Hammerspace Unveils the Fastest File System in the World for Training Enterprise AI Models at Scale

Hammerspace, the company orchestrating the Next Data Cycle, unveiled the high-performance NAS architecture needed to address the requirements of broad-based enterprise AI, machine learning and deep learning (AI/ML/DL) initiatives and the widespread rise of GPU computing both on-premises and in the cloud. This new category of storage architecture – Hyperscale NAS – is built on the tenants required for large language model (LLM) training and provides the speed to efficiently power GPU clusters of any size for GenAI, rendering and enterprise high-performance computing.

Using Clinician Big Data to Alleviate a Struggling Workforce

In this contributed article, Charlie Lougheed, CEO and co-founder of Axuall, explains why healthcare needs to adjust its thinking and in what ways clinician big data can make impactful changes throughout the healthcare industry, from credentialing to attrition.

Video Highlights: A Code-Specialized LLM Will Realize AGI — with Jason Warner

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by poolside co-founder and CEO Jason Warner who sheds light on how code-specialized LLMs could vastly outperform generalized counterparts like GPT-4.

Generative AI Report – 3/1/2024

Welcome to the Generative AI Report round-up feature here on insideBIGDATA with a special focus on all the new applications and integrations tied to generative AI technologies. We’ve been receiving so many cool news items relating to applications and deployments centered on large language models (LLMs), we thought it would be a timely service for readers to start a new channel along these lines. The combination of a LLM, fine tuned on proprietary data equals an AI application, and this is what these innovative companies are creating. The field of AI is accelerating at such fast rate, we want to help our loyal global audience keep pace.

Heard on the Street – 2/29/2024

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

Securing GenAI in the Enterprise

Opaque Systems released a new whitepaper titled “Securing GenAI in the Enterprise.” Enterprises are chomping at the bit to use GenAI to their benefit but they are stuck. Data privacy is the number one factor that stalls GenAI initiatives. Concerns about data leaks, malicious use, and ever-changing regulations loom over the exciting world of Generative AI (GenAI), specifically large language models (LLMs).

Microsoft Expands AI Reach with Partnership with French Start-up Mistral

Microsoft has taken another notable step forward in the realm of artificial intelligence by announcing a strategic partnership with Mistral, a French AI start-up based in Paris. This move further exemplifies Microsoft’s commitment to broaden its presence in the rapidly evolving AI industry beyond its existing alliance with OpenAI and at a global scale.

NEW RESEARCH: Growing Database Complexity Will Fuel Significant Skills Gaps in 2024

Increasing complexity, the rapid adoption of emerging technologies and a growing skills gap are the biggest concerns facing IT leaders in 2024, according to The State of the Database Landscape, a major new survey from end-to-end Database DevOps provider Redgate. 

AI Integration in the Service Industry: Navigating Challenges and Leveraging Human Expertise 

In this contributed article, Edwin Pahk, SVP of Presales & Customer Success at Aquant, discusses how AI is revolutionizing industries, especially in the service sector involving complex equipment maintenance, by promising efficiency and innovation. However, its integration faces challenges like adapting infrastructure, cultural shifts, and the need for evolved employee skills, requiring a human-in-the-loop approach and transforming AI vendors from mere providers to strategic advisors, while democratizing AI skills across the workforce.

A Brief Overview of the Strengths and Weaknesses Artificial Intelligence 

In this contributed article, editorial consultant Jelani Harper suggests that since there are strengths and challenges for each form of AI, prudent organizations will combine these approaches for the most effective results. Certain solutions in this space combine vector databases and applications of LLMs alongside knowledge graph environs, which are ideal for employing Graph Neural Networks and other forms of advanced machine learning.