Play it forward your Intelligence

AI Agents

GeoGPT+ Tutorial: Web-Ready Map Visuals From GIS Forest Fire Data

Author(s): John Loewen, PhD Originally published on Towards AI. Step-by-step guide on using the GPT-4 GeoGPT+ plug-inDall-E image: impressionist painting of a heat map on a computer screen hovering over a forest fire GeoGPT+ improves geospatial analysis by providing real-time data integration and visualization of spatial data. What kind of mapping can GPTGeo+ (Geo+) create for me? A month or so ago, I wrote an article on how to leverage GPT-4 to create Python data visualization code to visualize a map of forest fires near my home. I have a NASA Forest Fires dataset in CSV format (with fire location data and intensity). It is ready for use, and I am curious what functionality is available with the Geo+ tool. Let’s find out what Geo+ can do with this data. The forest fire situation in Canada over the past 10 years or so has become very bad. Particularly in British Columbia, where I am from. Leading to awful pollution days like this: Forest fire smoke on the left, normal day on the right (Photo: David Loewen) To show the monthly effects of forest fires, I want to create a data visual that shows forest fires over time (by month) for British Columbia. And with all of it’s visualization capabilities, I want Geo+ to help me out! Let’s work together and step through how to get this done. I… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI

Read More

Machine Learning in Chemistry

Author(s): Tony Flores Originally published on Towards AI. Image adapted from Adobe Stock Machine learning is becoming a significant tool in the field of chemistry, providing new opportunities in various areas such as drug discovery and materials science. Machine learning algorithms, especially neural networks, are effective at identifying complex patterns in chemical data, which can lead to new insights and speed up processes that were previously dependent on traditional, more time-consuming methods. As we examine the impact of machine learning on chemistry, we will look at its uses, and how it not only simplifies regular tasks but also leads to advancements in understanding molecular complexities. Recently, the combination of machine learning and chemistry has made significant progress. Researchers are using advanced models like CNNs and RNNs for tasks such as creating new drugs, predicting toxicology, and modeling quantitative structure-activity relationships. The pursuit of models that are interpretable and explainable is becoming more important, giving scientists a better understanding of why predictions are made. Additionally, the use of multi-modal data and the development of transfer learning techniques are expanding what can be achieved in predicting material properties and optimizing synthesis planning. These recent trends highlight the growing collaboration between machine learning and chemistry, pushing scientific research into new areas and influencing… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI

Read More

Jacek Zukowski

Some challenges came from operating on various language versions of SAP, but in most cases, we have selectors that can match the data.

Read More