Tag Archives: cheat_sheet

Intel’s Gaudi 3 is the latest generation of AI accelerators designed to provide high-performance, cost-effective solutions for AI training and inference tasks, particularly for large language models (LLMs) and generative AI applications. According to Intel, Gaudi 3 offers several practical benefits for AI teams, including: Increased performance: Gaudi 3 delivers 4x AI compute for BF16,Continue reading “Intel’s Gaudi 3: A Promising Contender in the AI Accelerator Arena”

Trends, Applications, and Implications for the Future of Work. The U.S. Census Bureau conducted a study to provide real-time information on the use of Artificial Intelligence (AI) by businesses in the United States. The study aims to gather data on the types of AI used, the applications of AI in business functions, the impact ofContinue reading “AI Adoption in the U.S.”

As a seasoned observer of the tech industry, the recent lawsuit filed by the U.S. Department of Justice (DOJ) against Apple, which accuses the company of wielding an iPhone monopoly, presents an important examination of competition and innovation within the smartphone sector. The DOJ’s complaint leverages striking statistics, highlighting Apple’s commanding 70% market share inContinue reading “Apple vs. DOJ: Weighing the Arguments in the Lawsuit Against Apple”

Nvidia’s shift from being primarily a chip provider to becoming a full-fledged platform provider, akin to tech giants like Microsoft or Apple, is a bold move that signals the company’s ambition to play a central role in shaping the AI ecosystem. The introduction of the Nvidia Inference Microservice (NIM), a container system for easily deployingContinue reading “Nvidia’s GTC 2024 Announcements: Shaping the Future of AI with Integrated Platforms and Powerful Chips”

Large language models (LLMs) have exploded in capability and adoption over the past couple years. They can generate human-like text, summarize documents, translate between languages, and even create original images and 3D designs based on text descriptions. Companies remain highly bullish on LLMs, with most either actively experimenting with or already partially implementing the technologyContinue reading “Managing the Risks and Rewards of Large Language Models”

NYT Sues OpenAI: Copyright Infringement in the Age of AI As a technologist observing the intersection of AI and law, the New York Times lawsuit against OpenAI is a critical juncture. This isn’t merely a legal dispute; it symbolizes the delicate balance between innovation and regulation. My primary concern lies in the potential chilling effectContinue reading “Mimicry or Transformation? Fair Use and Copyright Clash Over AI Training Methods”

Apple Tackles Memory and Computational Demands of Large Language Models. In a recent paper, Apple addresses the substantial computational and memory demands of large language models (LLMs), which present difficulties when attempting to operate them on devices with limited DRAM. These issues are pivotal due to: The prohibitive memory requirements for LLMs that surpass theContinue reading “Apple’s AI Leap: Bridging the Gap in On-Device Intelligence”

This cheat sheet provides an overview of applications of machine learning in finance, as described in the working paper “Financial Machine Learning” by Bryan T. Kelly and Dacheng Xiu. Return Forecasting Description: Using machine learning models like neural networks to predict future returns on financial assets and portfolios. Examples: Bridgewater Associates, Two Sigma, quant hedgeContinue reading “Financial Machine Learning”

This cheat sheet provides an overview of Gemini’s capabilities, development process, early reviews and potential future directions. What is Gemini? Gemini is a natively multimodal foundation model developed by Google that can understand and reason across multiple data modalities such as text, images, audio, video, and more in an integrated fashion. Unlike previous AI systemsContinue reading “Gemini Cheat Sheet: Google’s State-of-the-Art Multimodal Assistant Explained”