Skip to content Skip to sidebar Skip to footer

Tech News

Google DeepMind Researchers and Others Investigate Scaling Deep Reinforcement Learning by Classifying Training Value Functions

Deep reinforcement learning (RL) heavily relies on value functions, which are typically trained through mean squared error regression to ensure alignment with bootstrapped target values. However, while cross-entropy classification loss effectively scales up supervised learning, regression-based value functions pose scalability challenges in deep RL. In classical deep learning, large neural networks show proficiency at handling classification…

Read More

The machine learning study conducted by Tel Aviv University unveils a crucial correlation between Mamba and Self-Attention Layers.

Recent research highlights the value of Selective State Space Layers, also known as Mamba models, across language and image processing, medical imaging, and data analysis domains. These models are noted for their linear complexity during training and quick inference, which notably increases throughput and facilitates the efficient handling of long-range dependencies. However, challenges remain in…

Read More

Introducing Apollo: An Open-Source, Lightweight, Multilingual Medical Language Model, Aimed at Making Medical AI Accessible to 6 Billion People

Researchers from Shenzhen Research Institute of Big Data and The Chinese University of Hong Kong, Shenzhen, have introduced Apollo, a suite of multilingual medical language models, set to transform the accessibility of medical AI across linguistic boundaries. This is a crucial development in a global healthcare landscape where the availability of medical information in local…

Read More

The AI exploration carried out by Stanford delves into the process of tracing back and identifying the root of the query.

Researchers have explored the limitations of online content portals that allow users to ask questions for better comprehension, such as during lectures. Current Information Retrieval (IR) systems are noted for their ability to answer user questions, but they often fail in assisting content providers, like educators, in identifying the specific part of their content that…

Read More

Transforming Neural Network Design: The Rise and Influence of DNA Models in Searching Neural Architecture

Neural Architecture Search (NAS) is a process that utilizes machine learning to automate the design of neural networks. This development has marked a significant shift from traditional manual design processes and is considered pivotal in paving the way for future advancements in autonomous machine learning. Despite these benefits, adopting NAS in the past has been…

Read More

Transforming Robotic Surgery with Neural Networks: Defeating Catastrophic Forgetfulness by Maintaining Privacy during Continuous Learning in Semantic Division

Deep Neural Networks (DNNs) have demonstrated substantial prowess in improving surgical precision by accurately identifying robotic instruments and tissues through semantic segmentation. However, DNNs grapple with catastrophic forgetting, signifying a rapid performance decline on previously learned tasks when new ones are introduced. This poses significant problems, especially in cases where old data is not accessible…

Read More

Optimizing Trajectory through Exploration: Leveraging Success and Failure for Improved Autonomous Agent Learning

Artificial intelligence possesses large language models (LLMs) like GPT-4 that enable autonomous agents to carry out complex tasks within various environments with unprecedented accuracy. However, these agents still struggle to learn from failures, which is where the Exploration-based Trajectory Optimization (ETO) method comes in. This training introduced by the Allen Institute for AI; Peking University's…

Read More

Introducing SafeDecoding: A Unique Safety-Conscious Decoding AI Method for Protection Against Jailbreak Attacks

Despite remarkable advances in large language models (LLMs) like ChatGPT, Llama2, Vicuna, and Gemini, these platforms often struggle with safety issues. These problems often manifest as the generation of harmful, incorrect, or biased content by these models. The focus of this paper is on a new safety-conscious decoding method, SafeDecoding, that seeks to shield LLMs…

Read More

Huawei’s AI research unveils DenseSSM, an innovative machine learning methodology designed to optimize the transfer of concealed data amongst various levels in State Space Models (SSMs).

The field of large language models (LLMs) has witnessed significant advances thanks to the introduction of State Space Models (SSMs). Offering a lower computational footprint, SSMs are seen as a welcome alternative. The recent development of DenseSSM represents a significant milestone in this regard. Designed by a team of researchers at Huawei's Noah's Ark Lab,…

Read More

This Chinese AI report presents ShortGPT: A Fresh AI Method for Trimming Extensive Language Models (LLMs) rooted in Layer Redundancy.

The rapid development in Large Language Models (LLMs) has seen billion- or trillion-parameter models achieve impressive performance across multiple fields. However, their sheer scale poses real issues for deployment due to severe hardware requirements. The focus of current research has been on scaling models to improve performance, following established scaling laws. This, however, emphasizes the…

Read More

Improving the Security of Large Language Models (LLM) to Protect Against Threats from Fine-Tuning: A Strategy Using Enhanced Backdoor Alignment

Large Language Models (LLMs) such as GPT-4 and Llama-2, while highly capable, require fine-tuning with specific data tailored to various business requirements. This process can expose the models to safety threats, most notably the Fine-tuning based Jailbreak Attack (FJAttack). The introduction of even a small number of harmful examples during the fine-tuning phase can drastically…

Read More