature, attracted financial contributions by others to the non-profit. 250. In 2023, on information and belief, Altman and OpenAI, Inc. breached their agreement with Musk by, among other things: a. Failing to publicly disclose the non-profit’s research and development, including details on GPT-4, GPT-4T, and GPT-4o’s architecture, hardware, training method, and training computation; b. Licensing and/or furnishing OpenAI, Inc.’s GPT-4 and related technology exclusively to Microsoft, and by concentrating it in this single giant for-profit corporation; c. Permitting Microsoft, a publicly traded for-profit business, to occupy a seat on OpenAI, Inc.’s Board of Directors and exert undue influence and control over OpenAI’s activities; d. Closing off OpenAI, Inc.’s technology for profit and erecting a “paywall” excluding the public from open usage of GPT-4 and related technology to advance Defendants and Microsoft’s own private commercial interests; e. Self-dealing and manipulating the non-profit’s assets to enrich themselves by, for example causing OpenAI, Inc. to excessively patronize businesses in which Altman owns a significant interest, for his personal enrichment; and f. Currently working to convert the non-profit into a fully for-profit commercial entity. 251. Defendants’ obligations to perform were not waived nor were their breaches and/or failures to perform justified and/or excused. Defendants intentionally concealed their wrongful conduct, which prevented Musk from discovering their scheme, notwithstanding his exercise of due diligence. 252. As a direct and proximate result of Altman and OpenAI, Inc.’s conduct, acts, and omissions alleged hereinabove, Defendants have deprived Musk of the benefit of the parties’ agreement and have caused Musk to suffer damages, including but not limited to the financial contributions he made to OpenAI, Inc., the loss of the time and resources he expended to direct research and recruit talent and damage to his reputation, in an amount to be adjudicated and determined at trial, but which vastly exceeds $75,000, plus prejudgment interest. 253. Musk has no adequate remedy at law for many of the injuries he suffered as a result of Defendants’ breaches and failures, and such injuries cannot reasonably, adequately, or precisely be measured or compensated in damages. Accordingly, Musk also seeks and is entitled to specific performance of Defendants’ contractual obligations. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Lead Image by Jonathan Kemper on Unsplash Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 18 of 29. Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 18 of 29. HackerNoon’s Legal PDF Series here COUNT VI: BREACH OF EXPRESS CONTRACT (Against Altman and OpenAI, Inc.) 247. Plaintiff re-alleges and incorporates by reference paragraphs 1 through 246 inclusive, as though fully set forth herein. 248. In a series of express written correspondence in 2015, Altman and Musk entered into a valid, enforceable, and binding agreement to co-found a non-profit entity (OpenAI, Inc.) to develop leading AI/AGI technology. Per their founding agreement, which under Altman’s leadership was ratified and/or adopted by OpenAI, Inc., Musk was to contribute capital, provide integral advice, and use his stature and track record to recruit leading scientific talent, attracting further contributions to OpenAI, Inc. In exchange, and as consideration for Musk’s contributions, Altman promised that OpenAI, Inc. (i) would be a non-profit and develop AI/AGI for the benefit of humanity, not personal profits, and (ii) to that end, would make OpenAI, Inc.’s technology largely open source, subject only to genuine safety considerations, and to not conceal nor concentrate its technology for proprietary commercial reasons. 249. Musk fulfilled all of his obligations and has performed and/or complied with all terms and conditions of the agreement that he was required to perform and/or comply with, except those which were waived and/or excused, or the non-performance of which was justified, and is in no matter or respect in breach of said agreement. From OpenAI, Inc.’s founding in 2015 through September 2020, Musk contributed more than $44 million to OpenAI, Inc., provided key advice on research to be conducted, played an integral role in recruiting world-class talent to OpenAI, Inc. like its Chief Scientist Dr. Sutskever, and due to his participation and stature, attracted financial contributions by others to the non-profit. 250. In 2023, on information and belief, Altman and OpenAI, Inc. breached their agreement with Musk by, among other things: a. Failing to publicly disclose the non-profit’s research and development, including details on GPT-4, GPT-4T, and GPT-4o’s architecture, hardware, training method, and training computation; b. Licensing and/or furnishing OpenAI, Inc.’s GPT-4 and related technology exclusively to Microsoft, and by concentrating it in this single giant for-profit corporation; c. Permitting Microsoft, a publicly traded for-profit business, to occupy a seat on OpenAI, Inc.’s Board of Directors and exert undue influence and control over OpenAI’s activities; d. Closing off OpenAI, Inc.’s technology for profit and erecting a “paywall” excluding the public from open usage of GPT-4 and related technology to advance Defendants and Microsoft’s own private commercial interests; e. Self-dealing and manipulating the non-profit’s assets to enrich themselves by, for example causing OpenAI, Inc. to excessively patronize businesses in which Altman owns a significant interest, for his personal enrichment; and f. Currently working to convert the non-profit into a fully for-profit commercial entity. 251. Defendants’ obligations to perform were not waived nor were their breaches and/or failures to perform justified and/or excused. Defendants intentionally concealed their wrongful conduct, which prevented Musk from discovering their scheme, notwithstanding his exercise of due diligence. 252. As a direct and proximate result of Altman and OpenAI, Inc.’s conduct, acts, and omissions alleged hereinabove, Defendants have deprived Musk of the benefit of the parties’ agreement and have caused Musk to suffer damages, including but not limited to the financial contributions he made to OpenAI, Inc., the loss of the time and resources he expended to direct research and recruit talent and damage to his reputation, in an amount to be adjudicated and determined at trial, but which vastly exceeds $75,000, plus prejudgment interest. 253. Musk has no adequate remedy at law for many of the injuries he suffered as a result of Defendants’ breaches and failures, and such injuries cannot reasonably, adequately, or precisely be measured or compensated in damages. Accordingly, Musk also seeks and is entitled to specific performance of Defendants’ contractual obligations. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 05, 2024, deadline.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. deadline.com Lead Image by Jonathan Kemper on Unsplash Lead Image by Jonathan Kemper on Unsplash Jonathan Kemper on Unsplash

Musk Claims OpenAI Violated Original Partnership Deal

Craig Wright's Tangled Web of Lies in Bitcoin's Origin Story

The emergence and growing sophistication of AI detectors like Originality.ai has made it increasingly difficult for AI writer users to evade their detection. From students and business professionals to content creators and marketers, people are more likely to get flagged by these stringent detectors. As such, there is a desperate need for new bypass strategies that will effectively allow AI users to make their content undetectable and bypass Originality.ai detections. In this article, we will explore all the methods that can help you achieve high human scores on it. Read on for all the answers! Everything You Need To Know About Originality.AI Originality.ai was launched in November 2022 by Jon Gillham after he sold a content marketing agency he had founded previously. Having used AI writing tools for SEO-marketing purposes, he anticipated a growing need for an online detection solution that could help users to quickly distinguish between AI and human-written content. This resulted in the creation of Originality.ai, which was one of the first AI tools capable of detecting content generated by popular language models like GPT-3.5/4, Claude, etec. The AI detection tool claims to have a 94%+ accuracy rate and over the past two years, the company has kept on upgrading its AI detection model having already released its 3.0 version in 2024. The platform is one of the few AI detectors well-suited for enterprise use, as it provides access to team management features. It also facilitates API integration and supports multilingual AI detection in 15 different languages. Originality.ai is also unique in that it has a pay-as-you-go option that lets you conduct AI checks without a subscription. How to Bypass Originality.ai Detection? Originality.ai is a popular AI detection tool used by many industry-leading platforms, companies and institutions. As such, it is essential to know how to bypass it if you want to publish or submit AI content without being flagged. Luckily, several unique strategies can help you outsmart it. Let’s explore them in detail below. Use an AI Humanizer When it comes to making AI content undetectable, the use of an AI humanizer like BypassGPT is the easiest way to achieve this. Using highly advanced algorithms, the platform is trained and developed to rewrite AI text until it is undetectable by AI detectors Unlike typical rewriting tools, it makes comprehensive alterations to the text.  This involves reworking the entire content in terms of word choice, phrasing, and even syntax. In doing so, it erases common indicators that AI detectors like Originality.ai tend to spot as AI. For this reason, BypassGPT consistently produces high human score output without fail. See the test sample humanized by BypassGPT on Originality.ai: The platform has a wide range of features and benefits that include: Features: Next-level AI humanization technology to ensure output is 100% undetectable
ChatGPT watermark removal capabilities
Built-in AI checkers integrated with popular AI detector tools for collective AI scoring Benefits: Humanizes any type of AI content including emails, reports, articles, ad copy, etc.
Humanizes AI text from any language model, be it ChatGPT, GPT-4, Gemini, Jasper, etc.
Produces 100% plagiarism-free and contextually-accurate output each time Aside from an AI humanizer, there are other alternative methods that you can use to bypass Originality.ai detection. However, do keep in mind that these methods will require a certain level of manual input. Let’s quickly break them down in detail. Include Real-Time Information AI tools generate content based on pre-existing information. As such, they rarely produce real-time facts and statistics on current events. You can make your content seem more credible and human-written by including new information and data that is yet to be widely publicized. Use Informal Language AI content tends to be heavily formal and impersonal, which is a common indicator that AI detectors look out for. Instead, you should try to incorporate more informal vocabulary and relatable phrases like slang words to help make it sound more casual and human. Eliminate Repetitive Wording One of the most common indicators of AI generation is the presence of repetitive wording. To this end, you should always replace any overused terms or phrases with more casual language to help make the content seem less robotic and predictable. Mention Personal Experiences One way to make AI content sound more human is to include personal anecdotes, opinions, and even stories. Since AI writers can’t replicate real human experiences and thoughts, it helps you make the content sound more relatable and therefore undetectable by AI detectors. Be Precise With Your Desired Voice When using AI language models like ChatGPT, you should always specify your desired tone and style in the AI prompts. For example, you can ask it to generate an article in a conversational tone. This helps ensure it produces less generic-sounding results, and can probably bypass Originality. Use Figurative Language Metaphors, similes, and other forms of figurative language are often more effectively used by human writers than AI. They add richness to text, make it more expressive and help you keep it undetectable by AI detectors. Vary Sentence Length AI often generates text with uniform sentence lengths or lengthy sentences. Humans, however, tend to use a variety of sentence lengths. Breaking long sentences down to shorter ones, mixing short, medium, and long sentences can make your text seem more natural. Include References or Citations If the text includes facts or information from specific sources, make sure to include references or citations. This makes you comply with good writing practices, and can help you avoid the detection of Originality. Make Grammar and Syntax Errors Deliberately inserting grammatical or syntactical errors into your text can be a good way to avoid AI detection, as AI is not likely to make mistakes. But be extra careful about where you include these errors, as making them too noticeable will reduce your text readability. Reasons To Bypass Originality.ai Detection? Able to bypass Originality.ai detection can bring you a lot of benefits: Avoid Spam Filters: Spammy AI content is something many spam filters are trained to spot instantly. To ensure your content reaches its intended recipients, you should humanize it to bypass Originality.ai. This helps to confirm the content sounds authentic and human-like enough to avoid being marked as spam. Improve Content Readability: It can often be boring for audiences to read AI-generated content because it typically comes off as bland and formulaic. However, if you can humanize it in a way it bypasses Originality.ai, this does well to confirm that the content sounds authentic enough to keep readers engaged.
Boost SEO Rankings: If you want AI content to rank highly on SERPs, it must meet Google’s strict content guidelines. The easiest way to ensure it complies is by humanizing it well enough to bypass Originality.ai. This helps your content look less like unhelpful AI-generated content that can result in SEO penalties and affect your rankings. Conclusion Originality.ai is a very sensitive AI content detector and can easily flag your content as AI generated. You must be able to effectively make your AI content undetectable and this detailed guide holds all the solutions you need to bypass Originality.ai. Take your time to review each one and feel free to implement them into your content writing workflow for more human-like results. Based on overall effectiveness, our top recommendation for guaranteed results is to use BypassGPT. This platform is powerful enough to have a high success rate in avoiding the detection of Originality.ai. The emergence and growing sophistication of AI detectors like Originality.ai has made it increasingly difficult for AI writer users to evade their detection. From students and business professionals to content creators and marketers, people are more likely to get flagged by these stringent detectors. As such, there is a desperate need for new bypass strategies that will effectively allow AI users to make their content undetectable and bypass Originality.ai detections. In this article, we will explore all the methods that can help you achieve high human scores on it. Read on for all the answers! Everything You Need To Know About Originality.AI Originality.ai was launched in November 2022 by Jon Gillham after he sold a content marketing agency he had founded previously. Having used AI writing tools for SEO-marketing purposes, he anticipated a growing need for an online detection solution that could help users to quickly distinguish between AI and human-written content. Jon Gillham Jon Gillham This resulted in the creation of Originality.ai, which was one of the first AI tools capable of detecting content generated by popular language models like GPT-3.5/4, Claude, etec. The AI detection tool claims to have a 94%+ accuracy rate and over the past two years, the company has kept on upgrading its AI detection model having already released its 3.0 version in 2024. The platform is one of the few AI detectors well-suited for enterprise use, as it provides access to team management features. It also facilitates API integration and supports multilingual AI detection in 15 different languages. Originality.ai is also unique in that it has a pay-as-you-go option that lets you conduct AI checks without a subscription. How to Bypass Originality.ai Detection? Originality.ai is a popular AI detection tool used by many industry-leading platforms, companies and institutions. As such, it is essential to know how to bypass it if you want to publish or submit AI content without being flagged. Luckily, several unique strategies can help you outsmart it. Let’s explore them in detail below. Use an AI Humanizer When it comes to making AI content undetectable, the use of an AI humanizer like BypassGPT is the easiest way to achieve this. Using highly advanced algorithms, the platform is trained and developed to rewrite AI text until it is undetectable by AI detectors BypassGPT BypassGPT Unlike typical rewriting tools, it makes comprehensive alterations to the text.  This involves reworking the entire content in terms of word choice, phrasing, and even syntax. In doing so, it erases common indicators that AI detectors like Originality.ai tend to spot as AI. For this reason, BypassGPT consistently produces high human score output without fail. See the test sample humanized by BypassGPT on Originality.ai: The platform has a wide range of features and benefits that include: Features: Next-level AI humanization technology to ensure output is 100% undetectable ChatGPT watermark removal capabilities Built-in AI checkers integrated with popular AI detector tools for collective AI scoring Next-level AI humanization technology to ensure output is 100% undetectable ChatGPT watermark removal capabilities Built-in AI checkers integrated with popular AI detector tools for collective AI scoring Benefits: Humanizes any type of AI content including emails, reports, articles, ad copy, etc. Humanizes AI text from any language model, be it ChatGPT, GPT-4, Gemini, Jasper, etc. Produces 100% plagiarism-free and contextually-accurate output each time Humanizes any type of AI content including emails, reports, articles, ad copy, etc. Humanizes AI text from any language model, be it ChatGPT, GPT-4, Gemini, Jasper, etc. Produces 100% plagiarism-free and contextually-accurate output each time Aside from an AI humanizer, there are other alternative methods that you can use to bypass Originality.ai detection. However, do keep in mind that these methods will require a certain level of manual input. Let’s quickly break them down in detail. Include Real-Time Information AI tools generate content based on pre-existing information. As such, they rarely produce real-time facts and statistics on current events. You can make your content seem more credible and human-written by including new information and data that is yet to be widely publicized. Use Informal Language AI content tends to be heavily formal and impersonal, which is a common indicator that AI detectors look out for. Instead, you should try to incorporate more informal vocabulary and relatable phrases like slang words to help make it sound more casual and human. Eliminate Repetitive Wording One of the most common indicators of AI generation is the presence of repetitive wording. To this end, you should always replace any overused terms or phrases with more casual language to help make the content seem less robotic and predictable. Mention Personal Experiences One way to make AI content sound more human is to include personal anecdotes, opinions, and even stories. Since AI writers can’t replicate real human experiences and thoughts, it helps you make the content sound more relatable and therefore undetectable by AI detectors. Be Precise With Your Desired Voice When using AI language models like ChatGPT, you should always specify your desired tone and style in the AI prompts. For example, you can ask it to generate an article in a conversational tone. This helps ensure it produces less generic-sounding results, and can probably bypass Originality. Use Figurative Language Metaphors, similes, and other forms of figurative language are often more effectively used by human writers than AI. They add richness to text, make it more expressive and help you keep it undetectable by AI detectors. Vary Sentence Length AI often generates text with uniform sentence lengths or lengthy sentences. Humans, however, tend to use a variety of sentence lengths. Breaking long sentences down to shorter ones, mixing short, medium, and long sentences can make your text seem more natural. Include References or Citations If the text includes facts or information from specific sources, make sure to include references or citations. This makes you comply with good writing practices, and can help you avoid the detection of Originality. Make Grammar and Syntax Errors Deliberately inserting grammatical or syntactical errors into your text can be a good way to avoid AI detection, as AI is not likely to make mistakes. But be extra careful about where you include these errors, as making them too noticeable will reduce your text readability. Reasons To Bypass Originality.ai Detection? Able to bypass Originality.ai detection can bring you a lot of benefits: Avoid Spam Filters: Spammy AI content is something many spam filters are trained to spot instantly. To ensure your content reaches its intended recipients, you should humanize it to bypass Originality.ai. This helps to confirm the content sounds authentic and human-like enough to avoid being marked as spam. Avoid Spam Filters: Spammy AI content is something many spam filters are trained to spot instantly. To ensure your content reaches its intended recipients, you should humanize it to bypass Originality.ai. This helps to confirm the content sounds authentic and human-like enough to avoid being marked as spam. Improve Content Readability: It can often be boring for audiences to read AI-generated content because it typically comes off as bland and formulaic. However, if you can humanize it in a way it bypasses Originality.ai, this does well to confirm that the content sounds authentic enough to keep readers engaged. Boost SEO Rankings: If you want AI content to rank highly on SERPs, it must meet Google’s strict content guidelines. The easiest way to ensure it complies is by humanizing it well enough to bypass Originality.ai. This helps your content look less like unhelpful AI-generated content that can result in SEO penalties and affect your rankings. Improve Content Readability: It can often be boring for audiences to read AI-generated content because it typically comes off as bland and formulaic. However, if you can humanize it in a way it bypasses Originality.ai, this does well to confirm that the content sounds authentic enough to keep readers engaged. Boost SEO Rankings: If you want AI content to rank highly on SERPs, it must meet Google’s strict content guidelines. The easiest way to ensure it complies is by humanizing it well enough to bypass Originality.ai. This helps your content look less like unhelpful AI-generated content that can result in SEO penalties and affect your rankings. Conclusion Originality.ai is a very sensitive AI content detector and can easily flag your content as AI generated. You must be able to effectively make your AI content undetectable and this detailed guide holds all the solutions you need to bypass Originality.ai. Take your time to review each one and feel free to implement them into your content writing workflow for more human-like results. Based on overall effectiveness, our top recommendation for guaranteed results is to use BypassGPT . This platform is powerful enough to have a high success rate in avoiding the detection of Originality.ai. BypassGPT BypassGPT

How to Bypass Originality AI Detection

অরিজিনালিটি এআই ডিটেকশন কিভাবে বাইপাস করবেন

So umgehen Sie die Originalitätserkennung durch KI

Cómo evitar la detección de IA de originalidad

Comment contourner la détection d'originalité par l'IA

मौलिकता AI पहचान को कैसे बायपास करें

Stories, News & Mentions about ORIGIN

Categories

Trending Topics

Stories, News & Mentions about ORIGIN

Origin’s Stories on HackerNoon

Origin's News & Mentions

Categories

Trending Topics