AI companies are generating synthetic data to train machine learning systems.Why it matters: Using computer-generated data to train AI systems can help address privacy concerns and cut down on bias while meeting the needs of models that operate in highly specific environments.Stay on top of the latest market trends and economic insights with Axios Markets. Subscribe for freeHow it works: A synthetic data set is artificially created, rather than scraped from the real world.For a computer vision system being trained on facial recognition, that might mean a dataset of artificially generated human faces in lieu of online photos of real people pulled off the internet — often without their explicit consent. "This allows you to train systems in a completely virtual domain," says Yashar Behzadi, the CEO of Synthesis AI, which generates synthetic data for computer vision models.Details: Synthetic data has been used for some time in robotics and autonomous vehicles, which need to be trained with highly specific data — like the precise 3D position of an object — that can be expensive or difficult to pull from the real world.But as concerns about AI bias and privacy grow, synthetic data makes it possible to generate data sets that can be molded to specification, allowing AI researchers to counter the bias that can be built into the real world."If we want to be robust against skin color or skin tone or demographics, any element that may not be well-represented, you can just model your distribution to equally representing each of those categories," says Behzadi.Yes, but: The real world contains outliers that synthetic data generators may not think to cover, which could leave models unprepared for certain situations.And it's still up to the generators of synthetic data to ensure that their datasets are fairer than what might be picked up in the real world. The bottom line: Synthetic data can be even better than the real thing, but only if it's designed the right way. Like this article? Get more from Axios and subscribe to Axios Markets for free.

AI companies are generating synthetic data to train machine learning systems.Why it matters: Using computer-generated data to train AI systems can help address privacy concerns and cut down on bias while meeting the needs of models that operate in highly specific environments.Stay on top of the latest market trends and economic insights with Axios Markets. Subscribe for freeHow it works: A synthetic data set is artificially created, rather than scraped from the real world.For a computer vision system being trained on facial recognition, that might mean a dataset of artificially generated human faces in lieu of online photos of real people pulled off the internet — often without their explicit consent. "This allows you to train systems in a completely virtual domain," says Yashar Behzadi, the CEO of Synthesis AI, which generates synthetic data for computer vision models.Details: Synthetic data has been used for some time in robotics and autonomous vehicles, which need to be trained with highly specific data — like the precise 3D position of an object — that can be expensive or difficult to pull from the real world.But as concerns about AI bias and privacy grow, synthetic data makes it possible to generate data sets that can be molded to specification, allowing AI researchers to counter the bias that can be built into the real world."If we want to be robust against skin color or skin tone or demographics, any element that may not be well-represented, you can just model your distribution to equally representing each of those categories," says Behzadi.Yes, but: The real world contains outliers that synthetic data generators may not think to cover, which could leave models unprepared for certain situations.And it's still up to the generators of synthetic data to ensure that their datasets are fairer than what might be picked up in the real world. The bottom line: Synthetic data can be even better than the real thing, but only if it's designed the right way. Like this article? Get more from Axios and subscribe to Axios Markets for free.

These are troubled times for artificial intelligence developers: never has there been such potential in the field of machine learning, which relies on users' personal information for training--however, data regulation and public perception of digital privacy have never been sterner, either. The 2018 Cambridge Analytica scandal was a watershed moment: personal data from 87 million Facebook users were covertly used for political campaigning. This event, and the frequent news of security breaches in social networks, in operating systems and in cloud servers have eroded public trust. Earlier this year, Google admitted that its employees listen to recordings of conversations held between clients and the company's smart speaker. Technologists are on a quest for privacy-protecting artificial intelligence, which has led to the proposal of new techniques like federated learning.

Imagine having a rat clinging to your back, sucking out your fat stores. That's similar to what infested bees endure when the Varroa destructor mite comes calling. Some bees fight back, wiggling, scratching, and biting until the mites depart for friendlier backs. Now, researchers, professional beekeepers, and hobbyists are working on ways to breed into bees these mite-defeating behaviors to rid them of these damaging pests. Host Sarah Crespi and Staff Writer Erik Stokstad discuss the tactics of, and the hurdles to, pesticide-free mite control.

We haven't reached widespread adoption of AI in enterprises yet, but it's coming. More enterprises have adopted stream processing, though, because it can enable a variety of mission-critical use cases. A new AI algorithm could help detect Alzheimer's disease early. A new report explains why a number of factors determine whether you need cloud or on-premise solutions for AI and HPC.

We haven't reached widespread adoption of AI in enterprises yet, but it's coming. More enterprises have adopted stream processing, though, because it can enable a variety of mission-critical use cases. A new AI algorithm could help detect Alzheimer's disease early. A new report explains why a number of factors determine whether you need cloud or on-premise solutions for AI and HPC.

Artificial intelligence (AI) holds real potential for improving both the speed and accuracy of medical diagnostics. But before clinicians can harness the power of AI to identify conditions in images such as X-rays, they have to'teach' the algorithms what to look for. Identifying rare pathologies in medical images has presented a persistent challenge for researchers, because of the scarcity of images that can be used to train AI systems in a supervised learning setting. Professor Shahrokh Valaee and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets. "In a sense, we are using machine learning to do machine learning," says Valaee, a professor in The Edward S. Rogers Sr. "We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays."

Identifying rare pathologies in medical images has presented a persistent challenge for researchers, because of the scarcity of images that can be used to train AI systems in a supervised learning setting. Professor Shahrokh Valaee and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets. "In a sense, we are using machine learning to do machine learning," says Valaee, a professor in The Edward S. Rogers Sr. "We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays." Valaee is a member of the Machine Intelligence in Medicine Lab (MIMLab), a group of physicians, scientists and engineering researchers who are combining their expertise in image processing, artificial intelligence and medicine to solve medical challenges. "AI has the potential to help in a myriad of ways in the field of medicine," says Valaee.

IMAGE: On the left of each quadrant is a real X-ray image of a patient's chest and beside it, the syntheisized X-ray formulated by the DCGAN. Under the X-ray images are... view more Artificial intelligence (AI) holds real potential for improving both the speed and accuracy of medical diagnostics. But before clinicians can harness the power of AI to identify conditions in images such as X-rays, they have to'teach' the algorithms what to look for. Identifying rare pathologies in medical images has presented a persistent challenge for researchers, because of the scarcity of images that can be used to train AI systems in a supervised learning setting. Professor Shahrokh Valaee and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets.

Artificial intelligence (AI) holds real potential for improving both the speed and accuracy of medical diagnostics. But before clinicians can harness the power of AI to identify conditions in images such as X-rays, they have to'teach' the algorithms what to look for. Identifying rare pathologies in medical images has presented a persistent challenge for researchers, because of the scarcity of images that can be used to train AI systems in a supervised learning setting. Professor Shahrokh Valaee and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets. "In a sense, we are using machine learning to do machine learning," says Valaee, a professor in The Edward S. Rogers Sr. "We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays."