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3 weeks 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).
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3 weeks 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.
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4 weeks ago | 
datasciencecentral.com | Zachary Amos |Manoj Kumar |Alan Morrison |Dan Wilson
AI chatbots seem to grow more advanced with each passing month. The idea of a computer handling ordinary tasks sounds great, but how do AI chatbots compare to human educators? Understanding the strengths and weaknesses of people versus machines in education allows administrators to make more informed decisions about when to invest in equipment and what areas today’s teachers excel in that robots likely never will.
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1 month ago | 
datasciencecentral.com | Saqib Jan |Dan Wilson |J. Rusnak |Bill Schmarzo
The breakthrough advancements in computing power, deluge of data, and ever-sharpening algorithms have converged to make AI the defining technology of our age. Across industries and across continents, AI has become the focal point for innovation and disruption, for the very future of how work gets done—now operationalized and embedded into core processes, from customer service and supply chains to product development itself.
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1 month ago | 
datasciencecentral.com | Dan Wilson |J. Rusnak |Bill Schmarzo |Eric Ethridge
Interview w/ Sumit Gupta – Business Intelligence Engineer at NotionIn our latest episode of the AI Think Tank Podcast, I had the pleasure of sitting down with Sumit Gupta, a business intelligence engineer at Notion, author of The Tableau Workshop, and an expert in modern data stacks, marketing analytics, and business intelligence.
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1 month ago | 
datasciencecentral.com | Jelani Harper |Dan Wilson |Alan Morrison
There’s a really good reason almost every credible vector database—or enterprise application of this technology—incorporates re-ranking models, or re-rankers. It’s not just because these deep neural networks score vector retrieval engines’ results so they’re more useful for search and Retrieval Augmentation Generation (RAG). Quite simply, it’s because for most vector database use cases, no one has actually fine-tuned the embedding model on an organization’s specific, or proprietary, data.
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1 month ago | 
datasciencecentral.com | Dan Wilson |J. Rusnak |Bill Schmarzo |Eric Ethridge
Interview w/ Larry NamerThe AI Think Tank Podcast has always been a platform for exploring the future of intelligent systems, machine learning, and data science. But this time, we had the pleasure of diving into the intersection of AI and entertainment media with an industry legend, Larry Namer. As a founder of E! Entertainment Television, Larry has shaped the global media landscape in ways few have.
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1 month ago | 
datasciencecentral.com | J. Rusnak |Dan Wilson |Bill Schmarzo |Eric Ethridge
Large Language Models (LLMs) are a natural language processing tool under the category of generative artificial intelligence. They have the power to transform the insurance and reinsurance industries by improving a multitude of processes.
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1 month ago | 
datasciencecentral.com | Bill Schmarzo |Eric Ethridge |Dan Wilson
In the blog “Economic Power of Entity Propensity Models Are Transforming the Game”, I talked about how my childhood fascination with the board game of Strat-O-Matic baseball fueled my interest in analytics and mastering the power of player-level Entity Propensity Models (EPMs). Since then, I have learned the critical role of EPMs as modern-day AI-driven assets that could transform the organization’s economic value curve.
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1 month ago | 
datasciencecentral.com | Eric Ethridge |Dan Wilson |Bill Schmarzo |Vincent Granville
The rapid development and adoption of artificial intelligence (AI) has been incredible. When it comes to generative AI alone (GenAI), 65% of respondents in a recent McKinsey Global Survey said their companies regularly use the technology, doubling findings from just 10 months earlier. Moreover, three-quarters anticipate that in the years ahead, AI will bring significant or disruptive change to their industries. Without question, AI is rapidly revolutionizing areas of business operations.
