The world of artificial intelligence (AI) is a rapidly evolving landscape, with new AI models and techniques emerging at a dizzying pace. One of the most significant developments in recent years has been the rise of foundation models. These large-scale neural networks, trained on vast amounts of data, serve as a base for various applications, offering a level of versatility and adaptability that has revolutionized the field. But why are there so many of these models, and why do we need them?
To understand the proliferation of foundation models, it’s essential to grasp their function and the concept of transfer learning that underpins them. Foundation models are pre-trained on large datasets, allowing them to learn patterns and structures in the data. This pre-training phase enables the models to apply the information they’ve learned to new, different situations, a process known as transfer learning. This adaptability is one of the key reasons why there are so many foundation models – each one can be fine-tuned for a specific task, making them incredibly versatile.
Why are there so many AI foundation models?
One of the most compelling examples of this versatility is the use of foundation models in analyzing NASA’s earth science data. NASA has about 70 petabytes of earth science data from satellite images, a figure expected to reach 300 petabytes by 2030. Analyzing this vast amount of data traditionally requires human experts to annotate features, a time-consuming and labor-intensive process. Foundation models, however, can expedite this process by extracting the structure of raw natural images, significantly reducing the manual effort required in data analysis.
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In collaboration with IBM, NASA has created an AI Foundation model for Earth observations, known as the “IBM NASA Geospatial model.” This model, which is available as open-source on Hugging Face, uses a transformer AI architecture to turn raw data into a compressed representation that captures the data’s basic structure. The IBM NASA Geospatial model has been fine-tuned to map the extent of past US floods and wildfires, providing valuable data that can be used to predict future areas of risk.
The versatility of the IBM NASA Geospatial model doesn’t stop there. With additional fine-tuning, the model can be redeployed for tasks like tracking deforestation, predicting crop yields, or monitoring greenhouse gases. This adaptability is not unique to the IBM NASA Geospatial model; it’s a characteristic of foundation models in general. Clark University, for instance, is adapting the IBM NASA Geospatial model for other applications, including time series, segmentation, and similarity search.
The thousands of open-source foundation models available on platforms like Hugging Face are trained and tuned on a wide variety of data, allowing them to be adapted to meet specific needs. This adaptability multiplies the usefulness of data, such as that from NASA, by enabling these models to be tailored to new use cases.
The reason there are so many foundation models is that they offer a level of versatility and adaptability that is unparalleled in the field of AI. By leveraging the concept of transfer learning, these models can be fine-tuned for specific tasks, making them incredibly useful in a wide range of applications. Whether it’s analyzing large-scale data sets like those from NASA or being adapted for new use cases, foundation models are a testament to the power and potential of AI.
Source : IBM
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