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The emergence of “opensource” AI models: issues of transparency and accountability under scrutiny

Industry, Machine learning, Open Source, UncategorizedJune 27, 2024173Views0Likes0Comments
In the rapidly advancing world of generative AI, the term "open source" has become widely used. Traditional open-source software refers to code freely available for anyone to view, modify, and distribute, fostering a sense of knowledge-sharing and collaborative innovation. However, in the sphere of AI, this definition can become blurred and problematic. Due to the…

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Path: A Machine Learning Technique for Educating Small-Scale (Sub-100M Parameter) Neural Data Retrieval Models Utilizing a Minimum of 10 Gold Relevance Labels

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJune 27, 2024134Views0Likes0Comments
The use of pretrained language models and their creative applications have contributed to significant improvements in the quality of information retrieval (IR). However, there are questions about the necessity and efficiency of training these models on large datasets, especially for languages with scant labeled IR data or niche domains. Researchers from the University of Waterloo,…

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UCLA’s latest machine learning study discovers unanticipated inconsistencies and roughness within the in-context decision boundaries of LLMs.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJune 27, 2024219Views0Likes0Comments
Researchers have been focusing on an effective method to leverage in-context learning in transformer-based models like GPT-3+. Despite their success in enhancing AI performance, the method's functionality remains partially understood. In light of this, a team of researchers from the University of California, Los Angeles (UCLA) examined the factors affecting in-context learning. They found that…

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New research on machine learning from UCLA reveals surprising inconsistencies and roughness in in-context decision boundaries of LLMs.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJune 27, 2024169Views0Likes0Comments
Advanced language models such as GPT-3+ have shown significant improvements in performance by predicting the succeeding word in a sequence using more extensive datasets and larger model capacity. A key characteristic of these transformer-based models, aptly named as "in-context learning," allows the model to learn tasks through a series of examples without explicit training. However,…

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An algorithm developed by MIT aids in predicting the occurrence of severe weather conditions.

Algorithms, Artificial Intelligence, Climate, Climate change, Computer modeling, Defense Advanced Research Projects Agency (DARPA), Disaster response, Earth and atmospheric sciences, Environment, Fluid dynamics, Hurricanes, Machine learning, Mechanical engineering, Natural disasters, Planetary science, Research, School of Engineering, Uncategorized, WeatherJune 27, 2024204Views0Likes0Comments
Global climate models predict future weather conditions, but these models are limited in their ability to provide detailed forecasts for specific locations. Policymakers often need to supplement these coarse-scale models with high-resolution ones to predict local extreme weather events. However, the accuracy of these predictions heavily depends on the initial coarse model’s accuracy. Themistoklis Sapsis,…

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An algorithm originating from MIT assists in predicting the occurrence rate of severe weather conditions.

Algorithms, Artificial Intelligence, Climate, Climate change, Computer modeling, Defense Advanced Research Projects Agency (DARPA), Disaster response, Earth and atmospheric sciences, Environment, Fluid dynamics, Hurricanes, Machine learning, Mechanical engineering, Natural disasters, Planetary science, Research, School of Engineering, Uncategorized, WeatherJune 27, 2024158Views0Likes0Comments
To better predict the risks of extreme weather events due to climate change, scientists at MIT have developed a method that refines the predictions from large, coarse climate models. The key to this approach is leveraging machine learning and dynamical systems theory to make the climate models' large-scale simulations more realistic. By correcting the climate…

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Unstructured Unveils a Seamless Serverless API: The Easiest, Quickest, and Most Cost-Efficient Method to Make Business Data Ready for AI

AI Shorts, Artificial Intelligence, Editors Pick, Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJune 26, 2024181Views0Likes0Comments
Unstructured, a major innovator in data transformation, has launched the Unstructured Serverless API, a breakthrough solution designed to streamline the processing and preparation of enterprise-level data for artificial intelligence (AI) applications. Not only does this offer a more straightforward approach, but it significantly accelerates the process and reduces costs. The Unstructured Serverless API is a…

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An algorithm originating from MIT aids in predicting the occurrence rate of severe weather conditions.

Algorithms, Artificial Intelligence, Climate, Climate change, Computer modeling, Defense Advanced Research Projects Agency (DARPA), Disaster response, Earth and atmospheric sciences, Environment, Fluid dynamics, Hurricanes, Machine learning, Mechanical engineering, Natural disasters, Planetary science, Research, School of Engineering, Uncategorized, WeatherJune 26, 2024194Views0Likes0Comments
Scientists from MIT have developed a technique that helps to fine-tune predictive models for extreme weather events by combining machine learning and dynamical systems theory. Currently, climate models are run decades and even centuries in advance to assess a community's risk to extreme weather but these generally operate at a rough resolution. As a result,…

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Scientists at Stanford University Suggest SleepFM: The Initial Multi-Mode Base Model for Sleep Examination.

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJune 26, 2024155Views0Likes0Comments
Sleep monitoring is a crucial part of maintaining overall health, yet accurately assessing sleep and diagnosing disorders is a complex task due to the need for multi-modal data interpretation typically obtained through polysomnography (PSG). Current methods often depend on extensive manual evaluation by trained technicians, making them time-consuming and susceptible to variability. To address these…

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An algorithm developed at MIT assists in predicting the occurrence rate of severe weather conditions.

Algorithms, Artificial Intelligence, Climate, Climate change, Computer modeling, Defense Advanced Research Projects Agency (DARPA), Disaster response, Earth and atmospheric sciences, Environment, Fluid dynamics, Hurricanes, Machine learning, Mechanical engineering, Natural disasters, Planetary science, Research, School of Engineering, Uncategorized, WeatherJune 26, 2024159Views0Likes0Comments
Researchers at the Massachusetts Institute of Technology (MIT) have developed a new method to improve the accuracy of large-scale climate models. These models, used by policymakers to understand the future risk of extreme weather like flooding, often lack precise data for smaller scales without considerable computational power. By combining machine learning with dynamical systems theory,…

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An algorithm developed from MIT assists in predicting the occurrence rate of severe weather conditions.

Algorithms, Artificial Intelligence, Climate, Climate change, Computer modeling, Defense Advanced Research Projects Agency (DARPA), Disaster response, Earth and atmospheric sciences, Environment, Fluid dynamics, Hurricanes, Machine learning, Mechanical engineering, Natural disasters, Planetary science, Research, School of Engineering, Uncategorized, WeatherJune 26, 2024161Views0Likes0Comments
A team of scientists from MIT's Department of Mechanical Engineering has developed a new method using machine learning to correct and enhance prediction accuracy in climate models. These advancements could provide significantly greater insights into the frequency of extreme weather events with more localized precision, improving the ability to plan and mitigate for future climatic…

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Innovating Adapter Methods: Qualcomm AI’s Sparse High Rank Adapters (SHiRA) for Quick and Efficient Implementation in Extensive Language Models

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Tech News, Technology, UncategorizedJune 25, 2024192Views0Likes0Comments
Large language models (LLMs) and latent variable models (LVMs) can present significant challenges during deployment, such as balancing low inference overhead and the rapid change of adapters. Traditional methods, such as Low Rank Adaptation (LoRA), often result in increased latency or loss of rapid switching capabilities. This can prove particularly problematic in resource-constrained settings like…

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