Artificial Intelligence
A research team from multiple universities has introduced a unique approach to Indirect Reasoning (IR) for enhancing the reasoning capability of Large Language Models (LLMs). The method leverages contrapositives and contradictions, resulting in significant improvements in overall reasoning skills, especially when combined with conventional direct reasoning tactics. This advancement signifies a major step in developing…
Generalist AI systems have made significant progress in computer vision and natural language processing, benefitting various applications. However, the lack of physical and spatial reasoning in these systems limits their full potential. Google DeepMind’s BootsTAP method addresses this by accurately representing motions in videos, utilizing real-world data, and a teacher-student model to enhance performance.
Guardrails is an open-source Python package designed to validate and correct outputs of large language models (LLMs). It introduces “rail spec,” allowing users to define expected structure and types, including quality criteria for bias and bugs. Its notable features include compatibility with various LLMs, Pydantic-style validation, and real-time streaming support. Guardrails provides a valuable solution…
Graph-based machine learning is undergoing a transformation driven by Graph Neural Networks (GNNs). Traditional GNNs face challenges with long-range dependencies in graphs. Graph Mamba Networks (GMNs) by Cornell University researchers integrate State Space Models to offer a solution, excelling in capturing long-range dependencies and computational efficiency. GMNs open new avenues for graph learning. [50 words]
LAION, in collaboration with the ELLIS Institute Tübingen, Collabora, and the Tübingen AI Center, is developing BUD-E, an innovative voice assistant aiming to revolutionize human-AI interaction. Their model prioritizes natural and empathetic responses with a low latency of 300-500 ms, and invites global contributions for further advancements. BUD-E’s features include real-time interaction, context memory, multi-modal…
EPFL’s groundbreaking study at the intersection of machine learning and neural networks sheds light on the dynamics of dot-product attention layers. They reveal a phase transition from positional to semantic learning, impacting the design and implementation of attention-based models. The research’s theoretical insights and practical contributions promise to enhance the capabilities of machine learning models…
Gemma is designed for ethical AI development using the research and technology utilized for creating Gemini models.
A team of researchers has investigated the emergence of reasoning ability in Large Language Models (LLMs) through pre-training and next-token prediction. They suggest that LLMs acquire reasoning abilities through intensive pre-training and may use reasoning paths to infer new information. The study demonstrates the effectiveness of using unlabeled reasoning paths, providing a reasonable explanation for…
The emergence of Multimodality Large Language Models (MLLMs) like GPT-4 and Gemini has spurred interest in combining language understanding with vision. While models like BLIP and LLaMA-Adapter show promise, they need more training data. Researchers have developed SPHINX-X, which significantly advances MLLMs, demonstrating superior performance and generalization while offering a platform for multi-modal instruction tuning.
Programming by example is a field in AI focused on automating processes by generating programs based on input-output examples. It faces challenges in abstraction and reasoning, addressed by neural and neuro-symbolic methods. Researchers at the University of Amsterdam introduced CodeIt, which uses program sampling and hindsight relabeling to improve AI’s ability to solve complex tasks.…
Google’s research team has developed the Gemini 1.5 Pro model, a highly efficient AI that excels in integrating complex information from textual, visual, and auditory sources. The model’s innovative multimodal mixture-of-experts architecture enables it to process extensive data sets with near-perfect recall and understanding across modalities, revolutionizing AI’s potential.
The text discusses the significance of natural language generation in AI, focusing on recent advancements in large language models like GPT-4 and the challenges in evaluating the reliability of generated text. It presents a new method, Non-exchangeable Conformal Language Generation through Nearest Neighbor, which aims to provide statistically-backed prediction sets during model inference. The method…
AWS AI Labs has unveiled CODE SAGE, a groundbreaking bidirectional encoder representation model for programming code. It uses a two-stage training scheme and a vast dataset to enhance comprehension and manipulation of code. This model outperforms existing ones in code-related tasks and opens new possibilities for deep learning in understanding and utilizing programming languages.
Meta researchers have developed V-JEPA, a non-generative AI model aimed at enhancing the reasoning and planning abilities of machine intelligence. Utilizing self-supervised learning and a frozen evaluation approach, V-JEPA efficiently learns from unlabeled data and excels in various video analysis tasks. It outperforms previous methods in fine-grained action recognition and other tasks.
Google DeepMind’s research has led to a significant advancement in length generalization for transformers. Their approach, featuring the FIRE position encoding and a reversed data format, enables transformers to effectively process much longer sequences with notable accuracy. This breakthrough holds promise for expanding the practical applications and capabilities of language models in artificial intelligence.
Large language models (LLMs) aligning with human expectations is crucial for societal benefits. Reinforcement learning from human feedback (RLHF) and direct alignment from preferences (DAP) are approaches discussed. A new study introduces Online AI Feedback (OAIF) for DAP, combining online flexibility and efficiency. Empirical comparisons demonstrate OAIF’s effectiveness, especially in aligning LLMs online.
This research from UC Berkeley analyzes the evolving role of large language models (LLMs) in the digital ecosystem, highlighting the complexities of in-context reward hacking (ICRH). It discusses the limitations of static benchmarks in understanding LLM behavior and proposes dynamic evaluation recommendations to anticipate and mitigate risks. The study aims to enhance the development of…
Infographics and user interfaces share design concepts and visual languages. To address the complexity of each, Google Research introduced ScreenAI, a Vision-Language Model (VLM) capable of comprehending UIs and infographics. ScreenAI achieved remarkable performance on various tasks and released three new datasets to advance the field. Learn more in the research paper.
Large Language Models (LLMs) such as GPT, PaLM, and LLaMa have enhanced AI and NLP by enabling machines to comprehend and produce human-like content. Finetuning is crucial to adapt these generalist models to specialized activities. Approaches include Parameter Efficient Fine Tuning (PEFT), Supervised Finetuning with hyperparameter tweaking, transfer learning, and few-shot learning, and Reinforcement Learning…
This survey explores the burgeoning field of prompt engineering, which leverages task-specific instructions to enhance the adaptability and performance of language and vision models. Researchers present a systematic overview of over 29 techniques, categorizing advancements by application area and emphasizing the transformative impact of prompt engineering on model capabilities. Despite notable successes, challenges such as…