11 articles
Articles, guides and practical notes about AI & Emerging Technology, with useful context on what matters, how it works and where the interesting details are.
9 articles · authority 33/100
Jersey Finance’s analysis argues that responsible AI adoption depends on staff who can test outputs, recognise limits and retain accountability—raising difficult questions about training, oversight and the development…
AI agents that can act on systems need more than a shared service account. A Zero Trust design gives each run a verifiable identity, short-lived authority, policy checks at the tool boundary and an auditable record of consequential actions.
Private AI, public cloud AI and hybrid AI suit different workloads. The right choice depends on data boundaries, latency, demand, model requirements, operational importance and the organisation’s ability to tolerate provider or network disruption.
Jersey Finance’s analysis argues that responsible AI adoption depends on staff who can test outputs, recognise limits and retain accountability—raising difficult questions about training, oversight and the development of junior professionals.
Jersey’s ageing population, healthcare rebuild and compact geography create a plausible opening for health innovation. But a credible ecosystem will depend on integrated care, trusted data governance, clinical capacity and evidence that pilots improve outcomes.
Tiangong Ultra’s 9.39-second preliminary 100-metre run is a major result for bipedal locomotion and autonomous control. It does not show that humanoid robots can yet work safely, reliably or independently in real-world settings.
AWS AI can suit teams with sound AWS practices, usable data and a bounded problem. But Bedrock or SageMaker AI access alone does not establish readiness: costs, security, governance, skills, lock-in and measurable value all need testing.
The enterprise rush to deploy AI agents is running into a familiar problem in a new form: identity. If an agent can act, call tools, move data or trigger workflows, it needs a proper identity model, not a vague service account and a hope that logs will be enough. That shifts the discussion from ‘what can the model do?’ to ‘what can this agent prove it is allowed to do, and how is that enforced?’
AWS AI services address different jobs: Amazon Bedrock for generative applications, SageMaker AI for custom machine learning, pre-built APIs for recognised tasks, and Amazon Q for employee and developer assistance.
AWS AI is a portfolio rather than a single product. This guide explains where Amazon Bedrock, SageMaker AI, Amazon Q and specialist services fit, and how to choose between foundation models, machine learning platforms, assistants and managed APIs.
Digital Jersey’s Let’s Build AI Project Lab helps teams turn defined use cases into working prototypes, with data, governance and deployment questions addressed before an AI experiment is treated as a production project.
