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Data is the lifeblood of many organizations, but it can be hard to get your hands on when data protection protocols are in place. With synthetic data you needn’t worry about access time-consuming roadblocks because this new technology bypasses them entirely!
Consider one financial institution with rich streams containing strategic information for decision makers–it was so highly protected that gaining internal use rights was practically impossible without outside help from specialists who knew how break through these security measures
Synthetic data is the answer to all of your problems. It can protect you from a myriad security threats and vulnerabilities by eliminating any chance that hackers might have at accessing company or customer information without consent, while also maintaining privacy for everyone involved in its creation!
What is synthetic data ?
AI-based synthetic data has been used to bring statistical properties and patterns of real world events into a different context.
The goal is reproduce the probability distribution or sampling theorem, but this time with new variables that weren’t present in any previous datasets
Now you can train machine learning and computer vision models faster than ever! With synthetic data, companies are able to quickly create large sets of training examples. This is a huge advantage for those who need access and time on their hands – it’s like having your own personal data set ready at any moment.
The power to analyze large datasets with speed and accuracy, without the need for third party data sets that are prohibitively expensive.
Synthetic data gives companies access to mimicked real world synthetic datasets and images made from high-quality analyzed real world sources at a low cost – enabling them not only see how their business would perform but also make informed decisions about where it needs improvement or success!
Why is it so hard ?
While the benefits of synthetic data are compelling, realizing them can be difficult. Generating synthetic files requires an organization to do more than just plug in AI tools that analyze their own datasets; this complex process needs people with specialized skill sets and understanding about machine learning and deep learning algorithms who have advanced knowledge on how these technologies work together as well specific frameworks tailored for each task at hand.
What’s next ?
Synthetic data is a complex and often tricky area to work in. Organizations should be sure that the value will outweigh any drawbacks before getting involved with it, which can include pitfalls from doing so wrong or having incorrect assumptions when creating synthetic datasets for use within your company’s operations.
Data-driven leaders who want their organization to be successful should make it a priority and never abandon the idea of using data and analytics as an integral part in decision making.
Engineers at the University of Waterloo combined two existing deep-learning AI techniques to identify players by their sweater numbers with 90-per-cent accuracy.
“That is significant because the only major cue you have to identify a particular player in a hockey video is jersey number,” said Kanav Vats, a Ph.D. student in systems design engineering who led the project. “Players on a team otherwise appear very similar because of their helmets and uniforms.”
Player identification is one aspect of a complicated challenge as members of the Vision and Image Processing (VIP) Lab at Waterloo work with industry partner Stathletes Inc. on AI software to analyze player performance and produce other data-driven insights.
The researchers built a data set of more than 54,000 images from National Hockey League games—the largest data set of its kind—and used it to train AI algorithms to recognize sweater numbers in new images.
Accuracy was boosted by representing the number 12, for instance, as both a two-digit number and two single digits, 1 and 2, put together, an approach known in the field of AI as multi-task learning.
“Using different representations to teach the same thing can improve performance,” Vats said. “We combined a wholistic representation and a digit-wise representation with great results.”
The research team is also developing AI to track players in video, locate them on the ice and recognize what they are doing, such as taking a shot or checking an opposing player, for integration in a single system.
Check out some cool artificial intelligence companies in San Diego, including Pagarba Solutions.