Moca Network, a flagship project ... communications technology platforms, today announced the launch of OKI Club, a premium membership program that integrates Web3 technology into SK Planet’s OK Cashbag app, which is South Korea’s largest integrated rewards service....

Moca Network and SK Planet launch OKI Club, marking first large-scale enterprise use of AIR Kit for Web3 onboarding | Currency News | Financial and Business News | Markets Insider

Moca Network and SK Planet have launched OKI Club, a significant Web3 onboarding initiative using AIR Kit, integrated within SK Planet's OK Cashbag app.

Moca Network and SK Planet Unveil OKI Club, Pioneering Web3 Integration

It is no longer just buses and trains, traffickers are increasingly taking the air mode to quickly shift victim girls and women from source to destination states.The issue took centre stage at a seminar on the problem of human trafficking organised by National Commission for Women and the Central Industrial Security Force in New Delhi

Need to step up airport vigil as traffickers ‘flying victims’ to destination states’

Delta Air Lines says the pilot accused of threatening to shoot the plane’s captain during a flight no longer works for the airline

Delta says pilot accused of threatening to shoot the captain no longer works for the airline

A first group of civilian evacuees from Gaza crossed into Egypt under a Qatari-mediated deal on Wednesday while Israeli forces bombed the Palestinian enclave from land, sea and air anew as they pressed their offensive against Hamas militants.

First evacuees leave Gaza after another night of Israeli bombardments

- An alphabet book and a notebook lie on top of an elevated wooden floorboard of a middle school in Let Yet Kone village in Tabayin township in the Sagaing region of Myanmar on Sept. 17, 2022, the day after an air strike hit the school.

Researchers hope tracking senior Myanmar army officers can ascertain blame for human rights abuses

The Houthi attacks will be hard to respond to from sea, and the naval version of Iron Dome is designed for self-defense or protecting a nearby ship'

Could Israel’s New Sa’ar 6 Corvettes Give Its Southern Flank Greater Defensive Depth Against Houthi Attacks?

A recent alteration of an NCAA rule could put Harbaugh himself square in the association's crosshairs ... again

Even if unaware, Jim Harbaugh can be held responsible by NCAA for Michigan sign-stealing scandal

On Oct. 31, the inspector general's office for the U.S. Department of Transportation confirmed that plane captain Jonathan J. Dunn has been indicted over an August 2022 incident in which he allegedly threatened to shoot his co-pilot over his decision to divert the plane because one of the passengers on board needed medical help.

A pilot reportedly threatened to shoot his co-captain in the air

It’s the worst nightmare for the people that have to plan an attack and for the people that have to execute it,” says Walker Mills, a Marine Corps captain who has studied and written about tunnel warfare.

The Tunnel War

airedefined

Navigating AI Integration in Retail: The Change Management Compass

AIR is a Canadian startup focusing on a new AI training approach that allows the tackling of challenges that are too complex for traditional machine learning methods. We are doing this by building a toolset for AIs and people to learn continuously from each other. AIR developed Cogment, the world's first open-source framework to provide the means to train people and AI together, in multi-agent, multi-human, multi-rewards contexts. HackerNoon Reporter: Please tell us briefly about your  background. Dorian Kieken (Founder, President): Before founding the AI start-up AIR (formerly Age of Minds), I shipped 17 video games in multiple roles ranging from Design to Production, including the acclaimed games Mass Effect 2 (Game of the year 2010) and Mass Effect 3 (RPG of the year 2012). I co-founded the BioWare Montreal studio and helped it grow to 100+ people. I introduced and evolved agile production methodologies & tools to multiple companies, and drove the implementation of a distributed leadership/teal system. I'm also an active member of the Montreal AI ethics group and a strong believer in taking key actions today to nudge humanity towards a good future. Greg Szriftgiser (Co-founder, Design): I spent 20 years professionally studying, writing about, then finally designing and writing for the video games industry and its exploration of interactivity and dynamic systems. I became particularly interested in procedural storytelling (how to design intelligent systems able to create compelling narratives through various storytelling devices), which led to AI research and a passion for the struggles and achievements of machine learning as a way for humans to achieve more. François Chabot (Co-founder, Technology): I come from a computer engineering background with a specialization in artificial intelligence. Prior to co-founding AIR, I worked in video games (Capcom), mostly building AI and Gameplay systems in heavily resource-constrained scenarios, notably Dead Rising 2, real-time hardware-in-the-loop simulators (Opal-RT), and malware search (Google). I have a great interest in the relationship between people and machines, both when it comes to programmers and people using programs. Nowadays, I see myself as multidisciplinary glue that can bring various disparate systems together. Craig Vachon (CEO): After grad school, I couldn’t find a job, so I started a small company. I got extremely lucky and sold that company to a Japanese firm two years later. After a few more lucky professional experiences (and having lived as an expat for 12 years), I decided to spend my efforts helping fellow entrepreneurs solve important human challenges as an angel investor. I joined AIR because I like to think we’re tackling one of the more impactful issues that humanity faces—how humans will continue to benefit from/steer/trust AI. What's your startup called? And in a sentence or two, what does it do? It's called AIR, which stands for AI Redefined. We create technology for “steerable AI”, leveraging the advent of AI to the benefit of humankind through human-AI collaboration technology. Our open-source AI orchestration platform, Cogment, allows AI practitioners to design, train and deploy complex intelligent ecosystems, mixing multiple humans and artificial agents of various kinds to bring about results that are more context-aware (ethical, empathetic, explainable). What is the origin story? After enjoying great success in the game industry, and what was a dream-job for me with recognition in a fantastic studio, I felt motivation slipping away around 2014. It coïncided with the birth of my first daughter. I discovered that actually, this loss of motivation came from not working on something more meaningful that will help the next generation. Dorian: Among the many challenges this next generation will face (climate change, energy, water, etc.), I identified AI, and in particular, human/AI alignment, as the most important one that I could get involved in. I got strongly engaged in the Montreal AI Ethics group and ultimately, it led to the creation of AIR that is tackling this human/AI alignment problem. I met Dorian around the time I was diving into AI myself, through common friends, and we immediately clicked on several topics, as well as how we thought the AI conundrums could be tackled. Greg: We also shared this desire to see how we could leverage what we had learned in the video game industry to tackle something more impactful with regard to the various global challenges on the horizon. Throughout my career, I had the good fortune to be exposed to a number of fascinating, seemingly disparate domains and technologies. On top of that, trying my hand at the startup process had always been on my radar ever since I left university. Francois: So once I reached a point in my professional development where I was starting to feel confident enough in my skills to take a crack at it, I looked back at everything that had interested me and that I had become good at so far: The way humans connect and interact with machines, Simulations, Machine Learning, etc… and pondered how to bring it all together into something truly novel, interesting, and useful. During that process, I was introduced to Dorian, and the rest is history. I wasn’t a part of the origin of this company. I joined this year (2021) to help productize and accelerate the adoption of the Team’s amazing technology. Craig: What do you love about your team, and why are you the ones to solve this problem? The dual expertise in both the world of AI and the world of simulation/video games (human-AI interaction) makes for a very unique team of people with expertise in more than one area. It's something that makes the team especially well-suited for the problem we tackle because it needs to be tackled from multiple points of view. Dorian: Not just the AI point of view which centers around technology, rewards, methods of training, but also from the environment where agents evolve and interact with each other and with human users. One parallel that comes to mind is the birth of CGI animated movies, which required people with dual skill sets in Art/animation and computer science to truly blossom. Beyond the multiple skill sets people have in the team, there is also a very important cultural aspect in my opinion, of a human-centered perspective on the world. Emotional maturity, going hand in hand with a very high prevalence in each and every member of AIR's team of an almost child-like curiosity in diverse topics makes for a very particular blend of talents and personalities to tackle this long-term vision of synergy between humans and AI. Greg: The team is mission-driven and diverse in terms of its skill sets and expertise. They fully understand and appreciate the opportunity we’ve been given to ensure ‘humans and AI elevate each other.’ Craig: If you weren’t building your startup, what would you be doing? Dorian: Probably finding another way to nudge the future of AI and humans in a good direction. Maybe through another existing company like DeepMind? Or an organization like the Montreal AI Ethics Institute? Dorian: Probably something related to Video Games, Interaction Design, and/or Storytelling, I suppose, but with the missing excitement of working on a truly transformative technology with a mission bigger than myself. Greg: I would probably be doing something very similar: Tackling hard problems that affect how people relate to technology in some way, and doing so in a healthy collaborative environment. The startup is a means to this end for me. I’m not big on entrepreneurship for the sake of entrepreneurship. François: Helping other great founders pursue worthwhile goals. Craig: At the moment, how do you measure success? What are your core metrics? In the long term, on how human-centric the training of AIs becomes, and how successfully we move past the narrow "AI behaving without context" era we are currently in, to reach towards AI with a broader understanding of context. Dorian: In the short term, it's about the scope and type of collaborations our open-source platform enables, and our ability to generate license-based revenue from customers that value context/steerability/trust from their AI results. Craig: What’s most exciting about your traction to date? The collaborations we have with CRL (Chandar Research Lab at Mila) on Hanabi and the University of Alberta on complex multi-agent problems come to mind. Dorian: From a more general perspective, I'm very excited to see more and more thought pieces, actual work, and studies popping up and coming to similar conclusions we did: that by designing intelligent systems with human participation from the get-go, issues like bias, trust, or explainability can be addressed at the same time as the ability for those systems to achieve better results, thanks to the complementarity between the different strengths humans and AI bring to the table. Greg: : One of our aerospace customers admitted we had made him a hero as we accelerated his roadmap in AI by two full years. (How often do you hear that from the head of a Business Unit?) Craig What technologies are you currently most excited about, and most worried about? And why? AI in general. I'm excited about how it can have a positive impact on humanity, and I’m worried about how this impact could also be negative. Dorian: There is no doubt in anyone's mind, I think, that AI can help us solve very important issues, and bring us much-needed help; but used poorly, it can also exacerbate those issues or even create new ones. It's really similar, I think, to any groundbreaking shift of the technological paradigm in which, or with which, we build our societies. Many people will probably talk about the Internet as the most recent example of a profoundly world-changing technological paradigm shift, and they would be right, but even though we now have around 3 decades of hindsight, I think it's fair to say the jury is still out on whether our societies are better for it or not. Greg: AI will probably impact us at least similarly, if not much more profoundly; that is why I think we need to actively work at making it more accessible to everyone, but also more human-centric. I am excited about the future of how we express what we want computer systems to do. Technologies that are being widely deployed right now feel like we are dipping our toes towards expressing intent in a more semantically meaningful manner. Francois: The massive rise of Python as a programming language and Kubernetes as a deployment platform are but baby steps in that general direction. I am convinced the next generation of “telling machines what we want from them” will open the door to some truly amazing things, and that this is coming a lot sooner than most people would expect. As far as worries go, I am particularly concerned about our willingness to deploy socially disruptive systems based on technologies for which we only have proof of concepts exhibiting critical flaws with the assumption that functional implementations are within our grasp. Image-driven emotion detection and blockchain are particularly egregious examples of this in my mind. Today’s narrow AI provides only the thinnest veneer of humanity. And yet, without an iota of context, common sense, empathy, or morality, we want #AI to solve significant human problems. We will do better (with ). Craig: Cogment.AI What drew you to get published on HackerNoon? What do you like most about our platform? Getting nominated as a Startup of the Year was a very nice surprise! HackerNoon offers a very open, accessible platform for people to learn and share knowledge, with accessible tools everyone can use from anywhere; in that sense, it aligns pretty well with how we think about what the future of AI should be :) What advice would you give to the 21-year-old version of yourself? I was already driven by passion back then. Probably something along the lines of "a healthy mind in a healthy body". Something I only learned in my late twenties. Dorian: Better care of the body (training, food, etc…) leads to better sleep which, in return, gives you more time for all the cerebral activities. "Pay more attention to the irrational and the way humans behave, I swear it's fascinating!" Oh, and, I'd also tell myself: Greg: "sleep more, you idiot, stop pulling multiple all-nighters in a row, circadian rhythm is more important than you think". Don’t second-guess yourself so much. The shame of being proven wrong is only fleeting, but the missed opportunities often don’t come back. Francois: Say “yes” more often. The best adventures, (plus laughter/learning) always start by saying, yes. Craig: What is something surprising you've learned this year that your contemporaries would benefit from knowing? From a business standpoint, that the scaling potential of a business and its recurring licensing revenue is more important to investors than the number of revenues! Also, from a cultural perspective, how much face-to-face human contact is important, particularly after the past year of Covid-19 isolation. We are social creatures more than ever. Dorian: The idea in itself is no surprise to me, but the extent to which it verifies surprises me regularly: that what reality is or could be is irrelevant in the face of what human culture dictates it is or should be. I am also surprised by how much more fragmented and inward-facing culture seems to become in the age of the so-called globalization of information. Greg: Learning to skateboard as an adult feels like rebelling against the rules of physics themselves. The sequence of events required for it to barely function is unlikely. Everything about the process is an unstable equilibrium. Yet here you are, spitting in the face of Newton’s third law. Francois: Two things: Hairy, audacious goals are life-affirming; And the sequel (of the comedic spy thriller I published last year) isn’t as easy as it ought to be. Craig: Don’t forget to vote for R as Startup of the Year in AI Montreal.

Tackling a new Human-Centric AI  Paradigm: Startup Interview with AIR Cofounders

AWS AI Services Scratch Extension In my previous , I have mentioned that we should prepare our children for the future. In the future, to succeed our children should have skills like creative thinking and problem solving. STEM education can improve this skills. post According to MIT Professor , coding is the new literacy. A visual programming language called could stimulate the curiosity we know it exists in the children from the day they have born. You can read stories about how it changes children’s lives , and . Mitchel Resnick Scratch here here here As I am working with , I have been wondering how we could teach AI to our children easily. Then I came across , an extension framework to Scratch for adding new functionality to Scratch. With ScratchX, you can write extensions to integrate Scratch with anything you imagine. There are sample extensions for , , and . AWS AI services ScratchX LEGO WeDo Arduino Raspberry Pi Then I thought I can make a Scratch extension for using AWS AI services like Polly and Translate. And it is . here You can try sample project that uses Amazon Polly to speak the text you have entered in the language you choose. Also you can translate text between languages and speak the translated text in target language. here Below is a video recording of the sample project. Developing The Extension If you wonder how the extension is developed, let’s develop the extension step by step together. A minimal Scratch Extension is created like below. After creating the extension object, we should register it by calling ScratchExtensions.register with the name of the extension and a descriptor which defines the blocks that will be created. For more information on making Scratch Extensions, see . here We can develop our extension in three steps: Create the descriptor Implement the methods Try the extension in ScratchX Let’s start. Create the descriptor The descriptor defines the blocks that will be created after the extension is registered. Created blocks will be shown in ‘More Blocks’ section of ScratchX. The blocks of our extension are defined as below. First block is for initializing the AWS JavaScript SDK in the browser. Amazon Polly blocks are the next two blocks. And last blocks are for Amazon Translate service. Parameters of blocks aredefined as ‘say %s’. We can use lists to choose parameters from with ‘%m.sourceLanguages’ syntax, where ‘%m.’ says that we want to use a list and sourceLanguages shows the name of the list. In ScratchX lists are known as menus and menus are defined with ‘menus’ field. Blocks of our extension will look like below. Blocks are shown in ‘More Blocks’ section 2. Implement the methods After we defined our blocks, we will implement the methods that will be executed when blocks are used. For more information on method details, you can look . Usage of AWS services in the browser with AWS JavaScript SDK is explained in detail in . extension JavaScript file my previous blog post 3. Try the extension in ScratchX In order to try our extension, we should load it from a URL. I recommend for this. MiniWeb HTTP Server After you can run the server in your directory at 8080 port, you can try it in your browser by visiting the address below. I recommend Google Chrome browser, ScratchX is not working in Firefox. IE will not load the extension from http: url, although it works over https: url. https://scratchx.org/?url=http://localhost:8080/awsAiServicesScratchExtension.js When ScratchX warned you, click ‘I understand, continue’ to load and try the extension. Summary In this post, I have shown how a Scratch Extension can be created easily for using AWS AI services in Scratch. You can find the code . For more information on making Scratch Extensions, see . here here For more information on how AWS AI services can be used in the browser using AWS JavaScript SDK, please see my previous blog post: You Can’t Fail To Learn A New Language With These AWS AI Services: Translate, Rekognition, Polly Also, you might like my other post about creating a serverless app for saving allergy information with the picture of children and searching the allergy information later with the picture of the children by using face recognition methods of Amazon Rekognition: Serverless Allergy Checker with Amazon Rekognition, Lex, Polly, DynamoDB, S3 and Lambda I will continue to write about different use cases of AWS AI service. I will add object and text detection features to the extension using Amazon Rekognition. I am developing a Chrome Browser Extension for reading the selected text in the page you are viewing using Amazon Polly and detecting Authy authentication codes from the camera using Amazon Rekognition. Please stay tuned or follow. If you like this post, please share, clap or follow. Thanks for reading.

AI Is Hard? It’s Child’s Play With This AWS AI Services Scratch Extension

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Method and 3.1 Phase 1: Taxonomy Generation 3 Method and 3.1 Phase 1: Taxonomy Generation 3.2 Phase 2: LLM-Augmented Text Classification 3.2 Phase 2: LLM-Augmented Text Classification 4 Evaluation Suite and 4.1 Phase 1 Evaluation Strategies 4 Evaluation Suite and 4.1 Phase 1 Evaluation Strategies 4.2 Phase 2 Evaluation Strategies 4.2 Phase 2 Evaluation Strategies 5 Experiments and 5.1 Data 5 Experiments and 5.1 Data 5.2 Taxonomy Generation 5.2 Taxonomy Generation 5.3 LLM-Augmented Text Classification 5.3 LLM-Augmented Text Classification 5.4 Summary of Findings and Suggestions 5.4 Summary of Findings and Suggestions 6 Discussion and Future Work, and References 6 Discussion and Future Work, and References A. Taxonomies A. Taxonomies B. Additional Results B. Additional Results C. Implementation Details C. Implementation Details D. Prompt Templates D. Prompt Templates 2 RELATED WORK Taxonomy Generation. Prior work in taxonomy generation falls into manual and automatic approaches. Handcrafted taxonomies, beyond being expensive to construct, tend to be developed for specific downstream tasks (e.g., web search intent analysis [4, 20], chatbot intent detection [31]), or tied to the development of specific datasets [22, 25]. On the other hand, automated approaches scale better but either rely on term extraction from corpora to obtain labels, which may hinder interpretability and/or coverage [24, 33], or else require a set of seeds for the taxonomy in order to generate new labels [32]. TnT-LLM, in contrast, is automatic, abstractive (i.e., labels describe the corpus but need not be directly extracted from it), and does not require any seed labels. Moreover, TnT-LLM treats taxonomy generation and text classification as interrelated problems in an end-to-end pipeline, whereas prior work has tended to focus mainly on the quality of the taxonomy produced, without considering its downstream utility for classification. Taxonomy Generation TnT-LLM TnT-LLM Text Clustering and Topic Modeling. Text clustering and topic modeling “invert” the traditional approach of defining a label set, then applying the labels on the corpus. Given a set of documents, such approaches first group the documents into topical clusters using various definitions of textual similarity, then post-hoc label or summarize the clusters [1, 29]. While traditional approaches in theory accomplish the same goals as TnT-LLM, they suffer due to a lack of interpretability [5], as they typically do not assign intelligible labels to clusters. More recently, attempts have been made to overcome these problems by using LLMs for topic modeling [18, 30], though these approaches still require supervision through either a predefined taxonomy [30] or a seed set of topics [18]. Text Clustering and Topic Modeling. TnT-LLM LLMs as Annotators. Recent work has explored using LLMs to replace human annotation for labor-intensive tasks such as search relevance quality labeling [28], topic and stance detection [8], and various computational social science labeling tasks [34]. These studies have found that, in general, LLMs perform on par or even better than crowd-workers [15], often at a fraction of the cost. In the same vein, we explore using LLMs as annotators for text classification, although our main goal is to scale the process by distilling LLMs’ label-specific capabilities into more efficient, lightweight classifiers. LLMs as Annotators. 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 Authors:
(1) Mengting Wan, Microsoft Corporation and Microsoft Corporation;
(2) Tara Safavi (Corresponding authors), Microsoft Corporation;
(3) Sujay Kumar Jauhar, Microsoft Corporation;
(4) Yujin Kim, Microsoft Corporation;
(5) Scott Counts, Microsoft Corporation;
(6) Jennifer Neville, Microsoft Corporation;
(7) Siddharth Suri, Microsoft Corporation;
(8) Chirag Shah, University of Washington and Work done while working at Microsoft;
(9) Ryen W. White, Microsoft Corporation;
(10) Longqi Yang, Microsoft Corporation;
(11) Reid Andersen, Microsoft Corporation;
(12) Georg Buscher, Microsoft Corporation;
(13) Dhruv Joshi, Microsoft Corporation;
(14) Nagu Rangan, Microsoft Corporation. Authors: Authors: (1) Mengting Wan, Microsoft Corporation and Microsoft Corporation; (2) Tara Safavi (Corresponding authors), Microsoft Corporation; (3) Sujay Kumar Jauhar, Microsoft Corporation; (4) Yujin Kim, Microsoft Corporation; (5) Scott Counts, Microsoft Corporation; (6) Jennifer Neville, Microsoft Corporation; (7) Siddharth Suri, Microsoft Corporation; (8) Chirag Shah, University of Washington and Work done while working at Microsoft; (9) Ryen W. White, Microsoft Corporation; (10) Longqi Yang, Microsoft Corporation; (11) Reid Andersen, Microsoft Corporation; (12) Georg Buscher, Microsoft Corporation; (13) Dhruv Joshi, Microsoft Corporation; (14) Nagu Rangan, Microsoft Corporation.

Prior Approaches to Text Mining: Taxonomy, Clustering, and LLM Annotation

In 2019, a robot capable of creating art was developed by Engineered Arts under the guidance of Aidan Meller. This innovation raised fascinating questions: what is art? And can something produced by a machine even be considered art? The robot, named Ai-Da, quickly gained attention for her eerie ability to create abstract and surreal images—some of which were labeled "self-portraits." She even has her own social media presence. At the time, the world watched in mild curiosity, intrigued but not alarmed. The conversation centered more on aesthetics and philosophical musings than on ownership. Nobody was too concerned about intellectual property rights or copyright laws. robot capable of creating art robot capable of creating art is But the thing about advancement is that change begets change. Fast forward to 2022, when the release of ChatGPT changed everything. Suddenly, generative AI was not just a cool tech demo; it became accessible to everyone—from hobbyists to professionals to everyday users. What seemed like a fascinating novelty quickly turned into a disruptive force in creative industries. Now, a much louder conversation is emerging, one that dives straight into legal and ethical quicksand: who owns AI-generated art? Does ownership lie with the user who prompted the algorithm? The developer who wrote the code? Or the company that owns the model? And what about the artists whose original works were scraped to train these systems? Studio Ghibli Embroiled in an IP Struggle Generative AI tools such as ChatGPT, CGDream, DALL·E, and Midjourney have completely redefined what it means to be an artist. With just a few lines of text, users can produce illustrations, paintings, and even animations that mimic the styles of established artists and studios. The controversy surrounding AI-generated images that mimic the distinctive aesthetic of Studio Ghibli has become a flashpoint in this larger debate. CGDream CGDream Online forums and social media are flooded with Ghibli-inspired AI artworks—some so convincing that fans mistake them for unreleased concept art. While impressive, this has sparked outrage among creatives who see it as theft, plain and simple. Advocates are now calling for tighter regulations to protect artists' rights in the age of machine-made media. Mr. Chua, an intellectual property lawyer, explained to The Straits Times that “no one can claim exclusive rights over a style,” citing a notable legal precedent involving musician Ed Sheeran, who was sued for allegedly copying another song. The case was ultimately dismissed because chord progressions—like art styles—cannot be copyrighted. However, this doesn’t mean artists are without recourse. If AI-generated content crosses the line into direct reproduction of copyrighted elements (like specific characters or original compositions), that’s when legal trouble begins. The Straits Times The Case for Ethical Oversight As one of the most beloved animation studios in the world, many creatives hope Studio Ghibli will lead the charge in confronting the unchecked rise of AI art. The concern isn’t just about imitation—it’s about dilution of creative labor. Artists who spend years refining their craft are now competing with machines that can mimic their styles in seconds, without credit, compensation, or consent. Just as OpenAI faces lawsuits for training language models on copyrighted materials without permission—such as the one from Scarlett Johansson, who alleges her voice was used to train a voice assistant without her consent—visual artists are demanding similar accountability. But so far, unless an AI blatantly recreates iconic characters like Totoro or Princess Mononoke, current copyright law remains toothless. Who Owns AI-Generated Images? Here lies the core dilemma. In most jurisdictions, copyright is only granted to works created by humans. This legal principle was reinforced in the infamous “monkey selfie” case, where a macaque used a photographer’s camera to take a selfie. The courts ruled that because the image was not created by a human, it wasn’t eligible for copyright. So, if AI isn’t human—and if it autonomously generates a piece of art—then who can own the rights to that creation? Currently, many AI platforms state in their terms of service that users have rights to the content they generate. However, this is more of a policy stance than a legal one. The truth is, there’s still no global legal framework that definitively answers this question. And if we really dig deeper, another issue bubbles to the surface: the dataset. These AI systems are trained on millions (if not billions) of images scraped from the internet, often without the consent of the original creators. This includes everything from amateur sketches on DeviantArt to professional illustrations from licensed portfolios. users users If an AI learns to draw “like” a particular artist by analyzing thousands of their works, is that theft or inspiration? And should that artist be entitled to royalties or recognition when their style is emulated? The Grey Zone: Copyright, Culture, and the Future Right now, the legal consensus leans toward considering AI-generated images as public domain—meaning nobody owns them. But this could change as litigation continues and as governments begin crafting new legislation. Some countries are already taking tentative steps. The European Union, for example, has introduced draft AI regulations that include transparency requirements for generative systems. Meanwhile, the U.S. Copyright Office has begun rejecting registrations for AI-generated content unless there is “significant human authorship.” This leaves us in a grey zone—a sort of copyright limbo where users believe they own what they create, platforms claim limited liability, and original artists have little to no recourse. It's a situation that benefits tech companies the most, while creatives are left to navigate an evolving digital Wild West. What Happens Next? The path forward is murky. Any meaningful solution will likely require an unprecedented level of international cooperation. After all, the internet doesn’t recognize borders, and neither does AI-generated content. For copyright laws to truly be effective in this space, they’ll need to be enforceable globally—a challenge that has never been successfully met before. enforceable enforceable In the meantime, artists are fighting back in the ways they can: through lawsuits, public campaigns, and even by creating “poisoned” data that disrupts how AI systems train on their work. There’s also a growing call for new forms of digital licensing that would allow artists to opt in—or opt out—of having their work used to train generative systems. Whether AI art becomes a democratizing force or a destructive one depends on how we, as a society, choose to regulate it. One thing is clear: the conversation is only just beginning, and the rules of ownership in the digital age are still being written. This story was authored under HackerNoon’s Business Blogging Program. This story was authored under HackerNoon’s Business Blogging Program. This story was authored under HackerNoon’s Business Blogging Program. This story was authored under HackerNoon’s Business Blogging Program. Business Blogging Program

Who Owns AI Art? A Deep Dive into Copyright, Intellectual Property, and Liability

من يملك الفن الفني؟ غرق عميق في حقوق الملكية الفكرية والمسؤولية

¿Quién posee el arte de la IA? una profundización en el derecho de autor, la propiedad intelectual y la responsabilidad

Tko posjeduje umjetnost AI? duboko uranjanje u autorsko pravo, intelektualno vlasništvo i odgovornost

Chi possiede l'arte AI? una profonda immersione in copyright, proprietà intellettuale e responsabilità

누가 AI 예술을 소유합니까? 저작권, 지적 재산권 및 책임에 대한 깊은 몰입

Quem possui a arte de IA? um mergulho profundo em direitos autorais, propriedade intelectual e responsabilidade

Cine deține arta AI? o scufundare profundă în drepturile de autor, proprietatea intelectuală și răspunderea

Кто владеет искусством ИИ? глубокое погружение в авторское право, интеллектуальную собственность и ответственность

AI Art අයිති කාටද? Copyright, Intellectual Property, and Liability වෙත ගැඹුරු පිපිරීමක්

Nani ni mmiliki wa sanaa ya AI? Uvuvi wa kina katika mamlaka, mali ya akili, na wajibu

Prompt Injection Is What Happens When AI Trusts Too Easily

AI Agents Aren’t Production Ready - and Access Control Might Be the Reason

AI agenti nejsou připraveni na výrobu - a kontrola přístupu může být důvodem

Οι πράκτορες τεχνητής νοημοσύνης δεν είναι έτοιμοι για παραγωγή - και ο έλεγχος πρόσβασης μπορεί να είναι ο λόγος

Los agentes de IA no están listos para la producción - y el control de acceso podría ser la razón

Os axentes de IA non están listos para a produción - e o control de acceso pode ser a razón

एआई एजेंट उत्पादन तैयार नहीं हैं - और एक्सेस नियंत्रण कारण हो सकता है

AIエージェントは生産準備ができていない - アクセス制御が原因かもしれません

«Қазақтелеком» АҚ-ның телекоммуникациялар желісін қуаттылығы 1 млн. абонент Next Generation Network (NGN) технологиясына көшіру

Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la Pamba la 100%

ສະ ຫນັບ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ ສະ ຫນູນ

Agentes de IA não estão prontos para produção - e controle de acesso pode ser a razão

Активісти не готові до виробництва - і контроль доступу може бути причиною

This Simple App Lets You See How Hollywood Uses Color to Mess With Your Emotions

هذا التطبيق البسيط يمنعك من رؤية كيف تستخدم هوليوود اللون لتناقش عواطفك

Ova jednostavna aplikacija omogućuje vam da vidite kako Holivud koristi boju da meša s vašim emocijama

Diese einfache App lässt Sie sehen, wie Hollywood Farbe verwendet, um mit Ihren Emotionen zu verwechseln

Esta sencilla aplicación te permite ver cómo Hollywood usa el color para mezclar con tus emociones

Esta aplicación sinxela permítelle ver como Hollywood usa a cor para confundir coas súas emocións

このシンプルなアプリは、ハリウッドがあなたの感情と混同するために色を使用する方法を見せてくれます

ეს მარტივი აპლიკაცია საშუალებას გაძლევთ იხილოთ, თუ როგორ Hollywood გამოიყენებს ფერი თქვენი რეპუტაციაზე

Na na na na na na na na na na na na na na na na

Deze eenvoudige app laat je zien hoe Hollywood kleuren gebruikt om je emoties te verwarren

This Simple App lets you see how Hollywood uses color to mess with your emotions இந்த எளிய பயன்பாட்டை நீங்கள் உங்கள் உணர்வுகளை எப்படி பயன்படுத்துகிறீர்கள் என்று பார்க்கலாம்

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