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Educators advise that AI should be used to assist, not replace, learning. Educators advise that AI should be used to assist, not replace, learning. How to use ChatGPT at university without cheating: 'Now it's more like a study partner' The ubiquitous AI tool has a divisive effect on educators with some seeing it a boon and others a menace. So what should you know about where to draw the line between check and cheat? For many students, ChatGPT has become as standard a tool as a notebook or a calculator.

'It's going to be a life skill': educators discuss the impact of AI on university education Artificial intelligence is changing how students learn and the world they'll graduate into. OpenAI CEO Sam Altman recently told a US podcast that if he was graduating today, "I would feel like the luckiest kid in all of history." Altman, whose company developed and released ChatGPT in November 2022, believes the transformative power of AI offers unprecedented opportunities for young people. Yes, there will be job displacement, but "this always happens," says Altman, "and young people are the best at adapting to this." New, more exciting jobs will emerge, full of greater possibilities. For UK sixth-formers and their families looking at universities, trying to make the best possible choices about what to study - and where - in the age of generative AI, Altman's words may offer some comfort.

The survey uncovered worries about the advance of AI, with only 17% saying it was a good substitute for human interaction. The survey uncovered worries about the advance of AI, with only 17% saying it was a good substitute for human interaction. UK workers wary of AI despite Starmer's push to increase uptake, survey finds It is the work shortcut that dare not speak its name. A third of people do not tell their bosses about their use of AI tools amid fears their ability will be questioned if they do. Research for the Guardian has revealed that only 13% of UK adults openly discuss their use of AI with senior staff at work and close to half think of it as a tool to help people who are not very good at their jobs to get by.

I Wasn't Sure I Wanted Anthropic to Pay Me for My Books--I Do Now Anthropic agreed to a $1.5 billion settlement for authors whose books were used to train its AI model. As an author who fits that description, I've come around to the idea. A billion dollars isn't what it used to be--but it still focuses the mind. At least it did for me when I heard that the AI company Anthropic agreed to an at least $1.5 billion settlement This came after a judge issued a summary judgement that it had pirated the books it used. The proposed agreement--which is still under scrutiny by the wary judge--would reportedly grant authors a minimum $3,000 per book.

When you purchase through links in our articles, we may earn a small commission. Microsoft's Copilot AI text-to-speech gets new, cleaner'scripted mode' Copilot Audio Expressions can now read text aloud in a more direct fashion without altering its content. Microsoft's AI Manager Mustafa Suleyman recently unveiled in a social media post a new feature called "Scripted Mode" in Copilot Labs for turning written scripts into text-to-speech. Copilot Labs is an experimental platform where you can try out Microsoft's newest AI features that are still in development. Scripted mode just dropped for audio generation in Copilot Labs (c/o our new MAI-Voice-1 model).

The Download: America's gun crisis, and how AI video models work We can't "make American children healthy again" without tackling the gun crisis This week, the Trump administration released a strategy for improving the health and well-being of American children. The report was titled--you guessed it--Make Our Children Healthy Again. It suggests American children should be eating more healthily. And they should be getting more exercise. This week's news of yet more high-profile shootings at schools in the US throws this disconnect into even sharper relief. Experts believe it is time to treat gun violence in the US as what it is: a public health crisis.

Welcome back to In the Loop, new twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? When you think about the top echelon of the world's tech elites, Larry Ellison probably doesn't spring to mind. But on Wednesday, the 81-year-old chairman of Oracle briefly became the richest person in the world with a net worth of almost $400 billion, overtaking Elon Musk. Ellison's $100-billion jump was the biggest single-day gain ever, and the result of a promising Oracle growth forecast in which they advertised hundreds of billions of dollars in inbound revenue from AI companies using Oracle's cloud computing capabilities.

How do AI models generate videos? With powerful video generation tools now in the hands of more people than ever, let's take a look at how they work. It's been a big year for video generation. In the last nine months OpenAI made Sora public, Google DeepMind launched Veo 3, the video startup Runway launched Gen-4. All can produce video clips that are (almost) impossible to distinguish from actual filmed footage or CGI animation. This year also saw Netflix debut an AI visual effect in its show, the first time video generation has been used to make mass-market TV.

In-context learning (ICL) allows a language model to improve its problem-solving capability when provided with suitable information in context. Since the choice of in-context information can be determined based on the problem itself, in-context learning is analogous to human learning from teachers in a classroom. Recent works (Didolkar et al., 2024a; 2024b) show that ICL performance can be improved by leveraging a frontier large language model's (LLM) ability to predict required skills to solve a problem, popularly referred to as an LLM's metacognition, and using the recommended skills to construct necessary in-context examples. While this skill-based strategy boosts ICL performance in larger models, its gains on small language models (SLMs) have been minimal, highlighting a performance gap in ICL capabilities. We investigate this gap and show that skill-based prompting can hurt SLM performance on easy questions by introducing unnecessary information, akin to cognitive overload. To address this, we introduce AdaptMI, an adaptive approach to selecting skill-based in-context Math Instructions for SLMs. Inspired by cognitive load theory from human pedagogy, our method only introduces skill-based examples when the model performs poorly. We further propose AdaptMI+, which adds examples targeted to the specific skills missing from the model's responses. On 5-shot evaluations across popular math benchmarks and five SLMs (1B--7B; Qwen, Llama), AdaptMI+ improves accuracy by up to 6% over naive skill-based strategies.