Large language model
Advancements in Large Language Models (LLMs) enabled by Natural Language Processing and Generation have broad applications. However, their biased representations of human viewpoints stemming from pretraining data composition have prompted researchers to focus on data curation. A recent study introduces the AboutMe dataset to address these biases and the need for sociolinguistic analysis in NLP.
The emergence of large language models has led to rapid advancements in Mixture-of-Experts (MoE) architecture. The DeepSeekMoE model introduced by DeepSeek-AI innovatively addresses challenges in expert specialization through fine-grained expert segmentation and shared expert isolation. Experimental results demonstrate the scalability and performance superiority of DeepSeekMoE, with potential at an unprecedented scale of 145B parameters.
DeepMind’s AlphaGeometry, a new AI system, excels in solving complex Olympiad-level geometry problems, achieving a milestone in AI’s ability for mathematical problem-solving. By combining a neural language model with a symbolic deduction engine and using synthetic training examples, it outperformed previous AI models, approaching human gold medalist levels. This breakthrough opens new possibilities for mathematics…
Advancements in artificial intelligence and machine learning have revolutionized molecular property prediction in drug discovery and design. The SGGRL model from Zhejiang University introduces a multi-modal approach, combining sequence, graph, and geometry data to overcome the limitations of traditional single-modal methods. The model’s intricate fusion layer produces more accurate predictions, marking a potential breakthrough in…
The Billion-Scale Approximate Nearest Neighbor Search Challenge at NeurIPS aims to advance large-scale ANNS. Pinecone’s innovative algorithms excelled across all four tracks: Filter, Sparse, OOD, and Streaming. Pinecone demonstrated exceptional performance, outperforming the winners by up to 2x, solidifying their position as a leader in vector search technology. [49 words]
Recent research from UC Berkeley and New York University explores the deficiencies in multimodal large language models (MLLMs) caused by visual representation issues. The study uncovers the shortcomings of pre-trained vision and language models and introduces a new benchmark, MMVP, to assess the visual capacities of MLLMs. The researchers propose Mixture-of-Features (MoF) methods to enhance…
Scientists have created a soft fluidic switch using an ionic polymer artificial muscle, capable of lifting objects 34 times its weight with ultra-low power. Its small size and light weight allow for use in industrial areas like soft electronics, smart textiles, and biomedical devices, offering precise fluid control in tight spaces.
Ed Newton-Rex, former VP of Audio at Stability AI, has launched ‘Fairly Trained,’ a non-profit certifying generative AI companies for ethical training data practices, aiming to address concerns over data scraping and copyright infringement. The initiative has already certified nine companies and introduced the ‘Licensed Model certification’ to ensure ethical use of training data.
The StableRep model improves AI training by using synthetic imagery to generate diverse images from text prompts, addressing data collection challenges and offering more efficient and cost-effective training options.
The text discusses the potential risks and limitations of relying on external servers for AI applications. It introduces Jan as an open-source alternative that operates entirely offline, addressing privacy concerns. Jan is designed to run on various hardware setups, offering customization and seamless integration with compatible applications. With a commitment to open-source principles, Jan presents…
Machine learning in healthcare aims to revolutionize medical treatment by predicting tailored outcomes for individual patients. Traditional clinical trials often fail to represent diverse patient populations, hindering the development of effective treatments. Researchers are turning to machine learning algorithms to estimate personalized treatment effects, promising a future of personalized and effective healthcare.
Language models are increasingly used as dialogue agents in AI applications, facing challenges in customizing for specific tasks. A new self-talk methodology, introduced by researchers, involves two models engaging in self-generated conversations to streamline fine-tuning and generate a high-quality training dataset. This innovative approach enhances dialogue agents’ performance and opens new avenues for specialized AI…
OpenAI unveils a comprehensive strategy to counter misinformation during elections using advanced AI tools. The company aims to prevent misuse of its technology by blocking creation of deceptive chatbots and pausing its use in political campaigning. OpenAI plans to add digital watermarks to generated images for tracking. Collaboration with the National Association of Secretaries of…
FedTabDiff, a collaborative effort by researchers from University of St.Gallen, Deutsche Bundesbank, and International Computer Science Institute, introduces a method, leveraging Denoising Diffusion Probabilistic Models (DDPMs), to generate high-quality mixed-type tabular data without compromising privacy. It demonstrates exceptional performance in financial and medical datasets, addressing privacy concerns in AI applications.
AI systems are rapidly advancing in two categories: Predictive AI and Generative AI, demonstrated by Large Language Models. The NIST AI Risk Management Framework emphasizes the need for secure and reliable AI operations. A study by NIST Trustworthy and Responsible AI outlines a comprehensive taxonomy and strategies for controlling Adversarial Machine Learning (AML) attacks. Read…
Large language models have revolutionized natural language processing, with recent models like Tower catering to translation tasks in 10 languages. Developed by researchers at Unbabel, SARDINE Lab, and MICS Lab, Tower outperforms other open-source models and offers features like automatic post-editing and named-entity recognition. The researchers aim to release TowerEval for evaluating language models against…
A recent study examines the application of robotic-assisted joint replacement in revision knee situations. It evaluates the implant positions before and after revision surgeries using a state-of-the-art robotic arm system in a series of revision total knee arthroplasties (TKA).
Advancements in large language models (LLMs) have made interactive conversational AI in healthcare possible. Google DeepMind developed AMIE, an AI system designed to take medical histories and engage in diagnostic discussions, which outperformed primary care physicians in diagnostic accuracy and patient communication in a remote trial. The research aims to address limitations for real-world clinical…
Researchers from Columbia University have introduced hierarchical causal models to address causal questions in hierarchical data. This innovative method involves advanced algorithms, machine learning techniques, and hierarchical Bayesian models to enable rapid, accurate, and real-time data processing, demonstrating potential to transform data processing in contemporary data-rich environments. (50 words)
Researchers at NVIDIA and University of California, San Diego, have developed an innovative method for high-fidelity 3D geometry rendering in Generative Adversarial Networks (GANs). Based on SDF-based NeRF parametrization, the approach utilizes learning-based samplers to accelerate high-resolution neural rendering and demonstrates state-of-the-art 3D geometric quality on FFHQ and AFHQ datasets. Despite commendable achievements, limitations include…