Generative models are advancing in the field of Artificial Intelligence (AI). The concept of intelligent interaction with the physical environment requires planning at low and high levels. A research team from Google Deepmind, MIT, and UC Berkeley has proposed Video Language Planning (VLP) to combine text-to-video and vision-language models. VLP aims to facilitate visual planning…

Researchers have developed a few-shot-based tuning framework called LAMP for text-to-video (T2V) generation. Existing methods for T2V either require extensive data or result in aligning with template videos. LAMP addresses this challenge by using a few-shot approach, allowing a text-to-image diffusion model to learn motion patterns. It significantly improves video quality and generation freedom. LAMP…

Woodpecker is a new approach that aims to fix hallucinations in Multimodal Large Language Models (MLLM), such as GPT-4V. By connecting the MLLM to the internet, Woodpecker allows the model to validate its generated descriptions using relevant internet data, leading to self-correction. It builds a visual knowledge base from the image and uses it to…

This article discusses the evolution of Data/ML platforms and their support for complex MLOps practices. It explains how data infrastructures have evolved from simple systems like online services and OLTP/OLAP databases to more sophisticated setups like data lakes and real-time data/ML infrastructures. The challenges and solutions at each stage are described, as well as the…

Entropy regularization is a technique used in reinforcement learning (RL) to encourage exploration. By adding an entropy bonus to the reward function, RL algorithms strive to maximize the entropy or randomness of the actions taken. This helps to explore new possibilities and avoid premature convergence to suboptimal actions. Entropy regularization offers benefits such as improved…

CLIN (Continually Learning Language Agent) is an innovative architecture that allows language agents to adapt and improve their performance over time. It introduces a dynamic textual memory system that focuses on causal abstractions and enables the agent to learn and refine its performance. CLIN exhibits rapid adaptation and efficient generalization across diverse tasks and environments,…

Researchers from Google Research and the University of Toronto have developed a zero-shot agent for autonomous learning and task execution in live computer environments. The agent, built on top of PaLM2, a large language model, uses a single set of instruction prompts for all activities and demonstrates high task completion rates on the MINIWOB++ benchmark.…

A new technique called Meta-learning for Compositionality improves the capability of tools like ChatGPT to make compositional generalizations. It surpasses current methods and even matches or exceeds human performance in some cases.

This post describes the implementation of text-to-image search and image-to-image search using a pre-trained model called uform, which is inspired by Contrastive Language Image Pre-Training (CLIP). The post provides code snippets for implementing these search functions and explains how cosine similarity is used to calculate similarity between text and images. The results of the searches…