The first batch of synthetic data startups that emerged thus targeted the autonomous vehicle end market. By 2019, that figure had reached 10 billion simulated miles.Īs Andreessen Horowitz general partner Chris Dixon put it back in 2017: “Right now, you can almost measure the sophistication of an autonomy team-a drone team, a car team-by how seriously they take simulation.” In 2016, for instance, Waymo generated 2.5 billion miles of simulated driving data to train its self-driving system (compared to 3 million miles of driving data collected from the real world). These simulated worlds make it possible to automatically produce thousands or millions of permutations of any imaginable driving scenario-e.g., changing the locations of other cars, adding or removing pedestrians, increasing or decreasing vehicle speeds, adjusting the weather, and so on.įor years now, the leading autonomous vehicle players-Waymo, Cruise, Aurora, Zoox-have all invested heavily in synthetic data and simulation as a core part of their technology stack. So instead, AV companies developed sophisticated simulation engines to synthetically generate the requisite volume of data and efficiently expose their AI systems to the “long tail” of driving scenarios. Given how unpredictable and unbounded the world is, it would take literally hundreds of years of real-world driving to collect all the data required to build a truly safe autonomous vehicle. Collecting real-world driving data for every conceivable scenario an autonomous vehicle might encounter on the road is simply not possible. Synthetic data and autonomous vehicles are a particularly natural fit for one another given the challenges and importance of “edge cases” in the world of AVs. To begin with, because the AV sector has attracted more machine learning talent and investment dollars than perhaps any other commercial application of AI, it is often the catalyst for foundational innovations like synthetic data. It is no surprise that synthetic data got its start in the world of autonomous vehicles.

While the concept of synthetic data has been around for decades, it was in the autonomous vehicle sector that the technology first found serious commercial adoption starting in the mid-2010s. It is a technology worth paying attention to. The rise of synthetic data will completely transform the economics, ownership, strategic dynamics, even (geo)politics of data. “We can simply say that the total addressable market of synthetic data and the total addressable market of data will converge,” said Ofir Zuk, CEO/cofounder of synthetic data startup Datagen. 'The president's panel will take it up, and they'll probably bring in more perspectives.Needless to say, massive business opportunities will result. I think that’s probably a mistake,' Kaebnick told. 'I was the only one on the panel who didn't have a vested stake in it one way or the other. Only one witness, Gregory Kaebnick, a bioethicist at the Hastings Center, a nonprofit that studies the ethics of biotechnology, could be said to be an outside observer of the synthetic biology industry. 'That is, through advances in synthetic biology, we can engineer these same safe, reliable, industrial microorganisms to produce biofuels that will work within our existing transportation infrastructure.' We are now re-engineering the artemisinin-producing microbes to produce drop-in biofuels,' he said. 'Fortuitously, artemisinin is a hydrocarbon, a fundamental building block for fuel. Keasling made the smoothest transition from his scientific work, coming up with a way to produce the anti-malarial drug artemisinin in yeast, which could substantially reduce the cost of its distribution, to his sales pitch. Venter, Keasling and Endy all have ties to companies trying to make money from synthetic biological techniques. In fact, the hearing was technically an oversight task, but it played out closer to a gee-whiz commercial for the new firms that are trying to commercialize the technology.

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