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Artificial Intelligence

Introducing Torchchat: A Versatile Infrastructure for Speeding Up Llama 3, 3.1, along with Other Extensive Language Models on Laptop, Desktop, and Mobile Devices.

The rapid development of Large Language Models (LLMs) has transformed multiple areas including generative AI, Natural Language Understanding, and Natural Language Processing. However, hardware constraints have often limited the ability to run these models on devices such as laptops, desktops, or mobiles. In response to this, the PyTorch team has developed Torchchat, a versatile framework…

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Darts: A Brand-New Python Repository for Intuitive Prediction and Abnormality Identification in Time Series Data

Time series data, which involves sequential observations recorded over time, is essential in various aspects of life including business and environmental studies. There are numerous models and tools available for time series analysis, but their diverse APIs and complexities pose challenges to users. To address these difficulties, a company called Unit8 developed Darts, an open-source…

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Darts: An Innovative Python Library for User-Accessible Predictions and Irregularity Identification in Time Series

Time series data is prevalent in various sectors, including weather forecasting, business strategizing, and complex systems monitoring. Effective processing of this data can aid in areas like strategic business planning and anomaly detection. Despite the availability of numerous tools for time series analysis, their complexities often pose challenges to the user. Addressing this issue, a…

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The Gemma 2-2B model has been launched, featuring an advanced text generation capability with 2.6 billion parameters, enhanced security measures, and the ability to deploy on the device itself.

Google's AI research team, DeepMind, has unveiled Gemma 2 2B, its new, sophisticated language model. This version, supporting 2.6 billion parameters, is optimized for on-device use and is a top choice for applications demanding high performance and efficiency. It holds enhancements for handling massive text generation tasks with more precision and higher levels of efficiency…

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To develop a superior artificial intelligence assistant, commence by replicating the illogical actions exhibited by humans.

Researchers from MIT and the University of Washington have created a model that considers the computational constraints of an agent, which could be a human or a machine, resulting in a more accurate prediction of the agent's actions. Humans, despite having sophisticated decision-making abilities, are often irrational and tend to behave suboptimally due to computational constraints.…

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This compact microchip can protect user information and boost effective computing on a mobile phone.

Researchers from MIT and the MIT-IBM Watson AI Lab have developed a machine-learning accelerator that strengthens the security of health-monitoring apps and other AI-powered devices. These apps and devices, which can help manage chronic diseases or track fitness progress, run on complex machine-learning models. This requires substantial data transfer between a central memory server and…

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A dataset on artificial intelligence paves new ways for identifying tornadoes.

A newly released open-source dataset could revolutionize the prediction and detection of tornadoes by using machine learning. Called TorNet, the Massachusetts Institute of Technology's dataset is composed of radar returns from thousands of tornadoes in the last 10 years. Alongside the dataset, models trained on it, which demonstrate the capacity of machine learning to identify…

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Researchers from Carnegie Mellon University Investigate Professional Advice and Tactical Variations in Multi-Agent Mimic Learning.

Carnegie Mellon University researchers are exploring the complexities of multi-agent imitation learning (MAIL), a mediation strategy in which a group of agents (like drivers on a road network) are coordinated through action recommendations, despite the mediator lacking knowledge of their utility functions. The challenge of this approach lies in specifying the quality of those recommendations,…

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Researchers from Carnegie Mellon University Study Guidance from Experts and Strategic Departures in Multi-Agent Imitation Learning.

Researchers from Carnegie Mellon University are examining the challenge of a mediator coordinating a group of strategic agents without knowledge of their underlying utility functions, referred to as multi-agent imitation learning (MAIL). This is a complex issue as it involves providing personalised, strategic guidance to each agent without a comprehensive understanding of their circumstances or…

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Baidu AI introduces a comprehensive self-reasoning structure to enhance the dependability and trackability of Retrieval-Augmented Generation (RAG) systems.

Researchers from Baidu Inc., China, have unveiled a self-reasoning framework that greatly improves the reliability and traceability of Retrieval-Augmented Language Models (RALMs). RALMs augment language models with external knowledge, decreasing factual inaccuracies. However, they face reliability and traceability issues, as noisy retrieval may lead to incorrect responses, and a lack of citations makes verifying these…

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