AI Technology & Industry Review
In a new paper Language to Rewards for Robotic Skill Synthesis, a Google DeepMind research team proposes a new paradigm to leverage reward functions to interface language and low-level robot actions, which enables non-technical users to steer novel and intricate robot actions without large amount of data or expert knowledge to engineer low-level primitives.
In a new paper Textbooks Are All You Need, a Microsoft’s research team crafts ‘textbook quality’ data for training large language model for code, the resulting phi-1 model improves the state-of-the-art large language models (LLMs) with mere 1.3B-parameter.
In a new paper Fast Segment Anything, a research team from Chinese Academy of Sciences, University of Chinese Academy of Sciences, Objecteye Inc. and Wuhan AI Research presents FastSAM, a real-time solution for the segment anything task that achieves comparable performance to SAM while drastically reducing computational demands.
In a new paper Scaling Open-Vocabulary Object Detection, a DeepMind research team introduces OWLv2 model, an optimized architecture with improved training efficiency and applies and OWL-ST self-training recipe to the proposed OWLv2 to substantially improves detection performance, achieving state-of-the-art result on open-vocabulary detection task.
In a new paper Infinite Photorealistic Worlds using Procedural Generation, a Princeton University research team presents Infinigen, a procedural photorealistic 3D scenes generator that is capable to generate unlimited, diverse training data of the natural world, substantially expands the coverage of existing synthetic data.
In a new paper High-Fidelity Audio Compression with Improved RVQGAN, a Descript research team presents Improved RVQGAN, a high fidelity universal audio compression model that combines advances in high-fidelity audio generation and improved adversarial and reconstruction losses to achieve 90x compression of 44.1 KHz audio at only 8kbps bandwidth.
The BAAI 2023 Conference in Beijing successfully closed on June 10. With two busy days of agenda, the host Beijing Academic of Artificial Intelligence (BAAI) welcomed numerous renowned AI scholars, seasoned industry leaders, and enthusiastic AI researchers to share their insights on the latest AI hot topics.
In a new paper Prodigy: An Expeditiously Adaptive Parameter-Free Learner, a research team from Samsung AI Center and Meta AI presents two novel modifications, Prodigy and Resetting, to enhance the D-Adaptation method’s worst-case non-asymptotic convergence rate, achieving faster convergence rates and better optimization outputs.
In a new paper Image Captioners Are Scalable Vision Learners Too, a DeepMind research team presents CapPa, a image captioning based pretraining strategy that and can compete CLIP and exhibit favorable model and data scaling properties, verifying that a plain image captioning can be a competitive pretraining strategy for vision backbones.
In a new paper FinGPT: Open-Source Financial Large Language Models, a research team from Columbia University and New York University (Shanghai) presents FinGPT, an end-to-end open-source financial large language models (FinLLMs) that democratize financial data to encourage researchers and practitioners to developer user-specified FinLLMs.
In a new paper From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces, a research team from Google and DeepMind proposes PIX2ACT, a Transformer-based image-to-text model that is able to generate outputs corresponding to mouse and keyboard actions based solely on pixel-based screenshots from Graphical User Interfaces (GUIs).
In a new paper CodeTF: One-stop Transformer Library for State-of-the-art Code LLM, a Salesforce AI research team develop CodeTF, an open-source one-stop comprehensive Python library that provides a seamless interface for training and inferencing on code intelligence tasks, aiming to facilitate easy integration of state-of-the-art language models into real-world applications.
In a new paper Faster sorting algorithms discovered using deep reinforcement learning, a DeepMind research team introduces AlphaDev, a deep reinforcement learning agent which is capable to automatically discover correct and efficient sorting algorithms that achieves superior performance then previously known human benchmarks.
In a new paper Orca: Progressive Learning from Complex Explanation Traces of GPT-4, a Microsoft research team introduces Orca, a 13-billion parameter model that learns explanation traces; step-by-step thought processes; and complex instructions from GPT-4 to significantly boosts SOTA instruction-tuned models.
In a new paper Birth of a Transformer: A Memory Viewpoint, a Meta AI research team introduces a new synthetic setup to explore the structure and evolution of transformer language models, aiming to provide insights of the global vs in-context learning of LLMs.
In a new paper LLaVA-Med: Training a Large Language-and-Vision Assistant, a Microsoft research team proposes a Large Language and Vision Assistant for BioMedicine (LLaVA-Med), which can be trained in less than 15 hours and demonstrates strong multimodal conversational capability, aiding inquiries about biomedical image.
In a new paper Bigger, Better, Faster: Human-level Atari with human-level efficiency, a research team from Google DeepMind, Mila and Universite de Montreal presents a value-based RL agent, which they call faster, better, faster (BBF), that achieves super-human performance on the Atari 100K benchmark on single GPU.
In a new paper How Does Generative Retrieval Scale to Millions of Passages? a research team from Google Research and University of Waterloo performs the first empirical study of generative retrieval across various corpus scales, even scaling up to the entire MS MARCO passage ranking task that contains 8.8M passages, aiming to provide insights on scaling generative retrieval to millions of passages.
In the new paper DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining, a research team from Google and Stanford University introduces Domain Reweighting with Minimax Optimization (DoReMi), a domain weight optimization strategy that leverages distributionally robust optimization (DRO) to substantially speed up effective language model pretraining.