Data Science Central

2 weeks ago | datasciencecentral.com | Vincent Granville

Standard LLMs rely on prompt engineering to fix problems (hallucinations, poor response, missing information) that come from issues in the backend architecture. If the backend (corpus processing) is properly built from the ground up, it is possible to offer a full, comprehensive answer to a meaningful prompt, without the need for multiple prompts, rewording your query, having to go through a chat session, or prompt engineering.

2 weeks ago | datasciencecentral.com | Erika Balla

Data pipeline diagrams function as blueprints that transform unprocessed data into useful information. According to an IBM study, 39% of businesses anticipate increased revenue and reduced operating costs, and 44% of businesses expect quick data to help them make better decisions. These figures demonstrate the importance of fast data pipelines for businesses looking to stay ahead. Businesses that use predictive models driven by AI have seen a 20% improvement in risk management.

2 weeks ago | datasciencecentral.com | Saqib Jan

Software development cycles accelerate constantly, pushing quality assurance teams to keep pace. However, the pressure engineering leaders face to ensure quality under the speed and complexity modern pipelines require is also immense. And simply doing more of the same old way of things isn’t enough with the advancing user demands. Interestingly, while much of the focus has been on accelerating coding or transforming creative workflows, GenAI is profoundly reshaping quality assurance.

3 weeks ago | datasciencecentral.com | Kevin Vu

While traditional deep learning techniques have excelled at handling structured data like images and text, they often struggle when faced with irregular, complex data like molecules and networks of data. Geometric Deep Learning (GDL) is a machine learning methodology suitable for solving problems with irregular and complex data.

1 month ago | datasciencecentral.com | Jelani Harper |Vincent Granville |Dan Wilson

The quantum leap forward in natural language technologies attributed to foundation models, LLMs, and modern vocal applications of AI is due in no small part to the mastery of the concept of attention. When training and deploying the aforementioned models, attention mechanisms account for several things. One of the most valuable is allowing models to look back at different parts of a conversation or text and determine how that context relates to present inputs (or lines of text).