Choropleth maps, which utilize color schemes to visualize spatial patterns and trends, are simple yet effective tools for geographic data analysis. As such, color scheme design is a critical aspect of choropleth map creation. The traditional coloring methods offered by GIS tools such as ArcGIS and QGIS are not user-friendly for non-professionals. On the one hand, these tools provide numerous color schemes, making it hard to decide which one best matches the theme. On the other hand, it is difficult to fulfill some ambiguous and personalized coloring needs of users, such as requests for 'summer-like' map colors. To address these shortcomings, we develop a novel system that leverages a large language model and map color design principles to generate contextually relevant and user-aligned choropleth map color schemes. The system follows a three-stage process: Data processing, which provides an overview of the data and classifies the data into meaningful classes; Color Concept Design, where the color theme and color mode are conceptualized based on data characteristics and user intentions; and Color Scheme Design, where specific colors are assigned to classes based on generated color theme, color mode, and user requirements. Our system incorporates an interactive interface, providing necessary visualization for choropleth map color design and allowing users to customize and refine color choices flexibly. Through user studies and evaluations, the system demonstrates acceptable usability, accuracy, and flexibility, with users highlighting the tool's efficiency and ease of use.

Feedback is crucial for every design process, such as user interface (UI) design, and automating design critiques can significantly improve the efficiency of the design workflow. Although existing multimodal large language models (LLMs) excel in many tasks, they often struggle with generating high-quality design critiques -- a complex task that requires producing detailed design comments that are visually grounded in a given design's image. Building on recent advancements in iterative refinement of text output and visual prompting methods, we propose an iterative visual prompting approach for UI critique that takes an input UI screenshot and design guidelines and generates a list of design comments, along with corresponding bounding boxes that map each comment to a specific region in the screenshot. The entire process is driven completely by LLMs, which iteratively refine both the text output and bounding boxes using few-shot samples tailored for each step. We evaluated our approach using Gemini-1.5-pro and GPT-4o, and found that human experts generally preferred the design critiques generated by our pipeline over those by the baseline, with the pipeline reducing the gap from human performance by 50% for one rating metric. To assess the generalizability of our approach to other multimodal tasks, we applied our pipeline to open-vocabulary object and attribute detection, and experiments showed that our method also outperformed the baseline.

Color coding, a technique assigning specific colors to cluster information types, has proven advantages in aiding human cognitive activities, especially reading and comprehension. The rise of Large Language Models (LLMs) has streamlined document coding, enabling simple automatic text labeling with various schemes. This has the potential to make color-coding more accessible and benefit more users. However, the impact of color choice on information seeking is understudied. We conducted a user study assessing various color schemes' effectiveness in LLM-coded text documents, standardizing contrast ratios to approximately 5.55:1 across schemes. Participants performed timed information-seeking tasks in color-coded scholarly abstracts. Results showed non-analogous and yellow-inclusive color schemes improved performance, with the latter also being more preferred by participants. These findings can inform better color scheme choices for text annotation. As LLMs advance document coding, we advocate for more research focusing on the "color" aspect of color-coding techniques.

Harmony level prediction is receiving increasing attention nowadays. Color plays a crucial role in affecting human aesthetic responses. In this paper, we explore color harmony using a fuzzy-based color model and address the question of its universality. For our experiments, we utilize a dataset containing attractive images from five different domains: fashion, art, nature, interior design, and brand logos. We aim to identify harmony patterns and dominant color palettes within these images using a fuzzy approach. It is well-suited for this task because it can handle the inherent subjectivity and contextual variability associated with aesthetics and color harmony evaluation. Our experimental results suggest that color harmony is largely universal. Additionally, our findings reveal that color harmony is not solely influenced by hue relationships on the color wheel but also by the saturation and intensity of colors. In palettes with high harmony levels, we observed a prevalent adherence to color wheel principles while maintaining moderate levels of saturation and intensity. These findings contribute to ongoing research on color harmony and its underlying principles, offering valuable insights for designers, artists, and researchers in the field of aesthetics.

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Want your bedroom to look like a "sunset in Paris"? A new AI project will help you find the right color scheme from a simple text description. Danny Richman's Free GPT-3 Color Palette Generator uses machine learning to pick out colors associated with text phrases. You might, for example, enter "baby nursery" or "New England" or "Martian sunset" and the AI will find a palette of four colors associated with that term. Richman's generator, which is built in Google Sheets and is free for anyone to use, also returns the HEX values, making it easy for web designers to apply those colors on their websites, for example.

Nikolay Ironov had been working as a graphic designer for more than a year before he revealed his secret. Lebedev Studio -- Russia's largest design company -- Ironov had already worked on more than 20 commercial projects, creating everything from beer bottle labels to startup logos. But Ironov was not the person he claimed to be. In fact, the designer was not a person at all. Lebedev Studio revealed the truth to its clients: their logos had been created by an AI system.

Amazon's Echo has been our long-time favorite in the lineup of Alexa-enabled smart speakers and displays. Since its introduction in 2015, the Echo has stood out in an increasingly congested marketplace for its sound quality, far-field voice technology, and minimal footprint. The new Echo is no exception to that trend with its improved speaker, rounded design, and ever-helpful Alexa. Let's dig a little deeper into what the third-generation Echo is about and whether you should get one. The third-generation Echo can put out decent sound with its 0.8-inch tweeter and 3-inch woofer. While you might find the Echo's entry-level smaller sibling, the Dot, appealing for its lower price point, the Echo is a reasonable place to get started with smart speakers because it manages to produce respectable sound quality for its size and sub-$100 cost.

Every website needs to provide a great user experience for its visitors. This is a principle that has been true since the earliest days of the Internet. However, it is becoming even more important, especially as user expectations are increasing and Google has started heavily relying on engagement statistics from analytics data as part of its ranking algorithm. This is one big reason that multilingual responsive design is so important and more achievable than ever. This has created some significant challenges for businesses that operate in different regions. If they don't offer the best possible user experience for those customers, then their rankings can drop for keywords some of the languages is that provide a lot of traffic.

It seems like innovation just can't leave designers alone. At first, it was the computer revolution; As Adrian Shaughnessy put it, the introduction of Macintosh meant "no more mechanical artwork, no more paste-up, no more typesetters, no more expensive retouchers" for designers. The second major shock to the field was the explosion of the Internet. No longer designers had the comfort of fixed-size, one-format print publishing. With most businesses looking to add a website to their public image, designers now had to make sure their artwork would look good on all possible screen ratios and sizes.