Artificial Intelligence

Foundation models are large AI models trained on massive datasets. They’re versatile and powerful because they can be fine-tuned for various tasks. This fine-tuning leverages transfer learning: the model first learns general patterns from a broad dataset, then is adapted with smaller, domain-specific datasets for specific problems like translation or image recognition. This saves time and data, letting organisations use advanced AI without starting from scratch. Further reading What are foundation models? - IBM What Are Foundation Models? - Nvidia What is fine-tuning? - IBM

Risks around Agentic AI Meredith Whittaker, president of the Signal Foundation, discusses some of the risks around Agentic AI. Further reading Signal President Meredith Whittaker calls out agentic AI as having ‘profound’ security and privacy issues - TechCrunch

Further reading Small Language Models Are the New Rage, Researchers Say - Wired UK (Apple News link)

Companies are rapidly integrating AI into their operations, from customer service chatbots to advanced analytics tools. And if organisations are using AI, then so are their third party vendors, the companies processing data on their behalf. Do we know how our vendors are using our data, and how will we manage that risk? AI and Third-Party Risk: What’s at Stake? AI amplifies third-party risk in several ways: Data Leakage: Information entered into AI tools could be stored, reused, or exposed. Are your inputs contributing to a model that may resurface these comments elsewhere? Opaque Data Practices: What AI systems are your third parties using? Are they using in-house proprietary models? How clear are you on data usage, retention, and any onward sharing? Model Vulnerabilities: If an AI model is compromised, its outputs could become inaccurate or biased, damaging trust and operations. What obligations do you have, and how do you manage this risk? Supply Chain Risks: Many AI solutions will rely on a network of sub-processors, expanding the risk landscape. Do you know where your data ends up? The Key Questions to Ask Yourself and Your Vendors To manage third-party risk effectively in the AI era, you need to ask the right questions: ...

Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI models share structured context - including messages, observations and actions - in a predictable and machine-readable way. Released in early 2024, MCP has been described as an “API for AI models”. However, describing it as just an API underplays its fundamental purpose: to provide a common, structured language for inter-model communication, which goes beyond a simple API call and introduces new considerations for systems design and governance. ...