Grandparents who play with, read to, and look after their grandkids score better on cognitive tests. Breakthroughs, discoveries, and DIY tips sent six days a week. From physical fitness to doing puzzles to going out with friends, there's a laundry list of advice out there to help protect our brains from cognitive decline as we age . Taking care of grandchildren may also help brain health, according to new research from the American Psychological Association published today in the journal . "Many grandparents provide regular care for their grandchildren--care that supports families and society more broadly," Flavia Chereches, a study co-author and Ph.D. candidate at Tilburg University in the Netherlands, said in a statement.

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Well, Who Do You Know? AI-powered startup Fortell has become a secret handshake for the privileged hearing-impaired crowd who swear by the product. Now, it wants to be in your ears. A secret is percolating at dinner parties, salons, and cocktail gatherings among the august New York City elite. It's whispered in the circles of financial masters of the universe, Hollywood stars, and owners of sports teams. Many haven't--or if they did hear, they might not have made out the words through noisy cross-conversations. Once they do know--particularly if they're boomers--they want it desperately. Fortell is a hearing aid, one that claims to use AI to provide a dramatically superior aural experience. The chosen few included in its beta test claim that it seems to top the performance of high-end devices they'd been unhappily using. These testers have made pilgrimages to Fortell's headquarters on the fifth floor of a WeWork facility in New York City's trendy SoHo neighborhood, where they were fitted for the hearing aids--which from the outside look pretty much like standard, over-the-ear, teardrop-shaped devices. But the big moment comes when a Fortell staffer takes them down to street level.

We're a week into the school summer holidays here in England, and I wonder how many parents who started out determined to keep their children completely away from screens are now beginning to feel the strain. When my sons were much younger, I often had these idyllic images in my head of day trips to the seaside, back garden treasure hunts, paddling in the river, visiting relatives … an endless series of character forming experiences which I imagined in grainy Kodachrome colours. Then I'd be faced with the reality of having a job, and also the, let's say, limited attention span of my sons. Sheepishly, we'd end up allowing some Fortnite time to catch our breath. There is so much pressure and guilt around children and gaming, especially during long school breaks, and I think we need to seriously redress our outlook as a society.

An AI-powered death clock promises to predict the exact day you'll die, right down to the second. The Death Clock app, available for download in Google and Apple stores, analyzes life choices users regularly make, their past habits, health conditions and family history of disease to'accurately' determine when they will die. Users are asked to put in a number of health markers like their cholesterol and blood-sugar levels, as well as their workout schedule, water intake, mental health and the current state of their romantic and plutonic relationships. The app is backed by data from 1,200 international life expectancy studies that looked at 53 million participants, including information from the Centers for Disease Control and Prevention. Although it seemed to be a morbid exercise, I took the test to see exactly how the results would play out.

Long-term memory is important for chatbots and dialogue systems (DS) to create consistent and human-like conversations, evidenced by numerous developed memory-augmented DS (MADS). To evaluate the effectiveness of such MADS, existing commonly used evaluation metrics, like retrieval accuracy and perplexity (PPL), mainly focus on query-oriented factualness and language quality assessment. However, these metrics often lack practical value. Moreover, the evaluation dimensions are insufficient for human-like assessment in DS. Regarding memory-recalling paradigms, current evaluation schemes only consider passive memory retrieval while ignoring diverse memory recall with rich triggering factors, e.g., emotions and surroundings, which can be essential in emotional support scenarios. To bridge the gap, we construct a novel Memory-Augmented Dialogue Benchmark (MADail-Bench) covering various memory-recalling paradigms based on cognitive science and psychology theories. The benchmark assesses two tasks separately: memory retrieval and memory recognition with the incorporation of both passive and proactive memory recall data. We introduce new scoring criteria to the evaluation, including memory injection, emotion support (ES) proficiency, and intimacy, to comprehensively assess generated responses. Results from cutting-edge embedding models and large language models on this benchmark indicate the potential for further advancement. Extensive testing further reveals correlations between memory injection, ES proficiency, and intimacy.

Math reasoning is a highly active area of Large Language Model (LLM) research because it is a hallmark of artificial intelligence. However, few works have explored how math reasoning is encoded within LLM parameters and if it is a skill that can be isolated within a model. Doing so could allow targeted intervention to improve math performance without altering non-math behavior and foster understanding of how models encode math reasoning. We introduce Math Neurosurgery (MathNeuro), a method for isolating math-specific parameters in LLMs using only forward passes. MathNeuro builds on existing work by using weights and activations to calculate parameter importance, but isolates math-specific parameters by removing those important for general language tasks. Pruning parameters MathNeuro identifies deletes a LLM's math reasoning ability without destroying its general language ability. Scaling these parameters by a small constant improves a pretrained or instruction-tuned LLM's performance by 4-17% on GSM8K while leaving non-math behavior unaltered. MathNeuro is also data efficient: most of its effectiveness holds when identifying math-specific parameters using a single sample. MathNeuro highlights the potential for future work to intervene on math-specific parameters.

In the current landscape of automatic language generation, there is a need to understand, evaluate, and mitigate demographic biases as existing models are becoming increasingly multilingual. To address this, we present the initial eight languages from the MASSIVE MULTILINGUAL HOLISTICBIAS (MMHB) dataset and benchmark consisting of approximately 6 million sentences representing 13 demographic axes. We propose an automatic construction methodology to further scale up MMHB sentences in terms of both language coverage and size, leveraging limited human annotation. Our approach utilizes placeholders in multilingual sentence construction and employs a systematic method to independently translate sentence patterns, nouns, and descriptors. Combined with human translation, this technique carefully designs placeholders to dynamically generate multiple sentence variations and significantly reduces the human translation workload. The translation process has been meticulously conducted to avoid an English-centric perspective and include all necessary morphological variations for languages that require them, improving from the original English HOLISTICBIAS. Finally, we utilize MMHB to report results on gender bias and added toxicity in machine translation tasks. On the gender analysis, MMHB unveils: (1) a lack of gender robustness showing almost +4 chrf points in average for masculine semantic sentences compared to feminine ones and (2) a preference to overgeneralize to masculine forms by reporting more than +12 chrf points in average when evaluating with masculine compared to feminine references. MMHB triggers added toxicity up to 2.3%.