Most Read: NASA, IBM Create AI Model to Predict Weather; Flying Car Company Taps AI for Vehicle Design
Also inside, how AI and IoT are reshaping agriculture, ‘future you’ AI allows you to meet your future self and more
Here are the most-read stories on AI Business this week.
NASA, IBM Create AI Model to Predict Weather
IBM, NASA and the Oak Ridge National Laboratory have launched a new open-source AI model for weather and climate applications they said goes beyond normal forecasting to predict weather patterns at a variety of scales.
The new model called Prithvi WxC has been pre-trained on 40 years of Earth observation data from NASA's Modern-Era Retrospective Analysis for Research and Applications, Version 2.
It was created using a novel design and training regime that enables it to tackle tasks beyond forecasting unlike many existing weather AI models, the organizations said. For example, it can be fine-tuned to global, regional and local scales, making it suited to a range of weather studies.
How AI and IoT Are Reshaping Agriculture
Agriculture – the cultivation of domestic plants and animals – is believed to have begun in earnest some 12,000 years ago in the so-called Fertile Crescent of the Middle East. The management and harvest of plants likely began much earlier, but the techniques developed in this region allowed humans to form large settlements that developed into the complex, urban centers that now define our species. This was the first of a series of agricultural revolutions.
The increasing sophistication of agriculture in the ensuing centuries has supported an ever-burgeoning population. A second agricultural revolution began in Britain in the 17th century and included the introduction of new irrigation techniques, fertilizers and means of transporting agricultural products. And the projected population collapse of the 20th century was averted by the Green Revolution or third agricultural revolution, beginning in the 1940s, which saw huge increases in crop yields due to new fertilizers and pesticides.
Discover agriculture's fourth revolution
Flying Car Company Taps AI for Vehicle Design
Japanese eVTOL (electric vertical takeoff and landing) vehicle maker SkyDrive is turning to artificial intelligence (AI) to aid in the design of its flying vehicles.
SkyDrive partnered with Braid Technologies to use AI to generate thousands of design patterns to fine-tune the structure of its electric aerial vehicles (EAV).
Scientists, engineers and designers at startup Braid Technologies use AI, physics and mathematics to automatically discover high-performance advanced engineering designs.
“We have been working with Braid to find new ways to optimize the structure of SkyDrive’s next-generation eVTOL,” said Arnaud Coville, chief development officer of SkyDrive. “Rather than using generalized techniques like topology optimization, we were inspired by their advanced and creative technology that deals with a large number of parameters that impact the weight of the structure.”
‘Future You’ AI Allows You to Meet Your Future Self
A new tool would allow you to meet your future self to improve your future self-continuity and well-being using artificial intelligence.
Developed by researchers from MIT, Harvard, the University of California and Thailand’s Business-Technology Group, “future you” offers a short, interactive, single-session digital chat with the user's future self.
The system uses a large language model drawing on information the user provides to help generate a relatable, virtual version of themselves at age 60 that can answer questions about their future life as well as offer advice and insights on future paths.
Find out how to meet your future self
RAG to the Rescue
Large language models (LLMs) have captured the public imagination with their ability to generate human-like responses. But the ability to create sonnets and write code within seconds will rarely deliver tangible value or ROI for businesses. Instead, it’s the accuracy, specificity and domain expertise that make AI tools useful.
Retrieval augmented generation (RAG) is the key to providing this missing layer of detail. More importantly, it’s unlocking new possibilities for generative AI applications in industries that have so far been unable or unwilling to implement this technology.
Large language models (LLMs) have captured the public imagination with their ability to generate human-like responses. But the ability to create sonnets and write code within seconds will rarely deliver tangible value or ROI for businesses. Instead, it’s the accuracy, specificity and domain expertise that make AI tools useful.
Retrieval augmented generation (RAG) is the key to providing this missing layer of detail. More importantly, it’s unlocking new possibilities for generative AI applications in industries that have so far been unable or unwilling to implement this technology.
About the Author
You May Also Like