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”
Author Archives: Ben Lorica
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”
Expanding AI Horizons: The Rise of Function Calling in LLMs
Subscribe • Previous Issues 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. AtContinue reading “Expanding AI Horizons: The Rise of Function Calling in LLMs”
Harnessing WebGPU for Next-Gen AI Applications: Insights and Implications
WebGPU is a relatively new web standard that provides direct access to graphics processors (GPUs) in web browsers. It allows web developers to leverage the parallel computing power of GPUs for graphics rendering and general purpose computing right in the browser. At a high level, WebGPU gives web apps near-direct access to the GPU, reducingContinue reading “Harnessing WebGPU for Next-Gen AI Applications: Insights and Implications”
Articulate Medical Intelligence Explorer
The Articulate Medical Intelligence Explorer (AMIE) is a DeepMind research initiative pushing the boundaries of AI in medical diagnostic conversations. AMIE tackles key challenges in this domain, aiming to improve dialogue quality, diagnostic accuracy, and scalability for more effective healthcare interactions. Firstly, AMIE focuses on conducting engaging and informative dialogues. It asks clarifying questions toContinue reading “Articulate Medical Intelligence Explorer”
PRISM: A Breakthrough in AI-Driven Pancreatic Cancer Detection
An innovative AI system developed at MIT offers hope for the early detection of pancreatic ductal adenocarcinoma (PDAC), one of the deadliest forms of cancer. PRISM, short for Predicting Risk Intelligence System Model, leverages the power of machine learning and big data to mine electronic health records for subtle clues that may indicate early-stage PDAC.Continue reading “PRISM: A Breakthrough in AI-Driven Pancreatic Cancer Detection”
Beyond Nvidia: Exploring New Horizons in LLM Inference
The landscape of large language models (LLMs) and Generative AI (GenAI) is undergoing rapid transformation, fueled by surging interest from executives and widespread interest across diverse sectors. Over one-third of CxOs have already embraced GenAI in their operations, and nearly half are actively preparing to invest in this cutting-edge technology. In 2024, LLMs are setContinue reading “Beyond Nvidia: Exploring New Horizons in LLM Inference”