Over the last 9 months, my usage of general-purpose language models like OpenAI’s API has decreased as I’ve learned to leverage open-source models fine-tuned for specific tasks. Anyscale’s user-friendly Fine Tuning service has accelerated this transition by making it easy to craft accurate, efficient custom models. Despite the initial investment in creating labeled datasets, theContinue reading “From Supervised Fine-Tuning to Online Feedback”
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How Generative AI is Transforming Healthcare
Subscribe • Previous Issues Generative AI in Healthcare: Beyond the Horizon of Modern Medicine Whenever a new technology emerges, I like to explore its application across various sectors, especially those that are highly regulated such as financial services and healthcare. These sectors, with their exacting standards and stringent regulations, provide a robust framework for evaluating the maturityContinue reading “How Generative AI is Transforming Healthcare”
Generative AI’s Impact on Healthcare
The healthcare sector is enormously complex, requiring advanced tools to unlock innovation. As highlighted in our latest report, generative AI brings transformative potential across healthcare, from accelerating drug discovery to optimizing hospital operations. Explore the Report: Clinical Support and Documentation: Dive into how AI is revolutionizing the way clinicians interact with patient data, enhancing decision-makingContinue reading “Generative AI’s Impact on Healthcare”
localllm and the Promise and Pitfalls of Running LLMs Locally
localllm is an open-source framework that aims to democratize the use of large language models (LLMs) by enabling their efficient operation on local CPUs. This circumvents the need for expensive and scarce GPUs. It provides developers with an easy way to access state-of-the-art quantized LLMs from Hugging Face through a simple command-line interface. localllm canContinue reading “localllm and the Promise and Pitfalls of Running LLMs Locally”
AMD’s Expanding Role in Shaping the Future of LLMs
In my recent exploration of emerging hardware options for Large Language Models (LLMs), AMD’s offerings have emerged as particularly promising. In this analysis, I delve deeper into the factors that position AMD GPUs favorably for leveraging the growth of LLMs and Generative AI. These factors range from performance and efficiency gains in demanding AI tasksContinue reading “AMD’s Expanding Role in Shaping the Future of LLMs”
Improving Data Privacy in AI Systems Using Secure Multi-Party Computation
In the financial services sector and beyond, accessing comprehensive data for building models and reports is a critical yet challenging task. During my time working in financial services, we aimed to use data to understand customers fully, but siloed information across separate systems posed significant obstacles to achieving a complete view. This issue underscores theContinue reading “Improving Data Privacy in AI Systems Using Secure Multi-Party Computation”
Favorable Winds for AMD in the GenAI Chip Market
Subscribe • Previous Issues AMD’s Expanding Role in Shaping the Future of LLMs In my recent exploration of emerging hardware options for Large Language Models (LLMs), AMD’s offerings have emerged as particularly promising. In this analysis, I delve deeper into the factors that position AMD GPUs favorably for leveraging the growth of LLMs and Generative AI. TheseContinue reading “Favorable Winds for AMD in the GenAI Chip Market”
A Critical Look at Red-Teaming Practices in Generative AI
The rapid advancement of generative AI (GenAI) models, such as DALL-E and GPT-4, promises new creative capabilities, yet also raises critical safety and security concerns. As these models become more powerful and widespread, a pressing question emerges: How can we rigorously assess risks before real-world deployment? The answer lies in red-teaming. Red-teaming involves subjecting AIContinue reading “A Critical Look at Red-Teaming Practices in Generative AI”
Designing for the Future: Key Principles for Generative AI Applications
Generative AI offers promising new capabilities, but it also poses unique challenges for design and ethical implementation. A new paper from IBM, “Design Principles for Generative AI Applications”, tackles these issues head-on by outlining actionable strategies rooted in rigorous research. As companies race to capitalize on generative AI, these design principles serve as indispensable guidesContinue reading “Designing for the Future: Key Principles for Generative AI Applications”
Function Calling AI: Transforming Text Models into Dynamic Agents
Function Calling, particularly relevant in large language models (LLMs), is a transformative feature that significantly broadens the capabilities of these frontier models. This feature allows AI models to go beyond basic text generation and language understanding by interacting with and executing external functions. At its essence, Function Calling in AI entails the invocation or executionContinue reading “Function Calling AI: Transforming Text Models into Dynamic Agents”