Explores using multimodal vision AI models like LLaVA for advanced UI/UX test automation, moving beyond traditional methods.
Introducing CloudSecGPT, a specialized AI model trained on cloud security documentation to provide interactive insights and troubleshooting help.
An opinion piece arguing against using AI-generated images, highlighting ethical concerns and the negative impact on professional illustrators' livelihoods.
The author critiques the focus on speculative AI risks at global summits, arguing for addressing real issues like corporate power and algorithmic bias instead.
A technical guide on using Meta AI's Segment Anything model to perform object segmentation on satellite imagery from Maxar.
A developer shares insights from building an AI audit prototype, discussing the importance of defensibility and lessons from banking model audits.
Explores the potential and implications of using AI to automate mathematical theorem proving, framing it as a 'tame' problem solvable by machines.
A framework for developing a generative AI strategy, based on a talk exploring practical approaches for leaders and teams.
A personal blog post reflecting on books read, AI's impact, and developer experience, with a focus on technology and purpose.
Analyzes Geoffrey Hinton's technical argument comparing biological and digital intelligence, concluding digital AI will surpass human capabilities.
Explores using logic programming and Prolog for semi-supervised clustering, arguing it's more intuitive than traditional algorithms for rule-based problems.
Explores the strategic importance of Generative AI and how to build sustainable competitive advantages (moats) in AI products.
An analysis of ChatGPT's future, predicting challenges like company dilution, failed competition, and a potential acquisition.
Announcing the release of the 'transformer' R package on CRAN, implementing a full transformer architecture for AI/ML development.
A critical analysis of GPT-4's capabilities, questioning the 'miracle' narrative and exploring the technical foundations behind its success.
Analyzes the rise of AI as a platform shift, comparing its early stages to the historical growth and evolution of cloud computing.
A guide on managing the overwhelming volume of AI/ML research, sharing strategies and tools for prioritizing and staying updated effectively.
A reflection on past skepticism of deep learning and why similar dismissal of Large Language Models (LLMs) might be a mistake.
Argues against the 'lossy compression' analogy for LLMs like ChatGPT, proposing instead that they are simulators creating temporary simulacra.
A software developer analyzes ChatGPT's impact on coding and language, arguing that human developers remain essential despite AI's puzzle-solving abilities.
