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In a remarkable breakthrough, researchers from Google, Carnegie Mellon University, and Bosch Center for AI have a pioneering method for enhancing the adversarial robustness of deep learning models, showcasing significant advancements and practical implications. To set a headstart, the key takeaways from this research can be placed around the following points: Effortless Robustness through Pretrained…

Artificial Intelligence (AI) and Machine Learning (ML) have grown significantly over the past decade or so, making remarkable progress in almost every field. Be it natural language, mathematical reasoning, or even pharmaceuticals, in today’s age, ML is the driving factor behind innovative solutions in these domains. Chemistry is also one such field where ML has…

Subcellular protein localization is key to unlocking the intricate details of cellular function, particularly within the complex milieu of tumor microenvironments. While providing a broad view, traditional proteomics methods often miss the nuanced, sub-cellular information crucial for a complete understanding. Researchers have noted that current techniques, such as immunofluorescence, tend to aggregate protein expressions within…

Many people are now interested in the state space model (SSM) because of how recent research has advanced. Modern SSMs, which derive from the classic state space model, benefit from concurrent training and excel at capturing long-range dependencies. Process sequence data across many activities and modalities using SSM-based methods like linear state-space layers (LSSL), structured…

Large Language Models (LLMs) are crucial to maximizing efficiency in natural language processing. These models, central to various applications ranging from language translation to conversational AI, face a critical challenge in the form of inference latency. This latency, primarily resulting from traditional autoregressive decoding where each token is generated sequentially, increases with the complexity and…

In computing, there’s a common challenge when it comes to speeding up the process of running complex language models, like those used in large language understanding tasks. These models, often known as LLMs, require significant computational power, and researchers are always on the lookout for ways to make them faster and more efficient. Some existing…

Alignment has become a pivotal concern for the development of next-generation text-based assistants, particularly in ensuring that large language models (LLMs) align with human values. This alignment aims to enhance LLM-generated content’s accuracy, coherence, and harmlessness in response to user queries. The alignment process comprises three key elements: feedback acquisition, alignment algorithms, and model evaluation….

In a recent study, a team of researchers from Google DeepMind has introduced AlphaGeometry, an Artificial Intelligence (AI) system that can easily solve geometry Olympiad questions almost as well as a human gold medallist. Olympiad-level mathematical theorem proofs are noteworthy accomplishments that represent sophisticated automated reasoning abilities, especially in the difficult field of pre-university mathematics….

Machine translation, a crucial aspect of Natural Language Processing, has significantly increased. Yet, a primary challenge persists: producing translations beyond mere adequacy to reach near perfection. Traditional methods, while effective, often need to be improved by their reliance on large datasets and supervised fine-tuning (SFT), leading to limitations in the quality of the output. Recent…

Large Language Models (LLMs) are increasingly employed for various domains, with use cases including creative writing, chatbots, and semantic search. Many of these applications are inherently subjective and require generations catering to different demographics, cultural and societal norms, or individual preferences. Through their large-scale training, current language models are exposed to diverse data that allows…