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.

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.

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.”

Vero AI Evaluates 10 Leading Generative AI Models Using Its Comprehensive VIOLET Framework to Gauge Responsible AI 

Vero AI, an analytical engine and scoreboard that helps enterprises fully harness the potential of advanced technology including artificial intelligence while minimizing risk, announced the findings of its inaugural “Generating Responsibility: Assessing AI Using AI” report.

Why Accelerating Data Engineering Across Public Clouds and Private Data Centers is a Game Changer

In this contributed article, Rob Gibbon, Product Manager at Canonical, suggests that data engineers typically know what they need to get done. The problem is that their environment doesn’t always make it easy. If you’re working on premise, it can be hard to get data-intensive solutions off the ground quickly. However, cloud solutions come with lock-in and unpredictable pricing. The game-changer in this scenario is a hybrid solution that will allow you to accelerate data engineering.

Video Highlights: Deep Reinforcement Learning for Maximizing Profits — with Prof. Barrett Thomas

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by Dr. Barrett Thomas, an esteemed Research Professor in at the University of Iowa’s College of Business, to delve deep into Markov decision processes and how they relate to Deep Reinforcement Learning.

The Power of Data Visualization: Techniques and Best Practices

In this contributed article, freelance writer Ainsley Lawrence discusses how data visualization is a powerful tool that can help viewers quickly analyze and assess the status or results of an analysis. Good visualization can make even the largest and most complex datasets relatively straightforward to interpret.

How Can Companies Protect their Data from Misuse by LLMs? 

In this contributed article, Jan Chorowski, CTO at AI-firm Pathway, highlights why LLM safety begins at the model build and input stage, rather than the output stage – and what this means in practice; how LLM models can be engineered with safety at the forefront, and the role that a structured LLM Ops model plays; and the role of data chosen to train models, and how businesses can appropriately select the right data to feed into LLMs