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Sway AI

swayai

Professionals in the book publishing industry are terrified of artificial intelligence — and for good reason. While it isn’t all bad, it has the potential to bring the sector to its knees. Not to mention, it has already harmed many creatives. Are their fears justified or unwarranted? AI’s Emerging Role in the Publishing Industry Thanks to its versatility, AI has emerged in most parts of the publishing industry. In 2023, 47% of book publishers based in the United States used it for marketing, 25% for editorial work, and 12% for content generation. Companies can use it in writing, editing, advertising, or administrative roles — it can do almost everyone’s job to some extent. AI Isn’t the First Threat, But It’s the Biggest Many disruptors have affected the publishing industry. Most recently, e-books have started replacing physical books. According to one estimate, about 30% of pages will be digitally printed by 2028, up from 5% in 2023. Many publishing houses and self-publishers are migrating to digital. Before that, digital printing shook up the offset printing press. Although AI isn’t the first disruptor, it’s the biggest so far. The publishing sector has withstood others because they were all natural evolutions to outdated processes. Machine learning (ML) and large language models are different — they replace existing technology instead of improving it. Who needs writers, editors, and agents when algorithms can do their work for free? Writers, Editors, and Publishers Are Terrified of AI Most professionals in book publishing are terrified of generative AI. Its versatility could bring about the sector’s downfall. A handful of high-quality models can write a novel, proofread, edit, design a cover, make marketing materials, post on social media for publicity, distribute digital copies, and handle accounting. For now, AI can’t perform any of these jobs better than a human. One industry professional said the content it generates is “crude at best,” arguing it isn’t comparable to what a real writer can produce. Even so, it might not matter when it can pump out pages at an inhuman speed. AI will inevitably affect the entire industry even if every author, editor, and publisher agrees not to use it. This technology’s accessibility is its main driving force. Dozens of leading models — and thousands more in-house systems — exist. Almost anyone can use ChatGPT, Midjourney, LaMDA, or Claude to make, market, and distribute AI-generated books. The Negative Impact of AI on the Publishing Industry There’s abundant evidence of AI’s negative impacts on the book publishing industry. AI Is Stealing People’s Jobs There’s truth to the fear that AI is set to steal people’s jobs. While more job ads now stress human-written content is a hard requirement and more companies are investing in software to detect AI-generated content, the publishing sector’s pushback hasn’t been enough — and people are being forced to find new careers. Olivia Lipkin, a copywriter, said her assignments dwindled after ChatGPT came out. She even claimed management changed her title to “Olivia/ChatGPT” on the company’s messaging platform. Within a few months of the generative model’s release, Lipkin was let go without explanation. Eventually, she discovered her managers had realized AI was much cheaper. It’s Stealing Their Work, Too Algorithms can’t come up with content on their own — they must use others’ work. Since most books are copyrighted, AI companies have resorted to piracy and copyright infringement. Books3 is a data set of nearly 200,000 pirated novels used to train generative AI systems. Most of the content was published within the last 20 years, so many living authors are affected. This issue isn’t limited to bad actors and shady characters, either. OpenAI, ChatGPT’s creator, argued creating models would be impossible unless it was allowed to use copyrighted material. It claimed public domain content wouldn’t “meet the needs” of people. Regardless of the statement’s truth, it shows how unconcerned AI companies are about content theft. There Aren’t Protections Yet Despite the issue of AI-driven copyright infringement being a real threat to authors, literary agents and publishing houses, no real regulations exist to protect them yet. Recently, a 24-page document revealed that Midjourney had a database of around 16,000 individuals it allegedly stole art from. Apparently, it was a list for model training. Even though the list — which contained specific names — seems like it would prompt swift regulatory action, things aren’t as open and shut as they should be. In many countries, a lawsuit is the only recourse creators have. Realistically, there’s no guarantee they’ll be successful. Not to mention, many would have to fight against well-funded tech giants. The Positive Impact of AI on the Publishing Industry AI wouldn’t be able to disrupt the book publishing industry if it was inferior to what people currently use. In other words, a silver lining does exist — there are potential positive impacts. AI Can Take on the Mundane Work Most writers spend more time than they’d like on mundane — but necessary — tasks like proofreading and spellchecking. The same can be said about literary agents hassling publishers or marketers tweaking designs for the nth time. AI can take over these boring, repetitive duties. Since ML models evolve over time, they can create and maintain searchable databases of relevant marketing avenues, contract work or agents to query. Publishing companies can leverage AI tools to help editors check for grammar or continuity mistakes, accelerating time to market. AI Makes Human Content Stand Out Publishing AI-generated books is a get-rich-quick scheme. Natural language processing technology isn’t advanced yet — readers and literary agents can quickly identify this kind of content. Fortunately, many view it as cheap, poor quality or lazy. To avoid AI-generated books, readers must search for authors. In doing so, they’ll spotlight creators and learn to appreciate content better. Many companies are already taking advantage of this fact, using “100% human-generated content” as a selling point. The Gray Area Where Its Impact Could Go Either Way AI’s impact isn’t purely black and white — there’s a massive gray area where things could go either way. Navigating these subtleties is tricky. AI Has Editorial and Publishing Sway Book publishing is notoriously difficult. Even now-famous authors have dealt with round after round of rejections. An ML model in a decision-making role could evaluate content at an unparalleled pace, helping more people get published. It may occasionally make biased or illogical decisions, but those aren’t exactly uncommon. Numerous cases exist where a book goes through dozens of rejections before becoming a best seller — well-trained literary agents and publishers make mistakes, too. Self-Publishing Is Easier Than Ever Anybody with access to AI can ask a model to write a book, design a cover, and generate hype on social media. This technology makes self-publishing easier than ever, meaning previously overlooked and underserved groups can fulfill their dream of publishing a novel. Unfortunately, bad actors are taking advantage of the simplicity of AI-driven self-publishing. One author, Jane Friedman, found six fake titles under her name in a few months. Others claim they spend hours reporting listings just for new ones to pop up the next week. Another potential issue with AI-powered self-publishing is that authors won’t need professional help anymore. If most people go this route — which they might because getting published traditionally can be next to impossible — the sector could collapse. The Future of Book Publishing Hinges on AI Although every big disruptor causes general worry, AI is different. For one, it’s accelerating at a pace publishers can’t keep up with. It’s also like no technology people have ever seen before — what other invention can autonomously read, learn, write, edit and publish a novel? Professionals’ fear seems to be justified. For now, there’s no telling if generative models will transform, replace or topple this industry. Professionals in the book publishing industry are terrified of artificial intelligence — and for good reason. While it isn’t all bad, it has the potential to bring the sector to its knees. Not to mention, it has already harmed many creatives. Are their fears justified or unwarranted? AI’s Emerging Role in the Publishing Industry AI’s Emerging Role in the Publishing Industry Thanks to its versatility, AI has emerged in most parts of the publishing industry. In 2023, 47% of book publishers based in the United States used it for marketing, 25% for editorial work, and 12% for content generation. Companies can use it in writing, editing, advertising, or administrative roles — it can do almost everyone’s job to some extent. 47% of book publishers 47% of book publishers AI Isn’t the First Threat, But It’s the Biggest AI Isn’t the First Threat, But It’s the Biggest Many disruptors have affected the publishing industry. Most recently, e-books have started replacing physical books. According to one estimate, about 30% of pages will be digitally printed by 2028, up from 5% in 2023. Many publishing houses and self-publishers are migrating to digital. Before that, digital printing shook up the offset printing press. about 30% of pages about 30% of pages Although AI isn’t the first disruptor, it’s the biggest so far. The publishing sector has withstood others because they were all natural evolutions to outdated processes. Machine learning (ML) and large language models are different — they replace existing technology instead of improving it. Who needs writers, editors, and agents when algorithms can do their work for free? Writers, Editors, and Publishers Are Terrified of AI Most professionals in book publishing are terrified of generative AI. Its versatility could bring about the sector’s downfall. A handful of high-quality models can write a novel, proofread, edit, design a cover, make marketing materials, post on social media for publicity, distribute digital copies, and handle accounting. For now, AI can’t perform any of these jobs better than a human. One industry professional said the content it generates is “crude at best,” arguing it isn’t comparable to what a real writer can produce. Even so, it might not matter when it can pump out pages at an inhuman speed. arguing it isn’t comparable arguing it isn’t comparable AI will inevitably affect the entire industry even if every author, editor, and publisher agrees not to use it. This technology’s accessibility is its main driving force. Dozens of leading models — and thousands more in-house systems — exist. Almost anyone can use ChatGPT, Midjourney, LaMDA, or Claude to make, market, and distribute AI-generated books. The Negative Impact of AI on the Publishing Industry There’s abundant evidence of AI’s negative impacts on the book publishing industry. AI Is Stealing People’s Jobs There’s truth to the fear that AI is set to steal people’s jobs. While more job ads now stress human-written content is a hard requirement and more companies are investing in software to detect AI-generated content, the publishing sector’s pushback hasn’t been enough — and people are being forced to find new careers. is a hard requirement is a hard requirement Olivia Lipkin, a copywriter, said her assignments dwindled after ChatGPT came out. She even claimed management changed her title to “Olivia/ChatGPT” on the company’s messaging platform. Within a few months of the generative model’s release, Lipkin was let go without explanation. Eventually, she discovered her managers had realized AI was much cheaper. said her assignments dwindled said her assignments dwindled It’s Stealing Their Work, Too Algorithms can’t come up with content on their own — they must use others’ work. Since most books are copyrighted, AI companies have resorted to piracy and copyright infringement. Books3 is a data set of nearly 200,000 pirated novels used to train generative AI systems. Most of the content was published within the last 20 years, so many living authors are affected. of nearly 200,000 pirated novels of nearly 200,000 pirated novels This issue isn’t limited to bad actors and shady characters, either. OpenAI, ChatGPT’s creator, argued creating models would be impossible unless it was allowed to use copyrighted material. It claimed public domain content wouldn’t “meet the needs” of people. Regardless of the statement’s truth, it shows how unconcerned AI companies are about content theft. argued creating models would be impossible argued creating models would be impossible There Aren’t Protections Yet Despite the issue of AI-driven copyright infringement being a real threat to authors, literary agents and publishing houses, no real regulations exist to protect them yet. Recently, a 24-page document revealed that Midjourney had a database of around 16,000 individuals it allegedly stole art from. Apparently, it was a list for model training. database of around 16,000 individuals database of around 16,000 individuals Even though the list — which contained specific names — seems like it would prompt swift regulatory action, things aren’t as open and shut as they should be. In many countries, a lawsuit is the only recourse creators have. Realistically, there’s no guarantee they’ll be successful. Not to mention, many would have to fight against well-funded tech giants. The Positive Impact of AI on the Publishing Industry AI wouldn’t be able to disrupt the book publishing industry if it was inferior to what people currently use. In other words, a silver lining does exist — there are potential positive impacts. AI Can Take on the Mundane Work Most writers spend more time than they’d like on mundane — but necessary — tasks like proofreading and spellchecking. The same can be said about literary agents hassling publishers or marketers tweaking designs for the nth time. AI can take over these boring, repetitive duties. Since ML models evolve over time, they can create and maintain searchable databases of relevant marketing avenues, contract work or agents to query. Publishing companies can leverage AI tools to help editors check for grammar or continuity mistakes, accelerating time to market. AI Makes Human Content Stand Out Publishing AI-generated books is a get-rich-quick scheme. Natural language processing technology isn’t advanced yet — readers and literary agents can quickly identify this kind of content. Fortunately, many view it as cheap, poor quality or lazy. To avoid AI-generated books, readers must search for authors. In doing so, they’ll spotlight creators and learn to appreciate content better. Many companies are already taking advantage of this fact, using “100% human-generated content” as a selling point. The Gray Area Where Its Impact Could Go Either Way AI’s impact isn’t purely black and white — there’s a massive gray area where things could go either way. Navigating these subtleties is tricky. AI Has Editorial and Publishing Sway Book publishing is notoriously difficult. Even now-famous authors have dealt with round after round of rejections. An ML model in a decision-making role could evaluate content at an unparalleled pace, helping more people get published. It may occasionally make biased or illogical decisions, but those aren’t exactly uncommon. Numerous cases exist where a book goes through dozens of rejections before becoming a best seller — well-trained literary agents and publishers make mistakes, too. Self-Publishing Is Easier Than Ever Anybody with access to AI can ask a model to write a book, design a cover, and generate hype on social media. This technology makes self-publishing easier than ever, meaning previously overlooked and underserved groups can fulfill their dream of publishing a novel. Unfortunately, bad actors are taking advantage of the simplicity of AI-driven self-publishing. One author, Jane Friedman, found six fake titles under her name in a few months. Others claim they spend hours reporting listings just for new ones to pop up the next week. found six fake titles found six fake titles Another potential issue with AI-powered self-publishing is that authors won’t need professional help anymore. If most people go this route — which they might because getting published traditionally can be next to impossible — the sector could collapse. The Future of Book Publishing Hinges on AI Although every big disruptor causes general worry, AI is different. For one, it’s accelerating at a pace publishers can’t keep up with. It’s also like no technology people have ever seen before — what other invention can autonomously read, learn, write, edit and publish a novel? Professionals’ fear seems to be justified. For now, there’s no telling if generative models will transform, replace or topple this industry.

Why the Book Publishing Industry Is Terrified of AI

কেন বই প্রকাশনা শিল্প এআই নিয়ে আতঙ্কিত

Warum die Buchverlagsbranche Angst vor KI hat

Por qué la industria editorial de libros está aterrorizada por la IA

Pourquoi l'industrie de l'édition de livres est terrifiée par l'IA

पुस्तक प्रकाशन उद्योग एआई से क्यों भयभीत है?

出版業界がAIを恐れる理由

도서출판업계가 AI를 두려워하는 이유

Por que a indústria editorial de livros tem pavor de IA

Почему книгоиздательская индустрия боится искусственного интеллекта

Kitap Yayıncılığı Endüstrisi Neden Yapay Zekadan Korkuyor?

Tại sao ngành xuất bản sách lại sợ hãi AI

图书出版业为何惧怕人工智能

Startups of The Year 2023 Winners: Europe

Are you aware of how the were carried out when there was no internet or computers? Back then, stock exchanges had active trading floors filled with brokers and traders. To make a trade or a purchase, they had to shout or use hand signals to alert others about their buy or sell orders. It looked a whole lot like an auction at a fish market today. buying and selling of stocks But then came computers and the internet to change the game completely. That not only replaced the archaic trading system on the floors of exchanges but also made it possible for individual investors to participate in markets on an equal footing. But that wasn't the last market-altering shift that would happen. The Algorithmic or Automated Trading Era Next came the era - where computers are programmed to trade independently when specific criteria are met. This made it possible to execute complex trading strategies with pinpoint precision. Investors and brokers gained the ability to plot their strategies many moves in advance. And they could also protect themselves from downside risk with automated stop-loss orders. algorithmic or automated trading The new trading systems also made a variety of possible. They took automated trading to the next level by allowing computers to trade based on a variety of price signals analyzed in real-time. And since the computers can execute a near-limitless number of trades in an instant, high-frequency trading to exploit every minute price movement became the norm. useful algorithmic trading tools Algorithmic trading strategies also came with another advantage. Traders could use software like and platforms like to backtest their strategies against historical market data. That meant they would know their strategy's odds of success before committing real money to it. It marked the final shift from intuition-based trading to data-backed analytical trading. MetaTrader 5 TradingView Enter Artificial Intelligence But algorithmic trading wasn't perfect. Using algorithmic trading, we can tell a computer to do A when B occurs. For example, an order to buy a stock (A) when RSI<30 (B). When condition B (RSI<30) is met, the computer performs action A (buys the stock). That's automated trading. Humans program and specify conditions in advance and the computer does everything else. But preprogrammed strategies can fall apart when confronted with contradictory or unexpected data. It's a vulnerability that has already spurred in the recent past. numerous mass-selloffs Artificial intelligence (AI), however, goes a step further. AI trading solutions can train on multiple types of data to understand how and why markets move as they do. Applying AI to the task of stock trading can allow computers to figure out those A and B conditions themselves and get results with minimum risk. And they should also have the ability to shift strategies on the fly with no further human intervention. Human Intelligence vs. Artificial Intelligence The great thing about AI systems is that they can process gigantic amounts of information in mere seconds, far exceeding the ability of human traders. The fintech app , for example, built proprietary AI-driven algorithms to instantly analyze information from a variety of data sources. It considers historical company and stock data, international markets, news announcements and social sentiment, financial reports, and even stocks' compatibility with investors' existing portfolios. Front The result is to quickly show investors the potential risk for any investment, so they can make more informed decisions in a fraction of the time. AI systems consider all the technical, fundamental, sentimental, and lots of other micro and macro factors to make the correct evaluation in real-time. They bring some of the art of the old-school intuition-based trader together with the power of data-backed analysis. And stock trading AI systems are improving fast. This past June, researchers at the University of Cagliari reported the results of a project that used a convolutional neural network (CNN) to power a buy and hold stock trading strategy. The CNN trained on historical S&P 500 data and could predict the live market with 50% accuracy. And with some minor modifications, . it did better than break even Is AI a Game Changer? While it's still too early to declare AI the next big thing in stock trading, all of the early signs are pointing in that direction. Already, basic AI systems can do a better-than-average job at predicting market movements. And they don't suffer from the tendency to , as human traders do. They approach every situation with cool rationality – which means they can exploit market movements that run counter to the underlying data. trade based on emotions AI can also protect traders from mistakes and work to stabilize markets, unlike algorithmic trading. A wide range of tasks (automation spectrum) can be performed without human intervention through AI that was impossible before. fat-finger trading With AI, can evaluate millions of data points and carry out trades at the most favorable price points, and analysts can make market predictions with increased accuracy. And AI can also help large trading firms to mitigate risk while aiming at the highest possible gains. Robo-advisors However, to perform such functions, a large amount of data is required. An AI model can only provide superior predictions when the primary dataset it learns from is large and diverse. And developers must ensure to utilize training methods that don't leave the AI with blind spots or inherent biases. Only then can an AI-based trading system have a solid foundation upon which to build. Pitfalls of AI in Trading With that said, some factors could prevent AI from coming to dominate financial markets. As with any other type of technical innovation, there are bound to be missteps along the way. And considering the speed of today's markets and their growing dependence on automation, even a slight hiccup could cause havoc. And there's also the possibility of AI being used for purposeful market manipulation. Back in 2010, a rogue trader used an algorithm to try and sway markets to their advantage – . With AI in the mix, such schemes might grow ever-more sophisticated, depending on what kind of fail-safe mechanisms evolve to constrain automated traders. with disastrous results And there's still the matter of AI's immaturity as a technology. Even now, as impressive as AI trading systems are, they can't fully replicate human intuition. That could leave AI systems open to external manipulation. For example, suppose an AI system tracks an influencer, Mr. X, using sentiment analysis. It would be up to its training data to inform a decision on how to weigh Mr. X's opinions. The AI can't yet factor in real-time contextual data. However, a seasoned human trader might have known that Mr. X's followers, given his history, would discount his opinion and act accordingly – producing a contradictory result. And if an individual influencer carried enough weight with an AI, they might decide to try and influence that AI's decision-making for their own benefit. The possibility of such outside manipulation – however remote – will remain a stumbling block for AI trading systems. That is until they evolve further and can make more intuitive decisions on their own. AI Trading Offers an Edge Over Algorithm Trading What is clear, however, is that AI-powered trading holds several advantages over its algorithm-only predecessors. First, there's the fact that an algorithm-driven system is always limited by the skills of the person using it. Because they cannot conduct independent analysis, they won't always deliver profitable results. An AI-powered system, by contrast, can make decisions based on rich data in real-time. AI can process massive amounts of data far faster than a human trader ever could, which increases the chances that it will make sound and reasoned decisions. And it can make those decisions fast enough to exploit even the tiniest market movements, or to counteract unexpected market swings by remaining flexible. Managing Risks All of these factors mean that AI is already one of the best tools available to forecast which stocks to invest in. The risk-management benefits of current-generation AI tools alone make it more than worth using. And for that reason, it's no surprise that AI is already in use by the stock markets themselves for that purpose. NASDAQ, for example, adopted an AI-powered anti-fraud system at the end of 2019. It's purpose-built to that caused the flash crash referenced earlier. And because the system can learn to spot other types of malicious activity, it may come to act as a check on some of the pitfalls of AI-powered trading identified here. detect and defeat the exact type of market manipulation The Bottom Line The bottom line is that AI has already revolutionized the stock market. It may not be as visible as the technologies it's displacing, but its influence is undeniable. And this is just the beginning. Moving forward, there's an excellent chance that AI will continue to evolve new feature sets to aid investors, brokers, and market regulators. But even at this early stage, AI-powered trading tools can reduce the time, in-depth knowledge, and intuition necessary to build a strong portfolio for the long term. We haven't reached the point where human traders become irrelevant, but today's AI has closed the gap between casual investors and professional traders considerably. And given how fast that has happened – it won't be long before AI becomes the new foundation of the stock markets of the future. Photos licensed via contributor's Adobe Stock account, by metamorworks, peshkov, AndSus.

The Evolution and Future of AI in the Stock Market

Disclaimer: This is not financial advice. Article inspired from InWara . For more details visit terms and conditions. Blockchain is widely associated with one of its applications, cryptocurrencies but this is hardly the only use-case for this technology. Blockchain is essentially a digital ledger where data is stored on a network of computers or so called nodes. The computers on this network check the data systematically, and makes sure it hasn’t been tampered with. A blockchain network inherently brings security and transparency into the system, hence can be used in sectors where there is a lack of those features. Real estate 2.0 Real estate is the latest among a multitude of industries that are poised to be impacted by blockchain technology. Similar to how many age-old industries that are plagued with inefficiencies, real estate market could also benefit from the advantages inherently to blockchain technology. Blockchain could enable direct transfer of assets between two parties without the need for a trusted intermediary. There are several other potential benefits to implementing Blockchain technology in real estate, which we’ve covered in detail a . previous article Briefly blockchain could impact the Real Estate market by Eliminating the need for a trusted intermediary Facilitating a platform for direct transactions between buyer and seller Increasing deal flow and general access to deals Eliminating cumbersome formalities and procedures Making transactions more secure and transparent Eliminating unnecessary costs for administration etc and thus helping both parties save money. Some of these benefits are either inherent or are programmable into blockchain networks. Some features that could be expected to be available in the future is tokenization of real world assets and facilitating real estate transactions using smart contracts. Enterprises focus on real world applications Applications of blockchain is often restricted to cryptocurrencies by entrepreneurs and developers alike but the past few months have witnessed enterprises bringing in ideas that have a real world impact. This trend can be observed in the funding received by startups in real estate, trading and investing sectors. In January 2019, real estate blockchain enterprises have displaced sectors such as Financial services, Fintech and Cryptocurrency and is now ranked second in terms of funds raised. Funds raised by various sectors in January 2019 Source: InWara’s monthly report: January 2019 Despite the reigning bearish market sentiment, real estate startups raised an impressive $80 million. Only second to trading and investing startups which raised $81 million. Interestingly Canadian startup single handedly raised all $80 million in capital in real estate sector. Of the dozens of startups in real estate sector, Propy has recently achieved an important milestone that could provide a glimpse into the future of real estate transactions. Chelle coin Propy Propy is a US-based real estate blockchain enterprise and it recently made headlines when it concluded the first residential transaction exclusively on a blockchain platform facilitated by Ethereum smart contracts. According to InWara’s ICO+STO database the company managed to raise ~$13 million during its token sale which concluded in 2017. recently published a report pertaining to the details of the property, according to the report the real estate asset sold for approximately $1 million and was facilitated using smart contracts. CEO of Propy thinks that despite blockchain’s nascency, it is still an optimal solution to simplify buying real estate easier. Natalia was quoted saying “Over the past two decades, Silicon Valley has become one of the world’s hottest property markets, denoting the perfect example for a market where high levels of trust and transparency throughout the transaction process are paramount. Forbes Natalia Karayaneva , While still considered a nascent technology, the utilization of blockchain is the optimal trustworthy way of streamlining the lengthy and non-transparent process of buying a home. We look forward to facilitating hundreds more of these transactions in the near future, easing the process of buying a home for all parties involved,” signaling the company’s ambitious plans of initiating a paradigm shift in real estate transactions. Propy concluded its first property transaction entirely using Bitcoin. This deal in spite of being revolutionary, portrayed the company as being solely into real estate transactions using cryptocurrencies. The company is now trying to educate agents on how buyers can leverage blockchain by releasing a which allows agents to become certified in cryptos. Despite these incredible developments, blockchain powered real estate market still has to cross a regulatory minefield before it can become universally adopted. crypto certified agent program Then there are who believe Blockchain cannot deliver on all its promises like , Farzam argues that there will always be a need for a trusted intermediary, to ‘verify’ if a digital token is actually backed by the high-value physical asset as claimed by the token distributors Farzam Ehsani , co-founder and CEO of VALR.com a digital asset trading platform.

Blockchain technology swaying away from crypto, to revamp real estate?

Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Conclusion, References

Trends and Predictions: How Social Media Influencers Shape Consumer Buying Decisions

Authors:
(1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work;
(2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work;
(3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro
Motivation
Related Work
Dataset
Experiments and Evaluation
Results and Analysis
Conclusion and Future Work
Limitations and References
A. Appendix 8. Limitations • In the plot generation dataset, the Wikipedia plots are sometimes not written by professional content writers from the film industry. Therefore these plots may fail to include the main events of the movie. • In a few cases, the model fails to generate coherent events along with the abrupt introduction of characters in the plots and scenes. • Although it has been noticed only a few times, the plot or scene generated contains repeated clauses or phrases. • The model hallucinates and generates factually incorrect things, making it incapable of generating biographies or documentaries. • The plot or scene may not abide by the theme of the input or genre mentioned along with the prompt. References Selmer Bringsjord and David Ferrucci. 1999. Artificial intelligence and literary creativity: Inside the mind of brutus, a storytelling machine. Psychology Press. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. arXiv preprint arXiv:1805.04833. Angela Fan, Mike Lewis, and Yann Dauphin. 2019. Strategies for structuring story generation. arXiv preprint arXiv:1902.01109. S. Field. 1979. Screenplay: The Foundations of Screenwriting. A Delta book. Dell Publishing Company. Chenyang Huang, Osmar R Zaiane, Amine Trabelsi, and Nouha Dziri. 2018. Automatic dialogue generation with expressed emotions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 49–54. Ting-Hao Huang, Francis Ferraro, Nasrin Mostafazadeh, Ishan Misra, Aishwarya Agrawal, Jacob Devlin, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, et al. 2016. Visual storytelling. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies, pages 1233–1239. Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, and Karthik Sankaranarayanan. 2017. Story generation from sequence of independent short descriptions. arXiv preprint arXiv:1707.05501. Frederick Jelinek, Robert L. Mercer, Lalit R. Bahl, and J. Baker. 1977. Perplexity—a measure of the difficulty of speech recognition tasks. Journal of the Acoustical Society of America, 62. Mihir Kale and Abhinav Rastogi. 2020. Template guided text generation for task-oriented dialogue. arXiv preprint arXiv:2004.15006. Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541. Rensis Likert. 1932. A technique for the measurement of attitudes / by Rensis Likert. Archives of psychology ; no. 140. [s.n.], New York. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318. Rafael Pérez Ý Pérez and Mike Sharples. 2001. Mexica: A computer model of a cognitive account of creative writing. Journal of Experimental & Theoretical Artificial Intelligence, 13(2):119–139. Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021. Mauve: Measuring the gap between neural text and human text using divergence frontiers. Advances in Neural Information Processing Systems, 34:4816–4828. Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, and Jianfeng Gao. 2020. Plotmachines: Outline conditioned generation with dynamic plot state tracking. arXiv preprint arXiv:2004.14967. Mark O Riedl and Robert Michael Young. 2010. Narrative planning: Balancing plot and character. Journal of Artificial Intelligence Research, 39:217–268. Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, and Zhiting Hu. 2019. Targetguided open-domain conversation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5624–5634, Florence, Italy. Association for Computational Linguistics. Zhe Wang, Wei He, Hua Wu, Haiyang Wu, Wei Li, Haifeng Wang, and Enhong Chen. 2016. Chinese poetry generation with planning based neural network. arXiv preprint arXiv:1610.09889. Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, and Haifeng Wang. 2019. Proactive human-machine conversation with explicit conversation goal. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3794–3804, Florence, Italy. Association for Computational Linguistics. Rui Yan. 2016. i, poet: Automatic poetry composition through recurrent neural networks with iterative polishing schema. In IJCAI, volume 2238, page 2244. Zheng Zhao, Shay B Cohen, and Bonnie Webber. 2020. Reducing quantity hallucinations in abstractive summarization. arXiv preprint arXiv:2009.13312. Yutao Zhu, Ruihua Song, Zhicheng Dou, Jian-Yun Nie, and Jin Zhou. 2020. ScriptWriter: Narrative-guided script generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8647–8657, Online. Association for Computational Linguistics. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Authors: Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro Motivation Related Work Dataset Experiments and Evaluation Results and Analysis Conclusion and Future Work Limitations and References A. Appendix Abstract and Intro Abstract and Intro Motivation Motivation Related Work Related Work Dataset Dataset Experiments and Evaluation Experiments and Evaluation Results and Analysis Results and Analysis Conclusion and Future Work Conclusion and Future Work Limitations and References Limitations and References A. Appendix A. Appendix 8. Limitations • In the plot generation dataset, the Wikipedia plots are sometimes not written by professional content writers from the film industry. Therefore these plots may fail to include the main events of the movie. • In a few cases, the model fails to generate coherent events along with the abrupt introduction of characters in the plots and scenes. • Although it has been noticed only a few times, the plot or scene generated contains repeated clauses or phrases. • The model hallucinates and generates factually incorrect things, making it incapable of generating biographies or documentaries. • The plot or scene may not abide by the theme of the input or genre mentioned along with the prompt. References Selmer Bringsjord and David Ferrucci. 1999. Artificial intelligence and literary creativity: Inside the mind of brutus, a storytelling machine. Psychology Press. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. arXiv preprint arXiv:1805.04833. Angela Fan, Mike Lewis, and Yann Dauphin. 2019. Strategies for structuring story generation. arXiv preprint arXiv:1902.01109. S. Field. 1979. Screenplay: The Foundations of Screenwriting. A Delta book. Dell Publishing Company. Chenyang Huang, Osmar R Zaiane, Amine Trabelsi, and Nouha Dziri. 2018. Automatic dialogue generation with expressed emotions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 49–54. Ting-Hao Huang, Francis Ferraro, Nasrin Mostafazadeh, Ishan Misra, Aishwarya Agrawal, Jacob Devlin, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, et al. 2016. Visual storytelling. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies, pages 1233–1239. Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, and Karthik Sankaranarayanan. 2017. Story generation from sequence of independent short descriptions. arXiv preprint arXiv:1707.05501. Frederick Jelinek, Robert L. Mercer, Lalit R. Bahl, and J. Baker. 1977. Perplexity—a measure of the difficulty of speech recognition tasks. Journal of the Acoustical Society of America, 62. Mihir Kale and Abhinav Rastogi. 2020. Template guided text generation for task-oriented dialogue. arXiv preprint arXiv:2004.15006. Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541. Rensis Likert. 1932. A technique for the measurement of attitudes / by Rensis Likert. Archives of psychology ; no. 140. [s.n.], New York. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318. Rafael Pérez Ý Pérez and Mike Sharples. 2001. Mexica: A computer model of a cognitive account of creative writing. Journal of Experimental & Theoretical Artificial Intelligence, 13(2):119–139. Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021. Mauve: Measuring the gap between neural text and human text using divergence frontiers. Advances in Neural Information Processing Systems, 34:4816–4828. Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, and Jianfeng Gao. 2020. Plotmachines: Outline conditioned generation with dynamic plot state tracking. arXiv preprint arXiv:2004.14967. Mark O Riedl and Robert Michael Young. 2010. Narrative planning: Balancing plot and character. Journal of Artificial Intelligence Research, 39:217–268. Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, and Zhiting Hu. 2019. Targetguided open-domain conversation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5624–5634, Florence, Italy. Association for Computational Linguistics. Zhe Wang, Wei He, Hua Wu, Haiyang Wu, Wei Li, Haifeng Wang, and Enhong Chen. 2016. Chinese poetry generation with planning based neural network. arXiv preprint arXiv:1610.09889. Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, and Haifeng Wang. 2019. Proactive human-machine conversation with explicit conversation goal. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3794–3804, Florence, Italy. Association for Computational Linguistics. Rui Yan. 2016. i, poet: Automatic poetry composition through recurrent neural networks with iterative polishing schema. In IJCAI, volume 2238, page 2244. Zheng Zhao, Shay B Cohen, and Bonnie Webber. 2020. Reducing quantity hallucinations in abstractive summarization. arXiv preprint arXiv:2009.13312. Yutao Zhu, Ruihua Song, Zhicheng Dou, Jian-Yun Nie, and Jin Zhou. 2020. ScriptWriter: Narrative-guided script generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8647–8657, Online. Association for Computational Linguistics. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

"Kurosawa": A Script Writer's Assistant: Limitations and References

"কুরোসাওয়া": একজন স্ক্রিপ্ট লেখকের সহকারী: সীমাবদ্ধতা এবং উল্লেখ

„Kurosawa“: Der Assistent eines Drehbuchautors: Einschränkungen und Referenzen

"Kurosawa": asistente de guionista: limitaciones y referencias

"Kurosawa" : un assistant de scénariste : limites et références

"कुरोसावा": एक पटकथा लेखक का सहायक: सीमाएँ और संदर्भ

「黒澤」脚本家アシスタント：限界と参考文献

"구로사와": 각본가의 조수: 제한 사항 및 참고 사항

"Kurosawa": um assistente de roteirista: limitações e referências

«Куросава»: помощник сценариста: ограничения и ссылки

"Kurosawa": Bir Senaryo Yazarının Asistanı: Sınırlamalar ve Referanslar

"Kurosawa": Trợ lý biên kịch: Hạn chế và tài liệu tham khảo

“黑泽明”：编剧助手的局限性与借鉴

HEPT MOVES ON

As companies welcome the exciting benefits of Generative AI for getting more work done, they also face a tricky problem: Gen AI Hallucinations. Ideally, AI-generated results should be reliable without human assistance. But sometimes, AI can hallucinate or generate fictitious content that poses a great risk. When wrong information gets out, it doesn't just cause embarrassment—it can hurt a company's reputation and erode people’s trust. So, while everyone wants to use Gen AI to be more efficient, they must also be careful to avoid these mistakes that could damage their image. What is a Gen AI Hallucination? , seen in Large-Language Models (LLMs) like ChatGPT, often leads to inaccurate information and responses, ranging from slight deviations from facts to entirely fabricated content. Gen AI hallucination LLMs operate by predicting the next word in a response based on the user's input, lacking independent reasoning abilities, which can result in errors. Given the natural fluency and coherence of the generated text, fact-checking is essential to prevent the . dissemination of false information Example: An infamous incident of AI hallucination occurred with Bard when asked about discoveries from the James Webb Space Telescope. Bard claimed the telescope was the first to capture images of an exoplanet outside our solar system. But this information was false and was quickly debunked through fact-checking. Why Do Gen AI Hallucinations Happen? Hallucinations in generative AI models often stem from the following: Irrelevant or subpar information fetched by the retrieval system can degrade output quality, causing errors or misinformed responses. Inaccurate context retrieval: Poor user prompts can mislead the LLM, resulting in responses based on wrong or unsuitable context. Additionally, if the underlying app prompt is ineffective this could also hurt the GenAI app’s information retrieval process. Ineffective prompts: Difficulty with idioms, slang, or non-English languages may result in incorrect or illogical responses, especially if the retrieval system fails to obtain or decipher relevant context in these languages. Complex language challenges: Why Are Gen AI Hallucinations a Problem? AI hallucinations pose serious risks beyond technical errors, . Let’s briefly examine all the problems caused by Generative AI hallucinations. impacting your brand's reputation and consumer trust Reputational Damage The reputation and status of the impacted party may be seriously damaged when AI-generated content is deliberately used to propagate false information or defamatory statements. This involves a bad reputation, a decline in trust, and financial consequences. It can be difficult to reverse or undo the harm caused once false information has been disseminated, which can have a long-lasting impact on relationships with partners, consumers, and the general public. Legal Consequences If we look at the legal aspects of AI hallucinations, we’ll find tons of them. These legal consequences range from defamation, invasion of privacy, or copyright infringement. This could also happen unintentionally. Imagine you’re just exploring AI but end up creating something that someone doesn’t. In that case, you’ll have to face legal charges if it becomes viral. A few years back in 2019, one such event happened when a went viral. He was caught on camera making offensive and untrue remarks. The company's stock price dropped significantly, but it was a joke. After that, the business took the makers of the deepfake video to court. deepfake video claiming to be Mark Zuckerberg Medical Mistakes Hallucinations caused by generative AI can potentially cause medical errors by presenting false or misleading information. For example, errors in the data produced by the AI model may lead to a misdiagnosis or an ineffective course of therapy if medical personnel rely on AI-generated content for diagnostic or therapeutic suggestions. A study published in raised issues with commercial AI algorithms used to diagnose dermatology. The study discovered that these algorithms, intended to evaluate pictures of skin lesions, showed notable errors, especially when differentiating between benign and malignant lesions. This demonstrates how crucial it is to thoroughly validate and continuously assess AI algorithms in the healthcare industry. JAMA Network Open in 2020 Compliance Challenges Compliance issues can occur when AI hallucinations result in the creation of content that does not adhere to industry norms or legal constraints. For instance, businesses may face legal and financial repercussions if by unintentionally revealing private information. AI-generated material violates data privacy laws Creating biased or discriminating information against equality and fairness could be one way to do this. Organizations must implement robust governance frameworks and quality assurance processes to address compliance challenges associated with AI hallucinations. Financial Loss Hallucination in Artificial Intelligence can also result in financial loss. That occurs if inaccurate content creation causes expensive mistakes or interferes with corporate processes. For instance, if financial reports produced by AI are erroneous or contain misleading information. It forces the investors or companies to make poor investment decisions that result in losses. Similarly, inaccurate or misinterpreted customer preferences might cause AI-generated marketing initiatives to fall flat with target demographics. Thus wasting money and missing out on sales possibilities. Disruption of User Trust AI hallucinations can create fake information that closely mimics reality, which can seriously undermine consumer trust. This may result in disseminating false information, swaying public opinion, and growing fake news. Users will find it easier to trust sources and separate fact from fiction when AI-generated material is identical to real information. This erodes public confidence in disseminating information and may significantly affect public trust. Are Artificial Intelligence Hallucinations Always Bad? Hallucinations, despite their potential risks, can hold value. LLMs exhibit a unique set of strengths and weaknesses. While they may struggle with tasks traditionally associated with computer proficiency, such as search capabilities, they excel in storytelling, creativity, and aesthetics. Hallucinations in AI should be reframed as a feature rather than a flaw within AI systems. Marketers can leverage this by prompting AI to hallucinate its interface, enabling it to perform tasks that are otherwise challenging or costly to measure. For instance, marketers could assign scores to various objects based on their alignment with the brand and then task AI with identifying potential lifelong consumers based on those scores. Ultimately, manipulating these hallucinations could lead to innovative solutions and enhanced outcomes in the advertising and marketing space.

Gen AI Hallucinations: The Good, the Bad, and the Costly

A lone boy stood, staring at the star-speckled sky, his mind drawn to the girl just a few feet from where he lay. She was a beauty, that one. He couldn’t believe she was his. Every single thing she did screamed Art. The way she linked her fingers together, the way she smiled. The one girl who knew so much about him, yet… she didn’t shy away from his questionable past. As the emotions threatened to pull him under, he shook his head to clear his thoughts and face what was before him. “A shooting star!” She screamed in excitement as she pointed at that far-off speck in the sky, becoming larger and larger by the second. “That is no shooting star.” He managed to say, fear clogging his throat. He took in the lush grass swaying in the night wind, the beauty of the stars that more or less blotted out the sky. Then, he turned to look at the girl on the ground a few feet away. She was sitting up by then, fear morphing her once beautiful features into something unspeakable. “What is that?” Her voice quaked as she asked, and he closed his eyes momentarily to stop the bile from going up his throat. “Our death.” He said, not mincing words. His throat bobbed with unspoken words and something else underlying. “I’ve always loved you. I just- I just wanted you to know. This once.” He squeezed the words out of his mouth, wrangling them like one would do to a piece of wet cloth. “You? I’ve always seen you as just a friend.” She turned towards him then, his eyes red from tears. But he was grateful for that moment. He got her to look at him finally. And her eyes shone with regret. The screams came first as the she saw earlier was big enough to blot out the stars in the sky. And the heat. God, the heat. He felt as though he was being cooked from the inside out. shooting star The sky boiled like a cauldron; giant drops of fire rained down from the clouds, turning the lush green grass into something out of Hades’s fantasy. Blades of grass crumbled before his eyes. Withering without as much as a touch from him and fizzing out of existence, flames taking its place. The greenery is overtaken by the force that darkened the universe and turned rivers into lakes of fire. She screamed the most. He didn’t. He just watched the shooting star fall and gave a small smile. She was never his to own anyway. * Darren. That was his name. The man-boy with the power to manipulate space at his whims. He was a prodigy even among his peers in Earth 75. They had hoverboards to trapeze through space. That’s right, hoverboards. Fuelled with nothing more than gravel and sand. “Gloating again? That’s not very of you.” He rolled his eyes at the voice that came from just behind him. Glenna. His childhood friend. If you can count a hundred years as childhood. Before they settled—before the metamorphosis, they never grew older than the teenage age. But, after settling for five years and being a pain in the butt for half of those he called his cadre, he realized that he was ready for metamorphosis. But then, Glenna wasn’t. Hence, he had to stop halfway and wait for her to be. ladylike “I’m not a lady, for crying out loud. This is argument number what? Five thousand and seven?” He groaned, using flawless fingers to massage his forehead. Glenna just laughed as she walked towards him. Or, more like, she glided. She had more interest in the hoverboards than he did. The room was no larger than two children’s playroom, yet it had enough space due to his meticulousness. Not a strand of space is out of place, exactly how he wanted it to be. “Number five thousand and eight. You missed one.” She laughed, planting a wet kiss on his skin. He would have gone as red as beetroot if she didn’t deftly move the conversation elsewhere. “So, I need to weave a new dress. You’ve been called a genius for so long that it has become music to my ears. I need a strand of space to weave into a cloak I’m almost done making.” She tendered her request before him, and he took in her presence. She was young, after refusing the metamorphosis for the third time. Yet.. she had eyes of peace. The brown of her irises reminded him of worlds he hadn’t dared to touch for fear of breaking them apart. He’d seen them in his wakings. They were peaceful and oh, so beautiful. The serenity in them was what drew him to them at first. “Okay, I will. But why just a strand? Why not a whole chunk?” He asked as she raised her eyebrows at him, and he almost melted into the chair he sat on. If you can call it sitting. It was a haphazard view, an upturned table, yet he was upright. The world he’d come to know extended universes and eternities. It was a world of madness, but at least it was his. “One sec.” He said, raising his hand in mock surrender. Then, everything stopped. As his concentration cleared the level of the primordial beings, he tore a portal through the world. It was a red wiggly thing struggling to close, but he held it open with sheer willpower alone. When he found out that portals were alive, he decided to lord over them one way or another. They didn’t make it easy for him, though. While it wiggled, he stretched a hand into the portal and drew a strand out of that limitless vastness, loping it through his fingers like a struggling worm. It shimmered in and out of existence, blue like the untold skies, red as fiery dragons and demons of worlds he’d only read about. “Got you.” He smiled as he held a strand. Then, the world went to hell. Before he could remove his hand from the portal, it yawned like a gaping chasm and swallowed him whole. He was screaming, but Glenna was still in timeless mode. She wouldn’t know what happened. That is mercy. That has to be. Then, the world shut itself as the portal consumed the world he’d known all of his existence and winked out of reality. The screams didn’t come. Nothing did. * Carrion. That was his name. His name. His name. He tried to hold on to it, but it slipped out of his grasp like an eel, and he followed after it, crawling after his name with it on the tip of his tongue. What was his name again? Ca.. Ca what? What was his name? The man with scars lining his body like the universe's stars was lost. He tried to think. He tried to remember where he was. Yes. He was fighting Archanoid—the mother of spiders. One could even call her a deity. He dodged an incoming web made of madness and silk, woven with anger and hate. She was large, towering above him like he was a pesky fly. He hated that. He was the warrior who battled nightmares and abominations. He had to win. But then, what was his name? His name.. what was it again? He flew out of the way as an incubator shattered where he stood one second earlier. She wasn’t playing. “Foolish mortal. Do you think you can harm me? It doesn’t matter who you are, World Walker. You can’t escape from me.” She spoke, venom dripping out of those pincer-like things. They made a clanging sound as he swallowed, looking into those red eyes that had intelligence to them, something beyond the years. “One last chance. I will give it to you. Remember, pride and ego cannot win this war. Maybe you and the others can.” And then, before he could ask what she meant.. a scary sharp finger elongated from her finger, and she him out like a fly. flicked There was screaming. And then, there was nothing. * He woke up with a groan. There were several groans across him, and he was shocked to realize that he wasn’t dead. He wasn’t scorched. But he didn’t remember what his name was. He remembered her, recognized the girl who took him as a friend before the world became a sea of fire. Why, then, was he unhurt? The others stood on shaky feet, an otherworldly feel to them. There was something he just couldn’t place. They wore things he’d never heard of, or perhaps the dark skins rippling with fire and the darkness of the deep. Maybe it was the blades of darkness in the place where teeth were meant to be, but there was something.. out of place. One of them wore nightmares on his skin; he could see death. Death rippled with muscles and scars that seemed to blot out the skin. But strangely, he didn’t feel fear. “So, an explanation is in order?” He said to no one in particular. Even the being who looked as though he was 25 and 15 at the same time. As though the world couldn’t decide on his age and let him be both instead. He noticed that this being wasn’t standing on his two feet. He was.. hovering. “We’ll see soon enough.” A boy his age spoke, and he stared at him in wonderment, wondering how alike they were. The way the boy squinted made it glaring that he used glasses to see. Same with him. Even though he didn’t even know their names, they were about ten. Some had hands for feet, and others weren’t even walking on the bare ground. They just.. existed. There was no way to explain their existence, so he didn’t bother. “An explanation.. for this. All of this.” Another boy spoke, and he didn’t balk this time. He just waited. “Here it comes.” The boy who spoke was already running. “Here, what comes? What?” He asked, but everybody else had cleared out of that barren field. The world he was in was empty. There is no sign of green. No sign of brown either. It was as though the world was drained out of colour, and no matter where he looked, despair loomed. He felt them before he saw them. He knew who they were before they spoke. And even without an introduction, he knew everything about them. The Destroyers Of Worlds. They smiled; one hovered just like that man-boy. One had skin with uncountable scars. And then, he saw himself. That cruel smile, that deadly presence. He felt a chill that had nothing to do with the weather. Someone dragged him by the scruff of his hood, and he felt.. everything. There was no other way to describe what happened. Then, he turned to stare at the other him with a cruel smile as that being let out an inferno where he was standing a moment before. It was… hot. But strangely, he wasn’t burning. He wasn’t even singed. He was.. okay. The man-boy dropped him on the ground unceremoniously, and he muttered his thanks, but the others just raised their eyebrows, staring at the beings beyond them. Beings with their faces. Beings with enough power to destroy universes. It was them. Ageless. Timeless. With a snap of a finger, they were gone like they didn’t just lay waste to a large expanse of space. “I think I know what’s going on…” A tenacious finger went up, and the squinted boy was allowed to speak. “That is us. Timeless us. And.. we destroyed our universes. Now.. we have to fight against the us that are world destroyers.” “We don’t have a choice to decline?” The man-boy asked. “We have doomed worlds. This.. is penance.” The boy spoke again, his eyes filled with trepidation and resignation. “So, we were summoned here to kill our future selves? That’s a sick sense of humor.” “This world is sick, and when the Arachnoid told me, I didn’t believe it.” The man with the scars spoke, and his scars seemed alive, melding with his body. “Can you tell us what an Arachnoid is?” Another asked. So, he told them. Everything. “Pride and ego, huh?” A guy spoke, and they finally understood what they had to do. “This is depraved. All of this. Depraved. What the hell?” He spoke, shaking with anger. “Hell doesn’t exist, apparently. Future us made sure the world became it.” A brittle laughter from the man-boy. “This is it, then? We die in a land of no name? Our names were stolen from us. I can’t dredge up a single memory.” The scarred man said, eyes smouldering. I could tell he was pissed off. “This is our penance.” The squinting boy said again, and I was tempted to throw him out of the universe. Such a goody two shoes. “I don’t care. Whatever I have to do, I will do it. And it doesn’t matter who’s on the line. I have to go back home. Also, you can’t fight fire with water. Not in my world, anyway. It takes a monster to kill another. We will take the unorthodox approach. Did they want to seclude us and destroy us? World Walkers, indeed. Bastards more likely. I won’t take that. Neither should any of you. We have the power. Can’t you feel it? We can change things.” The man-boy spoke fervently, his eyes filled with loss, anger, and something else. “That’s right. We won’t let them decide for us. If we have to die, we might as well raise hell before we do. Now, we need a name.” The hooded boy spoke, and the others nodded in approval. “How does sound?” There were hoots of approval, some going as far as clapping. Then, they stared at the sky at once. Drained of colour. But then, the World between Worlds had always been a resting place for World Walkers. The Restorer of Worlds They had to fight. And this time, they had to fight themselves. Their memories returned, and they could see themselves in the past, present, and future—the Destroyer of Worlds. No more. They decided not to make the same mistakes. Not again. This time, they’d restore all they destroyed. As the squinty-eyed kid said, it was penance.

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