BRAIN, an LA-based ad agency, launched Goody-2, described as the world’s most responsible AI model and “outrageously safe”. Although it playfully declines to answer certain questions, it highlights the potential impact of overly stringent alignment principles on AI functionality. While Goody-2 is comedic, it sheds light on the balance needed in AI development.

The FCC has banned the use of AI-generated voices in robocalls to consumers, following a scandal involving a fake President Biden voice. FCC Chairwoman Jessica Rosenworcel warned of robocall fraud and misinformation. The ruling also sets limits on using AI voices to interact with consumers and exempts civil services and politicians for non-commercial use.

Nomic AI introduces Nomic Embed, an open-source, auditable text embedding model with an 8192 context length. It outperforms closed-source models like OpenAI’s text-embedding-ada-002, emphasizing transparency and reproducibility. Nomic Embed is built through a multi-stage contrastive learning pipeline, achieving superior performance on benchmarks. This release marks a significant advancement in the field of text embeddings.

Researchers introduce SCALEEVAL, a framework utilizing multiple LLM agents engaging in agent-debate to evaluate LLMs as responders. It reduces reliance on costly human annotation, balancing efficiency and human judgment for accurate assessments. It exposes effectiveness and limitations of LLMs in varied scenarios, advancing scalable evaluation methods crucial for expanding LLM applications.

Pinterest researchers have introduced a reinforcement learning framework to fine-tune diffusion models, addressing issues like bias and fairness. The method outperforms existing models, demonstrating generality, robustness, and the ability to generate diverse images. It achieved better results across various tasks and encourages further research to enhance diffusion models. [50 words]

Graph Transformers face scalability challenges due to high computational costs. Existing methods fail to adequately address data-dependent contexts. Graph Neural Networks have introduced innovations like BigBird and Performer to reduce computational demands. Researchers have introduced Graph-Mamba, integrating a selective State Space Model into the GraphGPS framework, promising significant improvements in computational efficiency and scalability.

Large Language Models (LLMs) like ChatGPT and Llama have shown remarkable performance in AI applications, but concerns about misuse and security vulnerabilities persist. Researchers have introduced the concept of weak-to-strong jailbreaking attacks, which exploit weaker models to manipulate larger ones. Token Distribution Fragility Analysis and Experimental Validation aim to address these vulnerabilities. For more details,…

Large Vision-Language Models (LVLMs) bridge visual perception and language processing. Huawei researchers address the challenge of hallucinations in LVLMs, proposing innovative strategies and interventions. Refinements in data processing and model architecture enhance accuracy and reliability, reducing hallucinations. The study emphasizes the need for continued innovation to realize LVLMs’ full potential in interpreting and narrating the…

There’s a shift towards creating powerful and efficient language models for real-world use, dealing with computational constraints and domain-specific needs. Apple researchers propose hyper-networks and mixtures of experts as solutions, achieving high performance with less computational cost. This research promises to expand AI applicability in resource-constrained environments. For more details, refer to the paper.