'Some people go into AI relationships purposefully, some out of curiosity, and others accidentally.' 'Some people go into AI relationships purposefully, some out of curiosity, and others accidentally.' The women in love with AI chatbots: 'I vowed to him that I wouldn't leave him' The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. A young tattoo artist on a hiking trip in the Rocky Mountains cozies up by the campfire, as her boyfriend Solin describes the constellations twinkling above them: the spidery limbs of Hercules, the blue-white sheen of Vega. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. Somewhere in New England, a middle-aged woman introduces her therapist to her husband, Ying.

Semantic text representation is a fundamental task in the field of natural language processing. Existing text embedding (e.g., SimCSE and LLM2Vec) have demonstrated excellent performance, but the values of each dimension are difficult to trace and interpret. Bag-of-words, as classic sparse interpretable embeddings, suffers from poor performance. Recently, Benara et al. (2024) propose interpretable text embeddings using large language models, which forms "0/1" embeddings based on responses to a series of questions. These interpretable text embeddings are typically high-dimensional (larger than 10,000). In this work, we propose Low-dimensional (lower than 500) Dense and Interpretable text embeddings with Relative representations (LDIR). The numerical values of its dimensions indicate semantic relatedness to different anchor texts through farthest point sampling, offering both semantic representation as well as a certain level of traceability and interpretability. We validate LDIR on multiple semantic textual similarity, retrieval, and clustering tasks. Extensive experimental results show that LDIR performs close to the black-box baseline models and outperforms the interpretable embeddings baselines with much fewer dimensions. Code is available at https://github.com/szu-tera/LDIR.

While screenwriters strike, partly over the threat from artificial intelligence, playwrights are busy writing about AI. Lauren Gunderson's Anthropology is the second world premiere in a week featuring pseudo-humanity – after Alan Ayckbourn's Constant Companions – and the third such London play in six months, following Jordan Harrison's Marjorie Prime and Andrew Stein's Disruption. Gunderson, an American whose I and You was a 2018 Hampstead success, creates Merril, a software engineer, whose sister Angie has been missing for a year after failing to reach home one night. From the phone, laptop and online footprint the young woman left behind, Merril sculpts a virtual Angie. The early scenes are a Merril duologue with a disembodied voice, like a digital Krapp's Last Tape, but Gunderson and director Anna Ledwich sensibly open up this closed circuit so that we see three, or by some counts four, others.

Artificial intelligence, or AI as it's called, has been a buzzword for nearly a decade already, yet sometimes it still feels as though we're just in the early stages of discovering what predictive analytics and machine learning can do for enterprises. Nowhere is this truer than in marketing and sales functions. According to Forrester, as of 2017 marketing and sales accounted for more than 50 percent of all AI investments. But when you look at investors who have already sunk serious money into AI projects, only 45 percent have seen any results at all. And among those who are seeing results, 25 percent agree that they've become more effective in their business processes.

Over the last few decades, the telecom industry has rapidly shifted from basic phone and internet services to a far more evolved space featuring mobile, wearables and automation, making it one of the biggest businesses in the world currently and always upgrading to the cutting edge technology. According to IDC, 63.5% of telecommunications organizations are making new technology investments for AI systems. While having to be on the bleeding edge of technology is a good thing for customers and the competition. The industry itself is a great candidate for adopting AI driven solutions which offer the hope of reduced costs and increased efficiencies through automation. Needless to say, frontrunners have already started playing with AI solutions and deploying them across various business areas including customer-facing and internal processes.

In 1950, Alan Turing, already famous for helping to crack the German Enigma code during World War II, devised the Turing test to define intelligence in machines. Could a computer, Turing asked, fool a human into thinking he was interacting with another person, or imitate human responses so well that it would be impossible for a person to tell the difference? If the machine could, Turing proposed, it could be considered intelligent. Turing's thought experiment spawned scores of science-fiction tales, such as the 2015 hit movie Ex Machina. Now, artificial intelligence (AI) and autonomous algorithms are not only passing the Turing test every day but, more importantly, are making and saving money for the businesses that deploy them.

In 1950, Alan Turing, already famous for helping to crack the German Enigma code during World War II, devised the Turing test to define intelligence in machines. Could a computer, Turing asked, fool a human into thinking he was interacting with another person, or imitate human responses so well that it would be impossible for a person to tell the difference? If the machine could, Turing proposed, it could be considered intelligent. Turing's thought experiment spawned scores of science-fiction tales, such as the 2015 hit movie Ex Machina. Now, artificial intelligence (AI) and autonomous algorithms are not only passing the Turing test every day but, more importantly, are making and saving money for the businesses that deploy them.

In 1950, Alan Turing, already famous for helping to crack the German Enigma code during World War II, devised the Turing test to define intelligence in machines. Could a computer, Turing asked, fool a human into thinking he was interacting with another person, or imitate human responses so well that it would be impossible for a person to tell the difference? If the machine could, Turing proposed, it could be considered intelligent. Turing's thought experiment spawned scores of science-fiction tales, such as the 2015 hit movie Ex Machina. Now, artificial intelligence (AI) and autonomous algorithms are not only passing the Turing test every day but, more importantly, are making and saving money for the businesses that deploy them.