Science Space Solar System What night is van Gogh's'The Starry Night'? New analysis says it's in July. Astronomy, weather data, and even letters referencing cicadas indicate he may have painted what the sky looked like around July 22, 1889. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Located in the Museum of Modern Art, New York, New York.

Animating and simulating crowds using an agent-based approach is a well-established area where every agent in the crowd is individually controlled such that global human-like behaviour emerges. We observe that human navigation and movement in crowds are often influenced by complex social and environmental interactions, driven mainly by language and dialogue. However, most existing work does not consider these dimensions and leads to animations where agent-agent and agent-environment interactions are largely limited to steering and fixed higher-level goal extrapolation. We propose a novel method that exploits large language models (LLMs) to control agents' movement. Our method has two main components: a dialogue system and language-driven navigation. We periodically query agent-centric LLMs conditioned on character personalities, roles, desires, and relationships to control the generation of inter-agent dialogue when necessitated by the spatial and social relationships with neighbouring agents. We then use the conversation and each agent's personality, emotional state, vision, and physical state to control the navigation and steering of each agent. Our model thus enables agents to make motion decisions based on both their perceptual inputs and the ongoing dialogue. We validate our method in two complex scenarios that exemplify the interplay between social interactions, steering, and crowding. In these scenarios, we observe that grouping and ungrouping of agents automatically occur. Additionally, our experiments show that our method serves as an information-passing mechanism within the crowd. As a result, our framework produces more realistic crowd simulations, with emergent group behaviours arising naturally from any environmental setting.

AI models trained on enormously large datasets, specifically large language or vision-language models, represent a phenomenon that has by far outgrown the notion of "being just a tool". Regardless of how and for what purpose such models are practically employed, they in themselves represent a valuable object of study. "In themselves" in this context includes not only all the culturally dependent patterns learned from the training data, but also the culturally dependent attempts to control, modify or erase culturally dependent patterns in the training data. Focusing on the comparative analysis of outputs from two very popular text-to-image synthesis models, DALL E 2 [1] and Stable Diffusion [2], this paper tries to tackle the pros and cons of striving towards culturally agnostic vs. culturally specific AI models. Implemented in one way or another, most commonly the "guiding principle" behind many existing text-to-image generators is the ground-breaking vision-language model CLIP and its emerging derivatives (e.g.

If you've been following Apple's announcements from the past year, you know that they are heavily invested in machine learning. Ever since they introduced Core ML last year at WWDC 2017, there are tons of apps which have sprung up which harness the power of machine learning. However, one challenge developers always faced was how to create the models? Luckily, Apple solved our question last winter when they announced the acquisition on Turi Create from GraphLab. Turi Create is Apple's tool which can help developers simplify the creation of their own custom models. With Turi Create, you can build your own custom machine learning models.

This is a torch implementation of the paper A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. The paper presents an algorithm for combining the content of one image with the style of another image using convolutional neural networks. Applying the style of different images to the same content image gives interesting results. The algorithm allows the user to trade-off the relative weight of the style and content reconstruction terms, as shown in this example where we port the style of Picasso's 1907 self-portrait onto Brad Pitt: By resizing the style image before extracting style features, we can control the types of artistic features that are transfered from the style image; you can control this behavior with the -style_scale flag. Below we see three examples of rendering the Golden Gate Bridge in the style of The Starry Night.

Remember last summer's influx of convolutional neural network art, which took the form of hallucinogenic-like DeepDream images, like the one above? Prompted by a blog post and code release by a team of Google engineers, haunting composite generation--also known as inceptionism, as a nod to the movie-related internet meme "we need to go deeper"--became the poster child for artificial neural networks. In "A Neural Algorithm of Artistic Style" Leon Gatys, Alexander Ecker and Matthias Bethge describe it as a system that "uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images." Imagine your vacation photos rendered in the style of Pablo Picasso, or Leonardo da Vinci's Mona Lisa painted in the style of Vincent Van Gogh's Starry Night. You can see that example directly, in "Machines and Metaphors," a blog post by artist and programmer Gene Kogan.

This is a torch implementation of the paper A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. The paper presents an algorithm for combining the content of one image with the style of another image using convolutional neural networks. Applying the style of different images to the same content image gives interesting results. The algorithm allows the user to trade-off the relative weight of the style and content reconstruction terms, as shown in this example where we port the style of Picasso's 1907 self-portrait onto Brad Pitt: By resizing the style image before extracting style features, we can control the types of artistic features that are transfered from the style image; you can control this behavior with the -style_scale flag. Below we see three examples of rendering the Golden Gate Bridge in the style of The Starry Night.

The nature of artistic style is something of a mystery to most people. Think of Vincent Van Gogh's Starry Night, Picasso's work on cubism, or Edvard Munch's The Scream. All have a powerful, unique style that humans recognize easily. Deep neural networks are revolutionizing the way machines recognize and interpret the world. Machine vision now routinely outperforms humans at tasks such as object and face recognition, something that was unimaginable just a few years ago.