Identifying fine-grained book genres is essential for enhancing user experience through efficient discovery, personalized recommendations, and improved reader engagement. At the same time, it provides publishers and marketers with valuable insights into consumer preferences and emerging market trends. While traditional genre classification methods predominantly rely on textual reviews or content analysis, the integration of additional modalities, such as book covers, blurbs, and metadata, offers richer contextual cues. However, the effectiveness of such multi-modal systems is often hindered by incomplete, noisy, or missing data across modalities. To address this, we propose IMAGINE (Intelligent Multi-modal Adaptive Genre Identification NEtwork), a framework designed to leverage multi-modal data while remaining robust to missing or unreliable information. IMAGINE learns modality-specific feature representations and adaptively prioritizes the most informative sources available at inference time. It further employs a hierarchical classification strategy, grounded in a curated taxonomy of book genres, to capture inter-genre relationships and support multi-label assignments reflective of real-world literary diversity. A key strength of IMAGINE is its adaptability: it maintains high predictive performance even when one modality, such as text or image, is unavailable. We also curated a large-scale hierarchical dataset that structures book genres into multiple levels of granularity, allowing for a more comprehensive evaluation. Experimental results demonstrate that IMAGINE outperformed strong baselines in various settings, with significant gains in scenarios involving incomplete modality-specific data.

"Blurb writing is a mini art form," Iris Murdoch once wrote in a letter to former Penguin blurb writer Elizabeth Buchan. And like many other art forms, companies have been experimenting with the idea that it could be created without an artist. A German company that provides digital book distribution and marketing services to publishers has announced it will integrate ChatGPT, a chatbot that answers questions by drawing on publicly available internet data, into its software. "During the beta phase, publishers can test the benefits of the artificial intelligence tool for their digital book marketing," states Bookwire, adding that it will only use ChatGPT if a publisher agrees and the disclaimer that the company "does not assume any responsibility for the content created by ChatGPT". But there is also another question that needs to be asked.

"It was uproar," she says, "We saw cars on fire." Her flat is in the East End district of Spitalfields in a Georgian house, which she bought 25 years ago, complete with a little shop that she ran for years as an organic grocer and tea room until the rates got too high, and she let it out to an upmarket chocolatier. It's as if a scene from Dickens's The Old Curiosity Shop has been dropped into a satire about prosperity Britain: the quaint old shopfront is still intact, while outside it a lifesize sculpture of a rowing boat full of people sits surreally in the middle of the street, and a little further along, a herd of large bronze elephants frolics. These public artworks only arrived a few weeks ago, Winterson explains, as part of a grand plan to pedestrianise the area, and make it more buzzy, just at the moment that the sort of well-heeled office workers who bought upmarket chocolates are abandoning it owing to the Covid pandemic. We're at a transitional moment in so many ways, she says – a perfect moment to launch a book that reassesses the past while staring the future in the face.

Plenty of prominent scientists have Wikipedia pages. But while checking to see if someone specific has a Wikipedia page is a quick Google search away, figuring out who should be on Wikipedia but isn't--and then writing an entry for him or her--is much trickier. For example, you may or may not have heard of Christina Economos. She doesn't have a Wikipedia page about her, although she's a professor at Tufts University and the New Balance Chair in childhood nutrition. But while she lacks a Wikipedia page, she does have a very short stub describing who she is professionally on a website made by a company called Primer.

On any given day, hordes of people consult online reviews to help them pick out where to eat, what to watch, and products to buy. We trust that these reviews are reliable because they come from everyday folk just like us. But, what if the feedback blurbs on sites ranging from Amazon to iTunes could be faked -- not just by nefarious humans, but by AI? That's what researchers from University of Chicago tried to do, with surprising results. Not only did the Yelp restaurant reviews written by their neural network manage to pass for the real thing, but people even found the posts to be useful. As part of their attack method, the researchers utilized a deep learning program known as a recurrent neural network (RNN).

Yesterday morning I woke up, put on a pot of coffee, and checked the news. I wanted to revisit the New Hampshire primary results that had rolled in the night before. I opened Quartz's new app and was greeted with a text message: "Yep, it's really happening: Trump and Sanders won big in New Hampshire." Below it appeared side-by-side portraits of Trump's scowl and Bernie's grin. To read more, I tapped a ready-made text reply containing a donkey, an elephant, and an American flag emoji.