AI for science (AI4S) is an emerging research field that aims to enhance the accuracy and speed of scientific computing tasks using machine learning methods. Traditional AI benchmarking methods struggle to adapt to the unique challenges posed by AI4S because they assume data in training, testing, and future real-world queries are independent and identically distributed, while AI4S workloads anticipate out-of-distribution problem instances. This paper investigates the need for a novel approach to effectively benchmark AI for science, using the machine learning force field (MLFF) as a case study. MLFF is a method to accelerate molecular dynamics (MD) simulation with low computational cost and high accuracy. We identify various missed opportunities in scientifically meaningful benchmarking and propose solutions to evaluate MLFF models, specifically in the aspects of sample efficiency, time domain sensitivity, and cross-dataset generalization capabilities. By setting up the problem instantiation similar to the actual scientific applications, more meaningful performance metrics from the benchmark can be achieved. This suite of metrics has demonstrated a better ability to assess a model's performance in real-world scientific applications, in contrast to traditional AI benchmarking methodologies. This work is a component of the SAIBench project, an AI4S benchmarking suite. The project homepage is https://www.computercouncil.org/SAIBench.

We are living in an era that is showing massive growth in data and computing power. We have seen a lot of progress in machine learning and deep learning, but there is an ever-growing need for more intelligent, more explainable AI. Most people's perception of artificial intelligence boils down to either science fiction, or what we call conventional AI. The foundations of conventional AI are numerical techniques like data analytics, including statistical analysis, modeling, and machine learning. This has been the primary approach to AI over the past few decades with significant success by numerous companies in many industries.

Beyond Limits, a leading developer of advanced artificial intelligence (AI) solutions, today announced its CEO AJ Abdallat will be a key presenter at the annual Montgomery Summit in Santa Monica, California, March 6-7. Abdallat will present the company's breakthrough approach to AI (artificial intelligence) to Montgomery's global community of over 1,000 venture capitalists, innovators, and executive leaders. The presentation will showcase how Beyond Limits cognitive AI solutions go beyond conventional AI to solve complex business problems for the world's most demanding industries. Beyond Limits combines human knowledge with machine learning. Conventional "black box" approaches like machine learning, deep learning or neural networks cannot explain their reasoning.

There are two types of AI, computation intelligence known as CI and Conventional AI. Conventional AI relates to machine learning and analysis. The machine is programmed to do particular tasks in a particular way. Computation intelligence involves the machine learning. Rather than only having particular skills, the machine will develop the ability to do more as it absorbs information.