In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field. However, there is still a lack of an effective framework for the overall assessment of AI4Science, particularly from a holistic perspective on data quality and model capability. Therefore, in this study, we propose SciHorizon, a comprehensive assessment framework designed to benchmark the readiness of AI4Science from both scientific data and LLM perspectives. First, we introduce a generalizable framework for assessing AI-ready scientific data, encompassing four key dimensions: Quality, FAIRness, Explainability, and Compliance which are subdivided into 15 sub-dimensions. Drawing on data resource papers published between 2018 and 2023 in peer-reviewed journals, we present recommendation lists of AI-ready datasets for both Earth and Life Sciences, making a novel and original contribution to the field. Concurrently, to assess the capabilities of LLMs across multiple scientific disciplines, we establish 16 assessment dimensions based on five core indicators Knowledge, Understanding, Reasoning, Multimodality, and Values spanning Mathematics, Physics, Chemistry, Life Sciences, and Earth and Space Sciences. Using the developed benchmark datasets, we have conducted a comprehensive evaluation of over 20 representative open-source and closed source LLMs. All the results are publicly available and can be accessed online at www.scihorizon.cn/en.

Pattern Recognition, as the name suggests is "recognizing the patterns" in simple terms. We see flowers around us and we classify them into different categories based on the number of petals, color, etc, depending on the pattern. Similarly, machines can also try to identify patterns and classify them, right? Pattern Recognition is an important scientific discipline whose goal is to identify patterns, categorizing the objects into various classes or categories. These objects could be images or signal waveforms or any measures that need to be classified.

High performance computing (HPC) refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve problems in science, engineering, or business. HPC is usually realized by means of computer clusters or supercomputers. Interestingly, the June 2019 edition of the list of TOP500 supercomputer sites marks a milestone in its history because, for the first time, all 500 systems deliver a petaflop or more. But looking at the development of single core performance reveals that it has stopped growing due to heat dissipation and energy consumption issues. As a result, substantial performance growth has started to come only from parallelism, which, in turn, means that sequential programs will not run faster on successive generations of hardware.

University of Oxford scientists led research that used artificial intelligence to generate accurate machine learning emulator algorithms for accelerating simulations billions of times, for all scientific disciplines. Researchers led by the University of Oxford in the U.K. used artificial intelligence to generate accurate machine learning emulator algorithms for accelerating simulations billions of times, for all scientific disciplines. The neural network-based emulators absorb the inputs and outputs of a full simulation, seeking patterns and learning to guess what the model would do with new inputs while avoiding the need to run the full simulation many times. The Deep Emulator Network Search (DENSE) method randomly inserts computation layers between network inputs and outputs and trains the system with the limited data, so added layers that improve performance are more likely to end up in future variations. DENSE-produced emulators for 10 simulations in physics, astronomy, geology, and climate science were 100,000 to 2 billion times faster than the models with the addition of specialized graphical processing chips--and were highly accurate.

According to the 2018 report of the World Economic Forum, AI will be increasingly present in our professional and personal lives. Some people think that this is a great opportunity which will revolutionize the world and our daily lives. Others see it as a threat to jobs and freedom. According to J. G. Ganascia, a researcher at the Sorbonne-University Computer Laboratory (LIP6), AI refers to "a scientific discipline that aims to break down intelligence into elementary functions, to the point where a computer can be built to simulate them." The fields of AI and machine learning offer infinite opportunities for research and development and building solutions that can help human civilization and socities.

Learn from well designed, well-crafted study materials on Machine Learning ML, Statistics, Python, Artificial Intelligence AI, Tensorflow, AWS, Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Face Detection, Business Intelligence BI, Regression, Hypothesis Testing, Algebra, Adaboost Regressor, Gaussian, Heuristic, Numpy, Pandas, Metplotlit, Seaborn, Forecasting, Distribution, Normalization, Trend Analysis, Predictive Modeling, Fraud Detection, Neural Network, Sequential Model, Data Visualization, Data Analysis, Data Manipulation, KNN Algorithm, Decision Tree, Random Forests, Kmeans Clustering, Vector Machine, Time Series Analysis, Market Basket Analysis. Get the skills to work with implementations and develop capabilities that you can use to deliver results in a machine learning project. This program will help you build the foundation for a solid career in Machine learning Tools. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions.

Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. Random Forest Algorithm in Machine Learning: Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

Are machines likely to become smarter than humans? No, says Jean-Gabriel Ganascia: this is a myth inspired by science fiction. The computer scientist walks us through the major milestones in artificial intelligence (AI), reviews the most recent technical advances, and discusses the ethical questions that require increasingly urgent answers. A scientific discipline, AI officially began in 1956, during a summer workshop organized by four American researchers – John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon – at Dartmouth College in New Hampshire, United States. Since then, the term "artificial intelligence", probably first coined to create a striking impact, has become so popular that today everyone has heard of it. This application of computer science has continued to expand over the years, and the technologies it has spawned have contributed greatly to changing the world over the past sixty years.

This post was originally published on Experfy's Blog. I am a recruiter specialised in the field of data science. The idea for this project arose because one of the most common questions I am asked is: "how do I obtain a position as a data scientist?" It is not just the regularity of this question that got my attention, but also the diverse backgrounds from where it was coming from. To name a few, I have had this conversation with: software engineers, database developers, data architects, actuaries, mathematicians, academics (of various disciplines), biologists, astronomers, theoretical physicists – I could go on. And through these conversations, it has become apparent that there is a huge amount of misinformation out there, which has left people confused about what they need to do, in order to break into this field.

It is often said that women are absent from the sciences. But this is not true. Although a gender gap remains in the sciences overall, the gap is closing. Women are now more likely than men to earn undergraduate degrees in biology, and they are almost as likely as men to earn undergraduate degrees in chemistry and math. There are, however, several scientific disciplines that women are still much less likely than men to choose to study: computer science, engineering and physics.