Google has officially released TensorFlow 2.21. The most significant update in this release is the graduation of LiteRT from its preview stage to a fully production-ready stack. Moving forward, LiteRT serves as the universal on-device inference framework, officially replacing TensorFlow Lite (TFLite
In industrial recommendation systems, the shift toward Generative Retrieval (GR) is replacing traditional embedding-based nearest neighbor search with Large Language Models (LLMs). These models represent items as Semantic IDs (SIDs)—discrete token sequences—and treat retrieval as an autoregressive d
Generative AI’s current trajectory relies heavily on Latent Diffusion Models (LDMs) to manage the computational cost of high-resolution synthesis. By compressing data into a lower-dimensional latent space, models can scale effectively. However, a fundamental trade-off persists: lower information den
To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.
The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks, the conversation is shifting from raw parameter counts to architectural efficiency. Liquid AI team is leading this charge with the release o
Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counterparts—and even cascaded pipelines—on language understanding tasks. We term this shortfall the text-speech understanding
The development of large language models (LLMs) has been defined by the pursuit of raw scale. While increasing parameter counts into the trillions initially drove performance gains, it also introduced significant infrastructure overhead and diminishing marginal utility. The release of the Qwen 3.5 M
In his new book, A World Appears, Michael Pollan argues that artificial intelligence can do many things—it just can’t be a person.
In the world of Generative AI, latency is the ultimate killer of immersion. Until recently, building a voice-enabled AI agent felt like assembling a Rube Goldberg machine: you’d pipe audio to a Speech-to-Text (STT) model, send the transcript to a Large Language Model (LLM), and finally shuttle text
In this post, we examine how Bedrock Robotics tackles this challenge. By joining the AWS Physical AI Fellowship, the startup partnered with the AWS Generative AI Innovation Center to apply vision-language models that analyze construction video footage, extract operational details, and generate label
