Hawk’s Post

💡 How do you transform your AFC operations without disrupting your current systems? The concept of AI overlays is a game-changer for financial institutions. 🌟 By leveraging existing tech stacks while improving outcomes like alert prioritization, false positive reduction, and crime detection, it's a win-win for innovation and efficiency. The collaboration with Deloitte brings cutting-edge insights to this challenge. 📄 I highly recommend exploring the whitepaper for actionable strategies and real-world applications. 💬 Let's discuss: How is your organization tackling AFC modernization while keeping costs and resources in check? #ArtificialIntelligence #FinancialCrime #RegTech #AFC #Innovation #AIOverlay#AML

I wanted to pass along a few simple revisions to basic contract forms that in-house and external lawyers can implement to help their clients capitalize on the benefits of AI while reducing potential risks and liabilities.

Can we use AI to make AI safer? OpenAI has been working on a novel approach they call Rule-Based Rewards (RBRs) an alternative to reinforcement learning from human feedback (RLHF) which has traditionally been used for fine-tuning language models to help ensure AI systems behave safely and align with human values. RBRs have already been integrated into OpenAI’s safety stack since GPT-4 launch, ensuring their models respond appropriately in sensitive scenarios. They found this innovation not only enhances the safety of AI systems but also makes them more efficient and adaptable. By reducing reliance on human data collection, RBRs help streamline the training process, making it quicker and more cost-effective among other advantages. But should RBRs replace human feedback? Even OpenAI notes there are limitations and ethical considerations that come into play. This approach is part of OpenAI’s ongoing efforts to explore new methods for AI safety, aiming to strike the perfect balance between helpfulness and safety. We invite researchers and practitioners to explore the potential of RBRs in their own work and contribute to advancing the field of safe AI. Read the full article here to learn more about this model and OpenAI's ongoing efforts to explore new methods that aim to strike the perfect balance between helpfulness and safety. #AI #Safety #Innovation #OpenAI #RBR #ArtificialIntelligence #TechNews #MachineLearning #AIResearch

Congratulations to OpenAI on their groundbreaking paper "Rule Based Rewards for Language Model Safety"! https://lnkd.in/gQJSJNTe What a good read for a rainy Sunday afternoon. This is exactly what the field needs right now - a clever way to cut down on all those time-consuming human annotations while really cranking up the data flywheel. It's pretty cool how they're basically putting #neurosymbolicAI ideas into practice, using rules to steer the whole data-driven learning process. Talk about a smart move! Interestingly, this work shares some conceptual similarities with our #MLSys 2024 paper "Fine-Tuning Language Models Using Formal Methods Feedback". https://lnkd.in/gDppf23d Our focus on rule satisfaction in #autonomous driving allows for direct #formal #verification with predefined rules as a reward model, simplifying our problem space. Another key difference lies in the workflow: RBR first generates labels/feedback, then produces responses. Our method instead first generates responses, then applies formal methods for feedback. Since publishing, we've been exploring extensions to handle open scenarios where rules aren't explicitly given, including experiments with LLMs as judges to bootstrap from limited human data. It's bittersweet to see similar ideas emerge from OpenAI (friends don't say "scooped by" :-), but we're excited and inspired by the parallel development! Looking ahead, there are several exciting directions for future research. A particularly promising avenue would be combining RBR with formal verification methods to establish even stronger safety guarantees for language models. Another critical area of exploration is developing techniques to automatically derive or learn rules from data, potentially reducing the need for manual rule creation and allowing for more adaptive systems. As we push these boundaries, it will also be crucial to address how to handle conflicting rules or edge cases that may arise in complex real-world scenarios. Eager to see how the community builds on these advances! #AI #MachineLearning #Safety #OpenAI

For workers in professional service industries such as the legal, tax & accounting, risk & fraud, and government operations sectors, adapting to new trends or developments in the way they work is common. Now, however, a much larger change: the impact of generative AI. How professional service firms integrate GenAI systems into their daily work while trying to understand what it means for the future of their profession is pivotal.    To better understand this, the Thomson Reuters Institute is proud to publish the 2024 Generative AI in Professional Services report: https://lnkd.in/gWnF34qP

Important read on AI. A notable observation to me is that while 85% of legal responders said Generative AI could be applied to their work, and 51% said it should be applied to their work, only 29% of legal responders said they have received training. Legal colleagues, what are your thoughts on that?

OpenAI developed Rule Based Rewards (RBRs), a method that improves AI safety by using automated feedback and specific rules instead of extensive human-labeled data, allowing for better control and easier updates of model behavior RBRs work by breaking down desired behaviors into simple propositions (like "contains a brief apology" or "avoids judgmental language"), using these to create rules for different scenarios, and then using an AI system to evaluate model responses against these rules, generating a reward signal that guides the AI towards safer and more appropriate behavior

🚀 Enhancing AI Safety with Rule-Based Rewards (RBRs) 🌟 OpenAI’s recent research highlights the transformative impact of Rule-Based Rewards (RBRs) on AI safety and reliability. By introducing and defining clear, step-by-step rules, RBRs guide AI behaviour, ensuring it aligns with safety standards without constantly relying on human feedback. This innovation, used since the GPT-4 launch, offers a streamlined, cost-effective method to maintain model alignment with human values. Key Benefits for AI Developers: 1. Increased Safety and Reliability: RBRs reduce instances of unnecessary refusals and enhance the model’s ability to respond appropriately to complex queries. 2. Cost-Effective and Efficient: Minimises the need for extensive human data, accelerating training processes and reducing costs. 3. Flexibility and Scalability: Easily updateable rules adapt to evolving safety guidelines, ensuring continued alignment without extensive retraining. We’re excited to leverage these advancements to deliver our clients smarter, safer AI solutions - pushing the boundaries of what’s possible with AI! #AI #OpenAI #MachineLearning #AIsafety #Innovation

Just read an interesting paper from OpenAI on a new approach to AI safety training. 🤖🛡️ ...And then I asked Claude to explain it to me in terms I could understand. Full disclosure...Gemini helped me draft this post... but the exercise really helped get a clearer understanding about what is meant by "AI safety" and "responsible AI" and why it may actually be better for humans to do less to accomplish both. The gist: Researchers have developed a method using Rule-Based Rewards (RBRs) that makes AI safety training more efficient and adaptable. By using automated AI feedback instead of relying heavily on human data, they've found a way to create safer AI systems without sacrificing usefulness. Give me a simple example that illustrates this: Imagine you're training a robot to be a helpful assistant in a kitchen. The goal is for the robot to be both safe and useful. Traditional method (relying heavily on human data): In this approach, you'd have humans watch the robot work in the kitchen and give feedback on every action. They'd say things like "Good job handling that knife safely" or "No, don't put metal in the microwave!" This process is time-consuming and expensive, as you need many humans to provide a lot of feedback. New method (using Rule-Based Rewards): Instead, you create a set of clear rules for kitchen safety and usefulness, such as: "Always hold knives by the handle" "Don't mix raw meat with other foods" "Preheat the oven before baking" You then program these rules into an AI system that can automatically evaluate the robot's actions. This AI system watches the robot and provides instant feedback based on these predefined rules. The benefits of this new approach: Efficiency: The AI can provide feedback much faster than humans, allowing for more rapid training. Consistency: The rules are applied consistently, without human variability. Adaptability: If you want to add a new safety rule (e.g., "Always turn off the stove after cooking"), you can simply add it to the rule set without retraining everything from scratch. Balance: By carefully crafting rules that cover both safety and usefulness, you ensure the robot learns to be safe without becoming overly cautious and ineffective. In this way, the researchers have created a method that can train AI systems to be safe more quickly and flexibly, while still ensuring they remain useful for their intended tasks. Key takeaways: -Cost and time-efficient -Easily updatable as safety standards evolve -Improved accuracy in classifying safe responses -Maintains a balance between safety and functionality Why is it important? This could be a game-changer in developing AI systems that are both powerful, more accurate, and responsible. Will be curious to see how this impacts the future of AI development in alignment and ethical frameworks.