Three Critical Insights about Artificial Intelligence at the Edge

Three Critical Insights about Artificial Intelligence at the Edge

Artificial Intelligence applications have vastly increased in recent years all around the world. Cloud computing has become an important aspect of AI progress as the number of business operations at work has increased.

Artificial Intelligence (AI) is progressing from science fiction to enterprise-wide scalability. Even ten years ago, AI workloads were almost exclusively used by a handful of extremely lucrative businesses with the financial means to experiment and recruit a big team of data scientists.

AI is being leveraged not only inside enormous data processing facilities, but also at the edge. This pattern is expected to continue in the future years. Here are three things that executive leaders should be aware of in order to take advantage of the change.

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Open-source is a boon to businesses

Open-source is supporting the rise of AI as developers learn from the community to make software elements more accessible for a variety of use cases. The open-source community functions like a school of fish, turning quickly and collectively in response to changing conditions.

Open-source is enabling industries to rapidly evolve by developing new scaling capabilities, in part because firms no longer require a huge team of data scientists for each industry. There was a time when there was concern that a dearth of data scientists might stifle AI’s growth. However, open-source AI tools are already being used by software developers from all walks of life for a variety of business advances.

The amount of video data available is enormous

One thing that is lacking is an understanding of the tremendous quantity of information that video can provide organizations, especially at higher resolutions. Video will play a huge role in how businesses function and stay competitive, from industrial settings to retail optimization. However, because video data is so large, sending better-resolution images to a centralized cloud for inferencing tasks is expensive. It’s significantly preferable to perform inference at the site of deployment and then send the results to a centralized cloud for further model training.

As a result, deploying AI at the edge makes business sense, but it also increases the need for more computing at the edge. When it comes to the total cost of ownership (TCO), the edge is less expensive, but it also feeds a symbiotic relationship: the more success with AI deployments, the higher the need for edge computing, and better edge processing provides more value from those AI deployments. This compounding relationship is spawning a slew of new business options, ranging from content distribution to yet-to-be-released services.

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More changes are on the way

Since innovation in AI is moving at such a breakneck pace, businesses will have to pivot to new software and hardware as new trends gain traction in the developer community. Industry experts suggest looking for micro services frameworks that can scale so that organizations can take advantage of opportunities as they arise.

There isn’t a field where Artificial Intelligence won’t play a role at the cutting edge. Organizations can better prepare for how AI at the edge will alter businesses in the next few years by embracing the value open-source will continue to have within the developer community, knowing the significance of video information across fields, and preparing for continuous changes and rapid pivots.

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Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain