Large language model
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.
Human Machine Interfaces (HMIs) facilitate user interaction with various devices and technologies. Innovations are enhancing their intuitiveness and efficiency. A Spanish research team has created a structured dataset from human-machine interactions using custom-built UIs, aiding in the development of adaptive interfaces. The open-source dataset and analysis tools support advancements in personalized UIs, while highlighting future…
Particle Swarm Optimization (PSO) is a nature-inspired algorithm used to find optimal solutions in complex, high-dimensional spaces, like supply chain problems. It utilizes ‘particles’ that represent candidate solutions, influenced by personal and global bests. PSO efficiently outperforms brute-force grid searches, requiring significantly fewer computations, and can handle problems impractical for grid searches. The article demonstrates…
Google’s Duet AI enhances G-Suite productivity by simplifying complex tasks in Sheets, personalizing Meet backgrounds, generating images in Slides, improving writing in Docs, and drafting emails in Gmail. These AI-powered features streamline analysis, meetings, visualization, writing, and email management across various applications.
Part 2 of an article on Wave Data Feature Engineering focuses on spectral features. Techniques like FFT help convert time-domain signals into frequency-domain, providing insights on dominant frequencies and power distribution through features such as spectral entropy, kurtosis, PSD, and Harmonic Ratios. The next part will discuss Wavelet Transform, Demodulation, RQA, and signal generation for…
Researchers critically evaluated foundational models scGPT and Geneformer for single-cell biology, assessing zero-shot performance on tasks like cell clustering and batch effect correction. Despite efforts, both models demonstrated suboptimal performance, often underperforming compared to baseline models. The study suggests future research focus on the relationship between pretraining and downstream task performance.
The study explores the environmental impact of deep learning in pathology, advocating for the use of simpler models and model pruning to reduce CO2 emissions. Strategies include minimizing data inputs and selecting specific tissue regions. Findings suggest pruned models maintain accuracy while offering sustainability, promoting a balance between technological growth and ecological care in healthcare…
Caching stores function call results to optimize repeated computations, saving time and resources. Strategies include LRU, LFU, FIFO, LIFO, MRU, and RR. Considerations are memory footprint, access, insertion, and deletion times. Python’s functools.lru_cache and other libraries facilitate caching implementation, offering features like maximum cache size, hit/miss stats, and expiration times.