Edge is the next wave of AI

Original article was published on Artificial Intelligence on Medium

Edge is the next wave of AI

Edge computing provides an opportunity to turn AI data into real-time value across almost every industry. The intelligent edge is the next stage in the evolution and success of AI technology.

As adoption rates rise for artificial intelligence and machine learning (ML), the ability to process large amounts of data in the form of algorithms for computational purposes becomes increasingly important. To help make the expanding use of data applications across billions of connected devices more efficient and valuable, there is growing momentum to migrate the processing from centralized third-party cloud servers to decentralized and localized processing on-device, commonly referred to as edge computing.

Take a look around your house, office or even the next store you visit, and you’ll start to notice that internet-connected devices are bringing us closer than ever before to a world of ubiquitous computing and ambient intelligence. As these Internet of Things (IoT) devices become increasingly commonplace, people will start to expect computing to be more integrated into their lives, to anticipate, understand and seamlessly meet their needs. They will expect software to respond to spoken natural language, gestures, body language and emotion, and for it to understand the physical world and the rich context surrounding each user as they navigate their personal life, their work and the world around them.

What is the reasoning behind your Edge strategy?

Earlier, the world saw a transformation with cloud technologies. Cloud enables us to provide intelligence at multiple locations.

However, times have changed. The use-cases and convenience that users want today do not have any room for latency, which is why cloud is becoming cumbersome to handle. If you want your user to have a seamless experience, then you need to launch a cloud for that region, which could be expensive.

Also, understand that the device or programme waits to take the input, sends it to the cloud and awaits the decision.

What we are doing is bringing the AI capabilities of the cloud to the device itself so that decisions can be taken in real time without network connectivity. It is what we call the Edge AI

AI at the edge is enabling organizations to use the massive amounts of data collected on sensors and devices to create smart manufacturing, healthcare, aerospace and defense, transportation, telecoms, and cities to provide engaging customer experiences. We call this next wave of computing the intelligent edge and intelligent cloud. When we take the power of the cloud down to the device — the edge — we provide the ability to respond, reason and act in real time and in areas with limited or no connectivity.

Edge AI takes the algorithms and processes the data as close as possible to the physical system — in this case, locally on the hardware device. The advantage is that the processing of data does not require a connection. The computation of data happens near the network edge, where the data is developed, instead of in a centralized data-processing center. Determining the right balance between how much processing can and should be done on the edge will become one of the most important decisions for device, technology, and component providers.

What are some of the real-life use cases that we could see?

Other than connected cars or tele-medicine, these chipsets could power back-end tech such as active stereo solutions for face identification, AI-based video encoding and accelerate real-time human pose recognition.

Now imagine what each of these technologies could do. Face identification solution meets an accuracy rate that can allow payments to happen securely. In India, it could be used as a solution to lower cost of face identification and can be feasible for mass production to help financial inclusion.

Human pose recognition technology can be used on a wide array of popular camera applications with augmented reality (AR) in mind, that can have varied applications in retail, security and body/physique-related apps or games. Our AI video-encoding technology can be used in several sectors such as security or insurance.

The advancement of AI and machine learning is providing numerous opportunities to create smart devices that are contextually aware of their environment. The demands placed on smart machines will benefit from the growth in multi-sensory data that can compute with greater precision and performance

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