Model Predictive Control (MPC) is widely used in fields such as power systems and robotics. A recent study from Carnegie Mellon University focused on the convergence characteristics of a sampling-based MPC technique called Model Predictive Path Integral Control (MPPI). The research led to the development of a new method called CoVariance-Optimal MPC (CoVO-MPC), which outperformed…

Researchers from Meta and NYU introduce Self-Rewarding Language Models, addressing limitations in traditional reward models by training a self-improving reward model. Utilizing LLM-as-a-Judge prompting and Iterative DPO, the model iteratively improves instruction-following and reward-modeling abilities, outperforming existing models. This novel approach signifies promising progress in language model training beyond human-preference-based reward models.

Researchers from Google, Carnegie Mellon University, and Bosch Center for AI have developed a pioneering method to enhance adversarial robustness of deep learning models. The innovative approach achieves top-tier adversarial robustness using pretrained models, without the need for complex fine-tuning. The groundbreaking research has significant implications for various domains, including autonomous vehicles, cybersecurity, healthcare, and…

After human annotation, a machine-learning model automatically replicates the same annotations from tagged pictures, aiming to meet defined standards. Image annotation categorizes and labels images for object identification, crucial for computer vision, robotics, and autonomous driving. Notable image annotation tools for 2024 include Markup Hero, Keylabs, Labelbox, Scale, Supervisely, and others, each offering unique features…

AI and ML have advanced in various fields, including chemistry. However, challenges persist for smaller datasets. PythiaCHEM, an ML toolkit, addresses this with tailored tools for predictive models in chemistry. It’s implemented in Python, organizes modules and integrates with other toolkits. Researchers showcased its effectiveness in classifying anion transporters and predicting enantioselectivity, highlighting its flexibility…

Researchers have developed PEPSI (Protein Expression Polarity Subtyping in Immunostains) to analyze subcellular protein localization in tumor microenvironments, crucial for understanding immune responses in cancer. It identifies distinct immune cell states by computing cell surface biomarker polarity from immunofluorescence imaging data and has shown potential for predicting patient survival outcomes, revolutionizing precision medicine.

The state space model (SSM) is gaining interest due to advancements, benefiting from concurrent training to capture long-range dependencies. Vision Mamba (Vim) aims to overcome obstacles in visual backbone design. It combines position embeddings and bidirectional SSMs for global context modeling. Vim shows promise for image modeling and dense prediction with efficient computation. For more…

The New Hampshire attorney general’s office is investigating an AI-generated robocall impersonating President Biden, aiming to dissuade voter participation in the primary election. The incident is described as illegal, with concerns about AI being weaponized in elections. This follows Pennsylvania candidate Shamaine Daniels’s use of AI-driven robocalls, indicating the growing concern about AI in election…

A study by MIT’s Computer Science and Artificial Intelligence Laboratory assessed AI’s potential to replace human jobs, focusing on computer vision. It found AI can automate 1.6% of US worker wages, but economically replace only 23%. Customizing AI for specific tasks is costly, while language models like GPT-4 may have broader economic adoption potential. AI’s…

Marlin is an innovative solution to speed up complex language models, such as LLMs, which typically require significant computational power. It addresses limitations of existing methods, offering near-ideal speedups for larger batch sizes. Marlin’s smart techniques optimize GPU use and ensure consistent performance, making it a standout performer in computational linguistics.