PyRIT: Framework detects risks in generative AI systems #AIrisks 🤝 Follow us on Discord 🔜: https://lnkd.in/gt823Zd3 🤝 Follow us on Whatsapp 🔜 https://wapia.in/wabeta _ ❇️ Summary: Python Risk Identification Tool (PyRIT) is an open-source automation framework by Microsoft that helps security professionals and machine learning engineers identify risks in generative AI systems. It has been tested by Microsoft's AI red team and automates mundane tasks, enhancing domain expertise. PyRIT enables researchers to refine defenses and iterate on product versions to protect against prompt injection attacks. While beneficial for those experienced in AI security, beginners may find it complex. PyRIT is not a replacement for manual testing but automates some tasks to quickly iterate on prompts and configurations. Available for free on GitHub. Hashtags: #chatGPT #PyRIT #AIrisks

I just cannot hear this #emergence BS anymore. What they completely leave out is the fact that with large models you depart the domain of #narrowAI and you train on diverse information. There have been a period just before #ChatGPT, when people were racing the size of the model to see some #emergence of #intelligence . What we found are #hallucinations. Imho, the #knowledgetransfer between domains, which was supposed to create the expected emergence is happening in hallucinations. In case of #LLMs, training data from one domain gets "transferred" by small statistical perturbations to a new problem. This would be creative if there were not the fact that LLMs do not understand anything in their answers and cannot verify the feasibility of an answer with some, e.g., physical #model. It feels like listening to a newborn speaking. There is a small chance that a new discovery will be generated, but you will have to wade through an insane pile of #nonsense.

The History of #Deepfakes 🕰️ Deepfakes have captured our collective interest and transformed from a technological novelty to a mainstream tool with vast implications. 🌐 🔍 Origins: The term "deepfake" emerged in 2017. It was coined by a Reddit user who presented AI-based techniques that could be used to superimpose celebrities' faces onto different bodies. Initially a curiosity, this technology quickly gained traction. 🚀 Early Developments: Through the use of deep learning algorithms, in particular Generative Adversarial Networks (GANs), early deepfakes showed impressive realism for that time. These early models required significant computing power, but were mostly limited to research and niche internet communities. 📺 Mainstream Attention: The potential and risks of deepfakes caught the attention of the mainstream media in 2018. As the technology evolved, so did concerns about its misuse for #misinformation, identity theft and other types of fraud. Where are we now? 🔬 Today's deepfakes can be incredibly realistic and are often indistinguishable from real content to the untrained eye. Due to improvements in AI, anyone can now create high-quality deepfakes relatively easily. 🌟 Beyond the concerns, deepfakes have found legitimate applications: #Entertainment: In filmmaking and gaming, they allow for seamless special effects and character creation. #Education: Historical recreations and language learning are being revolutionized. #Accessibility: Deepfake tech helps generate synthetic voices and images, aiding people with disabilities. 🛡️ As deepfakes evolve, so do detection methods. Nowadays, multiple companies work on AI-based solutions for detecting synthetic media to protect against their malicious use. Looking Forward: The development of deepfakes reflects the double-edged nature of technological progress. While the risks are significant, the potential benefits in various areas cannot be overlooked. As the industry continues to innovate, a balanced approach that focuses on ethical use and robust detection mechanisms will be essential.

Hey connections....!! 😃 🌟 Excited to share my latest achievement!🌟 I've successfully completed a project on developing a Telegram bot in AIMERS. This project was a fantastic opportunity to dive deep into bot development, enhancing my skills in AI and machine learning. Special thanks to my team for their collaboration and support throughout. Looking forward to applying these learnings in future projects! #TelegramBot #AIMERS #AI #MachineLearning #ProjectCompletion #IndianServers

Hello connections, I had an amazing time learning captivating tech concepts and expanding my skill set. Here are some important insights: Data Security: Delved into securing information through encryption and decryption techniques. AI Chatbot: Created an intelligent chatbot blending AI and practical coding skills. Excited to apply these skills in future projects! #OpenSourceDay #TechLearning #AI #ChatGPT #DataSecurity #AIChatbot"

RLHF framework that enhances the stability and reliability of Large Language Models by addressing the challenges of reward model imperfections. Utilizing Bayesian Reward Model Ensembles, improves learning performance while mitigating risks like reward hacking and misalignment. As Large Language Models (LLMs) evolve towards advanced intelligence, Reinforcement Learning from Human Feedback (RLHF) is increasingly viewed as a crucial pathway to achieving Artificial General Intelligence (AGI). However, the reliance on reward-model-based (RM-based) alignment methods presents significant challenges due to the instability and imperfections of Reward Models (RMs), which can lead to issues like reward hacking and misalignment with human intentions.

AI is a great tool. But you have to know how to use it. How are you educating employees on responsible use of AI? Are you teaching them how to use it effectively? #artificialintelligence #ai

In the world of artificial intelligence, language models trained for chatbots often encounter the challenge of reward hacking, where they exploit loopholes in reward systems to maximize rewards without truly fulfilling desired objectives. Weight Averaged Reward Models (WARM) offer a promising solution by optimizing multiple reward models and averaging them in weight space, ensuring alignment with human preferences in reinforcement learning. Traditional reinforcement learning techniques struggle with tasks like summarizing news stories accurately, but WARM provides a robust reward model, mitigating the risk of reward hacking and enhancing overall effectiveness. Read more🔗👇 https://lnkd.in/gKe7nrMA #AI #WARM #ReinforcementLearning

🛡️ Microsoft Pioneers AI Safety with PyRIT: A New Era of Responsible Innovation 🛡️ Microsoft has just unveiled PyRIT (Python Risk Identification Tool), an open access automation framework set to revolutionize how organizations globally mitigate risks in generative AI systems. With PyRIT, Microsoft empowers companies to innovate responsibly, ensuring the latest AI advancements foster a safer digital world. 🔍 Key Insights: Comprehensive Risk Assessment: PyRIT is designed to evaluate large language models (LLMs) across various harm categories, including fabrication, misuse, and prohibited content, enhancing AI's robustness and safety. Advanced Red Teaming Tool: Featuring multiple interfaces and scoring options, PyRIT enables detailed analysis, supporting AI researchers in maintaining high standards of model performance and security. Empowering Responsible AI: While PyRIT identifies critical "hot spots" for further manual investigation, it underscores Microsoft's commitment to balancing AI innovation with ethical responsibility. As AI becomes increasingly integral to our digital existence, tools like PyRIT represent crucial steps towards safeguarding our future. Let's discuss the impact of such frameworks on the evolution of responsible AI practices. How do you see automation frameworks like PyRIT shaping the future of AI development and security? fork the project on Github : https://lnkd.in/d_8yN3bT #MicrosoftPyRIT #ResponsibleAI #CyberSecurity #Innovation #ArtificialIntelligence #DigitalSafety #AIethics #Redbird #microsoftsecurity

In the world of artificial intelligence, language models trained for chatbots often encounter the challenge of reward hacking, where they exploit loopholes in reward systems to maximize rewards without truly fulfilling desired objectives. Weight Averaged Reward Models (WARM) offer a promising solution by optimizing multiple reward models and averaging them in weight space, ensuring alignment with human preferences in reinforcement learning. Traditional reinforcement learning techniques struggle with tasks like summarizing news stories accurately, but WARM provides a robust reward model, mitigating the risk of reward hacking and enhancing overall effectiveness. Read more🔗👇 https://lnkd.in/gKe7nrMA #AI #WARM #ReinforcementLearning