Artificial Intelligence
A study compares vision models on non-standard metrics beyond ImageNet. Models like ConvNet and ViT, trained using supervised and CLIP methods, are examined. Different models show varied strengths, which a single statistic cannot fully measure. This emphasizes the need for new benchmarks and evaluation metrics for precise model selection in specific contexts.
Text-to-image synthesis technology has transformative potential, but faces challenges in balancing high-quality image generation with computational efficiency. Progressive Knowledge Distillation offers a solution. Researchers from Segmind and Hugging Face introduced Segmind Stable Diffusion and Segmind-Vega, compact models that significantly improve computational efficiency without sacrificing image quality. This innovative approach has broad implications for the application…
Bioplanner, a recent research introduced by researchers from multiple institutions, addresses the challenge of automating the generation of accurate protocols for scientific experiments. It focuses on enhancing long-term planning abilities of language models, specifically targeting biology protocols using the BIOPROT1 dataset, showing superior performance of GPT-4 over GPT-3.5 in various tasks. [50 words]
On January 13, 2024, Nishith Desai Associates introduced NaiDA, an AI Bot tailored for legal professionals. With advanced technology and vast resources, NaiDA aims to revolutionize legal practices by offering personalized services, comprehensive research assistance, and time efficiency. The firm emphasizes responsible AI adoption and plans for continuous technological advancements.
Researchers have developed MAGNET, a new non-autoregressive approach for audio generation that operates on multiple streams of audio tokens using a single transformer model. This method significantly speeds up the generation process, introduces a unique rescoring method, and demonstrates potential for real-time, high-quality audio generation. MAGNET shows promise for interactive audio applications.
Generative AI, fueled by deep learning, has revolutionized fields like education and healthcare. Time-series forecasting plays a crucial role in anticipating future events from historical data. Researchers at Delft University explored the use of diffusion models in time-series forecasting, presenting state-of-the-art outcomes and insights for scholars and researchers. For more information, please refer to the…
This text discusses the use of multiple model forms for capturing and forecasting components of complex time series. It explores the application of mixed models for time series analysis and forecasting, utilizing various model tools to capture trend, seasonality, and noise components. The methods are demonstrated using real-world road traffic incident data from the UK.
The text discusses using the HuggingFace Text Generation Inference (TGI) toolkit to run large language models in a free Google Colab instance. It details the challenges of system requirements and installation, along with examples of running TGI as a web service and using different clients for interaction. Overall, the article demonstrates the feasibility and benefits…
The study delves into the impact of reasoning step length on the Chain of Thought (CoT) performance in large language models (LLMs). It finds that increasing reasoning steps in prompts improves LLMs’ reasoning abilities, while shortening them diminishes these capabilities. The study also highlights the task-dependent nature of these findings and emphasizes the importance of…
Researchers from Stanford and Greenstone Biosciences have developed ADMET-AI, a machine-learning platform utilizing generative AI and high-throughput docking to rapidly and accurately forecast drug properties. The platform’s integration of Chemprop-RDKit and 200 molecular features enables it to excel in predicting ADMET properties, offering exceptional speed and adaptability for drug discovery.
This article discusses three techniques to prevent memory overflow in data-related Python projects. It covers using __slots__ to optimize memory usage, lazy initialization to delay attribute initialization until needed, and generators to efficiently handle large datasets. These approaches enhance memory efficiency, reduce memory footprint, and improve overall performance in Python classes.
The text discusses the growing influence of large language models (LLMs) on information extraction (IE) in natural language processing (NLP). It highlights research on generative IE approaches utilizing LLMs, providing insights into their capabilities, performance, and challenges. The study also proposes strategies for improving LLMs’ reasoning and suggests future areas of exploration.
Recent advancements in speech generation have led to remarkable progress, with the introduction of the PHEME TTS system by PolyAI. The system focuses on achieving lifelike speech synthesis for modern AI applications, emphasizing adaptability, efficiency, and high-quality conversational capabilities. Comparative results demonstrate PHEME’s superior performance in terms of efficiency and synthesis quality.
Researchers from Codec Avatars Lab, Meta, and Nanyang Technological University have developed URHand, a Universal Relightable Hand model. It achieves photorealistic representation and generalization across viewpoints, poses, illuminations, and identities by combining physically based rendering and neural relighting. The model outperforms baseline methods and showcases adaptability beyond studio data, offering quick personalization. Read about the…
Summary: Explore the deployment of a real machine learning (ML) application with AWS and FastAPI. Access the full article on Towards Data Science.
Google Deepmind has developed AutoRT, utilizing foundation models to enable the autonomous deployment of robots in diverse environments with minimal human supervision. It leverages vision-language and large language models to generate task instructions and ensure safety through a robot constitution framework. AutoRT facilitates large-scale robotic data collection and enhances robotic learning and autonomy in real-world…
Researchers introduced a more efficient approach to enhancing large language models’ multilingual capabilities. By integrating a small set of diverse multilingual examples into the instruction-tuning process, they achieved significant improvement in the models’ performance across multiple languages. This approach offers a resource-effective pathway to developing globally applicable multilingual models.
Genetic algorithms are highlighted as an efficient tool for feature selection in large datasets, showcasing how it can be beneficial in minimizing the objective function via population-based evolution and selection. A comparison with other methods is provided, indicating the potential and computational demands of genetic algorithms. For more in-depth details, the full article can be…
Efficient Feature Selection via CMA-ES (Covariance Matrix Adaptation Evolution Strategy) explores the challenge of feature selection in model building for large datasets. With a particular focus on using evolutionary algorithms, this article introduces SFS (Sequential Feature Search) as a baseline technique and delves into a more complex approach – CMA-ES (Covariance Matrix Adaptation Evolution Strategy).…
This week at the CES tech expo, AI took center stage as companies unveiled new products. Standout releases included LG and Samsung’s mobile smart home AI assistants and NVIDIA’s new chips for local AI processing. Additionally, OpenAI faced legal challenges, and AI’s impact on art, robotics, and societal risks was a significant theme.