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CompeteAI: An AI structure that comprehends the competitive behavior of extensive language model-based constituents.

Competition is vital in shaping all aspects of human society, including economics, social structures, and technology. Traditionally, studying competition has been reliant on empirical research, which is limited due to issues with data accessibility and a lack of micro-level insights. An alternative approach, agent-based modeling (ABM), advanced from rule-based to machine learning-based agents to overcome…

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Could the Next Medical Innovation be Concealed in Simple Text? Introducing NATURAL: A Procedure for Inferring Cause and Effect from Non-Formatted Text Data in Hours, Instead of Years.

Causal effect estimation is a vital field of study employed in critical sectors like healthcare, economics, and social sciences. It concerns the evaluation of how modifications to one variable cause changes in another. Traditional approaches for this assessment, such as randomized controlled trials (RCTs) and observational studies, often involve structured data collection and experiments, making…

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SGLang: An Organized Production Language for Enhancing the Performance of Intricate Language Model Programs

Recent advancements in large language models (LLMs) have expanded their utility by enabling them to complete a broader range of tasks. However, challenges such as the complexity and non-deterministic nature of these models, coupled with their propensity to waste computational resources due to redundant calculations, limit their effectiveness. In an attempt to tackle these issues, researchers…

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LoRA-Pro: An Innovative Machine Learning Method for Eliminating the Performance Discrepancy Between Low-Rank Adaptation and Comprehensive Fine-Tuning

The methods of parameter-efficient fine-tuning (PEFT) are essential in machine learning as they allow large models to adapt to new tasks without requiring extensive computational resources. PEFT methods achieve this by only fine-tuning a small subset of parameters while leaving the majority of the model unchanged, aiming to make the adaptation process more efficient and…

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This article from Google DeepMind introduces Conditioned Language Policies (CLP): A system of artificial intelligence algorithms designed to optimize language models across various objectives.

Reinforcement Learning (RL) finetuning is an integral part of programming language models (LMs) to behave in a particular manner. However, in the current digital landscape, RL finetuning has to cater to numerous aims due to diverse human preferences. Therefore, multi-objective finetuning (MOFT) has come to the forefront as a superior method to train an LM,…

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An Evaluation of Leading Libraries for Generative AI Embeddings

Generative AI has made significant strides in recent times, increasing the need for text embeddings which convert textual data into dense vector representations, facilitating the processing of text, images, audio, etc., by models. Different embedding libraries have come to the fore in this space, each with unique pros and cons. This article provides a comparison…

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OpenDevin: A sophisticated AI platform designed to cultivate robust AI agents that emulate the interaction methods of a human developer.

A team of scholars from various universities and tech organizations have proposed OpenDevin, a revolutionary platform that aids in the development of AI agents capable of performing a broad range of tasks like a human software developer. Current AI algorithms often struggle with complex operations, lacking flexibility and generalization. Existing frameworks for AI development fall…

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In order to create an enhanced AI assistant, begin by mimicking the unpredictable actions of people.

Researchers at MIT and the University of Washington have developed a model that can predict an agent's potential computational limitations, and therefore their decision-making process, simply by observing past behaviour. Referred to as an "inference budget," this could enable AI systems to better predict human behaviour. The research paper demonstrates this modelling method within the…

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This small microchip can protect user information while promoting effective processing on a mobile device.

Researchers from MIT and the MIT-IBM Watson AI Lab have developed a novel machine-learning accelerator that can protect sensitive data like health records from two common types of cybersecurity threats while efficiently running large AI models. This advancement could make an noticable impact on challenging AI applications, such as augmented and virtual reality, autonomous driving…

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Julie Shah has been appointed as the chief of the Aeronautics and Astronautics Department.

Julie Shah, the H.N. Slater Professor in Aeronautics and Astronautics, has been appointed as the new head of the Department of Aeronautics and Astronautics (AeroAstro) at MIT, effective from May 1. Shah, an alumnus of MIT, is renowned for her exceptional interdisciplinary leadership skills and significant technical strides in the arena of robotics and Artificial…

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Researchers at IBM suggest a fresh approach to AI, which requires no training, to lessen illusions in Large Language Models.

Large language models (LLMs), used in applications such as machine translation, content creation, and summarization, present significant challenges due to their tendency to generate hallucinations - plausible sounding but factually inaccurate statements. This major issue affects the reliability of AI-produced copy, particularly in high-accuracy-required domains like medical and legal texts. Thus, reducing hallucinations in LLMs…

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