The hype surrounding generative AI has created an aura of excitement and urgency among evangelists. It might lead one to believe that Fortune 500 companies are quickly embracing large language models (LLMs) and transforming corporate America into one giant chatbot. However, the reality is quite different. The implementation of generative AI is moving far slower
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Computer Vision (CV) has undergone a remarkable evolution over the past few decades, permeating various aspects of our daily lives. While the average person may perceive it as a new and exciting innovation, the truth is that CV has been developing since the 1970s. The early foundations laid during that time have paved the way
As the debate intensifies about the use of copyrighted works in training large language models (LLMs), questions arise regarding the ability to alter or edit these models to remove their knowledge of such works without requiring extensive retraining or rearchitecting. In a groundbreaking new paper, co-authors Ronen Eldan of Microsoft Research and Mark Russinovich of
California-based startup Nucleus AI, comprised of talented individuals from Amazon and Samsung Research, has recently announced the launch of their first product – a revolutionary 22-billion-parameter large language model (LLM). This general-purpose model, which can be fine-tuned for various tasks and applications, is available under an open-source MIT license as well as a commercial license.
Zoom, the popular video communication platform, has recently unveiled several exciting updates and new products at its annual Zoomtopia conference. Among the headline announcements is Zoom Docs, an AI-powered, multi-user cloud documentation solution integrated directly into the Zoom platform. Eric Yuan, the CEO of Zoom, emphasized their commitment to evolving the platform with powerful AI
Microsoft has recently launched AutoGen, an open-source Python library that aims to revolutionize the development of large language model (LLM) applications. As described by Microsoft, AutoGen serves as a framework for streamlining the orchestration, optimization, and automation of LLM workflows. By harnessing the power of LLMs, such as GPT-4, AutoGen introduces the concept of “agents,”
Big tech companies and venture capitalists are heavily investing in artificial intelligence (AI) labs that specialize in generative models, resulting in a profound transformation of the AI research landscape. This gold rush mentality has led to multi-billion-dollar investments, such as Amazon’s $4 billion in AI lab Anthropic and Microsoft’s staggering $10 billion investment in OpenAI.
No matter where you are, AI, ChatGPT, and related tools are dominating discussions in various industries. The global generative AI market is witnessing significant growth, projected to exceed $22 billion by 2025 with a CAGR of over 27%. The imminent question is how this advancement will transform our operations in diverse sectors. As we venture
As the world delves deeper into the realms of generative AI, one might wonder why the fintech industry, particularly financial advisors, has lagged behind in its adoption of this transformative technology. However, a New York City-based fintech startup called Vise is determined to change this trend. While it faced substantial challenges in recent years, including
Navigating the complexities of ensuring timely payments and maintaining payment integrity can be an arduous task for any business. However, for B2B companies, the challenges of securing payments are magnified due to various factors such as business solvency, clever accounting practices, bankruptcy, litigation, and more. While these issues are relatively rare in the B2C space,