MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels https://lnkd.in/d-4S9g6b "Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and query distribution, provides rich information for various kinds of downstream tasks and encourages research in various areas, such as generic end-to-end neural indexer models, generic embedding models, and next generation information access system with large language models. MS MARCO Web Search offers a retrieval benchmark with three web retrieval challenge tasks that demand innovations in both machine learning and information retrieval system research domains. As the first dataset that meets large, real and rich data requirements, MS MARCO Web Search paves the way for future advancements in AI and system research. MS MARCO Web Search dataset is available at: https://lnkd.in/dJemmzTy."
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𝟳. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀: Implement memory-augmented neural networks or other architectures enabling the model to store and retrieve information from past experiences. This memory capability allows the model to learn from historical data and adapt to changing patterns. 𝟴. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺: Establish a real-time feedback loop collecting information about the AI system's performance in the live environment. This feedback aids in adjusting the model's parameters, enhancing its decision-making abilities over time. 𝟵. 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Develop mechanisms for dynamic feature engineering capable of adapting to changes in the distribution of input data. This involves continuous updates and re-evaluation of feature relevance for the current context. 𝟭𝟬. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗨𝗽𝗱𝗮𝘁𝗶𝗻𝗴: Implement systems for the ongoing monitoring of model performance and initiate updates or retraining based on predefined criteria. This ensures the AI system remains effective and aligned with real-world conditions. 𝟭𝟭. 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Employ transfer learning techniques, enabling the AI model to apply knowledge gained from one task to enhance performance in a different yet related task. This accelerates learning in new domains. 𝟭𝟮. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽: Incorporate human feedback into the learning process, allowing users or domain experts to provide input, corrections, or annotations. This contributes to the ongoing improvement of the model. Implementing self-learning capabilities in AI necessitates thoughtful consideration of the application domain, ethical considerations, and potential risks associated with continuous adaptation. Regular monitoring, validation, and quality assurance are crucial to ensure that the self-learning AI system delivers cutting-edge results in real time while maintaining accuracy and reliability. #artificialintelligence #bigdata #algorithms #intelligence #innovation #successmindset #Career Image/Video Credits – Open Source
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Computer vision encompasses techniques for capturing, processing, scrutinizing, and comprehending digital images. It involves extracting intricate data from the real world, often in high dimensions, to generate numerical or symbolic insights, such as informed decisions. This domain resides within the realm of computer science, more specifically, artificial intelligence (AI), where computers are trained to glean information from digital images or multi-dimensional datasets, subsequently leveraging this knowledge to make informed decisions or offer recommendations. For instance, AI-driven algorithms can analyze medical images to detect and diagnose diseases with greater accuracy and speed than traditional methods. To know more about Computer vision Click 👉 https://lnkd.in/gqgGNWkc #computerscience #aidriven #algorithms #techniques #technology
Computer Vision: What and How? - Daaslabs Blog
daaslabs.ai
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📢 Exciting news for AI and computer vision enthusiasts! A new AI paper has been released, providing a comprehensive analysis of computer vision backbones. The study compares popular pretrained models and explores their strengths and weaknesses. Key findings include the superior performance of supervised convolutional networks over transformers, the sensitivity of ViTs to parameters and pretraining data, and the effectiveness of vision-language modeling using CLIP models. The research team emphasizes the importance of ongoing evaluation and improvement in this dynamic field. Check out the paper for more details! #AI #ComputerVision #MachineLearning #DeepLearning #DataScience #BigData #Research #CLIP
📢 Exciting news for AI and computer vision enthusiasts! A new AI paper has been released, providing a comprehensive analysis of computer vision backbones. The study compares popular pretrained models and explores their strengths and weaknesses. Key findings include the superior performance of supervised convolutional networks over transformers, the sensitivity of ViTs to parameters and pretr...
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#day21 #statisticsinAI Mathematics and statistics are vital in AI, forming the core of algorithms, model representation, and optimization. They enable effective data analysis, machine learning algorithms, and validation, ensuring AI practitioners can develop, understand, and enhance intelligent systems with a solid foundation in quantitative reasoning. #aicommunity #30daysoflearning
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💫 Computer Vision technology plays a significant role in businesses when it comes to Artificial Intelligence (AI) applications. Computer Vision is an interdisciplinary field that enables machines to interpret and understand visual information from the world, just like humans do. Computer Vision technology is used in businesses within the realm of Artificial Intelligence: 1. Real-time Decision Making: Computer Vision enables machines to process visual data in real-time, allowing businesses to make immediate decisions based on the analysed information. 2. Unstructured Data Analysis: While traditional data analysis mostly focuses on structured data, Computer Vision allows businesses to extract valuable insights from unstructured visual data, such as images and videos. 3. Human-like Perception: With advancements in deep learning and neural networks, Computer Vision systems can achieve a level of perception that is closer to human capabilities. 4. Cross-domain Applications: Computer Vision technology has a wide range of applications across various industries, from healthcare and manufacturing to retail and entertainment. #AI #imagegeneration #newimage #creativity #newtechnology #dalleimagegenerater #graspcorn #gc.
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As part of the National Artificial Intelligence Research Resource (NAIRR) pilot, the U.S. research community can access SambaNova Systems generative AI technology via Argonne National Laboratory's ALCF Testbed systems. SambaNova Systems is pleased to collaborate on this effort to broaden access to AI resources and enhance U.S. competitiveness. We support making the benefits of AI accessible to all. #publicsector #llm #genai #generatieveai #llm #hpc
As part of the National Artificial Intelligence Research Resource (NAIRR) pilot, the U.S. research community can request access to Argonne National Laboratory's ALCF AI Testbed systems for projects focused on advancing safe, secure, and trustworthy AI. The initial call for proposals is now open through March 1, 2024. To submit a proposal, visit https://lnkd.in/gacwkDn6 The ALCF AI Testbed, which consists of novel AI accelerators from Cerebras Systems, Graphcore, Groq, and SambaNova Systems, provides advanced capabilities for a diverse set of #AI workloads, including training, fine-tuning, and inference for a wide gamut of models (e.g., large language models, foundation models, and computer vision models). "The NAIRR pilot will enhance the scope of research enabled by the ALCF AI Testbed and provides an entry point for researchers who may have been unaware of the availability of such resources to advance their scientific pursuits." – Venkatram Vishwanath, ALCF AI and Machine Learning Team Lead
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As part of the National Artificial Intelligence Research Resource (NAIRR) pilot, the U.S. research community can request access to Argonne National Laboratory's ALCF AI Testbed systems for projects focused on advancing safe, secure, and trustworthy AI. The initial call for proposals is now open through March 1, 2024. To submit a proposal, visit https://lnkd.in/gacwkDn6 The ALCF AI Testbed, which consists of novel AI accelerators from Cerebras Systems, Graphcore, Groq, and SambaNova Systems, provides advanced capabilities for a diverse set of #AI workloads, including training, fine-tuning, and inference for a wide gamut of models (e.g., large language models, foundation models, and computer vision models). "The NAIRR pilot will enhance the scope of research enabled by the ALCF AI Testbed and provides an entry point for researchers who may have been unaware of the availability of such resources to advance their scientific pursuits." – Venkatram Vishwanath, ALCF AI and Machine Learning Team Lead
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How would you explain GANs? GANs or Generative Adversarial Networks are type of AI architecture that have revolutionized the field of synthetic data generation since their inception. --> GANs consist of two distinct neural networks that engage in a dynamic rivalry : The Generator The Discriminator #Generator : is tasked with generating synthetic data, such as images, based on random noise or some other input. Its goal is to produce data that is indistinguishable from real data. #Discriminator: The discriminator is trained to distinguish between real data (e.g., real images) and fake data (e.g., images generated by the generator). Its objective is to correctly classify whether a given input is real or fake. #Processing During training, the generator aims to produce increasingly realistic data to fool the discriminator, while the discriminator aims to become more adept at distinguishing between real and fake data. This #adversarial process encourages both networks to improve their performance iteratively. -->Once trained, the generator can be used to produce new, synthetic data that resembles the training data. GANs have been widely used in various applications, including image generation, image-to-image translation, super-resolution, and data augmentation. #Note: while GANs have shown remarkable capabilities in generating realistic data, they can also be challenging to train and prone to mode collapse, where the generator only produces a limited variety of outputs. Research on addressing these challenges and improving the stability and performance of GANs is on! #deeplearning #neuralnetworks #artificialintelligence #machinelearning #generatieveai #GANs
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🌟 **Unlocking the Potential of Image Embeddings!** In the ever-evolving domain of #ComputerVision, image embeddings play a pivotal role in transforming the way we analyze and interpret visual data. By converting images into a numerical format, image embeddings enable machines to 'see' and 'understand' visual content, paving the way for innovations in #ImageRecognition, #ObjectDetection, and #VisualSearch. Imagine having a system that can accurately identify objects, people, and even emotions from images, thereby revolutionizing sectors like healthcare, e-commerce, and security. 💡 **Why are Image Embeddings Crucial?** - Enhances the efficiency and accuracy of image recognition systems. - Facilitates the development of intelligent visual search engines. - Contributes to advancements in #AugmentedReality and #VirtualReality. 📈 **Applications of Image Embeddings:** - Facial Recognition - Automatic Image Tagging - Visual Recommender Systems - Medical Image Analysis 🌟 **Popular Techniques in Image Embeddings:** - Convolutional Neural Networks (CNNs) - Transfer Learning - Autoencoders Embark on a journey with image embeddings and transform the way we interact with the visual world! Share your thoughts and experiences in the comments section below. #DataScience #MachineLearning #ArtificialIntelligence #DeepLearning #AI #ML #TechnologyInnovation #ComputerVision
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"How AI mathematicians might finally deliver human-level reasoning" via New Scientist (included in suggested "Long Reads" from our latest newsletter). Ask most AI researchers what needs to be done to produce an AI with general intelligence and the list will be long. Two of the more important skills that artificial intelligence still lacks are reasoning and planning, and these articles each take a look at techniques being employed to make advances in those areas. One of the most interesting parts of the research described here is the way these experimental models are being trained: Some algorithms are studying mathematical proofs so that they can achieve human-like levels of reasoning; others are examining the complex strategy game Diplomacy so that they can learn to think ahead. While the progress so far is incremental, these two stories make it clear that researchers are not shying away from trying to imbue artificial intelligence with capabilities that are still considered uniquely human. https://lnkd.in/etmpBH3J
How AI mathematicians might finally deliver human-level reasoning
newscientist.com
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