Oracle Corp. today announced the general availability of Oracle AI Database 26ai and Oracle Autonomous AI Lakehouse, both aimed at supporting artificial intelligence training and inference across ...

Discover how Tinker and Ray are utilized to fine-tune text-to-SQL models, enhancing AI capabilities in generating efficient SQL queries. In an innovative approach to advancing text-to-SQL models, ...

We're trying to use SQL schema files from multiple Smart Data Model Git repositories and we've noticed that in some occasions unrelated enums with the same name are defined. This means that it is ...

Abstract: State Space Models (SSMs) are powerful tools for mod-eling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

Snowflake has thousands of enterprise customers who use the company's data and AI technologies. Though many issues with generative AI are solved, there is still lots of room for improvement. Two such ...