Augmented Reality (AR) enhances the real world by integrating virtual content, yet ensuring the quality, usability, and safety of AR experiences presents significant challenges. Could Vision-Language Models (VLMs) offer a solution for the automated evaluation of AR-generated scenes? Could Vision-Language Models (VLMs) offer a solution for the automated evaluation of AR-generated scenes? In this study, we evaluate the capabilities of three state-of-the-art commercial VLMs -- GPT, Gemini, and Claude -- in identifying and describing AR scenes. For this purpose, we use DiverseAR, the first AR dataset specifically designed to assess VLMs' ability to analyze virtual content across a wide range of AR scene complexities. Our findings demonstrate that VLMs are generally capable of perceiving and describing AR scenes, achieving a True Positive Rate (TPR) of up to 93% for perception and 71% for description. While they excel at identifying obvious virtual objects, such as a glowing apple, they struggle when faced with seamlessly integrated content, such as a virtual pot with realistic shadows. Our results highlight both the strengths and the limitations of VLMs in understanding AR scenarios. We identify key factors affecting VLM performance, including virtual content placement, rendering quality, and physical plausibility. This study underscores the potential of VLMs as tools for evaluating the quality of AR experiences.

Now, the company is bringing classic characters from TV shows, anime and games including Pac-Man and Hello Kitty to life with augmented reality in Google Search. The expansion into animated characters makes sense considering Pac-Man is the most searched animated icon on Google, leading the pack by a long way: worldwide search interest in Pac-Man is more than double the next most-searched character, Hello Kitty. Now, anime is more popular than video games worldwide, with interest for anime climbing to its highest peak on record in the past month. Google Search has made these characters completely interactive, users can watch ghosts chasing after Pac-Man or Gundam swoop into their living room! To date the characters listed that are viewable in augmented reality include Evangelion, Hello Kitty, Gomora, Gundam, Pac-Man and Ultraman.

The impact of machine learning applications – from machine vision to voice recognition – will be massively enhanced via the deployment of 800,000 machine learning centric chips in data centres this year – roughly quadruple the volume in 2016. And one billion smartphone users will create augmented reality (AR) content in 2018 globally, with half of UK smartphone users doing so. The findings stand out in Deloitte UK's TMT Predictions 2018 report, from its technology-focused practice, published today. A surge in deployment of FPGAs (field programmable gate arrays) and ASICs (application-specific integrated circuits) - will be a core driver in the use of machine learning. FPGAs and ASICs will represent between a quarter and a third of all machine learning chips deployed this year.