Artificial Intelligence
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…
Studying scaling laws in large language models is crucial for optimizing their performance in tasks like translation. Challenges include determining the impact of pretraining data size on downstream tasks and developing strategies to enhance model performance. New scaling laws by researchers predict translation quality based on pretraining data size, offering insights for effective model training…
Reinforcement learning encompasses model-based (MB) and model-free (MF) algorithms. The Diffusion World Model (DWM) is a novel approach addressing inaccuracies in world modeling. DWM predicts long-horizon outcomes and enhances RL performance. By combining MB and MF strengths, DWM achieves state-of-the-art results, bridging the gap between the two approaches. This new framework presents promising advancements in…
CodeCompose, utilized by Meta developers, enhanced its AI-powered code authoring tool to provide multiline suggestions. The transition addressed challenges such as workflow disruption and latency concerns. Model-hosting optimizations improved multiline suggestion latency by 2.5 times, with significant productivity gains. Despite minor opt-outs, multiline suggestions have proven effective, aiding code completion and discovery.
Researchers have introduced the Listwise Preference Optimization (LiPO) framework, reshaping language model alignment as a listwise ranking challenge. LiPO-λ emerges as a powerful tool leveraging listwise data to enhance alignment, bridging LM preference optimization and Learning-to-Rank, setting new benchmarks, and driving future research. This approach signals a new era of language model development. [45 words]
Adobe introduces AI Assistant in Adobe Acrobat, a generative AI technology integrated into document workflows. This powerful tool offers productivity benefits for a wide range of users, from project managers to students. Adobe emphasizes responsible AI development and outlines a vision for future AI-powered document experiences, including intelligent creation and collaboration support.
Gary Marcus, a prominent AI researcher and critic of deep learning, discusses AI’s current state during a walk in Vancouver. He’s unimpressed with new AI models such as Google DeepMind’s Gemini and OpenAI’s Sora, criticizing their lack of understanding and the potential for exploitation. Marcus advocates for clearer rules and ethical practices in AI.
Researchers from Stanford University and Bauplan have developed the NEGOTIATION ARENA, a framework to evaluate Large Language Models’ (LLMs) negotiation capabilities. The study demonstrates LLMs’ evolving sophistication, adaptability, and strategic successes, while also highlighting their irrational missteps. This research offers insights into creating more reliable and human-like AI negotiators, paving the way for future applications…
Large language models (LLMs) offer powerful language processing but require significant resources. Binarization, reducing model weights to one bit, reduces computational demand. Existing quantization techniques face challenges at low bit widths. Researchers introduced BiLLM, a 1-bit post-training quantization scheme for LLMs, achieving ultra-low bit quantization without significant loss of precision. For more information, see the…
Mathematical reasoning is essential for solving complex real-world problems. However, developing large language models (LLMs) specialized in this area is challenging due to limited diverse datasets. Existing approaches rely on closed-source datasets, but the research team from NVIDIA has introduced OpenMathInstruct-1, a novel open-licensed dataset comprising 1.8 million problem-solution pairs. The dataset has shown significant…
The intersection of artificial intelligence and chess has been a testing ground for computational strategy and intelligence. Google DeepMind’s groundbreaking study trained a transformer model with 270 million parameters on 10 million chess games using large-scale data and advanced neural architectures. The model achieves grandmaster-level play without traditional search algorithms and demonstrates the critical role…