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AI Shorts

The team of researchers from Texas A&M University presents ComFormer, a new machine learning method for predicting properties of crystal materials.

Research in materials science is increasingly focusing on the rapid discovery and characterization of materials with specific attributes. A key aspect of this research is the comprehension of crystal structures, which are naturally complex due to their periodic and infinite nature. This complexity presents significant challenges when attempting to model and predict material properties, difficulties…

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Arc2Face Leads the Way in Realistic Face Image Generation Using ID Embeddings

The production of realistic human facial images has been a long-standing challenge for researchers in machine learning and computer vision. Earlier techniques like Eigenfaces utilised Principal Component Analysis (PCA) to learn statistical priors from data, yet they notably struggled to capture the complexities of real-world factors such as lighting, viewpoints, and expressions beyond frontal poses.…

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Sakana AI has introduced an innovative process known as Evolutionary Model Merge. It’s a novel method of machine learning that automates the development of basic models.

In the world of machine learning, large language models (LLMs) are a significant area of study. Recently, model merging or the combination of multiple LLMs into a single framework has fascinated the researcher's community because it doesn't require any additional training. This reduces the cost of creating new models considerably, sparking an interest in model…

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Common Corpus: A Vast Open-Source Database for Training LLMs

The debate over the necessity of copyrighted materials to train top Artificial Intelligence (AI) models continues to be a hot topic within the AI industry. This discussion was fueled further when OpenAI proclaimed to the UK Parliament in 2023 that it's 'impossible' to train these models without using copyrighted content, resulting in legal disputes and…

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UC Berkeley and Microsoft Research are redefining our understanding of visuals. Their approach of scaling at scale is proving to be more effective and sophisticated than larger models.

In the ever-evolving fields of computer vision and artificial intelligence, traditional methodologies favor larger models for advanced visual understanding. The assumption underlying this approach is that larger models can extract more powerful representations, prompting the construction of enormous vision models. However, a recent study challenges this wisdom, with a closer look at the practice of…

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LLM4Decompile: An Open-Source Broad Language Models Focused on Decompiling with a Strong Emphasis on Code Execution and Recompiling Capabilities

Decompilation is a pivotal process in software reverse engineering facilitating the analysis and interpretation of binary executables when the source code is not directly accessible. Valuable for security analysis, bug detection, and the recovery of legacy code, the process often needs assistance in generating a human-readable and semantically accurate source code, which is a substantial…

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MinusFace: Transforming Facial Recognition Privacy through Feature Deduction and Channel Mixing – An Innovative Research by Fudan University and Tencent

The increasing use of facial recognition technologies is a double-edged sword, wherein it provides unprecedented convenience, but also poses a significant risk to personal privacy as facial data could unintentionally reveal private details about an individual. As such, there is an urgent need for privacy-preserving measures in these face recognition systems. A pioneering approach to this…

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EasyJailbreak: A Comprehensive Machine Learning Platform to Improve LLM Security by Streamlining Jailbreak Attack Development and Evaluation in Response to New Threats.

Jailbreak attacks aim to identify and address security vulnerabilities in Language Models (LLMs) by bypassing their safety protocols. Despite significant advancements in LLMs, particularly in the area of natural language processing, they remain prone to such attacks. Given the increasing sophistication of new jailbreak techniques, the need for robust defense methodologies has grown. These methods,…

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Microsoft researchers have unveiled Garnet: An open-source cache-store system designed to speed up applications and services more effectively.

Microsoft researchers have introduced Garnet, a versatile and highly performant cache-store system designed to support the rapidly evolving needs of modern applications. Traditional cache-stores have struggled to keep pace with the increasing complexity and demands of interactive web applications, driving the creation of this new, open-source solution. As opposed to its predecessor, Garnet handles not…

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Amazon AI unveils DataLore: A new machine learning structure which elucidates data modifications from the original dataset to its enhanced format to promote trackability.

Data scientists and engineers often encounter difficulties when collaborating on machine learning (ML) tasks due to concerns about data reproducibility and traceability. Software code tends to be transparent about its origin and modifications, but it's often hard to ascertain the exact provenance of the data used for training ML models and the transformations conducted. To tackle…

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IBM’s Alignment Studio aims to maximize AI compliance for rules related to context.

Researchers from IBM Research have developed a new architecture, dubbed Alignment Studio, which enables developers to mould large language models (LLMs) to fit specific societal norms, laws, values and regulations. The system is designed to mitigate ongoing challenges in the artificial intelligence (AI) sector surrounding issues such as hate speech and inappropriate language. While efforts…

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