Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting Ben Affleck'scores $600m deal' with Netflix to sell his AI film start-up Long hair over 45 is ageing and try-hard. I've finally cut mine off. Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Is this the BEST job in the world? Meet the'dog matchmaker' who matches guide dog puppies with visually impaired owners If you've ever wondered what the world's best job really is, this unique role could be a very strong contender. Ellie Brown, 30, is a'dog matchmaker' or, as she is officially known, a Guide Dog Mobility Specialist.

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Three Day Rule promises to make matchmaking affordable by integrating AI matchmakers and coaches into a dating app. But what's the point when humans looking for connection are just talking through AI chatbots? AI coaches are trained by real human matchmakers. More in-depth prompts to help find matches based not only on looks. Profile is also shared off-app with white-glove matchmaking service.

A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to

Robotic assembly remains a significant challenge due to complexities in visual perception, functional grasping, contact-rich manipulation, and performing high-precision tasks. Simulation-based learning and sim-to-real transfer have led to recent success in solving assembly tasks in the presence of object pose variation, perception noise, and control error; however, the development of a generalist (i.e., multi-task) agent for a broad range of assembly tasks has been limited by the need to manually curate assembly assets, which greatly constrains the number and diversity of assembly problems that can be used for policy learning. Inspired by recent success of using generative AI to scale up robot learning, we propose MatchMaker, a pipeline to automatically generate diverse, simulation-compatible assembly asset pairs to facilitate learning assembly skills. Specifically, MatchMaker can 1) take a simulation-incompatible, interpenetrating asset pair as input, and automatically convert it into a simulation-compatible, interpenetration-free pair, 2) take an arbitrary single asset as input, and generate a geometrically-mating asset to create an asset pair, 3) automatically erode contact surfaces from (1) or (2) according to a user-specified clearance parameter to generate realistic parts. We demonstrate that data generated by MatchMaker outperforms previous work in terms of diversity and effectiveness for downstream assembly skill learning. For videos and additional details, please see our project website: https://wangyian-me.github.io/MatchMaker/.

Schema matching -- the task of finding matches between attributes across disparate data sources with different tables and hierarchies -- is critical for creating interoperable machine learning (ML)-ready data. Addressing this fundamental data-centric problem has wide implications, especially in domains like healthcare, finance and e-commerce -- but also has the potential to benefit ML models more generally, by increasing the data available for ML model training. However, schema matching is a challenging ML task due to structural/hierarchical and semantic heterogeneity between different schemas. Previous ML approaches to automate schema matching have either required significant labeled data for model training, which is often unrealistic or suffer from poor zero-shot performance. To this end, we propose Matchmaker - a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker also self-improves in a zero-shot manner without the need for labeled demonstrations via a novel optimization approach, which constructs synthetic in-context demonstrations to guide the language model's reasoning process. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches, highlighting its potential to accelerate data integration and interoperability of ML-ready data.

One of the most gruelling hurdles in any new relationship is when it becomes time to meet the parents. But now Tinder has come up with a way to make sure your partner has the familial seal of approval before they've even been introduced. The dating app has created a tool called Matchmaker, which allows users to offer up to 15 friends, family members or guardians 24 hours to scrutinise their possible matches. They can view the profiles and make suggestions without having an account of their own – and, fortunately, cannot start messaging on your behalf. Once the session ends, Tinder users can review the profiles recommended by their matchmakers before making a final decision on whether or not they see them as a good fit.

It's a question from the parents that every singleton dreads: are you dating anyone at the moment? But the days of dismissing their questioning could be a thing of the past, thanks to Tinder's latest feature. The dating app has launched Tinder Matchmaker, which lets your friends and fmaily view and suggest potential matches for you. 'For years, singles have asked their friends to help find their next match on Tinder,' said Melissa Hobley, Chief Marketing Officer at Tinder. 'Tinder Matchmaker brings your circle of trust into your dating journey and helps you see the possibilities you might be overlooking from the perspective of those closest to you.'

Tinder has rolled out a new feature dubbed "Tinder Matchmaker" that will allow users' family and friends to access the dating app and make recommendations for potential matches. The matchmakers do not need to have a Tinder profile to view or suggest possible pairings. Hypothetically, that means anyone from your grandmother to your ex-boyfriend could help you select a new profile to match with. A Tinder user will need to launch a "Tinder Matchmaker session" either directly from a profile card or within the app's settings. If you see a potential match, you can share a unique link with up to 15 individuals in a 24-hour period.

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