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Tech at the Edge: Low-Code Smart Solutions, VMware's CAM + Endesa Tots Up its Call Center with AI

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NEW YORK - May 16, 2019 - Amzeal -- Intelligent Connectivity, encompassing 5G, artificial intelligence, internet of things and Big Data, is paving the way for a new era which is set to impact almost every aspect of our daily lives.

In a new episode of "Tech at the Edge", Xnor unveils its fix for helping developers build low-code smart and edge-based solutions, we hear about VMWare's solution for reducing the carbon footprint of data centers, and find out how Spanish energy company Endesa transformed its call center with the help of IBM's Watson.

In our top story, despite recent attempts to broaden its currency, artificial intelligence is still run almost exclusively on expensive hardware running in the cloud and restricted to a handful of companies in the world.

Even with these new tools (and there are indeed a host of them) AI still requires expertise in deep learning where specialized teams must design, train and implement solutions. Deploying these models at the edge requires solving a host of constraints, including memory, power and latency, which makes developments for on-device AI almost impossible.

But tech can change the status quo quickly. The chimera is figuring out a way to give developers access to deep learning models, which could then be deployed on resource-constrained devices such as car dash cams and home security cameras with a few lines of code.

Check out the newest episode of Tech at the Edge on YouTube (https://youtu.be/5NVg0EYoLF8)



Let me introduce you to Xnor. They have recently launched AI2GO, a self-serve AI platform that enables developers, device creators and companies to build smart, edge-based solutions without training.

AI2GO contains more than a hundred fully trained models optimized to run on resource-constrained devices such as mobile, wearables, smart cameras, remote sensors and more. AI2GO models are being used today to build solutions for retail analytics, smart home and industrial internet of things.

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"By providing access to deep learning that can readily run on-device, we believe we afford all companies, regardless of team, budget or hardware, the opportunity to participate in this new era of AI innovation" says Ali Farhadi, CXO at Xnor.

Using AI2GO seems pretty straightforward. First the user selects their preferred hardware, something like Raspberry Pi, Linux, Ambarella, or Toradex. Then they chooses an AI use case, for example a "pet classifier for a home security camera."

Users can then tune their model for latency and memory footprint in order to fit within the user's set of constraints, and view the available models ranked by accuracy. The user can then download a module containing a deep learning model, also known as an inference engine.

Meanwhile, an increasing reliance on technologies such as mobile and cloud computing has led to significant energy usage from data centers. To mitigate their impact on the environment, companies need to acknowledge their environmental footprint and implement products and services that automate and optimize this process, thereby reducing energy outputs.

That's where VMware's new Carbon Avoidance Meter can help. It provides data center operators with near-real time "carbon scores" and recommendations for reducing this footprint. The CAM bases carbon scores on telemetry data.

"We are committed to empowering our customers with the resources and tools they need to u­ltimately reduce environmental impact across our vast customer and partner ecosystem" said Ray O'Farrell, CTO at VMware.

VMware's R&D organization developed CAM as a proof of concept through its internal product incubator, xLabs. The product represents another key step toward VMware's ambitious 2020 sustainability goals and commitment to creating transformative technology to power a better future for the planet.

In other news from the "Edge", as energy companies spur innovation and accelerate digital transformation journeys, there is incredible potential to utilize artificial intelligence. But AI platforms have to scale, embed easily, and provide industry-specific capabilities.

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Case in point, Endesa, the largest Spanish energy power company, wanted to transform its call center into an AI contact center to help deliver quick and personalized customer service.

Endesa´s contact center manages around 10 million calls a year with human agents. So the company enlisted IBM, and the Watson Assistant. To meet its challenges, Endesa built a virtual assistant to enable clients to get immediate answers to their inquiries, and empowering call center agents to focus on more complex requests and selling products and services.

"We found that our live agents are asked with the same routine, non-complex questions 40% of their time" says Jorge Honorio Domínguez, Innovation Director at Endesa. "Our assistant built on IBM Watson gives customers 24/7 access to resolve these issues and more importantly, it allows our human agents to focus on the questions that require more in-depth problem solving -improving our service to customers significantly."

Customers can ask the assistant to handle transactional requests like providing bill duplicates, changing bank account information or paying a bill. Hosted on the IBM Cloud and integrated with Endesa's back-office processes, the assistant currently manages 40,000 customer calls per month, but could soon handle 90,000 calls.

Since the launch of its Watson assistant in July 2018, Endesa's overall client satisfaction has increased 4.5%.

"Tech at the Edge" is produced by The Tech Video Project in New York City. and sponsored by RestonLogic, cloud wizards leveraging over 10 years experience helping companies automate, transform and build highly-secure and stable systems. RestonLogic offers a complete suite of IT and cybersecurity solutions from managed services to strategic advisory. Visit RestonLogic dot com to book a strategy session today.

Disclaimer: The blurbs highlighted on Breach Force are available for information purposes only, and don't necessarily reflect opinions of our editors.

Source: The Tech Video Project
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