Rockets: A Good Analogy for AI Language Models

In this contributed article, Varun Singh, President and co-founder of Moveworks, sees rockets as a fitting analogy for AI language models. While the core engines impress, he explains the critical role of Vernier Thrusters in providing stability for the larger engine. Likewise, large language models need the addition of smaller, specialized models to enable oversight and real-world grounding. With the right thrusters in place, enterprises can steer high-powered language models in the right direction.

Unveiling Jamba: AI21’s Groundbreaking Hybrid SSM-Transformer Open-Source Model

AI21, a leader in AI systems for the enterprise, unveiled Jamba, the production-grade Mamba-style model – integrating Mamba Structured State Space model (SSM) technology with elements of traditional Transformer architecture. Jamba marks a significant advancement in large language model (LLM) development, offering unparalleled efficiency, throughput, and performance.

Heard on the Street – 4/25/2024

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.

Artificial Intelligence Means Smaller Teams Doing More with Less Makes the Small Autonomous Teams Structure Even More Important 

In this contributed article, Brady Brim-DeForest, CEO of Formula.Monks, discusses how the more that we incorporate AI technology into white collar workflows in large organizations, the more that it becomes important to lean into the work structures that make humans function at their best.

Nature Communications Publishes Zapata AI Research on Generative AI for Optimization

Zapata Computing Holdings Inc. (Nasdaq: ZPTA), the Industrial Generative AI company, announced that its foundational research on generator-enhanced optimization (GEO) has been published in the esteemed Nature Communications journal. The research, titled “Enhancing Combinatorial Optimization with Classical and Quantum Generative Models,” introduces Generator-Enhanced Optimization (GEO), a novel optimization method that leverages the power of generative modeling to suggest high-quality candidate solutions to complex optimization problems.

What AI Could, Should, and Would Do

In this contributed article, Dr. Chirag Shah, professor in the Information School at the University of Washington, highlights how we are at a crossroads in our relationship with AI where what we choose now can have a huge impact on the future of AI and that of humanity. So the question is — how do we make good choices? Let’s start by examining two extreme visions of AI.

Video Highlights: Gemini Ultra — How to Release an AI Product for Billions of Users — with Google’s Lisa Cohen

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by Lisa Cohen, Google’s Director of Data Science and Engineering, to discuss the launch of Gemini Ultra. Discover the capabilities of this cutting-edge large language model and how it stands toe-to-toe with GPT-4.

What Happens When We Train AI on AI-Generated Data?

In this contributed article, Ranjeeta Bhattacharya, senior data scientist within the AI Hub wing of BNY Mellon, points out that In the world of AI and LLMs, finding appropriate training data is the core requirement for building generative solutions. As the capabilities of Generative AI models like Chat GPT, DALL-E continues to grow, there is an increasing temptation to use their AI-generated outputs as training data for new AI systems. However, recent research has shown the dangerous effects of doing this, leading to a phenomenon called “model collapse.”

Where Artificial Intelligence Is Making a Difference in Healthcare

In this contributed article, Rajesh Viswanathan, Chief Technology Officer for Inovalon, discusses how for the past year, AI was at the center of conversations throughout healthcare. While the potential for AI to revolutionize healthcare is clear, from care delivery to enhancing operational efficiencies and accelerating research, many organizations are still figuring out where to begin.

Heard on the Street – 4/18/2024

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.