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Improving Language Model Reasoning using Expert Iteration: Narrowing the Divide via Reinforcement Learning

Language Learner Models (LLMs) are rapidly advancing, displaying impressive performance in math, science and coding tasks. This progress is in part due to advancements in Reinforcement Learning from Human Feedback (RLHF) and instruction fine-tuning, which align LLMs more closely with human behaviors and preferences. Moreover, innovative prompting strategies, like Chain-of-Thought and Tree-of-Thoughts, have augmented LLM…

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Responsibilities of a Talent Consultant: Unlocking Potential in the Work Environment

The talent advisor plays an integral role in an organization, managing not only recruitment but also aligning talent management strategies with the company's business objectives. Unlike traditional recruiters focused on filling immediate job vacancies, talent advisors operate with a broader view, encompassing the entire employee lifecycle including development, retention, and succession planning. Key responsibilities of a…

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Aidoc’s CEO Discusses the Contrast Between Precision and Generative AI in AI in 5

"AI in 5" is a video series airing insights from Elad Walach, CEO of Aidoc, who takes viewers through an insightful exploration of different facets of clinical Artificial Intelligence (AI) within a time frame of five minutes or less. The aim of this series is to present the complexities of AI in a quick, easy-to-understand…

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WMDP identifies and mitigates the harmful utilization of LLM through the process of unlearning.

Researchers, including experts from Scale AI, the Center for AI Safety, and leading academic institutions, have launched a benchmark to determine the potential threat large language models (LLMs) may hold in terms of the dangerous knowledge they contain. Using a new technique, these models can now "unlearn" hazardous data, preventing bad actors from using AI…

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Scientists utilize Artificial Intelligence to investigate the impact of genetics on heart structure.

A global team of researchers led by the University of Manchester used Artificial Intelligence (AI) to investigate the role genetics play in forming the structure of the heart's left ventricle. They utilized unsupervised deep learning to examine over 50,000 three-dimensional MRI images from the UK Biobank. The goal was to establish a better understanding of…

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Researchers from Mayo Clinic have created an AI system driven by hypothesis, specifically for cancer studies.

In a bid to advance the role of artificial intelligence (AI) in medical and healthcare research, researchers from Mayo Clinic have developed an innovative AI technology, "hypothesis-driven AI". This diverges from conventional data-driven AI models that primarily excel at identifying patterns within large volumes of data but often struggle to incorporate existing scientific knowledge or…

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Google AI Presents ‘Croissant’: A New Metadata Format Designed for Datasets Prepared for Machine Learning

When developing machine learning (ML) models with pre-existing datasets, professionals need to understand the data, interpret its structure, and decide which subsets to use as features. The significant range of data formats poses a barrier to ML advancement. These may include text, structured data, photos, audio, and video, to name a few examples. Even within…

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Unleashing Advanced Visual AI: The Revolutionary Abilities of Image-Based World Models and Combined-Embedding Predictive Structures

Computer vision researchers frequently concentrate on developing powerful encoder networks for self-supervised learning (SSL) methods, intending to generate image representations. However, the predictive part of the model, which potentially contains valuable information, is often overlooked post-pretraining. This research introduces a distinctive approach that repurposes the predictive model for various downstream vision tasks rather than discarding…

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Researchers from the University of North Carolina at Chapel Hill have presented a new guidance AI strategy called Contrastive Region Guidance (CRG). This method, which doesn’t require training, empowers open-source Vision-Language Models (VLMs) to react to visual cues.

Recent advancements in large vision-language models (VLMs) have demonstrated great potential in performing multimodal tasks. However, these models have shortcomings when it comes to fine-grained region grounding, inter-object spatial relations, and compositional reasoning. These limitations affect the model's capability to follow visual prompts like bounding boxes that spotlight vital regions. Challenged by these limitations, researchers at…

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Chatbot Field: A Public Framework for Assessing Language Models using Collective, Binary Human Choices

The development of large language models (LLMs) has significantly expanded the field of computational linguistics, moving beyond traditional natural language processing to include a wide variety of general tasks. These models have the potential to revolutionize numerous industries by automating and improving tasks that were once thought to be exclusive to humans. However, one significant…

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Improving Language Model Analysis using Expert Iteration: Bridging the Disparity via Reinforcement Learning

The progress in Language Learning Models (LLMs) has been remarkable, with innovative strategies like Chain-of-Thought and Tree-of-Thoughts augmenting their reasoning capabilities. These advancements are making complex behaviors more accessible through instruction prompting. Reinforcement Learning from Human Feedback (RLHF) is also aligning the capabilities of LLMs more closely with human predilections, further underscoring their visible progression. In…

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