High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific applications such as weather and climate analysis. Unfortunately, despite the growing development of new deep learning models for weather and climate, there is a scarcity of curated, pre-processed machine learning (ML)-ready datasets. Curating such high-quality datasets for developing new models is challenging particularly because the modality of the input data varies significantly for different downstream tasks addressing different atmospheric scales (spatial and temporal). Here we introduce WxC-Bench (Weather and Climate Bench), a multi-modal dataset designed to support the development of generalizable AI models for downstream use-cases in weather and climate research. WxC-Bench is designed as a dataset of datasets for developing ML-models for a complex weather and climate system, addressing selected downstream tasks as machine learning phenomenon. WxC-Bench encompasses several atmospheric processes from meso-$\beta$ (20 - 200 km) scale to synoptic scales (2500 km), such as aviation turbulence, hurricane intensity and track monitoring, weather analog search, gravity wave parameterization, and natural language report generation. We provide a comprehensive description of the dataset and also present a technical validation for baseline analysis. The dataset and code to prepare the ML-ready data have been made publicly available on Hugging Face -- https://huggingface.co/datasets/nasa-impact/WxC-Bench

AI in Education Market size valued at USD 4 billion in 2022 and is anticipated to witness over 10% CAGR from 2023 to 2032, owing to the growing inclination towards personalized learning. Increasing reliance on technological reinforcement and conventional techniques has rendered traditional education models no longer sufficient to sustain the sector. In order to fulfill the evolving demands of students and educators, edtech startups are transforming and improving the education sphere by disrupting traditional technologies and advancing existing learning methods. As of January 2023, there are 30 EdTech Unicorns worth $89 billion worldwide. The lack of skilled professionals is a major factor restricting the adoption of AI across the education industry.

Global artificial intelligence (AI) software spending will grow 21.3 per cent to US$62 billion next year, according to analyst firm Gartner. Knowledge management, virtual assistants, autonomous vehicles, digital workplace and crowdsourced data will make up the top five use cases for AI software spending in 2022. The forecast report focused on applications with AI embedded in them, such as computer vision software, as well as software that is used to build AI systems. "Use cases that deliver significant business value, yet can be scaled to reduce risk, are critical to demonstrate the impact of AI investment to business stakeholders," Gartner senior research director Alys Woodward said. Demand for AI technologies and associated market growth is closely tied to organisational AI maturity levels, the report said.