Dynam.AI Unveils Revolutionary Contextualized Machine Learning Capability as Key Part of Client Services

Dynam.AI, an artificial intelligence (AI) software development firm automating critical decision-making for the industrial sector, announced a groundbreaking capability available to all clients: Context Augmented Machine Learning (CAML). CAML leverages scientific “first principles” and the laws of physics, human behavior, and macroeconomic conditions to simulate the physical world for more realistic, trustworthy analytics. This allows industrial businesses to automate manual processes, improving their critical decision making and increasing ROI with fewer resources. Dynam.AI’s in-house development platform, using the patent-pending CAML method, has been proven to speed up AI model development for clients by up to 65%.

“We have solved some previously unsolvable industry challenges for our clients using CAML, and are excited to introduce this groundbreaking AI capability to the industrial sector,” said Diana Shapiro, CEO of Dynam.AI. “Dynam.AI is a team of data scientists and engineers who are passionate about advancing artificial intelligence. Our novel data science methods and in-house platform empower us to complete projects faster, more accurately, and with less data. CAML is just one of the tools we have in our workbench to partner with clients—but it’s one that will improve the discipline of machine learning as we know it.”

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What CAML Does

CAML combines existing data and models to expand features for analysis, introducing common-sense information that a human would innately use but a non-human algorithm would not, such as the laws of physics. It allows Dynam.AI to do two important things for clients: 1.) create richer data models for AI solutions that are smarter and better able to adapt to the real world, and 2.) solve data “cold cases” that traditional machine learning methods are unable to crack.

“Traditional machine learning is only able to work with the data it’s given,” said Dimtiry Fisher, Chief Scientific Officer at Dynam.AI. “Not all data sets or data collection methods are created equal, and none of them are perfect. This is why it can take so long to develop AI solutions for the industrial sector that actually do what they’re supposed to, such as detect anomalies or optimize a process. But when your AI has access to scientific first principles, it can use the data it’s given much more efficiently and effectively. This translates into better results with available data, and results that cannot be obtained based on data alone, leading to better decision intelligence and better solution performance.”

CAML aids developers and data scientists who are working on projects in object detection, medical imaging, sensor fusion, predictive diagnostics, satellite, and aerial imaging, thermography, anomaly detection, and much more. This capability has allowed Dynam.AI clients to solve long-standing problems with image and sensor data, developing solutions up to 65% faster.

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How Dynam.AI Clients are Using CAML

The Dynam.AI team recently helped American Robotics, a subsidiary of Ondas Holdings, Inc., advance their AI-powered anomaly detection and alerts, which flag loss of containment in oil and gas operations. American Robotics, which is the first company approved by the FAA to operate commercial drones remotely, has worked with Dynam.AI using CAML to create a capability to detect and identify potential oil leaks early, i.e. loss of containment. These new loss of containment capabilities will enable automatic detection of hydrocarbon losses at oil and gas facilities, ensuring worker safety and mitigating potential environmental, financial and reputational risk.

“American Robotics and Ondas Holdings are valued partners of Dynam.AI, and this project demonstrates the benefit of using first principles in conjunction with machine learning,” said Shapiro. “We look forward to sharing the many other exciting advances we are helping our industry clients progress using context augmentation.”

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[To share your insights with us, please write to sghosh@martechseries.com]

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