Neural Information Processing Systems http://nips.cc/

Understanding convergence of stochastic gradient descent (SGD) based optimization algorithms can help deal with enormous machine learning problems.

Scarcity of labeled training data remains the long pole in the tent for building performant language technology and generative AI models. Transformer models -- particularly LLMs -- are increasingly being used to mitigate the data scarcity problem via synthetic data generation. However, because the models are black boxes, the properties of the synthetic data are difficult to predict. In practice it is common for language technology engineers to 'fiddle' with the LLM temperature setting and hope that what comes out the other end improves the downstream model. Faced with this uncertainty, here we propose Data Kernel Perspective Space (DKPS) to provide the foundation for mathematical analysis yielding concrete statistical guarantees for the quality of the outputs of transformer models. We first show the mathematical derivation of DKPS and how it provides performance guarantees. Next we show how DKPS performance guarantees can elucidate performance of a downstream task, such as neural machine translation models or LLMs trained using Contrastive Preference Optimization (CPO). Limitations of the current work and future research are also discussed.

Researchers risk fire, explosion or poisoning by allowing AI to design experiments, warn scientists. The use of AI models in scientific laboratories risks enabling dangerous experiments that could cause fires or explosions, researchers have warned. Such models offer a convincing illusion of understanding but are susceptible to missing basic and vital safety precautions. In tests of 19 cutting-edge AI models, every single one made potentially deadly mistakes. Serious accidents in university labs are rare but certainly not unheard of.

A network, which is used to model interactions or communications among a set of agents or nodes, is arguably among one of the most common and important representations for modern complex data.

However, the existing literature on parallel inference almost exclusively focuses on Euclidean data and parameters.