Opinion

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Allister Frost, a former Microsoft leader and expert on business transformation.

Allister Frost: Tackling workforce anxiety for AI integration success

adoption, ai, AI (Artificial Intelligence), AI and Us, AI Business Strategy, Artificial Intelligence, Culture, enterprise, ethics, Features, governance, Governance, Regulation & Policy, Human-AI Relationships, Inside AI, integration, Interviews, Opinion, Society, Trust, Bias & Fairness, workforce, World of Work

Navigating workforce anxiety remains a primary challenge for leaders as AI integration defines modern enterprise success. For enterprise leaders, deploying AI is less a technical hurdle than a complex exercise in change management. The reality for many organisations is that, while algorithms offer efficiency, the human element dictates the speed of adoption. Data from the […]

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Why some “breakthrough” technologies don’t work out

ai, AI (Artificial Intelligence), App, Artificial Intelligence, Culture, Opinion, TR10 2026

Every year, MIT Technology Review publishes a list of 10 Breakthrough Technologies. In fact, the 2026 version is out today. This marks the 25th year the newsroom has compiled this annual list, which means its journalists and editors have now identified 250 technologies as breakthroughs. A few years ago, editor at large David Rotman revisited

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The Law Society: Current laws are fit for the AI era

adoption, ai, AI (Artificial Intelligence), AI and Us, AI in Action, Artificial Intelligence, ethics, Features, Governance, Regulation & Policy, government, Inside AI, Law, legal, Legal Industry AI, Opinion, regulation

As ministers push to loosen rules to speed up AI adoption, The Law Society argues that lawyers just need to know how current laws apply. The Department for Science, Innovation & Technology (DSIT) recently launched a call for evidence on a proposed ‘AI Growth Lab’. This cross-economy sandbox is designed to accelerate the deployment of

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How to avoid machine learning pitfalls: a guide for academic researchers

#General, Artificial Intelligence, arXiv, Creative Commons, D. Learning, Educación | 🇬🇧 Education, English (Articles), English (Paper), Formación | 🇬🇧 Training, Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, Machine Learning (Article), Machine Learning (Paper), news, Numbered, Open Access, Opinion, Papers, Research, Researcher

This document gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended primarily as a guide for research students, and focuses on issues that are of particular concern within academic research, such as the need to do

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Generative AI hype distracts us from AI’s more important breakthroughs

Artificial Intelligence, Hype Correction, Opinion

On April 28, 2022, at a highly anticipated concert in Spokane, Washington, the musician Paul McCartney astonished his audience with a groundbreaking application of AI: He began to perform with a lifelike depiction of his long-deceased musical partner, John Lennon. Using recent advances in audio and video processing, engineers had taken the pair’s final performance

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How Confessions Can Keep Language Models Honest?

Artificial Intelligence, Beginner, Generative AI, Large Language Models, Opinion

When a person admits they made a mistake, something surprising happens. The confession often restores trust rather than breaking it. People feel safer around someone who owns their errors than someone who hides them. Accountability builds confidence. What if AI models can do the same? Most AI systems give confident answers, even when they are

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