NOLA AI, Inc.

Nivedita. Great read. We are so proud of your commitment to sharing your knowledge with others. Thank you for being a part of our team.

Learn about recent exciting developments at NOLA AI and our vision for the future. Damon Kirin will be speaking at 2:00pm on Monday, October 11, 2024.

?? Are Bigger Language Models Always Better? The Surprising Truth ?? Hi everyone, Damon Kirin here from NOLA AI. In our latest Trip Report episode, we dive into the scaling paradox facing today's AI industry. ???? Recent research published in Nature shows that as language models grow bigger, their reliability can actually decline. These huge models are more likely to confidently provide wrong answers—what we call hallucinations, or as some might say, straight-up "bullshitting." ?? Meanwhile, another fascinating study from Google DeepMind suggests a smarter way forward: instead of scaling up endlessly, why not optimize how we use compute during inference? By letting smaller models "think a bit longer" on tough questions, they can outperform much larger ones. At NOLA AI, we're all about building reliable, efficient models that align with real-world needs—because sometimes smarter beats bigger. ???? Check out the full video where we break down what these findings mean for the future of AI and how enterprises can rely on AI that's affordable, dependable, and trustworthy. ????? Watch the video here ??

So you wanna learn about a hot new research paper that surveys all the latest in Small Language Models around the globe? Just drop it into NotebookLM and get an awesome podcast that really nails some great insights! It often seems like Google is on the sidelines but they made something special with this one, and not just because of the 'audio magic', it's a helluva research tool. If you are into the current state of SLMs check out the original paper right here https://lnkd.in/gt3ZDwrw

?? The Trip Report is here! ?? At NOLA AI, we’re tackling one of the biggest challenges in enterprise AI today—hallucinations in large language models. ?? In our first episode of The Trip Report, CFO and Co-Founder Damon Kirin breaks down the latest solutions from Microsoft that aim to prevent and correct hallucinations in AI. From Advanced Query Categorization to real-time Corrections, these tools might be game-changers, but are they sustainable for businesses in the long run? ?? Key insights covered in this episode: ? The trade-offs between prevention and correction of hallucinations. ? The impact of these tools on AI performance and business operations. ? Why transforming the core design of AI models is essential for lasting progress.

THE AI BOOK FOR THE REST OF US...In the rapidly evolving world of AI, success isn’t just about having the best algorithms or the most data—it’s about building diverse, well-rounded teams that understand the big picture. That’s why books like Why Machines Learn: The Elegant Math Behind Modern AI by Anil Ananthaswamy are so valuable. For those who aren’t engineers but play vital roles in AI companies—like marketing, PR, and management—grasping the foundational concepts of machine learning can transform how they contribute to the team’s goals. This book does a phenomenal job of breaking down complex AI concepts into clear, engaging explanations that resonate beyond the technical audience. It offers non-engineering team members a high-level understanding of what’s driving the AI revolution, helping bridge the gap between departments. In an industry where collaboration is key, providing a shared knowledge base empowers everyone to speak the same language and work toward common objectives. Gifting this book to your team isn’t just a thoughtful gesture—it’s an investment in building a cohesive, informed team that can think strategically about the future of AI. When every team member, regardless of their role, understands the core ideas shaping AI, it leads to smarter conversations, more innovative thinking, and a stronger company culture. https://lnkd.in/gD7v5PqA

?? NOLA AI CEO Mitch Klein discusses the risks of releasing large language models (LLMs) too soon ?? We have solutions. Follow Us. #LLMs #SLMs #Science #Math #EdgeDevices #ClimateChange #MoreMathLessGPUs. #NOLAtech #LousianaCompanies #DeepTech

In the world of generative AI, bigger isn’t always better. According to this recent Computerworld article, smaller language models are gaining traction, offering businesses a faster, more secure, and more cost-effective alternative to the massive LLMs that have dominated the field. These Small Language Models, or SLMs, are transforming how companies approach AI—bringing agile, customizable solutions that run locally, fine-tune easily, and reduce risks of data breaches and hallucinations. This shift aligns perfectly with NOLA AI’s mission. With our Atomic Framework, we’re pushing the boundaries of efficiency and accuracy, making advanced AI accessible without the overwhelming costs and complexities of traditional large models. By focusing on smaller, powerful solutions, NOLA AI is not just keeping pace but leading the charge—helping businesses deploy AI that’s nimble, precise, and ready for the real world.