Exploring the Links Between A.I., Deep-Learning & Big Data

The term artificial intelligence (AI) has been around for nearly 60 years. But it is only recently that AI is revolutionizing industries as diverse as health care, retail, journalism, aerospace, manufacturing, and, now transportation, with the potential to profoundly affect how people live, work, and play.

Utilizing deep-learning enables us to better understand what is being captured through vision —the actual context of a given situation, and therefore reason and comprehend what the video and data is showing us. Our customer community realizes immediate advantages through our ‘edge computing’ approach; realizing gains in data analysis, performance, timeliness and efficiency in data transport.

The confluence of AI and deep-learning technology offers a big opportunity to create, manage and store meaningful Big Data. Deep-learning technologies are the key to structuring large amounts of data more efficiently. We use deep-learning to better handle our video- and visual-based data, and it is this data that is driving some of this Big Data Progression.

Our deep understanding of AI, Deep Learning, Edge Computing and Cloud creates opportunities that can positively affect individuals, corporations and ultimately communities around the world – impacting every minute of the day along with long-term technical aspirations.

Edge Computing and the promise of providing meaningful data

Industrial companies have used technology for decades to automate processes and streamline operations. Data from sensors became fertile ground for cloud computing and big data analytics to extract new insights from operations.

The benefits from this approach increased visibility to the source of data – however, companies continue to struggle with access and accuracy.

‘Edge’ computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data. In the context of Industrial Internet of Things (IIoT), where industrial machines are the “things” from which data is sourced, edge computing is where ‘software’ meets physical ‘machines’.

Edge computing works at the individual device, vehicle, or operator level. Among its benefits are:

• Enable the gathering of massive information from mobile environment; serving as an aggregation and control point
• Real-time or near real-time data analysis — as the data is analyzed at the device level, not in a distant data center or cloud
• Lower operating costs due to the smaller data management expenses of local devices vs. clouds and data centers
• Reduced network traffic because only meaningful data is transmitted from local devices; reducing network costs

Utilization of TeraFLOP processing to provide texture and context

The Driveri™ device was created using a TeraFLOP processor. A teraflop is a measure of a computer’s speed and can be defined as: A trillion floating point operations per second. 10 to the 12th power floating-point operations per second. In practical terms, it means this processor enables the Driveri™ device to be its own supercomputer with the computational power required to run Netradyne Deep Learning algorithms and engines.

Driveri™ captures every aspect of the driving experience – ranging from the condition of a traffic light in relation to the vehicle, to calculating following distance of vehicles on the road, to the texture of the road environment Netradyne’s utilization of a TeraFLOP processor is needed for computations that require a large dynamic range.

Our Driveri™ safety system is able to detect, reason and alert fleet managers about their driver’s behavior in meaningful, near-real time ways. We believe that Netradyne is the first “true” vision-based company in the Commercial Fleet Segment to utilize the computational power of a TeraFLOP processor.

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