In the "Startup-up, Scale-up" review report published last year, chancellor Rachel Reeves promised to make Britain the "high growth, start-up hub of the world". Now, almost six months into the new government, entrepreneurs remain encouraged by the promises made in the Labour manifesto. "The ambition embodied in Great British Energy and the 2030 decarbonization targets is precisely what we need and deserve," says Shilpika Gautam, CEO of greentech startup Opna, about Labour's energy policies. "It's high time the UK caught up with the policy and financing innovations in other countries, such as the Inflation Reduction Act in the US." Amit Gudka, founder of Field, agrees: "We welcome Labour's plans to double onshore wind, triple solar and quadruple offshore wind by 2030. These plans are ambitious, but not unrealistic, provided the Government continues to make clear policy decisions and create a stable policy and regulatory environment."

In this paper, we propose Knowledge Base augmented Language Model (KBLaM), a new method for augmenting Large Language Models (LLMs) with external knowledge. KBLaM works with a knowledge base (KB) constructed from a corpus of documents, transforming each piece of knowledge in the KB into continuous key-value vector pairs via pre-trained sentence encoders with linear adapters and integrating them into pre-trained LLMs via a specialized rectangular attention mechanism. Unlike Retrieval-Augmented Generation, KBLaM eliminates external retrieval modules, and unlike in-context learning, its computational overhead scales linearly with KB size rather than quadratically. Our approach enables integrating a large KB of more than 10K triples into an 8B pre-trained LLM of only 8K context window on one single A100 80GB GPU and allows for dynamic updates without model fine-tuning or retraining. Experiments demonstrate KBLaM's effectiveness in various tasks, including question-answering and open-ended reasoning, while providing interpretable insights into its use of the augmented knowledge.

If the pickled bodies, partial skeletons and stuffed carcasses that fill museums seem a little, well, quiet, fear not. In the latest coup for artificial intelligence, dead animals are to receive a new lease of life to share their stories – and even their experiences of the afterlife. More than a dozen exhibits, ranging from an American cockroach and the remnants of a dodo, to a stuffed red panda and a fin whale skeleton, will be granted the gift of conversation on Tuesday for a month-long project at Cambridge University's Museum of Zoology. Equipped with personalities and accents, the dead creatures and models can converse by voice or text through visitors' mobile phones. The technology allows the animals to describe their time on Earth and the challenges they faced, in the hope of reversing apathy towards the biodiversity crisis.

Morgan Neville knows not everything we talk about will make it into this story. After making dozens of documentaries, he knows that in order to be told properly, the best stories have to leave some parts out. Built using audio interviews with collaborators like Kendrick Lamar and Missy Elliott--many of which Neville conducted remotely during Covid-19 lockdowns--it's a biopic of Williams' life animated entirely with Lego. Because Williams' career as a hitmaker spans 30-plus years, and given the fact that animation is expensive, Neville knew he had to leave some stuff out. "People say, 'Oh, the interviews are so great.' And I'm like, 'Yeah, I used the good ones,'" he says, sitting in a restaurant off of Central Park, a few days before Piece by Piece's New York premiere.

Way back in 2011 Marc Andreessen, a venture capitalist with aspirations to be a public intellectual, published an essay entitled "Why Software Is Eating the World", predicting that computer code would take over large swaths of the economy. Thirteen years on, software now seems to be chomping its way through academia as well. This, at any rate, is one possible conclusion to be drawn from the fact that the computer scientist Geoffrey Hinton shares the 2024 Nobel prize in physics with John Hopfield, and that the computer scientist Demis Hassabis shares half of the Nobel prize in chemistry with one of his DeepMind colleagues, John Jumper. The award to Hassabis and Jumper was, in a way, predictable, for they built a machine – AlphaFold2 – that enables researchers to solve one of the toughest problems in biochemistry: predicting the structure of proteins, the building blocks of biological life. Their machine has been able to predict the structure of virtually all the 200m proteins that researchers have identified.

What's that feature called on pixel phones? I forget what Android in general about Android specifics. But yes, there there was like a magic erase option there, too Yeah, I was going to say magic eraser, but that is a that's a clean thing it's something like that too, but It works really well like in terms of highlighting a specific object and removing it there are instances where it's too big and it can't like extrapolate like what should be a background so it looks really messy but sometimes like it just like smooths out a bright ugly object in the background was just like general unfocused stuff and that actually may be better.

We present a novel form of explanation for Reinforcement Learning, based around the notion of intended outcome. These explanations describe the outcome an agent is trying to achieve by its actions. We provide a simple proof that general methods for post-hoc explanations of this nature are impossible in traditional reinforcement learning. Rather, the information needed for the explanations must be collected in conjunction with training the agent. We derive approaches designed to extract local explanations based on intention for several variants of Q-function approximation and prove consistency between the explanations and the Q-values learned.

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world.

Despite the well-established importance of feedback in education, the application of Artificial Intelligence (AI)-generated feedback, particularly from language models like ChatGPT, remains understudied in translation education. This study investigates the engagement of master's students in translation with ChatGPT-generated feedback during their revision process. A mixed-methods approach, combining a translation-and-revision experiment with quantitative and qualitative analyses, was employed to examine the feedback, translations pre-and post-revision, the revision process, and student reflections. The results reveal complex interrelations among cognitive, affective, and behavioural dimensions influencing students' engagement with AI feedback and their subsequent revisions. Specifically, the findings indicate that students invested considerable cognitive effort in the revision process, despite finding the feedback comprehensible. Additionally, they exhibited moderate affective satisfaction with the feedback model. Behaviourally, their actions were largely influenced by cognitive and affective factors, although some inconsistencies were observed. This research provides novel insights into the potential applications of AI-generated feedback in translation teachingand opens avenues for further investigation into the integration of AI tools in language teaching settings.

It is a common refrain that artificial intelligence is coming to take all our jobs, and now it seems that Nobel prizewinners are no exception. Two of the awards this year, for physics and chemistry, have been claimed by people working in the field of AI – much to the chagrin of some researchers in areas more traditionally recognised by these categories. What does the rise of the AI Nobel mean for the future of science? "These prizes reflect two different ways of reckoning with the relationship between AI and science:…