IBM - poor AI skills undermining UK leadership ambitions This article is sponsored by:
A new IBM survey suggests that whilst the UK has laid out its vision for being an AI powerhouse, a lack of skills is holding it back.

A new IBM survey suggests that whilst the UK has laid out its vision for being an AI powerhouse, a lack of skills is holding it back.
Sometimes the AI ethics conversation appears to be a morass of well-meaning platitudes, without forward direction. A notable article via the Carnegie Council for Ethics in International Affairs brings these issues to a head.
Does the latest academic treatise on ethical robotics and AI really tell us anything new?
This week - real world AI use cases get a closer look, while retailers grapple with consumer (dis)loyalty. Blockchain for identity verification and gets a surprising nod, and Atlassian reviews its outage (and gets reviewed). AI also whiffs.
The London Legacy Development Corporation is using real-time data to help ensure the effective flow of people and vehicles around the park.
Midea is using data-rich insight from AI specialist Birdie to give its customers the features they want.
Cloud data migrations can bring significant operational benefits - not to mention opening up AI/ML use cases. That doesn't mean these migrations are easy. Getting migrations right starts with data governance.
This week - lessons from UX accessibility keep us on our toes. Microsoft and Google buck the tech earnings blues - thanks to cloud. The metaverse gets an enterprise review, and NoOps wins buzzword bingo. As always, your whiffs.
Building out the future, leveraging AI off Big Data.
Digital twins can increase predictability of future maintenance of mission-critical, high-cost, and often complex assets, thereby lowering risk for organizations in asset-intensive industries. Michael Ouissi, Chief Customer Officer, IFS, examines the value for three such industries.
The developer branch of the Salesforce Ohana is gathering in San Francisco today. Salesforce co-founder Parker Harris explains the importance of this and points to what's to come.
Do data scientists really spend 80% of their time wrangling data? Last time around, we examined this notion. But when it comes to data management, how can machine learning change data platforms for the better?
This week - employee experience takes a big return-to-office hit, but what's next? Cloud adoption may be surging, but on-premises systems still have traction. Atlassian's outage yields lessons, and event planners get a hybrid event challenge. As always, your whiffs.
Many of the issues around AI and ethics are understood, but what practical steps should we take to protect citizens?
A key policy voice sets out what she believes should happen next in rolling out the government’s AI Strategy.
Do data scientists really squander the bulk of their time cleaning data sets? Not necessarily - but for robust machine learning models, we do need better data management platforms.