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

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

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Cách vượt qua tính năng phát hiện AI độc đáo

如何绕过原创 AI 检测

Hey everyone, In today’s world, AI-generated content is everywhere. It’s becoming increasingly common to see articles, blog posts, and even social media updates created by AI. This has raised a lot of questions and concerns, especially among original content creators who pour their heart and soul into each piece they craft. How can these creators continue to stand out? Or how can AI enhance their creative power without compromising their unique voice and authenticity? I had a great chat recently with one of Saner.ai’s early users, and that really got me thinking about these questions. Let me share that with you. The Chat with a Content Creator So, here’s the story. This user, let’s call him Alex, is a full-time worker and wants to build his personal brand on LinkedIn. He told me that he had been searching for a solution to help him store and develop his ideas. He wanted something more than just a digital notepad or a traditional content management tool. What Alex needed was a companion in his creative process — something that could help him refine and evolve his thoughts and ideas popping up in his life into valuable content. Interestingly, Alex mentioned that he doesn’t want AI content generators. You know, those tools that churn out articles in mass but often lack authenticity and a personal touch. Alex feels that these tools, while efficient, can’t capture his unique voice and style. And I totally get that. Authenticity is key, especially when you’re building a personal brand in this AI world. What Alex was looking for was an AI assistant, not just a generator. He needed a tool that could help him store ideas quickly, recommend ideas based on information he saved, and brainstorm ideas together. That’s how Alex came to Saner.ai. He hopes it will allow him to achieve this outcome. AI in Content Creation This conversation with Alex really got me reflecting on the broader implications of AI in the content creation space. As AI technology advances, it’s becoming increasingly embedded in various aspects of our lives, including the way we create and consume content. However, there’s a significant distinction between using AI to generate a flood of generic, mass-produced content and using AI to support the creation of original, thoughtful content. Firstly, let’s talk about the increase in AI-generated content. There’s no denying the efficiency of AI when it comes to producing large volumes of text quickly. For businesses looking to populate their blogs, social media, and other platforms with regular updates, AI content generators, which can create hundreds of content for SEO purposes in days instead of months, is surely tempting. However, this comes with a downside. The content produced by these generators often lacks the nuance, personality, and authenticity that human writers bring to the table. It may feel impersonal and disconnected. If your purpose is to establish an emotional connection with your audience, then this solution can fall short in a world that’s already saturated with information. On the other hand, using AI to support original content creation can be beneficial. This approach is about using AI to curate, ask, brainstorm, and correct grammar. Imagine having an AI assistant who helps you brainstorm ideas, organize your thoughts, and provide tailored suggestions to improve your writing. This doesn’t just save time — it enhances the creative process, allowing you to focus more on developing your unique ideas and less on the manual work of organizing and maintaining a database. Furthermore, AI can be a powerful tool for overcoming common creative challenges. For instance, writer’s block is something every writer faces at some point. An AI assistant can help break through this barrier by offering new perspectives and sparking inspiration. It can suggest new angles, provide relevant knowledge or even pose questions that help you think differently about your topic. This kind of support is invaluable, especially for creators who are striving to maintain a consistent output of high-quality content. What makes an ideal AI Tool for Original Content Creators? So with that, here is what I envision for an Ideal AI Content Creation assistant. Idea Development and Storage: A method and space where creators can quickly capture, store their ideas, and develop them over time. This isn’t just about keeping notes; it’s about creating an environment where ideas can grow and evolve.
Chat with your knowledge base: Just ask to get insights out of what you saved in real-time. Ideas, thoughts, articles.. everything
Curated Assistance: It suggests relevant knowledge to the content you are creating, making use of the knowledge you saved
Brainstorming with multiple AIs: You have the ability to converse with multiple AI models in the same space where you are creating your content. It improves the focus and range of ideas. Hearing from users like Alex reinforces my belief in the importance of these features. It isn’t about generating content for the sake of quantity; it’s about enhancing the quality of the content that creators are passionate about. What do you think about this approach? Have you had any experiences with a great AI tool in content creation? Hey everyone, In today’s world, AI-generated content is everywhere. It’s becoming increasingly common to see articles, blog posts, and even social media updates created by AI. This has raised a lot of questions and concerns, especially among original content creators who pour their heart and soul into each piece they craft. How can these creators continue to stand out? Or how can AI enhance their creative power without compromising their unique voice and authenticity? I had a great chat recently with one of Saner.ai’s early users, and that really got me thinking about these questions. Let me share that with you. The Chat with a Content Creator So, here’s the story. This user, let’s call him Alex, is a full-time worker and wants to build his personal brand on LinkedIn. He told me that he had been searching for a solution to help him store and develop his ideas. He wanted something more than just a digital notepad or a traditional content management tool. What Alex needed was a companion in his creative process — something that could help him refine and evolve his thoughts and ideas popping up in his life into valuable content. Interestingly, Alex mentioned that he doesn’t want AI content generators. You know, those tools that churn out articles in mass but often lack authenticity and a personal touch. Alex feels that these tools, while efficient, can’t capture his unique voice and style. And I totally get that. Authenticity is key, especially when you’re building a personal brand in this AI world. What Alex was looking for was an AI assistant, not just a generator. He needed a tool that could help him store ideas quickly, recommend ideas based on information he saved, and brainstorm ideas together. That’s how Alex came to Saner.ai. He hopes it will allow him to achieve this outcome. AI in Content Creation This conversation with Alex really got me reflecting on the broader implications of AI in the content creation space. As AI technology advances, it’s becoming increasingly embedded in various aspects of our lives, including the way we create and consume content. However, there’s a significant distinction between using AI to generate a flood of generic, mass-produced content and using AI to support the creation of original, thoughtful content. Firstly, let’s talk about the increase in AI-generated content. There’s no denying the efficiency of AI when it comes to producing large volumes of text quickly. For businesses looking to populate their blogs, social media, and other platforms with regular updates, AI content generators, which can create hundreds of content for SEO purposes in days instead of months, is surely tempting. However, this comes with a downside. The content produced by these generators often lacks the nuance, personality, and authenticity that human writers bring to the table. It may feel impersonal and disconnected. If your purpose is to establish an emotional connection with your audience, then this solution can fall short in a world that’s already saturated with information. On the other hand, using AI to support original content creation can be beneficial. This approach is about using AI to curate, ask, brainstorm, and correct grammar. Imagine having an AI assistant who helps you brainstorm ideas, organize your thoughts, and provide tailored suggestions to improve your writing. This doesn’t just save time — it enhances the creative process, allowing you to focus more on developing your unique ideas and less on the manual work of organizing and maintaining a database. Furthermore, AI can be a powerful tool for overcoming common creative challenges. For instance, writer’s block is something every writer faces at some point. An AI assistant can help break through this barrier by offering new perspectives and sparking inspiration. It can suggest new angles, provide relevant knowledge or even pose questions that help you think differently about your topic. This kind of support is invaluable, especially for creators who are striving to maintain a consistent output of high-quality content. What makes an ideal AI Tool for Original Content Creators? So with that, here is what I envision for an Ideal AI Content Creation assistant. Idea Development and Storage: A method and space where creators can quickly capture, store their ideas, and develop them over time. This isn’t just about keeping notes; it’s about creating an environment where ideas can grow and evolve. Chat with your knowledge base: Just ask to get insights out of what you saved in real-time. Ideas, thoughts, articles.. everything Curated Assistance: It suggests relevant knowledge to the content you are creating, making use of the knowledge you saved Brainstorming with multiple AIs: You have the ability to converse with multiple AI models in the same space where you are creating your content. It improves the focus and range of ideas. Idea Development and Storage: A method and space where creators can quickly capture, store their ideas, and develop them over time. This isn’t just about keeping notes; it’s about creating an environment where ideas can grow and evolve. Idea Development and Storage: Idea Development and Storage: Chat with your knowledge base: Just ask to get insights out of what you saved in real-time. Ideas, thoughts, articles.. everything Chat with your knowledge base : Chat with your knowledge base Curated Assistance: It suggests relevant knowledge to the content you are creating, making use of the knowledge you saved Curated Assistance : Curated Assistance Brainstorming with multiple AIs: You have the ability to converse with multiple AI models in the same space where you are creating your content. It improves the focus and range of ideas. Brainstorming with multiple AIs : Brainstorming with multiple AIs Hearing from users like Alex reinforces my belief in the importance of these features. It isn’t about generating content for the sake of quantity; it’s about enhancing the quality of the content that creators are passionate about. What do you think about this approach? Have you had any experiences with a great AI tool in content creation?

An AI Assistant for Original Content Creators vs. Mass AI Article Generators

**LJUBLJANA, Slovenia, April 29th, 2024/Chainwire/--**The explosive rise of Artificial Intelligence has sparked the first stages of a new knowledge revolution, comparable historically with the invention of the printing press or the world wide web. Its sudden growth also pointed at some of the threats and shortfalls such as hallucinations, bias, mishandling of intellectual property rights and even potential AI model collapses. Both the opportunities and challenges of this knowledge revolution are showing the need for convergence between Crypto, Internet and AI in a Verifiable Internet for AI proposed by OriginTrail. The Verifiable Internet for AI is effectively addressing AI’s shortfalls by decentralizing knowledge that AI systems use in their solutions. One of the pioneering implementations of the approach is Polkabot.AI - a Decentralized AI education hub on Polkadot which will see its full release in the coming months after receiving support from the Polkadot Treasury. https://www.youtube.com/embed/z7Nuv902BL8 Spearheading the development of Polkabot.ai is Trace Alliance, a collaborative hub that builds partnerships for creating and leveraging trusted knowledge in the age of AI. The solution is bringing the vision of decentralized AI to reality as it revolutionizes how anyone can interact and learn about the Polkadot ecosystem whether they are a novice getting through the first steps or a seasoned user looking for the latest updates. It achieves that by allowing a wider Polkadot ecosystem to get involved in creating a trusted knowledge base that Polkabot’s AI system will use to construct its responses. Unlike the solutions using solely generative AI, Polkabot is implementing a novel decentralized Retrieval Augmented Generation (dRAG) approach leveraging the OriginTrail Decentralized Knowledge Graph (DKG). Instead of Polkabot solely relying on AI to produce a generated response, it uses AI to construct a response using trusted inputs from the DKG populated by the wider Polkadot community. This gives the solution information provenance, respect for data ownership and gives the user a chance to verify each source and its issuer used in the final response. The trusted knowledge Polkabot’s AI systems access will be continuously expanded through community curation and knowledge publishing process called knowledge mining. The Polkabot.ai initiative received backing through an OpenGov treasury proposal, which was approved on April 23, 2024. As an AI-powered educational platform that tailors learning to each user, PolkaBot.AI represents a significant leap forward in how we interact with Polkadot's diverse content - whether within the ecosystem or in outward communications - enabling users to access trusted knowledge and get precise responses online. About OriginTrails OriginTrail is an ecosystem building Verifiable Internet for AI, aiming to combat misinformation worsened by AI adoption. It ensures the trustworthiness of information by allowing its origins to be tracked verifiably, promoting discoverability and integrity of knowledge for reliable AI. OriginTrail has applications in real-world assets (RWAs), search engines, question-answering systems, and generally knowledge-dependent applications (such as AI systems). Initially adopted in global supply chains to provide a trusted data sharing hub, OriginTrail now also supports the creation of a trusted AI knowledge ecosystem. It enhances the discoverability of crucial knowledge and verifies information provenance, crucial in the age of misinformation. OriginTrail's technology is increasingly vital across various sectors, including asset tokenization, construction, healthcare, and the metaverse, due to its ability to authenticate and secure information. Supported by world-class organizations like the British Standards Institution, SCAN, Polkadot, and Walmart, among others, OriginTrail is enhancing its trusted knowledge network. These partnerships extend its impact across multiple industries, emphasizing its role in advancing a verifiable knowledge web and supporting the economy of RWAs.Stay updated with the latest developments by following PolkaBotAI on X. Contact Martina Poberaj OriginTrail office@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, April 29th, 2024/Chainwire/--**The explosive rise of Artificial Intelligence has sparked the first stages of a new knowledge revolution, comparable historically with the invention of the printing press or the world wide web. Its sudden growth also pointed at some of the threats and shortfalls such as hallucinations, bias, mishandling of intellectual property rights and even potential AI model collapses. Both the opportunities and challenges of this knowledge revolution are showing the need for convergence between Crypto, Internet and AI in a Verifiable Internet for AI proposed by OriginTrail. The Verifiable Internet for AI is effectively addressing AI’s shortfalls by decentralizing knowledge that AI systems use in their solutions. One of the pioneering implementations of the approach is Polkabot.AI - a Decentralized AI education hub on Polkadot which will see its full release in the coming months after receiving support from the Polkadot Treasury. Polkabot.AI Polkabot.AI https://www.youtube.com/embed/z7Nuv902BL8 https://www.youtube.com/embed/z7Nuv902BL8 https://www.youtube.com/embed/z7Nuv902BL8 Spearheading the development of Polkabot.ai is Trace Alliance, a collaborative hub that builds partnerships for creating and leveraging trusted knowledge in the age of AI. The solution is bringing the vision of decentralized AI to reality as it revolutionizes how anyone can interact and learn about the Polkadot ecosystem whether they are a novice getting through the first steps or a seasoned user looking for the latest updates. It achieves that by allowing a wider Polkadot ecosystem to get involved in creating a trusted knowledge base that Polkabot’s AI system will use to construct its responses. Unlike the solutions using solely generative AI, Polkabot is implementing a novel decentralized Retrieval Augmented Generation (dRAG) approach leveraging the OriginTrail Decentralized Knowledge Graph (DKG). Instead of Polkabot solely relying on AI to produce a generated response, it uses AI to construct a response using trusted inputs from the DKG populated by the wider Polkadot community. This gives the solution information provenance, respect for data ownership and gives the user a chance to verify each source and its issuer used in the final response. The trusted knowledge Polkabot’s AI systems access will be continuously expanded through community curation and knowledge publishing process called knowledge mining. The Polkabot.ai initiative received backing through an OpenGov treasury proposal , which was approved on April 23, 2024. As an AI-powered educational platform that tailors learning to each user, PolkaBot.AI represents a significant leap forward in how we interact with Polkadot's diverse content - whether within the ecosystem or in outward communications - enabling users to access trusted knowledge and get precise responses online. OpenGov treasury proposal OpenGov treasury proposal About OriginTrails OriginTrail is an ecosystem building Verifiable Internet for AI, aiming to combat misinformation worsened by AI adoption. It ensures the trustworthiness of information by allowing its origins to be tracked verifiably, promoting discoverability and integrity of knowledge for reliable AI. OriginTrail has applications in real-world assets (RWAs), search engines, question-answering systems, and generally knowledge-dependent applications (such as AI systems). Initially adopted in global supply chains to provide a trusted data sharing hub, OriginTrail now also supports the creation of a trusted AI knowledge ecosystem. It enhances the discoverability of crucial knowledge and verifies information provenance, crucial in the age of misinformation. OriginTrail's technology is increasingly vital across various sectors, including asset tokenization, construction, healthcare, and the metaverse, due to its ability to authenticate and secure information. Supported by world-class organizations like the British Standards Institution, SCAN, Polkadot, and Walmart, among others, OriginTrail is enhancing its trusted knowledge network. These partnerships extend its impact across multiple industries, emphasizing its role in advancing a verifiable knowledge web and supporting the economy of RWAs.Stay updated with the latest developments by following PolkaBotAI on X . PolkaBotAI on X PolkaBotAI on X Contact Martina Poberaj OriginTrail office@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.

PolkaBotAI - Decentralizing AI With OriginTrail And Polkadot

DUBAI, United Arab Emirates, April 24th, 2024/Chainwire/--“Yoki Origins”, supported by Bandit Network and Brave Ads, introduces a gamified and rewarding experience for Astar zkEVM users, marking a significant milestone in Web3 adoption. Bandit Network, a leading provider of White-Labelled Quest solutions with Points SDK, proudly announces its collaboration with Astar Network to power the ongoing “Yoki Origins'' journey. Launched on March 8th, this innovative user onboarding journey coincides with the mainnet release of Astar zkEVM, a Layer 2 scaling solution for Ethereum. This collaboration represents a significant milestone in the evolution of Web3 infrastructure. Astar zkEVM, leveraging Ethereum's security and the Polygon CDK (Chain Development Kit), offers a secure and scalable platform for Web3 projects and enterprises. As the inaugural zero-knowledge EVM chain, Astar zkEVM unlocks access to Ethereum's extensive developer resources and industry-standard infrastructure providers. The "Yoki Origins'' journey, powered by Bandit Network's White-Labelled Quest solutions with Points SDK promoted by Brave Ads, introduces users to the world of Astar zkEVM through interactive experiences inspired by Japanese Yokais. Participants engage with Capsule Machines, or "Yo-Ports," to collect Yokis and artistic creations, fostering engagement and loyalty within the Astar ecosystem. "Bandit Network is thrilled to collaborate with Astar Network on the 'Yoki Origins' journey," said Sandesh B Suvarna, Founder of Bandit Network. "Our White-Labelled Quest solutions with Points SDK, along with promotion through Brave, provide a seamless and engaging experience for users, allowing them to immerse themselves in the Astar ecosystem while celebrating Japanese culture and heritage." Maarten Henskens, Head of Astar Network, commented, "Bandit Network's platform with Brave Ads has been instrumental in bringing the 'Yoki Origins' journey to life. This collaboration underscores our commitment to delivering innovative and engaging experiences to our community as we continue to expand the capabilities of Astar zkEVM." ** ** Luke Mulks, VP of Business Operations at Brave, highlights the integral role of Bandit Network's solutions in partnership with Brave Ads, "We’re excited for Brave Ads to be collaborating with Bandit Network's White-Labelled Quest solutions to bring awareness and growth for the Astar ecosystem. With collaborations like 'Yoki Origins', Brave Ads demonstrates the ability to use Quests as a growth accelerator. Brave Ads campaigns will elevate Quests beyond airdrop farmers, introducing Quests to millions of Brave Rewards users. In addition to earning BAT with their attention, Brave Rewards users will now be able to earn even more by participating in Quests through Astar and other participating Bandit Network partners advertising with Brave Ads." Brave offers Web3 promotional opportunities through Brave Ads for advertisers that pay with BAT for their campaigns, facilitating growth and adoption within the broader Web3 ecosystem. ** ** The "Yoki Origins" journey invites users to explore Astar zkEVM’s unique characters, participate in quests, and collect digital collectibles. Through interactive Capsule Machines, participants can obtain Yokis and artistic creations contributed by various artists, enterprises, and builders. The user journey aims to foster accessibility for individuals unfamiliar with Web3, incorporating social logins and credit card payments. Astar Network serves as a conduit for projects spanning enterprise, entertainment, and gaming to enter the Japanese market and beyond. By propelling global adoption of Web3, Astar aims to build an ecosystem fueled by both Polkadot and Polygon, positioning itself as a leading blockchain for the Japanese market. For more information about Bandit Network and the "Yoki Origins" journey, visit https://yoki.astar.network/en. About Bandit Network: Bandit Network is a leading provider of White-Labelled Quest solutions with Points SDK, offering a no-code platform for engaging and interactive experiences. With a focus on fostering user engagement and loyalty, Bandit Network empowers organisations to create immersive campaigns and events across various industries. Media Contact: Name: Sandesh B Suvarna Designation: Founder Company: Bandit Network Email: sandesh@bandit.network LinkedIn: https://www.linkedin.com/in/sandeshbsuvarna/ Twitter: https://twitter.com/sandeshsuvarna Telegram: https://t.me/SandeshBSuvarna Name: Luke Mulks Designation: VP - Business Operations Company: Brave Email: press@brave.com Twitter: https://twitter.com/lukemulks Twitter: https://twitter.com/brave Advertisers: https://ads.brave.com/ This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. DUBAI, United Arab Emirates, April 24th, 2024/Chainwire/-- “Yoki Origins”, supported by Bandit Network and Brave Ads , introduces a gamified and rewarding experience for Astar zkEVM users, marking a significant milestone in Web3 adoption. DUBAI, United Arab Emirates, April 24th, 2024/Chainwire/-- Bandit Network Bandit Network Brave Ads Brave Ads Bandit Network , a leading provider of White-Labelled Quest solutions with Points SDK, proudly announces its collaboration with Astar Network to power the ongoing “Yoki Origins'' journey. Launched on March 8th, this innovative user onboarding journey coincides with the mainnet release of Astar zkEVM, a Layer 2 scaling solution for Ethereum. This collaboration represents a significant milestone in the evolution of Web3 infrastructure. Bandit Network Bandit Network Astar zkEVM, leveraging Ethereum's security and the Polygon CDK (Chain Development Kit), offers a secure and scalable platform for Web3 projects and enterprises. As the inaugural zero-knowledge EVM chain, Astar zkEVM unlocks access to Ethereum's extensive developer resources and industry-standard infrastructure providers. The "Yoki Origins'' journey, powered by Bandit Network's White-Labelled Quest solutions with Points SDK promoted by Brave Ads, introduces users to the world of Astar zkEVM through interactive experiences inspired by Japanese Yokais. Participants engage with Capsule Machines, or "Yo-Ports," to collect Yokis and artistic creations, fostering engagement and loyalty within the Astar ecosystem. "Bandit Network is thrilled to collaborate with Astar Network on the 'Yoki Origins' journey," said Sandesh B Suvarna, Founder of Bandit Network. "Our White-Labelled Quest solutions with Points SDK, along with promotion through Brave, provide a seamless and engaging experience for users, allowing them to immerse themselves in the Astar ecosystem while celebrating Japanese culture and heritage." "Bandit Network is thrilled to collaborate with Astar Network on the 'Yoki Origins' journey," said Sandesh B Suvarna, Founder of Bandit Network. "Our White-Labelled Quest solutions with Points SDK, along with promotion through Brave, provide a seamless and engaging experience for users, allowing them to immerse themselves in the Astar ecosystem while celebrating Japanese culture and heritage." Maarten Henskens, Head of Astar Network, commented, "Bandit Network's platform with Brave Ads has been instrumental in bringing the 'Yoki Origins' journey to life. This collaboration underscores our commitment to delivering innovative and engaging experiences to our community as we continue to expand the capabilities of Astar zkEVM." Maarten Henskens, Head of Astar Network, commented, "Bandit Network's platform with Brave Ads has been instrumental in bringing the 'Yoki Origins' journey to life. This collaboration underscores our commitment to delivering innovative and engaging experiences to our community as we continue to expand the capabilities of Astar zkEVM." ** ** Luke Mulks, VP of Business Operations at Brave, highlights the integral role of Bandit Network's solutions in partnership with Brave Ads, "We’re excited for Brave Ads to be collaborating with Bandit Network's White-Labelled Quest solutions to bring awareness and growth for the Astar ecosystem. With collaborations like 'Yoki Origins', Brave Ads demonstrates the ability to use Quests as a growth accelerator. "We’re excited for Brave Ads to be collaborating with Bandit Network's White-Labelled Quest solutions to bring awareness and growth for the Astar ecosystem. With collaborations like 'Yoki Origins', Brave Ads demonstrates the ability to use Quests as a growth accelerator. Brave Ads campaigns will elevate Quests beyond airdrop farmers, introducing Quests to millions of Brave Rewards users. In addition to earning BAT with their attention, Brave Rewards users will now be able to earn even more by participating in Quests through Astar and other participating Bandit Network partners advertising with Brave Ads." Brave Ads campaigns will elevate Quests beyond airdrop farmers, introducing Quests to millions of Brave Rewards users. In addition to earning BAT with their attention, Brave Rewards users will now be able to earn even more by participating in Quests through Astar and other participating Bandit Network partners advertising with Brave Ads." Brave offers Web3 promotional opportunities through Brave Ads for advertisers that pay with BAT for their campaigns , facilitating growth and adoption within the broader Web3 ecosystem. for advertisers that pay with BAT for their campaigns for advertisers that pay with BAT for their campaigns ** ** The "Yoki Origins" journey invites users to explore Astar zkEVM’s unique characters, participate in quests, and collect digital collectibles. Through interactive Capsule Machines, participants can obtain Yokis and artistic creations contributed by various artists, enterprises, and builders. The user journey aims to foster accessibility for individuals unfamiliar with Web3, incorporating social logins and credit card payments. Astar Network serves as a conduit for projects spanning enterprise, entertainment, and gaming to enter the Japanese market and beyond. By propelling global adoption of Web3, Astar aims to build an ecosystem fueled by both Polkadot and Polygon, positioning itself as a leading blockchain for the Japanese market. For more information about Bandit Network and the "Yoki Origins" journey, visit https://yoki.astar.network/en . Bandit Network Bandit Network https://yoki.astar.network/en https://yoki.astar.network/en About Bandit Network: Bandit Network is a leading provider of White-Labelled Quest solutions with Points SDK, offering a no-code platform for engaging and interactive experiences. With a focus on fostering user engagement and loyalty, Bandit Network empowers organisations to create immersive campaigns and events across various industries. Bandit Network Bandit Network Media Contact: Name: Sandesh B Suvarna Designation: Founder Company: Bandit Network Email: sandesh@bandit.network LinkedIn: https://www.linkedin.com/in/sandeshbsuvarna/ Twitter: https://twitter.com/sandeshsuvarna Telegram: https://t.me/SandeshBSuvarna Name: Luke Mulks Designation: VP - Business Operations Company: Brave Email: press@brave.com Twitter: https://twitter.com/lukemulks Twitter: https://twitter.com/brave Advertisers: https://ads.brave.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.

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