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While real-world data are actively being collected to train these models, there are often scenarios that are unpredictable or infrequent that the model needs to be able to recognize in order to keep everyone involved safe.

More specifically, if we know that we don’t have data on people who wear glasses, then we can create that data artificially to ensure the models work for those wearing glasses.Īdditionally, an autonomous vehicle needs to be trained on all road situations, including different types of roads, different street sign languages, different extreme experiences, and whatever else is thrown its way. All these variables need to be added into the training dataset to ensure inclusivity.

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For example, facial recognition for your cell phone needs to be able to work for everyone, so it needs to be trained to identify skin color, hair color, hair type, different facial features, accessories like glasses or sunglasses, and more. And, although it can still inherit bias from the original set, this means it carries much less bias, if any at all.įor the dataset to be truly inclusive, it needs to cover every possible scenario and person who may use it. What sets synthetic data apart is that it’s not created by humans. For example, self-driving cars are more likely to recognize white pedestrians versus Black, which can result in major safety issues. No, these aren’t huge, obvious biases, but they are enough to skew applications on the basis of sex and race. Because these datasets are curated by humans, they often show the same biases as the people who create them. Just as bias is an ever-standing presence in society, it holds space in AI datasets as well. When used in conjunction with human-collected data, synthetic data can offer substantial benefits to businesses, including reduced cost of data and labor, increased speed of data collection, access to edge cases, and more-inclusive, less-biased datasets. Synthetic data allows companies to break away from traditional AI data limitations. However, it can be applied in nearly any program-leveraging computer vision, such as drones, security cameras, and various consumer electronics. Although the idea of synthetically generated data has been around for quite some time, its recent growth can be attributed in large part to the autonomous vehicles industry. Although extreme, these are real-life scenarios that we can’t just train a machine to recognize and react to with human-in-the-loop data alone.īy 2024, 60% of all AI data is expected to be synthetic data. Another example is training an autonomous vehicle to recognize a child running out in front of the car. For example, we don’t have enough of the data needed to train an alarm system to recognize a home intruder. There are real-world scenarios that happen but are not often documented enough to have the abundance of data needed to be able to train a machine to recognize it.

If a scenario had not occurred or been captured before, there was no data, which left a huge gap in the machine’s ability to understand that specific scenario. Originally, developers had to obtain training data that covered every possible scenario to accurately train successful AI models. Human-in-the-loop data training can take you a long way, but what about the cases in which we have no previous data? How can we teach an AI model to do something we don’t yet have the tools or data to do ourselves? Businesses are using AI to venture into previously unexplored territory.

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