Artificial Intelligence
The proposed adaptive weight decay method automatically adjusts the weight decay hyper-parameter during training to improve adversarial robustness and counter robust overfitting, without needing extra data, by dynamically basing it on classification and regularization loss gradients.
Researchers from KAIST developed Quatro++, which improves LiDAR SLAM by tackling sparsity and degeneracy through ground segmentation. It achieves better loop closing, precise mappings, and outperforms learning-based methods. Quatro++ enhances robust registration for ground vehicles and shows high success on the KITTI dataset, making it highly effective and versatile for both LiDAR and INS systems.
Researchers introduced a Physics-informed deep learning model to predict intratumoral fluid pressure and liposome accumulation, enhancing cancer treatment strategies. The model aims for accurate drug distribution insights, addressing inconsistencies in existing nanotherapeutic approaches and improving personalized therapy design. This marks a significant advancement in understanding tumor dynamics.
This paper introduces a versatile multimodal training scheme named 4M, which uses a unified Transformer encoder-decoder to handle various input/output modalities such as text, images, and semantic data, aiming to achieve a broad functionality similar to large language models in computer vision.
Apple is sponsoring the in-person NeurIPS conference in New Orleans from December 10-16, fostering research exchange on neural information processing in various disciplines. The summary doesn’t include Apple’s specific workshop and event schedules.
AWS’s suite of low-code and no-code ML tools, such as Amazon SageMaker Canvas, enables rapid, cost-effective machine learning model development without requiring coding expertise. Deloitte uses these tools to expedite project delivery and take on more clients, increasing accessibility and standardization while reducing time and costs, resulting in roughly 30-40% productivity gains in ML development…
As an analyst, to make impactful product changes, follow best practices and insights shared in the detailed guide available on the “Towards Data Science” platform.
Large language models often produce unreliable responses due to their factually incorrect claims and hallucinations, similar to human error. The paper introduces FLEEK, an automated tool designed to verify and correct factual inaccuracies, providing a solution to the cumbersome and time-consuming manual fact-checking process.
This paper introduces a benchmark for continual large-scale training of CLIP models on time-varying data without distinct task separation, addressing the challenges of training with daily-generated Petabytes of data. Accepted at NeurIPS 2023 workshop on Distribution Shifts.
The text introduces an exploration of OpenAI’s GPT architecture, with further details available on the Towards Data Science platform.
Researchers used AI to select and generate images, serving as tools to study the brain’s visual processing. This aims to enhance our understanding of vision organization and reduce biases from limited researcher-chosen images.
Researchers have successfully integrated 2D layered material into a compact electronic chip using a monolithic 3D approach for AI computing, enhancing multi-functional integration and advancing AI processing capabilities.
The GovAI Summit 2023, on December 5-6 in Arlington, VA, will explore AI’s public sector impact, featuring keynotes by AI experts and industry leaders. Lane Dilg from OpenAI and others will discuss AI’s role in government, healthcare, and security, focusing on ethical use amidst the evolving regulatory landscape. Discounted hotel rates are available.
The Biden administration has forced a Saudi Aramco-affiliated VC to sell its stake in the AI chip startup Rain Neuromorphics on national security grounds, as reviewed by CFIUS. This move reflects heightened U.S. vigilance over foreign tech investments and the strategic valuation of AI technology.
Researchers introduced DRESS, an LVLM trained with two types of Natural Language Feedback (critique and refinement) to better align with human values and improve interaction capabilities in multi-turn contexts. The approach uses conditional reinforcement learning and has shown improvements in alignment with human preferences based on the 3H criteria of helpfulness, honesty, and harmlessness. They…
Sports Illustrated faced criticism when it was revealed that they published articles by AI under fictitious author personas, as exposed by Futurism. The SI Union condemned the practice, while SI’s publisher blamed a third-party company for the content. This controversy emerges amidst financial struggles and increasing AI integration in journalism.
Researchers at Osaka University mapped human facial expressions’ mechanics to enhance androids’ emotional recognition. Analyzing 44 facial actions using 125 markers, they studied muscle and skin interactions. The findings may improve robotics, facial recognition, and medical diagnostics by providing data to recreate nuanced expressions in androids, mitigating the ‘uncanny valley’ effect.
A new study led by Hugging Face indicates considerable energy and carbon footprint in AI tasks, with image generation as the most intensive, equivalent to driving 4.1 miles. Text generation is less intensive. Research suggests choosing specialized AI models for tasks to reduce emissions, as day-to-day use significantly surpasses the carbon cost of AI training.
Deep machine learning, especially with neural networks, faces a challenge balancing interpretability and efficiency. White box probabilistic models are interpretable but outperformed by less interpretable deep neural networks. Tensor networks (TNs) offer a promising solution, enhancing both interpretability with quantum theories and efficiency on quantum and classical computers. Researchers at Capital Normal University and the…
Colleagues utilized Dask for partitioning data efficiently in training XGBoost models, allowing parallel processing across cores without overloading RAM. Experimentation indicated optimal partition size depends on dataset size, CPU, and RAM, with recommendations for handling data in small servers. Tips include averaging execution times and preferring smaller partitions if uncertain.