The 770-pound Bayeux Tapestry depicts the Norman conquest of England in 1066. Breakthroughs, discoveries, and DIY tips sent every weekday. Whether it's the morning paper, the games on the back of a cereal box, or just scrolling through social media, there is something nice about reading with a meal. For the monks living in St. Augustine's Abbey in Canterbury, England, one of the most famous (and phallic) tapestries in the world may have been their equivalent to the back of the cereal box. New research recently published in the journal claims that the 1,000-year-old Bayeux Tapestry may have served as mealtime reading.

Large vision-language models (LVLMs) have recently achieved rapid progress, exhibiting great perception and reasoning abilities concerning visual information. However, when faced with prompts in different sizes of solution spaces, LVLMs fail to always give consistent answers regarding the same knowledge point. This inconsistency of answers between different solution spaces is prevalent in LVLMs and erodes trust. To this end, we provide a multi-modal benchmark ConBench, to intuitively analyze how LVLMs perform when the solution space of a prompt revolves around a knowledge point. Based on the ConBench tool, we are the first to reveal the tapestry and get the following findings: (1) In the discriminate realm, the larger the solution space of the prompt, the lower the accuracy of the answers. We hope this paper will accelerate the research community in better evaluating their models and encourage future advancements in the consistency domain.

More and more, workers are presented with algorithms to help them make better decisions. But humans must trust those algorithms to follow their advice. The way humans view algorithmic recommendations varies depending on how much they know about how the model works and how it was created, according to a new research paper co-authored by MIT Sloan professorKate Kellogg. Prior research has assumed that people are more likely to trust interpretable artificial intelligence models, in which they are able to see how the models make their recommendations. But Kellogg and co-researchers Tim DeStefano, Michael Menietti, and Luca Vendraminelli, affiliated with the Laboratory for Innovation Science at Harvard, found that this isn't always true.

You type in words – however nonsensical or disjointed – and the algorithm creates a unique image based on your search. This is Dall-E 2, a startlingly advanced, image-generating AI trained on 250 million images, named after the surrealist artist Salvador Dalí and Pixar's Wall-E. While use of Dall-E 2 is currently limited to a narrow pool of people, Dall-E mini (or Craiyon) is a free, unrelated version that is open to the public. Drawing on 15m images, Dall-E mini's algorithm offers a smorgasbord of surreal images, complete with absurd compositions and blurred human forms. Already, trends have emerged: nuclear explosions, dumpster fires, toilets and giant eyeballs abound. On a dedicated Reddit thread, people delight in the images generated by the free, low-resolution version, which range from amusing (Kim Jong-un lego) to dark (The Last Supper by Salvador Dali), hellish (synchronized swimming in lava) and deeply disturbing (Steve Jobs introducing a guillotine). Like other machine-learning networks, this AI model seems biased in its images of people – who appear, perhaps unsurprisingly, overwhelmingly white and mostly male.