Research commentary from the UK's national institute for data science and AI, covering AI safety, public-sector deployment, health data and the social impact of automated systems.
Why I recommend it: Solid, evidence-based writing on AI policy — a useful counterweight to vendor blogs.
This is the only entry from Alan Turing Institute Blog in the hub so far.
Other resources that share this publication's topics.
Public resources and event highlights from an organization researching, building and governing technology around human flourishing. Covers AI, youth wellbeing and technology policy; the listing refers to free public material, not guaranteed free access to every event or program.
A DeepLearning.AI course built with Prior Labs, about 1.5 hours at the intermediate level, on using tabular foundation models such as TabPFN for prediction on spreadsheet-style data and connecting them to data science agents. Enrollment requires a free account.
A Cosign list compiled by Jack & Jill AI of 100 companies, mostly UK and European startups such as ElevenLabs, Cohere and Attio, with short company descriptions and links to their open jobs.
A KDnuggets guide by Vinod Chugani that goes round by round through current data science hiring processes, including AI-assisted coding rounds and generative AI evaluation questions, and lists ways candidates can prepare.
A Built In survey of job seekers on how AI tools shape which employers they find and consider. Among respondents who use AI for employer research, 66% say they applied to a company they had not considered and 53% say they decided not to apply somewhere because of what AI showed them. Built In sells employer branding services.
Artificial Analysis reports Muse Ember benchmark scores and task-cost estimates, including an Intelligence Index score of 36 and $0.14 per task in its evaluation. These are evaluator-specific results, not a verified Meta launch announcement or evidence of free model access.
