Data Science Central

Data Science Central is a premier online hub for professionals working with big data. It covers a wide range of topics, from analytics and data integration to visualization. The platform offers a vibrant community where users can engage with each other, access informative articles, seek technical help through forums, stay updated on the latest technologies and tools, and explore job opportunities in the industry.

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Articles

How to make AI work in QA

5 days ago | datasciencecentral.com | Saqib Jan

AI has radically changed Quality Assurance, breaking old inefficient ways of test automation, promising huge leaps in speed and the ability to test things we otherwise couldn’t easily test before. But getting AI to be trusted by QA teams is a major challenge across the industry. A big part of the difficulty stems from the “black box” nature of AI tools where the internal processing is abstracted away.
Stay ahead of the sales curve with AI-assisted Salesforce integration

2 weeks ago | datasciencecentral.com | Anas Baig

Any organization with Salesforce in its SaaS sprawl must find a way to integrate it with other systems. For some, this integration could be in the form of a platform-native connector or a custom script. However, these options have limitations: platform-native connectors are restricted to one platform, while custom connectors are difficult to build and maintain. So the remaining option is to use a reliable third-party Salesforce integration tool, like Exalate.
Generative AI in software development: Does faster code come at the cost of quality?

3 weeks ago | datasciencecentral.com | Scott Thompson

From creating comprehensive essays to writing intriguing fiction, there’s hardly anything untouched by the impact of generative AI. The technology has caught the attention of forward-thinking engineering companies, too. Many have attempted generative AI-assisted code development and seen measurable improvements in overall efficiency. However, the potential of this disruptive technology isn’t limited to code generation.
New Book: 0 and 1 - From Elemental Math to Quantum AI

3 weeks ago | datasciencecentral.com | Vincent Granville

This book opens up new research areas in theoretical and computational number theory, numerical approximation, dynamical systems, quantum dynamics, and the physics of numbers.
How to Design LLMs that Don't Need Prompt Engineering

1 month 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.
From raw data to real-time insights: Building high-speed data pipeline diagrams

1 month 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.
How generative AI is reshaping traditional QA strategies

1 month 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.
Geometric deep learning: AI beyond text & images

1 month 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.
Accounting for attention in speech-to-speech AI applications

2 months 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).
The Rise of Specialized LLMs for Enterprise

2 months ago | datasciencecentral.com | Vincent Granville |Dan Wilson

In this article, I discuss the main problems of standard LLMs (OpenAI and the likes), and how the new generation of LLMs addresses these issues. The focus is on Enterprise LLMs. Most of the LLMs still fall in that category. The first ones (ChatGPT) appeared around 2022, though Bert is an early precursor.