​ One of the main paradigms in machine learning is learning representations from several modalities. Pre-training broad pictures on unlabeled multimodal data and then fine-tuning ask-specific labels is a common learning strategy today. The present multimodal pretraining techniques are mostly derived from earlier research in… Read More »This AI Paper Proposes FACTORCL: A New Multimodal Representation Learning Method to Go Beyond Multi-View Redundancy Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

​ KTRL+F task is a knowledge-augmented in-document search problem that requires real-time identification of semantic targets within a document, incorporating external knowledge through a single natural query. Existing models face challenges such as hallucinations, low latency, and difficulty leveraging superficial knowledge. To address this, researchers… Read More »KAIST AI Researchers Introduce KTRL+F: A Knowledge-Augmented in-Document Search Task that Necessitates Real-Time Identification of Semantic Targets within a Document Adnan Hassan Artificial Intelligence Category – MarkTechPost

​ Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories, databases, and APIs without the need to fine-tune it. When using generative AI for question answering, RAG enables LLMs to… Read More »Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation Anastasia Tzeveleka AWS Machine Learning Blog

​ KT Corporation is one of the largest telecommunications providers in South Korea, offering a wide range of services including fixed-line telephone, mobile communication, and internet, and AI services. KT’s AI Food Tag is an AI-based dietary management solution that identifies the type and nutritional… Read More »KT’s journey to reduce training time for a vision transformers model using Amazon SageMaker Youngjoon Choi AWS Machine Learning Blog

​ Tencent AI Lab researchers address challenges in the reliability of retrieval-augmented language models (RALMs), which may retrieve irrelevant information, leading to misguided responses. The proposed approach, CHAIN-OF-NOTING (CON), aims to enhance RALM. CON-equipped RALMs exhibit substantial performance improvements across open-domain QA benchmarks, achieving notable… Read More »Tencent AI Lab Introduces Chain-of-Noting (CoN) to Improve the Robustness and Reliability of Retrieval-Augmented Language Models Adnan Hassan Artificial Intelligence Category – MarkTechPost

​ Reasoning efficiently across extended sequences is a major difficulty in machine learning. Recently, convolutions have emerged as a critical primitive for sequence modeling, supporting state-of-the-art performance in language modeling, time-series analysis, computer vision, DNA modeling, and more. Despite these impressive quality findings and additional… Read More »Stanford University Researchers Introduce FlashFFTConv: A New Artificial Intelligence System for Optimizing FFT Convolutions for Long Sequences Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​ Home Table of Contents Tools and Methodologies for Studying Causal Effects Randomization A/B Test Multi-Armed Bandits Natural Experiments Regression Discontinuity Instrumental Variable Observational Data Backdoor Criterion Propensity Score Matching Summary References Citation Information Tools and Methodologies for Studying Causal Effects This lesson is the… Read More »Tools and Methodologies for Studying Causal Effects Aritra Roy Gosthipaty and Ritwik Raha PyImageSearch

​ Researchers from Google Research, Google DeepMind, and the University of Waterloo introduce SWIM-IR, a synthetic retrieval training dataset encompassing 33 languages, addressing the challenge of limited human-labeled training pairs in multilingual retrieval. Leveraging the SAP (summarize-then-ask prompting) method, SWIM-IR is constructed to enable synthetic… Read More »A New AI Research Releases SWIM-IR: A Large-Scale Synthetic Multilingual Retrieval Dataset with 28 Million Training Pairs over 33 Languages Sana Hassan Artificial Intelligence Category – MarkTechPost

​ With the advent of AI, its use is being felt in all spheres of our lives. AI is finding its application in all walks of life. But AI needs data for the training. AI’s effectiveness relies heavily on data availability for training purposes. Conventionally,… Read More »Researchers from the University of Toronto Unveil a Surprising Redundancy in Large Materials Datasets and the Power of Informative Data for Enhanced Machine Learning Performance Rachit Ranjan Artificial Intelligence Category – MarkTechPost

​ Amidst persistent industry rumors, Microsoft’s long-anticipated revelation came to light during the Ignite conference, marking a pivotal moment in the tech landscape. The tech giant officially unveiled its in-house designed chips, a testament to its commitment to innovation and self-sufficiency across hardware and software… Read More »Microsoft Unveils Azure Custom Chips: Revolutionizing Cloud Computing and AI Capabilities Niharika Singh Artificial Intelligence Category – MarkTechPost