Computing has evolved from large mainframes to PCs, the internet, smartphones, and now wearables. Each leap forward required a new "bridge" – a software layer making the technology easier to use.
Generative AI is set to be that bridge for wearables. It promises to create on-demand, intuitive interfaces, turning wearables into powerful, general-purpose computers. Think of Meta's Orion AR glasses as a preview – AI dynamically creates the UI *as you need it*.
Generative AI is set to be that bridge for wearables. It promises to create on-demand, intuitive interfaces, turning wearables into powerful, general-purpose computers. Think of Meta's Orion AR glasses as a preview – AI dynamically creates the UI *as you need it*.
This website details MicroSims, simple animations/simulations generated using generative AI to aid in teaching concepts. It discusses limitations of system prompts, the importance of a MicroSim registry for training AI, and provides examples.
Local Large Language Models can convert massive DataFrames to presentable Markdown reports — here's how.
This article examines the dual nature of Generative AI in cybersecurity, detailing how it can be exploited by cybercriminals and simultaneously used to enhance defenses. It covers the history of AI, the emergence of GenAI, potential threats, and mitigation strategies.
The Institute of Foundation Models at MBZUAI focuses on advancing research in Generative AI, developing foundation models for various data types, and driving innovation in healthcare, climate change, and sustainability.
The article discusses the role of AI agents in generative AI, focusing on tool calling and reasoning abilities, and how they can be evaluated using benchmarks like BFCL and Nexus Function Calling Benchmark.
Amazon's AI shopping assistant Rufus helps customers make informed decisions by answering shopping-related questions using a custom language model and innovative techniques in generative AI.
The article explores the challenges associated with generative artificial intelligence systems producing inaccurate or 'hallucinated' information. It proposes a strategic roadmap to mitigate these issues by enhancing data quality, improving model training techniques, and implementing robust validation checks. The goal is to ensure that AI-generated content is reliable and trustworthy.
The article discusses the integration of Large Language Models (LLMs) and search engines, exploring two themes: Search4LLM, which focuses on enhancing LLMs using search engines, and LLM4Search, which looks at improving search engines with LLMs.
How simple prompt engineering can replace custom software
