WhichYear.com presents a visual guessing game challenging users to identify the year a photograph was taken. The site displays a photo and provides four year choices as possible answers. After selecting an answer, the correct year is revealed along with a brief explanation of the visual clues that point to that era. The game spans a wide range of photographic subjects and historical periods, testing players' knowledge of fashion, technology, and cultural trends.
The author details their process of building an AI system to analyze rugby footage. They leveraged computer vision techniques to detect players, the ball, and key events like tries, scrums, and lineouts. The primary challenge involved overcoming the complexities of a fast-paced, contact-heavy sport with variable camera angles and player uniforms. This involved training a custom object detection model and utilizing various data augmentation methods to improve accuracy and robustness. Ultimately, the author demonstrated successful tracking of game elements, enabling automated analysis and potentially opening doors for advanced statistical insights and automated highlights.
HN users generally praised the project's ingenuity and technical execution, particularly the use of YOLOv8 and the detailed breakdown of the process. Several commenters pointed out the potential real-world applications, such as automated sports analysis and coaching assistance. Some discussed the challenges of accurately tracking fast-paced sports like rugby, including occlusion and player identification. A few suggested improvements, such as using multiple camera angles or incorporating domain-specific knowledge about rugby strategies. The ethical implications of AI in sports officiating were also briefly touched upon. Overall, the comment section reflects a positive reception to the project with a focus on its practical potential and technical merits.
Two teenagers developed Cal AI, a photo-based calorie counting app that has surpassed one million downloads. The app uses AI image recognition to identify food and estimate its caloric content, aiming to simplify calorie tracking for users. Despite its popularity, the app's accuracy has been questioned, and the young developers are working on improvements while navigating the complexities of running a viral app and continuing their education.
Hacker News commenters express skepticism about the accuracy and practicality of a calorie-counting app based on photos of food. Several users question the underlying technology and its ability to reliably assess nutritional content from images alone. Some highlight the difficulty of accounting for factors like portion size, ingredients hidden within a dish, and cooking methods. Others point out existing, more established nutritional databases and tracking apps, questioning the need for and viability of this new approach. A few commenters also raise concerns about potential privacy implications and the ethical considerations of encouraging potentially unhealthy dietary obsessions, particularly among younger users. There's a general sense of caution and doubt surrounding the app's claims, despite its popularity.
VGGT introduces a novel Transformer architecture designed for visual grounding tasks, aiming to improve interaction between vision and language modalities. It leverages a "visual geometry embedding" module that encodes spatial relationships between visual features, enabling the model to better understand the geometric context of objects mentioned in textual queries. This embedding is integrated with a cross-modal attention mechanism within the Transformer, facilitating more effective communication between visual and textual representations for improved localization and grounding performance. The authors demonstrate VGGT's effectiveness on various referring expression comprehension benchmarks, achieving state-of-the-art results and highlighting the importance of incorporating geometric reasoning into vision-language models.
Hacker News users discussed VGGT's novelty and potential impact. Some questioned the significance of grounding the transformer in visual geometry, arguing it's not a truly novel concept and similar approaches have been explored before. Others were more optimistic, praising the comprehensive ablation studies and expressing interest in seeing how VGGT performs on downstream tasks like 3D reconstruction. Several commenters pointed out the high computational cost associated with transformers, especially in the context of dense prediction tasks like image segmentation, wondering about the practicality of the approach. The discussion also touched upon the trend of increasingly complex architectures in computer vision, with some expressing skepticism about the long-term viability of such models.
This Mozilla AI blog post explores using computer vision to automatically identify and add features to OpenStreetMap. The project leverages a large dataset of aerial and street-level imagery to train models capable of detecting objects like crosswalks, swimming pools, and basketball courts. By combining these detections with existing OpenStreetMap data, they aim to improve map completeness and accuracy, particularly in under-mapped regions. The post details their technical approach, including model architectures and training strategies, and highlights the potential for community involvement in validating and integrating these AI-generated features. Ultimately, they envision this technology as a powerful tool for enriching open map data and making it more useful for everyone.
Several Hacker News commenters express excitement about the potential of using computer vision to improve OpenStreetMap data, particularly in automating tedious tasks like feature extraction from aerial imagery. Some highlight the project's clever use of pre-trained models like Segment Anything and the importance of focusing on specific features (crosswalks, swimming pools) to improve accuracy. Others raise concerns about the accuracy of such models, potential biases in the training data, and the risk of overwriting existing, manually-verified data. There's discussion around the need for careful human oversight, suggesting the tool should assist rather than replace human mappers. A few users suggest other data sources like point clouds and existing GIS datasets could further enhance the project. Finally, some express interest in the project's open-source nature and the possibility of contributing.
Time Portal is a simple online game that drops you into a random historical moment through a single image. Your task is to guess the year the image originates from. After guessing, you're given the correct year and some context about the image. It's designed as a fun, quick way to engage with history and test your knowledge.
HN users generally found the "Time Portal" concept interesting and fun, praising its educational potential and the clever use of Stable Diffusion to generate images. Several commenters pointed out its similarity to existing games like GeoGuessr, but appreciated the historical twist. Some expressed a desire for features like map integration, a scoring system, and the ability to narrow down guesses by time period or region. A few users noted issues with image quality and historical accuracy, suggesting improvements like using higher-resolution images and sourcing them from reputable historical archives. There was also some discussion on the challenges of generating historically accurate images with AI, and the potential for biases to creep in.
Bild AI is a new tool that uses AI to help users understand construction blueprints. It can extract key information like room dimensions, materials, and quantities, effectively translating complex 2D drawings into structured data. This allows for easier cost estimation, progress tracking, and identification of potential issues early in the construction process. Currently in beta, Bild aims to streamline communication and improve efficiency for everyone involved in a construction project.
Hacker News users discussed Bild AI's potential and limitations. Some expressed skepticism about the accuracy of AI interpretation, particularly with complex or hand-drawn blueprints, and the challenge of handling revisions. Others saw promise in its application for cost estimation, project management, and code generation. The need for human oversight was a recurring theme, with several commenters suggesting AI could assist but not replace experienced professionals. There was also discussion of existing solutions and the competitive landscape, along with curiosity about Bild AI's specific approach and data training methods. Finally, several comments touched on broader industry trends, such as the increasing digitization of construction and the potential for AI to improve efficiency and reduce errors.
The blog post "Biases in Apple's Image Playground" reveals significant biases in Apple's image suggestion feature within Swift Playgrounds. The author demonstrates how, when prompted with various incomplete code snippets, the Playground consistently suggests images reinforcing stereotypical gender roles and Western-centric beauty standards. For example, code related to cooking predominantly suggests images of women, while code involving technology favors images of men. Similarly, searches for "person," "face," or "human" yield primarily images of white individuals. The post argues that these biases, likely stemming from the datasets used to train the image suggestion model, perpetuate harmful stereotypes and highlight the need for greater diversity and ethical considerations in AI development.
Hacker News commenters largely agree with the author's premise that Apple's Image Playground exhibits biases, particularly around gender and race. Several commenters point out the inherent difficulty in training AI models without bias due to the biased datasets they are trained on. Some suggest that the small size and specialized nature of the Playground model might exacerbate these issues. A compelling argument arises around the tradeoff between "correctness" and usefulness. One commenter argues that forcing the model to produce statistically "accurate" outputs might limit its creative potential, suggesting that Playground is designed for artistic exploration rather than factual representation. Others point out the difficulty in defining "correctness" itself, given societal biases. The ethics of AI training and the responsibility of companies like Apple to address these biases are recurring themes in the discussion.
This paper proposes a new method called Recurrent Depth (ReDepth) to improve the performance of image classification models, particularly focusing on scaling up test-time computation. ReDepth utilizes a recurrent architecture that progressively refines latent representations through multiple reasoning steps. Instead of relying on a single forward pass, the model iteratively processes the image, allowing for more complex feature extraction and improved accuracy at the cost of increased test-time computation. This iterative refinement resembles a "thinking" process, where the model revisits its understanding of the image with each step. Experiments on ImageNet demonstrate that ReDepth achieves state-of-the-art performance by strategically balancing computational cost and accuracy gains.
HN users discuss the trade-offs of this approach for image generation. Several express skepticism about the practicality of increasing inference time to improve image quality, especially given the existing trend towards faster and more efficient models. Some question the perceived improvements in image quality, suggesting the differences are subtle and not worth the substantial compute cost. Others point out the potential usefulness in specific niche applications where quality trumps speed, such as generating marketing materials or other professional visuals. The recurrent nature of the model and its potential for accumulating errors over multiple steps is also brought up as a concern. Finally, there's a discussion about whether this approach represents genuine progress or just a computationally expensive exploration of a limited solution space.
Large language models (LLMs) excel at mimicking human language but lack true understanding of the world. The post "Your AI Can't See Gorillas" illustrates this through the "gorilla problem": LLMs fail to identify a gorilla subtly inserted into an image captioning task, demonstrating their reliance on statistical correlations in training data rather than genuine comprehension. This highlights the danger of over-relying on LLMs for tasks requiring real-world understanding, emphasizing the need for more robust evaluation methods beyond benchmarks focused solely on text generation fluency. The example underscores that while impressive, current LLMs are far from achieving genuine intelligence.
Hacker News users discussed the limitations of LLMs in visual reasoning, specifically referencing the "gorilla" example where models fail to identify a prominent gorilla in an image while focusing on other details. Several commenters pointed out that the issue isn't necessarily "seeing," but rather attention and interpretation. LLMs process information sequentially and lack the holistic view humans have, thus missing the gorilla because their attention is drawn elsewhere. The discussion also touched upon the difference between human and machine perception, and how current LLMs are fundamentally different from biological visual systems. Some expressed skepticism about the author's proposed solutions, suggesting they might be overcomplicated compared to simply prompting the model to look for a gorilla. Others discussed the broader implications of these limitations for safety-critical applications of AI. The lack of common sense reasoning and inability to perform simple sanity checks were highlighted as significant hurdles.
The author trained a YOLOv5 model to detect office chairs in a dataset of 40 million hotel room photos, aiming to identify properties suitable for "bleisure" (business + leisure) travelers. They achieved reasonable accuracy and performance despite the challenges of diverse chair styles and image quality. The model's output is a percentage indicating the likelihood of an office chair's presence, offering a quick way to filter a vast image database for hotels catering to digital nomads and business travelers. This project demonstrates a practical application of object detection for a specific niche market within the hospitality industry.
Hacker News users discussed the practical applications and limitations of using YOLO to detect office chairs in hotel photos. Some questioned the business value, wondering how chair detection translates to actionable insights for hotels. Others pointed out potential issues with YOLO's accuracy, particularly with diverse chair designs and varying image quality. The computational cost and resource intensity of processing such a large dataset were also highlighted. A few commenters suggested alternative approaches, like crowdsourcing or using pre-trained models specifically designed for furniture detection. There was also a brief discussion about the ethical implications of analyzing hotel photos without explicit consent.
Summary of Comments ( 146 )
https://news.ycombinator.com/item?id=43715024
HN users generally found the "Which Year" game fun and well-executed, praising its simple yet engaging concept. Several commenters discussed the subtle cues they used to pinpoint the year, such as fashion trends, car models, image quality, and the presence or absence of digital artifacts. Some noted the difficulty increased with more recent years due to the faster pace of technological advancement and stylistic changes, while others appreciated the nostalgic trip through time. A few users shared their scores and playfully lamented their inability to distinguish between certain decades. The addictive nature of the game was a recurring theme, with some admitting they spent more time playing than intended. One commenter suggested adding a difficulty slider, while another expressed their enjoyment at being able to recognize specific cameras used in some photos.
The Hacker News post "Which year: guess which year each photo was taken" linking to whichyr.com generated a moderate number of comments, mostly discussing the difficulty of the game, strategies for guessing, and observations about societal and technological changes reflected in the photos.
Several commenters found the game surprisingly challenging. One noted the difficulty in distinguishing between certain decades, particularly the 70s, 80s, and 90s, highlighting how styles and technologies sometimes persisted or experienced revivals, making precise dating difficult. The subtle evolution of fashion and car designs were mentioned as particularly tricky aspects.
Some users shared strategies for narrowing down the year. Looking for specific technological clues like the presence of smartphones, the type of computers visible, or the style of headphones was a common tactic. Others mentioned focusing on fashion trends, car models, and background details like signage and store branding. One commenter specifically mentioned paying attention to the aspect ratio of photos as a potential clue.
A few comments touched on broader observations about societal and technological change. One user remarked on how quickly technology has evolved, referencing the rapid shift from bulky CRT monitors to sleek flat screens. Another pointed out the cyclical nature of fashion, noting how certain styles reappear over time. The game sparked reflections on the passage of time and the sometimes subtle but significant changes that occur from decade to decade.
Some commenters mentioned similar games or websites, suggesting alternatives or variations on the "guess the year" concept. There was some discussion of the user interface and potential improvements to the game's design.
While no single comment overwhelmingly dominated the discussion, the collection of comments provided a mix of perspectives on the game's difficulty, strategies for playing, and observations about the changing technological and cultural landscape reflected in the photographs. The overall sentiment seemed to be one of intrigued engagement with the challenge presented by the game.