Patronus AI, a leading AI safety company, has identified safety gaps in major AI systems, emphasizing the pressing need for improved safety measures in AI development and deployment. Their diagnostic test suite, SimpleSafetyTests, exposed vulnerabilities in open-source models, particularly in sensitive areas like suicide and child abuse. The findings highlight transparency issues in training data and underscore the importance of additional safeguards. Notably, Meta's Llama2 performed well, while models like Anthropic's Claude and Google's PaLM struggled, revealing weaknesses linked to training data and transparency limitations. Despite challenges, implementing safety prompts and content moderation can mitigate risks in AI systems. Source: https://venturebeat.com/ai/patronus-ai-finds-alarming-safety-gaps-in-leading-ai-systems/
A new report indicates the expanding landscape of effective altruism in AI security, emphasizing the growing significance of collaboration among AI researchers, policymakers, and industry leaders. The report underscores the increasing importance of proactive measures to address potential risks associated with AI and ensure its safe and ethical development. Notably, it highlights the interconnections between the effective altruism community and AI security, citing the influence of RAND Corporation researchers on the White House's AI Executive Order and financial support from Open Philanthropy, an effective altruism group. Additionally, the report notes that RAND's CEO, Jason Matheny, is a member of Anthropic's Long-Term Benefit Trust, and several researchers involved in the study have strong ties to the effective altruism community. Source: https://venturebeat.com/ai/the-widening-web-of-effective-altruism-in-ai-security-the-ai-beat/
Large Language Models (LLMs) are being experimented with online, introducing challenges as generative AI companies release them into the unregulated internet for bug discovery. This approach raises ethical concerns regarding biases and accuracy, highlighting the need for responsible data selection and bias mitigation in software development. Despite demands for safeguards, terms of service for generative AI lack accuracy guarantees, leaving users vulnerable to misinformation. The unstructured nature of LLM databases complicates selective information deletion, and the legal landscape struggles to keep up, resulting in disputes. Ongoing efforts to recalibrate quality standards for LLMs face challenges in identifying breakdowns and hallucinations in AI experiences. Source: https://venturebeat.com/ai/llms-unleashed-navigating-the-chaos-of-online-experimentation/