Plus: TikTok's potential new owners have deep pockets Given the draconian cuts to US federal funding for science, it's worth asking some hard-nosed money questions: How much should we be spending on R&D? How much value do we get out of such investments, anyway? To answer that, in several recent papers, economists have approached this issue in clever new ways. And, though they ask slightly different questions, their conclusions share a bottom line: R&D is, in fact, one of the better long-term investments that the government can make. This article is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what's coming next. We've been here before . Artists and musicians are finding new ways to make art using AI, by injecting friction, challenge, and serendipity into the process.

Nvidia boss'disappointed' by reported China chip ban The boss of Nvidia says he is disappointed that China has reportedly ordered its top technology companies to halt purchases of the firm's artificial intelligence (AI) chips. Jensen Huang added he would be patient in response to the move from China's internet regulator. There are a lot of places we can't go to, and that's fine, he told reporters on Wednesday. Mr Huang is one of a number of tech bosses, including Microsoft's Satya Nadella, accompanying US President Donald trump on his state visit to the UK. Nvidia - the world's leading chipmaker - had previously been banned from selling its most advanced chips to China, before Trump reversed the ban in July.

Located in China, Juno is a 17-country collaboration that will try to detect neutrinos and antineutrinos to learn more about their mass. Juno's sphere (bottom left) and photomultipliers (top right) for neutrino detection. Located 700 meters underground near the city of Jiangmen in southern China, a giant sphere--35 meters in diameter and filled with more than 20,000 tons of liquid--has just started a mission that will last for decades. This is Juno, the Jiangmen Underground Neutrino Observatory, a new, large-scale experiment studying some of the most mysterious and elusive particles known to science. Neutrinos are the most abundant particles in the universe with mass.

Trump claims Biden administration investigation into Turning Point tried to force Charlie Kirk'out of business' Dem-run city's iconic mall teeters on collapse as 93% of stores vacant after crime-wave causes stores to flee Extraordinary measures jail put in motion to keep Charlie Kirk assassin suspect alive: 'It's severe' Texas AG's mistress' shock new life after sex scandal was exposed Airbnb guest says he was'shaken down' for $10K after selfie inside California's viral'Invisible House' Astonishing moment Charlie Kirk's wife Erika loses Miss USA pageant to pro-trans rival... as Trump watches on Christian Brueckner walks free: Madeleine McCann chief suspect leaves prison... with prosecutors fearing he will immediately flee the country and never be seen again Psychiatrist reveals bombshell'motive' for Charlie Kirk's assassination: 'Fits the profile' The raging jealousy that destroyed Nashville's hottest young couple: 'Up to no good' Air Force One jumbo carrying Donald Trump to Britain'is involved in close encounter mid-air drama with jet on same altitude ordered to change course by frantic air controllers' Charlie Kirk's final video message is released as top conservatives prepare to speak at his memorial this weekend Dancing with the Stars descends into CHAOS: Foul-mouthed backstage rants erupt over'trainwreck' celebs... as one star goes'missing' and ugly rape allegations resurface Rapper DaBaby ignites backlash with music video depicting slain Ukrainian refugee's fatal Charlotte stabbing in controversial reenactment'Give me a f***ing break': Searing response of Elon Musk's trans daughter Vivian to my questions about Charlie Kirk's assassination... in an interview that takes a tearful turn ABC News reporter Matt Gutman blasted for hailing'very touching' texts between Charlie Kirk suspect and trans lover Meet the'world's cutest sea monster': Scientists discover an adorable snailfish nearly 10,800ft underwater - as amazed viewers compare it to a Pokémon When you go this deep, you'd expect any sign of life to be pretty terrifying. So scientists were delighted to discover an adorable new species of snailfish thousands of feet below the ocean's surface. The large-eyed pink creature was first detected in 2019 by researchers who were exploring the seafloor off California using a remotely operated vehicle at a depth of 3,268m (10,722ft). They came across this individual, an adult female 9.2cm (3.6 inches) long, happily swimming along amongst the crushing pressure, frigid cold and perpetual darkness. New analysis now reveals this animal was a species previously unknown to science - and has been named the bumpy snailfish (Careproctus colliculi).

Did Pakistan shoot down five Indian jets?

Ben & Jerry's co-founder quits over social activism row Ben & Jerry's co-founder Jerry Greenfield has left the ice cream maker after almost half a century at the firm, deepening a dispute with parent company Unilever. In a letter shared on social media by fellow co-founder Ben Cohen, Mr Greenfield said the Cherry Garcia maker had lost its independence after Unilever put a halt to its social activism. His exit marks the latest episode in a row that started in 2021 when Ben & Jerry's said it would stop selling its ice cream in Israeli settlements in the occupied West Bank and East Jerusalem. A spokesperson for The Magnum Ice Cream Company, which is being spun off from Unilever, said it was grateful to Mr Greenfield but disagreed with his stance. In his letter Mr Greenfield said leaving the firm was one of the hardest and most painful decisions he had ever made but he could no longer in good conscience work for a business that had been silenced by Unilever.

Bio-inspired algorithms utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. However, a plethora of these algorithms require a more rigorous review before making them applicable to the relevant fields. This survey categorizes these algorithms into eight groups: evolutionary, swarm intelligence, physics-inspired, ecosystem and plant-based, predator-prey, neural-inspired, human-inspired, and hybrid approaches, and reviews their principles, strengths, novelty, and critical limitations. We provide a critique on the novelty issues of many of these algorithms. We illustrate some of the suitable usage of the prominent algorithms in machine learning, engineering design, bioinformatics, and intelligent systems, and highlight recent advances in hybridization, parameter tuning, and adaptive strategies. Finally, we identify open challenges such as scalability, convergence, reliability, and interpretability to suggest directions for future research. This work aims to serve as a resource for both researchers and practitioners interested in understanding the current landscape and future directions of reliable and authentic advancement of bio-inspired algorithms.

Deep Equilibrium Models (DEQs) are an interesting class of implicit model where the model output is implicitly defined as the fixed point of a learned function. These models have been shown to outperform explicit (fixed-depth) models in large-scale tasks by trading many deep layers for a single layer that is iterated many times. However, gradient calculation through DEQs is approximate. This often leads to unstable training dynamics and requires regularisation or many function evaluations to fix. Here, we introduce Reversible Deep Equilibrium Models (RevDEQs) that allow for exact gradient calculation, no regularisation and far fewer function evaluations than DEQs. We show that RevDEQs achieve state-of-the-art performance on language modelling and image classification tasks against comparable implicit and explicit models.

Temporal reasoning about historical events is a critical skill for NLP tasks like event extraction, historical entity linking, temporal question answering, timeline summarization, temporal event clustering and temporal natural language inference. Yet efforts on benchmarking temporal reasoning capabilities of large language models (LLMs) are rather limited. Existing temporal reasoning datasets are limited in scale, lack multilingual coverage and focus more on contemporary events. To address these limitations, we present HistoryBank, a multilingual database of 10M+ historical events extracted from Wikipedia timeline pages and article infoboxes. Our database provides unprecedented coverage in both historical depth and linguistic breadth with 10 languages. Additionally, we construct a comprehensive question answering benchmark for temporal reasoning across all languages. This benchmark covers a diverse set of 6 temporal QA reasoning tasks, and we evaluate a suite of popular language models (LLaMA-3-8B, Mistral-7B, Gemma-2-9b, Qwen3-8B, GPT4o) to assess their performance on these tasks. As expected GPT4o performs best across all answer types and languages; Gemma-2 outperforms the other small language models. Our work aims to provide a comprehensive resource for advancing multilingual and temporally-aware natural language understanding of historical events. To facilitate further research, we will make our code and datasets publicly available upon acceptance of this paper.

This paper presents a novel approach to optimizing pricing and replenishment strategies in fresh food supermarkets by combining Long Short-Term Memory (LSTM) networks with Particle Swarm Optimization (PSO). The LSTM model, enhanced with an attention mechanism, is used to predict sales volumes, pricing trends, and spoilage rates over a seven-day period. The predictions generated by the LSTM model serve as inputs for the PSO algorithm, which iteratively optimizes pricing and replenishment strategies to maximize profitability while adhering to inventory constraints. The integration of cost-plus pricing allows for dynamic adjustments based on fixed and variable costs, ensuring real-time adaptability to market fluctuations. The framework not only maximizes profits but also reduces food waste, contributing to more sustainable supermarket operations. The attention mechanism enhances the interpretability of the LSTM model by identifying key time points and factors influencing sales, improving decision-making accuracy. This methodology bridges the gap between predictive modeling and optimization, offering a scalable solution for dynamic pricing and inventory management in fresh food retail and other industries dealing with perishable goods.