Converting a flat table to a good data model in Power Query

Challenges in AI Mathematical Reasoning Mathematical reasoning is a significant challenge for AI. While AI has made strides in natural language processing and pattern recognition, it still struggles with complex math problems that require human-like logic.…

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…

Understanding Agency in AI What is Agency? Agency is the ability of a system to achieve specific goals. This study highlights that how we assess agency depends on the perspective we use, known as the reference…

C4AI Command R+ 08-2024: Advancements in AI Models Overview Cohere For AI introduces the C4AI Command R+ 08-2024, a groundbreaking language model with 104 billion parameters. It features Retrieval Augmented Generation (RAG) and advanced tool-use functionalities,…

Retrieval Augmented Generation (RAG) has revolutionized open-domain question answering by using a retrieval module to find relevant context passages and a generative module to provide answers. However, vector search, one of the critical components, has limitations…

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…

Universities and Global Competition Universities are facing tough competition worldwide. Their rankings are increasingly linked to the United Nations’ Sustainable Development Goals (SDGs), which assess their social impact. These rankings affect funding, reputation, and student recruitment.…

The modern object detection heavily relies on deep learning models trained end-to-end with larger and more diverse datasets. Data augmentation offers a way to boost performance without adding new annotations. AWS AI’s research explores generative data…

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations AI is crucial in healthcare, particularly in vitro diagnostics (IVD) under the European IVDR. AI systems must provide explainable results to comply with regulatory…

GPT4All 3.0: Redefining Local AI Interaction In the rapidly evolving field of artificial intelligence, the accessibility and privacy of large language models (LLMs) have become pressing concerns. As major corporations seek to monopolize AI technology, there’s…

Researchers have developed Wonder3D, an innovative method for generating high-quality 3D models from single-view images. It addresses the limitations of existing approaches, such as time-consuming optimization and low-quality results. Wonder3D utilizes a cross-domain attention mechanism and…

PLMs have transformed Natural Language Processing, but their computational and memory needs pose challenges. The authors propose LoftQ, a quantization framework for pre-trained models. They combine low-rank approximation and quantization to approximate high-precision weights. Results show…

The StableRep model improves AI training by using synthetic imagery to generate diverse images from text prompts, addressing data collection challenges and offering more efficient and cost-effective training options.

Lincoln Laboratory is focused on reducing energy consumption in AI models through improved transparency and more efficient training methods.

Data Mapping as a Search Problem Data mapping is a critical process in data management, enabling the integration and transformation of data from various sources into a unified format. This approach provides a novel and effective…

SenseTime Unveils SenseNova 5.5: Setting a New Benchmark in AI Practical Solutions and Value SenseTime introduces the SenseNova 5.5, a cutting-edge AI model with real-time multimodal capabilities, enabling interactive experiences across various formats like audio, text,…

The Value of TabReD Benchmark for Tabular Machine Learning In recent years, the complexities of real-world industrial applications have posed challenges for traditional academic benchmarks for tabular machine learning. This can lead to overly optimistic performance…

Large-scale training of generative models on video and image data is explored, utilizing text-conditional diffusion models. A transformer architecture operates on video and image latent codes to enable generation of high-fidelity video. Sora, the largest model,…