Fair AI Data’s Post

Prediction: The answer to AI copyright problems is not in the courts, but in the systems themselves. 1. There's an open window of opportunity here for an online Git repo provider who blocks any AI scraping. Take Microsoft's toys away. 2. CoPilot doesn't, at this stage, understand what makes code work. It only knows how to copy, spin, and paste things it has already seen. The whole system is vulnerable to large scale injection of code with security holes, bugs, and other issues. Sub-Prediction: This is already happening. We've seen it with NPMs. State actors are already playing the long game. Lazy coders will not check AI code for problems. Problems will get in. Sub-Sub-Prediction: An AI to check your AI generated code is already underway somewhere. Sub-Sub-Sub-Prediction: AI on AI is a recursive doom loop. https://bit.ly/3zHOyU6

GitHub, Microsoft, and OpenAI are in trouble because their AI tool, Copilot, is said to have used open-source code without permission. The lawsuit says that the AI learned from publicly available code and this might be against copyright laws. This raises questions about what AI can and cannot do with our code. #TechLaw #AI https://lnkd.in/dhiS79QZ

I was reading an article on managing AI risks, and i noticed no mention of code reviews. Per my humble opinion you cannot identify risks in any system, especially an AI system, without looking at its code in detail. Yes, code review would be challenging to risk professionals with no development experience. In recent past, some experts have recommended algo reviews, but algos still need to be translated into a working code by developers, and it is not entirely unexpected that variances can creep in during implementation of these algos. There is a view that software testing should address all such risks. I disagree with this notion because the software testing might only focus on sanctity of the code itself, and not on more holistic risk-based testing of the code. Example: you write an code to develop an AI-enabled pebble sorter which automatically the tells the color, shape and size of a pebble from a giant heap of millions of pebbles. A software tester might focus on making sure the code works, and the code output is in acceptable accuracy range. However, a risk professional would focus the code review on things like: Is the code copied from a proprietary code? Is the color output infringing any copyrights? Is the data set used to train the AI model sufficient to provide a representative set for the universe of pebbles? etc. It is rare for developers to build new AI tools from scratch today, and they often use publicly available code to develop more mature AI tools. This is the root of my hypothesis that some of these tools that leverage other off-the-shelf code, and trained on public data sets, may not have been reviewed for different types of risks. An output-testing model may not be sufficient for an AI-model because of the range of inputs and outputs that are possible, and the fact that the outputs might evolve over time. However, in my experience risk teams rarely work at the code level. And since the risk teams usually develop the control frameworks (often in excel in or ppts!!), the tech teams are forced to follow the control documentation inherited from more conventional risk areas like finance and accounting, instead of addressing controls at the code level. I think this is a bridge the risk and tech teams will have to cross by addressing risks at code level in the new era of AI. For executives who want to jump headfirst onto the Gen AI bandwagon, this might be a sobering read.

As AI systems become more prevalent in coding and other creative fields, the legal frameworks surrounding their use must evolve to ensure fair and ethical practices. -- 🎯 We are targeting agencies and enterprises looking to deploy conversational models for teams of up to 1500 members, all at a highly competitive development cost. Don’t miss out—embrace AI now before it's too late! ✳ Learn more about it in a discovery call (link in comments). #AI #ConversationalAI #TeamEfficiency #TechInnovation #CompetitiveEdge #AIDevelopment #FutureOfWork #DigitalTransformation #AgileTeams #BusinessGrowth

Model Musings Intellectual Property: This is a big one, and not just for LLMs, but for any model. Why would I generalize to such an extent? Because of the word "model." By definition, it represents something else, and that something else was almost certainly created by someone else. When I build a model plane, it's not an actual SR-71 Blackbird. In fact, it only looks like it. It has no functioning engines. I'm not in danger of stealing or replicating the intellectual property (IP) behind the SR-71 blackbird. However, when a model produces exactly what it is modeled after, then we have a potential problem. And that's what an LLM does. It's trained on (modeled after) human language. And it produces ... human language. Now, no one has a copyright on human language in the general sense. However, there are bodies of human language that are copyrighted. And there can be styles that are copyrighted. I might produce an entirely novel body of language, but if its style closely resembles the style of someone for whom that style represents "property," then I'm in a danger zone. But, it gets even more complicated. Very few people are generating language with an LLM that they themselves created. The LLM was probably created by OpenAI, Meta, Anthropic, Mistral AI, or Google. So, when that model creates some body of language, who can/should have the copyright? The person who prompted the model? Or the entity who created the model? If neither can have the copyright, then can either be held liable for the text generated? Can either be held liable for how that text is used? And then there's the elephant in the room: code. Imagine using an LLM to generate/fix a significant portion of the codebase for a hugely successful commercial application. As of writing, I understand that x.AI retains ownership of everything that Grok produces while OpenAI specifically states that users retain ownership of both Input and Output. If I remain as objective as possible, I can understand both sides of an IP debate involving LLMs. The creators of the model invested an obscene amount of money to bring it into existence. But the existence of the model isn't producing anything valuable. The prompting of the model is what produces value. The question of who can capitalize on that value hasn't yet been set in stone. I use LLMs all the time but not for commercial applications. So, I'm not worried about these things affecting me. However, there are companies that exist solely because of what these LLMs can generate. That feels precarious to me. What do you think about LLMs and IP? What precedents do we have in the IP space that we can apply to text generated by LLMs? What are some important IP-related distinctions that need to be made soon to protect content creators?

The CTO of OpenAI recently gave an interview on the data used to train Sora and it made me question the legalities behind LLMs. My concerns surrounding intellectual property (IP) in large language models (LLMs) such as those developed by OpenAI and Google are steadily growing, and I wonder how they might shape the next wave of IP laws and regulations. One of my primary concerns revolves around the vast amounts of data these LLMs are trained on. These datasets often consist of copyrighted text, code, and creative works sourced from various sources across the internet (Public data*.) With LLMs generating outputs such as text, music, or art, there’s a significant risk I think that these outputs may bear resemblance to copyrighted material, potentially leading to infringement. The attribution and ownership of the creative output generated by LLMs pose another significant challenge. With their complex architectures and intricate training processes, determining who exactly owns the rights to the content produced becomes increasingly ambiguous leading down a path of blurring IP lines. Is it the developers who built the LLM, the companies that own the training data, or the LLMs themselves, or the physical data used to train the models? This lack of clarity makes me curious about the difficulties to assign credit, enforce copyright, and hold parties accountable for any infringement. Going off that the scope of originality arises concerning the outputs generated by LLMs. While models can produce remarkably creative content, I wonder whether such content qualifies as truly “original” or merely a remix of the vast corpus of data they were trained on. This ambiguity in determining the originality of LLM-generated content further complicates the application of existing copyright laws. I think this will influence the next wave of IP laws and regulations. Policymakers, legal experts, and industry stakeholders are actively engaging in discussions to address these challenges and develop frameworks that strike a balance between fostering innovation and protecting intellectual property rights. 1. Clarify ownership and attribution: Set clear guidelines for LLM content ownership and credit. 2. Update copyright laws: Adapt laws for LLM content, ensuring fair use and creator protection. 3. Implement data governance: Introduce protocols for data privacy and bias reduction. 4. Encourage collaboration: Promote cooperation for innovation and IP resolution in LLM tech. I think these concerns surrounding IP in LLMs are poised to shape the future trajectory of intellectual property laws and regulations.

You may have noticed today that Mumsnet announced we're taking legal action against Open AI and other AI scrapers. Not only is scraping without permission an explicit breach of our terms of use but the free scraping of publishers large and small, represents a fundamental challenge to the viability of the open web, with LLMs building models with scraped content from the sites they are trying to replace. At Mumsnet we’re in a stronger position than most because much of our traffic comes to us directly, and AI can never replicate the stories, humour and advice from tens of thousands of real user posts every day. However if these trillion-dollar giants are allowed to pillage content from publishers and get away with it they risk destroying many of them. We strongly believe that our conversational data is of incredible value to LLMs - most notably because the six billion+ words on Mumsnet is a unique record of twenty-four years of female conversation and AI models have gender bias baked in, which our data can help counter. However, we also recognise the need for a clear value exchange for this data. The response we received from Open AI when we approached them about a fair deal was that they were more interested in deals for datasets that are not easily accessible online - an admission similar to that of Microsoft CEO Mustafa Suleyman, who shared two weeks ago that machine-learning companies are perfectly within their rights to scrape content published online. Publishers need to work together to get a fair deal from AI companies and ensure we maintain the breadth and quality of independent content on the open web. https://lnkd.in/eYR3Fb2P.

🚨 Weekly Copyright Round-Up: Is generative AI's ability to replicate content no more infringing than the VCR? 💻©🗞 (For those of you who don't remember the VCR, you can ignore this post... 😆) ⚡ Recent Development: Microsoft is leveraging the precedent set by the legality of technologies like the VCR to dismiss claims in The New York Times' copyright infringement lawsuit against OpenAI and Microsoft. 👩⚖️ NYT's Infringement Suit: The Times alleges that Microsoft copied its stories to mimic its style using OpenAI's language models. 📢 Rebuttals: Microsoft's defense argues that such models are akin to past technologies and are not inherently illegal. The Times' counsel rebuts, stating that comparing language models to the VCR is flawed as VCR manufacturers didn't engage in massive copyright infringement. 🙋♂️ OpenAI's side-eye: OpenAI has similarly filed a motion to dismiss, claiming that the Times manipulated ChatGPT into reproducing copyrighted material. 🌎 Setting Precedent: The outcome of this lawsuit could shape the future of generative AI. Lawsuits like the one from the Times against OpenAI and Microsoft have the potential to rewrite how generative AI continues to grow as an industry. 1️⃣ The full text of Microsoft's memo in support of the Motion to Dismiss can be found here: https://lnkd.in/ewqsvZC9 2️⃣ The full text of NYT's complaint against Microsoft and OpenAI can be found here: https://lnkd.in/eR8KimNw

Is Synthetic Data the Solution? Let’s check this together. The issue of OpenAI infringing on authors’s work has been a recurring problem, and OpenAi has been facing a series of lawsuits about them using people’s copyright work to train their AI ChatGPT. OpenAI, like many companies, uses artificial intelligence (AI) to learn and understand language. To teach their AI systems, they need a lot of text examples, like books, articles, and conversations. This text data is what the AI learns from. I, as a person, am not always happy to see OpenAI facing this lawsuit, as I strongly commend what they are building; it’s something that’s useful to everyone. But I understand the fact that it’s at the detriment of copyright holders whose content is being used to train their AI without proper licensing or compensation. It’s a dilemma. I'm, however, happy to see that OpenAI is taking steps to salvage this situation. In an interview, the CEO of OpenAI, Mr. Atman, disclosed that the company is taking steps to remedy this problem with the development of synthetic data. He also believes that it’s another way of dealing with the issue of a shortage of data to train their AI. Synthetic data means using AI to create new text for training AI models. Instead of relying only on real-world data, AI can generate its own text to learn from. OpenAI thinks this approach could help prevent copyright issues. But will synthetic data really solve the problem? What happens to the text created by AI? Can it be protected by copyright laws, and is it considered a new work? It's complicated. One big question is who owns the copyright for text created by AI using synthetic data. Is it OpenAI, the company developing the AI, or the users who give instructions to generate the data? These questions are part of a larger debate about copyright and AI-generated content. Many laws require human creativity for copyright protection, which human lacks. an element many courts in most jurisdictions have ruled to be missing in respect of AI-generated content, robbing Ai developers and users from claiming copyright. Also, with AI, we can’t argue that copyright over synthetic data is vested in AI due to the human authorship policy in most jurisdictions. So the same problem. Well, that’s something OpenAI will deal with later. Using synthetic data might help them avoid copyright problems if original creators don't claim ownership. But there's still uncertainty about whether this new AI-generated text counts as a derivative work and whether consent is needed. Let’s see how this unfolds #syntheticdata #openai #copyrightinfringement #wipo https://lnkd.in/gfqMhrbh