AI meets AR: How Rokid Glasses bring multilingual, real-time intelligence to smart eyewear globally
Updated
March 3, 2026 3:50 PM
Rokid's smart glasses. PHOTO: ROKID
Rokid, a Chinese company specializing in AI-powered smart eyewear and human–computer interaction, has rolled out a major software update for the international version of its Rokid Glasses. This update makes it the first smart glasses manufacturer to natively support Google’s Gemini, alongside three other leading large language models: OpenAI’s ChatGPT, Alibaba’s Qwen and DeepSeek.
The integration is powered by Rokid’s device-to-cloud architecture, which enables users to switch between AI models on the fly. In practice, this means a traveler can receive a real-time translation in Japanese using one AI model, then quickly switch to ChatGPT to answer a technical query—without noticeable delay. The system also supports multi-modal inputs like voice and gestures, making interactions more intuitive for everyday use.
This is more than a routine software update. By combining AI models from both U.S. and Chinese developers, Rokid is making its smart glasses relevant to global users, with features that adapt to local languages and preferences while maintaining high performance.
These technological advancements have directly fueled Rokid’s international growth. Between November 2024 and October 2025, Shangpu Group data shows Rokid Glasses ranked No.1 in global sales for AI glasses with display functionality. Crowdfunding milestones further reflect this momentum: the product became the fastest smart glasses to raise over 100 million Japanese Yen on Japan’s MAKUAKE platform and broke Kickstarter records for smart eyewear.
Taken together, Rokid’s update highlights a shift in the smart glasses space: success increasingly comes from openness, flexibility and localized AI experiences rather than closed, single-platform ecosystems. By giving users choice, integrating global AI capabilities and bridging cultural and linguistic gaps, Rokid is positioning itself as a serious contender in the international AR and AI wearable market.
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Where smarter storage meets smarter logistics.
Updated
January 8, 2026 6:32 PM
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Kioxia's flagship building at Yokohama Technology Campus. PHOTO: KIOXIA
E-commerce keeps growing and with it, the number of products moving through warehouses every day. Items vary more than ever — different shapes, seasonal packaging, limited editions and constantly updated designs. At the same time, many logistics centers are dealing with labour shortages and rising pressure to automate.
But today’s image-recognition AI isn’t built for this level of change. Most systems rely on deep-learning models that need to be adjusted or retrained whenever new products appear. Every update — whether it’s a new item or a packaging change — adds extra time, energy use and operational cost. And for warehouses handling huge product catalogs, these retraining cycles can slow everything down.
KIOXIA, a company known for its memory and storage technologies, is working on a different approach. In a new collaboration with Tsubakimoto Chain and EAGLYS, the team has developed an AI-based image recognition system that is designed to adapt more easily as product lines grow and shift. The idea is to help logistics sites automatically identify items moving through their workflows without constantly reworking the core AI model.
At the center of the system is KIOXIA’s AiSAQ software paired with its Memory-Centric AI technology. Instead of retraining the model each time new products appear, the system stores new product data — images, labels and feature information — directly in high-capacity storage. This allows warehouses to add new items quickly without altering the original AI model.
Because storing more data can lead to longer search times, the system also indexes the stored product information and transfers the index into SSD storage. This makes it easier for the AI to retrieve relevant features fast, using a Retrieval-Augmented Generation–style method adapted for image recognition.
The collaboration will be showcased at the 2025 International Robot Exhibition in Tokyo. Visitors will see the system classify items in real time as they move along a conveyor, drawing on stored product features to identify them instantly. The demonstration aims to illustrate how logistics sites can handle continuously changing inventories with greater accuracy and reduced friction.
Overall, as logistics networks become increasingly busy and product lines evolve faster than ever, this memory-driven approach provides a practical way to keep automation adaptable and less fragile.