Google Deepmind has developed AutoRT, utilizing foundation models to enable the autonomous deployment of robots in diverse environments with minimal human supervision. It leverages vision-language and large language models to generate task instructions and ensure safety through a robot constitution framework. AutoRT facilitates large-scale robotic data collection and enhances robotic learning and autonomy in real-world…

Researchers introduced a more efficient approach to enhancing large language models’ multilingual capabilities. By integrating a small set of diverse multilingual examples into the instruction-tuning process, they achieved significant improvement in the models’ performance across multiple languages. This approach offers a resource-effective pathway to developing globally applicable multilingual models.

Genetic algorithms are highlighted as an efficient tool for feature selection in large datasets, showcasing how it can be beneficial in minimizing the objective function via population-based evolution and selection. A comparison with other methods is provided, indicating the potential and computational demands of genetic algorithms. For more in-depth details, the full article can be…

Efficient Feature Selection via CMA-ES (Covariance Matrix Adaptation Evolution Strategy) explores the challenge of feature selection in model building for large datasets. With a particular focus on using evolutionary algorithms, this article introduces SFS (Sequential Feature Search) as a baseline technique and delves into a more complex approach – CMA-ES (Covariance Matrix Adaptation Evolution Strategy).…

This week at the CES tech expo, AI took center stage as companies unveiled new products. Standout releases included LG and Samsung’s mobile smart home AI assistants and NVIDIA’s new chips for local AI processing. Additionally, OpenAI faced legal challenges, and AI’s impact on art, robotics, and societal risks was a significant theme.

FineMoGen is a new framework by S-Lab, Nanyang Technological University, and Sense Time Research, addressing challenges in generating detailed human motions. It incorporates a transformer architecture called Spatio-Temporal Mixture Attention (SAMI) to synthesize lifelike movements closely aligned with user inputs. FineMoGen outperforms existing methods, introduces zero-shot motion editing, and establishes a large-scale dataset for future…

Scientists have faced challenges in understanding the immune system’s response to infections. Current methods of predicting how immune receptors bind to antigens have limitations, leading to the development of DeepAIR, a deep learning framework that integrates sequence and structural data to improve accuracy. DeepAIR shows promising results in predicting binding affinity and disease identification, advancing…

NVIDIA introduces ‘Incremental FastPitch’, a variant of FastPitch, to enable real-time speech synthesis with lower latency and high-quality Mel chunks. The model incorporates chunk-based FFT blocks, training with receptive field-constrained chunk attention masks, and inference with fixed-size past model states. It offers comparable speech quality to parallel FastPitch but with significantly reduced latency.

The text discusses the concept of using Neural ODE to model dynamical systems with a focus on two case studies: system identification and parameter estimation. It covers the implementation details of the Neural ODE approach, including defining the neural network model, data preparation, training loop, assessment, and overall summary. The approach effectively approximates unknown dynamics…

The text introduces the concept of non-linearities in PyTorch for neural networks. It discusses how activation functions can help in solving complex problems and introduces the use of the Heart Failure prediction dataset in PyTorch. It also covers the implementation of neural network architectures and the impact of activation functions on model performance and training.…