How OpenAI is approaching 2024 worldwide elections
We’re working to prevent abuse, provide transparency on AI-generated content, and improve access to accurate voting information.
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This AI Paper from Meta AI and MIT Introduces In-Context Risk Minimization (ICRM): A Machine Learning Framework to Address Domain Generalization as Next-Token Prediction.
Artificial intelligence is advancing rapidly, but researchers are facing a significant challenge. AI systems struggle to adapt to diverse environments outside their training data, which is critical in areas like self-driving cars, where failures can have catastrophic consequences. Despite efforts by researchers to tackle this problem with algorithms for domain generalization, no algorithm has yet…
Can We Optimize AI for Information Retrieval with Less Compute? This AI Paper Introduces InRanker: a Groundbreaking Approach to Distilling Large Neural Rankers
The practical deployment of multi-billion parameter neural rankers in real-world systems poses a significant challenge in information retrieval (IR). These advanced neural rankers demonstrate high effectiveness but are hampered by their substantial computational requirements for inference, making them impractical for production use. This dilemma poses a critical problem in IR, as it is necessary to…
Can Large Language Models Understand Context? This AI Paper from Apple and Georgetown University Introduces a Context Understanding Benchmark to Suit the Evaluation of Generative Models
In the ever-evolving landscape of natural language processing (NLP), the quest to bridge the gap between machine interpretation and the nuanced complexity of human language continues to present formidable challenges. Central to this endeavor is the development of large language models (LLMs) capable of parsing and fully understanding the contextual nuances underpinning human communication. This…
This AI Paper from NTU and Apple Unveils OGEN: A Novel AI Approach for Boosting Out-of-Domain Generalization in Vision-Language Models
Large-scale pre-trained vision-language models, exemplified by CLIP (Radford et al., 2021), exhibit remarkable generalizability across diverse visual domains and real-world tasks. However, their zero-shot in-distribution (ID) performance faces limitations on certain downstream datasets. Additionally, when evaluated in a closed-set manner, these models often struggle with out-of-distribution (OOD) samples from novel classes, posing safety risks in…
Building an early warning system for LLM-aided biological threat creation
We’re developing a blueprint for evaluating the risk that a large language model (LLM) could aid someone in creating a biological threat. In an evaluation involving both biology experts and students, we found that GPT-4 provides at most a mild uplift in biological threat creation accuracy. While this uplift is not large enough to be conclusive,…
AI News Weekly – Issue #382: A Majority of AI decision makers worry about data privacy and security – Apr 25th 2024
Powered by rws.com In the News 80% of AI decision makers are worried about data privacy and security Organisations are hitting stumbling blocks in four key areas of AI implementation: Increasing trust, Integrating GenAI, Talent and skills, Predicting costs. artificialintelligence-news.com Sponsor When Generative AI Gets It Wrong, TrainAI Helps Make It Right TrainAI provides prompt…
