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One of the most pressing challenges in the evaluation of Vision-Language Models (VLMs) is related to not having comprehensive benchmarks that assess the full spectrum of model capabilities. This is because most existing evaluations are narrow in terms of focusing on only one aspect of the respective tasks, such as either visual perception or question… Read more

Recent progress in LLMs has spurred interest in their mathematical reasoning skills, especially with the GSM8K benchmark, which assesses grade-school-level math abilities. While LLMs have shown improved performance on GSM8K, doubts remain about whether their reasoning abilities have truly advanced, as current metrics may only partially capture their capabilities. Research suggests that LLMs rely on… Read more

Why we must develop methods, procedures and practices to make sure that improvements in some areas don’t eliminate LLMs’ other advantages.  Read More Read more

The data center industry is expanding rapidly to keep up with the flywheel growth of AI. While these data centers are necessary AI infrastructure, they store an AI company’s compute, they are expensive to build, seemingly more so to run, and they are a huge energy suck. Startups are looking to make data centers more… Read more

For the most sensitive parts of the human body, friction is the enemy. Here’s how to keep it at bay with our favorite lubes made of water, silicone, or natural oil. Read more

With S-tier horsepower and a gorgeous, curved glass case, this is one prebuilt gaming PC that looks as good as it performs. Read more

Automatic benchmarks like AlpacaEval 2.0, Arena-Hard-Auto, and MTBench have gained popularity for evaluating LLMs due to their affordability and scalability compared to human evaluation. These benchmarks use LLM-based auto-annotators, which align well with human preferences, to provide timely assessments of new models. However, high win rates on these benchmarks can be manipulated by altering output… Read more

Model merging is an advanced technique in machine learning aimed at combining the strengths of multiple expert models into a single, more powerful model. This process allows the system to benefit from the knowledge of various models while reducing the need for large-scale individual model training. Merging models cuts down computational and storage costs and… Read more

We tried the most popular curling irons on the market, and here are the 11 that stood out. Read more

Robotic task execution in open-world environments presents significant challenges due to the vast state-action spaces and the dynamic nature of unstructured settings. Traditional robots struggle with unexpected objects, varying environments, and task ambiguities. Existing systems, often designed for controlled or pre-scanned environments, lack the adaptability required to respond effectively to real-time changes or unfamiliar tasks.… Read more