Written by: Ryan Monsurate, Co-founder, CTO
In today's data-driven world, businesses are increasingly turning to artificial intelligence (AI) to solve complex problems and gain a competitive edge. But what happens when the data you have isn't perfect? What if some of your information is "noisy" or even seemingly irrelevant to the AI task at hand? At Farpoint, we're constantly exploring cutting-edge AI research to help our clients overcome these challenges. Today, we're excited to share a breakthrough that could significantly improve the accuracy of your AI models. It turns out that in some cases, even information that seems completely unrelated can still enhance the capabilities of your business's AI.
Imagine you're training an AI model to detect defective products on a manufacturing line. You diligently gather images of both good and bad products. You may not have millions of images, and so we could try to find external datasets to augment your data. But what if some of the images show unrelated objects like tools, packaging materials, or even shadows on the line? Traditional AI methods might struggle with these "irrelevant" images. The model can get confused by data that's not directly tied to the task. Or, the model would have to be painstakingly trained to remove that noise. What if instead, the model can be trained to learn about those seemingly irrelevant images and use them to boost the signal of the images that are relevant?
Recent research shows us that these seemingly irrelevant images can, in fact, be valuable, by improving the performance of AI models. This means your AI models can be more accurate, require less training data, and be more reliable in the real-world scenarios.
How is this possible? It's akin to learning more about a specific type of fruit by looking at the shapes, colors, and textures of all the other fruit in the world and the surrounding context it is shown in. The information on unrelated fruits can help you learn more about that one type of fruit that you are trying to understand. This research shows us that AI can also use these unrelated examples to get a better picture about the core information it is seeking.
Where could this approach benefit your business?
At Farpoint, we're dedicated to keeping our clients at the forefront of AI innovation. We understand that staying competitive requires leveraging cutting-edge techniques. The approach highlighted above demonstrates that we are aware of how to apply complex AI research in real-world business solutions. By applying this approach, we can improve your AI models' accuracy and reliability in the face of noisy or even seemingly irrelevant data.
By using this and similar methods, our teams were able to improve the accuracy of state-of-the-art models from 69% to 82% in certain scenarios.*
Don't let imperfect data hinder your AI initiatives. By leveraging breakthroughs like these, we can unlock hidden potential within your data and significantly improve the performance of your AI models. Contact us today to learn more about how Farpoint can help your business harness the power of the latest AI innovations.