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Artificial Intelligence Business: How you can profit from AI
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If you don’t have sufficient data to train deep learning algorithms, there are three ways to work around it: generate synthetic data, scrape/buy data from external sources or develop AI models that work well with small data.
Deep learning is very data-hungry — models are trained on huge sets of labeled data, e.g. millions of tagged animal images — and large amounts of labeled data are not available for specific applications. In such cases, training an AI model from scratch is often difficult, if not impossible.
As we’ve mentioned, one potential solution is to enlarge real datasets with synthetic data by generating more examples. This has been successfully used in autonomous driving, where autonomous vehicles drive millions of miles in photorealistic simulated environments that recreate situations like snowstorms and unusual pedestrian behavior and where acquiring real-world data is hard.
Similarly, researchers...
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